HHousen/ParaSCI · Datasets at Hugging Face

sentence1 stringlengths 16 446	sentence2 stringlengths 14 436
we perform testing on a set of representative dnn benchmarks , lenet-5 , vggnet for imagenet dataset .	we conduct empirical studies on the cifar-10 dataset with resnet-32 , resnet-110 .
for general results on the existence and uniqueness of variational solutions to spde we refer to .	for general results on the existence and uniqueness of solutions to spde we refer to .
for pre-training , given a short snippet of conversation , the model was tasked with predicting the next turn , similar to skip-thought .	given a short snippet of conversation , the model is tasked with predicting the next turn , similar to skip thought .
the cavity consists of a fixed mirror and a movable mirror separated by a distance l .	within this cavity , there is a distribution of mobile reactive particles which have not yet reached the interface .
lin and zhang proposed cosso to select features in smoothing spline regression by breaking up the regularization term into components on individual dimensions .	lin and zhang developed cosso for model selection in a smoothing spline anova model , with the penalty term being the sum of component norms .
on a transversal there is the natural transverse topology .	a transversal for s is a subset t which is a finite union of transversals of flowboxes .
lattice qcd is the only first principles tool for calculations of nonperturbative qcd effects .	lattice qcd is a method for calculating equilibrium properties of strongly interacting systems directly from the qcd lagrangian by numerical evaluation of the corresponding path integrals .
this coupling is inspired by generative adversarial networks .	these maps are usually trained using generative adversarial networks .
the axiom is the initial state of the system .	if axiom is a domain axiom , then ψ is in t .
gravity is a weak interaction , even at the enormous energies usually conceived for inflation , so the increment from each new pair is minuscule .	because gravity is a weak interaction , even at the scales of primordial inflation , the self-gravitation of inflationary gravitons can not be significant until the past light-cone has become enormous .
in we studied the complexity of shape expressions for open and closed shapes without negation .	in we studied the complexity of validation of shex in absence of negation , and for closed shape definitions only .
case will be larger than the pure product states case .	states case can be larger than the product states case .
the ordinate is the integrated time during which the detector exchange threshold has been lower than ht .	the ordinate is the ratio of the core-sw component separation divided by the core-ne component separation .
convolutional neural networks have shown its great effectiveness in computer vision tasks .	convolutional neural networks have achieved superior performance in many visual tasks , such as object classification and detection .
models based on convolutional neural networks and recurrent neural networks have achieved remarkable performance in many tasks , such as image classification .	deep learning techniques have been extensively researched for the tasks of segmentation of two dimensional images .
to this end , we use residual deep neural networks proposed by as feature detectors .	to achieve this goal , we choose wide residual bilstm networks .
a discriminatively-trained multiscale markov random fields were introduced in , in order to optimally fuse local and global features .	a discriminativelytrained multi-scale markov random field was introduced in , in order to optimally fuse local and global features .
such enhancement is the result of a cooperative effect arising from the interplay between deterministic and random dynamics in a nonlinear system .	the enhancement is the matrix element for a charge-1 excitation to decay into λ-anyons .
we apply the vgg16 model which is pre-trained on the imagenet classification task as our base cnn .	we use a cnn model based on the vgg-16 architecture pretrained on imagenet .
this ratio might depend indirectly on the temperature .	this result is independent of the temperature .
the scale invariant feature transform , called sift descriptor , has been proposed by and proved to be invariant to image rotation , scaling , translation , partly illumination changes , and projective transform .	the scale invariant feature transform , called sift descriptor , has been proposed by lowe and proved to be invariant to image rotation , scaling , translation , partly illumination changes .
instead , they suggest that the profile is a consequence of a near universal angular momentum distribution of the halos .	as previously suggested , the 1d profile is a good approximation to the true limb profiles from the 3d model .
deep neural networks have shown tremendous success in image recognition tasks , even surpassing human capability .	deep neural networks have shown impressive state-of-the-art results in the last years on numerous tasks and especially on image recognition .
the vertical short-dashed lines show the faint absolute magnitude limits considered in the sty estimate .	the vertical dot-dashed lines correspond to the faint absolute magnitude limits surveyed by the hdf data .
small-world networks tend to have a small average shortest path length and a clustering coefficient significantly higher than expected by random chance .	as largely explored in literature , small-world networks are characterized by a high clustering coefficient and a small average path length .
according to there is faithful irreducible frattini kg-module a of dimension 4 .	according to , there is a frattini f p g-module a which is faithful for g .
xu et al propose feature squeezing as a technique to harden nn models by detecting adversarial examples .	xu et al proposed feature squeezing as another approach for hardening the ml schemes against adversarial attacks .
to avoid technical difficulties , we assume our ring spectrum r to be an s-algebra in the sense of .	we work in the category of left r-modules where r is an s-algebra in the sense of and s stands for the sphere spectrum .
the generalized gradient approximation according to perdewburke-ernzerhof was employed for both the generation of the pseudopotentials and the exchange-correlation functional .	the generalized gradient approximation corrected functional by perdew et al is used for the exchange-correlation potential .
in this paper , we derive the average weight distributions of the multi-edge type ldpc code ensembles .	in this section , we derive the growth rate for the met-ldpc code ensemble .
after each purification step , the lab demon divides all pairs into two subensembles , according to the value of their error flags .	here , the lab demon divides all pairs into four subensembles , according to the value of their error flag .
many analytical properties and numerical results have been derived from these equations and related to cortical phenomena , for instance in the case of the problem of spatio-temporal pattern formation in spatially extended models .	many analytical and numerical results and properties have been derived from these equations and related to cortical phenomena , for instance for the problem of spatio-temporal pattern formation in spatially extended models .
gravitational higgs mechanism provides a non-perturbative and fully covariant definition of massive gravity .	the gravitational higgs mechanism gives a non-perturbative and fully covariant definition of massive gravity .
the set of all nilpotent semigroup varieties is definable .	an arbitrary abelian periodic group variety is definable .
the shift transition , introduced by nivre in the transition-based version , is an optimization that avoids the need to apply a sequence of no-arc transitions to empty the list λ 1 before reading a new input word .	duced by nivre in the transition-based version , is an optimization that avoids the need to apply a sequence of no-arc transitions to empty the list λ 1 before reading a new input word .
the main work in this thesis is to extend these results to products of three and four infinite dimensional globes .	one of the main result in this thesis characterises molecules in the product of three infinite dimensional globes , in terms of such subcomplexes .
the lightest neutralino which is the lightest susy particle is in the right mass range to become a dark matter candidate .	we will assume that the neutralino is the lsp .
deep learning has shown an great improvement in performance of several computer vision tasks in the recent years .	in recent years , deep learning approaches have led to a significant increase in performance on the task of image super-resolution .
in reinforcement learning , an agent interacts with an environment in a sequential manner , receiving feedback as it does so .	in the reinforcement learning paradigm , an agent in a system acts , observes , and receives feedback in the form of numerical signals .
we refer the reader to for the basic properties of o-minimal structures used in this paper .	we refer to for introductory material on o-minimal structures , as well as examples .
for imagenet , we use the top 800 pca components of some convolutional features extracted from inception-resnet-v2 .	we use the keras implementation of incpetion-resnetv2 pre-trained on imagenet dataset with 1000 classes .
there are only few automated theorem provers specially dedicated for deontic logic and used by deontic logicians .	however , there are only few automated theorem provers specially dedicated for deontic logic and used by deontic logicians .
the weights of the network are initialized using xavier normal initializer .	the weights in the softmax layer were initialized from the scaled uniform distribution .
in this paper , we study the dmt for various setups of multiple-antenna relay networks .	in this paper , we investigate the benefits of af relaying in multi-antenna multi-relay networks .
to accelerate the training of the siamese network , we add a batch normalization layer after each convolutional layer .	we adopt batch normalization into the architecture after every convolution layer .
kan , homotopy limits , completions and localizations , lecture notes in mathematics , vol .	rutter , spaces of homotopy self-equivalences , lecture notes in mathematics , vol .
the photon is the goldstone boson of the broken symmetry .	the photon is the lkp and there is insufficient phase space and the lkp photon .
complex models such as deep neural networks have shown remarkable success in applications such as computer vision , speech and time series analysis .	convolutional neural networks have achieved significant progress in computer vision tasks such as image classification .
deep convolutional neural networks have made significant breakthroughs in many visual understanding tasks including image classification .	deep convolutional neural networks have become one of the most important methods in computer vision tasks such as image classification .
in recent years , deep convolutional neural networks have demonstrated dramatic improvements in performance for computer vision tasks such as object classification , detection , and segmentation .	recently , deep convolutional neural network based approaches have been setting new state-of-the-art results not only for high-level computer vision tasks such as image classification , but also for low-level tasks such as image super-resolution .
this is the gravitational dielectric effect in the m-theory .	this is the gravitational myers effect in the type iia superstring theory .
moreover , wu and negi in defined the dual concept of effective capacity , which provides the maximum constant arrival rate that can be supported by a given departure process while satisfying statistical delay constraints .	wu and negi in defined the effective capacity as the maximum constant arrival rate that can be supported by a given channel service process while also satisfying a statistical qos requirement specified by the qos exponent θ .
in addition , sdn controllers can combine header fields from any stack layer , creating a crosslayer design that is very suitable for emerging wireless scenarios such as d2d communications .	in addition , sdn controllers can combine header fields from any stack layer , creating a crosslayer design that is suitable for emerging wireless scenarios such as d2d .
detailed discussions of the following definitions and theorems , along with their proofs , can be found in .	further details regarding the following definitions and theorems can be found in .
we conducted several experiments on cifar-10 with various network architectures , including resnet , wide resnet .	we experimented with several of the best performing deep architectures on imagenet like resnet-50 .
banerjee et al show that there is a bijection between regular exponential family distributions and bregman divergences .	in banerjee et al , they show that there is a one-to-one correspondence between bregman divergences and exponential family .
more recently , iwami et al derived and studied a model for autoimmunity , which makes explicit account of the virus dynamics and its interaction with the immune system by means of linear or nonlinear immune response .	more recently , iwami et al considered a model of immune response to a viral infection , in which they explicitly included the dynamics of a virus population .
more precisely , percolation is a purely geometric model in the sense that we have to populate all lattice sites with a prescribed probability p .	percolation is a weaker condition , since merging entails percolation but not vice versa .
shi et al performed dslf reconstruction as an optimization for sparsity in the continuous fourier domain .	shi et al explored sparsity in the continuous fourier domain to reconstruct densely-sampled lfs from a small set of samples .
logg , lr scott , ar terrel , topological optimization of the evaluation of finite element matrices , siam j .	logg , lr scott , optimizing the evaluation of finite element matrices , siam j .
codes over finite rings have been of interest after it was shown that some binary non-linear codes such as the kerdock , preparata and goethal codes are the gray images of linear codes over z 4 in .	recently , it has been observed that many important non-linear codes , such as kerdock and preparata codes , are related to linear codes over the ring z 4 of integers modulo 4 with the help of a gray map .
the chirality is a good quantum number only if the particle is massless .	chirality is a ubiquitous feature of the biological world .
the shot noise is the pure fluctuations in number which correspond to the fact that photoemission is a random process .	the shot noise is the other source of noise .
for spectral analysis , we used the xspec software package .	for data reduction we used the eso-eclipse software package .
in , statistical features , eg , maximum , minimum , mean , energy and entropy , are extracted from the csi sequences and fed to classifiers , eg , support vector machine , for human identification .	in , statistical features , eg , maximum , minimum , mean , energy and entropy , are extracted from the csi sequences and fed to classifiers , eg , support vector machine , for person recognition .
batch normalization and relu activation are applied after every convolutional layer .	batch normalization and leaky relu activation are applied after every convolutional layer .
after establishing single-letter expressions for the secrecy capacity region , we consider the parallel degraded compound multi-receiver wiretap channel .	using this theorem , we can establish the secrecy capacity region of the gaussian parallel degraded compound multi-receiver wiretap channel with layered messages as follows .
han et al proposed an iterative thresholding to remove unimportant weights with small absolute values .	han et al proposed the deep compression model , which effectively reduces the model size and the energy consumption .
it has been shown in the traditional cs framework that the availability of multiple measurement vectors can further improve performance by harnessing the joint sparsity pattern of different signals , also known as group sparsity .	with multiple measurement vectors in a cs framework , it is shown to further reduce the required number of samples by harnessing the joint sparsity pattern of different signals .
the proposed measure of distance between high density points is not euclidean distance , but diffusion distance , which is more adept at removing spurious modes , due to its incorporation of the geometry of the data .	more significantly , the proposed measure of distance between high density points is not euclidean distance , but diffusion distance , which is more adept at removing spurious modes , due to its incorporation of the geometry of the data .
we give the proof of the reverse inequality .	we give now the proof of the lemma we have used .
these networks comprise all sequences sharing a common shape that can be accessed by one point mutations , hence the name .	these networks consist of genes and proteins that interact with each other .
cluster algebras were introduced by fomin and zelevinsky and canonical bases .	cluster algebras were introduced by fomin and zelevinsky in in the context of canonical bases and total positivity .
the dimpled fiber taper was fabricated by modifying the procedure presented in refto use a cermanic mold for creating a dimple .	the dimpled fiber taper was fabricated by modifying the process of michael et al to use a ceramic edge as the dimple mold .
a certain choice of labeling events in space and time is called a coordinate system .	the coordinate system is the fourier analogue of the .
the problem can be solved in practical time scales by applying the belief propagation framework .	the problem can be solved in practical time scales by applying the conceptually different framework of belief propagation .
roem et al use skipthought vectors to encode sentences and model relations between the sentences .	kiros et al recently propose the skip-thought vectors to encode a sentence into a compact vector .
the generalized gradient approximation in the parametrization of perdew , burke and ernzerhof was used as approximation for the exchange and correlation functional .	we employed density functional theory within the generalized gradient approximation of the exchange and correlation functional , as parameterized by perdew , burke and ernzerhof .
a community can be thought as a group of nodes having a higher density of internal than external connections .	formally , a community is defined to be a set of cohesive nodes that have more connections inside than outside .
convolutional neural networks , cnn , have recently achieved state of the art performance in a number of computer vision tasks .	deep convolutional neural networks have made significant breakthroughs in many visual understanding tasks including image classification .
during this phase the detector response is generated from the energy deposition of the particles traversing it .	the particles are transported in the material of the detector , simulating their interaction with it and the energy deposition that generates the detector response .
detecting community structure in networks using edge prediction methods .	detecting communities using an edge prediction method .
graphene is a one-atom-thick sheet of sp2-bonded carbon atoms that are densely packed in a bipartite crystal lattice .	graphene is a zero-gap semi-metal whose electronic band structure shows linear dispersion near the charge neutral dirac point .
convolutional neural networks , trained on large scale data , have significantly advanced the state-of-theart on traditional vision problems such as object recognition .	deep convolutional neural networks have emerged as highly effective models for these large-scale visual recognition tasks .
lenagan , weakly multiplicative coactions of quantized function algebras , j .	sklyanin , algebraic structures related to reflection equations , j .
memory required by the agents to achieve deterministic rendezvous was studied in for general graphs .	the amount of memory required by the agents to achieve deterministic rendezvous was studied in for general graphs .
al have studied nlgm and modified nlgm in both spectral and finite element set ups , to name a few .	al have studied nlg and modified nlg in both spectral and finite element set ups , to name a few .
network motifs , or graphlets , are usually used to identify such higher-order interaction .	network motifs , or graphlets , are usually used to identify higher-order interactions .
we generate adversarial examples using an iterative version of the fast gradient sign method .	firstly , we craft the adversarial examples by using the fgsm algorithm .
we also adopt dropout upon the output layer of cnn .	besides l 2 regularization , we also use dropout to reduce overfitting .
the evolution of in-plane anisotropy in the films was attributed to the directional order mechanism .	the evolution of in-plane uniaxial anisotropy in fe thin films was attributed to the directional order mechanism .
a gan framework consists of two neural networks , a generator and a discriminator , which are trained in an adversarial manner to generate data approximating the training data distribution .	a gan consists of two neural networks known as a generator and discriminatorwe focus on conditional gans which , in addition to a gaussian input , learn to generate different outputs conditioned on known problem-specific characteristics .
the introduction of convolutional neural networks has led to the advancement of state-of-the-art performance on several fundamental computer vision tasks such as image classification .	convolutional neural networks have achieved state-of-the-art performance on visual tasks such as image and video recognition in the last few years .
several datasets such as afw , etc , have been constructed specifically for face detection .	many of these object detectors have been re-purposed for other tasks such as face detection .
quantum information is a young field of physics .	the quantum fisher information is a monotone metric on the quantum state space with the coordinate system θ .
because kinetic energy is the difference between total and interaction energies , it varies with time .	the kinetic energy is the volume integral of the kinetic energy density , and the potential energy is the volume integral of the potential energy available in the system .
over the past years , convolutional neural networks and recurrent neural networks have emerged as the state-of-the-art learning framework for action recognition .	more recently , deep networks , including 2d two-stream approaches , 3d space-time approaches like convolutional 3d , and the use of recurrent neural network units , have shown state-of-the-art performance .
neural networks , eg , have proven to be very adept parametric representations in image classification problems .	deep convolutional neural networks have been proven very useful in various tasks in computer vision including classification .
the gridded spectral data cubes were processed with the miriad software package for further analysis .	the data were calibrated , continuum subtracted and imaged using the miriad software package .
the set consisting of only the ld selection rule .	the set consisting of the input-consuming selection rules .

we perform testing on a set of representative dnn benchmarks , lenet-5 , vggnet for imagenet dataset .

we conduct empirical studies on the cifar-10 dataset with resnet-32 , resnet-110 .

for general results on the existence and uniqueness of variational solutions to spde we refer to .

for general results on the existence and uniqueness of solutions to spde we refer to .

for pre-training , given a short snippet of conversation , the model was tasked with predicting the next turn , similar to skip-thought .

given a short snippet of conversation , the model is tasked with predicting the next turn , similar to skip thought .

the cavity consists of a fixed mirror and a movable mirror separated by a distance l .

within this cavity , there is a distribution of mobile reactive particles which have not yet reached the interface .

lin and zhang proposed cosso to select features in smoothing spline regression by breaking up the regularization term into components on individual dimensions .

lin and zhang developed cosso for model selection in a smoothing spline anova model , with the penalty term being the sum of component norms .

on a transversal there is the natural transverse topology .

a transversal for s is a subset t which is a finite union of transversals of flowboxes .

lattice qcd is the only first principles tool for calculations of nonperturbative qcd effects .

lattice qcd is a method for calculating equilibrium properties of strongly interacting systems directly from the qcd lagrangian by numerical evaluation of the corresponding path integrals .

this coupling is inspired by generative adversarial networks .

these maps are usually trained using generative adversarial networks .

the axiom is the initial state of the system .

if axiom is a domain axiom , then ψ is in t .

gravity is a weak interaction , even at the enormous energies usually conceived for inflation , so the increment from each new pair is minuscule .

because gravity is a weak interaction , even at the scales of primordial inflation , the self-gravitation of inflationary gravitons can not be significant until the past light-cone has become enormous .

in we studied the complexity of shape expressions for open and closed shapes without negation .

in we studied the complexity of validation of shex in absence of negation , and for closed shape definitions only .

case will be larger than the pure product states case .

states case can be larger than the product states case .

the ordinate is the integrated time during which the detector exchange threshold has been lower than ht .

the ordinate is the ratio of the core-sw component separation divided by the core-ne component separation .

convolutional neural networks have shown its great effectiveness in computer vision tasks .

convolutional neural networks have achieved superior performance in many visual tasks , such as object classification and detection .

models based on convolutional neural networks and recurrent neural networks have achieved remarkable performance in many tasks , such as image classification .

deep learning techniques have been extensively researched for the tasks of segmentation of two dimensional images .

to this end , we use residual deep neural networks proposed by as feature detectors .

to achieve this goal , we choose wide residual bilstm networks .

a discriminatively-trained multiscale markov random fields were introduced in , in order to optimally fuse local and global features .

a discriminativelytrained multi-scale markov random field was introduced in , in order to optimally fuse local and global features .

such enhancement is the result of a cooperative effect arising from the interplay between deterministic and random dynamics in a nonlinear system .

the enhancement is the matrix element for a charge-1 excitation to decay into λ-anyons .

we apply the vgg16 model which is pre-trained on the imagenet classification task as our base cnn .

we use a cnn model based on the vgg-16 architecture pretrained on imagenet .

this ratio might depend indirectly on the temperature .

this result is independent of the temperature .

the scale invariant feature transform , called sift descriptor , has been proposed by and proved to be invariant to image rotation , scaling , translation , partly illumination changes , and projective transform .

the scale invariant feature transform , called sift descriptor , has been proposed by lowe and proved to be invariant to image rotation , scaling , translation , partly illumination changes .

instead , they suggest that the profile is a consequence of a near universal angular momentum distribution of the halos .

as previously suggested , the 1d profile is a good approximation to the true limb profiles from the 3d model .

deep neural networks have shown tremendous success in image recognition tasks , even surpassing human capability .

deep neural networks have shown impressive state-of-the-art results in the last years on numerous tasks and especially on image recognition .

the vertical short-dashed lines show the faint absolute magnitude limits considered in the sty estimate .

the vertical dot-dashed lines correspond to the faint absolute magnitude limits surveyed by the hdf data .

small-world networks tend to have a small average shortest path length and a clustering coefficient significantly higher than expected by random chance .

as largely explored in literature , small-world networks are characterized by a high clustering coefficient and a small average path length .

according to there is faithful irreducible frattini kg-module a of dimension 4 .

according to , there is a frattini f p g-module a which is faithful for g .

xu et al propose feature squeezing as a technique to harden nn models by detecting adversarial examples .

xu et al proposed feature squeezing as another approach for hardening the ml schemes against adversarial attacks .

to avoid technical difficulties , we assume our ring spectrum r to be an s-algebra in the sense of .

we work in the category of left r-modules where r is an s-algebra in the sense of and s stands for the sphere spectrum .

the generalized gradient approximation according to perdewburke-ernzerhof was employed for both the generation of the pseudopotentials and the exchange-correlation functional .

the generalized gradient approximation corrected functional by perdew et al is used for the exchange-correlation potential .

in this paper , we derive the average weight distributions of the multi-edge type ldpc code ensembles .

in this section , we derive the growth rate for the met-ldpc code ensemble .

after each purification step , the lab demon divides all pairs into two subensembles , according to the value of their error flags .

here , the lab demon divides all pairs into four subensembles , according to the value of their error flag .

many analytical properties and numerical results have been derived from these equations and related to cortical phenomena , for instance in the case of the problem of spatio-temporal pattern formation in spatially extended models .

many analytical and numerical results and properties have been derived from these equations and related to cortical phenomena , for instance for the problem of spatio-temporal pattern formation in spatially extended models .

gravitational higgs mechanism provides a non-perturbative and fully covariant definition of massive gravity .

the gravitational higgs mechanism gives a non-perturbative and fully covariant definition of massive gravity .

the set of all nilpotent semigroup varieties is definable .

an arbitrary abelian periodic group variety is definable .

the shift transition , introduced by nivre in the transition-based version , is an optimization that avoids the need to apply a sequence of no-arc transitions to empty the list λ 1 before reading a new input word .

duced by nivre in the transition-based version , is an optimization that avoids the need to apply a sequence of no-arc transitions to empty the list λ 1 before reading a new input word .

the main work in this thesis is to extend these results to products of three and four infinite dimensional globes .

one of the main result in this thesis characterises molecules in the product of three infinite dimensional globes , in terms of such subcomplexes .

the lightest neutralino which is the lightest susy particle is in the right mass range to become a dark matter candidate .

we will assume that the neutralino is the lsp .

deep learning has shown an great improvement in performance of several computer vision tasks in the recent years .

in recent years , deep learning approaches have led to a significant increase in performance on the task of image super-resolution .

in reinforcement learning , an agent interacts with an environment in a sequential manner , receiving feedback as it does so .

in the reinforcement learning paradigm , an agent in a system acts , observes , and receives feedback in the form of numerical signals .

we refer the reader to for the basic properties of o-minimal structures used in this paper .

we refer to for introductory material on o-minimal structures , as well as examples .

for imagenet , we use the top 800 pca components of some convolutional features extracted from inception-resnet-v2 .

we use the keras implementation of incpetion-resnetv2 pre-trained on imagenet dataset with 1000 classes .

there are only few automated theorem provers specially dedicated for deontic logic and used by deontic logicians .

however , there are only few automated theorem provers specially dedicated for deontic logic and used by deontic logicians .

the weights of the network are initialized using xavier normal initializer .

the weights in the softmax layer were initialized from the scaled uniform distribution .

in this paper , we study the dmt for various setups of multiple-antenna relay networks .

in this paper , we investigate the benefits of af relaying in multi-antenna multi-relay networks .

to accelerate the training of the siamese network , we add a batch normalization layer after each convolutional layer .

we adopt batch normalization into the architecture after every convolution layer .

kan , homotopy limits , completions and localizations , lecture notes in mathematics , vol .

rutter , spaces of homotopy self-equivalences , lecture notes in mathematics , vol .

the photon is the goldstone boson of the broken symmetry .

the photon is the lkp and there is insufficient phase space and the lkp photon .

complex models such as deep neural networks have shown remarkable success in applications such as computer vision , speech and time series analysis .

convolutional neural networks have achieved significant progress in computer vision tasks such as image classification .

deep convolutional neural networks have made significant breakthroughs in many visual understanding tasks including image classification .

deep convolutional neural networks have become one of the most important methods in computer vision tasks such as image classification .

in recent years , deep convolutional neural networks have demonstrated dramatic improvements in performance for computer vision tasks such as object classification , detection , and segmentation .

recently , deep convolutional neural network based approaches have been setting new state-of-the-art results not only for high-level computer vision tasks such as image classification , but also for low-level tasks such as image super-resolution .

this is the gravitational dielectric effect in the m-theory .

this is the gravitational myers effect in the type iia superstring theory .

moreover , wu and negi in defined the dual concept of effective capacity , which provides the maximum constant arrival rate that can be supported by a given departure process while satisfying statistical delay constraints .

wu and negi in defined the effective capacity as the maximum constant arrival rate that can be supported by a given channel service process while also satisfying a statistical qos requirement specified by the qos exponent θ .

in addition , sdn controllers can combine header fields from any stack layer , creating a crosslayer design that is very suitable for emerging wireless scenarios such as d2d communications .

in addition , sdn controllers can combine header fields from any stack layer , creating a crosslayer design that is suitable for emerging wireless scenarios such as d2d .

detailed discussions of the following definitions and theorems , along with their proofs , can be found in .

further details regarding the following definitions and theorems can be found in .

we conducted several experiments on cifar-10 with various network architectures , including resnet , wide resnet .

we experimented with several of the best performing deep architectures on imagenet like resnet-50 .

banerjee et al show that there is a bijection between regular exponential family distributions and bregman divergences .

in banerjee et al , they show that there is a one-to-one correspondence between bregman divergences and exponential family .

more recently , iwami et al derived and studied a model for autoimmunity , which makes explicit account of the virus dynamics and its interaction with the immune system by means of linear or nonlinear immune response .

more recently , iwami et al considered a model of immune response to a viral infection , in which they explicitly included the dynamics of a virus population .

more precisely , percolation is a purely geometric model in the sense that we have to populate all lattice sites with a prescribed probability p .

percolation is a weaker condition , since merging entails percolation but not vice versa .

shi et al performed dslf reconstruction as an optimization for sparsity in the continuous fourier domain .

shi et al explored sparsity in the continuous fourier domain to reconstruct densely-sampled lfs from a small set of samples .

logg , lr scott , ar terrel , topological optimization of the evaluation of finite element matrices , siam j .

logg , lr scott , optimizing the evaluation of finite element matrices , siam j .

codes over finite rings have been of interest after it was shown that some binary non-linear codes such as the kerdock , preparata and goethal codes are the gray images of linear codes over z 4 in .

recently , it has been observed that many important non-linear codes , such as kerdock and preparata codes , are related to linear codes over the ring z 4 of integers modulo 4 with the help of a gray map .

the chirality is a good quantum number only if the particle is massless .

chirality is a ubiquitous feature of the biological world .

the shot noise is the pure fluctuations in number which correspond to the fact that photoemission is a random process .

the shot noise is the other source of noise .

for spectral analysis , we used the xspec software package .

for data reduction we used the eso-eclipse software package .

in , statistical features , eg , maximum , minimum , mean , energy and entropy , are extracted from the csi sequences and fed to classifiers , eg , support vector machine , for human identification .

in , statistical features , eg , maximum , minimum , mean , energy and entropy , are extracted from the csi sequences and fed to classifiers , eg , support vector machine , for person recognition .

batch normalization and relu activation are applied after every convolutional layer .

batch normalization and leaky relu activation are applied after every convolutional layer .

after establishing single-letter expressions for the secrecy capacity region , we consider the parallel degraded compound multi-receiver wiretap channel .

using this theorem , we can establish the secrecy capacity region of the gaussian parallel degraded compound multi-receiver wiretap channel with layered messages as follows .

han et al proposed an iterative thresholding to remove unimportant weights with small absolute values .

han et al proposed the deep compression model , which effectively reduces the model size and the energy consumption .

it has been shown in the traditional cs framework that the availability of multiple measurement vectors can further improve performance by harnessing the joint sparsity pattern of different signals , also known as group sparsity .

with multiple measurement vectors in a cs framework , it is shown to further reduce the required number of samples by harnessing the joint sparsity pattern of different signals .

the proposed measure of distance between high density points is not euclidean distance , but diffusion distance , which is more adept at removing spurious modes , due to its incorporation of the geometry of the data .

more significantly , the proposed measure of distance between high density points is not euclidean distance , but diffusion distance , which is more adept at removing spurious modes , due to its incorporation of the geometry of the data .

we give the proof of the reverse inequality .

we give now the proof of the lemma we have used .

these networks comprise all sequences sharing a common shape that can be accessed by one point mutations , hence the name .

these networks consist of genes and proteins that interact with each other .

cluster algebras were introduced by fomin and zelevinsky and canonical bases .

cluster algebras were introduced by fomin and zelevinsky in in the context of canonical bases and total positivity .

the dimpled fiber taper was fabricated by modifying the procedure presented in refto use a cermanic mold for creating a dimple .

the dimpled fiber taper was fabricated by modifying the process of michael et al to use a ceramic edge as the dimple mold .

a certain choice of labeling events in space and time is called a coordinate system .

the coordinate system is the fourier analogue of the .

the problem can be solved in practical time scales by applying the belief propagation framework .

the problem can be solved in practical time scales by applying the conceptually different framework of belief propagation .

roem et al use skipthought vectors to encode sentences and model relations between the sentences .

kiros et al recently propose the skip-thought vectors to encode a sentence into a compact vector .

the generalized gradient approximation in the parametrization of perdew , burke and ernzerhof was used as approximation for the exchange and correlation functional .

we employed density functional theory within the generalized gradient approximation of the exchange and correlation functional , as parameterized by perdew , burke and ernzerhof .

a community can be thought as a group of nodes having a higher density of internal than external connections .

formally , a community is defined to be a set of cohesive nodes that have more connections inside than outside .

convolutional neural networks , cnn , have recently achieved state of the art performance in a number of computer vision tasks .

deep convolutional neural networks have made significant breakthroughs in many visual understanding tasks including image classification .

during this phase the detector response is generated from the energy deposition of the particles traversing it .

the particles are transported in the material of the detector , simulating their interaction with it and the energy deposition that generates the detector response .

detecting community structure in networks using edge prediction methods .

detecting communities using an edge prediction method .

graphene is a one-atom-thick sheet of sp2-bonded carbon atoms that are densely packed in a bipartite crystal lattice .

graphene is a zero-gap semi-metal whose electronic band structure shows linear dispersion near the charge neutral dirac point .

convolutional neural networks , trained on large scale data , have significantly advanced the state-of-theart on traditional vision problems such as object recognition .

deep convolutional neural networks have emerged as highly effective models for these large-scale visual recognition tasks .

lenagan , weakly multiplicative coactions of quantized function algebras , j .

sklyanin , algebraic structures related to reflection equations , j .

memory required by the agents to achieve deterministic rendezvous was studied in for general graphs .

the amount of memory required by the agents to achieve deterministic rendezvous was studied in for general graphs .

al have studied nlgm and modified nlgm in both spectral and finite element set ups , to name a few .

al have studied nlg and modified nlg in both spectral and finite element set ups , to name a few .

network motifs , or graphlets , are usually used to identify such higher-order interaction .

network motifs , or graphlets , are usually used to identify higher-order interactions .

we generate adversarial examples using an iterative version of the fast gradient sign method .

firstly , we craft the adversarial examples by using the fgsm algorithm .

we also adopt dropout upon the output layer of cnn .

besides l 2 regularization , we also use dropout to reduce overfitting .

the evolution of in-plane anisotropy in the films was attributed to the directional order mechanism .

the evolution of in-plane uniaxial anisotropy in fe thin films was attributed to the directional order mechanism .

a gan framework consists of two neural networks , a generator and a discriminator , which are trained in an adversarial manner to generate data approximating the training data distribution .

a gan consists of two neural networks known as a generator and discriminatorwe focus on conditional gans which , in addition to a gaussian input , learn to generate different outputs conditioned on known problem-specific characteristics .

the introduction of convolutional neural networks has led to the advancement of state-of-the-art performance on several fundamental computer vision tasks such as image classification .

convolutional neural networks have achieved state-of-the-art performance on visual tasks such as image and video recognition in the last few years .

several datasets such as afw , etc , have been constructed specifically for face detection .

many of these object detectors have been re-purposed for other tasks such as face detection .

quantum information is a young field of physics .

the quantum fisher information is a monotone metric on the quantum state space with the coordinate system θ .

because kinetic energy is the difference between total and interaction energies , it varies with time .

the kinetic energy is the volume integral of the kinetic energy density , and the potential energy is the volume integral of the potential energy available in the system .

over the past years , convolutional neural networks and recurrent neural networks have emerged as the state-of-the-art learning framework for action recognition .

more recently , deep networks , including 2d two-stream approaches , 3d space-time approaches like convolutional 3d , and the use of recurrent neural network units , have shown state-of-the-art performance .

neural networks , eg , have proven to be very adept parametric representations in image classification problems .

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

the gridded spectral data cubes were processed with the miriad software package for further analysis .

the data were calibrated , continuum subtracted and imaged using the miriad software package .

the set consisting of only the ld selection rule .

the set consisting of the input-consuming selection rules .