HHousen/ParaSCI · Datasets at Hugging Face

sentence1 stringlengths 16 446	sentence2 stringlengths 14 436
we utilize a simple encoder-decoder architecture inspired by u-net .	we use the u-net model design as our base fcn model architecture .
more recently , several works have assessed the robustness of deep neural networks to different regimes such as adversarial perturbations .	deep neural networks were firstly shown in to be vulnerable to adversarial examples , and many seminal methods have been developed in this literature .
the hamiltonian h is the lowest order in the expansion with respect to the tunneling interaction .	hamiltonian h and is the mean momentum of the detector after the measurement .
in both sections , the methods are sorted according to their average performance under the two working conditions .	in both parts of the table the various methods are sorted by increasing performance .
these models were trained with the adam optimizer for 20 epochs in each fold and a batch size of 32 .	all models were trained using the adam optimizer with minibatches of size 24 .
deep convolutional neural networks have achieved great success in various computer vision tasks , including object classification .	deep neural networks have shown improvement in state-of-the-art in different tasks , such as image classification .
as shown in the next proposition , these semirings are also simple .	in fact , we conjecture that this construction covers these semirings .
deep neural networks can be easily affected by slight perturbations , such as adversarial attacks and poisoning attacks .	recently , researchers have found that neural networks are vulnerable to adversarial examples , which are natural inputs polluted with small-magnitude , additive perturbations .
moreover , non-commutative field theory can be seen as an effective regime of string theory .	it also naturally leads to non-commutative systems within string theory .
the analysis of large markov chains is a recurring challenge in many important areas ranging from computer network dependability and performance .	t he analysis of large markov chains is a recurring challenge in many important areas ranging from quantitative security .
several techniques have been proposed to improve the generalization of networks such as dropout , which can be viewed as better regularization .	some regularization methods , such as dropout and so on , also improve the generalization ability of neural networks .
the ellipses denote the loops and arrows on them show drifting directions .	the ellipses denote operators that are suppressed by powers of µ .
xgboost is a gradient boosting library that uses decision trees as base learners .	specifically , xgboost is an optimized distributed gradient boosting library .
our notation for the vertex operator algebra , associated with l is stan- dard .	we use standard notation for vertex operator algebras and their modules .
lin et al proposed ic-stns that replaces the feature warping with transformation parameter propagation .	inspired by stns , lin et al proposed inverse compositional stns that replaces the feature warping with transformation parameter propagation .
in section iv , the results for realistic quantum dots are presented .	in section iii , we report the statistical properties of isolated quantum dots .
deep neural networks have been very successful in large-scale recognition and classification tasks , some even surpassing human-level accuracy .	deep convolutional neural networks have attracted a considerable attention due to their superior performance in image classification .
in this respect the problem has some similarity to the more explored problem of community detection in graphs .	the problem is analogous to the more explored problem of community detection in graphs .
timofte et al mentioned that backprojection can improve the quality of sr image .	timofte et al mentioned that backprojection could improve the quality of the sr images .
since gravity is a gauge theory , one has to take into account the complications due to the gauge fixing and ghost 19 terms .	we will assume that gravity is the only interaction responsible for growth of perturbations at large scales .
the above traceability matrix is for finding the critical non functional requirements for library system .	the above traceability matrix is for finding the critical non functional requirements for atm system .
local differential privacy is a strong notion of privacy motivated by differential privacy .	differential privacy is the standard notion of privacy for statistical databases .
quantum algorithms exploit quantum mechanical properties like superposition and entanglement and can solve certain problems exponentially faster than classical computers .	quantum computers can solve certain problems , such as simulation of quantum-mechanical systems , exponentially faster than the best classical algorithms known .
this arbitrariness is the same as the one encountered already in the galileonewton standard model , but here its appearance is much more jarring .	this arbitrariness is a disadvantage in the sense that additional input on the foliation seems necessary before one could attempt to reconstruct an entire quantum-corrected spacetime from our quantum theory .
inflation is the leading paradigm to describe the physical conditions that prevail in the very early universe .	inflation is one of the leading paradigms for explaining the physical conditions that prevailed in the very early universe .
two recent studies on the genetic association analysis of ordinal traits suggested that snp rs1972373 on chromosome 14 might be a disease susceptibility locus .	recent studies by wang et al and diao and lin suggested that snp rs1972373 on chromosome 14 might be a disease susceptibility locus .
selected far-ir line pairs normalized by lir , plotted against each other .	selected far-ir line luminosities , plotted against each other .
deep convolutional neural networks have led to a series of breakthrough for visual tasks .	deep convolutional neural networks have been proven very useful in various tasks in computer vision including classification .
for unexplained terminology and the basic results on vector lattices and semiprime f -algebras we refer to .	for all unexplained terminology about vector lattices we refer the reader to the standard texts .
deep neural network has aroused dramatically attention due to their extraordinary performance in computer vision and natural language processing tasks .	among them , convolutional neural networks have been demonstrated to be extremely successful in computer vision .
in , papazoglou and ferrari proposed an approach for the fast segmentation of foreground objects from background regions .	papazoglou and ferrari proposed an approach for the fast segmentation of foreground objects from background regions .
the breaking of the invariance of the weak interaction under the combined action of the charge conjugation and parity transformations is firmly established in the k-and b-meson systems .	the non-invariance of fundamental forces under the combined action of the charge conjugation and parity transformations , is well established in the k 0 and b 0 meson systems .
inflation is presently the most attractive scenario for early cosmology .	inflation is currently the leading paradigm of the very early universe cosmology .
the mineralization process reduces the lateral spacing between collagen molecules bringing them closer to one another .	the mineralization process reduces the lateral spacing between collagen molecules and bringing them closer to each other .
a cofibration is a morphism of dg-operads which has the left lifting property with respect to fibrations .	any -cofibration is a realization weak equivalence .
shi et al innova-tively integrated cnn and lstm in one end-to-end dl structure , named the convolutional lstm , which provided a brand-new idea for solving spatio-temporal sequence forecasting problems .	shi et al proposed the conv-lstm network , which combined cnn and lstm in one sequence to sequence learning framework , for precipitation nowcasting that was a typical spatio-temporal forecasting problem .
the amplitude is invariant under any permutations of the gravitons because there is no color ordering .	the amplitude is invariant under arbitrary permutations of external gravitons so the order of gravitons does not matter .
the two most significant types are the variational auto-encoders and generative adversarial networks .	the most popular ones are variational auto-encoders , and generative adversarial networks .
recently , reinforcement learning has witnessed a rise in popularity thanks to advances in deep neural networks .	deep reinforcement learning has recently achieved notable successes in a variety of domains .
observe that the above results are scale invariant .	note that the wilson line moduli ai are scale invariant .
one easily verifies that t is a normal operator .	one verifies directly that this is a linear map .
convolutional neural networks have been extremely successful across many domains in machine learning and computer vision .	convolutional neural networks have achieved state-of-the-art accuracy in many supervised learning challenges .
massive mimo has recently emerged as a key technology for 5g communications .	millimeter wave communication has drawn extensive attention as a promising technology for 5g cellular systems .
without loss of generality we assume that first triangle consists of the first vertices and the common edge joints the first part with the second part .	without loss of generality we assume that the first triangle consists of the first vertices and the common vertex belongs to the first part .
it follows that an edge e 2 creating a double cut at channel 2 in the second repetition of , 1 , 2 , .	it follows that , by property 5 , an edge e 4 creating a double cut at channel 4 in the second repetition of , 1 , 2 , .
these off-line methods consider mot as a global optimization problem and focus on designing various optimization algorithm such as network flow and so on .	typically , these methods model the mot task as a global optimization problem in various forms such as network flow .
driving behavior , however , is a dynamic process in nature which depends on past information .	for driving behavior , however , previous research demonstrates that it is dynamic process in nature which depends on past information .
all parameters of the networks were initialized with the xavier initializer .	the networks are initialized by xavier and biases values are set to zero at beginning .
more recently in , the author exhibited two infinite families of linear mrd codes which are not equivalent to generalized gabidulin codes .	most notably , in sheekey presents a construction of mrd-codes that are not equivalent to gabidulin codes .
dadarlat and guentner generalized property a to the class of exact spaces .	subsequently , it was generalized to the concept of exact spaces by dadarlat and guentner .
the model , shown diagrammatically in fig .	the model we analyze , shown diagrammatically in fig .
the disc counting information of is given by the superpotential related to the mirror curve .	the disc counting invariants of were given by the coefficients of the superpotential related to the mirror curve .
this type of nonlocal equation is known to influence the propagation of electromagnetic waves in plasmas .	this type of nonlocal equation appears in a lot of physical applications , for instance in the study of propagation of electromagnetic waves in plasmas .
recently , there has been a surge in interest in few-shot learning , where a model for novel classes is learned with only a few labeled examples .	few-shot learning has recently attracted significant attention , as it aims to produce models that can generalize from small amounts of labeled data .
millimeter wave communication is a promising technology for addressing the high throughput requirement for the fifth generation mobile networks .	massive mimo and millimeter wave communications are considered to be among the core technologies for next generation wireless networks since they can cope with the modern day demands of global mobile data traffic .
as demonstrated in , solving an extensive amount of opf problems to mimic short-term decision-making does not scale well to realistic grids , with thousands of nodes , generators and loads .	as demonstrated in , solving an extensive amount of unit commitment problems to mimic short-term decision-making does not scale well to realistic grids , with thousands of buses or more .
in theory , ontology is a formal , explicit specification of shared conceptualization .	an ontology is defined as a specification of a conceptualization .
pirsiavash et al developed a composition of hog features to model object and hand appearance during an activity .	pirsiavash et al created a first person dataset containing images from people fulfilling daily activity tasks .
we will return to this point in what follows .	we will return to this point in the conclusions section .
while the lightest neutralino is the lsp , which is stable and candidate to dark matter in this r-parity conserving model , the heavier neutralinos will decay into it .	the neutralino is a weakly interacting massive particle .
generalized langevin equation with fractional derivative and long-time correlation function .	generalized langevin equation and riemannliouville derivative .
as mentioned above , buhrman et al introduced the notion and provide the constructions of quantum fingerprinting .	buhrman et al exhibited a no-coin quantum protocol for the equality function with oqubits of communication .
in earlier works , hand-crafted features are used for action recognition from the skeleton .	earlier works used hand-crafted features for action recognition from the skeleton .
in the latter , we find that even in the case of moderate dark matter densities in the galactic center region , there are portions of the parameter space which will be probed by glast .	we find that even in case of moderately singular dark matter profiles , there are regions in the parameter space which will be probed by glast , especially in the high tan β case .
reinforcement learning is a sub-field of machine learning , by which the agent interacts with the environment to dynamically maximize its long-term expected rewards .	reinforcement learning is a learning technique that an agent learns from the interactions between the environment by trialand-error .
kepler frequency versus rotational neutron star mass , computed for the eoss of this paper .	neutron star mass versus central density , computed for the eoss of this paper .
bthis source was only detected in the soft band .	dthis source was detected in neither the full nor the hard band .
the area between the two curves contains all finite mass splittings between the quark masses .	all finite mass splittings are contained in the region between the two curves .
this construction is comparable to in terms of the parameter k , but outperforms it in terms of explicitness and field size .	the latter is comparable to in terms of the parameter k , but it is explicit , and may be defined over exponentially smaller fields .
it is pointed that ghz correlations of spins in original directions no longer hold and transfer to different directions in the moving frame .	we point out that , in the moving frame , ghz correlations of spins in original directions transfer to different directions due to the wigner rotation .
deep neural networks have been shown to be very efficient in image processing tasks such as content classification .	deep convolutional neural networks have demonstrated significant improvements over traditional approaches in many pattern recognition tasks .
perhaps surprisingly , the adversarial perturbations can be crafted even when the model details of the target dnn are totally unknown to an attacker , known as the black-box attack setting .	beyond the white-box attack setting where the target model is entirely transparent , visually imperceptible adversarial perturbations can also be generated in the black-box setting by only using the prediction results of the target model .
each of these couplings is a potential source for neutrino masses .	for these couplings , there is a window where the di-neutron is stable .
double field theory , is a framework that makes the t-duality symmetry manifest , thus highly constraining the allowed interactions of the effective theories under its reach .	double field theory is the proper framework that makes manifest the global osymmetry of the low-energy effective actions of closed string theory implied by t-duality for reviews .
in 1948 , shannon introduced the field of information theory as a set of tools for understanding the limits of one-way communication .	shannon launched the field of information theory with his seminal 1948 paper , a mathematical theory of communication .
the learned deep representations manifest invariant factors underlying different populations and are transferable from the original tasks to similar novel tasks .	deep representations manifest invariant factors underlying different populations and are transferable across similar tasks .
thus noise asymmetry is found to complement the effect of coupling asymmetry when the latter is absent .	we show that noise asymmetry induces effects similar to that of coupling asymmetry when the latter is absent .
the principles of 3d-pli have been explained in detail by larsen et al .	the measurement and signal analysis of 3d-pli have been described in detail by axer et al .
nc spaces appear in a natural fashion in the context of string theory under some backgrounds .	non-commutative spaces appear in a natural way in the context of string theory under some backgrounds .
so the ising order is best developed for gf near the quantum critical point .	in all cases , the order parameter is best enhanced in the vicinity of the quantum critical point .
neural networks have recently been shown to achieve outstanding performance in several machine learning domains such as image recognition .	convolutional neural networks have seen tremendous success across different problems including image classification .
from the point of view of informational organization , the microcolumn is likely the most basic and consistent information processing unit in the brain , instead of a single neuron .	from the point of view of informational organization , the fundamental processing unit in the brain is likely to be the microcolumn .
electronic structure calculations were performed using the quantum-espresso code functional .	hybrid functional calculations were performed using the hse06 functional .
scenario generation for stochastic programming .	evaluation of scenario-generation methods for stochastic programming .
for k-means and the spectral clustering methods we used the implementation present in scikit-learn python library .	for both tf-idf vectorization and svm classification , we used the scikitlearn library .
this assumption was confirmed by xps measurements .	this assumption is confirmed by xps measurement .
considerable research effort has been devoted to designing the convolutional neural network for graph-structured data .	finally , a recent line of work has focussed on using convolutional neural networks defined on graphs .
again we calculate the five lowest lying massgaps corresponding to the five lowest mass glueballs accessible with our choice of minimisation basis and ground state .	calculate the five lowest lying massgaps corresponding to the five lowest glueball masses accessible with our choice of ground state and minimisation basis .
reinforcement learning is the problem where an agent must interact with an unknown environment through trial-anderror in order to maximize its cumulative reward .	reinforcement learning is an important technique in training an agent to learn how to respond to a given environment .
finally , to solve the cf-based mdp , we propose a q-network learning method based on deep q-learning , with a particular training scheme that uniformly samples state transitions from different users .	to handle the cf-based mdp with continuous state space , we propose a qnetwork learning method based on dqn , which is essentially q-learning with function approximation .
rbp , as introduced in , is an informed scheduling strategy that updates first the message that maximizes an ordering metric called the residual .	rbp , as introduced in , is an informed scheduling strategy that proposes to update first the message that maximizes an ordering metric called residual .
supergravity is the theory of local supersymmetry that automatically implies general coordinate invariance .	supergravity is the best framework within which both quintessence and inflation can be described .
the cosmological constant is a tunable parameter , and in the limit that it vanishes there is a susic theory of quantum gravity in asymptotically flat spacetime .	generally , the cosmological constant is a decreasing function of time .
in , gupta and kumar derived theoretical bounds for the capacity of wireless ad hoc networks .	in particular , in their seminal work , gupta and kumar analyzed the capacity of wireless networks in the physical and protocol models .
this approach has been successfully extended to the multiple gp setting using a factorized approximation of the posterior across gps .	this approach has been successfully extended to the cgps setting using a factorized approximation of the posterior across gps .
one of the few works in this area is by kahrs , see related work below .	some of the few works in this area are by kahrs , see related work below .
going to two space one time dimensions greatly simplifies this issue due to the absence of ultraviolet divergences except in mass corrections .	in dimensions this issue becomes simplified due to the absence of ultraviolet divergences except in mass corrections .
mazzocco has shown that any solution of the garnier system having reducible linear monodromy can be expressed classically via particular solutions from the above proposition .	mazzocco has shown that the solutions of the garnier system , that have reducible linear monodromy , are classical functions and can be expressed via solutions of lauricella hypergeometric equations .
was supported by the national natural science foundation of china under grant no .	was supported in part by the national natural science foundation of china under grant nos .
the differences in pulsewave oscillations between second and fourth-gradient models allow to revisit papers introducing kinks versus pulses as in .	the differences in pulse-wave oscillations between second-and fourth-gradient models allow to revisit papers introducing kinks versus pulses as in .

we utilize a simple encoder-decoder architecture inspired by u-net .

we use the u-net model design as our base fcn model architecture .

more recently , several works have assessed the robustness of deep neural networks to different regimes such as adversarial perturbations .

deep neural networks were firstly shown in to be vulnerable to adversarial examples , and many seminal methods have been developed in this literature .

the hamiltonian h is the lowest order in the expansion with respect to the tunneling interaction .

hamiltonian h and is the mean momentum of the detector after the measurement .

in both sections , the methods are sorted according to their average performance under the two working conditions .

in both parts of the table the various methods are sorted by increasing performance .

these models were trained with the adam optimizer for 20 epochs in each fold and a batch size of 32 .

all models were trained using the adam optimizer with minibatches of size 24 .

deep convolutional neural networks have achieved great success in various computer vision tasks , including object classification .

deep neural networks have shown improvement in state-of-the-art in different tasks , such as image classification .

as shown in the next proposition , these semirings are also simple .

in fact , we conjecture that this construction covers these semirings .

deep neural networks can be easily affected by slight perturbations , such as adversarial attacks and poisoning attacks .

recently , researchers have found that neural networks are vulnerable to adversarial examples , which are natural inputs polluted with small-magnitude , additive perturbations .

moreover , non-commutative field theory can be seen as an effective regime of string theory .

it also naturally leads to non-commutative systems within string theory .

the analysis of large markov chains is a recurring challenge in many important areas ranging from computer network dependability and performance .

t he analysis of large markov chains is a recurring challenge in many important areas ranging from quantitative security .

several techniques have been proposed to improve the generalization of networks such as dropout , which can be viewed as better regularization .

some regularization methods , such as dropout and so on , also improve the generalization ability of neural networks .

the ellipses denote the loops and arrows on them show drifting directions .

the ellipses denote operators that are suppressed by powers of µ .

xgboost is a gradient boosting library that uses decision trees as base learners .

specifically , xgboost is an optimized distributed gradient boosting library .

our notation for the vertex operator algebra , associated with l is stan- dard .

we use standard notation for vertex operator algebras and their modules .

lin et al proposed ic-stns that replaces the feature warping with transformation parameter propagation .

inspired by stns , lin et al proposed inverse compositional stns that replaces the feature warping with transformation parameter propagation .

in section iv , the results for realistic quantum dots are presented .

in section iii , we report the statistical properties of isolated quantum dots .

deep neural networks have been very successful in large-scale recognition and classification tasks , some even surpassing human-level accuracy .

deep convolutional neural networks have attracted a considerable attention due to their superior performance in image classification .

in this respect the problem has some similarity to the more explored problem of community detection in graphs .

the problem is analogous to the more explored problem of community detection in graphs .

timofte et al mentioned that backprojection can improve the quality of sr image .

timofte et al mentioned that backprojection could improve the quality of the sr images .

since gravity is a gauge theory , one has to take into account the complications due to the gauge fixing and ghost 19 terms .

we will assume that gravity is the only interaction responsible for growth of perturbations at large scales .

the above traceability matrix is for finding the critical non functional requirements for library system .

the above traceability matrix is for finding the critical non functional requirements for atm system .

local differential privacy is a strong notion of privacy motivated by differential privacy .

differential privacy is the standard notion of privacy for statistical databases .

quantum algorithms exploit quantum mechanical properties like superposition and entanglement and can solve certain problems exponentially faster than classical computers .

quantum computers can solve certain problems , such as simulation of quantum-mechanical systems , exponentially faster than the best classical algorithms known .

this arbitrariness is the same as the one encountered already in the galileonewton standard model , but here its appearance is much more jarring .

this arbitrariness is a disadvantage in the sense that additional input on the foliation seems necessary before one could attempt to reconstruct an entire quantum-corrected spacetime from our quantum theory .

inflation is the leading paradigm to describe the physical conditions that prevail in the very early universe .

inflation is one of the leading paradigms for explaining the physical conditions that prevailed in the very early universe .

two recent studies on the genetic association analysis of ordinal traits suggested that snp rs1972373 on chromosome 14 might be a disease susceptibility locus .

recent studies by wang et al and diao and lin suggested that snp rs1972373 on chromosome 14 might be a disease susceptibility locus .

selected far-ir line pairs normalized by lir , plotted against each other .

selected far-ir line luminosities , plotted against each other .

deep convolutional neural networks have led to a series of breakthrough for visual tasks .

deep convolutional neural networks have been proven very useful in various tasks in computer vision including classification .

for unexplained terminology and the basic results on vector lattices and semiprime f -algebras we refer to .

for all unexplained terminology about vector lattices we refer the reader to the standard texts .

deep neural network has aroused dramatically attention due to their extraordinary performance in computer vision and natural language processing tasks .

among them , convolutional neural networks have been demonstrated to be extremely successful in computer vision .

in , papazoglou and ferrari proposed an approach for the fast segmentation of foreground objects from background regions .

papazoglou and ferrari proposed an approach for the fast segmentation of foreground objects from background regions .

the breaking of the invariance of the weak interaction under the combined action of the charge conjugation and parity transformations is firmly established in the k-and b-meson systems .

the non-invariance of fundamental forces under the combined action of the charge conjugation and parity transformations , is well established in the k 0 and b 0 meson systems .

inflation is presently the most attractive scenario for early cosmology .

inflation is currently the leading paradigm of the very early universe cosmology .

the mineralization process reduces the lateral spacing between collagen molecules bringing them closer to one another .

the mineralization process reduces the lateral spacing between collagen molecules and bringing them closer to each other .

a cofibration is a morphism of dg-operads which has the left lifting property with respect to fibrations .

any -cofibration is a realization weak equivalence .

shi et al innova-tively integrated cnn and lstm in one end-to-end dl structure , named the convolutional lstm , which provided a brand-new idea for solving spatio-temporal sequence forecasting problems .

shi et al proposed the conv-lstm network , which combined cnn and lstm in one sequence to sequence learning framework , for precipitation nowcasting that was a typical spatio-temporal forecasting problem .

the amplitude is invariant under any permutations of the gravitons because there is no color ordering .

the amplitude is invariant under arbitrary permutations of external gravitons so the order of gravitons does not matter .

the two most significant types are the variational auto-encoders and generative adversarial networks .

the most popular ones are variational auto-encoders , and generative adversarial networks .

recently , reinforcement learning has witnessed a rise in popularity thanks to advances in deep neural networks .

deep reinforcement learning has recently achieved notable successes in a variety of domains .

observe that the above results are scale invariant .

note that the wilson line moduli ai are scale invariant .

one easily verifies that t is a normal operator .

one verifies directly that this is a linear map .

convolutional neural networks have been extremely successful across many domains in machine learning and computer vision .

convolutional neural networks have achieved state-of-the-art accuracy in many supervised learning challenges .

massive mimo has recently emerged as a key technology for 5g communications .

millimeter wave communication has drawn extensive attention as a promising technology for 5g cellular systems .

without loss of generality we assume that first triangle consists of the first vertices and the common edge joints the first part with the second part .

without loss of generality we assume that the first triangle consists of the first vertices and the common vertex belongs to the first part .

it follows that an edge e 2 creating a double cut at channel 2 in the second repetition of , 1 , 2 , .

it follows that , by property 5 , an edge e 4 creating a double cut at channel 4 in the second repetition of , 1 , 2 , .

these off-line methods consider mot as a global optimization problem and focus on designing various optimization algorithm such as network flow and so on .

typically , these methods model the mot task as a global optimization problem in various forms such as network flow .

driving behavior , however , is a dynamic process in nature which depends on past information .

for driving behavior , however , previous research demonstrates that it is dynamic process in nature which depends on past information .

all parameters of the networks were initialized with the xavier initializer .

the networks are initialized by xavier and biases values are set to zero at beginning .

more recently in , the author exhibited two infinite families of linear mrd codes which are not equivalent to generalized gabidulin codes .

most notably , in sheekey presents a construction of mrd-codes that are not equivalent to gabidulin codes .

dadarlat and guentner generalized property a to the class of exact spaces .

subsequently , it was generalized to the concept of exact spaces by dadarlat and guentner .

the model , shown diagrammatically in fig .

the model we analyze , shown diagrammatically in fig .

the disc counting information of is given by the superpotential related to the mirror curve .

the disc counting invariants of were given by the coefficients of the superpotential related to the mirror curve .

this type of nonlocal equation is known to influence the propagation of electromagnetic waves in plasmas .

this type of nonlocal equation appears in a lot of physical applications , for instance in the study of propagation of electromagnetic waves in plasmas .

recently , there has been a surge in interest in few-shot learning , where a model for novel classes is learned with only a few labeled examples .

few-shot learning has recently attracted significant attention , as it aims to produce models that can generalize from small amounts of labeled data .

millimeter wave communication is a promising technology for addressing the high throughput requirement for the fifth generation mobile networks .

massive mimo and millimeter wave communications are considered to be among the core technologies for next generation wireless networks since they can cope with the modern day demands of global mobile data traffic .

as demonstrated in , solving an extensive amount of opf problems to mimic short-term decision-making does not scale well to realistic grids , with thousands of nodes , generators and loads .

as demonstrated in , solving an extensive amount of unit commitment problems to mimic short-term decision-making does not scale well to realistic grids , with thousands of buses or more .

in theory , ontology is a formal , explicit specification of shared conceptualization .

an ontology is defined as a specification of a conceptualization .

pirsiavash et al developed a composition of hog features to model object and hand appearance during an activity .

pirsiavash et al created a first person dataset containing images from people fulfilling daily activity tasks .

we will return to this point in what follows .

we will return to this point in the conclusions section .

while the lightest neutralino is the lsp , which is stable and candidate to dark matter in this r-parity conserving model , the heavier neutralinos will decay into it .

the neutralino is a weakly interacting massive particle .

generalized langevin equation with fractional derivative and long-time correlation function .

generalized langevin equation and riemannliouville derivative .

as mentioned above , buhrman et al introduced the notion and provide the constructions of quantum fingerprinting .

buhrman et al exhibited a no-coin quantum protocol for the equality function with oqubits of communication .

in earlier works , hand-crafted features are used for action recognition from the skeleton .

earlier works used hand-crafted features for action recognition from the skeleton .

in the latter , we find that even in the case of moderate dark matter densities in the galactic center region , there are portions of the parameter space which will be probed by glast .

we find that even in case of moderately singular dark matter profiles , there are regions in the parameter space which will be probed by glast , especially in the high tan β case .

reinforcement learning is a sub-field of machine learning , by which the agent interacts with the environment to dynamically maximize its long-term expected rewards .

reinforcement learning is a learning technique that an agent learns from the interactions between the environment by trialand-error .

kepler frequency versus rotational neutron star mass , computed for the eoss of this paper .

neutron star mass versus central density , computed for the eoss of this paper .

bthis source was only detected in the soft band .

dthis source was detected in neither the full nor the hard band .

the area between the two curves contains all finite mass splittings between the quark masses .

all finite mass splittings are contained in the region between the two curves .

this construction is comparable to in terms of the parameter k , but outperforms it in terms of explicitness and field size .

the latter is comparable to in terms of the parameter k , but it is explicit , and may be defined over exponentially smaller fields .

it is pointed that ghz correlations of spins in original directions no longer hold and transfer to different directions in the moving frame .

we point out that , in the moving frame , ghz correlations of spins in original directions transfer to different directions due to the wigner rotation .

deep neural networks have been shown to be very efficient in image processing tasks such as content classification .

deep convolutional neural networks have demonstrated significant improvements over traditional approaches in many pattern recognition tasks .

perhaps surprisingly , the adversarial perturbations can be crafted even when the model details of the target dnn are totally unknown to an attacker , known as the black-box attack setting .

beyond the white-box attack setting where the target model is entirely transparent , visually imperceptible adversarial perturbations can also be generated in the black-box setting by only using the prediction results of the target model .

each of these couplings is a potential source for neutrino masses .

for these couplings , there is a window where the di-neutron is stable .

double field theory , is a framework that makes the t-duality symmetry manifest , thus highly constraining the allowed interactions of the effective theories under its reach .

double field theory is the proper framework that makes manifest the global osymmetry of the low-energy effective actions of closed string theory implied by t-duality for reviews .

in 1948 , shannon introduced the field of information theory as a set of tools for understanding the limits of one-way communication .

shannon launched the field of information theory with his seminal 1948 paper , a mathematical theory of communication .

the learned deep representations manifest invariant factors underlying different populations and are transferable from the original tasks to similar novel tasks .

deep representations manifest invariant factors underlying different populations and are transferable across similar tasks .

thus noise asymmetry is found to complement the effect of coupling asymmetry when the latter is absent .

we show that noise asymmetry induces effects similar to that of coupling asymmetry when the latter is absent .

the principles of 3d-pli have been explained in detail by larsen et al .

the measurement and signal analysis of 3d-pli have been described in detail by axer et al .

nc spaces appear in a natural fashion in the context of string theory under some backgrounds .

non-commutative spaces appear in a natural way in the context of string theory under some backgrounds .

so the ising order is best developed for gf near the quantum critical point .

in all cases , the order parameter is best enhanced in the vicinity of the quantum critical point .

neural networks have recently been shown to achieve outstanding performance in several machine learning domains such as image recognition .

convolutional neural networks have seen tremendous success across different problems including image classification .

from the point of view of informational organization , the microcolumn is likely the most basic and consistent information processing unit in the brain , instead of a single neuron .

from the point of view of informational organization , the fundamental processing unit in the brain is likely to be the microcolumn .

electronic structure calculations were performed using the quantum-espresso code functional .

hybrid functional calculations were performed using the hse06 functional .

scenario generation for stochastic programming .

evaluation of scenario-generation methods for stochastic programming .

for k-means and the spectral clustering methods we used the implementation present in scikit-learn python library .

for both tf-idf vectorization and svm classification , we used the scikitlearn library .

this assumption was confirmed by xps measurements .

this assumption is confirmed by xps measurement .

considerable research effort has been devoted to designing the convolutional neural network for graph-structured data .

finally , a recent line of work has focussed on using convolutional neural networks defined on graphs .

again we calculate the five lowest lying massgaps corresponding to the five lowest mass glueballs accessible with our choice of minimisation basis and ground state .

calculate the five lowest lying massgaps corresponding to the five lowest glueball masses accessible with our choice of ground state and minimisation basis .

reinforcement learning is the problem where an agent must interact with an unknown environment through trial-anderror in order to maximize its cumulative reward .

reinforcement learning is an important technique in training an agent to learn how to respond to a given environment .

finally , to solve the cf-based mdp , we propose a q-network learning method based on deep q-learning , with a particular training scheme that uniformly samples state transitions from different users .

to handle the cf-based mdp with continuous state space , we propose a qnetwork learning method based on dqn , which is essentially q-learning with function approximation .

rbp , as introduced in , is an informed scheduling strategy that updates first the message that maximizes an ordering metric called the residual .

rbp , as introduced in , is an informed scheduling strategy that proposes to update first the message that maximizes an ordering metric called residual .

supergravity is the theory of local supersymmetry that automatically implies general coordinate invariance .

supergravity is the best framework within which both quintessence and inflation can be described .

the cosmological constant is a tunable parameter , and in the limit that it vanishes there is a susic theory of quantum gravity in asymptotically flat spacetime .

generally , the cosmological constant is a decreasing function of time .

in , gupta and kumar derived theoretical bounds for the capacity of wireless ad hoc networks .

in particular , in their seminal work , gupta and kumar analyzed the capacity of wireless networks in the physical and protocol models .

this approach has been successfully extended to the multiple gp setting using a factorized approximation of the posterior across gps .

this approach has been successfully extended to the cgps setting using a factorized approximation of the posterior across gps .

one of the few works in this area is by kahrs , see related work below .

some of the few works in this area are by kahrs , see related work below .

going to two space one time dimensions greatly simplifies this issue due to the absence of ultraviolet divergences except in mass corrections .

in dimensions this issue becomes simplified due to the absence of ultraviolet divergences except in mass corrections .

mazzocco has shown that any solution of the garnier system having reducible linear monodromy can be expressed classically via particular solutions from the above proposition .

mazzocco has shown that the solutions of the garnier system , that have reducible linear monodromy , are classical functions and can be expressed via solutions of lauricella hypergeometric equations .

was supported by the national natural science foundation of china under grant no .

was supported in part by the national natural science foundation of china under grant nos .

the differences in pulsewave oscillations between second and fourth-gradient models allow to revisit papers introducing kinks versus pulses as in .

the differences in pulse-wave oscillations between second-and fourth-gradient models allow to revisit papers introducing kinks versus pulses as in .