Datasets:

jonathan-roberts1

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SciFIBench

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[ "A) convergence curves of 3 neural networks with binary activations on the wsj1 dataset .", "B) the rmse convergence curves of synthetic data of our proposed methods ( 5db ) .", "C) the convergence curves of the four methods for training the neural network .", "D) convergence curves of different networks trained with resnet-50 architecture on imagenet .", "E) convergence curves of sr networks trained on observed data combined with different types of synthetic data ." ]

[ "A) mse versus snr for doa estimation in the case of mutual coupling .", "B) rmse versus snr of the proposed snr estimator .", "C) mse versus snr for doa estimation in the case of gain-phase error .", "D) rmse versus snr for esprit-based doa estimators .", "E) channel estimation mse versus snr ." ]

[ "A) power of the proposed 0.05-level test under the -community stochastic block models .", "B) the stochastic block model with 1000 and 1000 nodes in each community .", "C) the stochastic block model with 100 and 1900 nodes in each community .", "D) the stochastic block model with 700 and 1300 nodes in each community .", "E) the stochastic block model with 400 and 1600 nodes in each community ." ]

[ "A) average number of iterations required to decode the pldpc-hadamard code with r = 10 and k = 460 , 800 .", "B) average number of iterations for convergence by nash equilibrium computation algorithm .", "C) average number of iterations needed for the convergence of algorithm 1 , versus the number of data points n ( 1000 realizations ; k = 3 , n = 30 ) .", "D) average number of iterations needed for the convergence of algorithm 1 , versus the number of data points n ( 1000 realizations ; d = 10 , k = 3 ) .", "E) average number of iterations needed for the convergence of algorithm 1 , versus the number of data points n ( 1000 realizations ; d = 10 , n = 20 ) ." ]

[ "A) the evolution of numerical solutions of 2y to the optimal solution .", "B) the evolution of numerical solutions in the xy coordinate plane to the optimal solution .", "C) the evolution of numerical solutions of 2x to the optimal solution .", "D) the evolution of numerical solutions in the m m coordinate plane to the optimal solution .", "E) the evolution of numerical solutions of 1x to the optimal solution ." ]

[ "A) parallel case ( n = 8 ) : % ∆optsum .", "B) parallel case ( n = 8 ) : % ∆opt .", "C) parallel case ( n = 4 ) : % ∆opt for 12 runs .", "D) parallel case ( n = ) : % ∆optsum .", "E) parallel case ( n = 4 ) : % ∆optsum ." ]

[ "A) parallel case ( n = 8 ) : % ∆opty .", "B) parallel case ( n = 4 ) : % ∆opt .", "C) parallel case ( n = 4 ) : % ∆optsum .", "D) sequential case : % ∆optx .", "E) parallel case ( n = 8 ) : % ∆optsum ." ]

[ "A) : roc curves of different weight factor configurations in the range [ 100,200 ] .", "B) : roc curves of different threshold of the cnn probability with false positive suppression .", "C) : roc curves of multiple trainings of the same network configuration .", "D) : roc curves of different weight factor configurations in the range [ 1,100 ] .", "E) : roc curves of different step learning rate configurations ." ]

[ "A) : roc curves of different weight factor configurations in the range [ 1,100 ] .", "B) : roc curves of different step learning rate configurations .", "C) : roc curves of different weight factor configurations in the range [ 100,200 ] .", "D) : roc curves of different threshold of the cnn probability with false positive suppression .", "E) : roc curves of multiple trainings of the same network configuration ." ]

[ "A) throughput performance in 100 heterogeneous servers .", "B) throughput performance in 10 heterogeneous servers .", "C) throughput performance in 10 heterogeneous servers with poisson arrival and constant service .", "D) throughput performance in 10 heterogeneous servers with poisson arrival and bursty service .", "E) throughput performance in 50 heterogeneous servers ." ]

[ "A) throughput performance in 10 heterogeneous servers .", "B) delay performance under 10 homogeneous servers with bursty arrival and poisson service .", "C) throughput performance in 10 heterogeneous servers with poisson arrival and constant service .", "D) throughput performance in 10 heterogeneous servers with bursty arrival and poisson service .", "E) throughput performance in 10 heterogeneous servers with poisson arrival and bursty service ." ]

[ "A) delay performance under 10 homogeneous servers with bursty arrival and poisson service .", "B) heavy-traffic delay performance under 10 homogeneous servers with poisson arrival and constant service .", "C) delay performance under 10 homogeneous servers with poisson arrival and constant service .", "D) heavy-traffic delay performance under 10 homogeneous servers with poisson arrival and bursty service .", "E) delay performance under 10 homogeneous servers with poisson arrival and bursty service ." ]

[ "A) delay performance under 10 homogeneous servers with bursty arrival and poisson service .", "B) delay performance under 10 homogeneous servers with poisson arrival and constant service .", "C) heavy-traffic delay performance under 10 homogeneous servers with bursty arrival and poisson service .", "D) delay performance under 10 homogeneous servers with poisson arrival and bursty service .", "E) heavy-traffic delay performance under 10 homogeneous servers with poisson arrival and bursty service ." ]

[ "A) delay performance under 10 homogeneous servers with poisson arrival and bursty service .", "B) delay performance under 10 homogeneous servers with bursty arrival and poisson service .", "C) heavy-traffic delay performance under 10 homogeneous servers with poisson arrival and bursty service .", "D) delay performance under 10 homogeneous servers with poisson arrival and constant service .", "E) heavy-traffic delay performance under 10 homogeneous servers with bursty arrival and poisson service ." ]

[ "A) heavy-traffic delay performance under 10 homogeneous servers with poisson arrival and constant service .", "B) delay performance under 10 homogeneous servers with bursty arrival and poisson service .", "C) delay performance under 10 homogeneous servers with poisson arrival and bursty service .", "D) heavy-traffic delay performance under 10 homogeneous servers with bursty arrival and poisson service .", "E) heavy-traffic delay performance under 10 homogeneous servers with poisson arrival and bursty service ." ]

[ "A) precision-recall curves of different methods .", "B) precision-recall curves of our model ( candis ) against traditional methods .", "C) precision-recall curves for the proposed model and various baseline models .", "D) the precision/recall curves of previous state-of-the-art methods and our proposed framework .", "E) precision-recall curves for the proposed model and various baselines ." ]

[ "A) change of cumulative regret with respect to number of turns where s = 10 , n = 5 , α = 1 and µ = µ1 .", "B) change of cumulative regret with respect to number of turns where s = 10 , n = , α = 0 and µ = µ2 .", "C) change of cumulative regret with respect to number of turns where s = 10 , n = 5 , α = 1 and µ = µ2 .", "D) change of cumulative regret with respect to α where s = 10 , n = 5 .", "E) change of cumulative regret with respect to number of turns where s = 10 , n = 5 , α = 1 and µ = µ2 ." ]

[ "A) discrete transformation using 30 hidden states .", "B) discrete transformation using 5 hidden states .", "C) discrete transformation using 20 hidden states .", "D) eventid transformation using 15 hidden states .", "E) discrete transformation using 15 hidden states ." ]

[ "A) eventid transformation using 30 hidden states .", "B) eventid transformation using 15 hidden states .", "C) eventid transformation using 5 hidden states .", "D) eventid transformation using 20 hidden states .", "E) discrete transformation using 200 hidden states ." ]

[ "A) eventid transformation using 20 hidden states .", "B) eventid transformation using 30 hidden states .", "C) discrete transformation using 30 states .", "D) eventid transformation using 15 hidden states .", "E) eventid transformation using 5 hidden states ." ]

[ "A) discrete transformation using 15 hidden states .", "B) discrete transformation using 5 hidden states .", "C) discrete transformation using 20 hidden states .", "D) discrete transformation using 30 hidden states .", "E) eventid transformation using 31 hidden states ." ]

[ "A) roc curves on subset 10-20-30-100 .", "B) roc curves for dr detection .", "C) roc curves on full dataset .", "D) roc curves for different models .", "E) roc curves with w values ." ]

[ "A) error time-history of the crank-nicolson scheme .", "B) error time-history of the regularized scheme .", "C) error time-history of the lax-wendroff scheme .", "D) error time-history of the scheme of higher accuracy .", "E) time-histories of the solution for µ = 1 ." ]

[ "A) recognition error ( % ) on ar face database .", "B) recognition accuracy for gatech face database .", "C) recognition rates on faces 95 .", "D) recognition rates on faces 96 .", "E) recognition rates on faces 94 ." ]

[ "A) recognition rates on faces 96 .", "B) recognition rates on faces 94 .", "C) recognition rates on faces 95 .", "D) face recognition rate of pca-like methods for the color feret face database .", "E) recognition error ( % ) on ar face database ." ]

[ "A) the comparison of bpcrw with different α against rmss under the different decaying parameters λ. tkde is the source object .", "B) comparison of nmi for rmss under different λ with optimal nmi for pathsim under different α on bioin .", "C) comparison of nmi for rmss under different λ with optimal nmi for pathsim under different α on dblpc .", "D) the comparison of pathsim with different α against rmss under the different decaying parameters λ. tkde is the source object .", "E) comparison of nmi for rmss under different λ with optimal nmi for bscse under different α on dblpc ." ]

[ "A) sensitivity of bpcrw to different meta-paths on dblpr in terms of ranking with tkde as the source object .", "B) the comparison of pathsim with different α against rmss under the different decaying parameters λ. cikm is the source object .", "C) the comparison of pathsim with different α against rmss under the different decaying parameters λ. tkde is the source object .", "D) comparison of nmi for rmss under different λ with optimal nmi for bscse under different α on dblpc .", "E) the comparison of bpcrw with different α against rmss under the different decaying parameters λ. tkde is the source object ." ]

[ "A) the evolution of numerical solutions in the x y coordinate plane to the optimal solution .", "B) the evolution of numerical solutions of 1x to the optimal solution .", "C) the evolution of numerical solutions of 2x to the optimal solution .", "D) the evolution of numerical solutions of u to the optimal solution .", "E) the evolution of numerical solutions in the x y coordinate plane to the optimal solution ." ]

[ "A) ber results for bch ( 6,6 ) code .", "B) ber results for bch ( 127,106 ) code .", "C) ber results for bch ( 3,45 ) code trained with multiloss .", "D) ber results for bch ( 63,45 ) code .", "E) ber results for bch ( 63,5 ) code ." ]

[ "A) validation accuracy while training for wisconsin breast cancer dataset .", "B) validation error while training for wisconsin breast cancer dataset .", "C) training time error for breast cancer problem .", "D) training time error for breast cancer problem .", "E) training error while training for wisconsin breast cancer dataset ." ]

[ "A) error rate while training a single hmm .", "B) training time error for breast cancer problem .", "C) validation error while training for wisconsin breast cancer dataset .", "D) training error while training for wisconsin breast cancer dataset .", "E) validation accuracy while training for wisconsin breast cancer dataset ." ]

[ "A) time evolution of θ ( t ) of nodes with degree 20 for different values of δ20 .", "B) time evolution of the probability θ ( t ) for the iobt mean-field game and the finite iobt game .", "C) time evolution of the probability θ ( t ) for different values of βe20 .", "D) time evolution of the potential function v for different choices of α .", "E) time evolution of the acceptance probability of nodes with degree 20 for different values of βe20 ." ]

[ "A) variation ( interpolated ) of previous word λ about distance 0 .", "B) variation ( interpolated ) of posterior word λ about distance 0 .", "C) variation ( interpolated ) of posterior word λ about distance -2 .", "D) variation ( interpolated ) of previous word λ about distance 1 .", "E) variation ( interpolated ) of posterior word λ about distance -1 ." ]

[ "A) variation of error throughout the epochs .", "B) log-log error plot .", "C) error rates generated at different values of 351 .", "D) training error and test error ( up to 7500 epochs ) .", "E) error variation depending on the size of history ." ]

[ "A) the control inputs of the actuators of agent 1 .", "B) the control inputs of agent 1 with −8.5 ≤ u1 , j ( t ) ≤ 8.5 .", "C) the control inputs of agent 3 with −.5 ≤ u3 , j ( t ) ≤ .5 .", "D) the control inputs of the actuators of agent 3 .", "E) the control inputs of agent 2 with −8.5 ≤ u2 , j ( t ) ≤ 8.5 ." ]

[ "A) the control inputs of agent 2 with −8.5 ≤ u2 , j ( t ) ≤ 8.5 .", "B) the control inputs of the actuators of agent 1 .", "C) the control inputs of the actuators of agent 2 .", "D) the control inputs of agent 1 with −8.5 ≤ u1 , j ( t ) ≤ 8.5 .", "E) the control inputs of agent 3 with −.5 ≤ u3 , j ( t ) ≤ .5 ." ]

[ "A) the control inputs of agent 2 with −8.5 ≤ u2 , j ( t ) ≤ 8.5 .", "B) the control inputs of agent 1 with −8.5 ≤ u1 , j ( t ) ≤ 8.5 .", "C) the control inputs of the actuators of agent 3 .", "D) the control inputs of the actuators of agent 1 .", "E) the control inputs of agent 3 with −.5 ≤ u3 , j ( t ) ≤ .5 ." ]

[ "A) effect of number of epochs on model accuracy .", "B) mean accuracy versus number of epochs .", "C) number of epochs vs loss val and accuracy .", "D) accuracy vs. epochs .", "E) loss and accuracy vs epochs ." ]

[ "A) asc versus λ for different values of µd , µe , κd = κe = 3 , and md = me = 2 .", "B) spsc versus λ for different values of md , me , κd = κe = 3 , and µd = µe = 2 .", "C) spso versus λ for different values of µd , µe , κd = κe = 3 , and md = me = 2 .", "D) sopl versus λ for different values of md , me , κd = κe = 3 , µd = µe = 2 , and rs = 1 .", "E) sop versus λ for different values of md , me , κd = κe = 3 , µd = µe = 2 , and rs = 1 ." ]

[ "A) eδ2 versus snr for the rate /7 optimized code compared with hamming ( 7 , ) code .", "B) eδ2 versus snr for the rate 8/12 optimized code compared with hamming ( 12 , 8 ) code .", "C) eδ2 versus snr for the rate 3/8 optimized code compared with hadamard ( 8,3 ) code .", "D) eδ1 versus snr for the rate 4/7 optimized code compared with hamming ( 7,4 ) code .", "E) eδ2 versus snr for the rate 4/7 optimized linear code compared with hamming ( 7,4 ) code ." ]

[ "A) roc curves for nist special database 4 .", "B) roc curves when 300 points are used for training .", "C) roc curves for fvc2002 datasets .", "D) roc curves of the algorithms for ssi data .", "E) roc curves on lfw dataset ." ]

[ "A) roc curves of classification techniques .", "B) roc curves for each classification model .", "C) roc curves for the different machine learning models .", "D) roc curves for classifying process name spoof attacks .", "E) roc curves for classifying domain name spoof attacks ." ]

[ "A) roc curves for classifying domain name spoof attacks .", "B) roc curves for the different machine learning models .", "C) intra-domain roc curves for the classifiers on the computer science dataset .", "D) roc curves of classification techniques .", "E) roc curves for classifying process name spoof attacks ." ]

[ "A) matchings using pattern 3 .", "B) pattern counts .", "C) comparison of the matchings for different patterns .", "D) matchings using pattern 1 .", "E) matchings using pattern 2 ." ]

[ "A) matchings using pattern 3 .", "B) pattern counts .", "C) matchings using pattern 1 .", "D) comparison of the matchings for different patterns .", "E) matchings using pattern 2 ." ]

[ "A) matchings using pattern 1 .", "B) pattern counts .", "C) matchings using pattern 3 .", "D) comparison of the matchings for different patterns .", "E) matchings using pattern 2 ." ]

[ "A) the figure depicts pmd over snr for n = 104 , m = 300 , ks = 3 , u = 4 .", "B) the figure depicts pmd over snr for n = 1024 , m = 300 , ks = , u = 8 .", "C) the figure depicts pmd over snr for n = 1024 , m = 00 , ks = , u = 8 .", "D) the figure depicts pmd over snr for n = 1024 , m = 300 , ks = 6 , u = 4 .", "E) the figure depicts pmd over snr for n = 102 , m = 300 , ks = 3 , u = 16 ." ]

[ "A) the figure depicts pmd over snr for n = 1024 , m = 00 , ks = , u = 8 .", "B) the figure depicts pmd over snr for n = 102 , m = 300 , ks = 3 , u = 16 .", "C) the figure depicts pmd over snr for n = 104 , m = 300 , ks = 3 , u = 4 .", "D) the figure depicts pmd over snr for n = 1024 , m = 300 , ks = 6 , u = 4 .", "E) the figure depicts pmd over snr for n = 1024 , m = 300 , ks = , u = 8 ." ]

[ "A) the figure depicts pmd over snr for n = 1024 , m = 300 , ks = , u = 8 .", "B) the figure depicts pmd over snr for n = 104 , m = 300 , ks = 3 , u = 4 .", "C) the figure depicts pmd over snr for n = 102 , m = 300 , ks = 3 , u = 16 .", "D) the figure depicts pmd over snr for n = 1024 , m = 300 , ks = 6 , u = 4 .", "E) the figure depicts pmd over snr for n = 1024 , m = 00 , ks = , u = 8 ." ]

[ "A) f1-score evolution with increasing number of observations from the user in the production experiment .", "B) evolution of accuracy for the splitcifar100 dataset .", "C) prediction accuracies in the comprehension experiment with synthetic data .", "D) accuracy evolution with increasing number of observations from the user in the comprehension experiment with synthetic data .", "E) results of the experiment ( i ) , accuracy for each model on different data proportions ( english corpus ) ." ]

[ "A) the recognition rates using iclap , product-based ( icp*iclap ) and weighted sum-based ( icp+iclap ) hybrid fusion approaches against different number of touches .", "B) the recognition rates using iclap , product-based ( bow*icp ) and weighted sum-based ( bow+icp ) decision fusion approaches against different number of touches .", "C) the recognition rates using iclap , productbased ( bow*icp*iclap ) and weighted sum-based ( bow+icp+iclap ) decision fusion approaches with both tactile and kinesthetic sensing pipelines against different number of touches .", "D) the recognition rates using iclap , product-based ( bow*iclap ) and weighted sum-based ( bow*iclap ) hybrid fusion approaches against different number of touches .", "E) the recognition rates with icp , bow and iclap when different number of touches are taken ." ]

[ "A) the recognition rates using iclap , product-based ( bow*icp ) and weighted sum-based ( bow+icp ) decision fusion approaches against different number of touches .", "B) the recognition rates with icp , bow and iclap when different number of touches are taken .", "C) the recognition rates using iclap , product-based ( icp*iclap ) and weighted sum-based ( icp+iclap ) hybrid fusion approaches against different number of touches .", "D) the recognition rates using iclap , productbased ( bow*icp*iclap ) and weighted sum-based ( bow+icp+iclap ) decision fusion approaches with both tactile and kinesthetic sensing pipelines against different number of touches .", "E) the recognition rates using iclap , product-based ( bow*iclap ) and weighted sum-based ( bow*iclap ) hybrid fusion approaches against different number of touches ." ]

[ "A) the recognition rates using iclap , product-based ( bow*iclap ) and weighted sum-based ( bow*iclap ) hybrid fusion approaches against different number of touches .", "B) the recognition rates using iclap , product-based ( bow*icp ) and weighted sum-based ( bow+icp ) decision fusion approaches against different number of touches .", "C) the recognition rates with icp , bow and iclap when different number of touches are taken .", "D) the recognition rates using iclap , product-based ( icp*iclap ) and weighted sum-based ( icp+iclap ) hybrid fusion approaches against different number of touches .", "E) the recognition rates using iclap , productbased ( bow*icp*iclap ) and weighted sum-based ( bow+icp+iclap ) decision fusion approaches with both tactile and kinesthetic sensing pipelines against different number of touches ." ]

[ "A) the recognition rates using iclap , product-based ( bow*icp ) and weighted sum-based ( bow+icp ) decision fusion approaches against different number of touches .", "B) the recognition rates using iclap , product-based ( bow*iclap ) and weighted sum-based ( bow*iclap ) hybrid fusion approaches against different number of touches .", "C) the recognition rates using iclap , productbased ( bow*icp*iclap ) and weighted sum-based ( bow+icp+iclap ) decision fusion approaches with both tactile and kinesthetic sensing pipelines against different number of touches .", "D) the recognition rates with icp , bow and iclap when different number of touches are taken .", "E) the recognition rates using iclap , product-based ( icp*iclap ) and weighted sum-based ( icp+iclap ) hybrid fusion approaches against different number of touches ." ]

[ "A) accuracy @ topn on 80 % training data .", "B) accuracy of proposed methods with different k value set-up .", "C) comparison of top-p accuracy with existing techniques .", "D) top-k accuracy , mean average precision @ k , and mean recall @ k .", "E) top-k accuracy comparison with existing techniques ." ]

[ "A) ber performance of bicm-id with 4-d 16- and 16-qam over rayleigh fading channels .", "B) ber performance of bicm-id with 4-d 128- and 256-qam over rayleigh fading channels .", "C) error-floor bounds of ber for bicm-id with 4-d 16- and 32-qam over rayleigh fading channels .", "D) error-floor bounds of ber for bicm-id with 4-d 64-qam over rayleigh fading channels .", "E) error-floor bounds of ber for bicm-id with 4-d 8- and 256-qam over rayleigh fading channels ." ]

[ "A) function f ( x ) = x4 .", "B) graph of function f ( x ) = x .", "C) the function ϕε ( x ) for ε = 14 .", "D) the function f ( x ) given in ( 20 ) .", "E) function f ( x ) = x3 for x ∈ [ 1 , 3 ] ." ]

[ "A) pdcch al2 , rbs with 3db stronger interferences : [ 1:24 ] , tdla , s : 300ns .", "B) pdcch al , rbs with 3db stronger interferences : [ 1:12 ] , tdla , ds : 300ns .", "C) pdcch al1 , rbs with 3db stronger interferences : [ 1:1 ] , tdla , ds : 300ns .", "D) pdcch al8 , rbs with 3db stronger interferences : [ 1:12 ] , tdla , ds : 300ns .", "E) pdcch al1 , rbs with 3db stronger interferences : [ 1:24 ] , tdla , ds : 300ns ." ]

[ "A) ber comparison in 4×4 system between msm and generalized method which is proposed in [ 5 ] in terms of average number of molecules per bit .", "B) ber comparison for different m ’ s , with egc .", "C) ber of 2×2 system based on msm and 2×2 system based on proposed method in [ 5 ] in terms of average number of molecules .", "D) ber of 2×1 system based on msm and 1×1 system based on qcsk in terms of average number of molecules .", "E) ber comparison for differential stc schemes in a 4×1 system at 3 bits/s/hz ." ]

[ "A) optimal solutions for g variation ( i− 1 = ip ) .", "B) optimal solutions for v0i variation ( i− 1 > ip ) .", "C) optimal solutions for v0i variation ( i− 1 = ip ) .", "D) optimal solutions for t0i variation ( i− 1 > ip ) .", "E) optimal solutions for β variation ( i− 1 > ip ) ." ]

[ "A) execution time of multi-reference cosine ( 2000 to 4000 ) .", "B) execution time of multi-reference cosine 1000 3-grams .", "C) execution time of multi-reference cosine ( 6000 to 8000 ) .", "D) execution time of multi-reference cosine ( 4000 to 6000 ) .", "E) execution time of multi-reference cosine ( 0 to 2000 ) ." ]

[ "A) mean absolute error of multi-reference cosine with zero frequency 3-grams removed reference .", "B) mean absolute error of multi-reference cosine 1000 3-grams .", "C) mean absolute error of multi-reference cosine ( 6000 to 8000 ) .", "D) mean absolute error of multi-reference cosine 2000 3-grams .", "E) mean absolute error of multi-reference cosine 00 3-grams ." ]

[ "A) execution time of multi-reference cosine 1000 3-grams .", "B) execution time of multi-reference cosine ( 0 to 2000 ) .", "C) execution time of multi-reference cosine 2000 3-grams .", "D) execution time of multi-reference cosine ( 4000 to 6000 ) .", "E) execution time of multi-reference cosine 1500 3-grams ." ]

[ "A) execution time of multi-reference cosine ( 2000 to 4000 ) .", "B) execution time of multi-reference cosine 2000 3-grams .", "C) execution time of multi-reference cosine 1500 3-grams .", "D) execution time of multi-reference cosine 1000 3-grams .", "E) execution time of multi-reference cosine ( 6000 to 8000 ) ." ]

[ "A) execution time of multi-reference cosine ( 0 to 2000 ) .", "B) execution time of multi-reference cosine ( 2000 to 4000 ) .", "C) execution time of multi-reference cosine ( 4000 to 6000 ) .", "D) execution time of multi-reference cosine ( 6000 to 8000 ) .", "E) execution time of multi-reference cosine 2000 3-grams ." ]

[ "A) performance at different ψ of our methods .", "B) performance of our method on dev set for different γ .", "C) performance on hybrid datasets .", "D) performance of different models on several datasets .", "E) the performance of our method by varying λ ." ]

[ "A) convergence of the cost with iteration step for 30-bus model ( blue graph ) and for the polish model ( red graph ) .", "B) comprehensive convergence diagrams of objective functions with respect to full analysis ( blue ) and reanalysis ( red ) with scales of 160 80 .", "C) comprehensive convergence diagrams of objective functions with respect to full analysis ( blue ) and reanalysis ( red ) with scale of 80 80 .", "D) comparison of the convergence behavior between algorithm ( solid blue line ) and 3 ( dotted red line ) .", "E) comprehensive convergence diagrams of objective functions with respect to full analysis ( blue ) and reanalysis ( red ) with scale of 80 40 ." ]

[ "A) convergence of the cost with iteration step for 30-bus model ( blue graph ) and for the polish model ( red graph ) .", "B) comprehensive convergence diagrams of objective functions with respect to full analysis ( blue ) and reanalysis ( red ) with scale of 80 80 .", "C) comprehensive convergence diagrams of objective functions with respect to full analysis ( blue ) and reanalysis ( red ) with scale of 80 40 .", "D) comparison of the convergence behavior between algorithm ( solid blue line ) and 3 ( dotted red line ) .", "E) comprehensive convergence diagrams of objective functions with respect to full analysis ( blue ) and reanalysis ( red ) with scales of 160 80 ." ]

[ "A) sum rate versus transmit powers k = 16 users and bref = bits .", "B) sum rate versus transmit powers k = 8 users and bref = 8 bits .", "C) sum rate versus transmit powers k = 1 users and bref = 4 bits .", "D) sum rate versus transmit powers k = 16 users and bref = 2 bits .", "E) sum rate versus transmit powers k = 8 users and bref = 4 bits ." ]

[ "A) model accuracy on listops test set by size of training dataset .", "B) comparison of model performance vs. training size on the se07 data set in pearson ’ s r .", "C) influence of the training set size for the bach dataset .", "D) performance comparison on the nyt dataset .", "E) performance vs. training size ( log scale in x-axis ) on snli dataset ." ]

[ "A) time behaviour of the vehicles ’ collision point distance during the simulation run .", "B) velocity and distance between the vehicles with communication delay 0.333 s .", "C) time behaviour of the vehicles ’ acceleration during the simulation run .", "D) time behaviour of the vehicles ’ speed during the simulation run .", "E) time behaviour of the vehicles ’ speed ratio during the simulation run ." ]

[ "A) sop for various α1 with r1 = 0.1 , r2 = 0.2 , k = 2 , αj = 0.5 , p = 10 db , mu = me = mr = 2 , ε1 = 1.5 , ε2 = 2 , and ωe = −10 db .", "B) sop with dpa scheme for various k with r1 = 0.1 , r2 = 0.2 , αj = 0.5 , p = 10 db , mu = me = mr = 2 , ε1 = 1.5 , ε2 = 2 , ̟ = 0.1 , µ = 5 , and ωe = −5 db .", "C) sop for various k with r1 = 0.1 , r2 = 0.2 , mu = me = mr = 2 , p = 15 db , α1 = 0.2 , αj = 0.5 , ε1 = 1.5 , ε2 = 2 , and ωe = −12 db .", "D) sop with dpa scheme for various µ with r1 = 0.1 , r2 = 0.2 , αj = 0.5 , p = 10 db , mu = me = mr = 2 , k = 3 , ε1 = 1.5 , ε2 = 2 , ̟ = 0.2 , and ωe = −5 db .", "E) sop for various m with r1 = 0.1 , r2 = 0.2 , mu = me = mr = m , αj = 0.3 , k = 3 , p = 10 db , ε1 = 1.5 , ε2 = 2 , and ωe = −10 db ." ]

[ "A) links per year , positive covariance wind speed anomalies network after thresholding .", "B) heaviest links per year , positive covariance wind speed anomalies network .", "C) heaviest links per year , negative covariance wind speed anomalies network .", "D) links per year , positive covariance carbon emissions network after thresholding .", "E) links per year , negative covariance wind speed anomalies network after thresholding ." ]

[ "A) results for semi-black box fgs attack with standalone dae and dimensionality reduction defenses .", "B) results for black box fg attack with a cascaded dae and dimensionality reduction defense .", "C) results for semi-white box fgs attack with a cascaded dae and dimensionality reduction defense .", "D) results for semi-white box fg attack with a cascaded dae and dimensionality reduction defense .", "E) results for black box fgs attack with a cascaded dae and dimensionality reduction defense ." ]

[ "A) surowiec-south flow duration curves for 2025 scenarios .", "B) orrington-south flow duration curves for 2030 scenarios .", "C) orrington-south flow duration curves for 2025 scenarios .", "D) north-south flow duration curves for 2030 scenarios .", "E) north-south flow duration curves for 2025 scenarios ." ]

[ "A) orrington-south flow duration curves for 2030 scenarios .", "B) surowiec-south flow duration curves for 2030 scenarios .", "C) north-south flow duration curves for 2030 scenarios .", "D) surowiec-south flow duration curves for 2025 scenarios .", "E) north-south flow duration curves for 2025 scenarios ." ]

[ "A) surowiec-south flow duration curves for 2025 scenarios .", "B) orrington-south flow duration curves for 2030 scenarios .", "C) north-south flow duration curves for 2025 scenarios .", "D) north-south flow duration curves for 2030 scenarios .", "E) surowiec-south flow duration curves for 2030 scenarios ." ]

[ "A) the roc curves corresponding to ablation 1 .", "B) the roc curves corresponding to the ablation study .", "C) the roc curves corresponding to the ablation study .", "D) the roc curves corresponding to ablation 2 .", "E) the roc curves corresponding to ablation 3 ." ]

[ "A) classification accuracy vs. snr using an lstm with different degrees of uniform subsampling .", "B) classification accuracy vs. snr using a resnet with different degrees of uniform subsampling .", "C) classification accuracy vs. snr using a cldnn with different degrees of uniform subsampling .", "D) classification accuracy vs. snr using a cldnn with different degrees of magnitude rank subsampling .", "E) classification accuracy vs. snr using a cldnn with different degrees of random subsampling ." ]

[ "A) classification accuracy vs. snr using a cldnn with different degrees of uniform subsampling .", "B) classification accuracy vs. snr using an lstm with different degrees of uniform subsampling .", "C) classification accuracy vs. snr using a resnet with different degrees of uniform subsampling .", "D) classification accuracy vs. snr using an lstm with different degrees of random subsampling .", "E) classification accuracy vs. snr using an lstm with different degrees of magnitude rank subsampling ." ]

[ "A) classification accuracy vs. snr using an lstm with different degrees of uniform subsampling .", "B) classification accuracy vs. snr using a resnet with different degrees of random subsampling .", "C) classification accuracy vs. snr using a cldnn with different degrees of random subsampling .", "D) classification accuracy vs. snr using an lstm with different degrees of magnitude rank subsampling .", "E) classification accuracy vs. snr using an lstm with different degrees of random subsampling ." ]

[ "A) classification accuracy vs. snr using a cldnn with different degrees of uniform subsampling .", "B) classification accuracy vs. snr using a cldnn with different degrees of magnitude rank subsampling .", "C) classification accuracy vs. snr using a cldnn with different degrees of random subsampling .", "D) classification accuracy vs. snr using a resnet with different degrees of magnitude rank subsampling .", "E) classification accuracy vs. snr using an lstm with different degrees of magnitude rank subsampling ." ]

[ "A) mean rt vs. the number of commands for each condition .", "B) error rate vs. the number of commands for each condition .", "C) total time vs. the number of commands for each condition .", "D) mean mt vs. the number of commands for each condition .", "E) average condition numbers for different time increments ." ]

[ "A) number of explored nodes for randomly generated problems as a function of threshold δ .", "B) computation times for randomly generated problems without queue algorithm .", "C) computation times for randomly generated problems .", "D) number of explored nodes for randomly generated problems .", "E) computation times for randomly generated problems as a function of threshold δ ." ]

[ "A) number of explored nodes for randomly generated problems .", "B) computation times for randomly generated problems .", "C) number of explored nodes for randomly generated problems without queue algorithm .", "D) computation times for randomly generated problems as a function of threshold δ .", "E) number of explored nodes for randomly generated problems as a function of threshold δ ." ]

[ "A) nmpc charging : voltage over the single cells .", "B) cc-cv charging : voltage over the single cells .", "C) cc-cv charging : temperature of the single cells .", "D) voltage-based charging : voltage over the single cells .", "E) voltage-based charging : temperature of the single cells ." ]

[ "A) nmpc charging : voltage over the single cells .", "B) voltage-based charging : temperature of the single cells .", "C) cc-cv charging : voltage over the single cells .", "D) voltage-based charging : state of charge ( soc ) .", "E) voltage-based charging : voltage over the single cells ." ]

[ "A) voltage-based charging : voltage over the single cells .", "B) cc-cv charging : temperature of the single cells .", "C) nmpc charging : temperature of the single cells .", "D) cc-cv charging : voltage over the single cells .", "E) voltage-based charging : temperature of the single cells ." ]

[ "A) roc curves for both the standard and adpo line-based fracture detection scheme .", "B) figure illustrating roc curve for the standard line-based fracture detection .", "C) figure illustrating roc curve for the adpo line-based fracture detection .", "D) figure illustrating roc curve for the improved chfb fracture detection .", "E) figure illustrating roc curve for the standard chfb fracture detection ." ]

[ "A) figure illustrating roc curve for the improved chfb fracture detection .", "B) roc curves for both the standard and adpo line-based fracture detection scheme .", "C) figure illustrating roc curve for the standard chfb fracture detection .", "D) figure illustrating roc curve for the adpo line-based fracture detection .", "E) figure illustrating roc curve for the standard line-based fracture detection ." ]

[ "A) estimation error of f0 ( t ) .", "B) estimation of β under different noise levels .", "C) detection performance for varying level of noise parameter σ .", "D) estimation of γ under different noise levels .", "E) estimation of α under different noise levels ." ]

[ "A) roc curves of the target classifiers .", "B) roc plot of different classifiers using tfp-2 features .", "C) roc curve for vgo-based classifiers .", "D) roc plot for the 5 top-ranked classifiers without using the upvotes-related features .", "E) roc plot for the 4 top-ranked classifiers ." ]

[ "A) kaplan-meier curve for survival analysis .", "B) the kaplan-meier survival model for three groups of experience factors over a period of one month .", "C) the kaplan-meier survival model for two groups of activity factors over a period of two months .", "D) the kaplan-meier survival model for one group of divisional and non-divisional variables over a period of one month .", "E) the kaplan-meier survival model for two groups of experience factors over a period of one month ." ]

[ "A) the kaplan-meier survival model for one group of divisional and non-divisional variables over a period of one month .", "B) kaplan-meier curve for survival analysis .", "C) the kaplan-meier survival model for two groups of activity factors over a period of one month .", "D) the kaplan-meier survival model for three groups of experience factors over a period of one month .", "E) the kaplan-meier survival model for Group 1, Group 2 and Group 3 of experience factors over a period of one month ." ]

[ "A) held-out evaluation precision / recall curves for all nn-based models on nytfb-280k .", "B) precision-recall curves for the proposed model and various attention-based neural models .", "C) held-out evaluation precision / recall curves for pcnn+att , multir , dnmar and our proposed model pcnnnmar ( weighted ) on nytfb-68k .", "D) held-out evaluation precision / recall curves for pcnn+att model on original nytfb-280k and its shared-entity-pairs-removed version .", "E) the precision/recall curves of previous state-of-the-art methods and our proposed framework ." ]

[ "A) pibr4 ( λ ) versus λ .", "B) g ( λ̃1 ( δ ) ) versus λ̃1 ( δ ) in appendix e .", "C) α as a function of λ .", "D) the loss function eθ as function of ∆θ .", "E) f ( λ̃1 ( δ ) ) versus λ̃1 ( δ ) in appendix i ." ]

[ "A) p ( δ = j ) .", "B) µ versus q1 for p1 = 0.3 .", "C) p ( j=j ) for q = 0.7 , % ∗ = 0.7 .", "D) p ( j=j ) for q = 0.5 , % ∗ = 0 .", "E) ρ∗ ( α , p ) against p for different α ." ]

[ "A) excess probability of distortion for δ = 0.01 .", "B) probability distribution of wt { e∗ ( x ) } .", "C) asymptotic of the common distribution α and δ .", "D) probability distribution function of b̃ .", "E) probability distribution function of δ and b̃ ." ]

[ "A) frequency histogram with iteration planning as dependent variable and all the individual skills as factors .", "B) frequency histogram with productivity as dependent variable .", "C) productivity histogram of ds3 .", "D) frequency histogram for the second model with scale 2 as dependent variable .", "E) histogram of residuals ." ]

[ "A) the cumulative percentage of female first authored uk scopus-indexed journal articles 1996-2018 in each top cited percentile ( using nlcs values ) .", "B) the cumulative percentage of female first authored us scopus-indexed journal articles 1996-2018 in each top cited percentile ( using nlcs values ) .", "C) the cumulative percentage of female first authored canadian scopus-indexed journal articles 16-2018 in each top cited percentile ( using nlcs values ) .", "D) the cumulative percentage of female first authored new zealand scopus-indexed journal articles 1996-2018 in each top cited percentile ( using nlcs values ) .", "E) the cumulative percentage of female first authored irish scopus-indexed journal articles 1996-2018 in each top cited percentile ( using nlcs values ) ." ]