HHousen/ParaSCI · Datasets at Hugging Face

sentence1 stringlengths 16 446	sentence2 stringlengths 14 436
however , this problem is avoided in the ekpyrotic scenario where a scalar field with a steep and negativevalued potential always dominates over anisotropies in a contracting universe .	in the context of scalar field cosmology , this issue can be solved if there exists a phase with a steep and negative-valued potential which dominates over the anisotropies in the contracting phase .
therefore , tight closure is a kernel operator on the lattice of octagonal graphs , as was the case for strong closure .	tight closure is a technique in positive characteristic introduced by m .
to enhance the robustness of the parameters , we added the batch normalization layers following every convolutional layer .	we used relu nonlinearities and batch normalization after each convolutional layer .
generative adversarial networks and variational autoencoders are the two families of deep generative models that are widely adopted in applications .	generative adversarial networks are a subclass of generative models that have received a lot of attention because of their ability to generate realistic high quality images .
this insulating state with superconducting correlations is called a bose insulator .	strictly then , a mott insulator is a paramagnetic state with an odd number of particles per unit cell .
the diamond denotes the physical rho mass .	diamond is the hardest material , and the compression in such system will consequently lead to the rapid increase in pressure .
braunstein , quantum error correction for communication with linear optics , nature vol .	winkin , lq-optimal control of infinite-dimensional systems by spectral factorization , automatica , vol .
finally , the concatenated feature is passed through batchnormalization layers .	each convolutional layer is followed up with a batch normalization layer .
a plateau is a subgraph which is a p-plateau for some p .	the plateau is the unchanged characteristic feature of the long rectangular input wave packet .
for this reason , various construction methods based on calculating the reliability of the synthetic channels are discussed by density evolution .	for this reason , various code construction methods based on calculating the reliability of the synthetic channels are discussed by the density evolution .
convolutional neural networks are driving major advances in many computer vision tasks , 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 real breakthrough in this direction is due to bridgeland and to his notion of stability condition on triangulated categories .	the main tool in this paper is the notion of stability condition introduced by bridgeland .
however it is more plausible to say that there is a small range in parameter space where the lc is not unique .	it therefore seems at least plausible that there is a physical connection between the critical value of γ required for cloud fragmentation and the absence of a mass-radius relation in globular clusters 10 keith m .
by contrast , in the experiments mentioned above the wave packets extend initially over many unit cells .	in this paper we consider wave packets initially localized inside one unit cell .
the t-j model is the simplest model that captures some aspects of the high temperature superconductors .	the last sum in this expression , which involves operators acting at three different sites , is dropped and what is remaining is called the t-j model .
each iteration consists of a single orthogonal projection .	the iteration consists of a residual image that contains noise and low level source calculations plus a set of amplitudes and positions of the components removed .
moreover , this problem remains undecidable if it is restricted to strategies with finite memory .	moreover , the problem remains undecidable if one restricts to pure strategies that use finite memory .
in the past few years , massive mimo technology has attracted significant research attention for its ability to improve the spectral and energy efficiency .	massive multipleinput multiple-output has been considered as a new promising breakthrough technology due to its ability for achieving huge spectral and energy efficiencies .
eigen et al proposed a multi-scale architecture for predicting depths , surface normals and semantic labels .	eigen et al proposed a multi-scale architecture capable of extracting global and local information from the scene to estimate the corresponding depth map .
we then color x 1 x 2 , x 2 x 3 , x 3 x 4 , x 4 x 0 with 2 , 4 , 5 , 1 , respectively , and color x 0 x 1 from with respect to 2 and u φ .	color x 0 x 1 , x 1 x 2 , x 3 x 4 , x 4 x 0 with 2 , 5 , 5 , 4 , respectively , and then color x 2 x 3 from with respect to 5 and u φ .
zeghib , isometry groups and geodesic foliations of lorentz manifolds .	kowalsky , actions of non-compact simple groups of lorentz manifolds .
now we proceed on to define special identities in s-na-binear-rings .	now we proceed on to give examples of non-commutative bisemirings .
these problems are probably due to the explicit breaking of the diffeomorphism invariance in the theory .	these pathologies can be attributed to the explicit breaking of diffeomrphism invariance along the extra space dimension .
moreover , with six faulty edges , we can cut off s 1 from q r .	moreover , with seven faulty edges , we can cut off t 1 from q r .
in particular , the emergence of convolutional neural networks in computer vision has been replacing traditional computer vision technology .	in recent years , the field of computer vision and image recognition has been dominated by convolutional neural nets .
we also consider a vgg16 network fine-tuned for object detection on pascal .	we use the caffenet architecture and the fast-rcnn pipeline for the detection task .
simulations are conducted on a model of a ducted-fan vtol aerial drone , which was also used in .	simulations are conducted on a model of a vertical takeoff and landing aerial drone , also used in .
we have also demonstrated that the ring can be multiply loaded , and we have manipulated the velocity distribution of the atoms in the ring .	finally , we have performed simple manipulations of the longitudinal velocity distribution of the atoms in the ring .
the dual phase in which magnetic branes are confined is absent .	the dual phase , in which magnetic charges are confined , is absent .
then the above geometry is the full geometry rather than just the pointwise geometry , since the geometry is the same at each point .	the geometry of this space is called the acoustic geometry .
in this respect , we recall that the survey of the fractional order integration and differentiation is known as fractional calculus .	this calculus is the main ingredient of our approach .
data-driven approaches , in particular , deep learning with convolutional neural networks , have recently attained great success in many computer vision tasks such as image classification .	over the past few years , neural networks has been widely used in some domains , such as large vocabulary continuous speech recognition .
primitive subgroups of wreath products in product action .	innately transitive subgroups of wreath products in product action .
in particular , have been able to produce images that are now hard to distinguish from actual photos .	in particular , in 2017 , have been able to produce images that are now hard to distinguish from actual photos .
recent investigations have found that these measurements convey a lot of similarities between current and historical wireless propagation environments .	recent investigations have found that these data convey a lot of similarities between current and historical scenarios on user requirements and wireless propagation environments .
in fact , the dark matter particles have not yet been observed while there are several laboratories throughout the world searching for these particles using completely different techniques , see bertone et al .	however dark matter particles have not yet been discovered while there are several laboratories which look for these exotic particles using direct and indirect techniques , see bertone et al .
a similar phenomenon of extending an interval graph algorithm to cocomparability graphs by using an ldfs preprocessing step has also been observed for the longest path problem .	so far a similar phenomenon of extending an interval graph algorithm to cocomparability graphs by using an ldfs preprocessing step has only been observed for the longest path problem .
the molecular dynamics simulations were conducted using the lammps numerical code developed at sandia national laboratories .	the molecular dynamics simulations were carried out using the lammps numerical code .
some such distortion is a likely consequence of any flow , including those that are not meromorphic .	distortion there is a small amount of distortion over the irac fov .
representation learning with neural networks has brought remarkable successes to both academia and industry .	convolutional neural networks are enabling major advancements in a range of machine learning problems .
electron is a carrier of the electric charge and it is the orbiting particle in any atom .	as an electron is a product of spin and charge , then real electrons acquire an energy gap because of the spin gap , though charge excitations confined to the plane do not .
consequently , 1 partms were first presented in , but here we obtain some improvements and introduce the model of epartms , which represents a better approach to quantum computing .	however , such models are advantageous for the understanding of quantum computing and 1 partms were first presented in , but here we obtain some improvements and introduce the model of epartms , which represents a better approach to quantum computing .
they demonstrate that their approaches significantly outperform several existing algorithms , including paucboost .	they demonstrate that their approach , which uses a support vector method , significantly outperforms several existing algorithms , including paucboost .
the above couplings are invariant under the linear t-duality transformation at the level of two b-fields .	the new b-field couplings , however , are not invariant under the b-field gauge transformation .
supervised deep learning has recently improved the state-of-the-art in various tasks , such as image classification .	deep learning has led to significant improvements in many computer vision tasks such as image classification .
in our general formalism , by taking the appropriate coupling between dark sectors which enables the stable growth of structure , we have found that the effect of the interaction between dark sectors overwhelms that of the de perturbation on the growth function of dm perturbation .	by taking the appropriate coupling which enables the stable growth of structure , we find that the effect of the interaction between dark sectors overwhelms that of dark energy perturbation on the growth of dark matter perturbation .
malle , weights of markov traces and generic degrees .	geck , modular representations of hecke algebras .
we apply stochastic gradient descent with an adaptive learning rate for training .	we train our baseline models using stochastic gradient descent along with the adam update rules .
our proposed model with recursive social diffusion process borrows the recent advances of graph convolutional networks .	our design is mainly motivated by the recent success of graph convolutional networks in graph-based learning tasks .
inspired by this , bownik showed that there exists a linear functional , which maps all -atoms of h 1 into bounded scalars but does not admit a bounded extension to h 1 .	based on this fact , bownik constructed a surprising example of a linear functional defined on a dense subspace of h 1 , which maps all -atoms into bounded scalars , but yet can not extend to a bounded linear functional on the whole h 1 .
by the use of this notation , tsallis relative operator entropy can be rewritten by tλ .	the operator equality on tsallis relative operator entropy is also shown by considering the tensor product .
the missing ingredient is a high resolution precision measurement of the electron energy flux which would resolve the holes in the horseshoe distribution .	the missing ingredient is the quasigeodesic property of these pseudo-anosov flows which is needed to apply the main theorem .
its properties can be characterized by the space-time properties of the vector σd .	then its properties depend on the symmetries of spacetime .
in parallel , deep convolutional neural networks have proven their effectiveness in many computer vision fields such as object classification .	deep neural networks have gained popularity in recent years thanks to their achievements in many applications including computer vision , signal and image processing , speech recognition .
here a multiset is a collection in which the order of the entries does not matter , but multiplicities do .	a multiset m is a set which may hold multiple copies of its elements .
here we have classified plane symmetric static spacetimes according to conformal motion are constant or not .	thus we conclude that plane symmetric static spacetimes do not admit non trivial conformal motions apart from kvs or hvs , or the conformally flat cases .
each convolutional layer is followed by batch norm , before using the leaky-relu activation function .	every convolutional layer is followed by a batch normalization layer and a relu activation .
dai et al use the reinforcement learning approach to discover the adversarial attack , which is the only approach that support black-box attack comparing to other works .	dai et al proposed a reinforcement learning based attack method , which learns the generalizable attack strategy and only requiring prediction labels from the target classifier .
therefore , the lagrangian is a good starting point of our arguments .	consequently in the 5d lagrangian there is a counter term which cancels the above log divergence .
the askey-wilson polynomials in order to fix notations , we recall the basic properties of the askey-wilson polynomials in this section .	the askey-wilson function in this section we recall the definition of the askey-wilson function .
this discrepancy is a result of the absence of nearby rich clusters in the south .	we believe this discrepancy is a consequence of oversimplifying the renormalization group treatment by introducing a single soliton scale under the rg in ref .
success of convolutional neural networks over the past several years has lead to their extensive deployment in a wide range of computer vision tasks .	deep convolutional neural networks have gained tremendous attention recently due to their great success in boosting the performance of image classification .
we will describe this as natal precession .	we will refer to this as oscillatory pumping .
boundary value problems for pseudo-differential operators .	boundary value problems and singular pseudo-differential operators .
cosmos is a uk-ccc facility which is supported by hefce and pparc .	cosmos is used because it is the largest existing hst survey and furthermore it is closest to the euclid specifications .
recently , deep neural networks have achieved remarkable progress in computer vision .	deep neural networks have been widely applied in various fields , including computer vision he et al , among many others .
for the case in which only statistical information on the eavesdropper channel is available , the optimal power allocation is studied in terms of ergodic and outage secrecy rates .	for the case in which global channel state information is available , we aim to design the optimal jamming covariance matrix that maximizes the secrecy rate and mitigates loop interference associated with the fd operation .
we adopt a fully bayesian treatment of hyperparameters similar to snoek et al .	we adopt a fully bayesian treatment of hyperparameters in gp similar to snoek et al .
for exchange and correlation we applied the gradient corrected approach using the generalized gradient approximation functional following the approach suggested by perdew-burke-ernzerhof .	the exchange and correlation part of the total energy was approximated by the generalized gradient approximation using perdewburke-ernzerhof type of functional .
dybalski , spectral theory of automorphism groups and particle structures in quantum field theory .	bostelmann , phase space properties and the short distance structure in quantum field theory .
we now have all of the necessary tools to demonstrate the emergence of incommensurate effective theories in non-probabilistic systems .	this will give us the necessary framework to show the emergence of effective theories in non-probabilistic systems .
for this case , the existence of classical and weak solutions was investigated in .	for example , the existence of weak and smooth solutions time-dependent models was investigated , respectively , in .
spacetime diagram of the bubble nucleation .	spacetime diagram for the bubble collision .
quantum information is a new concept with no classical analogue and it is important to distinguish it from the state identity .	the quantum fisher information is a monotone metric on the quantum state space with the coordinate system θ .
reinforcement learning is a machine learning paradigm based on sequential interactions between an agent and an environment in which the agent attempts to learn a policy π to maximize a total reward .	reinforcement learning is a framework that pursues an optimal decision-making based on the maximization of a cumulative reward in the long-term .
we will give examples of these special interval semirings .	now we proceed onto give examples of interval group interval semirings .
we use the restricted definition of 1-interval connectivity from which says that for a given ring of nodes , in every round , the adversary can choose to remove at most one edge from the ring .	in this paper , we use a restricted version of 1-interval connectivity , proposed in , which requires that for a given ring of nodes , in each round an adversary chooses at most one edge of the ring and removes it for that round .
degeneracy is a common problem in galaxy decompositions , mostly because the model functions used are not selected based on physical criteria .	this degeneracy is a consequence of linearizing eqs .
exchange and correlation effects were treated in the generalized gradient approximation in the formulation of perdew , burke , and ernzerhof .	exchangecorrelation functionals were used in the perdewburke-ernzerhof form within the generalized gradient approximation .
tolhurst , dj , movshon , ja and thompson , id the dependence of response amplitude and variance of cat visual cortical neurones on stimulus contrast .	tolhurst , dj , movshon , ja and dean , af the statistical reliability of signals in single neurons in cat and monkey visual cortex .
deep convolutional neural networks have recently led to a rapid advance in the state-ofthe-art object recognition systems .	deep neural networks are at the core of state-of-the-art models for supervised tasks like image recognition and speech recognition .
solid-state and biological nanopores hold great promise as analytical single-molecule tools .	solid-state nanopores are finding increasingly widespread use in the study and sensing of single molecules .
analogy in function spaces via logistic regression .	analogical reasoning with relational bayesian sets .
due to high resolution of uwb signals , pulses received via multiple paths are usually resolvable at the receiver .	due to large bandwidths of uwb signals , design of high speed and low power adcs is an important issue for uwb receivers .
deep convolutional neural networks have made significant breakthroughs in many visual understanding tasks including image classification .	over the past few years , deep neural networks have driven advances in many practical problems , such as image classification .
in , ee is defined as the ratio between the average net data transmission rate and the power consumed for sending a given packet .	this metric has been defined in as the ratio between the average net data rate and the transmitted power .
again we use the method of moments together with asymptotic transfer results .	we use the method of moments together with asymptotic transfer results .
orbifolds which are not good are called bad 10 orbifolds .	orbifolds also naturally appears when there is a symmetry , such as in symplectic reductions or in the presence of group actions .
then the orbit consists of just one point , namely the origin .	moreover , since an orbit is a point , the differential character is just a smooth character of t .
in this subsection , we develop a more accommodating version of the general window movie lemma .	in this subsection , we develop a more accommodating version of the standard window movie lemma .
chimera states have been studied in nonlocally coupled excitable systems in the presence of noise .	recently , chimera states have been studied in nonlocally coupled type-i excitable systems .
the first architecture , which we call alexnet , corresponds to the architecture used by krizhevsky et al and has five convolutional layers interspersed with max pooling layers followed by two fully connected layers .	the first cnn model is the bvlc reference caffenet , whose architecture is similar to that of alexnet , that is , five convolution layers followed by two 4096-dimensional and one 1000-dimensional fully-connected layers .
to avoid the over-fitting problem in spatialtemporal model , karpathy et al collect a dataset of one million video clips for network training .	karpathy et al construct a video dataset of millions of videos for training cnns and also evaluate different temporal fusion approaches .
deep convolutional neural networks have been successful in many computer vision tasks including image classification .	along this direction , convolutional neural networks have been very successful in various computer vision and natural language processing tasks in recent years .
therefore , by simulating the composite system including the environment , we can study an open system on a quantum computer .	we note that the unitary time evolution for most of the physical hamiltonians can be efficiently simulated with a quantum computer .
this approach is often referred to as inverse optimal control or inverse reinforcement learning .	the most common approach for this is inverse reinforcement learning .
we assume that the magnetic field is a centered dipole .	we assume that the magnetic field is a centered dipole and we neglect the distortion of the magnetic field due to general relativity .
seiberg , comments on noncommutative perturbative dy namics , j .	tytgat , excitations in hot non-commutative theo ries , j .
a polish space is a separable , completely metrizable space .	a perfect polish space is a polish space with no isolated points .
recently , deep neural networks have substantially improved the state-of-the-art performances of various challenging classification tasks , including image based object recognition .	over the past few years , deep neural networks have driven advances in many practical problems , such as image classification .

however , this problem is avoided in the ekpyrotic scenario where a scalar field with a steep and negativevalued potential always dominates over anisotropies in a contracting universe .

in the context of scalar field cosmology , this issue can be solved if there exists a phase with a steep and negative-valued potential which dominates over the anisotropies in the contracting phase .

therefore , tight closure is a kernel operator on the lattice of octagonal graphs , as was the case for strong closure .

tight closure is a technique in positive characteristic introduced by m .

to enhance the robustness of the parameters , we added the batch normalization layers following every convolutional layer .

we used relu nonlinearities and batch normalization after each convolutional layer .

generative adversarial networks and variational autoencoders are the two families of deep generative models that are widely adopted in applications .

generative adversarial networks are a subclass of generative models that have received a lot of attention because of their ability to generate realistic high quality images .

this insulating state with superconducting correlations is called a bose insulator .

strictly then , a mott insulator is a paramagnetic state with an odd number of particles per unit cell .

the diamond denotes the physical rho mass .

diamond is the hardest material , and the compression in such system will consequently lead to the rapid increase in pressure .

braunstein , quantum error correction for communication with linear optics , nature vol .

winkin , lq-optimal control of infinite-dimensional systems by spectral factorization , automatica , vol .

finally , the concatenated feature is passed through batchnormalization layers .

each convolutional layer is followed up with a batch normalization layer .

a plateau is a subgraph which is a p-plateau for some p .

the plateau is the unchanged characteristic feature of the long rectangular input wave packet .

for this reason , various construction methods based on calculating the reliability of the synthetic channels are discussed by density evolution .

for this reason , various code construction methods based on calculating the reliability of the synthetic channels are discussed by the density evolution .

convolutional neural networks are driving major advances in many computer vision tasks , 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 real breakthrough in this direction is due to bridgeland and to his notion of stability condition on triangulated categories .

the main tool in this paper is the notion of stability condition introduced by bridgeland .

however it is more plausible to say that there is a small range in parameter space where the lc is not unique .

it therefore seems at least plausible that there is a physical connection between the critical value of γ required for cloud fragmentation and the absence of a mass-radius relation in globular clusters 10 keith m .

by contrast , in the experiments mentioned above the wave packets extend initially over many unit cells .

in this paper we consider wave packets initially localized inside one unit cell .

the t-j model is the simplest model that captures some aspects of the high temperature superconductors .

the last sum in this expression , which involves operators acting at three different sites , is dropped and what is remaining is called the t-j model .

each iteration consists of a single orthogonal projection .

the iteration consists of a residual image that contains noise and low level source calculations plus a set of amplitudes and positions of the components removed .

moreover , this problem remains undecidable if it is restricted to strategies with finite memory .

moreover , the problem remains undecidable if one restricts to pure strategies that use finite memory .

in the past few years , massive mimo technology has attracted significant research attention for its ability to improve the spectral and energy efficiency .

massive multipleinput multiple-output has been considered as a new promising breakthrough technology due to its ability for achieving huge spectral and energy efficiencies .

eigen et al proposed a multi-scale architecture for predicting depths , surface normals and semantic labels .

eigen et al proposed a multi-scale architecture capable of extracting global and local information from the scene to estimate the corresponding depth map .

we then color x 1 x 2 , x 2 x 3 , x 3 x 4 , x 4 x 0 with 2 , 4 , 5 , 1 , respectively , and color x 0 x 1 from with respect to 2 and u φ .

color x 0 x 1 , x 1 x 2 , x 3 x 4 , x 4 x 0 with 2 , 5 , 5 , 4 , respectively , and then color x 2 x 3 from with respect to 5 and u φ .

zeghib , isometry groups and geodesic foliations of lorentz manifolds .

kowalsky , actions of non-compact simple groups of lorentz manifolds .

now we proceed on to define special identities in s-na-binear-rings .

now we proceed on to give examples of non-commutative bisemirings .

these problems are probably due to the explicit breaking of the diffeomorphism invariance in the theory .

these pathologies can be attributed to the explicit breaking of diffeomrphism invariance along the extra space dimension .

moreover , with six faulty edges , we can cut off s 1 from q r .

moreover , with seven faulty edges , we can cut off t 1 from q r .

in particular , the emergence of convolutional neural networks in computer vision has been replacing traditional computer vision technology .

in recent years , the field of computer vision and image recognition has been dominated by convolutional neural nets .

we also consider a vgg16 network fine-tuned for object detection on pascal .

we use the caffenet architecture and the fast-rcnn pipeline for the detection task .

simulations are conducted on a model of a ducted-fan vtol aerial drone , which was also used in .

simulations are conducted on a model of a vertical takeoff and landing aerial drone , also used in .

we have also demonstrated that the ring can be multiply loaded , and we have manipulated the velocity distribution of the atoms in the ring .

finally , we have performed simple manipulations of the longitudinal velocity distribution of the atoms in the ring .

the dual phase in which magnetic branes are confined is absent .

the dual phase , in which magnetic charges are confined , is absent .

then the above geometry is the full geometry rather than just the pointwise geometry , since the geometry is the same at each point .

the geometry of this space is called the acoustic geometry .

in this respect , we recall that the survey of the fractional order integration and differentiation is known as fractional calculus .

this calculus is the main ingredient of our approach .

data-driven approaches , in particular , deep learning with convolutional neural networks , have recently attained great success in many computer vision tasks such as image classification .

over the past few years , neural networks has been widely used in some domains , such as large vocabulary continuous speech recognition .

primitive subgroups of wreath products in product action .

innately transitive subgroups of wreath products in product action .

in particular , have been able to produce images that are now hard to distinguish from actual photos .

in particular , in 2017 , have been able to produce images that are now hard to distinguish from actual photos .

recent investigations have found that these measurements convey a lot of similarities between current and historical wireless propagation environments .

recent investigations have found that these data convey a lot of similarities between current and historical scenarios on user requirements and wireless propagation environments .

in fact , the dark matter particles have not yet been observed while there are several laboratories throughout the world searching for these particles using completely different techniques , see bertone et al .

however dark matter particles have not yet been discovered while there are several laboratories which look for these exotic particles using direct and indirect techniques , see bertone et al .

a similar phenomenon of extending an interval graph algorithm to cocomparability graphs by using an ldfs preprocessing step has also been observed for the longest path problem .

so far a similar phenomenon of extending an interval graph algorithm to cocomparability graphs by using an ldfs preprocessing step has only been observed for the longest path problem .

the molecular dynamics simulations were conducted using the lammps numerical code developed at sandia national laboratories .

the molecular dynamics simulations were carried out using the lammps numerical code .

some such distortion is a likely consequence of any flow , including those that are not meromorphic .

distortion there is a small amount of distortion over the irac fov .

representation learning with neural networks has brought remarkable successes to both academia and industry .

convolutional neural networks are enabling major advancements in a range of machine learning problems .

electron is a carrier of the electric charge and it is the orbiting particle in any atom .

as an electron is a product of spin and charge , then real electrons acquire an energy gap because of the spin gap , though charge excitations confined to the plane do not .

consequently , 1 partms were first presented in , but here we obtain some improvements and introduce the model of epartms , which represents a better approach to quantum computing .

however , such models are advantageous for the understanding of quantum computing and 1 partms were first presented in , but here we obtain some improvements and introduce the model of epartms , which represents a better approach to quantum computing .

they demonstrate that their approaches significantly outperform several existing algorithms , including paucboost .

they demonstrate that their approach , which uses a support vector method , significantly outperforms several existing algorithms , including paucboost .

the above couplings are invariant under the linear t-duality transformation at the level of two b-fields .

the new b-field couplings , however , are not invariant under the b-field gauge transformation .

supervised deep learning has recently improved the state-of-the-art in various tasks , such as image classification .

deep learning has led to significant improvements in many computer vision tasks such as image classification .

in our general formalism , by taking the appropriate coupling between dark sectors which enables the stable growth of structure , we have found that the effect of the interaction between dark sectors overwhelms that of the de perturbation on the growth function of dm perturbation .

by taking the appropriate coupling which enables the stable growth of structure , we find that the effect of the interaction between dark sectors overwhelms that of dark energy perturbation on the growth of dark matter perturbation .

malle , weights of markov traces and generic degrees .

geck , modular representations of hecke algebras .

we apply stochastic gradient descent with an adaptive learning rate for training .

we train our baseline models using stochastic gradient descent along with the adam update rules .

our proposed model with recursive social diffusion process borrows the recent advances of graph convolutional networks .

our design is mainly motivated by the recent success of graph convolutional networks in graph-based learning tasks .

inspired by this , bownik showed that there exists a linear functional , which maps all -atoms of h 1 into bounded scalars but does not admit a bounded extension to h 1 .

based on this fact , bownik constructed a surprising example of a linear functional defined on a dense subspace of h 1 , which maps all -atoms into bounded scalars , but yet can not extend to a bounded linear functional on the whole h 1 .

by the use of this notation , tsallis relative operator entropy can be rewritten by tλ .

the operator equality on tsallis relative operator entropy is also shown by considering the tensor product .

the missing ingredient is a high resolution precision measurement of the electron energy flux which would resolve the holes in the horseshoe distribution .

the missing ingredient is the quasigeodesic property of these pseudo-anosov flows which is needed to apply the main theorem .

its properties can be characterized by the space-time properties of the vector σd .

then its properties depend on the symmetries of spacetime .

in parallel , deep convolutional neural networks have proven their effectiveness in many computer vision fields such as object classification .

deep neural networks have gained popularity in recent years thanks to their achievements in many applications including computer vision , signal and image processing , speech recognition .

here a multiset is a collection in which the order of the entries does not matter , but multiplicities do .

a multiset m is a set which may hold multiple copies of its elements .

here we have classified plane symmetric static spacetimes according to conformal motion are constant or not .

thus we conclude that plane symmetric static spacetimes do not admit non trivial conformal motions apart from kvs or hvs , or the conformally flat cases .

each convolutional layer is followed by batch norm , before using the leaky-relu activation function .

every convolutional layer is followed by a batch normalization layer and a relu activation .

dai et al use the reinforcement learning approach to discover the adversarial attack , which is the only approach that support black-box attack comparing to other works .

dai et al proposed a reinforcement learning based attack method , which learns the generalizable attack strategy and only requiring prediction labels from the target classifier .

therefore , the lagrangian is a good starting point of our arguments .

consequently in the 5d lagrangian there is a counter term which cancels the above log divergence .

the askey-wilson polynomials in order to fix notations , we recall the basic properties of the askey-wilson polynomials in this section .

the askey-wilson function in this section we recall the definition of the askey-wilson function .

this discrepancy is a result of the absence of nearby rich clusters in the south .

we believe this discrepancy is a consequence of oversimplifying the renormalization group treatment by introducing a single soliton scale under the rg in ref .

success of convolutional neural networks over the past several years has lead to their extensive deployment in a wide range of computer vision tasks .

deep convolutional neural networks have gained tremendous attention recently due to their great success in boosting the performance of image classification .

we will describe this as natal precession .

we will refer to this as oscillatory pumping .

boundary value problems for pseudo-differential operators .

boundary value problems and singular pseudo-differential operators .

cosmos is a uk-ccc facility which is supported by hefce and pparc .

cosmos is used because it is the largest existing hst survey and furthermore it is closest to the euclid specifications .

recently , deep neural networks have achieved remarkable progress in computer vision .

deep neural networks have been widely applied in various fields , including computer vision he et al , among many others .

for the case in which only statistical information on the eavesdropper channel is available , the optimal power allocation is studied in terms of ergodic and outage secrecy rates .

for the case in which global channel state information is available , we aim to design the optimal jamming covariance matrix that maximizes the secrecy rate and mitigates loop interference associated with the fd operation .

we adopt a fully bayesian treatment of hyperparameters similar to snoek et al .

we adopt a fully bayesian treatment of hyperparameters in gp similar to snoek et al .

for exchange and correlation we applied the gradient corrected approach using the generalized gradient approximation functional following the approach suggested by perdew-burke-ernzerhof .

the exchange and correlation part of the total energy was approximated by the generalized gradient approximation using perdewburke-ernzerhof type of functional .

dybalski , spectral theory of automorphism groups and particle structures in quantum field theory .

bostelmann , phase space properties and the short distance structure in quantum field theory .

we now have all of the necessary tools to demonstrate the emergence of incommensurate effective theories in non-probabilistic systems .

this will give us the necessary framework to show the emergence of effective theories in non-probabilistic systems .

for this case , the existence of classical and weak solutions was investigated in .

for example , the existence of weak and smooth solutions time-dependent models was investigated , respectively , in .

spacetime diagram of the bubble nucleation .

spacetime diagram for the bubble collision .

quantum information is a new concept with no classical analogue and it is important to distinguish it from the state identity .

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

reinforcement learning is a machine learning paradigm based on sequential interactions between an agent and an environment in which the agent attempts to learn a policy π to maximize a total reward .

reinforcement learning is a framework that pursues an optimal decision-making based on the maximization of a cumulative reward in the long-term .

we will give examples of these special interval semirings .

now we proceed onto give examples of interval group interval semirings .

we use the restricted definition of 1-interval connectivity from which says that for a given ring of nodes , in every round , the adversary can choose to remove at most one edge from the ring .

in this paper , we use a restricted version of 1-interval connectivity , proposed in , which requires that for a given ring of nodes , in each round an adversary chooses at most one edge of the ring and removes it for that round .

degeneracy is a common problem in galaxy decompositions , mostly because the model functions used are not selected based on physical criteria .

this degeneracy is a consequence of linearizing eqs .

exchange and correlation effects were treated in the generalized gradient approximation in the formulation of perdew , burke , and ernzerhof .

exchangecorrelation functionals were used in the perdewburke-ernzerhof form within the generalized gradient approximation .

tolhurst , dj , movshon , ja and thompson , id the dependence of response amplitude and variance of cat visual cortical neurones on stimulus contrast .

tolhurst , dj , movshon , ja and dean , af the statistical reliability of signals in single neurons in cat and monkey visual cortex .

deep convolutional neural networks have recently led to a rapid advance in the state-ofthe-art object recognition systems .

deep neural networks are at the core of state-of-the-art models for supervised tasks like image recognition and speech recognition .

solid-state and biological nanopores hold great promise as analytical single-molecule tools .

solid-state nanopores are finding increasingly widespread use in the study and sensing of single molecules .

analogy in function spaces via logistic regression .

analogical reasoning with relational bayesian sets .

due to high resolution of uwb signals , pulses received via multiple paths are usually resolvable at the receiver .

due to large bandwidths of uwb signals , design of high speed and low power adcs is an important issue for uwb receivers .

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

over the past few years , deep neural networks have driven advances in many practical problems , such as image classification .

in , ee is defined as the ratio between the average net data transmission rate and the power consumed for sending a given packet .

this metric has been defined in as the ratio between the average net data rate and the transmitted power .

again we use the method of moments together with asymptotic transfer results .

we use the method of moments together with asymptotic transfer results .

orbifolds which are not good are called bad 10 orbifolds .

orbifolds also naturally appears when there is a symmetry , such as in symplectic reductions or in the presence of group actions .

then the orbit consists of just one point , namely the origin .

moreover , since an orbit is a point , the differential character is just a smooth character of t .

in this subsection , we develop a more accommodating version of the general window movie lemma .

in this subsection , we develop a more accommodating version of the standard window movie lemma .

chimera states have been studied in nonlocally coupled excitable systems in the presence of noise .

recently , chimera states have been studied in nonlocally coupled type-i excitable systems .

the first architecture , which we call alexnet , corresponds to the architecture used by krizhevsky et al and has five convolutional layers interspersed with max pooling layers followed by two fully connected layers .

the first cnn model is the bvlc reference caffenet , whose architecture is similar to that of alexnet , that is , five convolution layers followed by two 4096-dimensional and one 1000-dimensional fully-connected layers .

to avoid the over-fitting problem in spatialtemporal model , karpathy et al collect a dataset of one million video clips for network training .

karpathy et al construct a video dataset of millions of videos for training cnns and also evaluate different temporal fusion approaches .

deep convolutional neural networks have been successful in many computer vision tasks including image classification .

along this direction , convolutional neural networks have been very successful in various computer vision and natural language processing tasks in recent years .

therefore , by simulating the composite system including the environment , we can study an open system on a quantum computer .

we note that the unitary time evolution for most of the physical hamiltonians can be efficiently simulated with a quantum computer .

this approach is often referred to as inverse optimal control or inverse reinforcement learning .

the most common approach for this is inverse reinforcement learning .

we assume that the magnetic field is a centered dipole .

we assume that the magnetic field is a centered dipole and we neglect the distortion of the magnetic field due to general relativity .

seiberg , comments on noncommutative perturbative dy namics , j .

tytgat , excitations in hot non-commutative theo ries , j .

a polish space is a separable , completely metrizable space .

a perfect polish space is a polish space with no isolated points .

recently , deep neural networks have substantially improved the state-of-the-art performances of various challenging classification tasks , including image based object recognition .

over the past few years , deep neural networks have driven advances in many practical problems , such as image classification .