HHousen/ParaSCI · Datasets at Hugging Face

sentence1 stringlengths 16 446	sentence2 stringlengths 14 436
obviously , gravity is a destabilizing effect since the heavy fluid is above the light one .	with gravity , there is a critical wave number , below which small amplitude solutions become unstable .
for the detection network , we adapt the design of the faster r-cnn object detector .	we use the faster rcnn object detector to obtain object detections in the inpainted images .
the toda lattice is one of the most important integrable systems .	the toda lattice is a prototypical example of a completely integrable system .
since the context of local regions is incorporated in addition here , this approach has proven highly effective in classification tasks .	since here the context of local regions is incorporated as well , this approach has proven highly effective in classification tasks .
we expect that , in the small pf limit , the jet function will not depend on these parameters .	we would thus expect the two jet functions to coincide in the small-pf limit .
recently , deep neural network based methods have lead to breakthroughs in several vision tasks , such as classification .	convolutional neural networks have recently become the par excellence base approach to deal with many computer vision tasks .
multi-task learning may improve the performance for each task as different tasks can influence each other by the shared representation .	multi-task learning leverages the task relatedness in the form of shared structures to jointly learn multiple tasks .
the quintessence is a slowly varying scalar field with a canonical kinetic energy term .	since quintessence is a in the following we study the isocurvature baryon number scalar field one expects its fluctuations in space-time .
a non admin user can generate report on his assignments only .	an admin ops user can generate report on any ops user .
the exchange correlation functional is approximated by the generalized gradient approximation as parametrized by perdew , burke and ernzerhof .	to account for the exchange-correlation potential we have used the generalized gradient approximation as formulated by perdew , burke and ernzerhof .
similar to prior work in scene graph generation , we adopt faster r-cnn as backbone in our object detection module .	we choose faster r-cnn architecture as our two-stage object detector that provides accurate bounding boxes .
during the past several years , the introduction of convolutional neural networks have dramatically improved the state-of-the-art performances of many computer vision tasks including face identification and verification .	in the past decade , deep convolutional neural networks have achieved significant success in a wide spectrum of applications , such as object classification and detection .
frames in hilbert spaces were introduced by duffin and schaeffer in 1952 , in the context of nonharmonic fourier series .	hilbert space frames were introduced by duffin and schaeffer in 1952 to address some deep questions in non-harmonic fourier series .
in recent years , convolutional neural networks has achieved remarkable results in a wide range of computer vision applications .	over the past few years , convolutional neural networks have become the leading approach in computer vision .
the assumption of having an essential family is natural since it is necessary for the existence of a nontrivial sparse resultant .	the assumption of having an essential family is natural since it is necessary for the existence of the corresponding sparse resultant .
deep neural networks have become popular acoustic models for state-of-the-art large vocabulary speech recognition systems .	modern state-of-the-art speech recognition systems are based on neural network acoustic models .
calibration and image deconvolution was carried out using the miriad package .	data reduction was performed using the miriad software .
it has been implemented using the widespread package scikit-learn .	the framework is implemented with the scikit-learn python library .
nonetheless , it has been shown that the limiting ensemble converges to the correct gaussian in the linear and finite-dimensional case , with the rateoin lp for lipschitz functionals with polynomial growth at infinity .	moreover , it was shown in that for finite-dimensional state-space the single level enkf converges to the kalman filtering distribution with the standard rate oin this case .
on the distribution of the length of the longest increasing subsequence of random permutations .	on the distributions of the lengths of the longest monotone subsequences in random words .
the tilde is to denote the model-dependence of this gap .	the tilde denotes the transverse amplitude .
many real-life networks turn out to share some common properties , one of them being that the degree distribution follows a power-law , and many more .	many real-world networks are often considered as the scale-free networks , in which the degree distribution follows power-law .
the axial-vector spectral functions at zero temperature for three lattice spacings .	the scalar bottomonium spectral function at zero temperature for different lattice spacings .
there is a construction that associates a reproducing kernel hilbert space to a positive definite kernel .	it is also shown in that a reproducing kernel uniquely defines a hilbert space of functions with a certain inner product .
we re-implemented netmf in pytorch for maximum efficiency and for improved scalability .	we implemented our models in pytorch using allennlp library .
the interaction between the valence electrons and ionic cores was described by the projector augmented wave method .	the electron-ion interaction was described within the projector augmented wave method with ni , o , and c states treated as valence states .
cosmology is a natural arena in which to put to the test alternative theories of gravity .	perhaps cosmology is the ideal setting in which to study possible stringy effects .
reaction , diffusion , and decay processes find applications in chemistry , physics , and applied mathematics .	reaction and diffusion processes find applications in chemistry , physics , and applied mathematics .
band structure calculations were performed under the framework of the generalized gradient approximation of density functional theory .	spin-polarized electronic structure calculations were performed using the perdew-burke-ernzerhof functional 38 for the exchange-correlation potential based on the generalized gradient approximation .
deep learning has been used as a dramatically powerful tool in computer vision tasks such as image recognition .	in particular , convolutional neural networks has been popular in vision and audio recognition areas .
epstein et al show that metric dimension can be solved in polynomial time on co-graphs and forests .	subsequently , epstein et al showed that this problem is np-complete on split graphs , bipartite and cobipartite graphs .
large-scale deep convolutional neural networks have been successfully applied to a wide variety of applications such as image classification .	deep convolutional neural networks have already achieved tremendous success on a variety of computer vision tasks such as image classification among many others .
the latter only comprises optical elements made from fused silica which are hydroxyl-catalysis bonded 8 to the zerodur baseplate so that a quasi-monolithic structure with superior stability and negligible sensitivity to thermal expansion is formed .	the bench only comprises optical elements made from fused silica which are hydroxide-catalysis bonded to the zerodur baseplate so that a quasi-monolithic structure with superior stability and negligible sensitivity to thermal expansion is formed .
in this section , we conduct extensive experiments on the imagenet dataset for reproducible proof of concept .	in this section , we use imagenet dataset to show the effectiveness of ipm in selecting the representatives for image classification task .
wafer-scale reduced graphene oxide films for nanomechanical devices .	conducting electrodes for organic electronics from reduced graphene oxide .
this idea comes from chapeau-blondeau , janez , and ferrier , who presented a relaxation scheme to locally optimize the length of a given steiner tree , starting from the minimal spanning tree .	this idea comes from who presented a relaxation scheme to locally optimize the length of a given steiner tree , starting from the minimal spanning tree .
we believe that separating information presentation from digital content is crucial to solving these problems .	we believe that useful , practical personalization of the experience of digital content can be achieved with context brokers .
dwork and naor first suggested proof-of-work in 1992 , applying it as a method to thwart spam email .	in 1992 , dwork and naor proposed proof-of-computation to combat junk mail .
such a geometry is a physical geometry , ie it is described completely by the world function , which is a half of the squared distance function .	the geometry consists of three edge states which can exchange quasiparticles by tunneling through the fractional hall fluid .
neural networks have recently been shown to achieve outstanding performance in several machine learning domains such as image recognition .	convolutional neural networks provide state-of-the-art results for many machine learning challenges , such as image classification .
the simulation was then stopped and the domain was doubled in each coordinate dimension .	the calculation was then stopped , the domain enlarged , and the calculation restarted within the larger domain .
the presence of human observers may artificially increase hand hygiene rates temporarily as the presence of other healthcare workers can induce peer effects to increase rates .	the presence of human observers may artificially increase hand hygiene rates temporarily just as the presence of other healthcare workers can induce peer effects to increase rates .
especially in computer vision , deep convolutional neural networks are actively applied to object recognition tasks like object classification .	convolutional neural networks have achieved exceptional results in many large-scale computer vision applications , particularly in image recognition task .
a clear penetration peak can be observed near zero field .	a clear penetration peak appears near zero field .
elliptic flow is a direct measure of collectivity .	because elliptic flow is a collective effect , it is a correlation of all the particles with the reaction plane .
ronneberger et al proposed a network structure called u-net focusing on an application to biomedical image segmentation problems providing only small datasets .	ronneberger et al proposed u-net , a u-shaped dnn particularly designed for biomedical image segmentation that adds a symmetric expanding path to enable precise localization that the prior methods lack of .
following a protocol p is said to be an all-case optimal solution to a decision task s in a context γ if it solves s and , moreover , p dominates every protocol p that solves s in γ .	following a protocol p is said to be an all-case optimal solution to a decision task s in a context γ if it solves s and , moreover , it dominates every protocol p that solves s in γ .
the region with pale green indicate mixed phase .	pale green shows the region of lg mixed phase .
recently , convolutional neural networks have been deployed successfully in a variety of applications , including imagenet classication .	deep neural networks have demonstrated success in many machine learning tasks , including image recognition , speech recognition , and even modelling mathematical learning , among many other domains .
entangled photon sources are basic platforms for quantum optical experiments and quantum information processing tasks like quantum key distribution .	entangled photon systems can function as quantum channels in some typical longdistance quantum communication proposals , such as quantum key distribution .
over the past few years , convolutional neural networks have become the leading approach in many vision-related tasks .	deep convolutional neural networks have led to major breakthroughs in many computer vision tasks .
it is based on pi-calculus , with the addition of primitive operations for quantum information processing .	cqp is based on the π-calculus with primitives for quantum information .
every idempotent different from 0 and 1a is called a nontrivial idempotent .	such an element is called a hermitian idempotent .
dynamical coulomb blockade in quantum point contacts .	quantum interference in atomic-sized point-contacts .
and asterisks denote orientifold image d6-branes .	the asterisks denote the time lags for which the absolute value of the correlation is largest .
the training of deep networks has been largely addressed by normalized initialization .	however , these neural networks benefit from recent improvements for regularization .
recently , linear programming decoding of linear codes was introduced by feldman , wainwright and karger .	in the recent decade , linear-programming decoding of linear block codes gains wide popularity since the seminal work by feldman .
the most recent advances of elm about its learning mechanism , biological understanding and fast deep learning perspectives can be found in .	the most recent advances of elm about its biological understanding and fast deep learning perspectives can be found in .
the dft calculations are done within the generalized gradient approximation and the perdewburke-ernzerhof exchange correlation function .	we employ generalized gradient approximation calculations with perdew-burke-ernzerhoff exchange and correlation functional .
the dashed lines correspond to the analytic approximations of eq .	the dashed line shows the analytic approximation of eq .
quantum entanglement is a type of correlation , but special because it can be shared only among quantum it has been the focus of foundational discussystems .	quantum entanglement is the most important resource in quantum information technology .
in fact , we show that this group as well as many other polish groups do not admit any nontrivial borel measure preserving actions .	we show that in full generality this theorem does not hold for actions of polish groups .
the em algorithm provides an iterative method for computing maximum likelihood estimates of the unknown parameters θ in a probabilistic model involving latent variables .	the em algorithm is an instrumental tool for evaluating the maximum likelihood estimator of latent variable models .
pattern recognition is the process of properties identification .	pattern recognition is the physical phenomenon of identification of physical quantities changes of a physical system by a physical pattern recognition mechanism .
model weights are initalised using the uniform initaliser of glorot and bengio while the bias terms are initalised to zero .	model parameters are initialized by normal distributions as glorot and bengio suggested .
the phase space is the product of the dual of a lie algebra g and r2 .	phase space is the two-dimensional space with coordinates .
this can be achieved by the well-known expectation-maximization algorithm .	this iterative algorithm is inspired by an expectation-maximization procedure .
it would be interesting to investigate antidots with different shapes and compare their properties with each other .	it would be interesting to investigate the properties of an inverse parabolic antidot experimentally .
we modify the public available cosmomc package to explore the parameter space using the markov chain monte carlo algorithm .	we use the cosmomc code to run mcmc simulations to explore the parameter space and obtain limits on cosmological parameters .
to solve the original smp , gale and shapley constructed an iterative algorithm -known as the gale-shapley algorithm , g-s algorithm or deferred-acceptance algorithm-to compute a particular stable matching of an smp instance .	to solve the standard smp , gale and shapley constructed an iterative algorithm -known as the gale-shapley algorithm , g-s algorithm or deferred-acceptance algorithm-to compute a particular solution of an smp instance .
the dissipation of the exciton-polariton lowers the revival amplitude but does not alter the revival time .	the temporal decay of the excitonpolariton lowers the revival amplitude but does not modify the revival time .
recently , convolutional neural networks have achieved remarkable success on many vision tasks such as object recognition .	deep neural networks have shown great success in computer vision and natural language processing tasks .
however , the efficiency is greatly compromised by the search space explosion problem .	existing studies show that the efficiency of apr techniques is greatly compromised by the search space explosion problem .
these models have demonstrated high performance in the domain of image processing .	these cnns have achieved state-of-the-art results in various fields , including object recognition .
this has been done for all mssm squark and gluino production processes at next-to-leading-logarithmic accuracy .	threshold resummation has been performed for all mssm squark and gluino production processes at next-to-leading-logarithmic accuracy .
the ordinate is the ratio of the cam lw2 flux within the pht-s aperture to a cam lw2-band-equivalent flux derived from the pht-s spectrum .	the ordinate is the flux normalized to the maximum .
a categorical approach to database semantics .	a category theory approach to conceptual data modeling .
we now show how do the usual equations of the wilemski-fixmann theory appear from this decoupling approximation .	we moreover discuss how do some well-known approximations appear from this exact scheme due to decoupling .
the final equality follows from the definition of quantum mutual information .	the first equality follows from the definition of coherent information .
the concept of spherical t-design is due to delsarte-goethals-seidel .	the notion of spherical designs is due to delsarte , goethals , seidel .
the existence question about lcd mds codes over a finite field of even characteristic has been completely addressed in .	the existence question about mds codes with complementary duals over f q has been completely addressed in .
though the alternative perspective itself does not provide further improvement on the known memoryrate tradeoff , it allows us to make a conceptual connection between the scheme in .	though the alternative construction itself does not provide further improvement on the memory-rate tradeoff , it allows us to make a conceptual connection between the scheme in .
architectures such as cyclegan are able to accomplish unpaired image-to-image translation by imposing consistency constraints in the original image domains a and b .	architectures such as cyclegan or dual-gan are able to accomplish unpaired imageto-image translation by imposing consistency constraints in the original image domains a and b .
deep neural networks have set new standards of performance in many machine learning areas such as image classification .	advances in both image-based learning and language-based learning using deep neural networks have made huge strides in difficult tasks such as object recognition .
to test this hypothesis we train a textual-visual embedding model on our data .	to embed video features in this space , we learn a neural-network based embedding function .
deep neural networks have demonstrated extraordinary success in a variety of fields such as computer vision .	deep neural networks have achieved state-of-the-art performance on a wide variety of machine learning tasks .
these kind of forces , if present , suppress relative motion between particles and therefore counteract the void-filling mechanism and thus decrease segregation .	these kinds of forces , if present , suppress relative motion between particles and therefore counteract the void-filling mechanism and thus decrease segregation .
recent achievements of deep neural networks make them an attractive choice in many computer vision applications including image classification .	large labeled datasets and computational power can be attributed as the main reason behind recent successes of deep neural networks in various computer vision tasks .
the fitting was performed using the idl routines in the mpfit package .	the model fitting was performed using the xspec software , using the χ 2 statistics .
etardx acm transactions on computational logic , vol .	xkn acm transactions on computational logic , vol .
many authors believe that having a publication in a higher-impact venue is yet another avenue for increasing the visibility of their work , which may lead to receiving more citations and consequently more rewards .	many authors believe that having a publication in a higher-impact venue is yet another way of increasing the visibility of their work , which may lead to receiving more citations and consequently more rewards .
deep neural networks have shown state-of-the-art performance on many computer vision problems , including semantic segmentation .	convolutional neural networks have recently exhibited great performance in various fields such as computer vision .
in this work , we analyze the consequences of lorentz violation in the massless neutrino sector .	in this paper , we study lorentz violation contribution to neutrino oscillation .
the completeness limits for the large fov are similar .	these completeness curves apply to the small fov .
community detection is a common challenge in the study of complex networks .	detecting communities in networks is a fundamental part of network analysis .
the burgers vector of a dislocation is a vectorial topological charge , and nematic order may be viewed as an ordering of the burgers vectors in the dislocation condensate .	the burgers vector of a dislocation is a vectorial topological charge , and nematic order may be viewed as an ordering of the burgers 3 vectors in the dislocation condensate .
region-based approaches with convolutional neural networks have achieved great success in object detection .	the convolutional neural network based approaches have been widely applied in object detection and recognition with promising performance .
we use a faster-rcnn model for region proposal and feature extraction .	we use faster r-cnn to obtain the box proposals and prior scores in video frames .
golovach , paulusma and song showed that if h is a tree then surjective h-colouring is polynomial-time solvable if h is loop-connected and np-complete otherwise .	golovach , paulusma and song showed that for any fixed tree h , the surjective hhomomorphism problem is polynomial-time solvable if the set of reflexive vertices in h induces a connected subgraph of h , and npcomplete otherwise .
the parallel plate geometry avoids the curvature effects found in the more common cylindrical couette experiments .	the parallel plate geometry allows for shear studies without the effects of curvature found in the more common couette experiments .

obviously , gravity is a destabilizing effect since the heavy fluid is above the light one .

with gravity , there is a critical wave number , below which small amplitude solutions become unstable .

for the detection network , we adapt the design of the faster r-cnn object detector .

we use the faster rcnn object detector to obtain object detections in the inpainted images .

the toda lattice is one of the most important integrable systems .

the toda lattice is a prototypical example of a completely integrable system .

since the context of local regions is incorporated in addition here , this approach has proven highly effective in classification tasks .

since here the context of local regions is incorporated as well , this approach has proven highly effective in classification tasks .

we expect that , in the small pf limit , the jet function will not depend on these parameters .

we would thus expect the two jet functions to coincide in the small-pf limit .

recently , deep neural network based methods have lead to breakthroughs in several vision tasks , such as classification .

convolutional neural networks have recently become the par excellence base approach to deal with many computer vision tasks .

multi-task learning may improve the performance for each task as different tasks can influence each other by the shared representation .

multi-task learning leverages the task relatedness in the form of shared structures to jointly learn multiple tasks .

the quintessence is a slowly varying scalar field with a canonical kinetic energy term .

since quintessence is a in the following we study the isocurvature baryon number scalar field one expects its fluctuations in space-time .

a non admin user can generate report on his assignments only .

an admin ops user can generate report on any ops user .

the exchange correlation functional is approximated by the generalized gradient approximation as parametrized by perdew , burke and ernzerhof .

to account for the exchange-correlation potential we have used the generalized gradient approximation as formulated by perdew , burke and ernzerhof .

similar to prior work in scene graph generation , we adopt faster r-cnn as backbone in our object detection module .

we choose faster r-cnn architecture as our two-stage object detector that provides accurate bounding boxes .

during the past several years , the introduction of convolutional neural networks have dramatically improved the state-of-the-art performances of many computer vision tasks including face identification and verification .

in the past decade , deep convolutional neural networks have achieved significant success in a wide spectrum of applications , such as object classification and detection .

frames in hilbert spaces were introduced by duffin and schaeffer in 1952 , in the context of nonharmonic fourier series .

hilbert space frames were introduced by duffin and schaeffer in 1952 to address some deep questions in non-harmonic fourier series .

in recent years , convolutional neural networks has achieved remarkable results in a wide range of computer vision applications .

over the past few years , convolutional neural networks have become the leading approach in computer vision .

the assumption of having an essential family is natural since it is necessary for the existence of a nontrivial sparse resultant .

the assumption of having an essential family is natural since it is necessary for the existence of the corresponding sparse resultant .

deep neural networks have become popular acoustic models for state-of-the-art large vocabulary speech recognition systems .

modern state-of-the-art speech recognition systems are based on neural network acoustic models .

calibration and image deconvolution was carried out using the miriad package .

data reduction was performed using the miriad software .

it has been implemented using the widespread package scikit-learn .

the framework is implemented with the scikit-learn python library .

nonetheless , it has been shown that the limiting ensemble converges to the correct gaussian in the linear and finite-dimensional case , with the rateoin lp for lipschitz functionals with polynomial growth at infinity .

moreover , it was shown in that for finite-dimensional state-space the single level enkf converges to the kalman filtering distribution with the standard rate oin this case .

on the distribution of the length of the longest increasing subsequence of random permutations .

on the distributions of the lengths of the longest monotone subsequences in random words .

the tilde is to denote the model-dependence of this gap .

the tilde denotes the transverse amplitude .

many real-life networks turn out to share some common properties , one of them being that the degree distribution follows a power-law , and many more .

many real-world networks are often considered as the scale-free networks , in which the degree distribution follows power-law .

the axial-vector spectral functions at zero temperature for three lattice spacings .

the scalar bottomonium spectral function at zero temperature for different lattice spacings .

there is a construction that associates a reproducing kernel hilbert space to a positive definite kernel .

it is also shown in that a reproducing kernel uniquely defines a hilbert space of functions with a certain inner product .

we re-implemented netmf in pytorch for maximum efficiency and for improved scalability .

we implemented our models in pytorch using allennlp library .

the interaction between the valence electrons and ionic cores was described by the projector augmented wave method .

the electron-ion interaction was described within the projector augmented wave method with ni , o , and c states treated as valence states .

cosmology is a natural arena in which to put to the test alternative theories of gravity .

perhaps cosmology is the ideal setting in which to study possible stringy effects .

reaction , diffusion , and decay processes find applications in chemistry , physics , and applied mathematics .

reaction and diffusion processes find applications in chemistry , physics , and applied mathematics .

band structure calculations were performed under the framework of the generalized gradient approximation of density functional theory .

spin-polarized electronic structure calculations were performed using the perdew-burke-ernzerhof functional 38 for the exchange-correlation potential based on the generalized gradient approximation .

deep learning has been used as a dramatically powerful tool in computer vision tasks such as image recognition .

in particular , convolutional neural networks has been popular in vision and audio recognition areas .

epstein et al show that metric dimension can be solved in polynomial time on co-graphs and forests .

subsequently , epstein et al showed that this problem is np-complete on split graphs , bipartite and cobipartite graphs .

large-scale deep convolutional neural networks have been successfully applied to a wide variety of applications such as image classification .

deep convolutional neural networks have already achieved tremendous success on a variety of computer vision tasks such as image classification among many others .

the latter only comprises optical elements made from fused silica which are hydroxyl-catalysis bonded 8 to the zerodur baseplate so that a quasi-monolithic structure with superior stability and negligible sensitivity to thermal expansion is formed .

the bench only comprises optical elements made from fused silica which are hydroxide-catalysis bonded to the zerodur baseplate so that a quasi-monolithic structure with superior stability and negligible sensitivity to thermal expansion is formed .

in this section , we conduct extensive experiments on the imagenet dataset for reproducible proof of concept .

in this section , we use imagenet dataset to show the effectiveness of ipm in selecting the representatives for image classification task .

wafer-scale reduced graphene oxide films for nanomechanical devices .

conducting electrodes for organic electronics from reduced graphene oxide .

this idea comes from chapeau-blondeau , janez , and ferrier , who presented a relaxation scheme to locally optimize the length of a given steiner tree , starting from the minimal spanning tree .

this idea comes from who presented a relaxation scheme to locally optimize the length of a given steiner tree , starting from the minimal spanning tree .

we believe that separating information presentation from digital content is crucial to solving these problems .

we believe that useful , practical personalization of the experience of digital content can be achieved with context brokers .

dwork and naor first suggested proof-of-work in 1992 , applying it as a method to thwart spam email .

in 1992 , dwork and naor proposed proof-of-computation to combat junk mail .

such a geometry is a physical geometry , ie it is described completely by the world function , which is a half of the squared distance function .

the geometry consists of three edge states which can exchange quasiparticles by tunneling through the fractional hall fluid .

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

convolutional neural networks provide state-of-the-art results for many machine learning challenges , such as image classification .

the simulation was then stopped and the domain was doubled in each coordinate dimension .

the calculation was then stopped , the domain enlarged , and the calculation restarted within the larger domain .

the presence of human observers may artificially increase hand hygiene rates temporarily as the presence of other healthcare workers can induce peer effects to increase rates .

the presence of human observers may artificially increase hand hygiene rates temporarily just as the presence of other healthcare workers can induce peer effects to increase rates .

especially in computer vision , deep convolutional neural networks are actively applied to object recognition tasks like object classification .

convolutional neural networks have achieved exceptional results in many large-scale computer vision applications , particularly in image recognition task .

a clear penetration peak can be observed near zero field .

a clear penetration peak appears near zero field .

elliptic flow is a direct measure of collectivity .

because elliptic flow is a collective effect , it is a correlation of all the particles with the reaction plane .

ronneberger et al proposed a network structure called u-net focusing on an application to biomedical image segmentation problems providing only small datasets .

ronneberger et al proposed u-net , a u-shaped dnn particularly designed for biomedical image segmentation that adds a symmetric expanding path to enable precise localization that the prior methods lack of .

following a protocol p is said to be an all-case optimal solution to a decision task s in a context γ if it solves s and , moreover , p dominates every protocol p that solves s in γ .

following a protocol p is said to be an all-case optimal solution to a decision task s in a context γ if it solves s and , moreover , it dominates every protocol p that solves s in γ .

the region with pale green indicate mixed phase .

pale green shows the region of lg mixed phase .

recently , convolutional neural networks have been deployed successfully in a variety of applications , including imagenet classication .

deep neural networks have demonstrated success in many machine learning tasks , including image recognition , speech recognition , and even modelling mathematical learning , among many other domains .

entangled photon sources are basic platforms for quantum optical experiments and quantum information processing tasks like quantum key distribution .

entangled photon systems can function as quantum channels in some typical longdistance quantum communication proposals , such as quantum key distribution .

over the past few years , convolutional neural networks have become the leading approach in many vision-related tasks .

deep convolutional neural networks have led to major breakthroughs in many computer vision tasks .

it is based on pi-calculus , with the addition of primitive operations for quantum information processing .

cqp is based on the π-calculus with primitives for quantum information .

every idempotent different from 0 and 1a is called a nontrivial idempotent .

such an element is called a hermitian idempotent .

dynamical coulomb blockade in quantum point contacts .

quantum interference in atomic-sized point-contacts .

and asterisks denote orientifold image d6-branes .

the asterisks denote the time lags for which the absolute value of the correlation is largest .

the training of deep networks has been largely addressed by normalized initialization .

however , these neural networks benefit from recent improvements for regularization .

recently , linear programming decoding of linear codes was introduced by feldman , wainwright and karger .

in the recent decade , linear-programming decoding of linear block codes gains wide popularity since the seminal work by feldman .

the most recent advances of elm about its learning mechanism , biological understanding and fast deep learning perspectives can be found in .

the most recent advances of elm about its biological understanding and fast deep learning perspectives can be found in .

the dft calculations are done within the generalized gradient approximation and the perdewburke-ernzerhof exchange correlation function .

we employ generalized gradient approximation calculations with perdew-burke-ernzerhoff exchange and correlation functional .

the dashed lines correspond to the analytic approximations of eq .

the dashed line shows the analytic approximation of eq .

quantum entanglement is a type of correlation , but special because it can be shared only among quantum it has been the focus of foundational discussystems .

quantum entanglement is the most important resource in quantum information technology .

in fact , we show that this group as well as many other polish groups do not admit any nontrivial borel measure preserving actions .

we show that in full generality this theorem does not hold for actions of polish groups .

the em algorithm provides an iterative method for computing maximum likelihood estimates of the unknown parameters θ in a probabilistic model involving latent variables .

the em algorithm is an instrumental tool for evaluating the maximum likelihood estimator of latent variable models .

pattern recognition is the process of properties identification .

pattern recognition is the physical phenomenon of identification of physical quantities changes of a physical system by a physical pattern recognition mechanism .

model weights are initalised using the uniform initaliser of glorot and bengio while the bias terms are initalised to zero .

model parameters are initialized by normal distributions as glorot and bengio suggested .

the phase space is the product of the dual of a lie algebra g and r2 .

phase space is the two-dimensional space with coordinates .

this can be achieved by the well-known expectation-maximization algorithm .

this iterative algorithm is inspired by an expectation-maximization procedure .

it would be interesting to investigate antidots with different shapes and compare their properties with each other .

it would be interesting to investigate the properties of an inverse parabolic antidot experimentally .

we modify the public available cosmomc package to explore the parameter space using the markov chain monte carlo algorithm .

we use the cosmomc code to run mcmc simulations to explore the parameter space and obtain limits on cosmological parameters .

to solve the original smp , gale and shapley constructed an iterative algorithm -known as the gale-shapley algorithm , g-s algorithm or deferred-acceptance algorithm-to compute a particular stable matching of an smp instance .

to solve the standard smp , gale and shapley constructed an iterative algorithm -known as the gale-shapley algorithm , g-s algorithm or deferred-acceptance algorithm-to compute a particular solution of an smp instance .

the dissipation of the exciton-polariton lowers the revival amplitude but does not alter the revival time .

the temporal decay of the excitonpolariton lowers the revival amplitude but does not modify the revival time .

recently , convolutional neural networks have achieved remarkable success on many vision tasks such as object recognition .

deep neural networks have shown great success in computer vision and natural language processing tasks .

however , the efficiency is greatly compromised by the search space explosion problem .

existing studies show that the efficiency of apr techniques is greatly compromised by the search space explosion problem .

these models have demonstrated high performance in the domain of image processing .

these cnns have achieved state-of-the-art results in various fields , including object recognition .

this has been done for all mssm squark and gluino production processes at next-to-leading-logarithmic accuracy .

threshold resummation has been performed for all mssm squark and gluino production processes at next-to-leading-logarithmic accuracy .

the ordinate is the ratio of the cam lw2 flux within the pht-s aperture to a cam lw2-band-equivalent flux derived from the pht-s spectrum .

the ordinate is the flux normalized to the maximum .

a categorical approach to database semantics .

a category theory approach to conceptual data modeling .

we now show how do the usual equations of the wilemski-fixmann theory appear from this decoupling approximation .

we moreover discuss how do some well-known approximations appear from this exact scheme due to decoupling .

the final equality follows from the definition of quantum mutual information .

the first equality follows from the definition of coherent information .

the concept of spherical t-design is due to delsarte-goethals-seidel .

the notion of spherical designs is due to delsarte , goethals , seidel .

the existence question about lcd mds codes over a finite field of even characteristic has been completely addressed in .

the existence question about mds codes with complementary duals over f q has been completely addressed in .

though the alternative perspective itself does not provide further improvement on the known memoryrate tradeoff , it allows us to make a conceptual connection between the scheme in .

though the alternative construction itself does not provide further improvement on the memory-rate tradeoff , it allows us to make a conceptual connection between the scheme in .

architectures such as cyclegan are able to accomplish unpaired image-to-image translation by imposing consistency constraints in the original image domains a and b .

architectures such as cyclegan or dual-gan are able to accomplish unpaired imageto-image translation by imposing consistency constraints in the original image domains a and b .

deep neural networks have set new standards of performance in many machine learning areas such as image classification .

advances in both image-based learning and language-based learning using deep neural networks have made huge strides in difficult tasks such as object recognition .

to test this hypothesis we train a textual-visual embedding model on our data .

to embed video features in this space , we learn a neural-network based embedding function .

deep neural networks have demonstrated extraordinary success in a variety of fields such as computer vision .

deep neural networks have achieved state-of-the-art performance on a wide variety of machine learning tasks .

these kind of forces , if present , suppress relative motion between particles and therefore counteract the void-filling mechanism and thus decrease segregation .

these kinds of forces , if present , suppress relative motion between particles and therefore counteract the void-filling mechanism and thus decrease segregation .

recent achievements of deep neural networks make them an attractive choice in many computer vision applications including image classification .

large labeled datasets and computational power can be attributed as the main reason behind recent successes of deep neural networks in various computer vision tasks .

the fitting was performed using the idl routines in the mpfit package .

the model fitting was performed using the xspec software , using the χ 2 statistics .

etardx acm transactions on computational logic , vol .

xkn acm transactions on computational logic , vol .

many authors believe that having a publication in a higher-impact venue is yet another avenue for increasing the visibility of their work , which may lead to receiving more citations and consequently more rewards .

many authors believe that having a publication in a higher-impact venue is yet another way of increasing the visibility of their work , which may lead to receiving more citations and consequently more rewards .

deep neural networks have shown state-of-the-art performance on many computer vision problems , including semantic segmentation .

convolutional neural networks have recently exhibited great performance in various fields such as computer vision .

in this work , we analyze the consequences of lorentz violation in the massless neutrino sector .

in this paper , we study lorentz violation contribution to neutrino oscillation .

the completeness limits for the large fov are similar .

these completeness curves apply to the small fov .

community detection is a common challenge in the study of complex networks .

detecting communities in networks is a fundamental part of network analysis .

the burgers vector of a dislocation is a vectorial topological charge , and nematic order may be viewed as an ordering of the burgers vectors in the dislocation condensate .

the burgers vector of a dislocation is a vectorial topological charge , and nematic order may be viewed as an ordering of the burgers 3 vectors in the dislocation condensate .

region-based approaches with convolutional neural networks have achieved great success in object detection .

the convolutional neural network based approaches have been widely applied in object detection and recognition with promising performance .

we use a faster-rcnn model for region proposal and feature extraction .

we use faster r-cnn to obtain the box proposals and prior scores in video frames .

golovach , paulusma and song showed that if h is a tree then surjective h-colouring is polynomial-time solvable if h is loop-connected and np-complete otherwise .

golovach , paulusma and song showed that for any fixed tree h , the surjective hhomomorphism problem is polynomial-time solvable if the set of reflexive vertices in h induces a connected subgraph of h , and npcomplete otherwise .

the parallel plate geometry avoids the curvature effects found in the more common cylindrical couette experiments .

the parallel plate geometry allows for shear studies without the effects of curvature found in the more common couette experiments .