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[ "A) probability of detection vs. probability of false alarm for n = 2 , m = 2 , l = 1 .", "B) probability of detection vs. probability of false alarm for n = 2 , m = 4 , l = 1 .", "C) probability of detection vs. probability of false alarm for n = , m = 4 , l = 1 .", "D) probability of detection vs. probability of false alarm for n = 2 , m = 8 , l = 1 .", "E) probability of detection vs. probability of false alarm for n = 8 , m = 2 , l = 1 ." ]

[ "A) probability of detection vs. probability of false alarm for n = , m = 4 , l = 1 .", "B) probability of detection vs. probability of false alarm for n = 2 , m = 8 , l = 1 .", "C) probability of detection vs. probability of false alarm for n = 8 , m = 2 , l = 1 .", "D) probability of detection vs. probability of false alarm for n = 8 , m = 8 , l = 1 .", "E) probability of detection vs. probability of false alarm for n = 2 , m = 4 , l = 1 ." ]

[ "A) success ratio : 8 processors .", "B) success ratio : 2 processors .", "C) success ratio : 16 processors .", "D) success ratio : 4 processors .", "E) wcrt comparison : 8 processors ." ]

[ "A) success ratio : 8 processors .", "B) success ratio : 2 processors .", "C) success ratio : 4 processors .", "D) success ratio : 16 processors .", "E) wcrt comparison : 16 processors ." ]

[ "A) averaged variation of κ against increasing amount of gaussian blur for reference images in tid2008 database .", "B) averaged variation of κ against increasing amount of gaussian blur and a fixed amount of gaussian noise for reference images in tid2008 database .", "C) averaged variation of ϕ ( fitness ) against increasing amount of gaussian blur and a fixed amount of gaussian noise for reference images in tid200 database .", "D) averaged variation of ϕ ( fitness ) against increasing amount of gaussian blur for reference images in tid2008 database .", "E) averaged variation of κ against increasing amount of gaussian noise for reference images in tid2008 database ." ]

[ "A) averaged variation of κ against increasing amount of gaussian blur and a fixed amount of gaussian noise for reference images in tid2008 database .", "B) averaged variation of ϕ ( fitness ) against increasing amount of gaussian blur for reference images in tid2008 database .", "C) averaged variation of ϕ ( fitness ) against increasing amount of gaussian noise for reference images in tid2008 database .", "D) averaged variation of κ against increasing amount of gaussian blur for reference images in tid2008 database .", "E) averaged variation of κ against increasing amount of gaussian noise for reference images in tid2008 database ." ]

[ "A) averaged variation of κ against increasing amount of gaussian noise for reference images in tid2008 database .", "B) averaged variation of ϕ ( fitness ) against increasing amount of gaussian noise for reference images in tid2008 database .", "C) averaged variation of ϕ ( fitness ) against increasing amount of gaussian blur and a fixed amount of gaussian noise for reference images in tid200 database .", "D) averaged variation of κ against increasing amount of gaussian blur and a fixed amount of gaussian noise for reference images in tid2008 database .", "E) averaged variation of ϕ ( fitness ) against increasing amount of gaussian blur for reference images in tid2008 database ." ]

[ "A) output signal at input current 400 µap-p and frequency 100mhz .", "B) output signal at input current 400 µap-p and frequency 1 mhz .", "C) amplitude modulation output for inputs 200mv 10mhz carrier and 50mv 1mhz modulating signal .", "D) input signal of 50 mhz without jitter .", "E) amplitude modulation output for inputs 200mv 10ghz carrier and 50mv 1ghz modulating signal ." ]

[ "A) input signal of 50 mhz without jitter .", "B) output signal at input current 400 µap-p and frequency 1 mhz .", "C) amplitude modulation output for inputs 200mv 10mhz carrier and 50mv 1mhz modulating signal .", "D) amplitude modulation output for inputs 200mv 10ghz carrier and 50mv 1ghz modulating signal .", "E) output signal at input current 400 µap-p and frequency 100mhz ." ]

[ "A) bit error rate ( ber ) vs signal to noise ratio ( snr ) performance of 16-fsk wsn for different number of sensors over rayleigh fading channels with probability of correct detection equal to unity .", "B) bit error rate ( ber ) vs signal to noise ratio ( snr ) performance of mfsk for 4 nodes wsn system over rayleigh fading channels for different modulation levels with probability of correct detection equal to unity .", "C) bit error rate ( ber ) vs signal to noise ratio ( snr ) performance of mfsk for 20 nodes wsn system over rayleigh fading channels for different modulation levels with probability of correct detection equal to unity .", "D) bit error rate ( ber ) vs signal to noise ratio ( snr ) performance of -fsk wsn for different number of sensors over rayleigh fading channels with probability of correct detection equal to unity .", "E) bit error rate ( ber ) vs signal to noise ratio ( snr ) performance of mfsk for 10 nodes wsn system over rayleigh fading channels for different modulation levels with probability of correct detection equal to unity ." ]

[ "A) decentralized influence spread : effect of β on the optimal cost and optimal τ .", "B) decentralized influence spread : effect of λ on the optimal cost and optimal τ .", "C) decentralized influence spread : effect of ψ on the optimal cost and optimal τ .", "D) decentralized influence spread : effect of d0 on the optimal cost and optimal τ .", "E) decentralized influence spread : effect of xd0d0 on the optimal cost and optimal τ ." ]

[ "A) oarae parameter values for different methods .", "B) parameter sensitivity analysis results .", "C) arae parameter values for different methods .", "D) performance comparison on accuracy .", "E) oatprate parameter values for different methods ." ]

[ "A) 4x4 rl beam to array phase error .", "B) 4x4 rl beam to array spillover coupling amplitude .", "C) 8x8 rl beam to array spillover coupling amplitude .", "D) 4x4 rl beam to array coupling amplitude .", "E) 8x8 rl beam to array coupling amplitude ." ]

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

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

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

[ "A) uncoded qpsk vs. coded higher-order modulations ( v=10 ) .", "B) uncoded 8-psk vs. coded higher-order modulations ( v=10 ) .", "C) comparison of bit error rates of uncoded modulations .", "D) uncoded ber of the compared precoders with different quantization levels and qpsk signaling .", "E) uncoded 16-qam vs. coded 64-qam ( v= ) ." ]

[ "A) pdf of rayleigh distribution .", "B) pdf of exponential distribution .", "C) the pdf of lognormal distributions with different σ .", "D) sigmoid distribution .", "E) pdf of modified logistic distribution ." ]

[ "A) the classification accuracy of self-adaptive , entropy-based , distance-based methods for training data labeling on wine .", "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 gdata1 ." ]

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

[ "A) the function ϕ ( x ) = 2x2 − x4 .", "B) the function ϕε ( x ) for ε = 14 .", "C) the function f ( β ) .", "D) function f ( x ) = x3 for x ∈ [ 1 , 3 ] .", "E) the successor function λryα for α = 0.9 and t = 1 ." ]

[ "A) recognition accuracy as a function of number of pca features .", "B) recognition accuracy for different numbers of pca features on iit delhi iris database .", "C) recognition accuracy for different numbers of pca features on casia-1000 iris database .", "D) recognition accuracy for extended yale database .", "E) recognition accuracy for the imagenet dataset ." ]

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

[ "A) comparing the number of dead sensor nodes in the proposed methods and leach and eleach protocols .", "B) comparison number of dead nodes for proposed method and leach , eleach protocols .", "C) comparison number of dead nodes in proposed method with leach and aleach protocols .", "D) comparison of number of dead sensor nodes between the proposed methods and leach and eleach protocols .", "E) comparison number of dead nodes for proposed method and eleach , aleach protocols ." ]

[ "A) illustrating the pos_max_surf_increasing_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_max_decreasing_sequence constraint of the example slot .", "E) illustrating the pos_max_min_increasing_sequence constraint of the example slot ." ]

[ "A) illustrating the pos_max_max_decreasing_sequence constraint of the example slot .", "B) illustrating the pos_min_min_decreasing_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_min_max_increasing_sequence constraint of the example slot ." ]

[ "A) optimal number of hash functions k∗ for different values of n and δ̂ .", "B) optimal number of replicas for different values of λ̂s/λf .", "C) the effective optimal threshold factors reffm , n for different values of m and n .", "D) compression gain as δ̂ increases and for different values of n .", "E) optimal filter size m∗ for different values of n and δ̂ ." ]

[ "A) simulation results for 3σ under the disturbance torque .", "B) simulation results for 1σ under the disturbance torque .", "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 3σ under the initial cmgs momentum error ." ]

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

[ "A) loss and accuracy vs epochs .", "B) number of epochs vs loss and accuracy .", "C) validation accuracies vs epochs .", "D) validation accuracy and loss after each epoch .", "E) accuracy vs. epochs ." ]

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

[ "A) error between actual velocity and estimated velocity .", "B) error between actual acceleration and estimated acceleration .", "C) velocity vs sample time.", "D) actual velocity vs estimated velocity .", "E) actual acceleration vs estimated acceleration ." ]

[ "A) error between actual acceleration and estimated acceleration .", "B) acceleration vs sample time.", "C) actual velocity vs estimated velocity .", "D) error between actual velocity and estimated velocity .", "E) actual acceleration vs estimated acceleration ." ]

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

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

[ "A) queue length statistics evolution for nbrw-pod algorithm with d = 2 and λ = 0.95. system is empty at time 0 .", "B) queue length statistics evolution for nbrw-pod algorithm with d = 2 and λ = 0.95. each queue has a length of 5 at time 0 .", "C) queue length statistics evolution for nbrw-pod algorithm with d = and λ = 0.95. each queue has a length of 5 at time 0 .", "D) queue length statistics evolution for nbrw-pod algorithm with d = 2 and λ = 0.9. system is empty at time 0 .", "E) queue length statistics evolution for nbrw-pod algorithm with d = 2 and λ = 0.85. system is empty at time 0 ." ]

[ "A) ber with respect to snr with nt = 100 , nr = 50 and ns = 1 .", "B) ber with respect to snr with nt = 100 , nr = 0 , mt = 3 , mr = 1 and l = 2 for different values of ns .", "C) ber with respect to snr with nt = 100 , nr = 0 and ns = 3 .", "D) ber with respect to snr with nt = 100 , nr = 50 , mt = 2 , mr = 2 and l = 2 for ns = 6 .", "E) ber with respect to snr with nt = 100 , nr = 50 , mt = 3 , mr = 1 and l = 2 for different values of ns ." ]

[ "A) fitting a quadratic model to hd and skd10000 .", "B) fitting a quadratic model to hd and skd1 .", "C) fitting a quadratic model to hd and skd2 .", "D) fitting a quadratic model to hd and skd1000 .", "E) fitting a quadratic model to hd and skd3 ." ]

[ "A) fitting a quadratic model to hd and skd10000 .", "B) fitting a quadratic model to hd and skd1 .", "C) fitting a quadratic model to hd and skd1000 .", "D) fitting a quadratic model to hd and skd2 .", "E) fitting a quadratic model to hd and skd3 ." ]

[ "A) strategy 2 , σ = 1 .", "B) strategy 1 , σ = 10 .", "C) strategy 3 , σ = 5 .", "D) strategy 2 , σ = 5 .", "E) strategy 2 , σ = 10 ." ]

[ "A) traffic distribution national freeway 03 at taichung city northern direction at 24 hours - seven days .", "B) traffic distribution national freeway 01 at taichung city northern direction at 24 hours - seven days .", "C) traffic distribution national freeway 03 at taichung city northern direction based on vehicle types .", "D) traffic distribution national freeway 03 at taichung city southern direction based on vehicle types .", "E) traffic distribution national freeway 01 at taichung city southern direction based on vehicle types ." ]

[ "A) ber performance of gsm using the proposed dnnbased detector with t-distributed noise .", "B) ber performance of gsm using the proposed dnnbased detector in varying channel .", "C) ber performance of gsm with the proposed dnnbased detector in the presence of correlated noise .", "D) ber performance of tmp receiver with different number of iterations for otfs system .", "E) ber performance of gsm with the proposed modularized dnn-based detector and single-dnn-based detector ." ]

[ "A) cost per time-slot as a function of the fetch cost ( m ) for c = 0.45 for job 0 .", "B) cost per time-slot as a function of the fetch cost ( m ) for c = 0.45 for job 2 .", "C) cost per time-slot as a function of the fetch cost ( m ) for c = 0.45 for job 1 .", "D) cost per time-slot as a function of the fetch cost ( m ) for c = 0.45 for job 3 .", "E) cost per time-slot as a function of storage cost ( c ) for m = 10 for job 3 ." ]

[ "A) convergence of the volumes of tumor proliferating cells for submodels with respect to the ground truth .", "B) quiescent cells volume vs simulation step for supermodels Su1, Su2, Su3.", "C) convergence of the volumes of tumor quiescent cells for supermodel with respect to the ground truth .", "D) convergence of the volumes of tumor quiescent cells for submodels with respect to the ground truth .", "E) convergence of tumor volumes for submodels with respect to ground truth ." ]

[ "A) the average age values with the age-optimal codeword lengths for λ ∈ { 2 , 3, 10 } for the pmf provided in ( 46 ) with the parameters n = 100 , s = 0.4. we vary k from 1 to n and observe that choosing k = 1 under the relatively high arrival rates ( λ = 10 ) minimizes the average age .", "B) the average age values with the age-optimal codeword lengths for λ ∈ { 2 , 10 } for the pmf provided in ( 27 ) with the parameters n = 100 , s = 0.. we vary k from 1 to n and observe that choosing k = 1 under the relatively high arrival rates ( λ = 10 ) minimizes the average age .", "C) the average age values with the age-optimal codeword lengths for λ ∈ { 0.3 , 0.5 , 1 } for the pmf provided in ( 6 ) with the parameters n = 100 , s = 0.. we vary k from 1 to n and indicate k that minimizes the average age for each λ with an arrow .", "D) the average age values with the age-optimal codeword lengths for different α values with the pmf provided in ( 27 ) with n = 100 , s = 0.2 for k = 70 when randomized highest k selective encoding is implemented .", "E) the average age values with the age-optimal codeword lengths for λ ∈ { 0. , 0.5 , 1 } for the pmf provided in ( 27 ) with the parameters n = 100 , s = 0.4. we vary k from 1 to n and indicate k that minimizes the average age for each λ with an arrow ." ]

[ "A) accuracies using fedcollabnn framework with 12 workers .", "B) accuracies using fedcollabnn framework with 10 workers .", "C) accuracies using fedcollabnn framework with 18 workers .", "D) accuracies using fedcollabnn framework with 1 workers .", "E) accuracies using fedcollabnn framework with 14 workers ." ]

[ "A) ergodic rate region for m1 = 3 , m2 = 3 , n = 3 , and pmax = 10 dbm .", "B) ergodic rate region for m1 = 2 , m2 = 2 , n = , and pmax = 20 dbm .", "C) ergodic rate region for m1 = , m2 = , n = 5 , and pmax = 20 dbm .", "D) ergodic rate region for m1 = 1 , m2 = 4 , n = 4 , and pmax = 10 dbm .", "E) ergodic rate region for m1 = 3 , m2 = 3 , n = 3 , pmax = 30 dbm , and π = 1 and 100 ." ]

[ "A) ebr-ltl-synth vs strix ( first conf . ) on syntcomp benchmarks .", "B) ebr-ltl-synth vs strix ( second conf . ) on syntcomp benchmarks .", "C) ebr-ltl-synth vs ltlsynt ( second conf . ) on syntcomp benchmarks .", "D) ebr-ltl-synth vs ltlsynt ( first conf . ) on syntcomp benchmarks .", "E) ebr-ltl-synth vs ssyft on scalable benchmarks ." ]

[ "A) the error performance of the ( 6486 , 219 ) qc-ldpc code given in example over the awgn channel .", "B) the error performance of the ( 3969 , 365 ) qc-ldpc code given in example over the awgn channel .", "C) the error performance of the ( 1096 , 10587 ) qc-ldpc code given in example over the awgn channel .", "D) the error performances of the ( 399 , 3243 ) qc-ldpc code given in example over the awgn channel decoded with the ibmpda and the spa .", "E) the error performance of the ( 16129 , 1437 ) qc-ldpc code given in example over the awgn channel ." ]

[ "A) the total distance measure with different window sizes on evolving graph 1 .", "B) the total distance measure with different window sizes on evolving graph 4 .", "C) the total distance measure with different window sizes on evolving graph 3 .", "D) the total distance measure with different window sizes on evolving graph 2 .", "E) the total distance measure with different window sizes on evolving graph 5 ." ]

[ "A) snr vs. the number of modules for classical modular method and our proposed optimized modular method for linear interpolati n .", "B) snr vs. the number of modules for classical modular method and our proposed optimized method for s & h interpolation .", "C) snr vs. the number of modules for classical modular method and our proposed optimized modular method for linear interpolation .", "D) snr vs. the number of modules for classical modular method and our proposed optimized modular method for s & h interpolation .", "E) snr vs. the number of modules for classical modular method and our proposed optimized method for s & h interpolation for 2-d signals ." ]

[ "A) ser performance at 4 bpcu for codes for8× 2 systems .", "B) ser performance at 6 bpcu for codes for4× systems .", "C) ser performance at 4 bpcu for codes for4× systems .", "D) ser performance at 6 bpcu for codes for× 3 systems .", "E) ser performance at 4 bpcu for codes for4× 2 systems ." ]

[ "A) a graph showing the relationship betweenǫ and 1 δ if we vary the values ofβ under fixed k .", "B) a graph showing the relationship between the values of k needed to achieve roughly the sameδ if we doubleβ .", "C) a graph showing the value ofǫ satisfied by a givenk if δ ≤ 10− with varying sampling probabilities .", "D) a graph showing the relationship betweenǫ and 1 δ with small k ’ s , varying k and fixing β .", "E) a graph showing the relationship betweenǫ and 1 δ if we vary the values of k under fixed β ." ]

[ "A) pk−l+1+t is below l and pk+t is below l ′ .", "B) both pk−l+1+t and pk+t are above l .", "C) pk−l+1+t is above while pk+t is below l .", "D) pk−l+1+t is below l and pk+t is above l ′ .", "E) pk−l+1−t is below l and pk+u−l−t is above l ." ]

[ "A) averaged variation of ϕ ( fitness ) against increasing amount of gaussian blur and a fixed amount of gaussian noise for reference images in tid200 database .", "B) averaged variation of κ against increasing amount of gaussian blur for reference images in tid2008 database .", "C) averaged variation of κ against increasing amount of gaussian blur and a fixed amount of gaussian noise for reference images in tid2008 database .", "D) averaged variation of ϕ ( fitness ) against increasing amount of gaussian noise for reference images in tid2008 database .", "E) averaged variation of ϕ ( fitness ) against increasing amount of gaussian blur for reference images in tid2008 database ." ]

[ "A) control signal of 1p for step input and load disturbance with istes based tuning .", "B) control signal of 2p for step input and load disturbance with istse based tuning .", "C) control signal of 1p for step input and load disturbance with itae based tuning .", "D) control signal of 1p for step input and load disturbance with itse based tuning .", "E) control signal of 1p for step input and load disturbance with istse based tuning ." ]

[ "A) decentralized influence spread : effect of β on the optimal cost and optimal τ .", "B) decentralized influence spread : effect of λ on the optimal cost and optimal τ .", "C) decentralized influence spread : effect of xd0d0 on the optimal cost and optimal τ .", "D) decentralized influence spread : effect of ψ on the optimal cost and optimal τ .", "E) decentralized influence spread : effect of d0 on the optimal cost and optimal τ ." ]

[ "A) average fitness obtained by the 4 optimizers against number of fitness function evaluations .", "B) the value of fitness function over ga iterations .", "C) average runtimes of the various methods .", "D) convergence of fitness function over the iterations .", "E) average fitness obtained by the five optimizers against number of fitness function evaluations ." ]

[ "A) number of bs 's in m-pam ( method i vs. method ii ) for cell=5 .", "B) number of bs 's in m-pam ( method i vs. method ii ) for cell=1.5 .", "C) number of bs 's when applying cwn-pam ( method i vs. method ii ) for cell=1.5 .", "D) number of bs 's when applying cwn-pam ( method i vs. method ii ) for cell=5 .", "E) comparison between number of bs 's when applying cwn-pam ( method i and ii ) vs. m-pam for cell range = 1.5 ." ]

[ "A) number of bs 's in cwn-pam by method ii for 30 nodes .", "B) number of bs 's in cwn-pam by method ii for 101 nodes .", "C) number of bs 's in cwn-pam by method i for 30 nodes .", "D) number of bs 's in cwn-pam by method i for 1 nodes .", "E) number of bs 's in m-pam by method i for 30 node ." ]

[ "A) mttd , raw feature [ 8-8 ] pair vs. enhanced features .", "B) dr , raw feature [ 4-2 ] pair vs. enhanced features .", "C) mttd , raw feature [ -2 ] pair vs. enhanced features .", "D) dr , raw feature [ 8-8 ] pair vs. enhanced features .", "E) dr , raw feature [ 12-12 ] pair vs. enhanced features ." ]

[ "A) mttd , raw feature [ 8-8 ] pair vs. enhanced features .", "B) mttd , raw feature [ -2 ] pair vs. enhanced features .", "C) dr , raw feature [ 12-12 ] pair vs. enhanced features .", "D) mttd , raw feature [ 12-12 ] pair vs. enhanced features .", "E) dr , raw feature [ 4-2 ] pair vs. enhanced features ." ]

[ "A) frequency response of the fir filter core .", "B) frequency response of the fifth order cic filter .", "C) frequency response of the fft core .", "D) frequency response of the 8-order halfband filter .", "E) frequency response of the iir filter core ." ]

[ "A) 8x8 rl beam to array spillover coupling amplitude .", "B) 4x4 rl beam to array spillover coupling amplitude .", "C) 4x4 rl beam to array phase error .", "D) 4x4 rl beam to array coupling amplitude .", "E) 8x8 rl beam to array coupling amplitude ." ]

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

[ "A) maximum ram usage ( mean ) of tasks running successfully ( python vs. other languages ) .", "B) lines of code ( mean ) of tasks compiling successfully ( python vs. other languages ) .", "C) number of solutions per task ( python vs. other languages ) .", "D) lines 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 ( all languages ) .", "B) page faults ( mean ) of tasks running successfully ( all languages ) .", "C) page faults ( min ) of tasks running successfully ( all languages ) .", "D) scalability ( min ) of tasks running successfully ( all languages ) .", "E) performance ( mean ) of tasks running successfully ( all languages ) ." ]

[ "A) convergence plot for tp8 .", "B) sample convergence graph .", "C) convergence plot for tp5 .", "D) convergence results of began .", "E) convergence plot for f5 during iterations ." ]

[ "A) ber results for bch ( 6,6 ) code .", "B) ber results for bch ( 127,106 ) code .", "C) ber results for bch ( 63,45 ) code trained with multiloss .", "D) ber results for bch ( 63,5 ) code .", "E) ber results for bch ( 63,46 ) code ." ]

[ "A) illustrating the pos_max_min_zigzag constraint of the example slot .", "B) illustrating the pos_max_width_zigzag constraint of the example slot .", "C) illustrating the pos_max_max_zigzag constraint of the example slot .", "D) illustrating the pos_min_min_zigzag constraint of the example slot .", "E) illustrating the pos_min_max_zigzag constraint of the example slot ." ]

[ "A) accuracy on cifar- depending on the dataset size .", "B) accuracy under different input sizes .", "C) accuracy of different hidden layer sizes .", "D) accuracy obtained on english dataset .", "E) accuracy with different data sizes ." ]

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

[ "A) optimal number of replicas for different values of λ̂s/λf .", "B) number of tests for d = 1 when varying n , θ0 , and θ1 .", "C) optimal number of hash functions k∗ for different values of n and δ̂ .", "D) compression gain as δ̂ increases and for different values of n .", "E) optimal filter size m∗ for different values of n and δ̂ ." ]

[ "A) eventid transformation using 15 hidden states .", "B) eventid transformation using 5 hidden states .", "C) eventid transformation using 20 hidden states .", "D) eventid transformation using 30 hidden states .", "E) discrete transformation using 4 hidden states ." ]

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

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

[ "A) accuracy vs. size of dataset for all algorithms .", "B) prediction performance in relation to training set size .", "C) performance vs. training size ( log scale in x-axis ) on snli dataset .", "D) classification versus training dataset size .", "E) comparison of model performance vs. training size on the se07 data set in pearson ’ s r ." ]

[ "A) pdcch al1 , rbs with 3db stronger interferences : [ 1:1 ] , tdla , ds : 300ns .", "B) pdcch al1 , rbs with 3db stronger interferences : [ 1:24 ] , tdla , ds : 300ns .", "C) pdcch al4 , rbs with 3db stronger interferences : [ 1:12 ] , tdla , ds : 300ns .", "D) pdcch al2 , rbs with 3db stronger interferences : [ 1:24 ] , tdla , s : 300ns .", "E) pdcch al8 , rbs with 3db stronger interferences : [ 1:12 ] , tdla , ds : 300ns ." ]

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

[ "A) convergence in hunl subgame 2 .", "B) convergence in hunl subgame 3 .", "C) convergence in hunl subgame 4 .", "D) convergence in hunl subgame 1 .", "E) convergence in hunl subgame ." ]

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

[ "A) error rate of the classifier of our sentence plan method , for different numbers of features .", "B) performance of different feature selection methods on orl data set .", "C) feature selection and classification error rates ( in % ) for different algorithms on the large kddcup99 data set .", "D) the error rate of classifiers on the letter datasets through data abstraction levels .", "E) test and training error rate of the classifier of our sentence plan generation method ." ]

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

[ "A) roc curves on detecting fake programs .", "B) roc curves for the different machine learning models .", "C) roc curves for each classification model .", "D) model roc curves for 25-minute windows , 6 users .", "E) roc curves on detecting unknown programs ." ]

[ "A) strategy 2 , σ = 1 .", "B) strategy 1 , σ = 10 .", "C) strategy 3 , σ = 5 .", "D) strategy 2 , σ = 10 .", "E) strategy 2 , σ = 5 ." ]

[ "A) impact of variance of β on optimal ∗ e .", "B) optimal solutions for β variation ( i− 1 = ip ) .", "C) impact of n on rapp ( l∗ , p∗ ) when φ < bv /u and δ ≤ δ3 .", "D) impact of variance of λ on optimal ∗ .", "E) total sample variance of the parameter vector θ̃ as function of α ." ]

[ "A) cost per time-slot as a function of the fetch cost ( m ) for c = 0.45 for job 3 .", "B) cost per time-slot as a function of storage cost ( c ) for m = 10 for job 3 .", "C) cost per time-slot as a function of the fetch cost ( m ) for c = 0.45 for job 0 .", "D) cost per time-slot as a function of the fetch cost ( m ) for c = 0.45 for job 2 .", "E) cost per time-slot as a function of the fetch cost ( m ) for c = 0.45 for job 1 ." ]

[ "A) current versus time and its approximation by 2-piecewise fifteenth degree polynomial .", "B) current versus time and its approximation by 2-piecewise fifth degree polynomial .", "C) voltage versus time and its approximation by 2-piecewise fifth degree polynomial .", "D) current versus time and its approximation by 2-piecewise tenth degree polynomial .", "E) current versus time and its approximation by a 30-degree polynomial ." ]

[ "A) performance of decoding the [ [ 256 , 32 ] ] code by different αc .", "B) performance of decoding the [ [ 256 , 32 ] ] code by different β .", "C) performance of decoding the [ [ 800 , 400 ] ] code by different αv .", "D) performance of decoding the [ [ 256 , 32 ] ] code by different αv .", "E) performance of decoding the [ [ 800 , 400 ] ] code by different αc ." ]

[ "A) performance of decoding the [ [ 256 , 32 ] ] code by different αv .", "B) performance of decoding the [ [ 800 , 400 ] ] code by different αv .", "C) performance of decoding the [ [ 256 , 32 ] ] code by different αc .", "D) performance of decoding the [ [ 800 , 400 ] ] code by different αc .", "E) performance of decoding the [ [ 256 , 32 ] ] code by different β ." ]

[ "A) organization b : effort by activity .", "B) organization a : effort by activity .", "C) organization a : defects removed by activity .", "D) organization c : effort by activity .", "E) evaluation results ." ]

[ "A) activities by hours .", "B) organization a : defects removed by activity .", "C) organization a : effort by activity .", "D) organization c : effort by activity .", "E) organization b : effort by activity ." ]

[ "A) accuracies using fedcollabnn framework with 12 workers .", "B) accuracies using fedcollabnn framework with 1 workers .", "C) accuracies using fedcollabnn framework with 18 workers .", "D) accuracies using fedcollabnn framework with 10 workers .", "E) accuracies using fedcollabnn framework with 14 workers ." ]

[ "A) error in displacement for the quartic solution using the variable horizon method ( vhm ) for δ = 1/4 and various values of m , with h = δ/m .", "B) displacement for = 0.01 using the variable horizon method ( vhm ) with δ = 1/2256 , 1/2512 , and 1/21024 .", "C) displacement for = 0.1 using the variable horizon method ( vhm ) with δ = 1/216 , 1/232 , and 1/264 .", "D) error in displacement using edm i with correction factor k ( x ) /κ given by ( 18 ) and ( 19 ) for various values of the horizon δ .", "E) error in displacement for the quartic solution using the variable horizon method ( vhm ) for δ = 1/8 and various values of m , with h = δ/m ." ]

[ "A) newsqa question length generated by different mppi reduction methods .", "B) squad question length generated by different mppi reduction methods .", "C) triviaqa question length generated by different mppi reduction methods .", "D) searchqa question length generated by different mppi reduction methods .", "E) natrualquestion question length generated by different mppi reduction methods ." ]

[ "A) expected job completion time e [ yk : n ] for pareto service time with additive scaling as a function of the diversity/parallelism parameter k. the number of workers ( job size ) is n = 12 , and task size per worker is s = n/k ( since both k and s are integers , we have k ∈ { 1 , 2 , 3 , 4 , 6 , 12 } . we use smooth dashed curves to connect the points corresponding to different allowed values of k for a given α . ) the pareto scale parameter is λ = 1. splitting or coding is optimal according to different α .", "B) expected job completion time e [ yk : n ] for bi-modal service time with additive scaling as a function of the diversity/parallelism parameter k. the straggling probability is 0.4. the number of workers ( job size ) is n = 12 , and task size per worker is s = n/k ( since both k and s are integers , we have k ∈ { 1 , 2 , 3 , 4 , 6 , 12 } . we use smooth dashed curves to connect the points corresponding to different allowed values of k for a given b . ) the optimal code rate decreases with increasing b , and it reaches the minimum at 1/3 .", "C) expected job completion time e [ yk : n ] for bi-modal service time with data-dependent scaling as a function of the diversity/parallelism parameter k. the straggling probability is 0.6. the number of workers ( job size ) is n = 12 , and task size per worker is s = n/k ( since both k and s are integers , we have k ∈ { 1 , 2 , 3 , 4 , 6 , 12 } . we use smooth dashed curves to connect the points corresponding to different allowed values of k for a given a . ) the optimal code rate decreases with increasing b .", "D) expected job completion time e [ yk : n ] for bi-modal service time with server-dependent scaling as a function of the diversity/parallelism parameter k. the straggling probability is 0.6. the number of workers ( also job size ) is n = , and task size per worker is s = n/k ( since both k and s are integers , we have k ∈ { 1 , 2 , 3 , 4 , 6 , } . we use smooth dashed curves to connect the points corresponding to different allowed values of k for a given d . ) thhe optimal code rate decreases with increasing b .", "E) expected job completion time e [ yk : n ] for ( shifted- ) exponential service time with additive scaling as a function of the diversity/parallelism parameter k. the number of workers ( job size ) is n = 12 , and task size per worker is s = n/k ( since both k and s are integers , we have k ∈ { 1 , 2 , 3 , 4 , 6 , 12 } . we use smooth dashed curves to connect the points corresponding to different allowed values of k for a given ∆ and w combination . ) in small w/∆ scenarios , splitting or maximal parallelism is the best ; in large w/∆ scenarios , there is a balance between diversity and parallelism ." ]

[ "A) accuracy vs. budget curve on sonar dataset .", "B) accuracy vs. budget curve on splice dataset .", "C) accuracy vs. budget curve on clean dataset .", "D) accuracy vs. budget curve on german dataset .", "E) accuracy vs. budget curve on vehicle dataset ." ]

[ "A) cdf distribution of outcoming retweets ( count weighted ) over two different sets of clear users ( users with zero copied events in our dataset ) and the users that were identified as bots ( with threshold t of copied events ) .", "B) cdf distribution of incoming retweets ( count weighted ) over two different sets of clear users ( users with zero copied events in our dataset ) and the users that were identified as bots ( with threshold t of copied events ) .", "C) cdf distribution of number of favoriters accounts ( percentage ) over two different sets of clear users ( users with zero copied events in our dataset ) and the users that were identified as bots ( with threshold t of copied events ) .", "D) cdf distribution of number of mentioned user ( count ) over two different sets of clear users ( users with zero copied events in our dataset ) and the users that were identified as bots ( with threshold t of copied events ) .", "E) cdf distribution of number of mentions of each user ( count ) over two different sets of clear users ( users with zero copied events in our dataset ) and the users that were identified as bots ( with threshold t of copied events ) ." ]

[ "A) simulated sequence of ae-nlm inference φu , gae ( · ) of algorithm 7 .", "B) se algorithm with β = 0 , ∈ [ 0 , 0.1 ] , τ = 0 ; simulation with µu = 500w .", "C) me algorithm with n ∈ [ 1 , 30 ] ; simulation with µu = 500w .", "D) se and me algorithm applied to simulation with µu = 00w .", "E) se algorithm with β = 0 , = 0 , τ ∈ [ 0 , 50 ] ; simulation with µu = 500w ." ]

[ "A) probability distribution function of residual in roll axis on the validation dataset .", "B) probability distribution function of residual in thrust axis on the validation dataset .", "C) error distribution of the prediction datasets using the proposed phycnn model .", "D) probability distribution function of residual in pitch axis on the validation dataset .", "E) probability distribution function of residual in yaw axis on the validation dataset ." ]