liangzid/robench2024b_all_seteessSCP-s · Datasets at Hugging Face

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A: In Section VI, we present numerical examples to demonstrate the performance of the proposed methodB: Conclusions are drawn in Section VII.C: The application of C2⁢-WORDsuperscriptC2-WORD\textrm{C}^{2}\textrm{-WORD}C start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT -WORD to pattern synthesis is presented in Section V	BCA	CBA	BAC	CAB	Selection 1
A: The utility of coverage size is denoted asB: Higher altitude indicates larger coverage size as shown in Fig. 1 (c)C: In order to support as many users as possible, UAVs are required to enlarge coverage size, which is equal to enlarge the coverage proportion in the mission area	CBA	ABC	ACB	CAB	Selection 1
A: Pascal VOC datasets: The PASCAL Visual Object Classes (VOC) Challenge (Everingham et al., 2010) was an annual challenge that ran from 2005 through 2012 and had annotations for several tasks such as classification, detection, and segmentationB: The segmentation task was first introduced in the 2007 challenge and featured objects belonging to 20 classesC: The last offering of the challenge, the PASCAL VOC 2012 challenge, contained segmentation annotations for 2,913 images across 20 object classes (Everingham et al., 2015).	BAC	BCA	BAC	ABC	Selection 4
A: The sum SE calculated by (11) and (28) with different numbers of t-UAVs and the given transmit power is shown in Fig. 10, respectively, to verify the influence of the inter-UAV interferenceB: In this paper, we mainly focus on the analog beam tracking without considering the inter-UAV interferenceC: It is shown that the sum SE of the scheme without interference calculated by (28) is similar with that of the scheme with interference calculated by (11) with the appropriate number of t-UAVs and the limited transmit power. The gap between the schemes increases as the power and the number of t-UAVs increase. Therefore, the inter-UAV interference can be neglected in the considered scenario.	BAC	ACB	CBA	CBA	Selection 1
A: Motivated by distributed statistical learning over uncertain communication networks, we study the distributed stochastic convex optimization by networked local optimizers to cooperatively minimize a sum of local convex cost functions. The network is modeled by a sequence of time-varying random digraphs which may be spatially and temporally dependentB: The local cost functions are not required to be differentiable, nor do their subgradients need to be bounded. The local optimizers can only obtain measurement information of the local subgradients with random noisesC: The additive and multiplicative communication noises co-exist in communication links. We consider the distributed stochastic subgradient optimization algorithm and prove that if the sequence of random digraphs is conditionally balanced and uniformly conditionally jointly connected, then the states of all local optimizers converge to the same global optimal solution almost surely. The main contributions of our paper are listed as follows.	ABC	CBA	BAC	BAC	Selection 1
A: MPC accounts for the real behavior of the machine and the axis drive dynamics can be excited to compensate for the contour error to a big extent, even without including friction effects in the model [4, 5]. High-precision trajectories or set points can be generated prior to the actual machining process following various optimization methods, including MPC, feed-forward PID control strategies, or iterative-learning control [6, 7], where friction or vibration-induced disturbances can be correctedB: In MPC, closed-loop performance is pushed to the limits only if the plant under control is accurately modeled, alternatively, the performance degrades due to imposed robustness constraints. Instead of adapting the controller for the worst case scenarios, the prediction model can be selected to provide the best closed-loop performance by tuning the parameters in the MPC optimization objective for maximum performance [8, 9, 10]C: Using Bayesian optimization-based tuning for enhanced performance has been further demonstrated for cascade controllers of linear axis drives, where data-driven performance metrics have been used to specifically increase the traversal time and the tracking accuracy while reducing vibrations in the systems [11, 12]. The approach has been successfully applied to linear and rotational axis embedded in grinding machines and shown to standardize and automate tuning of multiple parameters [13].	BCA	CAB	ABC	BCA	Selection 3
A: We show that B-CPP also achieves linear convergence for minimizing strongly convex and smooth objectives. B: In the second part of this paper, we propose a broadcast-like CPP algorithm (B-CPP) that allows for asynchronous updates of the agents: at every iteration of the algorithm, only a subset of the agents wake up to perform prescribed updatesC: Thus, B-CPP is more flexible, and due to its broadcast nature, it can further save communication over CPP in certain scenarios [63]	CBA	CAB	CBA	ACB	Selection 2
A: We can further differentiate Bar(new) and Bar(cont), representing respectively the beginning of a new bar and a continuation of the current bar and always have one of them before a Sub-bar token. This way, the tokens would always occur in a group of four for MIDI scores. For MIDI performances, six tokens would be grouped together, including Velocity and Tempo. Following the logic of Bar, if there is no tempo change, we simply repeat the tempo value.B: FigC: 1(a) shows that, except for Bar, the other tokens in a REMI sequence always occur consecutively in groups, in the order of Sub-bar, Pitch, Duration	CBA	CAB	ACB	CBA	Selection 2
A: 5, where the baseline is the result tested by feeding the speech sample sequence into the ASR module directly without considering communication problemsB: The CER results of DeepSC-SR and two benchmarks under the AWGN channels and the Rayleigh channels are shown in FigC: From the figure, DeepSC-SR obtains lower CER scores than the speech transceiver and text transceiver under all tested channel environments. Moreover, DeepSC-SR performs steadily when coping with dynamic channels and SNRs while the performance of two benchmarks is quite poor under dynamic channel conditions. In addition, DeepSC-SR significantly outperforms the benchmarks in the low SNR regime.	CAB	BAC	CBA	BCA	Selection 2
A: 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 %). All datasets have a 50/50 balance between positive (tumor present) and negative (tumor absent) samplesB: 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). C: The PCAM dataset was downloaded from the original website (https://github.com/basveeling/pcam). All images have a size of 96 x 96 pixels, in three colors	CAB	BCA	CAB	ACB	Selection 2
A: To this end, suppose we want to generate the étendue-expanded hologram of only a single scene. Then, the optimal complex wavefront modulation for the neural étendue expander would be the inverse Fourier transform of the target scene, and, as such, we do not require any additional modulation on the SLMB: The SLM therefore can be set to zero-phase modulation.C: Next, we analyze the expansion of étendue achieved with the proposed technique	CAB	BCA	ACB	BAC	Selection 2
A: Here are some commonly used classic combinations of loss functions. B: These combinations aim to balance the quality, details, and visual perception of the generated imageC: Mixed Loss: In SISR, there are also some classic combinations of loss functions that are widely used to guide the network towards generating high-quality HR images	ACB	BCA	ACB	CBA	Selection 4
A: We have hence set out to explore explainability in anti-spoofing. Our goals are to better understand what artefacts are being captured or discarded by different solutions based upon different input features, different machine learning solutions or different components of ensemble systems [11, 12]B: We are also hopeful of understanding why some spoofing attacks are more difficult to detect than others, e.g. the infamous A17 attack contained within the ASVspoof 2019 logical access (LA) database [13]. We hope too to understand why solutions that operate directly upon raw waveform inputs [8, 14, 15] can perform better than systems that operate upon hand-crafted spectro-temporal representations [12, 16], but worse for others, e.g. the A08 attack [15]C: Additional motivation comes from the numerous reports in the literature which show that some solutions apply greater attention to non-speech intervals than to speech intervals [17, 18, 19]. Here we hope to better understand whether or not these issues are evidence of database design shortcomings and/or whether they point towards issues relating to the behaviour of certain, specific countermeasure solutions. In case of the former, we hope that studies of explainability might contribute to the development of tools to assist with database quality control.	BAC	BAC	BAC	ABC	Selection 4
A: We discuss the algorithmic implementation of our framework to account for assumptions of our work in practice. For instance, our framework crucially relies on obtaining “unsafe” data which is hard to obtain in practice, and we propose a new algorithm to obtain unsafe datapoints as boundary points from the set of safe expert demonstrations based on reverse k𝑘kitalic_k-nearest neighbors.B: In contrast to our previous works [53, 55, 54], in which we assume perfect state knowledge, we focus on dealing with state estimation errorsC: Our paper additionally differs from [53, 55, 54] in its practical focus	ACB	CAB	ABC	ACB	Selection 2
A: Tx and Rx have co-located dual-polarized antennas, such that two antenna ports are available for each antenna element at a distinct spatial locationB: Compared to the case where the same number of antenna elements with only a single polarization is available, this leads to an increase in diversity and capacity, although the gain depends significantly on the XPD [15, 6]C: However, this also leads to doubling the number of antenna ports,	ABC	ACB	BCA	ACB	Selection 1
A: Another possibility is to try to perform a regularization by infimal regularization [8] for lower semicontinuous objective functionals. In [29], in a function space setting, Pock et alB: propose a high dimensional lifting of the Lagrangian formulation of (2) where the data-fit functional is non-convex. 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].C: Many works intent to find a convex proxy to a non-convex objective function. 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	ABC	BCA	ABC	BAC	Selection 2
A: Figure 1: The distribution of the mean radial error (MRE) when choosing a different image as a template in one-shot medical landmark detection taskB: The x-axis refers to MRE and the y-axis refers to the percentage of MRE lying in the corresponding rangesC: Evidently, the choice of template affects the performance significantly.	CBA	CAB	CAB	ABC	Selection 4
A: A request from one of the participating teams to evaluate a second version of their container after the completion of the challenge was accommodated and the method is suffixed with the term post-challenge (pc) to distinguish it from other methods. Table 4 gives an overview of the participating methodsB: These additional invited teams were also required to submit their singularity containers for evaluation on testing dataC: Detailed descriptions of the evaluated methods can be found in Appendix A. Invited methods are indicated with *.	CAB	BAC	ABC	CBA	Selection 2
A: Figure 1(a) presents the evolution of the smallest eigenvalue of the time-varying symmetric matrix M˙˙𝑀\dot{M}over˙ start_ARG italic_M end_ARG and it shows that the sufficient condition for continuous differentiability of τs(.)\tau_{s}(.)italic_τ start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT ( B: ), given in Corollary 8, is satisfied. Hence, for this case, the inter-event time function τs⁢(θ)subscript𝜏𝑠𝜃\tau_{s}(\theta)italic_τ start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT ( italic_θ ) is continuously differentiable and it is also periodic with period π𝜋\piitalic_π. FromC: rule (3b)	CBA	BAC	BCA	BAC	Selection 3
A: If the controller gains are chosen such that the following inequalities are satisfied,B: Let us also consider the unsafe set for this system to be (12) and the metric measuring the distance from this unsafe set to be given by (13)C: Consider the system (4) with boundary conditions (8)	CAB	BAC	CBA	BCA	Selection 3
A: In SS-Setting, a training sample is comprised of spectrum sensors’ received power readings. 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 followsB: First, we compute an FFT on the I/Q samples collected within a time window to get a power spectral density (PSD) plot. 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. C: 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	CAB	ACB	BAC	BCA	Selection 4
A: Similar algorithms have also been extended to cases where zeroth order [6, 30, 36, 26, 32] and second order [16] oracles are used instead of (sub)gradients. The works [6, 36] on bandit feedback consider the situation where there are time-varying inequality constraints. In such cases the algorithms proposed in [41] will be hard to implement because of the high computational resource demand of the projection operation. This motivates recent research on online optimization algorithms with time-varying constraints including the primal-dual algorithm proposed in [13, 37], a modified saddle-point method given in [9]. Other algorithms are also proposed to handle stochastic constraints [38] and cover continuous-time applications [27]B: In the case where only the values rather than the exact form of the cost function at are revealed to the decision maker, bandit feedback based online algorithms [6, 36] can be used to solve the problem, other methods such as forward gradient [22] have also been proposed recently to deal with the issue. The need for applications in large-scale systems has also led to extensive research on distributed OCO. Distributed online algorithms that achieve sublinear regret bound for convex optimization problems with static constraints can be found in [28, 12, 33]. For instance, [28] proposes a distributed version of the dynamic mirror descent algorithm which is a generalization of the classical gradient descent methods suitable for high-dimensional optimization problems. The work [19] proposes distributed online primal-dual algorithms for optimization problems with static coupled inequality constraints while the work [35] studies distributed online convex optimization with time-varying inequality constraints in the discrete-time setting. For a more detailed documentation of recent advances of online optimization, we refer the readers to the survey paper [17].C: In the seminal work of [41], the method of online gradient descent is proposed for OCO problems, where at each time step the decision maker performs one gradient descent step using the latest available information. A static regret upper bound that is sublinear in T𝑇Titalic_T is proved, where T𝑇Titalic_T is the length of the horizon. Under stronger assumptions on the cost functions such as strong convexity, an improved logarithmic regret bound can be achieved [11, 23, 10]. If future information is available, it can be used to further improve the performance of the online optimization algorithm in terms of regret bounds. The work [15] introduces an additional predictive step following the algorithm developed in [41], if certain conditions on the estimated gradient and descent direction are met	ABC	CAB	BAC	BCA	Selection 4
A: The results demonstrate a significant improvement in the discriminative performance of the model compared to our previous experiments. Specifically, the substantial improvement in model performance when moving from 1,000 images to 99,000 images with large training epochs indicates that data quantity plays a critical role in training deep CNN models. The model also did not overfit the data during the extended training period of 300 epochs.B: The corresponding evaluation metric table in Table 2 provides the final measures of evaluation criteria for each conditionC: The ROC curve shown in Figure 8(b) illustrates the improved performance of the classifier for different pathology conditions across various thresholds	BAC	CBA	ACB	ACB	Selection 2
A: However, the target of these datasets is the classification of a generic set of emotions instead of focusing on fear detection. This strategy makes it hard to obtain a robust model due to the lack of fear samplesB: One of the main shortcomings of training this specific fear detection system for Bindi is the lack of adequate datasets. Over the last decades, several datasets were published providing emotional labels together with auditory and physiological variables, such as DEAP [6], MAHNOB [7], WESAD [8], AMIGOS [9], FAU, Reg, and Ulm TSST Corpora [10], and BioSpeech [11]C: Moreover, gender perspective was not considered either, in spite of stimuli interpretation being strongly affected by gender [12]. Instead, a sufficient number of women volunteers and a balanced fear and non-fear set of emotions should be considered to fit the target users of the GBV application.	BAC	BCA	BCA	ABC	Selection 1
A: Table 1 presents the FID values for each GAN-based architectureB: StyleGAN2-ADA achieved the lowest FID value of 166166166166, while EBGAN was placed in last and its FID value of 380380380380 was the highestC: The smaller the FID value, the better is the quality of the generated image. Therefore, all further experiments only considered the StyleGAN2-ADA architecture for synthetic image generation.	BAC	ABC	BAC	CAB	Selection 2
A: 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.B: 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 setC: 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	CAB	BCA	BAC	CBA	Selection 1
A: However, as highlighted by [26], deploying DL models for privacy-preserving inference nowadays is predominantly achievable through Multi-Party Computation (MPC). This process necessitates multiple servers and incurs significant overhead, primarily attributed to the size of the DL model, especially when handling 3D image registration tasks within a DL-based framework [9].B: While PPIR focuses on the privacy-preserving formulation of classical image registration methods based on gradient-based optimization, throughout the past years the research community has been steering the attention towards deep learning (DL)-based image registration [42, 27, 46, 9]. Among the medical imaging application of privacy-preserving methodologies, Kaissis et alC: [26] discussed privacy-preserving FL with Secure Aggregation [13] and Differential Privacy [1] for 2D medical image classification tasks	BAC	CAB	ABC	ABC	Selection 2
A: In particular, the state transition of a low-rank MDP aligns with that in our low-rank POMDP model. Nevertheless, we remark that such states are observable in a low-rank MDP but are unobservable in POMDPs with the low-rank transition. Such unobservability makes solving a low-rank POMDP much more challenging than solving a low-rank MDP. B: To learn a sufficient embedding for control, we utilize the low-rank transition of POMDPsC: Our idea is motivated by the previous analysis of low-rank MDPs (Cai et al., 2020; Jin et al., 2020b; Ayoub et al., 2020; Agarwal et al., 2020; Modi et al., 2021; Uehara et al., 2021)	CAB	BCA	ACB	BAC	Selection 1
A: in 2⁢s+12𝑠12s+12 italic_s + 1 variables xjsubscript𝑥𝑗x_{j}italic_x start_POSTSUBSCRIPT italic_j end_POSTSUBSCRIPT at a point η=0𝜂0\eta=0italic_η = 0 where xi=0subscript𝑥𝑖0x_{i}=0italic_x start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT = 0, for 1≤i≤s1𝑖𝑠1\leq i\leq s1 ≤ italic_i ≤ italic_sB: Ex. 59 corresponds to s=3𝑠3s=3italic_s = 3C: Again in the	ABC	ACB	ACB	CBA	Selection 1
A: 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 errorB: Based on which, Theorem 1 proves the almost sure convergence of the algorithm. Then, Theorem 2 gives intuitive convergence conditions for the case with balanced conditional digraphs by Lemma 2C: 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. 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.	BAC	CBA	ABC	CBA	Selection 3
A: Middle column: Reconstructions using SDGLR for regularization. Right column: Reconstructions using SDGGLR for regularization.B: Left column: Corrupted imagesC: Figure 10: Image patch denoising and interpolation results (PSNR) using SDGLR versus SDGGLR	ACB	BAC	ACB	CBA	Selection 4
A: For both phases, we used Adam optimizer [20] and weight initialization as proposed by [21]. Mean squared error (MSE) loss was used for both phases. B: For the second phase, we train the Param-Net on MRiLab dataset. The weights of the auto-encoder are frozen during this phaseC: The training process comprises of two sequential phases: training of the auto-encoder, and training of the Param-Net. For the first phase, the auto-encoder was trained on a subset of Places-365 dataset [19] and fine-tuned using MR Image dataset	CBA	ABC	BCA	CAB	Selection 1
A: On the receiver side, since the receiving light intensity of SiPM needed to be accurately measured, an integrating sphere Thorlabs IS200-4 was used to generate equal photon flux among the LED, MicroFC-10010 SiPM, and optical power meter. As the LED light enters the integrating sphere, it experiences numerous diffusions and reflections, ultimately achieving an even distribution over the entire inner surface of the sphere. To reduce the light intensity reaching the SiPM, a Thorlabs neutral density (ND) filter with an optical density of 20 is usedB: After the amplification, the SiPM output pulse was captured by the LeCroy oscilloscope. The internal memory of the oscilloscope allowed a maximum of 10 ms waveform capture each time. When the PC remotely captures and saves sufficient waveform data from the oscilloscope, the PC analyses the data samples. C: This attenuation in intensity allows the SiPM to work within its linear response range and enhances the measurement precision. The SiPM output signal was then amplified by two Mini-Circuit amplifier blocks: ZX60-43-s+ and ZFL-1000+. The average gain and maximum bandwidth of ZX60-43-s+ is 18.6 dB and 4 GHz, and ZFL-1000+ is 17 dB and 1 GHz	CBA	CBA	ABC	ACB	Selection 4
A: Section 5.2 shows results considering the close-approach, which are further analyzed in Section 5.3 in Monte Carlo simulations. Finally, Section 5.4 considers a different shape onboard to assess how the proposed autonomous GN&C behaves.B: In Section 5.1 we make some comments on the far-approach phase of an asteroid mission, which is beyond the scope of this workC: This section analyzes and discusses the results obtained by applying the proposed autonomous GN&C approach	BCA	BAC	BCA	CBA	Selection 4
A: Given the increasing attention being paid to the integration of UAVs into 5G networks [18, 19], for the sake of completeness of this tutorial, we next discuss briefly the modeling of UAV wireless channels, which is currently an active research topic [95, 96]. Before presenting the UAV channel models, let us discuss some particularities of UAV communications. For small-sized UAVs, the receiver is close to the UAV’s power electronics and to the motors which run continuously. As a consequence, sometimes the UAV’s motors generate electromagnetic noise that can interfere with the UAV’s own receiver [97].B: In general, they are not appropriate for UAVs applications mainly because of the changing altitude of the UAVC: The channel models described in the previous section were originally developed for mobile communications considering ground users and are well-suited to ground MRs applications	BAC	ACB	CBA	ACB	Selection 3
A: Assumption 12 requires boundedness on the sum of two storage functions in terms of parts (but not all) of their argumentsB: An alternative assumption where the lower and upper bounds are functions of ‖z‖norm𝑧\\|z\\|∥ italic_z ∥ will be considered later. C: This resembles boundedness on a time-varying Lyapunov candidate function [Khalil, 2002, Th. 4.8] and a Lyapunov candidate function for partial stability [Haddad and Chellaboina, 2008, Th. 4.1]	CAB	BAC	CBA	ACB	Selection 4
A: The RCBF has a form that is easy to imagine as a barrier, while the ZCBF is defined outside the safe set, allowing the design of control laws with robustness.B: In the context of a CBF, the control objective is to make a specific subset, which is said to be a safe set, on the state space invariance forward in time (namely, forward invariance [2])C: There are various types of CBFs, the most commonly used currently are a reciprocal control barrier function (RCBF) [2, 4, 5] and a zeroing control barrier function (ZCBF) [2, 3, 6]: the RCBF is a positive function that diverges from the inside of the safe set toward the boundary, while the ZCBF is a function that is zero at the boundary of the safe set	ABC	BAC	CAB	BAC	Selection 3
A: Further, we will derive the closed-form relationship between the gSCR and the capacity ratio between the GFM and the GFL converters to simplify the analysis of how large the capacity should be to meet certain stability margins. B: On this basis, we will show in the next section that the integration of GFM converters has a similar effect to installing ideal voltage sources (i.e., infinite buses) in series with an equivalent internal impedance in the networkC: Combining the power grid strength quantified by gSCR in this section and the analysis of the voltage source behaviors of GFM converters in Section II, it is once again emphasized that it is necessary to install GFM converters to provide effective voltage source behaviors and thus enhance the power grid strength, which can be quantified by gSCR	BCA	CBA	BCA	BAC	Selection 2
A: The first and simplest way is to enlarge the model capacity by training the network with larger datasets and better strategiesB: Specifically, based on ImageNet [10], IPT [4] and HAT [6] conducted a sophisticated pre-training to excavate the capability of transformers in image processingC: LSDIR [29] introduced a large-scale dataset to exploit model capacity fully. RCAN-it [32] leveraged reasonable training strategies to help RCAN [58] regain SOTA performance. Generally, these approaches are universal for neural models but increase burdensome training and data collection consumption.	ACB	CAB	ACB	ABC	Selection 4
A: Figure 5: Trajectories for a confidence-parameterized FRTB: This causes the human’s FRT to update and expand, which leads the robot to deviate from its nominal trajectory (gray) to an adjusted trajectory (orange) to avoid collision with the human or the obstacle.C: The human moves towards its target (shaded blue), but its trajectory (blue) shows a sudden change of direction to avoid the unmodeled obstacle (black)	ACB	BCA	BAC	BAC	Selection 1
A: In addition, any attempt to intercept the transmission, by placing for instance an object in the first Fresnel zone, will lead to a channel distortion/disruption or a detectable change of the spatial channel experienced by the receiver. Such propagation anomaly can be detected during the channel estimation procedure. This concept for secure communication can be regarded as an intrusion-alarmed quasi-passive wireless network where eavesdropping attacks can be detected. In case of an anomaly, the central node might decide to redirect the path over a different RIS route.B: Due to the directional retransmission and confined wave propagation (via “wireless waveguides”), the directive RIS network configuration is expected to have a higher security level [41], comparable to hard-wired systems (physical security or layer-0 security). Eavesdropping becomes harder as signals can only be received along well-confined wave trajectoriesC: This is different from conventional physical layer security methods, where channel state information of the attacker as well as a larger coding block length, i.e. higher latency, is needed	BCA	CAB	BCA	ABC	Selection 2
A: Specifically, the parallel visual and haptic feedback transmissions should be aligned with each other when arriving at the manipulator, and consecutive C&C and feedback transmissions should be within the motion-to-photon delay constraint, which is defined as the delay between the movement of the user’s head and the change of the VR device’s display reflecting the user’s movement. Either violation of alignment in parallel links or latency constraint in consecutive links will lead to a break in presence (BIP) and cybersicknessB: To implement a closed-loop XR-aided teleoperation system, the wireless network is required to support mixed types of data traffic, which includes control and command (C&C) transmission, haptic information feedback transmission, and rendered 360∘superscript360360^{\circ}360 start_POSTSUPERSCRIPT ∘ end_POSTSUPERSCRIPT video feedback transmission [14]. As XR-aided teleoperation task relies on both parallel and consecutive communication links, how to guarantee the cooperation among these communication links to execute the task is of vital importanceC: Therefore, both parallel alignment and consecutive latency should be quantified into effectiveness-aware performance metrics to guarantee the success of XR-aided teleoperation. Moreover, due to the motion-to-photon delay, the control error between the expected trajectory and the actual trajectory will accumulate along with the time, which may lead to task failure. Hence, how to alleviate the accumulated error remains an important challenge that needs to be solved.	BAC	CAB	CAB	ACB	Selection 1
A: Table 4.1 shows both the computation times and the results of randomly drawing sampled power injections within the specified range of variability, computing the associated voltages by solving the power flow equations, and finding the number of false positive alarms (i.e., the voltage at a bus with a sensor is outside the sensor’s threshold but there are no voltage violations in the system). The results for the 33-bus and 141-bus test cases given in Table 4.1 illustrate the performance of the proposed reformulationsB: Whereas the KKT formulation is computationally intractable, our proposed reformulations find solutions within approximately one minute, where the MILP formulation with the BVR method typically exhibits the fastest performance. The solutions to the reformulated problems place a small number of sensors (two to four sensors in systems with an order of magnitude or more buses)C: No solutions suffer from false negatives since all samples where there is a voltage violation trigger an alarm. There are a number of false alarms prior to applying the AGD that after its application decrease dramatically to a small fraction of the total number of samples (1.34%percent1.341.34\%1.34 % and 0.01%percent0.010.01\%0.01 % in the 33-bus and the 141-bus systems, respectively). These observations suggest that our sensor placement formulations provide a computationally efficient method for identifying a small number of sensor locations and associated alarm thresholds that reliably identify voltage constraint violations with no false negatives (missed alarms) and few false positives (spurious alarms).	CBA	ABC	BAC	ACB	Selection 2
A: We replaced the original second dense layer in CNN-MLC with B𝐵Bitalic_B parallel BiLSTM layers, which results in the CNN-Mask backbone network. The B𝐵Bitalic_B BiLSTM layers take the sigmoid function as the activationsB: They are designed to learn B𝐵Bitalic_B ratio masks. See the following for the details. C: Inspired by [33], we designed a mask layer for both single and multiple speakers, implemented with Bi-directional Long Short-Term Memory (BiLSTM). Table II outlines the CNN architecture incorporating the mask layer	BCA	CAB	ABC	BAC	Selection 1
A: 10(c)B: For our second example, we observed that if the CNN were fed with images containing obstacles with a small apparent height (such as glass gates), it would often ignore the obstacle as if it were a traversable area. A simulation at one of these failure states exposed a glass door, as shown in figC: The network conveniently ignores the glass door as it can see the ground area through it.	CBA	ACB	BAC	CBA	Selection 3
A: This makes it non-trivial to implement exact batched inference for multi-blank RNN-Ts, since different utterances in the same batch might output blanks with different durations, making it hard to fully parallelize the computation.B: The multi-blank RNN-T method is ideal for on-device speech recognition in that it brings significant speedup in recognition speed as well as better accuracyC: With that being said, the method also supports batched inference to run on the server side. In Section 2.4, we mentioned that during inference, when a big blank symbol is emitted, it should advance the input t𝑡titalic_t by the duration corresponding to the big blank	ACB	ABC	CAB	ACB	Selection 3
A: Enhancing the real speech involving a scene, such as “Airport”. 2B: Adding another scene to the enhanced speech, such as “Street”. The signal noise ratio (SNR) of the real utterance is denoted by SNRreal. The SNR of the fake utterance is referred to as SNRfake. The SNRreal and SNRfake are both 5dB in the example.C: Figure 3: An example of acoustic scene manipulation for a fake utterance. The manipulation procedure consists of two steps: 1	BCA	ACB	BAC	CAB	Selection 1
A: 4 that the non-zero initial conditions, in general, don’t decay to zero in finite time. Next, we discuss the performance comparison of TV-OKID and the Information-state approachB: First, we show the importance of having a system identification technique that is immune to non-zero initial conditions. We show in FigC: For the oscillator, two experiments were performed - one with zero-initial conditions and another with non-zero initial conditions, and the results are shown in Fig. 5. The results for the nonlinear systems are shown in Fig 6. The error shown in the figures is the 1-norm of the mean error between the true response and the predicted response from 100 independent simulations, across all the output channels.	ABC	BAC	CBA	CBA	Selection 2
A: Figure 6: Average inference latency per frame under different communication rates in the multi-camera pedestrian occupancy prediction taskB: The overall latency comprises on-device computation time, transmission latency, and server-side computation timeC: All the methods achieve a MODA of around 87%.	ABC	CAB	CBA	BCA	Selection 1
A: The direct link between the sub-diffusion model parameter β𝛽\betaitalic_β and mean kurtosis is well established (Yang et al., 2022; Ingo et al., 2014, 2015). An important aspect to consider is whether mean β𝛽\betaitalic_β used to compute the mean kurtosis is alone sufficient for clinical decision making. While benefits of using kurtosis metrics over other DW-MRI data derived metrics in certain applications are clear, the adequacy of mean kurtosis over axial and radial kurtosis is less apparentB: In a different study a correlation was found between glomerular filtration rate and axial kurtosis is assessing renal function and interstitial fibrosis (Li et al., 2022a). Uniplor depression subjects have been shown to have brain region specific increases in mean and radial kurtosis, while for bipolar depression subjects axial kurtosis decreased in specific brain regions and decreases in radial kurtosis were found in other regions (Maralakunte et al., 2022). This selection of studies highlight future opportunities for extending the methods to additionally map axial and radial kurtosis. C: Most studies perform the mapping of mean kurtosis, probably because the DW-MRI data can be acquired in practically feasible times. Nonetheless, we can point to a few recent examples where the measurement of directional kurtosis has clear advantages. A study on mapping tumour response to radiotherapy treatment found axial kurtosis to provide the best sensitivity to treatment response (Goryawala et al., 2022)	BAC	ACB	CBA	BAC	Selection 2
A: (2021) presented a nonlinear optimization model where 1111d Euler equations were used to derive a model of the water flow in a DHG. On top of that, Machado et alB: (2022) built and described a modeling approach of DHGs, whose passivity properties were shown.C: To model DHGs in great physical detail, Krug et al	ACB	BAC	BCA	ABC	Selection 3
A: This is because by synergistically deploying charging and battery swapping stations, the platform can effectively reduce the charging cost and thus address this bottleneck. As shown in Figure 3b, compared to charging-only deployment, joint planning yields a charging cost reduction of up to 44.4% under a sufficient budget. As the budget increases, the charging cost of joint planning is even comparable to that of swapping only, which underscores the potential of joint planning to capitalize on the complementary strengths of charging and swapping facilities. We formalize the superiority of joint planning as follows.B: Specifically, under a tight budget, joint planning yields a total profit that is significantly higher than swapping-only deployment (i.e., by 11.7%). In this case, the major bottleneck is the budget as it is too limited to afford an extensive swapping network and thus cannot support a large EV fleet. Conversely, when the budget is generous, the bottleneck becomes the low fleet utilization arising from time-consuming plug-in charging. The total profit of joint planning is significantly higher than charging-only deployment under a large budget (i.e., by 17.5%)C: In contrast, joint planning combines the advantages of both charging and battery swapping stations, thereby harnessing the synergistic value between the two facilities. Under the joint planning scheme, the platform can not only address the scaling challenge of battery swapping by building charging stations at the early stage but also overcome the bottleneck of plug-in charging and enhance fleet utilization by building battery swapping stations. Consequently, joint planning outperforms other deployment strategies in terms of profit maximization. However, the synergistic benefit of joint planning depends on the budget level as well as the scenario it is compared to	CAB	CAB	CBA	BAC	Selection 3
A: It is noteworthy that DEviS exhibits superior robustness in uncertainty estimation compared to the conference version of the TBraTS method. Additionally, as depicted in Fig. 5 (c), the segmentation results of different methods show that under Gaussian noise, introducing the DEviS model can slightly improve the segmentation results of nnU-Net and V-Net, while U-Net and Attention-UNet with DEviS demonstrate more robust segmentation results. This is because nnU-Net and V-Net themselves possess certain noise resistance capabilities. B: The integration of DEviS enhances the robustness under different levels of Gaussian noise, Gaussian blur, and random masking. Taking the Attention-UNet method based on U-Net as an example, the introduction of DEviS results in an average increase of 12.6% and 5.1% in the Dice metric under Gaussian noise and Gaussian blur conditions, respectively. Similarly, considering the nnFormer based on the Transformer, the introduction of DEviS leads to an average increase of 2.21% and 5.93% in the Dice metric under Gaussian noise and random masking conditions, respectivelyC: 1) Comparison with U-Net and transformer based methods. As shown in Fig. 5 (a), under normal conditions, V-Net, Attention-UNet, nnU-Net, and nnFormer demonstrate comparable performance; however, their segmentation performance begins to degrade under different levels of Gaussian noise, Gaussian blur, and random masking, with the impact becoming more pronounced as the severity increases	BCA	CAB	CBA	ACB	Selection 3
A: Due to the fact that the onset feature represents the beginning of a cardiac activity, IBIs sequence from the onset feature is selected as the baseline for segmentationB: Firstly, the IBIs sequence of the onset feature is divided into q segments where each segment contains three consecutive IBIsC: The timestamps of this segmentation are used as references to guide the segmentation of IBIs sequences for maximum slope and systolic peak features. For each segment, if the starting points of IBIs from maximum slope and systolic features are within this segment, these IBIs are included as the candidate IBIs. Secondly, based on physiological phenomenons that true IBIs are expected to be close to their average IBIs and would not have drastic changes for short time, hence, an objective function as Equation (4) is proposed for the greedy fusion method to find local optimum IBIs in each segment.	BCA	BAC	BAC	ABC	Selection 4
A: Each RRDB contains multiple densely connected residual blocks, which enables features to be fully reused and propagated efficiently within each RRDB to extract richer features and utilize correlations between channels. Compared to standard residual blocks, RRDB achieve superior performance with significantly fewer parameters. B: RRDB combines the advantages of residual learning and dense connectionsC: The generator backbone uses Residual-in-Residual Dense Blocks (RRDBs), inspired by ESRGAN[20], a seminal work in single image super resolution with excellent generated results	CAB	ABC	CBA	ABC	Selection 3
A: In this paper, we consider DoA estimation in a reverberant enclosure consisting of desired sources along with interference sourcesB: Consequently, their identification as desired or interference is unknown as well. The power of the different sources is also unknown, and the interference sources could, in fact, be stronger than the desired sources with overlapping activity periods.C: We assume that the desired sources are constantly active, whereas the interference sources are only intermittently active. The number of sources, their locations, and their times of activity are all unknown	CAB	ACB	BAC	ABC	Selection 2
A: Researchers have been applying machine learning and deep learning techniques on human respiration data collected through various technologies for anomaly detectionB: Some of the common categories of features used in the literature were statistical features from the data (mean, standard deviation, skewness, kurtosis, root mean-square value, range etc.), signal-processing based features (Fourier co-efficients, autoregressive integrated moving average co-efficients, wavelet decomposition, mel-frequency cepstral coefficients, linear predictive coding etc.), and respiration related features (breathing rate, amplitude, inspiratory time, expiratory time etc.) [28, 29, 30, 11, 31, 24]. In some research efforts, deep neural networks were trained to recognize subtle features from breathing data before classification, thus making manual feature extraction redundant [19, 32, 9, 17, 26].C: Most of these efforts made use of handcrafted features to perform breathing data classification for anomaly detection	ABC	BAC	ACB	BAC	Selection 3
A: Moreover, O-RAN operation is divided into three different control loops [7]: the real-time (RT), near-RT, and non-RT loops executing at different time-scalesB: O-RAN adopts the functional split defined in 3GPP [6] and defines three distinct units [7]: the open central unit (O-CU), open distributed unit (O-DU), and open radio unit (O-RU)C: The resulting O-RAN architecture and standard names of interfaces between these elements, which enable practical implementation of many RAN operations, are depicted in Fig. 1(a).	BAC	BCA	ACB	CAB	Selection 1
A: In the reminder of this study, we focus on the partial dual problem (14)B: These problems are known to be very challenging, even if convex [29, Chapter 2]. In contrast, as we will see later in the manuscript, the use of the partial dual problem leads to a tractable inner minimization problem. This is one of the major differences with respect to the finite dimensional case in [23].C: The reason is that the dual problem (13), or other variations that keep the SINR constraints augmented, do not seem tractable, mostly because of the difficulties in solving the (infinite dimensional) inner minimization problem over the space of precoders subject to nontrivial information constraints	BAC	BCA	ACB	BCA	Selection 3
A: The suboptimality analysis of LQR, where a modeling error is present, has been considered in [18, 19, 20]B: As we consider the LQ setting, the performance analysis of LQ regulator (LQR) for unknown systems is also relevant, which has received renewed attention from the perspective of learning-based control [15, 16, 17]C: This analysis is an essential ingredient for further deriving performance guarantees for learning-based LQR controllers. It typically relies on the perturbation analysis of the Riccati equation [19, 20], which characterizes the solution of the Riccati equation under a modeling error. However, since the LQR concerns an infinite prediction horizon, the above analysis does not consider the effect of the prediction horizon and the terminal value function.	BCA	CBA	ACB	BAC	Selection 4
A: Although they are commonly employed for data assimilation in numerical weather prediction, they require large computational resources since they involve repeated solutions of the high-dimensional dynamics (1). Thus, they are not applicable in the context of embedded control systems, whose limited resources call for an inexpensive model such as the ROM (3). Since the ROM that we consider has linear dynamics, extensions of the Kalman filter for nonlinear dynamics such as the extended or unscented Kalman filters (Wan & Van Der Merwe, 2000; Julier & Uhlmann, 2004) are not relevant, and the vanilla Kalman filter remains the best choice of baseline.B: The ensemble Kalman filter and 4D-Var are two estimation techniques for high-dimensional systems such as those governed by PDEs (Lorenc, 2003)C: Before proceeding to the results, we discuss our choice of baseline	CAB	BCA	CBA	ACB	Selection 3
A: This kind of integration aids in capturing both local and global features and reducing the neglect of potentially diseased areas. Specifically, varying medical datasets may contain lesions of varying sizes. By capturing and understanding features across a broader range of scales, the hybrid model improves its ability to recognize and interpret features in unseen images, which may present lesions at varying scales. This extensive scale coverage enables the model to adapt to unseen images better, thereby significantly contributing to the overall performance and generalization capability.B: CNN has dominated the CV field over the past several years and is capable of capturing spatial hierarchies of the input features with successive convolution operations [5]. A typical CNN consists of several types of layers, including convolution layer, dropout layer, normalization layer, etc. Compared with CNN, the transformer is an emerging role in the CV field initially proposed in the natural language processing field. The core component of the transformer is the self-attention mechanism or attention in shortC: The attention mechanism in a transformer network creates interdependencies among different positions within a single sequence, enabling the model to compute a context-aware representation of each position in the sequence [6]. The primary distinction between the CNN- and the transformer-based methods is that CNN tends to emphasize local features, whereas the transformer is more oriented toward global features. As the visual input may contain lesions of varying scales, it is crucial to enhance the ability of the model to capture features at both local and global scales. To take advantage of both CNN and transformer, the combination of CNN and transformer has developed rapidly	BCA	CAB	CBA	BCA	Selection 2
A: The left and right imagers embed depth information to aid the generation of depth maps (with the help of a filter), and using these images in stereo-based localization algorithms might give lower accuracy compared to non-filtered imagesB: While using these data, the effect of the rolling shutter camera can be ignored, since the camera is placed on top of a relatively stable robot moving at a low speedC: The Mocap Studio is decorated with posters and objects with different textures to enable feature extraction in vision-based localization algorithms. Some example decorations can be seen in Fig. 3.	CAB	CBA	CBA	BAC	Selection 4
A: In this step, we are able to measure slowly and precisely, meaning repeated measurement is possible to increase the dynamic range of the diffraction pattern and reduce noise. So we can have a precise, high quality reference known before the reconstruction of the sample.B: The first step is referenced pattern zoomingC: Since most of the time, the reference is designed to be real and without fine structures, so it is easily reconstruced with conventional CDI algorithm such as HIO	CAB	ABC	CBA	BCA	Selection 1
A: While this does not affect the cost of the transfer itself, the additional round trip requires additional propellant and payload to be brought and stored at the depotB: The demand D𝐷Ditalic_D reflects the number of round trips to be conducted between a depot and its clientsC: As such, as D𝐷Ditalic_D increases, the saving in propellant mass resulting from placing the depot closer to the client increases.	ABC	CAB	BCA	BAC	Selection 4
A: It supports easy benchmarking of different speech representation models. In this paper, we chose to experiment with two pre-trained models, wav2vec [28] and HuBERT [11] to see how different augmentation techniques impact the performance of both PR and ASR tasks.B: Phoneme Recognition (PR) and Automatic Speech Recognition (ASR) are two of the most common speech-processing tasks that can greatly benefit from applying these augmentation techniquesC: We used S3PRL, an open-source toolkit that targets self-supervised learning for speech processing [38]	BCA	CBA	CAB	BAC	Selection 3
A: In S-Band, we already have existing terrestrial communication from 4G LTE devices. With the advent of mm-wave technology, terrestrial communication is also using the Ka-band in 5GB: So the satellite users will suffer from co-channel interference with the terrestrial users in both bands. To avoid this interference, we have to come up with efficient spectrum-sharing techniques to put the interference below a certain threshold ensuring proper decoding of the received signals.C: As discussed in Section II-D, the S-Band and Ka-Band are the target bands for NTN. On top of this limited spectrum allocation, we have interference from terrestrial users in these bands	BAC	BCA	CBA	CAB	Selection 2
A: To some degree, they represent distinct oscillation modes within the same hybrid system, triggered solely by initial conditions like forward speed and the torso’s heightB: This work not only provides crucial insights into the rationale behind utilizing multiple gaits at varying speeds but also holds the promise of an efficient and versatile strategy for generating reference trajectories for robotic systems with desired footfall sequences.C: When only one leg pair initiates movement out of phase, it simultaneously disrupts both leg permutation and time-reversal symmetries, leading to the discovery of four distinct half-bounding gaits, as depicted in Fig. 5. These solutions were identified using a single energy-conserving model without the need for additional control laws or actuation	ABC	BAC	BCA	ACB	Selection 3
A: The pure Python implementation implements the transform using matrix multiplication. Therefore, the speed of DCT-ResNets and BWT-ResNets is the same as in this implementation. B: Other layers are built in PyTorch so they are always accelerated by CUDAC: Note: “CUDA” and “Pure Python” represent whether CUDA acceleration is applied on the HT-Perceptron layers	BCA	BAC	ABC	CBA	Selection 4
A: During training, no additional bias corrections were applied to the network output, thus we expected GT values close to zero to be mapped to predicted up-to-scale depths close to zero (i.e. zero offset) (see Figure 4A).B: For each trained model the relationship between the GT and the predicted up-to-scale depths was analyzed on its respective test split. An MDE trained using projective geometry [30, 14] is expected to linearly rank the predicted depth per image [53]C: We started our analysis by separately training the MDE on datasets from various domains	ABC	ABC	CBA	BAC	Selection 3
A: Crude ControlB: A naïve system that forcefully modifies predicted values individually, this straw-man represents a controllable model that is entirely inconsistentC: Its default prosody is generated with NoControl, and then modified manually with the control points111Samples demonstrating TTS for these systems can be found here, anonymous-submission-563098.github.io/sparse-control.	ABC	CBA	BAC	ACB	Selection 1
A: Therefore, extended comparison of the ideal baseline scenario with several association policies is provided, and evaluation of diverse dominant interferer-based scenarios are highlighted. B: In summary, even though angular coordinates have appeared in the stochastic geometry-based analysis, their manifestation in the received power as a consequence of realistic beam management procedure and the overall effect on the system performance has not been studied, which is the main objective of this paperC: Consequently, this work proposes for the first time a stochastic geometry framework to study the implications of beam misalignment error in the association policy and the corresponding performance of a mmWave cellular network by adopting realistic 5G NR beam management-based procedures and practical equipment limitations	CAB	BAC	ACB	BCA	Selection 1
A: The proposed filters exploit the cubature and quadrature rules to approximate the recursive Bayesian integrals. The I-CKF’s stability conditions are easily achieved for a stable forward CKF. The developed inverse filters are also consistent, provided that the initial estimate pair is consistentB: Numerical experiments show that I-CKF and I-QKF outperform I-UKF even when they incorrectly assume the true form of the forward filter. However, I-QKF is computationally expensive, while I-CQKF provides reasonable estimates at lower computational costs. The non-trivial upshot of this result is that the forward filter does not need to be known exactly to the defender.C: We developed I-CKF, I-QKF, and I-CQKF to estimate the defender’s state, given noisy measurements of the attacker’s actions in highly non-linear systems. On the other hand, RKHS-CKF, as both forward and inverse filters, provides the desired state and parameter estimates even without any prior system model information. In the case of the perfect system model information, our developed methods can be further generalized to systems with non-Gaussian noises, continuous-time state evolution, or complex-valued states and observations	CAB	ABC	BCA	ACB	Selection 3
A: 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]. We demonstrate our approach through extensive numerical experiments.B: 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)C: 1) M-DBD with structured unknown continuous-valued parameters. 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. 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	CBA	ABC	ABC	ABC	Selection 1
A: Since we are comparing upper bounds, strictly speaking we cannot conclude that the actual Lipschitz constant of the SR-LASSO is larger than that of the LASSOB: However, the fact that these two upper bounds arise from the application of analogous proof techniques suggests this to be a reasonable conjectureC: This theoretical insight aligns with numerical evidence provided by Fig. 2(b) and the next subsection.	ABC	CAB	BCA	BCA	Selection 1
A: Some studies upsample each 2D LR medical slice to acquire the corresponding HR one, such as [8, 43, 47]B: [5] and Wang et al. [36] use 3D DenseNet-based networks to generate HR volumetric patches from LR ones. Yu et al. [45] build a transformer-based MISR network to address volumetric MISR challenges.C: On the other hand, Chen et al	ACB	BCA	BCA	CBA	Selection 1
A: For SZO-QQ, in case an infeasible iterate appears, which is a result of underestimated Lipschitz and smoothness constants, we can easily recover feasibility by using the last feasible iterate. Then, we need to enlarge the constants according to Remark 3.B: We also notice that SZO-QQ and LB-SGD satisfy sample feasibility while Extremum-Seeking generates infeasible iterates. Due to the lack of a recovering mechanism, after the first infeasible sample, most of the other samples of Extremum-Seeking are also infeasibleC: We discontinue the corresponding curve in Fig. 5 after the first infeasible sample	BAC	BCA	CBA	CAB	Selection 4
A: For the same power constraint, the computational complexities of the RSD or RCG method are nearly the same and are lower than those of the RTR and comparable methods. B: With the Riemannian ingredients derived in Section III, we propose three precoder design methods using the RSD, RCG and RTR in this sectionC: There is no inverse of the large dimensional matrix in the proposed Riemannian methods during the iterations, thereby enabling significant savings in computational resources	CBA	BCA	CAB	BAC	Selection 3
A: A SinGAN generative model is then used to realistically blend the contrast-altered tumor to the original image. The cascaded SinGAN generators are trained on the central slice and applied to every slice containing the tumor. B: For each tumor slice, tumor intensity values are linearly scaled by a factor λ𝜆\lambdaitalic_λ to perform contrast alterationC: Figure 3: Generative blending augmentation (GBA) of a vestibular schwannoma in a synthetic hrT2 image generated by CycleGAN (CrossMoDA 2022 dataset)	CBA	ABC	BCA	BAC	Selection 1
A: The implementation of the method is described in Section 3.2 and the results are presented and discussed in Section 4, including a comparison with other localization methods from the literatureB: Finally, conclusions are drawn in Section 5.C: The theoretical basis of the method is derived in Section 2 which is evaluated in a simulation setup described in Section 3.1	ACB	CAB	BCA	ACB	Selection 3
A: By exploiting existing results on rewriting signal temporal logic specifications to MILP constraints, we can efficiently solve an optimization problem to automatically synthesize a control policy that ensuresB: In this paper, we have developed a direct data-driven controller synthesis method for linear time-invariant systems subject to a temporal logic specification, which does not require an explicit modeling stepC: We build upon the promising results of the behavioral framework and the Fundamental Lemma, which allows us to obtain a characterization of the system after collecting a single sequence of input-output data from it	CBA	CAB	BCA	BAC	Selection 2
A: Specifically, the MQA framework naturally allows users to examine the response changes to different combinations of imaging and clinical data, and thus, the informative clinical elements can be identified as those contributing to statistically high prediction accuracy.B: It is achieved with the MQA framework and the attention mechanismC: The M3FMs are capable of identifying informative clinical elements both quantitatively and qualitatively, which offers certain explainability	CAB	CBA	ACB	ACB	Selection 2
A: (6) and (4) indicates the importance of fixing codebook during finetuning to protect the pre-trained clean speech prior. Comparison between ExpB: (6) and (5) demonstrates that the pre-trained prior knowledge in codebook is important to the clean speech restoration and downstream ASR.C: Furthermore, comparison between Exp	CBA	CAB	BCA	BAC	Selection 3
A: In order to decompose the strong coupling, we present the following representation of an arbitrary point located on the TX (RX) surface via using the center coordinates. Since the TX (RX) surface is divided into discrete antenna elements, an arbitrary point located on the TX (RX) surface belongs to a certain antenna element.B: It is quite challenging to eliminate those multiple integrals due to the complicated coupling among themC: The double integrals of (5) over TX and RX element areas can be expressed as multiple integrals over three spatial orientations (x𝑥xitalic_x, y𝑦yitalic_y and z𝑧zitalic_z orientations)	CBA	BCA	ABC	ABC	Selection 1
A: The second consists of a synchronous machine described by a third order model, to which a governor and automatic voltage regulator (AVR) representations are added. The grid side is represented by an infinite bus [10, 13]. B: The first one is the well known synchronisation loop of classic grid-following converters: the phase locked loop (PLL [24, 25, 26])C: In this section we present the model of two different test cases, proposed in the literature, to illustrate the tool capabilities, flexibility and performance	ACB	BAC	BCA	CBA	Selection 4
A: ResNet. ResNet was introduced in 2015 by Microsoft ResearchB: The model is designed to handle the vanishing-gradient problem in deep networks, which can lead to underfitting during training. The pre-trained ResNet model is used as a meaningful extractor of residuals, instead of features, from the raw data using identity shortcut connections [79]C: Refined variants of ResNet, such as ResNet-34, ResNet-50, and ResNet-101, have been created using various combinations of layers. ResNet has already been employed for IDS-based DTL [80, 81].	CBA	ABC	CAB	ACB	Selection 2
A: The proposed hybrid controller is run in Matlab R2020a on another computer running Windows 10, equipped with an Intel(R) i5-5200U CPU with a clock speed of 2.20 GHz and 12 GB RAM, referred to as Computer 2.B: The next simulation is performed using the Turtlebot3 Burger model in GazeboC: The simulation runs on a computer, equipped with 4 GB RAM, running Ubuntu 20.04 with the ROS Noetic distribution installed, which we refer to as Computer 1	ACB	ACB	CBA	CAB	Selection 4
A: The training settings followed the default configurations of 2D nnU-Net. Each model was trained on one A100 GPU with 1000 epochs and the last checkpoint was used as the final model. The DeepLabV3+ specialist models used ResNet50 [38] as the encoder. Similar to [3], the input images were resized to 224×224×32242243224\times 224\times 3224 × 224 × 3B: We employed the nnU-Net to conduct all U-Net experiments, which can automatically configure the network architecture based on the dataset properties. In order to incorporate the bounding box prompt into the model, we transformed the bounding box into a binary mask and concatenated it with the image as the model input. This function was originally supported by nnU-Net in the cascaded pipeline, which has demonstrated increased performance in many segmentation tasks by using the binary mask as an additional channel to specify the target locationC: The bounding box was transformed into a binary mask as an additional input channel to provide the object location prompt. Segmentation Models Pytorch (0.3.3) [39] was used to perform training and inference for all the modality-wise specialist DeepLabV3+++ models. Each modality-wise model was trained on one A100 GPU with 500 epochs and the last checkpoint was used as the final model.	CBA	BAC	CAB	CBA	Selection 2
A: An illustration of the proposed model is shown in Fig. 1. In this study, the gNBs and IAB-nodes formed two different tiers. Multi-hop backhaul IAB networks will not be considered because of the challenge of network configuration, and the feasibility of using stochastic geometry in this scenario [16]. B: The backhaul link is the wireless link between the gNB and the IAB-node, whereas the access link is between the UE and the gNB or the IAB-nodeC: More details of the 3GPP architecture can be found in our recent study [2]	CBA	ACB	CAB	ACB	Selection 3
A: Therefore, applying this result to the PMB density of the form (13) yieldsB: This type of single-target space was obtained when we introduced auxiliary variables to the PMB in (12), resulting in (13)C: A set-density defined in a single-target space that is the disjoint union of different sub-spaces can be used to define a density over a sequence of sets [22, Eq. 3.52]	CBA	BCA	CAB	ABC	Selection 1
A: For semantic communications, the accuracy can be measured by the task performance and easily quantified by semantic similarity for text transmission, character-error-rate for speech recognition, answer accuracy for VQA task, and so forth. However, the efficiency of semantic communications is usually hard to measure and quantify. Although the S-SE has been defined in [21], it can not be calculated due to the lack of semantic information quantification.B: A communication system is usually evaluated from two aspects, accuracy and efficiencyC: The conventional communications are measured by bit-error rate and bit transmission rate	BCA	CBA	CAB	CBA	Selection 3
A: Furthermore, a stability-preserving, adaptive rational Krylov (SPARK) algorithm [33] was developed to maintain model stability and is usually embedded in the CURE scheme to generate a family of stable ROMs whose orders are increased by sequential accumulation in a single MOR process. This embedded CURE scheme with SPARK algorithm (hereafter abbreviated by SPARK+CURE) was used in [34] to generate a family of physically-interpretable multi-fidelity surrogate LPMs for physical systems governed by PDEs.B: To overcome this limitation, the CUmulative REduction (CURE) scheme was proposed, which adaptively chooses the expansion frequency and incrementally increases the scale of the ROM by monotonically decreasing the norm of the error transfer function to zero through an accumulation process [33]C: However, the IRKA algorithm cannot ensure the error converges to a local minimum [32]	BAC	CAB	CBA	ACB	Selection 3
A: Calibration-aware training improves the match between accuracy and confidence, resulting in a lower ECE, for both frequentist and Bayesian learning, with CA-BNN achieving the lowest ECE. B: Bayesian learning is observed to yield better calibrated decisions than frequentist learning, as also manifest in the lower value of the ECEC: Fig. 3 shows the reliability diagrams (see Sec. II-B) for all four schemes	BCA	ACB	CAB	CBA	Selection 4
A: In this section, we perform a case study on decentralized sparse soft-max regression to verify the theoretical results and show the convergence performance of Prox-DBRO-SAGA and Prox-DBRO-LSVRG, where three kinds of Byzantine attacks (zero-sum attacks, Gaussian attacks, and same-value attacks) are consideredB: All simulations are carried out in Python (version 3.9) on a DELL server (Linux) with 20 Cores 40 Threads i9-10900X 3.70 GHz processor and 32GB memory. C: The communication networks are randomly generated by the Erdős-Rényi method, where Byzantine agents are also selected in a random way	ABC	BAC	ACB	CBA	Selection 3
A: The comparison between the proposed MaxViT-UNet and previous methodologies is presented in Table 3 on the MoNuSeg18 dataset and Table 4 on the MoNuSAC20 datasetB: For the MoNuSeg18 dataset, we performed binary semantic segmentation. Whereas the MoNuSAC20 challenge contains four types of nuclei, we performed multi-class semantic segmentation for the MoNuSAC20 dataset. The proposed MaxViT-UNet beats the previous techniques by a large margin on both datasets and proves the significance of the hybrid encoder-decoder architecture. C: For comparison on both datasets, UNet and Swin-UNet were trained using MMSegmentation [73] with the same hyper-parameters as the proposed technique	ABC	ACB	BCA	CBA	Selection 2
A: However, securing labels for new tasks is not always feasible. Furthermore, the success of these methods heavily depends on the number of supportive examples providedB: Another way towards the universal medical image segmentation is few/one-shot learning [8, 51, 42, 41]. In this setting, a pre-trained foundational model needs one or few labeled samples as the ’supportive examples’, to grasp a new specific taskC: For example, UniverSeg[8] achieves competitive performance with 64 supportive samples. But obtaining such amount of data can be challenging in real clinical practice.	ABC	BAC	ACB	ABC	Selection 2
A: The solution can account for the aggregate volume across all current Web3 Node RPC providers and comes equipped with an inbuilt difficulty modulation to dynamically adapt to the growing traffic in the Web3 industry. B: This paper introduces Relay Mining, a novel algorithm designed to scale efficiently to handle tens of billions of individual RPC requests dailyC: These requests are proven on a decentralized ledger via a commit-and-reveal scheme	ACB	ACB	BCA	CAB	Selection 4
A: We also performed AB preference tests by 15 listeners resulting in a total of 100 utterances to verify the generalization to different datasets conveniently. All the tests listed below are done in speaker F1, except the AB preference tests are done for both F1 and F2. B: We calculate the average RMSE of the F0 in Hertz of random 100 utterances in the validation set as an auxiliary metric to evaluate which model fit the pitch prosody betterC: Apart from the subjective tests, we also evaluate a commonly used objective metric called Root Mean Square Error (RMSE) of Fundamental Frequency (F0) for Ablation Studies in Section 4.2	CAB	CBA	CAB	BCA	Selection 2
A: There are two main criteria for optimizing speech pre-training: contrastive loss Oord et al. (2018); Chung and Glass (2020); Baevski et al. (2020) and masked prediction loss Devlin et al. (2018)B: (2021). Some recent work Chung et al. (2021) has combined the two approaches, achieving good performance for downstream automatic speech recognition (ASR) tasks. In this work, we leverage the success of self-supervised to enhance both the encoder and decoder to alleviate the data scarcity issue. C: Contrastive loss is used to distinguish between positive and negative samples with respect to a reference sample, while masked prediction loss is originally proposed for natural language processing Devlin et al. (2018); Lewis et al. (2019) and later applied to speech processing Baevski et al. (2020); Hsu et al	ABC	BAC	ACB	BCA	Selection 3
A: where δ=ℜ⁢(𝐙SS⁢(1,1))/M𝛿ℜsubscript𝐙SS11𝑀\delta=\mathfrak{R}(\mathbf{Z}_{\text{SS}}(1,1))/Mitalic_δ = fraktur_R ( bold_Z start_POSTSUBSCRIPT SS end_POSTSUBSCRIPT ( 1 , 1 ) ) / italic_M and 𝜽𝜽\boldsymbol{\theta}bold_italic_θ is calculated according to [13, EqB: (24)]C: First,	BCA	ACB	ABC	CBA	Selection 3

**A**: In Section VI, we present numerical examples to demonstrate the performance of the proposed method**B**: Conclusions are drawn in Section VII.**C**: The application of C2⁢-WORDsuperscriptC2-WORD\textrm{C}^{2}\textrm{-WORD}C start_POSTSUPERSCRIPT 2 end_POSTSUPERSCRIPT -WORD to pattern synthesis is presented in Section V

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**A**: The utility of coverage size is denoted as**B**: Higher altitude indicates larger coverage size as shown in Fig. 1 (c)**C**: In order to support as many users as possible, UAVs are required to enlarge coverage size, which is equal to enlarge the coverage proportion in the mission area

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**A**: Pascal VOC datasets: The PASCAL Visual Object Classes (VOC) Challenge (Everingham et al., 2010) was an annual challenge that ran from 2005 through 2012 and had annotations for several tasks such as classification, detection, and segmentation**B**: The segmentation task was first introduced in the 2007 challenge and featured objects belonging to 20 classes**C**: The last offering of the challenge, the PASCAL VOC 2012 challenge, contained segmentation annotations for 2,913 images across 20 object classes (Everingham et al., 2015).

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**A**: The sum SE calculated by (11) and (28) with different numbers of t-UAVs and the given transmit power is shown in Fig. 10, respectively, to verify the influence of the inter-UAV interference**B**: In this paper, we mainly focus on the analog beam tracking without considering the inter-UAV interference**C**: It is shown that the sum SE of the scheme without interference calculated by (28) is similar with that of the scheme with interference calculated by (11) with the appropriate number of t-UAVs and the limited transmit power. The gap between the schemes increases as the power and the number of t-UAVs increase. Therefore, the inter-UAV interference can be neglected in the considered scenario.

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**A**: Motivated by distributed statistical learning over uncertain communication networks, we study the distributed stochastic convex optimization by networked local optimizers to cooperatively minimize a sum of local convex cost functions. The network is modeled by a sequence of time-varying random digraphs which may be spatially and temporally dependent**B**: The local cost functions are not required to be differentiable, nor do their subgradients need to be bounded. The local optimizers can only obtain measurement information of the local subgradients with random noises**C**: The additive and multiplicative communication noises co-exist in communication links. We consider the distributed stochastic subgradient optimization algorithm and prove that if the sequence of random digraphs is conditionally balanced and uniformly conditionally jointly connected, then the states of all local optimizers converge to the same global optimal solution almost surely. The main contributions of our paper are listed as follows.

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**A**: MPC accounts for the real behavior of the machine and the axis drive dynamics can be excited to compensate for the contour error to a big extent, even without including friction effects in the model [4, 5]. High-precision trajectories or set points can be generated prior to the actual machining process following various optimization methods, including MPC, feed-forward PID control strategies, or iterative-learning control [6, 7], where friction or vibration-induced disturbances can be corrected**B**: In MPC, closed-loop performance is pushed to the limits only if the plant under control is accurately modeled, alternatively, the performance degrades due to imposed robustness constraints. Instead of adapting the controller for the worst case scenarios, the prediction model can be selected to provide the best closed-loop performance by tuning the parameters in the MPC optimization objective for maximum performance [8, 9, 10]**C**: Using Bayesian optimization-based tuning for enhanced performance has been further demonstrated for cascade controllers of linear axis drives, where data-driven performance metrics have been used to specifically increase the traversal time and the tracking accuracy while reducing vibrations in the systems [11, 12]. The approach has been successfully applied to linear and rotational axis embedded in grinding machines and shown to standardize and automate tuning of multiple parameters [13].

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**A**: We show that B-CPP also achieves linear convergence for minimizing strongly convex and smooth objectives. **B**: In the second part of this paper, we propose a broadcast-like CPP algorithm (B-CPP) that allows for asynchronous updates of the agents: at every iteration of the algorithm, only a subset of the agents wake up to perform prescribed updates**C**: Thus, B-CPP is more flexible, and due to its broadcast nature, it can further save communication over CPP in certain scenarios [63]

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**A**: We can further differentiate Bar(new) and Bar(cont), representing respectively the beginning of a new bar and a continuation of the current bar and always have one of them before a Sub-bar token. This way, the tokens would always occur in a group of four for MIDI scores. For MIDI performances, six tokens would be grouped together, including Velocity and Tempo. Following the logic of Bar, if there is no tempo change, we simply repeat the tempo value.**B**: Fig**C**: 1(a) shows that, except for Bar, the other tokens in a REMI sequence always occur consecutively in groups, in the order of Sub-bar, Pitch, Duration

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**A**: 5, where the baseline is the result tested by feeding the speech sample sequence into the ASR module directly without considering communication problems**B**: The CER results of DeepSC-SR and two benchmarks under the AWGN channels and the Rayleigh channels are shown in Fig**C**: From the figure, DeepSC-SR obtains lower CER scores than the speech transceiver and text transceiver under all tested channel environments. Moreover, DeepSC-SR performs steadily when coping with dynamic channels and SNRs while the performance of two benchmarks is quite poor under dynamic channel conditions. In addition, DeepSC-SR significantly outperforms the benchmarks in the low SNR regime.

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**A**: 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 %). All datasets have a 50/50 balance between positive (tumor present) and negative (tumor absent) samples**B**: 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). **C**: The PCAM dataset was downloaded from the original website (https://github.com/basveeling/pcam). All images have a size of 96 x 96 pixels, in three colors

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**A**: To this end, suppose we want to generate the étendue-expanded hologram of only a single scene. Then, the optimal complex wavefront modulation for the neural étendue expander would be the inverse Fourier transform of the target scene, and, as such, we do not require any additional modulation on the SLM**B**: The SLM therefore can be set to zero-phase modulation.**C**: Next, we analyze the expansion of étendue achieved with the proposed technique

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**A**: Here are some commonly used classic combinations of loss functions. **B**: These combinations aim to balance the quality, details, and visual perception of the generated image**C**: Mixed Loss: In SISR, there are also some classic combinations of loss functions that are widely used to guide the network towards generating high-quality HR images

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**A**: We have hence set out to explore explainability in anti-spoofing. Our goals are to better understand what artefacts are being captured or discarded by different solutions based upon different input features, different machine learning solutions or different components of ensemble systems [11, 12]**B**: We are also hopeful of understanding why some spoofing attacks are more difficult to detect than others, e.g. the infamous A17 attack contained within the ASVspoof 2019 logical access (LA) database [13]. We hope too to understand why solutions that operate directly upon raw waveform inputs [8, 14, 15] can perform better than systems that operate upon hand-crafted spectro-temporal representations [12, 16], but worse for others, e.g. the A08 attack [15]**C**: Additional motivation comes from the numerous reports in the literature which show that some solutions apply greater attention to non-speech intervals than to speech intervals [17, 18, 19]. Here we hope to better understand whether or not these issues are evidence of database design shortcomings and/or whether they point towards issues relating to the behaviour of certain, specific countermeasure solutions. In case of the former, we hope that studies of explainability might contribute to the development of tools to assist with database quality control.

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**A**: We discuss the algorithmic implementation of our framework to account for assumptions of our work in practice. For instance, our framework crucially relies on obtaining “unsafe” data which is hard to obtain in practice, and we propose a new algorithm to obtain unsafe datapoints as boundary points from the set of safe expert demonstrations based on reverse k𝑘kitalic_k-nearest neighbors.**B**: In contrast to our previous works [53, 55, 54], in which we assume perfect state knowledge, we focus on dealing with state estimation errors**C**: Our paper additionally differs from [53, 55, 54] in its practical focus

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**A**: Tx and Rx have co-located dual-polarized antennas, such that two antenna ports are available for each antenna element at a distinct spatial location**B**: Compared to the case where the same number of antenna elements with only a single polarization is available, this leads to an increase in diversity and capacity, although the gain depends significantly on the XPD [15, 6]**C**: However, this also leads to doubling the number of antenna ports,

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**A**: Another possibility is to try to perform a regularization by infimal regularization [8] for lower semicontinuous objective functionals. In [29], in a function space setting, Pock et al**B**: propose a high dimensional lifting of the Lagrangian formulation of (2) where the data-fit functional is non-convex. 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].**C**: Many works intent to find a convex proxy to a non-convex objective function. 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

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**A**: Figure 1: The distribution of the mean radial error (MRE) when choosing a different image as a template in one-shot medical landmark detection task**B**: The x-axis refers to MRE and the y-axis refers to the percentage of MRE lying in the corresponding ranges**C**: Evidently, the choice of template affects the performance significantly.

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**A**: A request from one of the participating teams to evaluate a second version of their container after the completion of the challenge was accommodated and the method is suffixed with the term post-challenge (pc) to distinguish it from other methods. Table 4 gives an overview of the participating methods**B**: These additional invited teams were also required to submit their singularity containers for evaluation on testing data**C**: Detailed descriptions of the evaluated methods can be found in Appendix A. Invited methods are indicated with *.

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**A**: Figure 1(a) presents the evolution of the smallest eigenvalue of the time-varying symmetric matrix M˙˙𝑀\dot{M}over˙ start_ARG italic_M end_ARG and it shows that the sufficient condition for continuous differentiability of τs(.)\tau_{s}(.)italic_τ start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT ( **B**: ), given in Corollary 8, is satisfied. Hence, for this case, the inter-event time function τs⁢(θ)subscript𝜏𝑠𝜃\tau_{s}(\theta)italic_τ start_POSTSUBSCRIPT italic_s end_POSTSUBSCRIPT ( italic_θ ) is continuously differentiable and it is also periodic with period π𝜋\piitalic_π. From**C**: rule (3b)

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**A**: If the controller gains are chosen such that the following inequalities are satisfied,**B**: Let us also consider the unsafe set for this system to be (12) and the metric measuring the distance from this unsafe set to be given by (13)**C**: Consider the system (4) with boundary conditions (8)

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**A**: In SS-Setting, a training sample is comprised of spectrum sensors’ received power readings. 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**B**: First, we compute an FFT on the I/Q samples collected within a time window to get a power spectral density (PSD) plot. 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. **C**: 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

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**A**: Similar algorithms have also been extended to cases where zeroth order [6, 30, 36, 26, 32] and second order [16] oracles are used instead of (sub)gradients. The works [6, 36] on bandit feedback consider the situation where there are time-varying inequality constraints. In such cases the algorithms proposed in [41] will be hard to implement because of the high computational resource demand of the projection operation. This motivates recent research on online optimization algorithms with time-varying constraints including the primal-dual algorithm proposed in [13, 37], a modified saddle-point method given in [9]. Other algorithms are also proposed to handle stochastic constraints [38] and cover continuous-time applications [27]**B**: In the case where only the values rather than the exact form of the cost function at are revealed to the decision maker, bandit feedback based online algorithms [6, 36] can be used to solve the problem, other methods such as forward gradient [22] have also been proposed recently to deal with the issue. The need for applications in large-scale systems has also led to extensive research on distributed OCO. Distributed online algorithms that achieve sublinear regret bound for convex optimization problems with static constraints can be found in [28, 12, 33]. For instance, [28] proposes a distributed version of the dynamic mirror descent algorithm which is a generalization of the classical gradient descent methods suitable for high-dimensional optimization problems. The work [19] proposes distributed online primal-dual algorithms for optimization problems with static coupled inequality constraints while the work [35] studies distributed online convex optimization with time-varying inequality constraints in the discrete-time setting. For a more detailed documentation of recent advances of online optimization, we refer the readers to the survey paper [17].**C**: In the seminal work of [41], the method of online gradient descent is proposed for OCO problems, where at each time step the decision maker performs one gradient descent step using the latest available information. A static regret upper bound that is sublinear in T𝑇Titalic_T is proved, where T𝑇Titalic_T is the length of the horizon. Under stronger assumptions on the cost functions such as strong convexity, an improved logarithmic regret bound can be achieved [11, 23, 10]. If future information is available, it can be used to further improve the performance of the online optimization algorithm in terms of regret bounds. The work [15] introduces an additional predictive step following the algorithm developed in [41], if certain conditions on the estimated gradient and descent direction are met

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**A**: The results demonstrate a significant improvement in the discriminative performance of the model compared to our previous experiments. Specifically, the substantial improvement in model performance when moving from 1,000 images to 99,000 images with large training epochs indicates that data quantity plays a critical role in training deep CNN models. The model also did not overfit the data during the extended training period of 300 epochs.**B**: The corresponding evaluation metric table in Table 2 provides the final measures of evaluation criteria for each condition**C**: The ROC curve shown in Figure 8(b) illustrates the improved performance of the classifier for different pathology conditions across various thresholds

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**A**: However, the target of these datasets is the classification of a generic set of emotions instead of focusing on fear detection. This strategy makes it hard to obtain a robust model due to the lack of fear samples**B**: One of the main shortcomings of training this specific fear detection system for Bindi is the lack of adequate datasets. Over the last decades, several datasets were published providing emotional labels together with auditory and physiological variables, such as DEAP [6], MAHNOB [7], WESAD [8], AMIGOS [9], FAU, Reg, and Ulm TSST Corpora [10], and BioSpeech [11]**C**: Moreover, gender perspective was not considered either, in spite of stimuli interpretation being strongly affected by gender [12]. Instead, a sufficient number of women volunteers and a balanced fear and non-fear set of emotions should be considered to fit the target users of the GBV application.

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**A**: Table 1 presents the FID values for each GAN-based architecture**B**: StyleGAN2-ADA achieved the lowest FID value of 166166166166, while EBGAN was placed in last and its FID value of 380380380380 was the highest**C**: The smaller the FID value, the better is the quality of the generated image. Therefore, all further experiments only considered the StyleGAN2-ADA architecture for synthetic image generation.

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**A**: 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.**B**: 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**C**: 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

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**A**: However, as highlighted by [26], deploying DL models for privacy-preserving inference nowadays is predominantly achievable through Multi-Party Computation (MPC). This process necessitates multiple servers and incurs significant overhead, primarily attributed to the size of the DL model, especially when handling 3D image registration tasks within a DL-based framework [9].**B**: While PPIR focuses on the privacy-preserving formulation of classical image registration methods based on gradient-based optimization, throughout the past years the research community has been steering the attention towards deep learning (DL)-based image registration [42, 27, 46, 9]. Among the medical imaging application of privacy-preserving methodologies, Kaissis et al**C**: [26] discussed privacy-preserving FL with Secure Aggregation [13] and Differential Privacy [1] for 2D medical image classification tasks

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**A**: In particular, the state transition of a low-rank MDP aligns with that in our low-rank POMDP model. Nevertheless, we remark that such states are observable in a low-rank MDP but are unobservable in POMDPs with the low-rank transition. Such unobservability makes solving a low-rank POMDP much more challenging than solving a low-rank MDP. **B**: To learn a sufficient embedding for control, we utilize the low-rank transition of POMDPs**C**: Our idea is motivated by the previous analysis of low-rank MDPs (Cai et al., 2020; Jin et al., 2020b; Ayoub et al., 2020; Agarwal et al., 2020; Modi et al., 2021; Uehara et al., 2021)

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**A**: in 2⁢s+12𝑠12s+12 italic_s + 1 variables xjsubscript𝑥𝑗x_{j}italic_x start_POSTSUBSCRIPT italic_j end_POSTSUBSCRIPT at a point η=0𝜂0\eta=0italic_η = 0 where xi=0subscript𝑥𝑖0x_{i}=0italic_x start_POSTSUBSCRIPT italic_i end_POSTSUBSCRIPT = 0, for 1≤i≤s1𝑖𝑠1\leq i\leq s1 ≤ italic_i ≤ italic_s**B**: Ex. 59 corresponds to s=3𝑠3s=3italic_s = 3**C**: Again in the

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**A**: 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**B**: Based on which, Theorem 1 proves the almost sure convergence of the algorithm. Then, Theorem 2 gives intuitive convergence conditions for the case with balanced conditional digraphs by Lemma 2**C**: 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. 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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**A**: Middle column: Reconstructions using SDGLR for regularization. Right column: Reconstructions using SDGGLR for regularization.**B**: Left column: Corrupted images**C**: Figure 10: Image patch denoising and interpolation results (PSNR) using SDGLR versus SDGGLR

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**A**: For both phases, we used Adam optimizer [20] and weight initialization as proposed by [21]. Mean squared error (MSE) loss was used for both phases. **B**: For the second phase, we train the Param-Net on MRiLab dataset. The weights of the auto-encoder are frozen during this phase**C**: The training process comprises of two sequential phases: training of the auto-encoder, and training of the Param-Net. For the first phase, the auto-encoder was trained on a subset of Places-365 dataset [19] and fine-tuned using MR Image dataset

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**A**: On the receiver side, since the receiving light intensity of SiPM needed to be accurately measured, an integrating sphere Thorlabs IS200-4 was used to generate equal photon flux among the LED, MicroFC-10010 SiPM, and optical power meter. As the LED light enters the integrating sphere, it experiences numerous diffusions and reflections, ultimately achieving an even distribution over the entire inner surface of the sphere. To reduce the light intensity reaching the SiPM, a Thorlabs neutral density (ND) filter with an optical density of 20 is used**B**: After the amplification, the SiPM output pulse was captured by the LeCroy oscilloscope. The internal memory of the oscilloscope allowed a maximum of 10 ms waveform capture each time. When the PC remotely captures and saves sufficient waveform data from the oscilloscope, the PC analyses the data samples. **C**: This attenuation in intensity allows the SiPM to work within its linear response range and enhances the measurement precision. The SiPM output signal was then amplified by two Mini-Circuit amplifier blocks: ZX60-43-s+ and ZFL-1000+. The average gain and maximum bandwidth of ZX60-43-s+ is 18.6 dB and 4 GHz, and ZFL-1000+ is 17 dB and 1 GHz

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**A**: Section 5.2 shows results considering the close-approach, which are further analyzed in Section 5.3 in Monte Carlo simulations. Finally, Section 5.4 considers a different shape onboard to assess how the proposed autonomous GN&C behaves.**B**: In Section 5.1 we make some comments on the far-approach phase of an asteroid mission, which is beyond the scope of this work**C**: This section analyzes and discusses the results obtained by applying the proposed autonomous GN&C approach

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**A**: Given the increasing attention being paid to the integration of UAVs into 5G networks [18, 19], for the sake of completeness of this tutorial, we next discuss briefly the modeling of UAV wireless channels, which is currently an active research topic [95, 96]. Before presenting the UAV channel models, let us discuss some particularities of UAV communications. For small-sized UAVs, the receiver is close to the UAV’s power electronics and to the motors which run continuously. As a consequence, sometimes the UAV’s motors generate electromagnetic noise that can interfere with the UAV’s own receiver [97].**B**: In general, they are not appropriate for UAVs applications mainly because of the changing altitude of the UAV**C**: The channel models described in the previous section were originally developed for mobile communications considering ground users and are well-suited to ground MRs applications

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**A**: Assumption 12 requires boundedness on the sum of two storage functions in terms of parts (but not all) of their arguments**B**: An alternative assumption where the lower and upper bounds are functions of ‖z‖norm𝑧\|z\|∥ italic_z ∥ will be considered later. **C**: This resembles boundedness on a time-varying Lyapunov candidate function [Khalil, 2002, Th. 4.8] and a Lyapunov candidate function for partial stability [Haddad and Chellaboina, 2008, Th. 4.1]

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**A**: The RCBF has a form that is easy to imagine as a barrier, while the ZCBF is defined outside the safe set, allowing the design of control laws with robustness.**B**: In the context of a CBF, the control objective is to make a specific subset, which is said to be a safe set, on the state space invariance forward in time (namely, forward invariance [2])**C**: There are various types of CBFs, the most commonly used currently are a reciprocal control barrier function (RCBF) [2, 4, 5] and a zeroing control barrier function (ZCBF) [2, 3, 6]: the RCBF is a positive function that diverges from the inside of the safe set toward the boundary, while the ZCBF is a function that is zero at the boundary of the safe set

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**A**: Further, we will derive the closed-form relationship between the gSCR and the capacity ratio between the GFM and the GFL converters to simplify the analysis of how large the capacity should be to meet certain stability margins. **B**: On this basis, we will show in the next section that the integration of GFM converters has a similar effect to installing ideal voltage sources (i.e., infinite buses) in series with an equivalent internal impedance in the network**C**: Combining the power grid strength quantified by gSCR in this section and the analysis of the voltage source behaviors of GFM converters in Section II, it is once again emphasized that it is necessary to install GFM converters to provide effective voltage source behaviors and thus enhance the power grid strength, which can be quantified by gSCR

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**A**: The first and simplest way is to enlarge the model capacity by training the network with larger datasets and better strategies**B**: Specifically, based on ImageNet [10], IPT [4] and HAT [6] conducted a sophisticated pre-training to excavate the capability of transformers in image processing**C**: LSDIR [29] introduced a large-scale dataset to exploit model capacity fully. RCAN-it [32] leveraged reasonable training strategies to help RCAN [58] regain SOTA performance. Generally, these approaches are universal for neural models but increase burdensome training and data collection consumption.

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**A**: Figure 5: Trajectories for a confidence-parameterized FRT**B**: This causes the human’s FRT to update and expand, which leads the robot to deviate from its nominal trajectory (gray) to an adjusted trajectory (orange) to avoid collision with the human or the obstacle.**C**: The human moves towards its target (shaded blue), but its trajectory (blue) shows a sudden change of direction to avoid the unmodeled obstacle (black)

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**A**: In addition, any attempt to intercept the transmission, by placing for instance an object in the first Fresnel zone, will lead to a channel distortion/disruption or a detectable change of the spatial channel experienced by the receiver. Such propagation anomaly can be detected during the channel estimation procedure. This concept for secure communication can be regarded as an intrusion-alarmed quasi-passive wireless network where eavesdropping attacks can be detected. In case of an anomaly, the central node might decide to redirect the path over a different RIS route.**B**: Due to the directional retransmission and confined wave propagation (via “wireless waveguides”), the directive RIS network configuration is expected to have a higher security level [41], comparable to hard-wired systems (physical security or layer-0 security). Eavesdropping becomes harder as signals can only be received along well-confined wave trajectories**C**: This is different from conventional physical layer security methods, where channel state information of the attacker as well as a larger coding block length, i.e. higher latency, is needed

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**A**: Specifically, the parallel visual and haptic feedback transmissions should be aligned with each other when arriving at the manipulator, and consecutive C&C and feedback transmissions should be within the motion-to-photon delay constraint, which is defined as the delay between the movement of the user’s head and the change of the VR device’s display reflecting the user’s movement. Either violation of alignment in parallel links or latency constraint in consecutive links will lead to a break in presence (BIP) and cybersickness**B**: To implement a closed-loop XR-aided teleoperation system, the wireless network is required to support mixed types of data traffic, which includes control and command (C&C) transmission, haptic information feedback transmission, and rendered 360∘superscript360360^{\circ}360 start_POSTSUPERSCRIPT ∘ end_POSTSUPERSCRIPT video feedback transmission [14]. As XR-aided teleoperation task relies on both parallel and consecutive communication links, how to guarantee the cooperation among these communication links to execute the task is of vital importance**C**: Therefore, both parallel alignment and consecutive latency should be quantified into effectiveness-aware performance metrics to guarantee the success of XR-aided teleoperation. Moreover, due to the motion-to-photon delay, the control error between the expected trajectory and the actual trajectory will accumulate along with the time, which may lead to task failure. Hence, how to alleviate the accumulated error remains an important challenge that needs to be solved.

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**A**: Table 4.1 shows both the computation times and the results of randomly drawing sampled power injections within the specified range of variability, computing the associated voltages by solving the power flow equations, and finding the number of false positive alarms (i.e., the voltage at a bus with a sensor is outside the sensor’s threshold but there are no voltage violations in the system). The results for the 33-bus and 141-bus test cases given in Table 4.1 illustrate the performance of the proposed reformulations**B**: Whereas the KKT formulation is computationally intractable, our proposed reformulations find solutions within approximately one minute, where the MILP formulation with the BVR method typically exhibits the fastest performance. The solutions to the reformulated problems place a small number of sensors (two to four sensors in systems with an order of magnitude or more buses)**C**: No solutions suffer from false negatives since all samples where there is a voltage violation trigger an alarm. There are a number of false alarms prior to applying the AGD that after its application decrease dramatically to a small fraction of the total number of samples (1.34%percent1.341.34\%1.34 % and 0.01%percent0.010.01\%0.01 % in the 33-bus and the 141-bus systems, respectively). These observations suggest that our sensor placement formulations provide a computationally efficient method for identifying a small number of sensor locations and associated alarm thresholds that reliably identify voltage constraint violations with no false negatives (missed alarms) and few false positives (spurious alarms).

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**A**: We replaced the original second dense layer in CNN-MLC with B𝐵Bitalic_B parallel BiLSTM layers, which results in the CNN-Mask backbone network. The B𝐵Bitalic_B BiLSTM layers take the sigmoid function as the activations**B**: They are designed to learn B𝐵Bitalic_B ratio masks. See the following for the details. **C**: Inspired by [33], we designed a mask layer for both single and multiple speakers, implemented with Bi-directional Long Short-Term Memory (BiLSTM). Table II outlines the CNN architecture incorporating the mask layer

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**A**: 10(c)**B**: For our second example, we observed that if the CNN were fed with images containing obstacles with a small apparent height (such as glass gates), it would often ignore the obstacle as if it were a traversable area. A simulation at one of these failure states exposed a glass door, as shown in fig**C**: The network conveniently ignores the glass door as it can see the ground area through it.

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**A**: This makes it non-trivial to implement exact batched inference for multi-blank RNN-Ts, since different utterances in the same batch might output blanks with different durations, making it hard to fully parallelize the computation.**B**: The multi-blank RNN-T method is ideal for on-device speech recognition in that it brings significant speedup in recognition speed as well as better accuracy**C**: With that being said, the method also supports batched inference to run on the server side. In Section 2.4, we mentioned that during inference, when a big blank symbol is emitted, it should advance the input t𝑡titalic_t by the duration corresponding to the big blank

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**A**: Enhancing the real speech involving a scene, such as “Airport”. 2**B**: Adding another scene to the enhanced speech, such as “Street”. The signal noise ratio (SNR) of the real utterance is denoted by SNRreal. The SNR of the fake utterance is referred to as SNRfake. The SNRreal and SNRfake are both 5dB in the example.**C**: Figure 3: An example of acoustic scene manipulation for a fake utterance. The manipulation procedure consists of two steps: 1

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**A**: 4 that the non-zero initial conditions, in general, don’t decay to zero in finite time. Next, we discuss the performance comparison of TV-OKID and the Information-state approach**B**: First, we show the importance of having a system identification technique that is immune to non-zero initial conditions. We show in Fig**C**: For the oscillator, two experiments were performed - one with zero-initial conditions and another with non-zero initial conditions, and the results are shown in Fig. 5. The results for the nonlinear systems are shown in Fig 6. The error shown in the figures is the 1-norm of the mean error between the true response and the predicted response from 100 independent simulations, across all the output channels.

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**A**: Figure 6: Average inference latency per frame under different communication rates in the multi-camera pedestrian occupancy prediction task**B**: The overall latency comprises on-device computation time, transmission latency, and server-side computation time**C**: All the methods achieve a MODA of around 87%.

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**A**: The direct link between the sub-diffusion model parameter β𝛽\betaitalic_β and mean kurtosis is well established (Yang et al., 2022; Ingo et al., 2014, 2015). An important aspect to consider is whether mean β𝛽\betaitalic_β used to compute the mean kurtosis is alone sufficient for clinical decision making. While benefits of using kurtosis metrics over other DW-MRI data derived metrics in certain applications are clear, the adequacy of mean kurtosis over axial and radial kurtosis is less apparent**B**: In a different study a correlation was found between glomerular filtration rate and axial kurtosis is assessing renal function and interstitial fibrosis (Li et al., 2022a). Uniplor depression subjects have been shown to have brain region specific increases in mean and radial kurtosis, while for bipolar depression subjects axial kurtosis decreased in specific brain regions and decreases in radial kurtosis were found in other regions (Maralakunte et al., 2022). This selection of studies highlight future opportunities for extending the methods to additionally map axial and radial kurtosis. **C**: Most studies perform the mapping of mean kurtosis, probably because the DW-MRI data can be acquired in practically feasible times. Nonetheless, we can point to a few recent examples where the measurement of directional kurtosis has clear advantages. A study on mapping tumour response to radiotherapy treatment found axial kurtosis to provide the best sensitivity to treatment response (Goryawala et al., 2022)

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**A**: (2021) presented a nonlinear optimization model where 1111d Euler equations were used to derive a model of the water flow in a DHG. On top of that, Machado et al**B**: (2022) built and described a modeling approach of DHGs, whose passivity properties were shown.**C**: To model DHGs in great physical detail, Krug et al

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**A**: This is because by synergistically deploying charging and battery swapping stations, the platform can effectively reduce the charging cost and thus address this bottleneck. As shown in Figure 3b, compared to charging-only deployment, joint planning yields a charging cost reduction of up to 44.4% under a sufficient budget. As the budget increases, the charging cost of joint planning is even comparable to that of swapping only, which underscores the potential of joint planning to capitalize on the complementary strengths of charging and swapping facilities. We formalize the superiority of joint planning as follows.**B**: Specifically, under a tight budget, joint planning yields a total profit that is significantly higher than swapping-only deployment (i.e., by 11.7%). In this case, the major bottleneck is the budget as it is too limited to afford an extensive swapping network and thus cannot support a large EV fleet. Conversely, when the budget is generous, the bottleneck becomes the low fleet utilization arising from time-consuming plug-in charging. The total profit of joint planning is significantly higher than charging-only deployment under a large budget (i.e., by 17.5%)**C**: In contrast, joint planning combines the advantages of both charging and battery swapping stations, thereby harnessing the synergistic value between the two facilities. Under the joint planning scheme, the platform can not only address the scaling challenge of battery swapping by building charging stations at the early stage but also overcome the bottleneck of plug-in charging and enhance fleet utilization by building battery swapping stations. Consequently, joint planning outperforms other deployment strategies in terms of profit maximization. However, the synergistic benefit of joint planning depends on the budget level as well as the scenario it is compared to

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**A**: It is noteworthy that DEviS exhibits superior robustness in uncertainty estimation compared to the conference version of the TBraTS method. Additionally, as depicted in Fig. 5 (c), the segmentation results of different methods show that under Gaussian noise, introducing the DEviS model can slightly improve the segmentation results of nnU-Net and V-Net, while U-Net and Attention-UNet with DEviS demonstrate more robust segmentation results. This is because nnU-Net and V-Net themselves possess certain noise resistance capabilities. **B**: The integration of DEviS enhances the robustness under different levels of Gaussian noise, Gaussian blur, and random masking. Taking the Attention-UNet method based on U-Net as an example, the introduction of DEviS results in an average increase of 12.6% and 5.1% in the Dice metric under Gaussian noise and Gaussian blur conditions, respectively. Similarly, considering the nnFormer based on the Transformer, the introduction of DEviS leads to an average increase of 2.21% and 5.93% in the Dice metric under Gaussian noise and random masking conditions, respectively**C**: 1) Comparison with U-Net and transformer based methods. As shown in Fig. 5 (a), under normal conditions, V-Net, Attention-UNet, nnU-Net, and nnFormer demonstrate comparable performance; however, their segmentation performance begins to degrade under different levels of Gaussian noise, Gaussian blur, and random masking, with the impact becoming more pronounced as the severity increases

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**A**: Due to the fact that the onset feature represents the beginning of a cardiac activity, IBIs sequence from the onset feature is selected as the baseline for segmentation**B**: Firstly, the IBIs sequence of the onset feature is divided into q segments where each segment contains three consecutive IBIs**C**: The timestamps of this segmentation are used as references to guide the segmentation of IBIs sequences for maximum slope and systolic peak features. For each segment, if the starting points of IBIs from maximum slope and systolic features are within this segment, these IBIs are included as the candidate IBIs. Secondly, based on physiological phenomenons that true IBIs are expected to be close to their average IBIs and would not have drastic changes for short time, hence, an objective function as Equation (4) is proposed for the greedy fusion method to find local optimum IBIs in each segment.

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**A**: Each RRDB contains multiple densely connected residual blocks, which enables features to be fully reused and propagated efficiently within each RRDB to extract richer features and utilize correlations between channels. Compared to standard residual blocks, RRDB achieve superior performance with significantly fewer parameters. **B**: RRDB combines the advantages of residual learning and dense connections**C**: The generator backbone uses Residual-in-Residual Dense Blocks (RRDBs), inspired by ESRGAN[20], a seminal work in single image super resolution with excellent generated results

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**A**: In this paper, we consider DoA estimation in a reverberant enclosure consisting of desired sources along with interference sources**B**: Consequently, their identification as desired or interference is unknown as well. The power of the different sources is also unknown, and the interference sources could, in fact, be stronger than the desired sources with overlapping activity periods.**C**: We assume that the desired sources are constantly active, whereas the interference sources are only intermittently active. The number of sources, their locations, and their times of activity are all unknown

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Selection 2

**A**: Researchers have been applying machine learning and deep learning techniques on human respiration data collected through various technologies for anomaly detection**B**: Some of the common categories of features used in the literature were statistical features from the data (mean, standard deviation, skewness, kurtosis, root mean-square value, range etc.), signal-processing based features (Fourier co-efficients, autoregressive integrated moving average co-efficients, wavelet decomposition, mel-frequency cepstral coefficients, linear predictive coding etc.), and respiration related features (breathing rate, amplitude, inspiratory time, expiratory time etc.) [28, 29, 30, 11, 31, 24]. In some research efforts, deep neural networks were trained to recognize subtle features from breathing data before classification, thus making manual feature extraction redundant [19, 32, 9, 17, 26].**C**: Most of these efforts made use of handcrafted features to perform breathing data classification for anomaly detection

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**A**: Moreover, O-RAN operation is divided into three different control loops [7]: the real-time (RT), near-RT, and non-RT loops executing at different time-scales**B**: O-RAN adopts the functional split defined in 3GPP [6] and defines three distinct units [7]: the open central unit (O-CU), open distributed unit (O-DU), and open radio unit (O-RU)**C**: The resulting O-RAN architecture and standard names of interfaces between these elements, which enable practical implementation of many RAN operations, are depicted in Fig. 1(a).

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**A**: In the reminder of this study, we focus on the partial dual problem (14)**B**: These problems are known to be very challenging, even if convex [29, Chapter 2]. In contrast, as we will see later in the manuscript, the use of the partial dual problem leads to a tractable inner minimization problem. This is one of the major differences with respect to the finite dimensional case in [23].**C**: The reason is that the dual problem (13), or other variations that keep the SINR constraints augmented, do not seem tractable, mostly because of the difficulties in solving the (infinite dimensional) inner minimization problem over the space of precoders subject to nontrivial information constraints

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**A**: The suboptimality analysis of LQR, where a modeling error is present, has been considered in [18, 19, 20]**B**: As we consider the LQ setting, the performance analysis of LQ regulator (LQR) for unknown systems is also relevant, which has received renewed attention from the perspective of learning-based control [15, 16, 17]**C**: This analysis is an essential ingredient for further deriving performance guarantees for learning-based LQR controllers. It typically relies on the perturbation analysis of the Riccati equation [19, 20], which characterizes the solution of the Riccati equation under a modeling error. However, since the LQR concerns an infinite prediction horizon, the above analysis does not consider the effect of the prediction horizon and the terminal value function.

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Selection 4

**A**: Although they are commonly employed for data assimilation in numerical weather prediction, they require large computational resources since they involve repeated solutions of the high-dimensional dynamics (1). Thus, they are not applicable in the context of embedded control systems, whose limited resources call for an inexpensive model such as the ROM (3). Since the ROM that we consider has linear dynamics, extensions of the Kalman filter for nonlinear dynamics such as the extended or unscented Kalman filters (Wan & Van Der Merwe, 2000; Julier & Uhlmann, 2004) are not relevant, and the vanilla Kalman filter remains the best choice of baseline.**B**: The ensemble Kalman filter and 4D-Var are two estimation techniques for high-dimensional systems such as those governed by PDEs (Lorenc, 2003)**C**: Before proceeding to the results, we discuss our choice of baseline

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**A**: This kind of integration aids in capturing both local and global features and reducing the neglect of potentially diseased areas. Specifically, varying medical datasets may contain lesions of varying sizes. By capturing and understanding features across a broader range of scales, the hybrid model improves its ability to recognize and interpret features in unseen images, which may present lesions at varying scales. This extensive scale coverage enables the model to adapt to unseen images better, thereby significantly contributing to the overall performance and generalization capability.**B**: CNN has dominated the CV field over the past several years and is capable of capturing spatial hierarchies of the input features with successive convolution operations [5]. A typical CNN consists of several types of layers, including convolution layer, dropout layer, normalization layer, etc. Compared with CNN, the transformer is an emerging role in the CV field initially proposed in the natural language processing field. The core component of the transformer is the self-attention mechanism or attention in short**C**: The attention mechanism in a transformer network creates interdependencies among different positions within a single sequence, enabling the model to compute a context-aware representation of each position in the sequence [6]. The primary distinction between the CNN- and the transformer-based methods is that CNN tends to emphasize local features, whereas the transformer is more oriented toward global features. As the visual input may contain lesions of varying scales, it is crucial to enhance the ability of the model to capture features at both local and global scales. To take advantage of both CNN and transformer, the combination of CNN and transformer has developed rapidly

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**A**: The left and right imagers embed depth information to aid the generation of depth maps (with the help of a filter), and using these images in stereo-based localization algorithms might give lower accuracy compared to non-filtered images**B**: While using these data, the effect of the rolling shutter camera can be ignored, since the camera is placed on top of a relatively stable robot moving at a low speed**C**: The Mocap Studio is decorated with posters and objects with different textures to enable feature extraction in vision-based localization algorithms. Some example decorations can be seen in Fig. 3.

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**A**: In this step, we are able to measure slowly and precisely, meaning repeated measurement is possible to increase the dynamic range of the diffraction pattern and reduce noise. So we can have a precise, high quality reference known before the reconstruction of the sample.**B**: The first step is referenced pattern zooming**C**: Since most of the time, the reference is designed to be real and without fine structures, so it is easily reconstruced with conventional CDI algorithm such as HIO

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Selection 1

**A**: While this does not affect the cost of the transfer itself, the additional round trip requires additional propellant and payload to be brought and stored at the depot**B**: The demand D𝐷Ditalic_D reflects the number of round trips to be conducted between a depot and its clients**C**: As such, as D𝐷Ditalic_D increases, the saving in propellant mass resulting from placing the depot closer to the client increases.

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Selection 4

**A**: It supports easy benchmarking of different speech representation models. In this paper, we chose to experiment with two pre-trained models, wav2vec [28] and HuBERT [11] to see how different augmentation techniques impact the performance of both PR and ASR tasks.**B**: Phoneme Recognition (PR) and Automatic Speech Recognition (ASR) are two of the most common speech-processing tasks that can greatly benefit from applying these augmentation techniques**C**: We used S3PRL, an open-source toolkit that targets self-supervised learning for speech processing [38]

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**A**: In S-Band, we already have existing terrestrial communication from 4G LTE devices. With the advent of mm-wave technology, terrestrial communication is also using the Ka-band in 5G**B**: So the satellite users will suffer from co-channel interference with the terrestrial users in both bands. To avoid this interference, we have to come up with efficient spectrum-sharing techniques to put the interference below a certain threshold ensuring proper decoding of the received signals.**C**: As discussed in Section II-D, the S-Band and Ka-Band are the target bands for NTN. On top of this limited spectrum allocation, we have interference from terrestrial users in these bands

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**A**: To some degree, they represent distinct oscillation modes within the same hybrid system, triggered solely by initial conditions like forward speed and the torso’s height**B**: This work not only provides crucial insights into the rationale behind utilizing multiple gaits at varying speeds but also holds the promise of an efficient and versatile strategy for generating reference trajectories for robotic systems with desired footfall sequences.**C**: When only one leg pair initiates movement out of phase, it simultaneously disrupts both leg permutation and time-reversal symmetries, leading to the discovery of four distinct half-bounding gaits, as depicted in Fig. 5. These solutions were identified using a single energy-conserving model without the need for additional control laws or actuation

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**A**: The pure Python implementation implements the transform using matrix multiplication. Therefore, the speed of DCT-ResNets and BWT-ResNets is the same as in this implementation. **B**: Other layers are built in PyTorch so they are always accelerated by CUDA**C**: Note: “CUDA” and “Pure Python” represent whether CUDA acceleration is applied on the HT-Perceptron layers

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**A**: During training, no additional bias corrections were applied to the network output, thus we expected GT values close to zero to be mapped to predicted up-to-scale depths close to zero (i.e. zero offset) (see Figure 4A).**B**: For each trained model the relationship between the GT and the predicted up-to-scale depths was analyzed on its respective test split. An MDE trained using projective geometry [30, 14] is expected to linearly rank the predicted depth per image [53]**C**: We started our analysis by separately training the MDE on datasets from various domains

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**A**: Crude Control**B**: A naïve system that forcefully modifies predicted values individually, this straw-man represents a controllable model that is entirely inconsistent**C**: Its default prosody is generated with NoControl, and then modified manually with the control points111Samples demonstrating TTS for these systems can be found here, anonymous-submission-563098.github.io/sparse-control.

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**A**: Therefore, extended comparison of the ideal baseline scenario with several association policies is provided, and evaluation of diverse dominant interferer-based scenarios are highlighted. **B**: In summary, even though angular coordinates have appeared in the stochastic geometry-based analysis, their manifestation in the received power as a consequence of realistic beam management procedure and the overall effect on the system performance has not been studied, which is the main objective of this paper**C**: Consequently, this work proposes for the first time a stochastic geometry framework to study the implications of beam misalignment error in the association policy and the corresponding performance of a mmWave cellular network by adopting realistic 5G NR beam management-based procedures and practical equipment limitations

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**A**: The proposed filters exploit the cubature and quadrature rules to approximate the recursive Bayesian integrals. The I-CKF’s stability conditions are easily achieved for a stable forward CKF. The developed inverse filters are also consistent, provided that the initial estimate pair is consistent**B**: Numerical experiments show that I-CKF and I-QKF outperform I-UKF even when they incorrectly assume the true form of the forward filter. However, I-QKF is computationally expensive, while I-CQKF provides reasonable estimates at lower computational costs. The non-trivial upshot of this result is that the forward filter does not need to be known exactly to the defender.**C**: We developed I-CKF, I-QKF, and I-CQKF to estimate the defender’s state, given noisy measurements of the attacker’s actions in highly non-linear systems. On the other hand, RKHS-CKF, as both forward and inverse filters, provides the desired state and parameter estimates even without any prior system model information. In the case of the perfect system model information, our developed methods can be further generalized to systems with non-Gaussian noises, continuous-time state evolution, or complex-valued states and observations

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**A**: 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]. We demonstrate our approach through extensive numerical experiments.**B**: 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)**C**: 1) M-DBD with structured unknown continuous-valued parameters. 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. 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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**A**: Since we are comparing upper bounds, strictly speaking we cannot conclude that the actual Lipschitz constant of the SR-LASSO is larger than that of the LASSO**B**: However, the fact that these two upper bounds arise from the application of analogous proof techniques suggests this to be a reasonable conjecture**C**: This theoretical insight aligns with numerical evidence provided by Fig. 2(b) and the next subsection.

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**A**: Some studies upsample each 2D LR medical slice to acquire the corresponding HR one, such as [8, 43, 47]**B**: [5] and Wang et al. [36] use 3D DenseNet-based networks to generate HR volumetric patches from LR ones. Yu et al. [45] build a transformer-based MISR network to address volumetric MISR challenges.**C**: On the other hand, Chen et al

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**A**: For SZO-QQ, in case an infeasible iterate appears, which is a result of underestimated Lipschitz and smoothness constants, we can easily recover feasibility by using the last feasible iterate. Then, we need to enlarge the constants according to Remark 3.**B**: We also notice that SZO-QQ and LB-SGD satisfy sample feasibility while Extremum-Seeking generates infeasible iterates. Due to the lack of a recovering mechanism, after the first infeasible sample, most of the other samples of Extremum-Seeking are also infeasible**C**: We discontinue the corresponding curve in Fig. 5 after the first infeasible sample

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**A**: For the same power constraint, the computational complexities of the RSD or RCG method are nearly the same and are lower than those of the RTR and comparable methods. **B**: With the Riemannian ingredients derived in Section III, we propose three precoder design methods using the RSD, RCG and RTR in this section**C**: There is no inverse of the large dimensional matrix in the proposed Riemannian methods during the iterations, thereby enabling significant savings in computational resources

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**A**: A SinGAN generative model is then used to realistically blend the contrast-altered tumor to the original image. The cascaded SinGAN generators are trained on the central slice and applied to every slice containing the tumor. **B**: For each tumor slice, tumor intensity values are linearly scaled by a factor λ𝜆\lambdaitalic_λ to perform contrast alteration**C**: Figure 3: Generative blending augmentation (GBA) of a vestibular schwannoma in a synthetic hrT2 image generated by CycleGAN (CrossMoDA 2022 dataset)

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**A**: The implementation of the method is described in Section 3.2 and the results are presented and discussed in Section 4, including a comparison with other localization methods from the literature**B**: Finally, conclusions are drawn in Section 5.**C**: The theoretical basis of the method is derived in Section 2 which is evaluated in a simulation setup described in Section 3.1

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**A**: By exploiting existing results on rewriting signal temporal logic specifications to MILP constraints, we can efficiently solve an optimization problem to automatically synthesize a control policy that ensures**B**: In this paper, we have developed a direct data-driven controller synthesis method for linear time-invariant systems subject to a temporal logic specification, which does not require an explicit modeling step**C**: We build upon the promising results of the behavioral framework and the Fundamental Lemma, which allows us to obtain a characterization of the system after collecting a single sequence of input-output data from it

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**A**: Specifically, the MQA framework naturally allows users to examine the response changes to different combinations of imaging and clinical data, and thus, the informative clinical elements can be identified as those contributing to statistically high prediction accuracy.**B**: It is achieved with the MQA framework and the attention mechanism**C**: The M3FMs are capable of identifying informative clinical elements both quantitatively and qualitatively, which offers certain explainability

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**A**: (6) and (4) indicates the importance of fixing codebook during finetuning to protect the pre-trained clean speech prior. Comparison between Exp**B**: (6) and (5) demonstrates that the pre-trained prior knowledge in codebook is important to the clean speech restoration and downstream ASR.**C**: Furthermore, comparison between Exp

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Selection 3

**A**: In order to decompose the strong coupling, we present the following representation of an arbitrary point located on the TX (RX) surface via using the center coordinates. Since the TX (RX) surface is divided into discrete antenna elements, an arbitrary point located on the TX (RX) surface belongs to a certain antenna element.**B**: It is quite challenging to eliminate those multiple integrals due to the complicated coupling among them**C**: The double integrals of (5) over TX and RX element areas can be expressed as multiple integrals over three spatial orientations (x𝑥xitalic_x, y𝑦yitalic_y and z𝑧zitalic_z orientations)

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**A**: The second consists of a synchronous machine described by a third order model, to which a governor and automatic voltage regulator (AVR) representations are added. The grid side is represented by an infinite bus [10, 13]. **B**: The first one is the well known synchronisation loop of classic grid-following converters: the phase locked loop (PLL [24, 25, 26])**C**: In this section we present the model of two different test cases, proposed in the literature, to illustrate the tool capabilities, flexibility and performance

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**A**: ResNet. ResNet was introduced in 2015 by Microsoft Research**B**: The model is designed to handle the vanishing-gradient problem in deep networks, which can lead to underfitting during training. The pre-trained ResNet model is used as a meaningful extractor of residuals, instead of features, from the raw data using identity shortcut connections [79]**C**: Refined variants of ResNet, such as ResNet-34, ResNet-50, and ResNet-101, have been created using various combinations of layers. ResNet has already been employed for IDS-based DTL [80, 81].

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Selection 2

**A**: The proposed hybrid controller is run in Matlab R2020a on another computer running Windows 10, equipped with an Intel(R) i5-5200U CPU with a clock speed of 2.20 GHz and 12 GB RAM, referred to as Computer 2.**B**: The next simulation is performed using the Turtlebot3 Burger model in Gazebo**C**: The simulation runs on a computer, equipped with 4 GB RAM, running Ubuntu 20.04 with the ROS Noetic distribution installed, which we refer to as Computer 1

ACB

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Selection 4

**A**: The training settings followed the default configurations of 2D nnU-Net. Each model was trained on one A100 GPU with 1000 epochs and the last checkpoint was used as the final model. The DeepLabV3+ specialist models used ResNet50 [38] as the encoder. Similar to [3], the input images were resized to 224×224×32242243224\times 224\times 3224 × 224 × 3**B**: We employed the nnU-Net to conduct all U-Net experiments, which can automatically configure the network architecture based on the dataset properties. In order to incorporate the bounding box prompt into the model, we transformed the bounding box into a binary mask and concatenated it with the image as the model input. This function was originally supported by nnU-Net in the cascaded pipeline, which has demonstrated increased performance in many segmentation tasks by using the binary mask as an additional channel to specify the target location**C**: The bounding box was transformed into a binary mask as an additional input channel to provide the object location prompt. Segmentation Models Pytorch (0.3.3) [39] was used to perform training and inference for all the modality-wise specialist DeepLabV3+++ models. Each modality-wise model was trained on one A100 GPU with 500 epochs and the last checkpoint was used as the final model.

CBA

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Selection 2

**A**: An illustration of the proposed model is shown in Fig. 1. In this study, the gNBs and IAB-nodes formed two different tiers. Multi-hop backhaul IAB networks will not be considered because of the challenge of network configuration, and the feasibility of using stochastic geometry in this scenario [16]. **B**: The backhaul link is the wireless link between the gNB and the IAB-node, whereas the access link is between the UE and the gNB or the IAB-node**C**: More details of the 3GPP architecture can be found in our recent study [2]

CBA

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Selection 3

**A**: Therefore, applying this result to the PMB density of the form (13) yields**B**: This type of single-target space was obtained when we introduced auxiliary variables to the PMB in (12), resulting in (13)**C**: A set-density defined in a single-target space that is the disjoint union of different sub-spaces can be used to define a density over a sequence of sets [22, Eq. 3.52]

CBA

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Selection 1

**A**: For semantic communications, the accuracy can be measured by the task performance and easily quantified by semantic similarity for text transmission, character-error-rate for speech recognition, answer accuracy for VQA task, and so forth. However, the efficiency of semantic communications is usually hard to measure and quantify. Although the S-SE has been defined in [21], it can not be calculated due to the lack of semantic information quantification.**B**: A communication system is usually evaluated from two aspects, accuracy and efficiency**C**: The conventional communications are measured by bit-error rate and bit transmission rate

BCA

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Selection 3

**A**: Furthermore, a stability-preserving, adaptive rational Krylov (SPARK) algorithm [33] was developed to maintain model stability and is usually embedded in the CURE scheme to generate a family of stable ROMs whose orders are increased by sequential accumulation in a single MOR process. This embedded CURE scheme with SPARK algorithm (hereafter abbreviated by SPARK+CURE) was used in [34] to generate a family of physically-interpretable multi-fidelity surrogate LPMs for physical systems governed by PDEs.**B**: To overcome this limitation, the CUmulative REduction (CURE) scheme was proposed, which adaptively chooses the expansion frequency and incrementally increases the scale of the ROM by monotonically decreasing the norm of the error transfer function to zero through an accumulation process [33]**C**: However, the IRKA algorithm cannot ensure the error converges to a local minimum [32]

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Selection 3

**A**: Calibration-aware training improves the match between accuracy and confidence, resulting in a lower ECE, for both frequentist and Bayesian learning, with CA-BNN achieving the lowest ECE. **B**: Bayesian learning is observed to yield better calibrated decisions than frequentist learning, as also manifest in the lower value of the ECE**C**: Fig. 3 shows the reliability diagrams (see Sec. II-B) for all four schemes

BCA

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Selection 4

**A**: In this section, we perform a case study on decentralized sparse soft-max regression to verify the theoretical results and show the convergence performance of Prox-DBRO-SAGA and Prox-DBRO-LSVRG, where three kinds of Byzantine attacks (zero-sum attacks, Gaussian attacks, and same-value attacks) are considered**B**: All simulations are carried out in Python (version 3.9) on a DELL server (Linux) with 20 Cores 40 Threads i9-10900X 3.70 GHz processor and 32GB memory. **C**: The communication networks are randomly generated by the Erdős-Rényi method, where Byzantine agents are also selected in a random way

ABC

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Selection 3

**A**: The comparison between the proposed MaxViT-UNet and previous methodologies is presented in Table 3 on the MoNuSeg18 dataset and Table 4 on the MoNuSAC20 dataset**B**: For the MoNuSeg18 dataset, we performed binary semantic segmentation. Whereas the MoNuSAC20 challenge contains four types of nuclei, we performed multi-class semantic segmentation for the MoNuSAC20 dataset. The proposed MaxViT-UNet beats the previous techniques by a large margin on both datasets and proves the significance of the hybrid encoder-decoder architecture. **C**: For comparison on both datasets, UNet and Swin-UNet were trained using MMSegmentation [73] with the same hyper-parameters as the proposed technique

ABC

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Selection 2

**A**: However, securing labels for new tasks is not always feasible. Furthermore, the success of these methods heavily depends on the number of supportive examples provided**B**: Another way towards the universal medical image segmentation is few/one-shot learning [8, 51, 42, 41]. In this setting, a pre-trained foundational model needs one or few labeled samples as the ’supportive examples’, to grasp a new specific task**C**: For example, UniverSeg[8] achieves competitive performance with 64 supportive samples. But obtaining such amount of data can be challenging in real clinical practice.

ABC

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Selection 2

**A**: The solution can account for the aggregate volume across all current Web3 Node RPC providers and comes equipped with an inbuilt difficulty modulation to dynamically adapt to the growing traffic in the Web3 industry. **B**: This paper introduces Relay Mining, a novel algorithm designed to scale efficiently to handle tens of billions of individual RPC requests daily**C**: These requests are proven on a decentralized ledger via a commit-and-reveal scheme

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Selection 4

**A**: We also performed AB preference tests by 15 listeners resulting in a total of 100 utterances to verify the generalization to different datasets conveniently. All the tests listed below are done in speaker F1, except the AB preference tests are done for both F1 and F2. **B**: We calculate the average RMSE of the F0 in Hertz of random 100 utterances in the validation set as an auxiliary metric to evaluate which model fit the pitch prosody better**C**: Apart from the subjective tests, we also evaluate a commonly used objective metric called Root Mean Square Error (RMSE) of Fundamental Frequency (F0) for Ablation Studies in Section 4.2

CAB

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Selection 2

**A**: There are two main criteria for optimizing speech pre-training: contrastive loss Oord et al. (2018); Chung and Glass (2020); Baevski et al. (2020) and masked prediction loss Devlin et al. (2018)**B**: (2021). Some recent work Chung et al. (2021) has combined the two approaches, achieving good performance for downstream automatic speech recognition (ASR) tasks. In this work, we leverage the success of self-supervised to enhance both the encoder and decoder to alleviate the data scarcity issue. **C**: Contrastive loss is used to distinguish between positive and negative samples with respect to a reference sample, while masked prediction loss is originally proposed for natural language processing Devlin et al. (2018); Lewis et al. (2019) and later applied to speech processing Baevski et al. (2020); Hsu et al

ABC

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Selection 3

**A**: where δ=ℜ⁢(𝐙SS⁢(1,1))/M𝛿ℜsubscript𝐙SS11𝑀\delta=\mathfrak{R}(\mathbf{Z}_{\text{SS}}(1,1))/Mitalic_δ = fraktur_R ( bold_Z start_POSTSUBSCRIPT SS end_POSTSUBSCRIPT ( 1 , 1 ) ) / italic_M and 𝜽𝜽\boldsymbol{\theta}bold_italic_θ is calculated according to [13, Eq**B**: (24)]**C**: First,

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Selection 3

Datasets:

liangzid
/

robench2024b_all_seteessSCP-s

Collection including liangzid/robench2024b_all_seteessSCP-s

robench-2024b