Datasets:

jonathan-roberts1

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SciFIBench

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[ "A) execution time .", "B) execution time ( ms ) on sorted arrays - log10 scale .", "C) execution time ( ms ) on sorted arrays .", "D) execution time ( ms ) on random arrays - log10 scale .", "E) execution time ( ms ) on random arrays ." ]

[ "A) the epdf of the time between commits of popular and unpopular projects .", "B) the epdf and ecdf of time between commits ( up to an interval of week ) .", "C) the epdf and ecdf of time between two consecutive commits of ( up to an interval of 1 day ) .", "D) the epdf of the median time between commits of popular and unpopular projects .", "E) the epdf and ecdf of median commit intervals per committer ." ]

[ "A) comments per line of code ( min ) of all tasks ( python vs. other languages ) .", "B) lines of code ( mean ) of all tasks ( python vs. other languages ) .", "C) lines of code ( mean ) of tasks compiling successfully ( python vs. other languages ) .", "D) comments per line of code ( mean ) of all tasks ( python vs. other languages ) .", "E) performance ( min ) of tasks running successfully ( python vs. other languages ) ." ]

[ "A) performance ( min ) of tasks running successfully ( python vs. other languages ) .", "B) comments per line of code ( min ) of all tasks ( python vs. other languages ) .", "C) lines of code ( mean ) of tasks compiling successfully ( python vs. other languages ) .", "D) comments per line of code ( mean ) of all tasks ( python vs. other languages ) .", "E) lines of code ( mean ) of all tasks ( python vs. other languages ) ." ]

[ "A) performance ( mean ) of tasks running successfully ( all languages ) .", "B) runtime ( mean ) of tasks running successfully ( all languages ) .", "C) scalability ( min ) of tasks running successfully ( all languages ) .", "D) maximum ram usage ( mean ) of tasks running successfully ( all languages ) .", "E) performance ( min ) of tasks running successfully ( all languages ) ." ]

[ "A) maximum ram usage ( mean ) of tasks running successfully ( all languages ) .", "B) performance ( min ) of tasks running successfully ( all languages ) .", "C) maximum ram usage ( min ) of tasks running successfully ( all languages ) .", "D) scalability ( mean ) of tasks running successfully ( all languages ) .", "E) scalability ( min ) of tasks running successfully ( all languages ) ." ]

[ "A) performance ( mean ) of tasks running successfully ( all languages ) .", "B) performance ( min ) of tasks running successfully ( all languages ) .", "C) page faults ( min ) of tasks running successfully ( all languages ) .", "D) page faults ( mean ) of tasks running successfully ( all languages ) .", "E) scalability ( mean ) of tasks running successfully ( all languages ) ." ]

[ "A) s-band microwave lna measured frequency response .", "B) simulated s–parameters of the 0db crossover .", "C) s-band microwave lna simulated stability µ-test .", "D) frequency response of the rsiw directional coupler .", "E) s-band microwave lna simulated frequency response ." ]

[ "A) mse of ule for n=1 and its theoretical curve in case when k = 1 , ts = 3µ , where µ is the mean of t .", "B) the mse of ule for n = n in case when k = 6 , and n1 = n2 = ... = n6 , where µ is the mean of t .", "C) comparison of mse normalized by n for different values of k with σ = 1 .", "D) the mse of mle and ule with cmlb as benchmark in case when k = 1 , n1 = n , and x = 0n1 .", "E) mse of decision feedback and theoretical decision-directed parameter estimation in case when k = 1 , ts = 3µ ." ]

[ "A) comparison of mse normalized by n for different values of k with σ = 1 .", "B) the mse of mle and ule with cmlb as benchmark in case when k = 1 , n1 = n , and x = 0n1 .", "C) mse of ule for n=1 and its theoretical curve in case when k = 1 , ts = 3µ , where µ is the mean of t .", "D) mse of decision feedback and theoretical decision-directed parameter estimation in case when k = 1 , ts = 3µ .", "E) the mse of ule for n = n in case when k = 6 , and n1 = n2 = ... = n6 , where µ is the mean of t ." ]

[ "A) comparison of bit error rates of uncoded modulations .", "B) uncoded qpsk vs. coded higher-order modulations ( v=10 ) .", "C) bit error rate for differents modulations .", "D) uncoded ber performance comparison with 16-qam modulation .", "E) bit error rates of the proposed decoders ." ]

[ "A) uncoded qpsk vs. coded higher-order modulations ( v=10 ) .", "B) uncoded 16-qam vs. coded 64-qam ( v= ) .", "C) uncoded 8-psk vs. coded higher-order modulations ( v=10 ) .", "D) comparison of bit error rates of uncoded modulations .", "E) comparison with ldpc coded modulation at r = 1 ." ]

[ "A) sad of the learned f2 features for coronary .", "B) sad of the learned f3 features for coronary .", "C) sad of the learned f1 features for coronary .", "D) sad of the learned f4 features for coronary .", "E) sad of the learned f5 features for coronary ." ]

[ "A) sad of the learned f1 features for coronary .", "B) sad of the learned f4 features for coronary .", "C) sad of the learned f5 features for coronary .", "D) sad of the learned f2 features for coronary .", "E) sad of the learned f3 features for coronary ." ]

[ "A) sad of the learned f5 features for coronary .", "B) sad of the learned f1 features for coronary .", "C) sad of the learned f3 features for coronary .", "D) sad of the learned f4 features for coronary .", "E) sad of the learned f2 features for coronary ." ]

[ "A) sad of the learned f4 features for coronary .", "B) sad of the learned f5 features for coronary .", "C) sad of the learned f2 features for coronary .", "D) sad of the learned f1 features for coronary .", "E) sad of the learned f3 features for coronary ." ]

[ "A) msd performance comparisons ( 𝜇 = 2.5 ) .", "B) msd performance comparisons ( 𝜇 = 1.5 ) .", "C) msd performance comparisons taps ( snr=8db ) .", "D) msd performance comparisons ( k=6 ) .", "E) msd performance comparisons ( k=4 ) ." ]

[ "A) msd performance comparisons taps ( snr=8db ) .", "B) msd performance comparisons ( 𝜇 = 1.5 ) .", "C) msd performance comparisons ( 𝜇 = 2.5 ) .", "D) msd performance comparisons ( k=6 ) .", "E) msd performance comparisons ( k=4 ) ." ]

[ "A) task allocation proportion for m-i team .", "B) task allocation proportion for s-c team .", "C) task allocation proportion for m-m team .", "D) task allocation proportion for m-c team .", "E) task allocation proportion for s-i team ." ]

[ "A) task allocation proportion for s-i team .", "B) task allocation proportion for m-m team .", "C) task allocation proportion for m-c team .", "D) task allocation proportion for m-i team .", "E) task allocation proportion for l-m team ." ]

[ "A) task allocation proportion for s-i team .", "B) task allocation proportion for m-i team .", "C) task allocation proportion for l-m team .", "D) task allocation proportion for m-m team .", "E) task allocation proportion for m-c team ." ]

[ "A) the classification accuracy of self-adaptive , entropy-based , distance-based methods for training data labeling on gdata1 .", "B) the classification accuracy of self-adaptive , entropy-based , distance-based methods for training data labeling on heart disease .", "C) the classification accuracy of self-adaptive , entropy-based , distance-based methods for training data labeling on gdata2 .", "D) the classification accuracy of self-adaptive , entropy-based , distance-based methods for training data labeling on wine .", "E) the classification accuracy of self-adaptive , entropy-based , distance-based methods for training data labeling on new thyroid ." ]

[ "A) the classification accuracy of self-adaptive , entropy-based , distance-based methods for training data labeling on gdata1 .", "B) the classification accuracy of self-adaptive , entropy-based , distance-based methods for training data labeling on heart disease .", "C) the classification accuracy of self-adaptive , entropy-based , distance-based methods for training data labeling on new thyroid .", "D) the classification accuracy of self-adaptive , entropy-based , distance-based methods for training data labeling on gdata2 .", "E) the classification accuracy of self-adaptive , entropy-based , distance-based methods for training data labeling on wine ." ]

[ "A) dynamics of the numbers of us-patents granted per million inhabitants of the usa during the period 1945-1980 ; the straight line is based on linear regression .", "B) dynamics of the numbers of us patents granted per million inhabitants of the usa during the period from 2006 ; the straight line is based on linear regression .", "C) dynamics of the numbers of us-patent granted per year per million inhabitants of the usa during the period 1840-1945 ; the straight line is based on linear regression .", "D) dynamics of the numbers of us-patents granted per million inhabitants of the .", "E) dynamics of the numbers of us patents granted per million inhabitants of the usa during the period 1979-2006 ; the straight line is based on linear regression ." ]

[ "A) dynamics of the numbers of us-patents granted per million inhabitants of the usa during the period 1945-1980 ; the straight line is based on linear regression .", "B) dynamics of the numbers of us-patent granted per year per million inhabitants of the usa during the period 1840-1945 ; the straight line is based on linear regression .", "C) dynamics of the numbers of us patents granted per million inhabitants of the usa during the period from 2006 ; the straight line is based on linear regression .", "D) dynamics of the numbers of us-patents granted per million inhabitants of the .", "E) dynamics of the numbers of us patents granted per million inhabitants of the usa during the period 1979-2006 ; the straight line is based on linear regression ." ]

[ "A) psnr based comparison with same size cipher different image dimensions .", "B) comparative analysis using psnr with variable image dimensions .", "C) comparative analysis of both methods using psnr with different images .", "D) comparison based on psnr with same image dimension and variable amount of cipher .", "E) comparative analysis using psnr with variable amount of embedded cipher ." ]

[ "A) execution time of qmul .", "B) execution time of smul .", "C) execution time of t3mul .", "D) execution time of kmul .", "E) execution time of omul ." ]

[ "A) the objective function values as iteration proceeds .", "B) objective value vs. number of iterations .", "C) objective function value versus the number of iterations .", "D) objective function value vs iteration index .", "E) the objective function value versus iteration ." ]

[ "A) substrate bw utilization comparison using 50 substrate nodes .", "B) substrate cpu utilization comparison using 50 substrate nodes .", "C) substrate resources utilization comparison using 50 substrate nodes .", "D) available substrate bw comparison using 50 substrate nodes .", "E) substrate cpu utilization comparison using 200 substrate nodes ." ]

[ "A) cost function along the iterations of the alternating optimization algorithm trajectory .", "B) change of the cost during iterations in example .", "C) normalized cost of the acs approach for different number of ants .", "D) normalized cost of the acs approach in different iterations .", "E) performance of the proposed acs algorithm with respect to normalized cost in each iteration number ." ]

[ "A) four-layer networks with different initial values .", "B) three-layer networks with different number of filters .", "C) three-layer networks with different filter sizes .", "D) four-layer networks with different filter sizes .", "E) comparison between three-layer and the four-layer networks ." ]

[ "A) three-layer networks with different number of filters .", "B) four-layer networks with different initial values .", "C) four-layer networks with different filter sizes .", "D) comparison between three-layer and the four-layer networks .", "E) three-layer networks with different filter sizes ." ]

[ "A) three-layer networks with different number of filters .", "B) four-layer networks with different initial values .", "C) three-layer networks with different filter sizes .", "D) comparison between three-layer and the four-layer networks .", "E) four-layer networks with different filter sizes ." ]

[ "A) comparison between three-layer and the four-layer networks .", "B) four-layer networks with different filter sizes .", "C) three-layer networks with different number of filters .", "D) four-layer networks with different initial values .", "E) three-layer networks with different filter sizes ." ]

[ "A) comparison of the dtit of non-blocking put operation .", "B) comparison of the dtit of non-blocking get operation .", "C) comparison of the bandwidth of the non-blocking put operation .", "D) comparison of the dtct of the blocking put operation .", "E) comparison of the dtct of the blocking get operation ." ]

[ "A) comparison of the bandwidth of the non-blocking get operation .", "B) comparison of the bandwidth of the blocking put operation .", "C) comparison of the dtit of non-blocking get operation .", "D) comparison of the bandwidth of the non-blocking put operation .", "E) comparison of the bandwidth of the blocking get operation ." ]

[ "A) comparison of the dtct of the blocking put operation .", "B) comparison of the dtct of the blocking get operation .", "C) comparison of the dtit of non-blocking get operation .", "D) comparison of the dtit of non-blocking put operation .", "E) comparison of the bandwidth of the blocking get operation ." ]

[ "A) standard deviation uncertainty in localization v/s beacon radius for number of beacons = 4 .", "B) standard deviation uncertainty in localization v/s beacon radius for number of beacons = 64 .", "C) standard deviation uncertainty in localization v/s beacon radius for number of beacons = 16 .", "D) standard deviation uncertainty in localization v/s beacon radius for number of beacons = 8 .", "E) standard deviation uncertainty in localization v/s beacon radius for number of beacons = 32 ." ]

[ "A) uncertainty in localization v/s beacon radius for number of beacons = 32 .", "B) uncertainty in localization v/s beacon radius for number of beacons = 64 .", "C) uncertainty in localization v/s beacon radius for number of beacons = 8 .", "D) uncertainty in localization v/s beacon radius for number of beacons = 4 .", "E) standard deviation uncertainty in localization v/s beacon radius for number of beacons = 4 ." ]

[ "A) uncertainty in localization v/s beacon radius for number of beacons = 4 .", "B) standard deviation uncertainty in localization v/s beacon radius for number of beacons = 8 .", "C) uncertainty in localization v/s beacon radius for number of beacons = 64 .", "D) uncertainty in localization v/s beacon radius for number of beacons = 8 .", "E) uncertainty in localization v/s beacon radius for number of beacons = 32 ." ]

[ "A) standard deviation uncertainty in localization v/s beacon radius for number of beacons = 64 .", "B) standard deviation uncertainty in localization v/s beacon radius for number of beacons = 16 .", "C) standard deviation uncertainty in localization v/s beacon radius for number of beacons = 32 .", "D) standard deviation uncertainty in localization v/s beacon radius for number of beacons = 8 .", "E) standard deviation uncertainty in localization v/s beacon radius for number of beacons = 4 ." ]

[ "A) standard deviation uncertainty in localization v/s beacon radius for number of beacons = 32 .", "B) standard deviation uncertainty in localization v/s beacon radius for number of beacons = 64 .", "C) standard deviation uncertainty in localization v/s beacon radius for number of beacons = 4 .", "D) standard deviation uncertainty in localization v/s beacon radius for number of beacons = 8 .", "E) standard deviation uncertainty in localization v/s beacon radius for number of beacons = 16 ." ]

[ "A) choose-first pw-rw with dynamic nodes : expected search length ls vs. pw length s for several d in a regular network .", "B) check-first pw-rw ( w = 5 ) with dynamic nodes : expected search length ls vs. pw length s for several d in a regular network .", "C) check-first pw-rw ( w = 5 ) with dynamic nodes : expected search length ls vs. pw length s for several l in a scale-free network .", "D) check-first pw-rw ( w = 5 ) with dynamic nodes : expected search length ls vs. pw length s for several d in a er network .", "E) choose-first pw-rw with dynamic nodes : expected search length ls vs. pw length s for several d in a er network ." ]

[ "A) choose-first pw-rw with dynamic resources : expected search length ls vs. pw length s for several d in a regular network .", "B) choose-first pw-rw with dynamic nodes : expected search length ls vs. pw length s for several d in a regular network .", "C) choose-first pw-rw with dynamic resources : expected search length ls vs. pw length s for several d in a er network .", "D) check-first pw-rw ( w = 5 ) with dynamic nodes : expected search length ls vs. pw length s for several d in a regular network .", "E) choose-first pw-rw with dynamic nodes : expected search length ls vs. pw length s for several d in a er network ." ]

[ "A) choose-first pw-rw with dynamic resources : expected search length ls vs. pw length s for several d in a regular network .", "B) check-first pw-rw ( w = 5 ) with dynamic nodes : expected search length ls vs. pw length s for several d in a er network .", "C) choose-first pw-rw with dynamic nodes : expected search length ls vs. pw length s for several d in a regular network .", "D) choose-first pw-rw with dynamic nodes : expected search length ls vs. pw length s for several d in a er network .", "E) choose-first pw-rw with dynamic resources : expected search length ls vs. pw length s for several d in a er network ." ]

[ "A) choose-first pw-rw with dynamic resources : expected search length ls vs. pw length s for several d in a regular network .", "B) check-first pw-rw ( w = 5 ) with dynamic nodes : expected search length ls vs. pw length s for several d in a regular network .", "C) choose-first pw-rw with dynamic nodes : expected search length ls vs. pw length s for several d in a regular network .", "D) check-first pw-rw ( w = 5 ) with dynamic nodes : expected search length ls vs. pw length s for several d in a er network .", "E) choose-first pw-rw with dynamic nodes : expected search length ls vs. pw length s for several d in a er network ." ]

[ "A) choose-first pw-rw with dynamic nodes : expected search length ls vs. pw length s for several d in a regular network .", "B) choose-first pw-rw with dynamic nodes : expected search length ls vs. pw length s for several d in a er network .", "C) check-first pw-rw ( w = 5 ) with dynamic nodes : expected search length ls vs. pw length s for several d in a er network .", "D) check-first pw-rw ( w = 5 ) with dynamic nodes : expected search length ls vs. pw length s for several d in a regular network .", "E) choose-first pw-rw with dynamic resources : expected search length ls vs. pw length s for several d in a er network ." ]

[ "A) choose-first pw-rw with dynamic nodes : expected search length ls vs. pw length s for several d in a er network .", "B) check-first pw-rw ( w = 5 ) with dynamic nodes : expected search length ls vs. pw length s for several l in a scale-free network .", "C) choose-first pw-rw with dynamic nodes : expected search length ls vs. pw length s for several d in a regular network .", "D) check-first pw-rw ( w = 5 ) with dynamic nodes : expected search length ls vs. pw length s for several d in a regular network .", "E) check-first pw-rw ( w = 5 ) with dynamic nodes : expected search length ls vs. pw length s for several d in a er network ." ]

[ "A) check-first pw-rw ( w = 5 ) with dynamic nodes : expected search length ls vs. pw length s for several l in a scale-free network .", "B) check-first pw-rw ( w = 5 ) with dynamic nodes : expected search length ls vs. pw length s for several d in a er network .", "C) check-first pw-rw ( w = 5 ) with dynamic nodes : expected search length ls vs. pw length s for several d in a regular network .", "D) choose-first pw-rw with dynamic nodes : expected search length ls vs. pw length s for several d in a regular network .", "E) choose-first pw-rw with dynamic nodes : expected search length ls vs. pw length s for several d in a er network ." ]

[ "A) function f ( x ) = x3 for x ∈ [ 1 , 3 ] .", "B) the function drawn with solid line is φξ ( x ) = ξ + ( 1 − ξ ) φ0 ( x ) with ξ = 0.1 , obtained from φ0 ( x ) = 0.2ϕ2,4 ( x ) + 0.4ϕ2,13 ( x ) + 0.4ϕ2,21 ( x ) ( dashed line ) .", "C) the function ϕε ( x ) for ε = 14 .", "D) the function a ( x ) .", "E) the function ϕ ( x ) = 2x2 − x4 ." ]

[ "A) classification accuracy .", "B) accuracy under different prediction percentage .", "C) accuracy of classification model for both classes .", "D) percentage of data used vs accuracy of -star classification .", "E) percentage of data used vs accuracy of positive/negative classification ." ]

[ "A) percentage of data used vs accuracy of five-star classification .", "B) accuracy ( in % ) vs. data size .", "C) classification accuracy vs. snr for synthetic data .", "D) accuracy of classification model for both classes .", "E) percentage of data used vs accuracy of positive/negative classification ." ]

[ "A) training cost on the arithmetic task when using associative lstm with 3 writing and reading heads .", "B) training cost on the variable assignment task .", "C) training cost on the arithmetic task .", "D) training cost on the xml task .", "E) training cost on the variable assignment task ." ]

[ "A) estimation of injury rate .", "B) estimation of the conflict rate .", "C) convergence of crash rate estimation .", "D) estimation of the crash rate .", "E) convergence of collision rate estimation ." ]

[ "A) estimation of injury rate .", "B) estimation of the conflict rate .", "C) convergence of injury rate estimation .", "D) estimation of the crash rate .", "E) convergence of collision rate estimation ." ]

[ "A) the accuracy of different number of training set .", "B) the average accuracy of test .", "C) comparison of f1 scores on the test set .", "D) classification evaluation for ohsumed dataset .", "E) f1-score comparison for different levels of correlation for the condmat dataset ." ]

[ "A) performance comparison between the optimal 2-jump algorithm and the halving 2-jump algorithm , as a function of β .", "B) performance comparison between the optimal 1-jump algorithm and the halving 1-jump algorithm , as a function of β .", "C) performance comparison between the optimal 3-jump algorithm and the halving 3-jump algorithm , as a function of β. notably , performance is nearly the same for all values of β > 5. the bigger discrepancy is observed for values of β close to 4 , for which also the jump steps between the two algorithms exhibit the larger gaps ( see figure 10 ) .", "D) comparison of the two jump choices between the 2-jump algorithm and the halving 2-jump algorithm , as a function of β. the first jump of each algorithm is always no smaller than the second one , and eventually they all attain the value 2π − β .", "E) comparison of jump choices between the optimal 1-jump algorithm and the halving 1-jump algorithm , as a function of β ." ]

[ "A) performance comparison between the optimal 3-jump algorithm and the halving 3-jump algorithm , as a function of β. notably , performance is nearly the same for all values of β > 5. the bigger discrepancy is observed for values of β close to 4 , for which also the jump steps between the two algorithms exhibit the larger gaps ( see figure 10 ) .", "B) performance comparison between the optimal 1-jump algorithm and the halving 1-jump algorithm , as a function of β .", "C) performance comparison between the optimal 2-jump algorithm and the halving 2-jump algorithm , as a function of β .", "D) comparison of jump choices between the optimal 1-jump algorithm, the halving 1-jump algorithm and the avg. 1-jump algorithm, as a function of β .", "E) comparison of the two jump choices between the optimal 2-jump algorithm and the halving 2-jump algorithm , as a function of β. the first jump of each algorithm is always no smaller than the second one , and eventually they all attain the value 2π − β ." ]

[ "A) ml normalized spectrum with perfect csi , θt = 23◦ , θj = 40◦ and pj = 4pt .", "B) ml normalized spectrum with perfect csi , θt = 43◦ , θj = −63◦ and pj = 2pt .", "C) ml normalized spectrum without csi , θt = 23◦ , θj = 40◦ and pj = 4pt .", "D) ml normalized spectrum without csi , θt = 43◦ , θj = −63◦ and pj = 2pt .", "E) ml normalized spectrum without csi , θt = 12◦ , θj = 50◦ and pj = pt ." ]

[ "A) ml normalized spectrum with perfect csi , θt = 43◦ , θj = −63◦ and pj = 2pt .", "B) ml normalized spectrum without csi , θt = 43◦ , θj = −63◦ and pj = 2pt .", "C) ml normalized spectrum without csi , θt = 16◦ , θj = 40◦ and pj = 4pt .", "D) ml normalized spectrum without csi , θt = 12◦ , θj = 50◦ and pj = pt .", "E) ml normalized spectrum with perfect csi , θt = 23◦ , θj = 40◦ and pj = 4pt ." ]

[ "A) ml normalized spectrum without csi , θt = 23◦ , θj = 35◦ and pj = 4pt .", "B) ml normalized spectrum with perfect csi , θt = 33◦ , θj = −63◦ and pj = 2pt .", "C) ml normalized spectrum without csi , θt = 43◦ , θj = −63◦ and pj = 2pt .", "D) ml normalized spectrum with perfect csi , θt = 23◦ , θj = 35◦ and pj = 4pt .", "E) ml normalized spectrum without csi , θt = 12◦ , θj = 50◦ and pj = pt ." ]

[ "A) ml normalized spectrum without csi , θt = 43◦ , θj = −63◦ and pj = 2pt .", "B) ml normalized spectrum without csi , θt = 23◦ , θj = 40◦ and pj = 4pt .", "C) ml normalized spectrum with perfect csi , θt = 43◦ , θj = −63◦ and pj = 2pt .", "D) ml normalized spectrum with perfect csi , θt = 28◦ , θj = 40◦ and pj = 4pt .", "E) ml normalized spectrum without csi , θt = 12◦ , θj = 50◦ and pj = pt ." ]

[ "A) average codeword ( x ( s ) ) performances of cstbcs and rc for 2× 2 im/dd mimo-owc with l = 4 .", "B) average codeword ( x ( s ) ) performances of cstbcs and rc for 2× 1 im/dd miso-owc with different variances .", "C) average codeword ( x ( s ) ) performances of cstbcs and rc for 2× 1 im/dd mimo-owc with l = 3 .", "D) average codeword ( x ( s ) ) performances of cstbcs and rc for n ×m im/dd mimo-owc with l = 3 and k = 5 .", "E) average codeword ( x ( p ) ) performances of cstbcs and rc for 2× 1 im/dd mimo-owc ." ]

[ "A) average codeword ( x ( s ) ) performances of cstbcs and rc for 2× 2 im/dd mimo-owc with l = 4 .", "B) average codeword ( x ( s ) ) performances of cstbcs and rc for n ×m im/dd mimo-owc with l = 3 and k = 5 .", "C) average codeword ( x ( p ) ) performances of cstbcs and rc for 2× 1 im/dd mimo-owc .", "D) average codeword ( x ( s ) ) performances of cstbcs and rc for 2× 1 im/dd mimo-owc with l = 3 .", "E) average codeword ( x ( s ) ) performances of cstbcs and rc for 2× 1 im/dd miso-owc with different variances ." ]

[ "A) taskset schedulability as the task utilization per vcpu increases .", "B) taskset schedulability as the number of tasks increases .", "C) taskset schedulability when tasks have medium memory intensity .", "D) taskset schedulability as the number of cores increases .", "E) taskset schedulability as the ratio of memory-intensive tasks increases ." ]

[ "A) average ber among all users against snr , m = 10 , n = 7 , 8 , 9 , 10 with ρ = 0 .", "B) average ber among all users against snr , m = 20 , n = 1 with ρ = 0 , 0.3 exp ( 0.5j ) , 0.6 exp ( 0.5j ) and 0.9 exp ( 0.5j ) .", "C) average ber among all users against snr , m = 20 , n = 14 , 17 , 19 , 20 with ρ = 0 .", "D) average ber performance against snr with m = 6 , n = 2 with different ρ. each user uses a -qam .", "E) average ber among all users against snr , m = 20 , n = 20 with ρ = 0 , 0.3 exp ( 0.5j ) and 0.6 exp ( 0.5j ) ." ]

[ "A) illustrating the pos_max_max_bump_on_decreasing_sequence constraint of the example slot .", "B) illustrating the pos_min_min_bump_on_decreasing_sequence constraint of the example slot .", "C) illustrating the pos_max_surf_bump_on_decreasing_sequence constraint of the example slot .", "D) illustrating the pos_min_max_bump_on_decreasing_sequence constraint of the example slot .", "E) illustrating the pos_max_min_bump_on_decreasing_sequence constraint of the example slot ." ]

[ "A) illustrating the pos_min_max_increasing_sequence constraint of the example slot .", "B) illustrating the pos_max_min_increasing_sequence constraint of the example slot .", "C) illustrating the pos_min_max_decreasing_sequence constraint of the example slot .", "D) illustrating the pos_max_min_decreasing_sequence constraint of the example slot .", "E) illustrating the pos_max_max_decreasing_sequence constraint of the example slot ." ]

[ "A) illustrating the pos_min_min_dip_on_increasing_sequence constraint of the example slot .", "B) illustrating the pos_max_min_dip_on_increasing_sequence constraint of the example slot .", "C) illustrating the pos_max_max_dip_on_increasing_sequence constraint of the example slot .", "D) illustrating the pos_min_max_dip_on_increasing_sequence constraint of the example slot .", "E) illustrating the pos_max_surf_dip_on_increasing_sequence constraint of the example slot ." ]

[ "A) illustrating the pos_max_min_decreasing_sequence constraint of the example slot .", "B) illustrating the pos_max_max_increasing_sequence constraint of the example slot .", "C) illustrating the pos_min_max_increasing_sequence constraint of the example slot .", "D) illustrating the pos_max_min_increasing_sequence constraint of the example slot .", "E) illustrating the pos_min_max_strictly_increasing_sequence constraint of the example slot ." ]

[ "A) illustrating the decreasing_width_increasing_sequence constraint of the example slot .", "B) illustrating the decreasing_width_strictly_increasing_sequence constraint of the example slot .", "C) illustrating the increasing_width_increasing_sequence constraint of the example slot .", "D) illustrating the decreasing_width_decreasing_sequence constraint of the example slot .", "E) illustrating the increasing_width_decreasing_sequence constraint of the example slot ." ]

[ "A) illustrating the max_max_dip_on_increasing_sequence constraint of the example slot .", "B) illustrating the max_min_dip_on_increasing_sequence constraint of the example slot .", "C) illustrating the min_min_dip_on_increasing_sequence constraint of the example slot .", "D) illustrating the sum_max_dip_on_increasing_sequence constraint of the example slot .", "E) illustrating the min_max_dip_on_increasing_sequence constraint of the example slot ." ]

[ "A) illustrating the max_min_bump_on_decreasing_sequence constraint of the example slot .", "B) illustrating the min_surf_bump_on_decreasing_sequence constraint of the example slot .", "C) illustrating the min_max_bump_on_decreasing_sequence constraint of the example slot .", "D) illustrating the min_min_bump_on_decreasing_sequence constraint of the example slot .", "E) illustrating the sum_min_bump_on_decreasing_sequence constraint of the example slot ." ]

[ "A) evolution of the error 2 vs. the wavenumber k , for m = 2 , nλ = 10 and p = 1 , ... , 6 .", "B) evolution of the error 2 vs. the discretization density nλ for k = 40 and p = 1 , ... , 4 .", "C) evolution of the error 2 vs. the wavenumber k for m = 2 , nλ = 10 and p = 1 , ... , 8 .", "D) evolution of the error 2 vs. the wavenumber k , for nλ = 10 and p = 1 , ... , 5 .", "E) evolution of the error 2 vs. the wavenumber k , for nλ = 20 and various approximation orders p = 1 , ... , 6 ." ]

[ "A) normalized revenues versus total number of ads for line count=40 .", "B) normalized revenues versus total number of ads for line count=45 .", "C) normalized revenues versus total number of ads for line count=35 .", "D) normalized revenues versus total number of ads for line count=30 .", "E) normalized revenues versus total number of ads for line count=25 ." ]

[ "A) roc curves for individual classes for the test data from the random forest model .", "B) roc curves for random forest algorithms .", "C) roc curves for naive bayes algorithms .", "D) roc curves for each classification model .", "E) roc curves for the test data from the random forest model ." ]

[ "A) sgj execution time ( type ii , 100 % connectivity ) .", "B) rjg execution time ( type i , 100 % connectivity ) .", "C) ngj execution time ( type ii , no connectivity ) .", "D) rgj execution time ( type ii , 100 % connectivity ) .", "E) sgj execution time ( type i , 100 % connectivity ) ." ]

[ "A) propulsion power coefficient .", "B) propulsion thrust coefficient .", "C) propulsion power at selected pitch angles and rpm .", "D) propulsion efficiency .", "E) propulsion efficiency at selected pitch angles and rpm ." ]

[ "A) propulsion efficiency at selected pitch angles and rpm .", "B) propulsion power at selected pitch angles and rpm .", "C) propulsion efficiency .", "D) propulsion power coefficient .", "E) propulsion thrust coefficient ." ]

[ "A) error probability as a function of m for the following settings : n = 20 , markovian sequence of states ρ = 0.95 , ε = 0.15 and α = 0.45 .", "B) error probability as a function of α for the following setting : n = 20 , markovian sequence of states ρ = 0.95 , ε = 0.15 , m = 30 and pmal = 1.0 .", "C) error probability as a function of α for the following setting : n = 20 , markovian sequence of states ρ = 0.95 , ε = 0.15 , m = and pmal = 1.0 .", "D) error probability as a function of α for the following setting : n = 20 , independent sequence of states ρ = 0.5 , ε = 0.15 , m = 10 and pmal = 1.0 .", "E) error probability as a function of α for the following setting : n = 20 , markovian sequence of states ρ = 0.95 , ε = 0.15 , m = 30 and pmal = 0.5 ." ]

[ "A) accuracy with varied number of neighbors k .", "B) comparison of domain discrepancy and target accuracy .", "C) accuracy with varied target value t .", "D) achievable accuracy for different target values .", "E) accuracy with missing data ." ]

[ "A) impacts of the reservation wage c on the gross profit f ∗ ( · ) , privacy level r∗ , subscription fee p∗s , and total data cost .", "B) gross profit f ∗ ( · ) , subscription revenue , subscription fee p∗s , and total data cost under varied privacy level r .", "C) impacts of the reservation wage c1 on the gross profit g∗c ( · ) , privacy levels r∗1 and r∗2 , and subscription fee p∗b .", "D) impacts of the degree of contingency γ on the gross profit g∗s ( · ) , privacy levels r∗1 and r∗2 , subscription fee p∗b , and total data cost .", "E) impacts of the degree of contingency γ on the gross profit g∗c ( · ) , privacy levels r∗1 and r∗2 , subscription fee p∗b , and total data cost ." ]

[ "A) gross profit f ∗ ( · ) , subscription revenue , subscription fee p∗s , and total data cost under varied privacy level r .", "B) impacts of the degree of contingency γ on the gross profit g∗s ( · ) , privacy levels r∗1 and r∗2 , subscription fee p∗b , and total data cost .", "C) impacts of the degree of contingency γ on the gross profit g∗c ( · ) , privacy levels r∗1 and r∗2 , subscription fee p∗b , and average data cost .", "D) impacts of the reservation wage c1 on the gross profit g∗c ( · ) , privacy levels r∗1 and r∗2 , and subscription fee p∗b .", "E) impacts of the reservation wage c on the gross profit f ∗ ( · ) , privacy level r∗ , subscription fee p∗s , and total data cost ." ]

[ "A) impacts of the reservation wage c1 on the gross profit g∗c ( · ) , privacy levels r∗1 and r∗2 , and subscription fee p∗b .", "B) impacts of the degree of contingency γ on the gross profit g∗s ( · ) , privacy levels r∗1 and r∗2 , subscription fee p∗b , and total data cost .", "C) impacts of the degree of contingency γ on the gross profit g∗c ( · ) , privacy levels r∗1 and r∗2 , subscription fee p∗b , and total data cost .", "D) impacts of the reservation wage c on the gross profit f ∗ ( · ) , privacy level r∗ , subscription fee p∗s , and total data cost .", "E) gross profit f ∗ ( · ) , subscription revenue , subscription fee p∗s , and total data cost under varied privacy level r ." ]

[ "A) change of expected accuracy interval with respect to the number of training samples observed on all three cnn models .", "B) recognition accuracy by using different numbers of training samples using svm .", "C) change of expected accuracy interval on class basis with respect to the number of training samples observed on cnn-3 for digit-0 , digit-1 and digit-5 .", "D) change of class specific dataset robustness with respect to the number of training samples observed on cnn-3 for digit-0 , digit-1 and digit-5 .", "E) change of robustness with respect to the number of training samples observed on all three cnn models ." ]

[ "A) change of robustness with respect to the number of training samples observed on all three cnn models .", "B) change of class specific dataset robustness with respect to the number of training samples observed on cnn-3 for digit-0 , digit-1 and digit-5 .", "C) accuracy for different numbers of training samples .", "D) change of expected accuracy interval on class basis with respect to the number of training samples observed on cnn-3 for digit-0 , digit-1 and digit-5 .", "E) change of expected accuracy interval with respect to the number of training samples observed on all three cnn models ." ]

[ "A) change of class specific dataset robustness with respect to the number of training samples observed on cnn-3 for digit-0 , digit-1 and digit-5 .", "B) change of robustness with respect to the number of training samples observed on all three cnn models .", "C) change of expected accuracy interval with respect to the number of training samples observed on all three cnn models .", "D) change of expected accuracy interval on class basis with respect to the number of training samples observed on cnn-3 for digit-0 , digit-1 and digit-5 .", "E) recognition accuracy by using different numbers of training samples using svm ." ]

[ "A) rank-50 cmc plot .", "B) rank-10 cmc plot .", "C) rank-5 cmc plot .", "D) rank-25 cmc plot .", "E) rank-1 cmc plot ." ]

[ "A) rank-5 cmc plot .", "B) rank-50 cmc plot .", "C) rank-1 cmc plot .", "D) rank-25 cmc plot .", "E) rank-10 cmc plot ." ]

[ "A) rank-50 cmc plot .", "B) rank-5 cmc plot .", "C) rank-1 cmc plot .", "D) rank-10 cmc plot .", "E) rank-25 cmc plot ." ]

[ "A) bounds and fer performance for bp and near-ml decoding of r = 1/ irregular ldpc codes of length n = 576 , where the following notations are used : “ shannon bound ” denotes the bound ( 4 ) , “ random code ” denotes the bound ( 6 ) computed for ensemble of linear codes , “ ldpc ( 4,8 ) -regular ” denotes the bound ( 6 ) computed for the gallager ensemble of the ( 4,8 ) -regular ldpc codes .", "B) bounds and fer performance for bp and near-ml decoding of r = 1/2 nonbinary ldpc code of length n = 1 over gf ( 28 ) [ 3 ] , where the following notations are used : “ shannon bound ” denotes the bound ( 4 ) , “ random code ” denotes the bound ( ) computed for ensemble of linear codes , “ nb ldpc ( 2,4 ) -regular ” denotes the bound ( ) computed for the gallager ensemble of binary images of the ( 2,4 ) -regular nb ldpc codes .", "C) bounds and fer performance for bp and near-ml decoding of r = 1/2 ( 4 , 8 ) -regular ldpc codes of length n = 96 , where the following notations are used : “ shannon bound ” denotes the bound ( 4 ) , “ random code ” denotes the bound ( 6 ) computed for ensemble of linear codes , “ ldpc ( 4,8 ) -regular ” denotes the bound ( 6 ) computed for the gallager ensemble of the ( 4,8 ) -regular ldpc codes .", "D) bounds and fer performance for bp and near-ml decoding of r = 1/3 irregular ldpc codes of length n = 576 , where the following notations are used : “ shannon bound ” denotes the bound ( ) , “ random code ” denotes the bound ( 6 ) computed for ensemble of linear codes , “ ldpc ( ,6 ) -regular ” denotes the bound ( 6 ) computed for the gallager ensemble of the ( ,6 ) -regular ldpc codes .", "E) bounds and fer performance for bp and near-ml decoding of r = 2/ irregular ldpc codes of length n = 576 , where the following notations are used : “ shannon bound ” denotes the bound ( 4 ) , “ random code ” denotes the bound ( 6 ) computed for ensemble of linear codes , “ ldpc ( ,9 ) -regular ” denotes the bound ( 6 ) computed for the gallager ensemble of the ( ,9 ) -regular ldpc codes ." ]

[ "A) hme : ǫz̈ ( ϕr , e ) and ǫz̈ ( ϕr , v ) vs. nϕr for the qr , fs and be heuristics .", "B) lme : ετz ( ϕr , e ) and ετz ( ϕr , e ) vs. nϕr for the qr , fs , and be heuristics .", "C) lme : ǫz̈ ( ϕr , e ) and ǫz̈ ( ϕr , v ) vs. nϕr for the qr , fs and be heuristics .", "D) lme : nop vs. nϕr for functions dq and [ mqq|δq ] for the qr , fs and be heuristics .", "E) lme : ǫτz ( ϕr , e ) vs. nop for functions dq and [ mqq|δq ] for the qr , fs and be heuristics ." ]

[ "A) lme : nop vs. nϕr for functions dq and [ mqq|δq ] for the or , fs and be heuristics .", "B) hme : ǫz̈ ( ϕr , e ) and ǫz̈ ( ϕr , v ) vs. nϕr for the qr , fs and be heuristics .", "C) lme : ǫz̈ ( ϕr , e ) and ǫz̈ ( ϕr , v ) vs. nϕr for the qr , fs and be heuristics .", "D) lme : ǫτz ( ϕr , e ) vs. nop for functions dq and [ mqq|δq ] for the qr , fs and be heuristics .", "E) lme : ετz ( ϕr , e ) and ετz ( ϕr , e ) vs. nϕr for the qr , fs , and be heuristics ." ]

[ "A) lme : nop vs. nϕr for functions dq and [ mqq|δq ] for the qr , fs and be heuristics .", "B) lme : ǫτz ( ϕr , e ) vs. nop for functions dq and [ mqq|δq ] for the qr , fs and be heuristics .", "C) lme : ǫz̈ ( ϕr , e ) and ǫz̈ ( ϕr , v ) vs. nϕr for the qr , fs and be heuristics .", "D) hme : ǫz̈ ( ϕr , e ) and ǫz̈ ( ϕr , v ) vs. nϕr for the or , fs and be heuristics .", "E) lme : ετz ( ϕr , e ) and ετz ( ϕr , e ) vs. nϕr for the qr , fs , and be heuristics ." ]

[ "A) median of information time as a function of degree .", "B) information spreading capacity as a function of eccentricity .", "C) information spreading capacity as a function of out-degree .", "D) information spreading capacity as a function of degree .", "E) information spreading capacity as a function of in-degree ." ]

[ "A) information spreading capacity as a function of in-degree .", "B) information spreading capacity as a function of eccentricity .", "C) median of information time as a function of degree .", "D) information spreading capacity as a function of degree .", "E) information spreading capacity as a function of out-degree ." ]

[ "A) results of different entropy coefficient λ .", "B) information spreading capacity as a function of in-degree .", "C) information spreading capacity as a function of eccentricity .", "D) information spreading capacity as a function of degree .", "E) information spreading capacity as a function of out-degree ." ]

[ "A) simulation results for 3σ under the disturbance torque .", "B) simulation results for 3σ under the initial cmgs momentum error .", "C) simulation results for 1σ under the disturbance torque .", "D) simulation results for 1σ under the initial cmgs momentum error .", "E) simulation results of the total momentum error magnitude under the initial cmgs momentum error ." ]

[ "A) simulation results for 1σ under the disturbance torque .", "B) simulation results for 1σ under the initial cmgs momentum error .", "C) simulation results of the total momentum error magnitude under the initial cmgs momentum error .", "D) simulation results for 3σ under the disturbance torque .", "E) simulation results for 3σ under the initial cmgs momentum error ." ]

[ "A) simulation results for 3σ under the disturbance torque .", "B) simulation results for 3σ under the initial cmgs momentum error .", "C) simulation results of the total momentum error magnitude under the initial cmgs momentum error .", "D) simulation results for 1σ under the initial cmgs momentum error .", "E) simulation results for 1σ under the disturbance torque ." ]