robench-2024b
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In summary, our work differs significantly from each of the above-mentioned works, and other literatures in UAV ad-hoc networks. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> c) There are two kinds of links between users, and the link supported by UAV is better.
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**A**: a) The UAV ad-hoc network supports user communications.
**B**: b) The coverage of a UAV depends on its altitude and field angle.
**C**: As far as we know, our proposed algorithm is capable of learning previous utilities and strategies, achieve NE with restricted information and constrained strategies sets, and update strategies synchronously, which significantly speed up the learning rate.
Figure 1: The topological structure of UAV ad-hoc networks.
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3.5 Sequenced Models
The Recurrent Neural Network (RNN) was designed for handling sequences. The long short-term memory (LSTM) network is a type of RNN that introduces self-loops to enable the gradient flow for long duration (Hochreiter and Schmidhuber, 1997). In the medical image analysis domain, RNNs have been used to model the temporal dependency in image sequences. Bai et al. <|MaskedSetence|> Similarly, Gao et al. <|MaskedSetence|> <|MaskedSetence|> Similarly, other works have also applied RNNs (LSTMs) (Alom et al., 2019; Chakravarty and Sivaswamy, 2018; Yang et al., 2017b; Zhao and Hamarneh, 2019a, b) to medical image segmentation.
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**A**: (2018) applied LSTM and CNN to model temporal relationship in brian MRI slices to improve segmentation performance in 4D volumes. Li et al.
**B**: (2018) proposed an image sequence segmentation algorithm by combining a fully convolutional network with a recurrent neural network, which incorporates both spatial and temporal information into the segmentation task.
**C**: (2019a) applied U-Net to obtain initial segmentation probability maps and further improve them using LSTM for pancreas segmentation from 3D CT scans.
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<|MaskedSetence|> <|MaskedSetence|> Recall that several efficient codebook-based beam training and tracking schemes have been proposed for conventional mmWave network with uniform ULA and UPA [22, 23]. These prior works inspire us to propose a specialized new codebook design and the corresponding codeword selection/processing strategy that can drive the CCA to achieve fast beam tracking in the highly dynamic UAV mmWave network. To this end, the properties of the CCA should be exploited in the design of the codebook, which are briefly discussed as follows.
Activated Subarray with Limited DREs: As shown in Fig. 1, given a certain azimuth angle, there are limited DREs that can be activated. Due to the directivity, the DREs of the CCA subarray at different positions are anisotropic, and this phenomenon is different from the UPA. <|MaskedSetence|>
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**A**: If an inappropriate subarray is activated, the beam angle may go beyond the radiation range of certain subarray elements, degrading the beam gain and SE.
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**B**: Note that directly solving the above beam tracking problem is very challenging, especially in the considered highly dynamic UAV mmWave network.
**C**: Therefore, developing new and efficient beam tracking solution for the CA-enabled UAV mmWave network is the major focus of our work.
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III. The co-existence of random graphs, subgradient measurement noises, additive and multiplicative communication noises are considered. Compared with the case with only a single random factor, the coupling terms of different random factors inevitably affect the mean square difference between optimizers’ states and any given vector. <|MaskedSetence|> It becomes more complex to estimate the mean square upper bound of the local optimizers’ states (Lemma 3.1). <|MaskedSetence|> Then, we prove that the mean square upper bound of the coupling term between states, network graphs and noises depends on the second-order moment of the difference between optimizers’ states and the given vector. <|MaskedSetence|>
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**A**: Finally, we get an estimate of the mean square increasing rate of the local optimizers’ states in terms of the step sizes of the algorithm (Lemma 3.2).
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**B**: What’s more, multiplicative noises relying on the relative states between adjacent local optimizers make states, graphs and noises coupled together.
**C**: We firstly employ the property of conditional independence to deal with the coupling term of different random factors.
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In this work, we use the model predictive contouring control (MPCC)
which is an MPC-based contouring approach to generate optimized tracking references. We account for model mismatch by automated tuning of both the MPC-related parameters and the low level cascade controller gains, to achieve precise contour tracking with micrometer tracking accuracy. The MPC-planner is based on a combination of the identified system model with the contouring terms. <|MaskedSetence|> <|MaskedSetence|> Additional constraints in the Bayesian optimization algorithm allow for balancing traversal time, accuracy, and minimization of oscillations, according to the specific crucial requirements of the application. <|MaskedSetence|>
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**A**: In our approach the tracking error is coupled with the progression along the path through the cost function.
**B**: We demonstrate enhanced performance in simulation for a 2-axis gantry, for geometries of different nature.
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**C**: The automated tuning of the parameters is performed using a cost that accounts for the global performance over the whole trajectory.
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The results show that MusicBERT achieves a testing accuracy of 37.25% for style classification and 77.78% for emotion classification. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> This finding is intriguing and suggests that the application of large-scale pre-training may yield substantial benefits in classifying the emotional content of a MIDI piece.
Tab. 2 also shows that the CP token representation tends to outperform the REMI one across different tasks for both the baseline models and the PTM-based models, demonstrating the importance of token representation for music applications.
To study whether the accuracy gain comes simply from a longer musical context enjoyed by CP, we also train “our model (performance)+++CP” with a sequence of length 128, obtaining 95.43, 80.32 and 64.04 accuracies for three-class melody classification, style classification and emotion classification, respectively..
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**A**: Conversely, in the emotion classification task, MusicBERT demonstrates impressive performance, surpassing our model (70.64%) by a significant margin.
**B**: Specifically, in the style classification task, MusicBERT exhibits clear signs of overfitting and falls short in performance when compared to our model (81.75%).
**C**: This outcome can be attributed to the limited size of the Pianist8 dataset, comprising only 411 songs.
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<|MaskedSetence|> <|MaskedSetence|> Moreover, the transcription is obtained from the recovered speech signals after passing through an automatic speech recognition (ASR) module. For the system, the adaptive multi-rate wideband (AMR-WB)[21] is used for speech source coding and 64-QAM is utilized for modulation. <|MaskedSetence|> Moreover, the ASR module aims to recover the text transcript accurately, which is realized by employing Deep Speech 2[23] model.
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**A**: Polar codes with successive cancellation list (SCL) decoding algorithm[22] is employed for channel coding, in which the block length is 512 and the list size is 4.
**B**: The first benchmark is a traditional communication system to transmit speech signals, named speech transceiver.
**C**: Particularly, the input of the system is the speech signals, which is restored at the receiver.
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The PCAM dataset was downloaded from the original website (https://github.com/basveeling/pcam). <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> The patches of 96 x 96 pixels images were automatically extracted from the CAMELYON dataset [13]. For each image, a positive label indicates that the 32 x 32 pixel center of the image contains at least one pixel annotated as tumor tissue (Figure 1).
Figure 1: Normal and tumor example images from the PCAM dataset. The red rectangle corresponds to the 32 x 32 pixels center. The presence of at least one pixel of tumor tissue in this region dictates a positive label (1), otherwise the image is labeled as negative(0).
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**A**: All images have a size of 96 x 96 pixels, in three colors.
**B**: All datasets have a 50/50 balance between positive (tumor present) and negative (tumor absent) samples.
**C**: The training set has 262,144 images (80 % of the total), the validation set has 32,768 images (10 %) and the test set also has 32,768 images (10 %).
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<|MaskedSetence|> By initializing the learning process with a uniform random expander we bias the optimized solution towards expanders that distribute energy throughout the eyebox, in contrast to a quadratic phase profiles[28] that concentrate the energy at fixed points. Thus, the viewer’s eye pupil can freely move within the eyebox and observe the wide field-of-view hologram at any location. We incorporate pupil-aware optimization[37] to preserve the perceived hologram quality at different eye pupil locations. See Supplementary Note 5 for findings.
Finally, we also investigate 3D étendue expanded holograms. <|MaskedSetence|> We note that existing methods on étendue expanded holography has focused on monochromatic 3D holograms[7, 28, 29]. Photon sieves[21] only achieves 3D color holography for sparse points. <|MaskedSetence|>
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**A**: See Supplementary Note 4 for a discussion of these findings.
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In addition to field-of-view, we also investigate the eyebox that is produced with neural étendue expansion.
**C**: We find that neural étendue expansion also enables higher fidelity étendue expanded 3D color holograms.
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2.5. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> In contrast, subjective methods are always based on human subjective judgments and are more related to evaluating the perceptual quality of the image. Based on the pros and cons of the two types of methods mentioned above, several assessment methods are briefly introduced in the following with respect to the aspects of image reconstruction accuracy, image perceptual quality, and reconstruction efficiency..
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**A**: However, they can only reflect the recovery of image pixels from a numerical point of view and are difficult to accurately measure the true visual effect of the image.
**B**: Objective methods commonly use a specific formulation to compute the results, which are simple and fair, thus becoming the mainstream assessment method in SISR.
**C**: Assessment Methods
The image quality assessment (IQA) can be generally divided into objective methods and subjective methods.
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2 Related work
Several related explainability studies have been reported previously. <|MaskedSetence|> The use of Gradient-weighted Class Activation Mapping (Grad-CAM) [25] to explain spoofing classifier behaviour is reported in [26]. It is applied to generate a binary saliency map for the network input layer. <|MaskedSetence|> Listening experiments show that the model uses the buzziness and rhythmic quality of speech sounds to distinguish between bona fide and spoofed speech. A study of replay detection [17] shows the impact of different replay attack configurations upon detection performance. The use of Local Interpretable Model-agnostic Explanations (LIME) [27] to generate both temporal and spectral explanations of model prediction behaviour for voice replay detection is reported in [28]. <|MaskedSetence|> These works show that non-speech intervals can provide discriminative information for spoofing detection. Related work in [19] shows that the duration of non-speech intervals in a synthetic speech and converted voice detection task can also be indicative of whether an utterance is bona fide or spoofed.
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**A**: Using an approach based upon the attenuation of distinct spectral components, [24] shows that artefacts indicative of different spoofing attacks are located within different sub-band intervals, and hence that they can be detected more reliably with front-ends that emphasise the same frequency range.
**B**: Input audio is then reconstructed using spectrograms masked with the binary saliency map.
**C**: The input speech spectrogram is first segmented into a number of temporal or spectral segments, before LIME is applied to learn their relative importance through experiments with and without their use for modelling.
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Learning with CBFs: Approaches that use CBFs during learning typically assume that a valid CBF is already given, while we focus on constructing CBFs so that our approach can be viewed as complementary. In [19], it is shown how safe and optimal reward functions can be obtained, and how these are related to CBFs. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> The authors in [24] collect data to episodically update the system model and the CBF controller. A similar idea is followed in [25] where instead a projection with respect to the CBF condition is episodically learned. Imitation learning under safety constraints imposed by a Lyapunov function was proposed in [26]. Further work in this direction can be found in
[27, 28, 29].
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**A**: In [23], it is shown how additive and multiplicative noise can be estimated online using Gaussian process regression for safe CBFs.
**B**: The authors in [20] use CBFs to learn a provably correct neural network safety guard for kinematic bicycle models.
**C**: The authors in [21] consider that uncertainty enters the system dynamics linearly and propose to use robust adaptive CBFs, as originally presented in [22], in conjunction with online set membership identification methods.
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<|MaskedSetence|> <|MaskedSetence|> [16] showed that space-time block coding (STBC) with single polarization outperforms STBC with dual polarization in Rayleigh and Ricean fading channels. A MIMO system with dual-polarized antenna elements can have lower spatial diversity but higher spatial multiplexing gain than a conventional MIMO system with single-polarized antennas, particularly, in Ricean fading channels with high K𝐾Kitalic_K-factor [17]. It is noteworthy that the extent of benefit from dual-polarized antennas depends on the associated schemes to exploit the characteristics of polarized wireless channel [15, 16, 17, 1, 6]. <|MaskedSetence|>
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**A**: Ref.
**B**:
Various other aspects of polarization in MIMO systems have been investigated as well.
**C**: Various channel sounding campaigns and channel models provide insights into the characteristics of wireless channel polarization [26, 21, 22, 20, 27, 28, 23, 29, 30].
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<|MaskedSetence|> In [7], adding a Lagrangian term to the regularization of a constrained non-convex minimization permits to build an equivalent minimization problem that is convex locally. <|MaskedSetence|> In [29], in a function space setting, Pock et al. <|MaskedSetence|> In the context of non-convex polynomial optimization, Lasserre’s hierarchies [26] are used to recast the original problem in a hierarchy of convex semi-definite positive problems which provide global convergence results. The drawback of this method is the computational cost that makes it impractical for high-dimensional problems. Finally, convex closure of submodular functions also permits to cast sparsity inducing objective functions (where the regularizer is a submodular function of the support) into convex problems [5].
Note that if one aims to find a non-convex, but continuous, regularization, several works of interest may be cited, such as the use of ℓpsuperscriptℓ𝑝\ell^{p}roman_ℓ start_POSTSUPERSCRIPT italic_p end_POSTSUPERSCRIPT minimization [21], SCAD [19], or CEL0 [33].
Nevertheless, in this paper, we focus on convex functions.
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Many works intent to find a convex proxy to a non-convex objective function.
**B**: Another possibility is to try to perform a regularization by infimal regularization [8] for lower semicontinuous objective functionals.
**C**: propose a high dimensional lifting of the Lagrangian formulation of (2) where the data-fit functional is non-convex.
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The predictions by one template from our method and random selection are visualized in Figure 4. Our predictions around ears and nose locate more closer to the ground-truth landmarks than those by random template, which has consistent performance in MRE, quantitatively.
Besides, another group of experiments are conducted on Hand Xray dataset. The proposed SCP is applied on Hand Xray dataset to obtain the suggested M𝑀Mitalic_M templates. Following settings in [42], evaluation model is built. <|MaskedSetence|> As reported above, MRE results of 5/10/15 templates perform a bit worse than that of 1 template (e.g., the MRE is 2.8912.8912.8912.891mm for 5 templates, but 2.6532.6532.6532.653mm for 1 template). The dataset size of Hand Xray (609 images) is much bigger than that of Cephalometric (150 images). <|MaskedSetence|> The smaller the number of templates is, the more randomness it underlies. <|MaskedSetence|>
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**A**: We speculate that the diversity of Hand Xray dataset could not be well "represented" by such small group of templates.
**B**: Results are listed in Table 1, showing reliable improvements (e.g., MRE reduced by 35.5%percent35.535.5\%35.5 % (4.1144.1144.1144.114mm to 2.6532.6532.6532.653mm)) .
**C**: So it makes sense the results tend to be stable when the number of templates increases.
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<|MaskedSetence|> The NICE-Net consists of a feature learning encoder and a coarse-to-fine registration decoder. The feature learning encoder has two identical, weight-shared paths to extract features from the fixed and moving images separately, which were then propagated to the coarse-to-fine registration decoder. The decoder performs multiple steps of coarse-to-fine registration in a single network iteration. Dual deep supervision, including a deep self-supervised loss based on image similarity (local normalized cross-correlation) and a deep weakly-supervised loss based on manually annotated landmarks (mean square error), was embedded into the NICE-Net (referred as NICE-Net-ds). <|MaskedSetence|> Then, the NICE-Net-ds was further trained for intra-patient registration with dual deep supervision. During inference, pair-specific fine-tuning was performed to improve the network’s adaptability to testing variations. <|MaskedSetence|>
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**A**: As the provided training set was relatively small (140 intra-patient image pairs), the NICE-Net-ds was first pretrained with inter-patient image pairs (280 ×\times× 279 pairs) to avoid overfitting.
**B**: This team adopted the recently proposed Non-Iterative Coarse-to-fine registration Network (NICE-Net) (Meng et al., 2022b) as the backbone and extended it by introducing dual deep supervision.
**C**: In addition, as the MRI scans provided by the challenge organizers had been rigidly registered to the same anatomical template, this method solely optimized for deformable image registration without considering affine registration.
Team CaMed.
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<|MaskedSetence|> <|MaskedSetence|> In Corollary 8, we also
provide a sufficient condition under which the function τs(.)\tau_{s}(.)italic_τ start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT ( . ) is
continuous. Note also that the angle map ϕ(.)\phi(.)italic_ϕ ( . <|MaskedSetence|>
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**A**: ) is continuous except at finitely many angles
θ𝜃\thetaitalic_θ.
**B**: assumptions on M(τ)𝑀𝜏M(\tau)italic_M ( italic_τ ), that in general the inter-event time
function τs(.)\tau_{s}(.)italic_τ start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT ( .
**C**: ) is a continuous.
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Control of PDE systems has been widely explored over the years [15, 16, 17, 18]. Similar to ODEs, notions of ISSt for PDE systems have garnered a lot of attention recently (see survey paper [19]). For example, PDE ISSt have been explored for reaction-diffusion systems [20], hyperbolic systems [21], [22], parabolic systems [23], parabolic PDE systems with boundary disturbances [24], [25], systems with distributed time-delays [26], and diffusion equation with time-varying distributed coefficients [27]. <|MaskedSetence|> In contrast to ISSt, ISSf has remained mostly unexplored in the context of PDEs. In [29], safety verification using barrier functionals for homogeneous distributed parameter systems has been considered. In this work, numerical strategies based on semi-definite programming has been used for the construction of barrier functionals. However, control performance under disturbances has not been considered in this work. Given the importance of maintaining system safety under disturbances, it is critical to consider control system design for PDE systems under these disturbances. In [30], safe control of Stefan system under disturbances is considered. In the framework proposed in [30], an operator is allowed to manipulate the control input as long as safety constraints are satisfied; however, the safety control overrides the operator control signal realizing a feedback control ultimately guaranteeing safety. The feedback law for safety control is designed utilizing backstepping, quadratic programming, and a control barrier function. <|MaskedSetence|> Specifically, we design a control law that employs feedback from the boundaries and an in-domain point, by utilizing a practical ISSf (pISSf) barrier functional characterization (inspired by the notion presented in [4]). <|MaskedSetence|> In this way, we ultimately propose a feedback control law that satisfies the conditions of both ISSt and pISSf.
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**A**: Subsequently, utilizing ISSt Lyapunov functional characterization, we prove that such designed safety control is also an input-to-state stabilizing control under certain additional conditions.
**B**: Notions of practical ISSt for PDEs have been explored in [28].
**C**: In our current work, we attempt an alternate approach to achieve safety control of a class of linear parabolic PDEs under disturbances.
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Collecting Training Samples. Recall that a sample in PU-Setting is comprised of a sample of PUs’ parameters (location and power) and the optimal power allocated to the SU. In SS-Setting, a training sample is comprised of spectrum sensors’ received power readings. <|MaskedSetence|> <|MaskedSetence|> Then, we compute the area under the PSD curve over the 1 MHz channel of interest (see below), and finally, convert the computed area to an appropriate unit.
Determining Labels (Optimal Power Allocated to SU). We essentially do a binary search to estimate the optimal power that can be allocated to SU. <|MaskedSetence|> This end-to-end communication system is implemented using GNU Radio.
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**A**: To determine whether PU to PUR transmission is incurring any harmful interference from SU, we have PU continuously streaming ASCII messages over the 1 MHz bandwidth channel centered at frequency 915.8 MHz, and check if the messages are successfully received at the PUR.
**B**: The location of entities is available by using a GPS dongle connected to the laptops as described below, and the sensor’s received power is computed as follows.
**C**: First, we compute an FFT on the I/Q samples collected within a time window to get a power spectral density (PSD) plot.
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To the best of our knowledge, Coordinate descent [31], as an important class of optimization algorithms, is not sufficiently analyzed by researchers in the online optimization community. In coordinate descent algorithms, most components of the decision variable are fixed during one iteration while the cost function is minimized with respect to the remaining components of the decision variable. The resulting problem is lower-dimensional and often much easier to solve. Thus, coordinate descent algorithms have great potential in applications such as machine learning, where iteration with full gradient information is computationally expensive. In [24], it is shown that for huge scale problems, coordinate descent can be very efficient. <|MaskedSetence|> Specifically, the dual problem of multi-agent optimal consensus results in a sum of functions with very loose coupling between the dual variables. Calculation of a component of the gradient of the dual function only involves computations and communications of a pair of agents (or processors). Moreover, it can also be implemented in a parallel fashion as shown in [3]. Therefore, sufficient effort has been made recently by researchers to develop theoretical performance guarantees of various coordinate descent algorithms [31]. In this paper, we aim to extend this appealing algorithm to solve OCO problems by providing an in-depth regret analysis for different types of online coordinate descent algorithms.
The main contributions of the paper can be summarized as follows. First, we extend the coordinate descent algorithms considered in [31] to the online case and provide their regret analysis. To the best of our knowledge, this is the first attempt to look at possibilities of using coordinate descent methods to solve OCO problems. Second, we provide an in-depth regret analysis of various coordinate descent algorithms with different rules, such as cyclic updating rules and random updating rules. <|MaskedSetence|> In particular, most existing literature on OCO are based on extensions of offline algorithms that monotonically reduce the distance from the decision variable to the set of solutions at each iteration. An example is the well-known online gradient descent [41, 11]. However, offline deterministic coordinate descent algorithm, although has provable convergence properties to the set of solutions, does not necessarily result in an updated variable that is closer to the set of solutions at each iteration. <|MaskedSetence|> Lastly, we show that the regret bounds achieved by our online coordinate descent algorithms are comparable to those achieved by the literature on centralized full-gradient based online algorithms.
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**A**: Another situation where one may find coordinate descent useful is dual decomposition based methods for distributed optimization, see [21] and references therein.
**B**: Specifically, we consider both random and deterministic online coordinate descent algorithms under assumptions commonly used in the literature.
**C**: We overcome this issue by using predictive like updates at each time which are detailed in Section 5.
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In Table 3 and Table 4, we compare the performance of our proposed model against single and multi-label prediction models for selected pathologies. Table 3 shows that our proposed multi-label approach was able to outperform single-label models. In Table 4, the results indicate that our proposed architecture outperforms Wang et al. Wang et al. (2017b) and Irvin et al. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|>
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**A**: Irvin et al.
**B**: (2019) in multiple detection whereas betters performance of CheXNext Rajpurkar et al.
**C**: (2018), which is the state-of-the-art chest x-ray disease prediction model, for cardiomegaly condition only..
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Emotional elicitation and labeling is a complex task, and sometimes the expected (or targeted) emotions are not the ones the volunteers experienced (or reported). The agreement between the target class and the self-reported discrete emotion annotations by the volunteers in this experiment is shown in the matrix in Figure 2, where it is observed as the ratio of times a targeted emotion is identified and felt as such by the volunteers. Thus, a value of 1.00 means a perfect agreement between the targeted emotion and the emotion felt, and 0.00 means no agreement.
As introduced before, only 8888 of the 12121212 emotions initially selected were included in WEMAC (see the Stimuli Section), although the 12121212 emotions were considered for the discrete emotion labeling (see the Measures Section). <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Tenderness and disgust are also quite well portrayed by the stimuli while anger is often taken as disgust or contempt, and amusement as joy or disgust.
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**A**: It is also observed that sadness, calm, joy and fear are the emotions best identified, being the agreement in the fear emotion especially relevant for the use case.
**B**: Analyzing this figure it can be found that the non-included emotions (attraction, contempt, hope and tedium) are very scarcely selected with the exception of the 17%percent1717\%17 % of times a stimulus expected to represent anger is taken as contempt.
**C**: It means that the number of targeted emotions is smaller than the reported ones in this matrix.
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<|MaskedSetence|> The authors highlighted the potential clinical applications of GANs concerning early- and late-stage AMD classification.
Burlina et al. [8] trained a Progressive GAN [29] on 133,821133821133,821133 , 821 color fundus images from 4,61346134,6134 , 613 age-related eye disease individuals to learn how to generate synthetic fundus images with and without AMD. <|MaskedSetence|> Recognition rates varied from around 84%percent8484\%84 % for the first specialist to about 89%percent8989\%89 % for the second. <|MaskedSetence|> While the outcomes show great potential, the authors did not verify the utilization of data augmentation during the training process with their approach. Furthermore, Burlina et al. [8] conducted their research using the Age-Related Eye Disease Study (AREDS) dataset. This dataset mainly includes participants from the United States, making it reflective of a North American demographic.
Anh et al [30] tested the FundusGAN to generate eye-fundus images for two eye disease: Age-related macular degeneration and Diabetic retinopathy and demonstrated its ability for the synthetic images to be generalisable for the two disease.However, Anh et al. [30] work was confined to a single dataset in which most participants were from India and lacked diversity. Thus, they could not evaluate their technique across different ethnicities, demographics, and equipment variations, and therefore, their study could not be tested for generalisability..
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**A**: Bellemo et al. [28] described the possible advantages and limitations towards synthetic retina image generation using GANs.
**B**: Two retina specialists were asked to distinguish between images with and without AMD for original and synthetic images.
**C**: The accuracy differences between synthetic and real images did vary slightly for both specialists.
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Among the available approaches, the concept of control invariant set is one of the most exploited historically, since it ensures the existence of some feedback law able to steer the closed-loop trajectories of the uncertain system within a prescribed state set 25, 6, 8, 37. This is traditionally achieved by associating a control Lyapunov function (CLF) with the invariant set design, which for polytopic systems has been proven to be universal, namely the stabilization of the linear uncertain system and the existence of a polyhedral CLF can be used interchangeably 7. <|MaskedSetence|> We will also refer to these policies as traditional stabilizing controllers for linear uncertain systems.
Once fixed feasible control inputs at the vertices of the invariant set have been computed, a variable structure controller either takes a convex combination of those values by exploiting the vertex reconstruction of any state belonging to such a set, or coincides with a purely linear gain stemming from a triangulation, i.e., a simplicial partition 16, of the underlying set. <|MaskedSetence|> If the simplicial partition-based implementation is considered, then one has also to account for the complexity of the resulting invariant set, which is typically high 6, 8, 49, 10, 2, 9. These methods can therefore require significant memory to store the vectors and/or matrices describing every simplicial partition and associated linear control gain. As a common drawback affecting both the implementations, however, fixing the input values at the vertices may result in poor control performance for the stabilization task.
A more sophisticated control method coincides with the selection-based policy. By requiring the online resolution of a nonlinear optimization problem, parametric in the current measured state, this method directly enforces a certain degree of contraction possessed by the CLF at every control step. <|MaskedSetence|>
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**A**: With a specific focus on discrete-time polytopic systems, an admissible control policy that actually makes a polyhedral CLF a suitable Lyapunov candidate for the closed-loop system is typically synthesized in two ways: through a variable structure 25, 46, 47, or a (minimal) selection-based controller 3.
**B**: These methods therefore require one to solve a linear program (LP) online or to generate a lookup table to identify the region in which the current state resides.
**C**: While solving a numerical optimization problem online provides flexibility and performance guarantees, the real-time computational efforts required complicate its application in polytopic linear systems characterized by high sampling rates..
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Fig. <|MaskedSetence|> <|MaskedSetence|> First and second column show respectively MRI and CT images. The third column shows the MRI transformed using Clear, while the fourth column shows the MRI transformed using PPIR(MPC). <|MaskedSetence|>
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**A**: The transformed images are highlighted by red and green frames, respectively.
**B**: 3: Qualitative results for diffeomorphic registration with CC between 3D medical images from the AbdomenMRCT dataset [25].
**C**: The images are presented in a 3×4343\times 43 × 4 grid, with the first row representing the axial axis, the second row the coronal axis, and the third row the sagittal axis.
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To this end, we identify a class of POMDPs with a low-rank structure on the state transition kernel (but not on the observation emission kernel), which allows prediction and control in a sample-efficient manner. More specifically, the transition admits a low-rank factorization into two unknown features, whose dimension is the rank. On top of the low-rank transition, we define a Bellman operator, which performs a forward update for any finite-length trajectory. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> It is worth mentioning that such a unified framework allows a variety of estimators (including maximum likelihood estimators and generative adversarial networks).
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**A**: To this end, we construct a confidence set of embeddings upon identifying and estimating the Bellman operator, which further allows efficient exploration via optimistic planning.
**B**: The Bellman operator allows us to further factorize the history across multiple steps to obtain its embedding, which assembles the per-step feature.
By integrating the two levels of representation learning, that is, (i) feature learning at each step and (ii) embedding learning across multiple steps, we propose a sample-efficient algorithm, namely Embed to Control (ETC), for POMDPs with infinite observation and state spaces.
**C**: The key to ETC is balancing exploitation and exploration along the representation learning process.
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III Main Results
The convergence and performance analysis of the algorithm (6) are presented in this section. First, Lemma 1 gives a nonnegative supermartingale type inequality of the squared estimation error. <|MaskedSetence|> <|MaskedSetence|> Whereafter, Corollary 2 gives more intuitive convergence conditions for the case with Markovian switching graphs and regression matrices. Finally, Theorem 3 establishes an upper bound for the regret of the algorithm by Lemma 3, and Theorem 4 gives a non-asymptotic rate for the algorithm. <|MaskedSetence|>
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**A**: The proofs of theorems, Proposition 1 and Corollary 2 are in Appendix A, and those of the lemmas in this section are in Appendix B.
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**B**: Then, Theorem 2 gives intuitive convergence conditions for the case with balanced conditional digraphs by Lemma 2.
**C**: Based on which, Theorem 1 proves the almost sure convergence of the algorithm.
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<|MaskedSetence|> One serves as input to the model, and the other as the ground truth corresponding to the desired parameter setting to compute the loss. <|MaskedSetence|> We generated these brain MRI scans for 200 random pairs of {TE, TR}. <|MaskedSetence|> The TE values ranged from 20 ms to 1s non-uniformly. The distribution was such that lower TE values were selected with higher probability. This was done because the scans were more sensitive toward changes in lower values of TE. The T1 and T2 relaxation times used by MRiLab were matrices of size 108×90×901089090108\times 90\times 90108 × 90 × 90 with values in the range 0s to 4.5s for T1 and 0s to 2.2s for T2. For each pair of {TE, TR}, we generated 24 different 2D axial MR slices of a 3D brain volume, so in total we obtained 4800 MR slices. We used 1500 samples of these slices for training, while the rest were kept for testing. The generated scans were rescaled to a 256×256256256256\times 256256 × 256 matrix.
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**A**:
For our training, we require the MRI scans in two different parameter settings of {TE, TR}.
**B**: The TR values were chosen uniformly at random in the range 1.2 s to 10s.
**C**: We use MRiLab [7] which is an MRI Simulator to generate these synthetic brain scans in different parameter settings of {TE, TR}.
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<|MaskedSetence|> Considering the peripheral connection between SiPM output and FPGA board, the peripheral module (PMOD) interface provided by the board was used [35]. The PMOD interface was developed by Digilent Inc. <|MaskedSetence|> The expected bandwidth of the PMOD interface is tens of megahertz. <|MaskedSetence|>
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**A**: Since the digital signal characteristics are not specified, the maximum speed for digital SiPM pulse detection was evaluated.
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**B**:
To demonstrate the real-time optical receiver with SiPM, an AMD/Xilinx PYNQ-Z1 evaluation board with Zynq-7000 SoC XC7Z020-1CLG400C FPGA was chosen as the platform to characterize SiPM output pulses.
**C**: for the low frequency, low I/O peripheral connections.
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We have purposely selected those specific asteroids to underscore the fact that our proposed guidance, navigation, and control (GN&C𝐺𝑁𝐶GN\&Citalic_G italic_N & italic_C) approach is not reliant on the size or shape of the asteroid. <|MaskedSetence|> <|MaskedSetence|> This preliminary analysis aids in determining whether solar radiation pressure or the asteroid’s elongated shape is the predominant factor influencing the mission. <|MaskedSetence|> However, it is important to note that selecting the most suitable approach may vary depending on the specific mission objectives and the characteristics of the individual asteroid.
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**A**: For scenarios where solar radiation pressure dominates, a sun-terminator orbit would be suitable, while for elongated asteroids, a retrograde equatorial orbit in the asteroid’s inertial frame would be generally preferable [40, 38, 39].
**B**: Once the initial assessment of the environment is conducted, the spacecraft can consider various profiles based on the overall characteristics of the asteroid as observed from a distance.
**C**: The mission profile can be customized based on the specific objectives of the mission and the available information about the asteroid’s environment and properties.
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The importance of such an interdisciplinary approach has also been recently recognized in [24, 25], where the authors proposed a simulation framework that allows for coordinating a robotics simulator (e.g. Robot Operating System (ROS)), a communications network simulator, and an antenna simulator. <|MaskedSetence|> Indeed, oversimplified models are often adopted for either the communications or the robotics aspects. This oversimplification causes the researchers to miss interesting results and opportunities, or even to derive techniques that would fail when tested on real robots equipped with real communication systems. <|MaskedSetence|> However, they still simplify either the robotics or communications aspects. For instance, the tutorial [19] discusses UAV communications with great detail, but treats the control and robotics aspects in a superficial manner. The authors in [19] mention that, to the best of their knowledge, no rigorous expression for the UAV energy consumption for a given trajectory has been derived. <|MaskedSetence|> On the other hand, the robotics community generally oversimplifies the communication model. For instance, the authors of [26] consider the problem of a team of data-gathering MRs and assume a binary disk model [27] for the communication channel. In this model, the communication is perfect as long as two MRs remain within a certain distance of each other. Such a model is far from reality, as we shall see in section III-A.
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**A**: Some tutorials have recently been published on communications-aware robotics problems.
**B**: This enables to accurately simulate the dynamics of the robot and the communications channel.
In the literature, however, CaR and RaC problems are often not addressed with such an interdisciplinary approach.
**C**: As we will show in subsection II-B, such a statement is imprecise and comes from a lack of understanding of the UAV dynamic models and control theory.
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In this paper, a general notion of dissipativity with dynamic supply rates was introduced for nonlinear systems, extending the notion of classical dissipativity. <|MaskedSetence|> In these results, both dynamical systems are characterised by compatible dissipation inequalities with respect to “coupled”
dynamic supply rates. Satisfaction of the dissipation inequalities is aided by the dynamics of possibly distinct auxiliary systems. The results were shown to recover several knowns results in the
literature. <|MaskedSetence|> This coupling test is simple to compute if the supply rate operators are chosen to be LTI. <|MaskedSetence|>
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**A**: A noteworthy specialisation of the results is a simple coupling test to verify whether the feedback interconnection of two nonlinear systems, each satisfying independent (Ψ,Π,Υ,Ω)ΨΠΥΩ(\Psi,\Pi,\Upsilon,\Omega)( roman_Ψ , roman_Π , roman_Υ , roman_Ω )-dissipation inequalities, is asymptotically stable.
**B**: Lyapunov and asymptotic stability analyses were performed for feedback interconnections of two
dissipative systems satisfying dissipativity with respect to dynamic supply rates.
**C**: Moreover, a meaningful comparison with the integral quadratic constraint based input-output approach to feedback stability was.
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In this paper, we propose a way of analyzing safety probability for a stochastic system via a CBF approach. The contributions of this paper are as follows. First, we propose an almost sure reciprocal control barrier function (AS-RCBF) ensuring the safety of a set with probability one, which is considered as a stochastic version of an extended RCBF in [5]; see also [4] (and note that the condition is relaxed around the boundary of the safe set compared with an RCBF in [1]). Second, we propose an almost sure zeroing control barrier function (AS-ZCBF) satisfying an inequality somewhat different from the one in [12]. Then, we suggest a new stochastic ZCBF for calculating a probability that a trajectory achieves a designed subset of a safe set before leaving the safe set. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|>
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**A**: Our stochastic ZCBF satisfies an inequality, which differs from the previous results in [9, 10, 11, 12, 13, 14, 15] because the inequality directly includes the diffusion coefficients.
**B**: In the procedure, we also provide control design strategies using AS-RCBF/AS-ZCBF and our stochastic ZCBF.
**C**: In addition, we demonstrate our stochastic ZCBF is available for stochastic systems including input constraints by simple examples.
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This paper aims at answering the above questions. <|MaskedSetence|> Note that a large admittance (or equivalently, a small impedance) indicates that the converter’s behavior is closer to a voltage source. <|MaskedSetence|> We show that only GFM control can provide effective voltage source behaviors even under different implementations, which justifies the necessity of installing GFM converters.
On this basis, we investigate the problem of how many GFM converters are needed to enhance power grid strength.
We review the relationship between power grid strength and the small signal stability of a multi-converter system. <|MaskedSetence|> Our analysis sheds some light on the question of how many GFM converters we will need from the perspective of power grid strength and small signal stability..
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**A**: By explicitly deriving how the integration of GFM converters affects the power grid strength, we link the capacity of GFM converters to the stability of a GFM-GFL hybrid system.
Then, we give recommendations for the capacity ratio between GFM and GFL converters to satisfy a (prescribed) desired stability margin.
**B**: To make a fair comparison, we consider the scenario where both GFL control and GFM control aim at regulating the AC voltage and active power.
**C**: Firstly, we compare the dynamical admittance/impedance models of GFL and GFM converters.
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<|MaskedSetence|> Many SR networks apply attention modules to exploit latent correlations among the immediate features. Following RCAN [58] that first adopted channel attention, SAN [8] leveraged second-order channel attention to adapt the channel-wise features through second-order statistics. Several works introduced spatial attention to enrich the feature maps, e.g., enhanced spatial attention in RFANet [33], and spatial-channel attention in HAN [41]. <|MaskedSetence|> Inspired by vision transformers [34, 47], self-attention has been employed in SR to capture long-term adaptability, e.g., IPT [4] and SwinIR [30]. <|MaskedSetence|> GRL [28] utilized varied SA to explicitly model image hierarchies from coarse to fine to improve the recovery quality.
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**A**: 2.2 Attention in Super-Resolution
The attention mechanism can be viewed as a discriminative selection process that focuses on informative regions and ignores the irrelevant noise of pending features.
**B**: More recently, DAT [7] leveraged SA along both channel and spatial dimensions and enabled an effective information aggregation to achieve a prominent record.
**C**: Additional CNN-based works have utilized and refined non-local attention (NLA) to obtain long-range correlations [40, 51] and achieved an appreciable performance gain.
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In practice, real-time reconfigurability in the range of milliseconds might be still difficult to achieve as it requires stringent timing requirements for the control channel. Alternatively, beam-hopping techniques that are popular in satellite communications [34] can be considered. Beam-hopping consists of serving sequentially users spots in turn according to a predetermined schedule. The periodic beam hopping time plan can be determined and updated based on the varying traffic demand and the RIS scattering pattern can be optimized based on long-term statistical channel information [35] which also reduces the training overhead (c.f. Section IV-A). <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Therefore, the RIS node is designed to support a medium number of wide initial access wide beams or, alternatively, a permanent directive link is dedicated between the access point and the RIS node. While the control overhead is reduced, synchronous operation (for instance via GPS) between the RIS nodes and the donor nodes is still required. A notable advantage of the redirective RIS system is the simultaneous beam hopping of multiple beams at full aperture gain, particularly when the RIS node is shared among several donor sites (e.g. Fig 2) as explained in the next subsection.
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**A**: This results in substantial initial access latency and a long beam-hopping period.
**B**: To allow for initial access, all potential beam directions are sequentially illuminated and scanned (beam sweeping) during multiple synchronization signal blocks (SSB).
**C**: Therefore, the reconfiguration needs to be done only occasionally with long cycle times and the requirements on the control channel are significantly relaxed.
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<|MaskedSetence|> However, during the testing phase, the whole learning model is transmitted between the server and devices. Federated pruning permanently removes neurons in either or both training and testing phases. <|MaskedSetence|> Thus, how to design federated pruning methods with low computation complexity needs to be investigated. Model-compression schemes decrease the model size via sparsification or
quantization. However, these methods slightly decrease the convergence rate and achieve a modest accuracy (about 85%percent\%%). <|MaskedSetence|>
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**A**: The pruning ratio should be carefully designed to guarantee learning accuracy, and extra computation latency is required to calculate the importance of parameters.
**B**: Thus, how to design a model compression algorithm with high learning accuracy still needs to be investigated.
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**C**:
Federated dropout randomly drops neurons during the training phase, which decreases communication and computation latencies and slightly improves learning accuracy.
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To address challenges associated with power flow nonlinearities, we employ a linear approximation of the power flow equations that is adaptive (i.e., tailored to a specific system and a range of load variability) and conservative (i.e., intend to over- or under-estimate a quantity of interest to avoid constraint violations). <|MaskedSetence|> As a sample-based approach, the CLAs are computed using the solution to a constrained regression problem across all samples within the range of power injection variability. They linearly relate the voltage magnitudes at a particular bus to the power injections at all PQ buses. <|MaskedSetence|> <|MaskedSetence|> The accuracy and conservativeness of our proposed method is based on the information of the location of DERs and their power injections variability. As inputs, our method uses the net load profiles including the size of PVs when computing the CLAs. In practice, this data can be obtained by leveraging the extensive existing research on load modeling and monitoring to identify the locations and capabilities of behind-the-meter devices (refer to, e.g., Grijalva2021 ; Schirmer2023 ).
An example of an overestimating CLA of the voltage magnitude at bus i𝑖iitalic_i is the linear expression
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**A**: These linear approximations can also effectively incorporate the characteristics of more complex components (e.g., tap-changing transformers, smart inverters, etc.), only requiring the ability to apply a power flow solver to the system.
**B**: These linear approximations are called conservative linear approximations (CLAs) and were first proposed in BUASON2022 .
**C**: Additionally, in the context of long-term planning, the CLAs can be readily computed with knowledge of expected DER locations and their potential power injection ranges.
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For the training and validation sets, we only need to generate simulation data for a single fixed array, either circular or linear, since that they will only be used to the training of the CNN-based DOA estimation. Specifically, each utterance was 2 seconds long. For each individual utterance, we generated a room. The length and width of the room were randomly generated from a range of [4,10]410[4,10][ 4 , 10 ] meters. The height of the room was randomly generated from [3,4]34[3,4][ 3 , 4 ] meters. <|MaskedSetence|> The heights of both the microphone array and the speakers were set to 1.31.31.31.3 meters. Each circular array or linear array contains 4 microphones with an aperture of 8 cm. The self-angle of the microphone array was randomly chosen. <|MaskedSetence|> The reverberation time T60 was randomly chosen from a range of [0.2,1.0]0.21.0[0.2,1.0][ 0.2 , 1.0 ] seconds. The SNR was randomly drawn from a range of [0,20]020[0,20][ 0 , 20 ] dB. Each training set comprises 24,000 utterances. Each validation set consists of 1,200 utterances.
For the test sets, we need to generate simulated data for ad-hoc microphone arrays, whose ad-hoc nodes are either circular arrays or linear arrays. Specifically, for each randomly generated room, we repeated the procedure of constructing the training data, except that (i) we randomly placed 10 ad-hoc nodes in the room and (ii) we placed B𝐵Bitalic_B speakers in the room with B={1,2}𝐵12B=\{1,2\}italic_B = { 1 , 2 }. We added diffuse noise with an SNR level randomly selected from [10,20,30]102030[10,20,30][ 10 , 20 , 30 ] dB. The SNR was calculated as an energy ratio of the average direct sound of all microphone channels to the diffuse noise. Note that, due to the potential large difference in distances between the nodes and speakers, the SNR at the nodes could vary in a wide range. Each test set consists of 1,200 utterances. <|MaskedSetence|>
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**A**: We used Pyroomacoustics [38] to generate the room impulse response.
**B**: To study the effects of different types of microphone arrays on performance, for each randomly generated test room, we applied exactly the same environmental setting (including the speech source, room environment, speaker positions, microphone node positions and self-angles) to both circular-array-based ad-hoc nodes and linear-array-based ad-hoc nodes..
**C**: A single microphone array and one to two speakers were randomly placed in the room.
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<|MaskedSetence|> Our BRAT slices indicate that the robot is able to traverse through hallways reasonably well; however, sometimes, it fails.
Figure 10:
(a) Notice the highlighted area in the top-right location of the BRAT for the robot heading of −π/2𝜋2-\pi/2- italic_π / 2 radians. <|MaskedSetence|> (b) On simulating the robot from one of the highlighted states, we saw that the CNN predicts a waypoint into the wall to its right and crashes the robot. We show the specific wall and its corresponding location on the top view with the magenta arrow. <|MaskedSetence|> We show the glass door and its corresponding location on the top view with the magenta arrow..
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**A**: Misunderstanding certain obstacles as traversable.
**B**: (c) Another situation was observed where the robot crashed into a glass door due to the low height of the wooden pane around it.
**C**: Even though the robot faces down (wrt the top view), it cannot escape from the recessed region.
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There are four sets in the SceneFake dataset: training, development, seen test and unseen test. We design an unseen test set to evaluate the generalization of the models. <|MaskedSetence|> <|MaskedSetence|> Our training, development and seen test sets are populated with utterances with 6 kinds of acoustic scenes: Airport, Bus, Park, Public, Shopping, Station, and the fake utterances therein are manipulated by using four kinds of speech enhancement methods: SSub, MMSE, Wiener, FullSubNet. Our unseen test test is populated with utterances with four kinds of acoustic scenes: Metro, Pedestrian, Street, Tram, and the fake utterances therein are manipulated by using two kinds of speech enhancement methods: WaveU-Net, GCRN. <|MaskedSetence|>
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**A**: The acoustic scenes are randomly sampled to mix with the utterances at 6 different SNRs each: -5dB, 0dB, 5dB, 10dB, 15dB and 20dB.
The data structure and the detailed configurations of acoustic scene manipulation in the SceneFake dataset are illustrated in Figure 4.
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**B**: The dataset consists of real and fake utterances with various scenes.
**C**: There are no overlaps among the speakers of training, development and seen test set.
The speakers of the unseen test set are identical to that of the seen test set.
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Figure 6: Results on the nonlinear models. <|MaskedSetence|> The identified observer in both cases is sensitive. Even small errors between the true response and the predicted response in the first few steps are amplified, leading to instability. Hence, we plot only the TV-OKID without the observer.
The results show that the information-state model can predict the responses accurately. The TV-OKID approach also can predict the response well in the oscillator experiment when the experiments have zero initial conditions, but it suffers from inaccuracy if the experiments have non-zero initial conditions as seen in Fig. 5b. In the case of fish and cart-pole, TV-OKID fails with the observer in the loop. We found that the identified open-loop Markov parameters predict the response well, but the prediction diverges from the truth when the observer is introduced, making the predictions useless. This observation further validates the hypothesis that the ARMA model cannot be explained by an observer in the loop system. Hence, we use only the estimated open-loop Markov parameters without the observer to show the performance of the TV-OKID prediction. <|MaskedSetence|> There is also the potential for numerical errors to creep in due to the additional steps taken in TV-OKID: determination of the time-varying Markov parameters from the time-varying observer Markov parameters, calculating the SVD of the resulting Hankel matrices and the calculation of the system matrices from these SVDs, as mentioned in [11]. On the other hand, the effort required to identify systems using the information-state approach is negligible compared to other techniques as the state-space model can be set up by just using the ARMA parameters. <|MaskedSetence|>
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**A**: The last q𝑞qitalic_q steps in OKID are ignored, as there is not sufficient data to calculate models for the last few steps, as discussed in Sec. 6.3.
**B**: The experiments for identifying the system were performed from zero-initial conditions and non-zero initial conditions.
**C**: More examples can be found in [1], where the authors use the information-state model for optimal feedback control synthesis in complex nonlinear systems..
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<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Moreover, multiple b-shell, multiple direction high quality DW-MRI data can take many minutes to acquire, which poses challenges for clinical imaging protocols involving a multitude of MRI contrasts already taking tens of minutes to execute. Reduction of DKI data acquisition times through parallel imaging, optimisation of b-shells and directions have been investigated (Zong et al., 2021; Heidemann et al., 2010; Zelinski et al., 2008), and DW-MRI data necessary for DKI analysis has been shown to supersede the data required for DTI (Veraart et al., 2011b). Therefore, an optimised DKI protocol can potentially replace clinical DTI data acquisitions without adversely affecting the estimation of DTI metrics.
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**A**: Recent clinical benefits of using kurtosis metrics over other DW-MRI derived measures have been demonstrated for grading hepatocellular carcinoma (Li et al., 2022b), prognosing chronic kidney disease (Liu et al., 2021), differentiating parotid gland tumours (Huang et al., 2021a), measuring response to radiotherapy treatment in bone tumour (Guo et al., 2022a) and glioblastoma (Goryawala et al., 2022), identifying tissue abnormalities in temporal lobe epilepsy patients with sleep disorders (Guo et al., 2022b) and brain microstructural changes in mild traumatic brain injury (Wang et al., 2022), monitoring of renal function and interstitial fibrosis (Li et al., 2022a), detecting the invasiveness of bladder cancer into muscle (Li et al., 2022d), aiding management of patients with depression (Maralakunte et al., 2022), delineating acute infarcts with prognostic value (Hu et al., 2022), predicting breast cancer metastasis (Zhou et al., 2022), diagnosing Parkinson’s disease (Li et al., 2022c), amongst others reported prior and not listed here.
The routine use of DKI in the clinic has nonetheless lagged due the inability to robustly estimate the kurtosis metric (Veraart et al., 2011a; Tabesh et al., 2010; Kuder et al., 2011; Henriques et al., 2021).
**B**: A known requirement for estimating kurtosis in DKI is to restrict the maximum b-value to 2000 s/mm2 ssuperscriptmm2\text{ s}/\text{mm}^{2}s / mm start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT-3000 s/mm2 ssuperscriptmm2\text{ s}/\text{mm}^{2}s / mm start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT for brain studies (Jensen et al., 2005; Jensen and Helpern, 2010; Poot et al., 2010), with the optimal maximum b-value found to be dependent on tissue type (Poot et al., 2010).
**C**: This suggests that the traditional kurtosis model is less accurate at representing the diffusion signal at large b-values.
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Despite a large volume of literature on charging infrastructure planning for electric ride-hailing fleets, most existing works focus on either charging or battery swapping stations, without considering their complementary effects. Among the few early attempts, Zhang et al. [47] studied the joint planning of swapping and charging stations for private EVs, and Zhang et al. [48] explored the charging demand management for electric taxis in the presence of a hybrid infrastructure network. <|MaskedSetence|> <|MaskedSetence|> Secondly, we integrate infrastructure planning decisions and operational decisions of the ride-hailing platform within a unified framework to account for their interdependence. <|MaskedSetence|>
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**A**: In contrast, this paper distinguishes itself from all previous studies in two key aspects.
**B**: To the best of our knowledge, these considerations have not been studied in the literature.
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**C**: Firstly, we consider a multimodal charging network where charging stations and battery swapping stations are jointly deployed to overcome their respective limitations and elicit the synergy value.
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<|MaskedSetence|> As shown in Fig. 3 (a), the comparison results between DEviS and other U-Net variants under different Gaussian noise levels and mask ratios are presented. Fig. 3 (a) indicates a gradual degradation in performance for U-Net, AU-Net, V-Net, and nnU-Net, particularly at higher mask ratios and noise levels. Upon applying DEviS, the results exhibit a certain degree of robustness to interference. When equipped with DEviS, U-Net demonstrates an average improvement of 10.6% and 8.9% in Dice metric under degraded conditions of Gaussian noise and random masking, respectively. <|MaskedSetence|> <|MaskedSetence|> It reveales that BQNAT, DU, PU, UE, and TTA methods were significantly affected by noise and masking, while the perturbation on U-Net, nnU-Net, and V-Net methods was relatively minor after applying DEviS. A comparison of uncertainty estimation results using ECE and UEO metrics indicated that U-Net, nnU-Net, and V-Net with DEviS achieved better uncertainty estimation. Visualizations of segmentation results and uncertainty estimation as shown in Fig. 3 (c), demonstrate that the proposed DEviS method provides more reliable uncertainty estimation for target edges and the noised or masked pixels..
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**A**:
1) Comparison with U-Net based methods.
**B**: Additionally, the generated uncertainty estimates, as illustrated in Fig. 3 (c), can be utilized by researchers and clinicians to discern the unreliability of the data.
2) Comparison with uncertainty-based methods.
**C**: As shown in Fig. 3 (a), the comparison results of the ECE and UEO metrics between the proposed method and other uncertainty estimation methods are presented.
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<|MaskedSetence|> One fusion approach is signal switching, where candidate fiducial points from a signal modality with the best signal quality are selected as final fiducial points in a certain segment. Singh and Sunkaria (2017) uses the sample entropy to assess the noise content in multiple signal modalities, such as ECG and arterial blood pressure (ABP) signals, and switch between them to enhance the accuracy of heartbeat detection. Aygun et al. <|MaskedSetence|> Another fusion approach related to voting method, where candidate fiducial points detected in each signal modality cast a vote to select final fiducial points for a certain segment. In the majority voting, the fiducial points that have most agreement among different signal modalities are selected as the final fiducial points (Yu et al., 2014). Furthermore, the vote could be weighted by the signal quality index or other evaluation metrics to select fiducial points with best quality (Rankawat and Dubey, 2017). <|MaskedSetence|> In a study, authors employ Bayesian Network to model the relationship between the ECG, ABP and classification for hidden states in a Hidden Markov Model (Zia and Arif, 2017).
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**A**: (2019) obtains the best set of IBI arrays from three PPG morphological features by selecting those segments with minimal standard deviation of IBI subarray.
**B**:
Fusion Methods of Physiological Signals.
Fusion approaches have been explored to enhance the accuracy of heartbeats detection by incorporating the information across different physiological signal modalities or multiple morphological features.
**C**: Other fusions are based on probabilistic models.
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<|MaskedSetence|> STSCI consists of two systems, the base system and the semantic enhancement system. <|MaskedSetence|> <|MaskedSetence|> The simulated-channel model is only used during the model training process to simulate a real-world wireless channel. This process is indicated by blue lines.
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**A**: The semantic enhancement system with the process indicated by red lines, on the other hand, includes a YOLONet for identifying key semantic content and an enhancement CNN network that utilizes extra information to enhance the transmission quality of the key semantic information.
**B**: The base system consists of a semantic encoder, a semantic decoder, and a simulated-channel model (trained only), with the process indicated by black lines.
**C**:
Figure 1: The framework of STSCI.
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<|MaskedSetence|> The top subfigure presents the results for the practical implementation that includes estimating the dimension, whereas the bottom subfigure presents the results for the oracle. We see that the Riemannian approach outperforms its Euclidean counterpart, by approximately 20dB20dB20\text{dB}20 dB. <|MaskedSetence|> In contrast, for the Euclidean approach, the SbSp method yields slightly lower SIRs than the DS method. <|MaskedSetence|>
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**A**: The reason is that the Riemannian mean better attenuates the interference sources, allowing for a better estimation of the signal subspace than the Euclidean mean.
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**B**: In addition, the oracle SbSp method is better than the practical SbSp.
In comparison to Figure 3(top), it can be seen that the Riemannian SbSp method results in higher output SIRs than the Riemannian DS method.
**C**:
Figure 4 is the same as Figure 3, but presenting the SbSp method with the addition of the intersection method, which appears in orange.
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<|MaskedSetence|> The data included regular breathing and six different types of anomalous breathing. Since breathing is an involuntary activity, humans have limited control over their breathing rates and depths. <|MaskedSetence|> Using a breathing machine or robot eliminated the need to establish ground truth as the robot operator controlled the breathing parameters for each scenario. <|MaskedSetence|> Therefore, the use of a robot allowed us to conduct research by finely adjusting various breathing parameters, as well as assessing the system’s performance and limitations.
Figure 3: The experimental setup diagram of the LWS system used for data collection..
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**A**: Therefore, it is extremely challenging for humans to consistently breathe at various prescribed rates and depths for the purpose of collecting training data, unless they have received specialized training to do so.
**B**: Moreover, the machine could consistently generate data for extended durations, resulting in a more comprehensive and reliable dataset.
**C**:
For the present study on infrared sensing for detecting respiratory anomalies, respiration data with precise frequencies and depths were necessary to create labeled training data and evaluate performance on test data.
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<|MaskedSetence|> <|MaskedSetence|> In the cell-free massive MIMO literature, the best performing joint precoders are typically designed from joint uplink combiners, motivated by a known uplink-downlink duality principle for fading channels [19, Ch. 4] [20, Ch. 6]. However, optimal joint precoders are generally unknown owing to the following two reasons. First, until very recently, optimal joint combiners were not known except for the relatively simple case of full CSI sharing within each cooperation cluster, an information constraint leading to so-called centralized combining. <|MaskedSetence|>
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**A**: Second, the known uplink-downlink duality principle for fading channels holds for a looser and somewhat less practical sum power constraint.
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Each AP must form its transmit signal as a function of the CSI and data bearing signals specified by the constraints, and no additional information exchange between the APs is allowed.
**C**: This is in contrast to related works such as [11], which covers iterative information exchange during precoding computation.
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<|MaskedSetence|> In this case, the model is estimated from data, and thus the modeling error is inevitable. Our suboptimality analysis can incorporate this modeling error to provide performance guarantees for these controllers.
Related work: When the model is exact, the suboptimality analysis of RHC controllers, with constraints or economic cost, has been studied extensively in [4, 5, 6] and references therein. However, performance analysis in a setting where the system model is uncertain or unknown is rare. The suboptimality analysis of RHC for linear systems with a structured parametric uncertainty is considered in [11]; however, the impact of the approximation in the terminal value function is not investigated. Other relevant works can be found in the performance analysis of learning-based RHC [12, 13, 14], where the controller actively explores the state space of the unknown system and the model is recursively updated. <|MaskedSetence|> <|MaskedSetence|>
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**A**: The impact of the modeling error has been investigated in the above analysis; however, the effect of the prediction horizon and the terminal value function on the control performance is not considered [14, 13, 12]..
**B**:
Moreover, we demonstrate an application of our analysis in the performance analysis of learning-based RHC controllers.
**C**: There, a control performance metric called regret is concerned, which measures the accumulative performance difference over a finite time window between the controller and the ideal optimal controller.
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A big challenge is that ROMs provide a simplified and imperfect description of the dynamics, which negatively affects the performance of the state estimator. One potential solution is to improve the accuracy of the ROM through the inclusion of additional closure terms (Ahmed et al., 2021). In this paper, we leave the ROM untouched and instead propose a new design paradigm for the estimator itself, which we call a reinforcement-learning reduced-order estimator (RL-ROE). <|MaskedSetence|> <|MaskedSetence|> Indeed, we show that in the limit of sparse measurements, the trained RL-ROE outperforms a Kalman filter designed using the same ROM and displays robust estimation performance across different dynamical regimes. <|MaskedSetence|>
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**A**: The RL-ROE is constructed from the ROM in an analogous way to a Kalman filter, with the crucial difference that the linear filter gain function, which takes in the current measurement data, is replaced by a nonlinear policy trained through reinforcement learning (RL).
**B**: The flexibility of the nonlinear policy, parameterized by a neural network, enables the RL-ROE to compensate for errors of the ROM while still taking advantage of the imperfect knowledge of the dynamics.
**C**: To our knowledge, the RL-ROE is the first application of RL to state estimation of parametric PDEs.
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However, as the CNN- and transformer-based methods focus more on capturing local and global features respectively, marrying these two methods can further enrich the local and global features extracted. <|MaskedSetence|> Firstly, the CNN and transformer are ensembled in tandem or parallel. For example, Tummala et al. [39] ensembled a variety of versions of the swin transformer and the final model output is obtained by averaging the predicted softmax vectors of individual models. Dai and colleagues [40] proposed a TransMed method, in which ResNet is leveraged to capture features, followed by the transformer. Liang [41] et al. developed a hybrid light-weight model architecture based on CNN and transformer, in which convolution layers and MobileViT blocks [42] are connected in tandem. Secondly, the CNN and transformer are married more intrinsically with complicated redesigned modules. A case in this is the MedViT proposed in 2023 [43] with the developed locally feed-forward network within the local transformer block. An alternative can be the EDCA-Net developed by Zhu et al. [44], consisting of the densely connected attention module with multiple densely connected channel-attentional feature units. In addition, Jiang et al. [45] developed an MXT architecture consisting of five stages. In MXT, the downsampling spatial reduction attention reduces resource usage while the multi-layer overlap patch tokenizes the images. Moreover, multi-label attention as well as the class token transformer block are incorporated, thereby providing a more effective procedure for multi-label scenarios. <|MaskedSetence|> Leveraging both local and global features captured, these methods demonstrate excellent performance.
To further enhance the feature capture and generalization ability, we propose a straightforward yet effective CECT approach. Different from existing models, CECT can extract features at both multi-local and global scales without complicated module design. Moreover, the contribution of local features at varying scales is controllable through our proposed ensemble coefficients. Compared with tandem or parallel combinations, our CECT comprises three CNN-based branches designed to identify features at multi-local scales instead of a specific local scale. <|MaskedSetence|>
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**A**: The recent methods of integrating CNN and transformer can be primarily categorized in two-fold.
**B**: Contrasting with the approaches with sophisticated module design, our CECT exhibits enhanced effectiveness and generalization ability with straightforward yet effective architecture.
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**C**: The convolution layers are integrated into the downsampling spatial reduction transformer block.
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<|MaskedSetence|> In our measurement campaign, 12121212 microphones (T-bone MM-1) are set up according to Fig. 2 and connected to a single sound card that is recording to a laptop. A stereo speaker is placed on top of the robot, as well as the 12121212th microphone.
The microphone on the robot is placed as close as possible to the speaker (sound source) and works as a reference to synchronize the speaker with the microphones. In addition to the 12121212 audio tracks, a synchronization pulse from the ground truth system on start and stop is recorded as a 13131313th track (“Sync”). To make calculations easier by viewing the sound source as a point source, only one side of the speaker is enabled (playing sound), and the head of the reference microphone is placed directly in front of the sound source. <|MaskedSetence|> <|MaskedSetence|> The sound level of every microphone is checked individually and the speaker is tested..
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**A**: Microphones are placed asymmetrically on the floor and on different heights to avoid microphones being co-linear or co-planar, as this may cause degeneracies when solving for positions.
**B**: All microphones, except the one on the robot, have two markers placed, as seen on the left side of Fig. 4.
The sampling frequency of the microphones is 96969696 kHz and, if required, the audio system can localize the speaker at the same frequency.
**C**: Audio-based localization uses an array of microphones to calculate the direction or location of a sound source by measuring different metrics, such as the time differences of arrival (TDOA) of sound signals at different microphones and triangulating the source’s position based on these TDOA measurements.
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<|MaskedSetence|> We employ specaugment and gaussian noise for phoneme recognition [20]. <|MaskedSetence|> <|MaskedSetence|> To augment the LibriSpeech data used in training, we used the open-source torchaudio library [39]. Our goal in this data augmentation was to enhance the total number of samples while maintaining the dataset’s balance and consistency.
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**A**: For ASR, we employ SpecAugment and Speed Perturbation [13].
**B**: We adopt these enhancements since prior research has demonstrated their effectiveness for the aforementioned tasks.
**C**:
3.3 Data Augmentation
Here, we employ three different types of task-specific augmentation.
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<|MaskedSetence|> So, intuitively, the CSI of this channel should contain information about the Doppler shift. This idea has been already explored in terrestrial networks to generate a model using ML[158, 113, 159]. The ground truth values or the labels are usually generated using the ephemeris information. Different channel characteristic variables like Rician K factor, azimuth AoA width, mean azimuth AoA and channel estimation errors are generated randomly, and averaged Power Spectral Density (PSD) is used as inputs with some preprocessing to a multi-layered FCNN to estimate the Doppler shift in [158]. <|MaskedSetence|> In [159], different time and frequency domain signals with various modulation schemes, delay profiles, and Signal to Noise Ratio (SNR) have been used as inputs to a hybrid CNN-LSTM model to estimate the Doppler shifts. In NTN, the research in this domain is still at the early stage The estimated CSI is used as input to a CNN model to estimate the Doppler shift in [160]. In the future, other potentially efficient SL models can be also explored to generate the real-time accurate Doppler shift in an online manner. In table IV, we summarize the AI approaches for Doppler shift estimation in NTN. Even though the DL techniques are found to be useful in estimating Doppler shift using channel parameters, Doppler shift can be also estimated by analyzing the predictable trajectory of the satellites. <|MaskedSetence|>
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**A**:
In wireless communication systems, due to the mobility of the transceivers, the channel between the transceivers changes significantly resulting in received signal power variation and Doppler shift.
**B**: Complexity analysis is required to justify the
applicability of these DL architectures replacing the state of art methods in real systems.
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**C**: In [113], RSRP values mapped from an ambiguity reducer are used to generate the weights for an MLP.
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Training ResNet-20 and its revisions follow the implementation in [1]. In detail, we use an SGD optimizer with a weight decay of 0.0001 and momentum of 0.9. <|MaskedSetence|> The initial learning rate is 0.1, and the learning rate is reduced by a factor of 1/10 at epochs 82, 122, and 163, respectively. <|MaskedSetence|> Then, we apply random cropping to get 32 by 32 images. Finally, we randomly flip images horizontally. We normalize the images with the means of [0.4914, 0.4822, 0.4465] and the standard variations of [0.2023, 0.1994, 02010]. <|MaskedSetence|>
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**A**: During the training, the best models are saved based on the accuracy of the CIFAR-10 test dataset, and their accuracy numbers are reported in Table V.
TABLE V: CIFAR-10 Experimental Results..
**B**: Data augmentation is implemented as follows: First, we pad 4 pixels on the training images.
**C**: Models are trained with a mini-batch size of 128 for 200 epochs.
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2 Related work
Over the years, various solutions were suggested to overcome the lack of target GT depth measurements for training MDEs to predict absolute depth from target images. <|MaskedSetence|> <|MaskedSetence|> A recent zero-shot model [28] successfully overcame the geometrical domain gap between the source and the target domain by training a transformer-based architecture on a variety of source datasets (containing more than 700,000 training images with GT) that were further augmented to support various focal lengths. <|MaskedSetence|> In our work, we show an alternative solution to close the geometrical domain gap that uses only few annotated source samples (validation/test splits, less than 3,000 images) with a significantly lighter model (x50 less parameters). In addition, since our solution also uses target domain images, it could be re-adjusted to the new domain.
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**A**: The first approach is implemented as zero-shot [44] (see Figure 2a), where a model is trained on source datasets, and used to infer depth on target images, in the hope of generalizing well on the new domain.
**B**: Here we cover the main approaches, that are also presented by category in Figure 2.
**C**: In addition, the camera parameters were embedded to enable zero-shot capabilities on various target datasets.
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<|MaskedSetence|> We explored two strategies: randomly selecting a certain number of PAFs or using iterative refinement (sequentially providing all the PAFs in descending order of reconstruction error). <|MaskedSetence|> 2) Providing a few PAFs at the beginning and the end of a sentence. <|MaskedSetence|> A detailed user study would evaluate the utility of our control framework in a range of practical settings.
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**A**: However, other strategies exist, including: 1) Providing PAFs for an entire word.
**B**: 3) Providing only F0 values.
**C**: 5 Limitations
We acknowledge that our work does not investigate all plausible strategies for selecting control points.
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With the rise of 5G and beyond communication systems, the use of multiple antennas at the BSs and the UEs has introduced beamforming capabilities as a central feature in 5G NR that leads to higher data rates. However, a series of beam management procedures are needed to ensure efficient handling and network operation. The selection of the best receiving beam is performed by measuring the average received signal power in each beam through exhaustive scanning in a set of candidate serving BSs. The maximum power-based association policy is governed by the distance dependent path loss and the transmitting and receiving antenna gain patterns. <|MaskedSetence|> In this ideal baseline scenario, the cell association decision is purely based on the Euclidean distance between the two nodes [3], [7]−--[12].
Many works in the literature have studied beam management techniques and procedures for 5G NR networks by adopting tools from stochastic geometry, since it captures the spatial randomness of network elements [13]−--[16]. Modeling the spatial locations of BSs and/or UEs as point processes allows the use of powerful tools from stochastic geometry to derive tractable analytical results for several key performance metrics. <|MaskedSetence|> <|MaskedSetence|> To address this issue, the authors in [18] develop a stochastic geometry framework and conduct a detailed performance analysis in terms of the average achievable rate and success probability. Going beyond the coverage probability and the achievable rate, in [19], the authors studied the average number of beam switching and handover events, in mmWave vehicular networks. Beam management techniques were also considered. In [20], both the impact of beamwidth in the reliability and throughput of a THz network and the impact of the highly directional antennas on the beam management procedures was investigated.
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**A**: However, the interference from other BSs is ignored.
**B**: Therefore, in [17], the authors study among others both the initial beam selection during BS handover and beam reselection technique in a mmWave cell.
**C**: However, either a binary valued antenna pattern, called flat-top pattern [4]−--[6], or ideal conditions with realistic patterns, i.e., perfect channel estimation and beam training, that imply full alignment between BS’s transmitting and UE’s receiving beams has been assumed in most cases.
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Prior research on inverse Bayesian filtering includes inverse hidden Markov model[10] for finite state-space and inverse Kalman filter (I-KF)[5] for linear Gaussian state-space models. These works do not address the highly non-linear counter-adversarial systems encountered in practice. In this regard, our recent work proposed inverse extended KF (I-EKF) for non-linear system settings in [11, 12]. <|MaskedSetence|> These filters generate deterministic points and propagate them through the non-linear functions to approximate the mean and covariance of the posterior density. While EKF is applicable to differentiable functions, SPKFs handle discontinuities. A popular SPKF is the unscented KF (UKF) [15], which utilizes unscented transform to generate sigma points and approximates the mean and covariance of a Gaussian distribution under non-linear transformation. <|MaskedSetence|> EKF, on the other hand, considers a linear approximation for the non-linear functions. <|MaskedSetence|>
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**A**: The basic intuition of unscented transform is that it is easier to approximate a probability distribution than it is to approximate an arbitrary non-linear function [15].
**B**: The corresponding inverse UKF (I-UKF) was proposed in our recent work [16, 17]..
**C**: However, even EKF performs poorly in case of severe non-linearities and modeling errors[13], for which our follow-up work [14] introduced inverses of several EKF variants such as high-order and dithered EKFs.
A more accurate approximation of a non-linear Bayesian filter than the advanced EKF variants is possible through derivative-free Gaussian sigma-point KFs (SPKFs).
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Preliminary results of our work appeared in [26], where the noiseless case of three-dimensional (3-D) SoMAN formulation was derived. In this paper, we apply our approach to estimate the communications messages and radar waveforms, formulate 3-D SoMAN in the presence of noise, provide detailed theoretical guarantees, incorporate steering vector errors in both gain and phase, address n-tuple M-DBD, and include additional numerical validations. <|MaskedSetence|> We exploit the sparsity of both radar and communications channels to formulate the recovery of unknown continuous-valued channel/signal parameters as a 3-D DBD problem. <|MaskedSetence|> This representation allows including the special structure of radar and communications signals in our M-DBD formulation.
2) 3-D SoMAN-based recovery. We formulate our problem as the minimization of the sum of two tri-variate atomic norms. However, the primal SoMAN problem does not directly yield a semidefinite program (SDP). We, therefore, turn to the dual problem and derive the SDP using the theories of positive hyperoctant trigonometric polynomials (PhTP) [28]. In the non-blind case, this approach has been previously employed for high-dimensional super-resolution (SR) [17] and bivariate radar parameter estimation [29]. <|MaskedSetence|>
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**A**: We demonstrate our approach through extensive numerical experiments..
**B**: Our main contributions are:
1) M-DBD with structured unknown continuous-valued parameters.
**C**: Following the approaches in [9, 27], we represent the unknown transmit radar signal (a periodic waveform) and communications messages in a low-dimensional subspace spanned by the columns of a known representation basis.
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Initially, some conventional methods like [12, 40] and widely-used interpolation methods like bicubic and tricubic interpolations [18] were employed in the early research.
Inspired by [11], recent studies have shifted their focus towards using deep learning-based super-resolution networks in the medical domain.
Lim et al. [20] employ deep learning-based super-resolution networks to upsample medical images.
Some studies upsample each 2D LR medical slice to acquire the corresponding HR one, such as [8, 43, 47]. <|MaskedSetence|> <|MaskedSetence|> [36] use 3D DenseNet-based networks to generate HR volumetric patches from LR ones.
Yu et al. <|MaskedSetence|>
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**A**: On the other hand, Chen et al.
**B**: [45] build a transformer-based MISR network to address volumetric MISR challenges..
**C**: [5] and Wang et al.
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<|MaskedSetence|> <|MaskedSetence|> Pattern search methods are extended in [13] to solve optimization problems with known constraints.
In case the explicit formulations of both objective and constraint functions are not available, the work [14] solves the problem by learning the functions using non-parametric models. However, this method only addresses linear programs. <|MaskedSetence|> A drawback of these approaches is the lack of a guarantee for sample feasibility (i.e., each sample satisfying the constraints). Therefore, they cannot be used for optimization tasks with hardware in the loop, since any infeasible sample may damage the hardware.
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**A**: Classical techniques for zeroth-order optimization can be classified as direct-search-based (where a set of points around the current point is searched for a lower value of the objective function), gradient-descent-based (where the gradients are estimated based on samples), and model-based (where a local model of the objective function around the current point is built and used for local optimization) [9, Chapter 9].
**B**: Examples of these three categories for unconstrained optimization are, respectively, pattern search methods [10], randomized stochastic gradient-free methods [11], and trust region methods [12].
**C**: When the unmodelled constraints are nonlinear, one can use two-phase methods [15, 16] where an optimization phase reduces the objective function subject to relaxed constraints and a restoration phase modifies the result of the first phase to regain feasibility.
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In general, the designs of WSR-maximization precoders under the power constraints mentioned above can be formulated as optimization problems with equality constraints. <|MaskedSetence|> In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point. By revealing the inherent geometric properties of the equality constraints, manifold optimization reformulates the constrained problems in Euclidean space as unconstrained ones on manifold. By defining the Riemannian ingredients associated with a Riemannian manifold, several Riemannian methods are presented for solving the unconstrained problems on manifold. In addition, manifold optimization usually shows promising algorithms resulting from the combination of insight from differential geometry, optimization, and numerical analysis. To be specific, by revealing that the precoders under TPC, PUPC or PAPC are on different Riemannian submanifolds, we can leverage this insight to transform the constrained problems into unconstrained ones on these submanifolds. Therefore, manifold optimization can provide a potential way for designing optimal WSR-maximization precoders under different power constraints in a unified framework.
In this paper, we focus on WSR-maximization precoder design for massive MIMO DL transmission and propose a matrix manifold framework applicable to TPC, PUPC and PAPC. We reveal the geometric properties of the precoders under different power constraints and prove that the precoder sets satisfying TPC, PUPC and PAPC form three different Riemannian submanifolds, respectively, transforming the constrained problems in Euclidean space into unconstrained ones on Riemannian submanifolds. To facilitate a better understanding, we analyze the precoder designs under TPC, PUPC and PAPC in detail. All the ingredients required during the optimizations on Riemannian submanifolds are derived for the three power constraints. <|MaskedSetence|> <|MaskedSetence|> Complexity analysis shows that the method using RCG is computationally efficient. The numerical results confirm the advantages of the RCG method in convergence speed and WSR performance.
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**A**: Further, we present three Riemannian design methods using Riemannian steepest descent (RSD), Riemannian conjugate gradient (RCG) and Riemannian trust region (RTR), respectively.
**B**: Recently, manifold optimization has been extensively studied and successfully applied to many domains [18, 19, 20, 21], showing a great advantage in dealing with smooth objective functions with challenging equality constraints.
**C**: Without the need to invert the large dimensional matrix during the iterations, Riemannian methods can efficiently save computational costs, which is beneficial in practice.
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Deep domain adaptation (DA) methods are being increasingly studied in medical image segmentation to reduce the domain shift effects [1, 10, 11]. In the context of cross-modal segmentation, we focus in particular on unsupervised domain adaptation (UDA) methods that do not rely on any prior knowledge of the labels of the target domain [12, 13, 14, 15, 16]. Typically, UDA methods for cross-modal segmentation involve two stages: unsupervised image-to-image (I2I) translation to learn intensity mappings between source and target domains, followed by supervised segmentation leveraging labels from the source domain [17, 18, 19, 20, 21, 22]. These two stages can also be combined into end-to-end models to benefit from label knowledge during I2I translation, at the cost of increased architectural complexity [17, 23, 24]. <|MaskedSetence|> diffusion models are now emerging [25], most of existing I2I translation methods are based on generative adversarial networks (GANs) [1, 4, 26, 17] that promote realistic outputs through a competition between a generator and a discriminator [27, 28]. <|MaskedSetence|> However, GAN-based methods tend to learn global image-level mappings, potentially disregarding smaller regions of interest (ROIs) like tumors that may be underrepresented in the training set [32, 33]. Maintaining a balance in the distributions of features of interest in the training and target domains becomes crucial for accurately translating such structures, which is of paramount importance to train a downstream segmentation model on the target modality. Tuning this proportion without prior knowledge of the test set’s composition remains an open problem. Lastly, an often overlooked aspect is the high variability and difficulty in reproducing the outputs of CycleGAN models [34, 35]. <|MaskedSetence|>
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**A**: Although newer generative paradigms based on e.g.
**B**: The most popular models for unsupervised I2I translation are CycleGAN and its variants [29, 30, 31].
**C**: It is common to retain the best performing model (as measured subjectively) from several trainings, which is not satisfactory due to the aleatoric nature of such practice.
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<|MaskedSetence|> <|MaskedSetence|> Based on three traveling wave measurements, the propagation medium is characterized and the origin of the event is localized. The method thus solves the problem of having to know the wave propagation characteristic of the transmission line in advance. This eliminates the setting as an input parameter for the algorithm, which is one of the limitations of existing methods because the characteristic of the propagation medium changes over time during the operation of the line. <|MaskedSetence|>
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5 Conclusion
In this paper, we propose a new online method for localizing events on transmission lines.
**B**: The method analyzes the traveling wave in the time-frequency domain of the wavelet transform.
**C**: At the same time, the characteristic of the transmission line is evaluated in a frequency domain, which improves the localization process by taking into account the dispersion effect.
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<|MaskedSetence|> Additionally, some part of the system is always left unmodeled due to limitations of first-principles-based modeling or uncertainty of the estimated model. These modeling errors propagate into the control design and result in unwanted behavior. A way to circumvent this issue is to directly synthesize a controller from data. By doing so, we avoid the possibility that an approximation error occurs in the modeling process. <|MaskedSetence|> <|MaskedSetence|> Unfortunately, those approaches usually require a large amount of data Recht (2019), while the approach developed in this paper requires only a single sequence of input-output data.
More specifically, in this work, we bring down correct-by-design control synthesis to the level of data by using a data-driven method
to directly synthesize a controller for both bounded and unbounded specifications.
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**A**: reinforcement learning Sutton and Barto (1999).
**B**: Due to the increasing complexity of systems, obtaining an accurate model has become a challenging task in practice Hjalmarsson (2005).
**C**: The interest in such direct data-driven control synthesis techniques is increased by the huge success of e.g.
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4.6 Transfer Learning
M3FM is designed for adaptability and generalization, enabling the enhancement of out-of-distribution task performance through transfer learning. <|MaskedSetence|> <|MaskedSetence|> For diverse clinical datasets, we can simply describe involved clinical data in free text to the model, without needing any modification on the M3FM architecture, as shown in Figure 7 (a). Specifically, adjusting to different output dimensions requires only the inclusion of a lightweight predictor. <|MaskedSetence|>
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**A**: To accommodate different image dimensions, the addition of a linear embedding layer suffices.
**B**: This capability extends to new tasks with varying image input dimensions, clinical data types, and output dimensions.
**C**: Consequently, M3FM can be easily fine-tuned to enable new tasks by leveraging the pre-trained model parameters.
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In comparison, our Transformer-based code predictor yields better ASR performance thanks to its stronger ability of global context modeling, i.e., Exp. <|MaskedSetence|> <|MaskedSetence|> (6) and (4) indicates the importance of fixing codebook during finetuning to protect the pre-trained clean speech prior.
Comparison between Exp. (6) and (5) demonstrates that the pre-trained prior knowledge in codebook is important to the clean speech restoration and downstream ASR.
In addition, Table V presents the effect of Transformer block numbers M𝑀Mitalic_M in Exp. <|MaskedSetence|>
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**A**: (3).
As shown in Fig. 4, the Transformer code predictor achieves higher prediction accuracy than CNN predictor and NN matching.
Furthermore, comparison between Exp.
**B**: (10)-(12)..
**C**: (6) vs.
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In channel modeling, the new features of H-MIMO inevitably introduce significant changes that should be addressed from a fundamental EM perspective. Specifically, the EM wave field, the actual transmission carrier in H-MIMO communications, is a spatial vector, and the communication distance tends to be in the near-field region, which allows the realization of communications with more polarization.
Conventional channel models, such as the classic Rayleigh fading channel model [14] and its correlated version [15], as well as the cluster-based channel model [16, 17], are generally built for far-field scenarios and are all based on mathematical abstractions, depicting the wireless channel via mathematical representations, while ignoring the physical phenomena of EM wave propagation. This is, however, insufficient to describe the wireless channel for H-MIMO communications. <|MaskedSetence|> <|MaskedSetence|> vector wave field and multi-polarization.
The above channel models undoubtedly fail to support the realistic vector wave field scenario, especially operating in the near-field region where the interactions among EM wave fields are abundant and complicated.
Going one step further, recent works [23, 24] proposed the EM-compliant near-field LoS channel models for H-MIMO, respectively, which are capable of depicting the vector wave field case. <|MaskedSetence|> The latter work only considers the parallel placement of surfaces, leading to the failure in capturing channel responses for arbitrary surface placements, which is the general case in practical deployments..
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**A**: However, the former work focuses on deriving a measurement-efficient model with high flexibility and mathematical tractability, whereas sacrificing the depiction accuracy to some extent.
**B**: As shown recently in [18, 19, 20], the authors describe a wireless channel following EM principles, where they studied the small-scale fading for scalar wave field in far-field scenarios.
As antenna surfaces tend to be large, the near-field line-of-sight (LoS) channel should be considered.
**C**: In recent studies [21, 22], the near-field LoS channel is described using a spherical wavefront propagation model, which is more of a mathematical abstraction without emphasis on EM propagation phenomena, e.g.
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More precisely, the existing frameworks [21, 22, 23] require users to manually write and solve SoSP problems to obtain the RoA approximation. In contrast, the proposed SOStab Matlab toolbox fully automates the SoSP aspect, eliminating the need for users to possess knowledge of the Moment-SoS hierarchy. <|MaskedSetence|> It outputs the stability certificate describing the RoA approximation and provides graphical representation in selected state coordinates. <|MaskedSetence|> <|MaskedSetence|>
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**A**: SOStab has been developed and made publicly available which allows users to compute RoA of non-linear dynamical systems.
To the best of the authors’ knowledge, the only existing toolbox for RoA approximation in a finite time horizon setting is SparseDynamicSystem [18], coded in the Julia language.
**B**: A notable distinction lies in the fact that the Julia toolbox exclusively supports polynomial dynamics, whereas SOStab is built on the Matlab codes supporting [12], specifically tailored for AC power systems that include phase variables and trigonometric dynamics.
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**C**: The toolbox operates with minimal input requirements, namely dynamics, state constraints, equilibrium point, time horizon, target set, and a complexity parameter d𝑑ditalic_d.
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Cloud computing, also known as on-demand computing, provides customers with a variety of services. Due to its rising popularity, it is susceptible to many intruders who can threaten the confidentiality and security of data stored in the cloud (Fig. 17). <|MaskedSetence|> The biggest issues of on-demand services are privacy and security, yet they are open to intrusion from any kind of assault. Existing IDSs face a number of difficulties as a result of the expanding size of cloud networks and the need to protect data privacy. These problems include a high computational cost, protracted training periods, and feature distributions that are not uniform, which results in poor model performance. These issues have been resolved via DTL.
However, privacy cannot be protected during data processing because current DTL-based techniques can only function in plaintext when separate domains and clouds are untrusted entities. Consequently, Xu et al. [181] developed a multi-source IDS-based DTL method that protects privacy. In their approach, first, the models from several SDs are encrypted and uploaded to the cloud using Paillier homomorphic encryption. Next, a DTL-based multi-source IDS based on encrypted XGBoost (E-XGBoost) for privacy-preserving technique is applied. <|MaskedSetence|> The model’s training period is substantially shorter, at the minute level, than it is at the more customary hour level. <|MaskedSetence|> [182] was to investigate whether a simplistic ML classifier with a modest common set of features, trained on a non-cloud dataset with a packet-based structure, can be applied to a cloud dataset incorporating time-based traffic using DTL to detect particular intrusions. This allows for analysis of the differences and similarities between assaults on cloud-based and non-cloud datasets, as well as recommendations for future research.
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**A**: Because of their dispersed nature, the most difficult aspect of cloud-based solutions is security.
**B**: The experimental findings demonstrate that the suggested method can effectively move the encryption models from a number of SDs to the TD, and the accuracy rate can reach 93.01% in ciphertext with no appreciable loss in detection performance compared to works in plaintext.
**C**: Similarly, the purpose of the study of Ahmadi et al.
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<|MaskedSetence|> On the other hand, in [18, Assumption 10], the obstacles are assumed to be smooth and sufficiently separated from each other. In [21], the authors proposed a discontinuous feedback control law for autonomous robot navigation in partially known two-dimensional environments. When a known obstacle is encountered, the control vector aligns with the negative gradient of the Navigation Function (NF). <|MaskedSetence|> <|MaskedSetence|> In our earlier work [22], we proposed a hybrid feedback controller design to address the problem of autonomous robot navigation in planar environments with arbitrarily shaped convex obstacles.
In the present work, which has been initiated in our preliminary conference paper [23], we consider the autonomous robot navigation problem in a two-dimensional space with arbitrarily-shaped non-convex obstacles which can be in close proximity with each other. Unlike [12], [19] and [22], wherein the robot is allowed to pass between any pair of obstacles, we require the existence of a safe path joining the initial and the target location, as stated in Assumptions 1 and 2. The main contributions of the present paper are as follows:
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**A**: However, when close to an unknown obstacle, the robot moves along its boundary, relying on the local curvature information of the obstacle.
**B**: This method is limited to point robots and, similar to [18], assumes smooth obstacle boundaries without sharp edges.
**C**: In [19, Definition 2], the proposed hybrid controller is applicable for known n−limit-from𝑛n-italic_n -dimensional environments with sufficiently disjoint elliptical obstacles.
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We curated a comprehensive dataset by collating images from publicly available medical image segmentation datasets, which were obtained from various sources across the internet, including the Cancer Imaging Archive (TCIA) [34], Kaggle, Grand-Challenge, Scientific Data, CodaLab, and segmentation challenges in the Medical Image Computing and Computer Assisted Intervention Society (MICCAI). All the datasets provided segmentation annotations by human experts, which have been widely used in existing literature (Supplementary Table 1-4). <|MaskedSetence|> To ensure uniformity and compatibility with developing medical image deep learning models, we converted the images to the widely used NifTI format. Additionally, grayscale images (such as X-Ray and Ultrasound) as well as RGB images (including endoscopy, dermoscopy, fundus, and pathology images), were converted to the png format. <|MaskedSetence|> For instance, CT images had intensity values ranging from -2000 to 2000, while MR images exhibited a range of 0 to 3000. In endoscopy and ultrasound images, intensity values typically spanned from 0 to 255. <|MaskedSetence|>
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**A**: We incorporated these annotations directly for both model development and validation.
The original 3D datasets consisted of Computed Tomography (CT) and Magnetic Resonance (MR) images in DICOM, nrrd, or mhd formats.
**B**: To facilitate stable training, we performed intensity normalization across all images, ensuring they shared the same intensity range..
**C**: Several exclusive criteria are applied to improve the dataset quality and consistency, including incomplete images and segmentation targets with branching structures, inaccurate annotations, and tiny volumes.
Notably, image intensities varied significantly across different modalities.
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<|MaskedSetence|> For the multi-path scenario, we relax Assumption 1, and the power of the desired signal is calculated based on Lemma 1 in [5]. This lemma states that for a large number of antennas, when the number of paths is much smaller than the number of antennas, the power of the desired signal converges to the power given by the strongest path, where the optimal RF beamformers are given by the AoA and AoD of the strongest path. <|MaskedSetence|> Therefore, we obtain a constant ergodic capacity across all subcarriers. By relaxing Assumption 1, γ[k]𝛾delimited-[]𝑘\gamma[k]italic_γ [ italic_k ] in (4) varies for each subcarrier, which gives us the convex curves in Fig. 5. It can be seen that Assumption 1 is a strong assumption, which gives us an upper band on ergodic capacity. <|MaskedSetence|> Moreover, by adding a multi-path to the picture, we can observe further degradation of ergodic capacity. This is because multi-path propagation introduces additional interference, which reduces the SINRSINR\mathrm{SINR}roman_SINR compared with that in the single path scenario. However, even if Assumption 1 is a strong assumption, it provides tractability in stochastic geometry analysis for OFDM modulation, because the statistics of raised cosine pulse shaping filters are still unknown in the literature and are difficult to derive owing to the complex impulse response of the filter [47, 48]..
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**A**: Without Assumption 1, the performance analyzed in the previous subsections is degraded.
**B**:
VI-D Effect of Assumption 1
In Fig. 5, we simulate the ergodic capacity for different links per subcarrier to analyze the effect of Assumption 1 by simulations and provide the corresponding ergodic capacity for the multi-path scenario with three paths for both the LoS and NLoS.
**C**: With Assumption 1, the single path channel is assumed to exhibit flat fading, that is, the channel gain is the same for all subcarriers.
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<|MaskedSetence|> That is, vector-type BP-SLAM is obtained assuming models for single-feature dynamics, single-feature death, sensor dynamics, and measurement models, but no feature births. Instead, it is assumed that, at the current time step, we know the multi-Bernoulli density of previous features and the multi-Bernoulli density of newly detected features. <|MaskedSetence|> Afterwards, an external PHD filter can be used in vector-based SLAM to model undetected features.
Instead, the set-type BP PMB-SLAM derivation works as follows. We require models for feature birth, single-feature dynamics, single-feature death, sensor dynamics, and measurements, similar to Bayesian multi-target tracking algorithms. Then, auxiliary variables are used to obtain the associated factor graph, and then the factor graph is solved using set-BP. <|MaskedSetence|>
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**A**: The resulting algorithm, with the minor modifications explained above, is similar to vector-BP SLAM with the external PHD filter in [13]..
**B**: Then, the factor graph is obtained.
**C**:
It is also important to realize that this paper provides an alternative derivation of the (slightly modified) vector-type BP-SLAM algorithm in [13] obtained from first principles using RFSs.
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Semantic information is regarded as the meaning of the source data underlying the concrete expression. <|MaskedSetence|> This indicates that (i) semantic information relies not only on the source data but also on the specific task, which is significantly different from the information defined by Shannon, and (ii) semantic information is obtained by removing the redundant information irrelevant to the task from the source data. <|MaskedSetence|> <|MaskedSetence|> Inspired by Chattopadhyay et al. [28], we define the semantic entropy as following.
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**A**: Consequently, the same data may contain different amounts of semantic information for different tasks.
**B**: More specifically, semantic information is the effective information contained in the source data for accomplishing a specific task.
**C**: For example, an image contains much more semantic information for the image reconstruction task than the image classification task.
In this regard, semantic entropy is usually used to measure the semantic information, which should depend on the source data and the task.
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To assess the quality of the ROMs, we used the backward Euler method [7] to simulate the DPM and all its surrogate models, using the same solver and time steps. The simulation results are presented in Figures 7 through 7, where the abscissa denotes computation time and the ordinate denotes the average displacement of the bracket’s top surface. <|MaskedSetence|> The solver employed in the simulations was the “sparse state space (sss)” toolbox in Matlab [40], which can analyze high-dimensional dynamical systems with state-space dimensions of O(104)𝑂superscript104O(10^{4})italic_O ( 10 start_POSTSUPERSCRIPT 4 end_POSTSUPERSCRIPT ) or more. <|MaskedSetence|> <|MaskedSetence|>
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**A**: This solver preserves system matrix sparsity, allowing it to use computationally efficient operations, such as sparse LU decompositions [41], that would otherwise be infeasible or time-consuming.
**B**: It is important to note that the simulations were only used to illustrate the accuracy of the surrogate models and were not necessary for our proposed model comparison method.
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**C**: Due to limited space, only a few ROM curves are labeled.
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In light of the mentioned limitations of both approaches, this paper proposes an integrated training framework, referred to as calibration-aware Bayesian neural networks (CA-BNNs). As described in Sec. <|MaskedSetence|> 2 and Sec. 3, the proposed training criterion applies a data-dependent regularizer that penalizes calibration errors, as in [8, 9], as well as a data-independent regularizer enforcing adherence to a prior density, while optimizing over a variational distribution, as in Bayesian learning. <|MaskedSetence|> 5, we also describe an improvement to the training strategy introduced in [8] that relies on fully differentiable calibration error metrics [9, 10]. Experiments presented in Sec. <|MaskedSetence|>
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**A**: 6 validate the proposed approach.
II BACKGROUND.
**B**: 4, after providing the necessary background in Sec.
**C**: As a secondary contribution, in Sec.
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<|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|> Section IV details the convergence results of the proposed algorithms. Case studies on decentralized learning problems with various Byzantine attacks to illustrate the effectiveness and performance of the proposed algorithms are carried out in Section V. Section VI concludes the paper and states our future direction. Some detailed derivations are placed to Appendix for coherence..
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**A**: Section II presents the basic notation, problem statement, problem reformulation, and setup of its robust variant.
**B**: The connection of the proposed algorithms with existing methods and the algorithm development are elaborated in Section III.
**C**:
I-D Organization
We provide the remainder of the paper in this part.
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3.5 Proposed Multi-Axis Attention-based Hybrid Decoder Block
The proposed Hybrid Decoder is designed by stacking layers of Mutil-Axis Attention-based MaxViT-blocks in a hierarchical architecture, with a TransposeConv layer at the start of each stage, as shown in Figure 2. <|MaskedSetence|> <|MaskedSetence|> Similar to Swin-UNet [14], our decoder contains three stages that are connected with the corresponding top three stages of the encoder. Features from the preceding decoder layer are transmitted through the TransposeConv layer inside a single decoder block in order to up-sample and match their shape with skip-path features. Semantically and spatially rich features are obtained by concatenating the up-sampled features with the associated skip-connection features. <|MaskedSetence|>
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**A**: The MaxViT blocks further enhance them using MBConv, local attention, and global attention sub-block.
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**B**: Similar to the encoder, we created a parameter-efficient decoder by using only two MaxViT blocks per stage.
**C**: The decoder also enjoys the global and local receptive fields at all stages and is able to better reconstruct output masks as compared to previous approaches.
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In Table 2, we compare the efficiency of our model with one/few-shot learning models across 14 test tasks. Additionally, we train 14 TransUNet on 14 test datasets to establish an upper-bound for the performance. <|MaskedSetence|> On average, it takes a user 27.47 seconds to annotate one image across 14 datasets, while prompting for one image only takes an average of 2.28 seconds. Moreover, the prompting process requires the users much less clinical background and domain knowledge, making it more practical. <|MaskedSetence|> <|MaskedSetence|> To establish an upper bound for performance, we individually train 14 task-specific TransUNet models for 14 held-out datasets. The run-time is its cumulative training time. The user-cost time is denoted as ∞\infty∞ since the user must annotate all training samples in using.
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**A**: Comparing with the fully-supervised upper-bound, One-Prompt only needs to train one time for all downstream tasks, which saves significant parameters, training run time, and user-cost time for the annotation.
Table 2: Model efficiency comparison with few/one-shot transfer learning models.
**B**: The One-Prompt Model also exhibits superior scale-up capability, showing a significant improvement of about 10% compared to smaller models and only a 3.23% decrease compared to the TransUNet upper-bound.
**C**: Unlike current one/few-shot models that require the fully-labeled images, One-Prompt only needs the users to simply prompt the image, significantly reducing the user-cost time.
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The network is composed of 20,000 nodes; however, this number does not correlate with the number of deployed blockchain nodes. The current version of the protocol cannot verifiably prove the real number of blockchain nodes, as the Servicers are permissionless, but a rough estimate can be obtained through the nodes’ service domains. The largest Servicer group has approximately 5,000 nodes (about 25% of the network), followed by two others with around 2,500 nodes each (about 12.5% of the network each). Then, the number of nodes by domain decreases. Around 90% of the network is held by 14 independent domains. <|MaskedSetence|> <|MaskedSetence|> <|MaskedSetence|>
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**A**: and that each node runner has at least two nodes per staked blockchain 555Accounting for redundancy or geographical distribution of blockchain nodes., the Pocket Network has more than 30 independent blockchain servers on each of the main served blockchains.
**B**: It is important to note that this is only an estimation; however, the number is significant in terms of decentralization and reliability.
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**C**: Assuming no blockchain node is shared among node runners 444This is a fair assumption given the competitive play between node runners.
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<|MaskedSetence|> We choose the FastSpeech2 with an emphasis embedding and the hierarchical prosodic module [19] as the baseline to compare fairly. <|MaskedSetence|> We use bert-base-chinese available on HuggingFace666https://huggingface.co/bert-base-chinese as our pre-trained BERT and fine-tuned with our model training. We trained all models to 250,000 steps on 4 Tesla V100-16GB GPUs with batch size 64. We modified the first anneal step to 200,000 due to the size of the fine-tuning dataset. <|MaskedSetence|> For all the other models, we first pre-trained the model to 180,000 steps with the pre-trained dataset and then fine-tuned it to 250,000 steps. For the two models with BERT, we use an independent learning rate with exponential decay of 0.7 rather than 0.5 for FastSpeech2 to prevent the BERT module from not converging. Besides, we use a default HiFiGAN [32] trained with the fine-tuning dataset as the vocoder for all TTS models.
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**A**: For our proposed model, the conformer encoder has 4 layers with both input and encoder dimensions of 256 and 2 attention heads, following the implementation and the default configurations of Espnet555https://github.com/espnet/espnet.
**B**:
3.2.1 TTS Training Configurations
We utilize the basic configuration of the Fastspeech2 [3] for the models listed below unless otherwise explained.
**C**: The baseline model is trained to 250,000 steps only with the fine-tuning dataset.
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For instance, a room with hard surfaces like concrete or glass reflects sound waves, whereas a room with soft surfaces such as carpets or curtains absorbs them. <|MaskedSetence|> Recent years have seen a surge in significant research Li et al. <|MaskedSetence|> (2021); Li et al. (2023); Huang et al. (2023b) addressing the language-visual modeling problem. For instance, Li et al. <|MaskedSetence|> (2021) have focused on large-scale image-text pairs pre-training via contrastive learning. Visual TTS open-ups numerous practical applications, including dubbing archival films, providing a more immersive and realistic experience in virtual and augmented reality, or adding appropriate sound effects to games.
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**A**: This variance can drastically impact the clarity and quality of the sound we hear.
To ensure an authentic and captivating experience, it is imperative to accurately model the acoustics of a room, particularly in virtual reality (VR) and augmented reality (AR) applications.
**B**: (2022) have proposed a unified video-language pre-training framework for learning robust representation, while Radford et al.
**C**: (2022); Radford et al.
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According to Frank & Schönherr (2021), a \acgmm trained on top of \acLFCC features performed best on the original WaveFake dataset. The \acgmm outperformed the deep RawNet2 proposed by Jung et al. (2020). RawNet2 processes raw and unmodified waveforms. <|MaskedSetence|> After the encoder, RawNet2 employs a recurrent layer to integrate information over time.
Instead of relying on recurrent connections, we process contextual information via dilated convolution. <|MaskedSetence|> We found dilated convolutions delivered improved run-time and fake detection accuracy. Furthermore, our network employs the PReLU Xu et al. (2015) activation function, which performs well in tandem with dilated convolution Zhang et al. (2017).
Figure 3: Structure of our \acfdcnn. The Conv2d blocks denote 2D-Convolution operations with hyperparameters (Output Channels, Kernel Size, Padding, Dilation). We always work with unit strides. Each Conv2d is preceded by a Batch Normalization Layer (Ioffe & Szegedy, 2015) and followed by a PreLU activation (Xu et al., 2015). The permutation operation permutes the first with the second dimension of the input (we consider the batch dimension to be dimension zero). <|MaskedSetence|>
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**A**: M𝑀Mitalic_M denotes the number of output channels from the convolutional layers before..
**B**: Dilated convolutions enlarge the receptive field without downsampling (Yu & Koltun, 2015).
**C**: A convolution-based encoder computes feature vectors.
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Notably, the self-trained method is extraordinary with two round training processes. The incorrect manual picking is removed between the two training processes. <|MaskedSetence|> Multiple training processes used in the self-trained network can correct these error.
Thus, the self-trained network performs well in the HR@5-9px, RMSE, MAE of Sudbury. <|MaskedSetence|> In the Brunswick test, the HR@1px of UPNet is 99%, indicating its reliability in picking surveys with relatively high SNR in real-world scenarios.
Furthermore, comparing end-to-end methods (CNNRNN and UPNet) with the methods of the segmentation plus threshold post process (the benchmark and the self-trained network), we conclude that the end-to-end method is more suitable for the FB picking task. <|MaskedSetence|> Moreover, unlike CNNRNN, UPNet can filter unstable pickings based on computed uncertainty. Specifically, the lowest RMSE verifies that UPNet is the most robust method for picking FB..
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**A**: However, the precision of the self-training network is insufficient, so both UPNet and CNNRNN exceed it on the HR@1px and HR@3px.
UPNet performs better than the self-trained on accuracy (MAE) and stability (RMSE) for another three folds.
Then, UPNet can outperform STA/LTA, the benchmark, and CNNRNN a lot in the test of four-folds, specifically in fold 3.
**B**: We analyze that the blurred boundary of the FB signal in the segmentation map causes the low HR@1px.
**C**: There are a few lousy manual picking in Sudbury.
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Figure 7 illustrates the reliability diagrams and prediction interval widths for 1-step ahead forecasts. <|MaskedSetence|> Notably, the reliability diagram of the proposed model fluctuates around the ideal case, albeit in close proximity. <|MaskedSetence|> While these constraints ensure that higher quantiles are no smaller than lower quantiles, they concurrently impact parameter estimation. <|MaskedSetence|> Overall, the performance of the proposed model in terms of reliability and sharpness is sound.
Table 2: The CRPS values of forecasts by the proposed and benchmark models with different lead times in case 2 (%).
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**A**: This behavior is attributed to the monotonicity constraint imposed on the proposed model.
**B**: A more in-depth analysis is provided in the subsequent subsection.
**C**: In this case, DeepAR continues to demonstrate the least reliability among all models.
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The paper is organized as follows. <|MaskedSetence|> <|MaskedSetence|> Similarly, in Section 4 we expose the results in RKHS theory which we leverage in this work. Our main contributions are contained in Section 5, which in particular details the methodologies we exposed at the previous steps 1) and 2) and corresponding learning rates. <|MaskedSetence|> Finally, in Section 7 we provide concluding remarks and some perspectives.
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**A**: Precision and computational complexity of our approach are discussed in Section 6.
**B**: To ease the reading of this section, we moved a more detailed description of the aforementioned results to Section 8 and their technical proofs to Appendix A.
**C**: After gathering basic notation and preliminary results in Section 2, in Section 3 we summarize both classical and less classical results about stochastic differential equations and corresponding relationships with the Fokker-Planck equation.
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Besides the issue mentioned above, sometimes the importance scores given by the WPR algorithm may converge to an undesired distribution in some heads: (1) tokens at the edge of the input image may get very high importance scores; (2) the importance score distribution may become nearly uniform. We provide visual examples of these cases in Supplementary Material Section E.2. <|MaskedSetence|> To mitigate the negative impact of these heads, we introduce the Variance-based Head Filter (VHF). <|MaskedSetence|> Heads with a distribution variance exceeding the maximum threshold or falling below the minimum threshold are excluded from the computation. <|MaskedSetence|>
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**A**: We compute the variance of the distribution in each head and set both a minimum and a maximum threshold for the variance.
**B**: Both heads in these cases do not provide helpful information and are even misleading.
**C**: Then the final importance score equation becomes:
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To perform PSD on the raw signal, simply run the “main.m” program using the default settings, which utilizes all PSD methods. The discrimination factors of each method are then calculated and used to generate histograms. An automatic double Gaussian distribution fitting process is then applied, where the neutron distribution is located on the right side of the histogram and the gamma-ray distribution is located on the left side. <|MaskedSetence|> The central point between the right side three-sigma point of the gamma-ray distribution and the left side three-sigma point of the neutron distribution is used as the dividing point between gamma-rays and neutrons. It should be noted that all PSD methods, except HQC-SCM and ZC, require manual parameter settings. By default, the parameters are optimized to achieve near-optimal performance for the given dataset. <|MaskedSetence|> Conversely, ZC is a parameter-free method that requires no additional tuning. <|MaskedSetence|>
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**A**: However, when using other datasets, these parameters must be adjusted accordingly.
**B**: The three-sigma points of each Gaussian distribution are used as the end of the distribution since they contain 99.74% of the distribution.
**C**: Additionally, HQC-SCM incorporates a genetic algorithm-based automatic parameter selection approach, which allows for direct implementation on other datasets.
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<|MaskedSetence|> This comparison underscores the differences in the overall quality of synthetic 3D brain MRIs produced by various methods. <|MaskedSetence|> 3D-α𝛼\alphaitalic_α-WGAN-GP results in even blurrier images with similar textures, while HA-GAN produces the blurriest images, which are consistently asymmetric. <|MaskedSetence|> Although no model perfectly replicates continuous vessels, 3D-α𝛼\alphaitalic_α-WGAN-GP images exhibit better vessel continuity but with unnaturally wide vessels. The 3D Pix2Pix model generates blurry images, but 3D DiscoGAN offers better performance, creating more realistic images yet lacking detailed brain features and presenting coarse gyri and sulci. In contrast, our proposed Med-DDPM model produces images that are significantly more realistic than those from all other baseline methods in terms of the overall quality of 3D brain MRI synthesis.
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**A**: 3.4 Generated Images
Fig. 2 presents coronal, sagittal, and axial slices of real brain MRI images alongside those generated by our proposed method, two conditional baseline models, and other unconditional 3D brain MRI synthesis models.
**B**: LDM occasionally generates images with uniform textures and clearer edges.
**C**: In terms of evaluating the overall quality of synthetic MRIs through visual assessment, the images generated by 3D StyleGAN appear blurry with wire mesh patterns.
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We perform two experiments to analyze to what extent context contributes to lip synchronization. First, we mask out 0.14s (7 frames) of the source audio and generate frames of the masked time steps. As shown in Fig.2, our work can still generate well-synchronized lips even with the absence of audio because it is able to attend to the surrounding phones and predict lip motion that fits into the masked region in context. <|MaskedSetence|> Such results verify that our model effectively incorporates context information in modeling lip movement of the talking face.
In addition, we generate varying sizes of the audio window. We take a frame in the middle as a target frame and increase the size of the input audio window at the frame-level. The maximum audio window for the LRW is ±plus-or-minus\pm±15 and LRS2 is ±plus-or-minus\pm±78. <|MaskedSetence|> As shown in Fig.3, on the LRW dataset, taking the entire audio window yields the best lip-sync performance. <|MaskedSetence|> But when we further experiment on the LRS2 using wider audio windows, we find that the effect of temporal audio information reaches the optimum 1.059 at around ±plus-or-minus\pm±13 frames. It demonstrates that the audio context of around 1.2 seconds assists in resolving ambiguities in the lip shapes of phones, improving spatio-temporal alignment.
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**A**: It reaches 1.162 in LMD at ±plus-or-minus\pm±15 frames.
**B**: The audio frames that lie outside the window are zero-padded and we measure the lip-sync quality of the generated target frame using LMD.
**C**: In contrast, the previous works cannot generate correct lip movements because they do not consider surrounding phones.
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<|MaskedSetence|> In contrast, the BSMSD is an ideal high-pass filter that disregards all frequency components before the cutoff frequency (here λ15subscript𝜆15\lambda_{15}italic_λ start_POSTSUBSCRIPT 15 end_POSTSUBSCRIPT), and the LPF detector is based on minimum and maximum operations, which are sensitive to noise.
The naive TV detector achieves the lowest probability of detection in all tested scenarios, since it lacks a normalization or subtracted term representing the non-smooth hypothesis, in contrast to the proposed detectors.
Finally, it should be noted that in Fig. 2.a, the LRT with the GMRF graph filter from (33) follows the true distribution of the data under both hypotheses, and can be regarded as an upper bound on the LRT performance. Similarly, the LRT with the Tikhonov Regularization graph filter from (34), and the LRT with Diffusion Kernel graph filter from (35), can be regarded as upper bounds on the LRT performance in Fig. <|MaskedSetence|> <|MaskedSetence|> In all of these figures, the proposed detectors achieve the upper bounds on the probability of detection.
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**A**: 2.c, respectively.
**B**: 2.b and Fig.
**C**: In addition, the superiority of our methods compared to the BSMSD and the LPF detector can be explained by the fact that the proposed detectors employ a weighted average of the filtered graph frequency components, with greater weight given to higher graph frequency components.
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<|MaskedSetence|> As illustrated in Fig. 1(a), clinical readers are trained to look across many images to identify those that show the aortic valve at sufficient quality and then use these “relevant” images to assess the valve’s health. Training an algorithm to mimic this expert diagnostic process is difficult. <|MaskedSetence|> <|MaskedSetence|> To make matters more difficult, each image’s view type is not typically recorded in digital health records during routine collection.
Multiple-instance learning (MIL) is a branch of weakly supervised learning in which classifiers can consume a variable-sized set of images to make one prediction.
Recent impressive advances in deep attention-based MIL have been published (Ilse et al., 2018; Lee et al., 2019; Sharma et al., 2021; Shao et al., 2021)..
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**A**: The challenge in developing an automated system for diagnosing AS is that each echocardiogram study consists of dozens of images or videos (typically 27-97 in our data) that show the heart’s complex anatomy from different acquisition angles.
**B**: Standard deep learning classifiers are designed to consume only one image and produce one prediction.
**C**: Automatic screening of echocardiograms requires the ability to make one coherent prediction from many images representing diverse view types.
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The field of music information retrieval (MIR) has long been facing challenges in data availability due to the costs associated with music audio annotation and country-specific copyright laws (Chen et al.,, 2019; Castellon et al.,, 2021). <|MaskedSetence|> Existing acoustic music pre-trained models primarily focus on tagging tasks and rely on supervised tagging labels for pre-training (Pons and Serra,, 2019; Spijkervet and Burgoyne,, 2021; McCallum et al.,, 2022; Huang et al.,, 2022). <|MaskedSetence|> Additionally, several models trained on inaccessible datasets or without publicly available codes and model weights make it difficult to reproduce or extend their approaches (McCallum et al.,, 2022; Castellon et al.,, 2021; Li et al.,, 2022; Zhu et al.,, 2021; Zhao and Guo,, 2021). Although some general-purpose audio representation models show potential for music audio representation learning, their performance is mostly evaluated on limited MIR downstream tasks (Saeed et al.,, 2021; Borsos et al.,, 2022; Wang et al.,, 2023). <|MaskedSetence|>
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**A**: To address this challenge, pre-trained language models (PLMs) for acoustic music have been proposed to provide reusable learned representations, enabling transfer learning for various downstream MIR tasks without the need for extensive data annotation (Castellon et al.,, 2021).
However, current acoustic music pre-trained models still have room for improvement in terms of providing open-source, generalisable, and lightweight learned representations suitable for both industrial and research applications (McCallum et al.,, 2022).
**B**: While some studies have explored contrastive learning for acoustic music pre-training, they face limitations in training data and model size, hampering the performance improvements (Choi et al.,, 2017; Li et al.,, 2022).
**C**: This lack of comprehensive evaluation hinders further studies and a thorough understanding of the pros and cons of existing models.
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5 Visual Interpretability
We visualize the most discriminative regions of several representative methods using the gradient-weighted class activation map (Grad-CAM) [29] in the Messidor-1 dataset for the DR task. As shown in Fig. 6, the proposed method showed the most accurate localization of diabetic lesions compared to the other baseline methods, e.g.. hard exudates, and hemorrhages. <|MaskedSetence|> When dealing with small lesion blocks, localized lesions within many patches tend to be averaged out and overlooked, with ViTs favoring semantic comparisons between patches. Consequently, this leads to methods like Swin-L and CrossFormer producing CAM regions that are overly broad, hindering the precise localization of smaller lesions. It is noteworthy that MIL-VT compels each patch token to pass through a MIL (Multiple Instance Learning) head, essentially engaging in a pseudo-label learning process. <|MaskedSetence|> <|MaskedSetence|> This situation underscores the importance of fine-tuning CNNs for improved performance. Finally, We observed that the ViT-based methods show inferior localization performance compared to CNN-based methods. However, other CNN-based baseline methods (i.e., ReXNet and CANet) only demonstrate coarse localization of the lesions. Whereas, the proposed method can accurately localize diabetic lesions. These findings suggest the importance of CNN in capturing small localized features for retinal disease diagnosis..
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**A**: Interestingly, The nn-mobilenet and ReXNet share the same model configuration, but the latter still struggles to accurately learn lesion representation.
**B**: We observed that this MIL attention mechanism tends to assign a uniform level of importance to all patch tokens, which disrupts the ability of ViT to learn the relationships between different patches.
Compared with CNN methods, the multi-task network of CANet presents fitting challenges, indicating that despite the relatedness of the tasks, DME does not significantly enhance lesion localization in DR, possibly due to divergent interest patterns between the two tasks.
**C**: This observation aligns with our initial hypothesis that ViTs are typically employed to model the similarities between different patches.
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<|MaskedSetence|> <|MaskedSetence|> This is a crucial point of departure from multiparametric techniques and deserves to be underlined. <|MaskedSetence|> In contrast, nonlinear models with non-convex cost functions may lead to a high degree of structural complexity, over and beyond the piecewise affine regime, of the optimal feedback, making it extremely difficult to find appropriate parametrization of such feedback maps.333In view of the current state of affairs of numerical analysis, parametrizing the optimal feedback (e.g., as along the lines of the Ritz method) does not appear to be a promising direction. Ours being an interpolation-driven technique, an approach via multiparametric programming turns out to be unnecessary in our setting; merely the ability to compute solutions to finite-horizon optimal control problems at each point of the feasible set is sufficient.
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**A**: (B)
The QuIFS algorithm applies to nonlinear systems and non-convex cost functions whenever the underlying optimal control problem admits a unique solution.
**B**: It relies on coarse properties of the optimal feedback such as Lipschitz continuity, etc., rather than more detailed local structural properties; information concerning such coarse properties may be distilled directly from the problem data.
**C**: Of course, the optimal feedback is piecewise affine in the linear/affine setting under appropriate hypotheses; this important observation is now classical and follows from the central results of multiparametric programming in this context.
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<|MaskedSetence|> When annotators disagree, majority voting and averaging are commonly used to derive single ground truth labels for training supervised machine learning systems. However, in many subjective tasks, there is usually no single “correct” answer. By enforcing a single ground truth, there’s a potential risk of ignoring the valuable nuance in each annotator’s evaluation and their disagreements. <|MaskedSetence|> The DEER approach proposed in this work could be beneficial to this concern as it models uncertainty in annotator disagreements and provides some explainability of the predictions.
While our method helps preserve minority perspectives, misuse of this technique might lead to ethical concerns. <|MaskedSetence|> Furthermore, since the proposed approach takes each annotation into consideration, it is important to protect the anonymity of annotators..
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**A**: This can cause minority views to be under-represented.
**B**:
In tasks involving subjective evaluations such as emotion recognition, it is common to employ multiple human annotators to give multiple annotations to each data instance.
**C**: Emotion recognition is at risk of exposing a person’s inner state to others and this information could be abused.
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