HHousen/ParaSCI · Datasets at Hugging Face

sentence1 stringlengths 16 446	sentence2 stringlengths 14 436
under a priori condition that the sound-soft scatterer is a ball or disk , it was proved in that the radius of the scatterer can be uniquely determined by a single phaseless far-field datum .	under the condition that the scatterer is a small sound-soft ball , liu and zhang proved that it can be uniquely determined by the modulus of the far-field datum measured at a fixed observation corresponding to a single incident plane wave .
deep learning has shown its effectiveness in many computer vision tasks , such as object detection .	deep learning has been successful in various high-level computer vision tasks .
quantum computers for instance can search faster and simulate better than their classical counterparts , and factor large numbers efficiently .	for instance , quantum computers can factor integers exponentially faster than the best known classical algorithms .
on the other hand , it is shown that on some of the datasets , the performance after pca w substantially drops compared with the raw descriptors .	on the other hand , it is shown that on some datasets , the performance after pca w substantially drops compared to the raw descriptors .
korniss , in the monte carlo method in the physical sciences , edited by j .	sloot , in parallel and distributed discrete event simulation , edited by c .
multitask learning is a widely used learning framework where similar tasks are considered jointly for the purpose of improving performance compared to learning the tasks separately .	multi-task learning is a learning paradigm which seeks to improve the generalization performance of a learning task with the help of some other related tasks .
by training the fed on a simulated faint-edges dataset , we developed a novel approach for edge detection in noisy images .	by training the u-net on a simulated faintedges dataset , we developed a new algorithms that deal with noise .
although in some particular cases chance constraints problems are convex , in general , these problems are not convex .	although , there are instances of convex chancecontrained problems , generally such problems are not convex .
deep neural networks have been very successful in large-scale recognition and classification tasks , some even surpassing human-level accuracy .	deep neural networks have demonstrated extraordinary success in a variety of fields such as computer vision .
we modify resnet-18 to generate a mask image as output .	we apply resnet-152 to extract the 2,048-dimensional image features .
we reduced the data using the miriad software package .	the data were reduced using the miriad package .
wengler , for the lep collaborations , these proceedings .	calderini , for the babar collaboration , these proceedings .
to automatically learn such patterns , we use the autoslog-ts weakly-supervised extraction pattern learner .	to bootstrap , we apply autoslog-ts , a weakly supervised pattern learner that only requires training sets os stories labeled broadly as positive or negative .
for infinite stream data publication , kellaris et al proposed a w-event privacy framework , protecting any event sequence occurring within any sliding window of w time stamps .	recently , kellaris et al tackled the limitations of event-level dp and user-level dp , and introduced the notion of w-event privacy where a sliding window methodology is applied .
the weights in each stream are initialized by pre-training on the imagenet dataset .	the initial weights of these network are pre-trained on the imagenet dataset .
deep learning and especially convolutional neural networks have revolutionized image-based tasks , eg , image classification .	deep neural networks have contributed to tremendous advances in computer vision during recent times .
this method utilizes the techniques used in generative adversarial networks .	the transformation model is trained using a generative adversarial network .
attention mechanism has been successfully applied in image captioning .	attention mechanisms have shown its efficiency in various tasks such as image captioning .
generally , the free energy is a difficult quantity to evaluate .	the free energy consists of the two main contributions , the interaction energy of the collapsed h-blocks do not contribute to the total elastic energy .
the imaging data were processed through a series of pipelines that perform astrometric calibration , photometric reduction , and photometric calibration , .	the data was passed through pipelines designed to perform astrometric calibration , photometric reduction , and photometric calibration .
in physics this map is called a gauge transformation .	transformation from a point in the phase space to the physically equivalent point is called gauge transformation .
this is due to the reductionist effect of weight distribution of highly connected nodes in these networks .	this paralellism is the result of both randomness of weight distribution and the lack of triangles in these networks .
a new polynomial-time algorithm for linear programming .	a polynomial algorithm in linear programming .
the metallicity is the best indicator for finding out which of these processes are most important .	the metallicity is the log of the ratio of the amount of iron to hydrogen in the stars relative to the sun .
a boolean satisfiability approach to the resource-constrained project scheduling problem .	an evolutionary algorithm for the resource-constrained project scheduling problem with minimum and maximum time lags .
sharp boundedness and regularizing effects of the integral menger curvature for submanifolds .	integral menger curvature for sets of arbitrary dimension and codi mension .
thus , it suffices to show that there is a unique equivalence class of proper up-to-center-epimorphisms .	it suffices to prove that m is a cauchy sequence .
the invariants here are where m is the absolute magnitude .	properties that remain the same up to isomorphism of graphs are called invariants .
both white gaussian noise and white poisson noise can be recovered from the dichotomous noise by taking suitable limits , so the results obtained here are quite generic .	both white gaussian noise and white poisson noise can be recovered from the dichotomous noise by taking suitable limits , so the results obtained are quite generic .
recently , convolutional neural networks achieve remarkable progresses in a variety of computer vision tasks , such as image classification .	convolutional neural networks have been proven to achieve astonishing results in different research areas such as face recognition .
the coproduct denotes concatenation of signatures .	the coproduct is a very important operation for our coarse-graining .
the superfluid phase is a condensate of singlet pairs , in which tunneling of individual atoms between the wells is suppressed and only singlet pairs are delocalized .	thus , this superfluid phase is a very useful system to examine how the detailed excitation spectra of collective excitations affect their tunneling properties .
deep learning has brought significant breakthroughs in many computer vision tasks , including object detection .	deep neural networks have significantly advanced the state-of-the-art performance for various machine learning problems .
our analysis uses several ideas introduced by kolipaka and szegedy .	we note that this concept originated in the work of kolipaka and szegedy .
for the stn network , we adopt the resnet-18 as our localization network .	for the image encoder , we use a resnet-18 architecture , pretrained on imagenet .
let sp σ be the category of symmetric spectra of simplicial sets as introduced in .	let denote a symmetric monoidal category of spectra , such as those given by .
since these conditions are obviously first order , transfer proves the object part of the proposition .	since these conditions are first order , the object part of the proposition follows .
the vgg-16 network consists of 16 layers and over 130 million weight parameters .	the classification network is constructed based on vgg 16-layer net .
adam was used as the optimizer to verify the validity of the proposed technique for different learning methods .	adam optimizer was employed as the optimizer with a initial learning rate of 1e-4 .
and asterisks denote orientifold image d6-branes .	the asterisks denote the nearby single-β clusters .
deep learning has led to significant improvements in many computer vision tasks such as image classification .	deep neural networks have demonstrated significant performance improvements in a wide range of computer vision tasks .
deep learning methods have achieved state of the art results on computer vision problems with supervision using large amounts of data .	in recent years , deep learning has demonstrated strong model capabilities and obtains very promising performances in many computer vision tasks .
deep learning approaches , in particularly deep convolutional neural networks , have achieved tremendous successes in various visual recognition tasks .	convolutional neural networks have gained remarkable success on a variety of visual recognition tasks .
therefore , the tetrahedron property holds for this system .	in this case we suppose that the tetrahedron property holds .
the conjecture was proved for log canonical thresholds on smooth varieties in .	this conjecture was eventually proved for the smooth case in .
this then enables us to characterize the k-addable corners and consequently , the saturated chains in the k-young lattice .	this enables us to show that the k-young lattice is isomorphic to the weak order on this quotient .
deep convolutional neural networks have made significant breakthroughs in many visual understanding tasks including image classification .	deep neural networks have been evolved to powerful predictive models with remarkable performance on computer vision tasks .
we initialize the weights as in and train googlenet from scratch .	we initialize the weights as in and train resnext 50 from scratch .
the geometric observable v0 is called euler characteristic .	but the euler characteristic is the same .
the degeneracy is the number of partitions of the level number into positive integers .	this degeneracy is the result of similar effects upon the relative heights of the first and second peak and is only broken by the presence of the third peak 13 in the spectrum .
we have found that in the large-n limit at weak nonhermiticity the two partition functions agree for an arbitrary and different number of quarks and conjugate anti-quarks , showing that they both belong to the same universality class .	at strong nonhermiticity we found that the partition functions of the two models can be mapped onto each other , provided we consider an equal number of quarks and conjugate anti-quarks .
however , if the gravitino is the lsp , the next-to-lightest supersymmetric particle decays to its standard model partner and a gravitino .	for the heavier gravitino , there is a very small region which is compatible with the bounds from the stau nlsp and the muon g 2 .
in , hardware demonstration of the benefit of inter-operator spectrum sharing was demonstrated .	in , the benefit of inter-operator spectrum sharing was demonstrated .
the em algorithm is an instrumental tool for evaluating the maximum likelihood estimator of latent variable models .	the em algorithm is one of the most popular methods for maximum likelihood identification of latent variable models .
the classical morrey spaces have been introduced by morrey in to study the local behavior of solutions of second order elliptic partial differential equations .	the classical morrey spaces l p , λ were originally introduced by morrey in to study the local behavior of solutions to second order elliptic partial differential equations .
the re- call is the fraction of ground truth boundary pixels that are matched by the boundaries defined by the algorithm .	the recall is the fraction of ground truth boundary pixels that are matched by the boundaries defined by the algorithm .
in recent years , deep neural networks have demonstrated impressive performance improvements on a wide range of challenging machine learning tasks .	deep neural networks have revitalized the field of machine learning by achieving accuracy levels beyond human perception in a variety of image , video , text and speech processing tasks .
some network community detection approaches like conductance measure similarity by the number of edges linking nodes to other nodes within the same community .	some community detection approaches measure similarity by the number of edges linking nodes to other nodes within the same community .
in , the authors proved that the class of good semigroups is actually larger than the one of value semigroups .	the properties of these semigroups were already considered in that it was proved that the class of good semigroups is larger than the one of value semigroups .
the direct simulation monte carlo method has been widely used in engineering applications for gas flows beyond the continuum limit .	this has applications for the direct simulation monte carlo method employed to model various aspects of rarefied gas flows .
the dlcq description involves a quantum-mechanical sigma model , and it was derived from matrix theory in .	this theory has a matrix-like dlcq description as a quantum mechanics on the moduli space of instantons .
each summarization layer is composed of a 1d convolutional layer , with a pre-defined number of filters of adjustable kernel sizes , followed by a batch normalization layer .	each convolutional layer consists of a convolution using the specified size of filters , followed by the relu activation function and batch normalization .
in the nnlo case , it is not difficult to obtain simple low-dimensional integrals .	in the nnlo case , it is possible to arrive at answers in the form of low-dimensional absolutely convergent integrals .
however , only relatively recently have localisation models capable of learning from challenging data such as pascal voc 2007 been proposed .	however , only relatively recently have localisation models capable of learning from challenging data such as the pascal voc 2007 dataset been proposed .
generative adversarial networks are generative models known for their ability to sample from complex and intractable distributions , inherent in tasks such as realistic image generation from natural scenes .	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 .
dft calculations were performed using the plane-wave pseudopotential code vasp .	dft calculations were performed using the vasp code based on spin-polarized dft .
we compare our models with 9 other state-of-the-art algorithms , including sf .	we compare our proposed salient object detector with 16 recent state-of-the-art saliency models , including drfi .
subjects therefore did not exert any effort to maintain the position of the left reference ankle , preventing the contribution of effort cues coming from the reference ankle to the sense of position during the test .	by doing so , participants did not exert any effort to maintain the position of the left reference ankle , preventing the contribution of effort cues coming from the reference ankle to the sense of position during the test .
in recent years , convolutional neural networkshave demonstrated great efficacy on computer vision tasks such as classification .	in recent years , deep convolutional networks have achieved remarkable results in many computer vision tasks .
the generalized gradient approximation was used with the perdewburke-ernzerhof functional to describe the exchange-correlation interaction .	the perdew-burke-ernzerhof scheme of the generalized gradient approximation is adopted to describe the exchange-correlation effect among electrons .
the panels is the power spectrum of the latter in the double-logarithmic scale .	each of the panels is a different assumption for the bss formation rate , as indicated on the top of the panel .
saut et al used decomposition to plan grasps and implemented dual-arm regrasp of complicated models .	saut et al used decomposition to plan grasps and implemented dual-arm regrasp of complicated mesh models .
it was recently studied by and song et al by using robust regression mixture models based on , respectively , the t distribution and the laplace distribution .	it was recently studied by bai et al and song et al by using robust regression mixture models based on , respectively , the t distribution and the laplace distribution .
elements of the two-dimensional integer lattice z2 are called sites .	together with a , these sites comprise another cell which tiles the remaining part of the lattice .
in , an optimal load control problem is formulated and a primary load-side control is derived as a partial primal-dual gradient algorithm for solving the olc problem .	in , an optimal load control problem is formulated and a ubiquitous primary load-side control is derived as a partial primal-dual gradient algorithm for solving the olc problem .
overlaid is the simple line fit discussed in the text .	overlaid is the measurement of the hmf from the 2dfgrs 2pigg catalogue and our expected constraint from the final gama survey .
inside each residual block , batch normalisation is applied on the output of the first convolutional layer , followed by a rectified linear activation function .	each residual block first applies a convolutional layer on the input , followed by batch normalisation and a rectified linear activation function .
quantum mechanics is a one dimensional field theory .	this is why they are called discrete quantum mechanics .
as well as giving properties of these operators , we also considered their complexity .	as well as giving properties of these operators , we also considered complexity questions .
convolutional neural networks have been extensively studied in the computer vision literature to tackle a variety of tasks , such as image classification .	convolutional neural networks , as one of the widely used deep learning methods , have been proven to be very successful for object recognition in images .
similarly we can define smarandache right ideal .	similarly we can define quasi minimal bi-ideal .
the runge-kutta methods are well-known examples of such schemes widely used in modern computational practice .	the rungekutta methods are well-known examples of such schemes widely used in modern computing practice .
we use the resnet50 architecture and its weights for initialization .	we use the 34 layers residual network to perform the classification task .
as an application we determine some characteristic numbers of del pezzo surfaces .	we will show that these surfaces are limits of del pezzo surfaces .
deep convolutional neural networks have gained tremendous attention recently due to their great success in boosting the performance of image classification .	with the development of convolutional neural networks , deep learning is widely used in saliency detection tasks .
preliminaries let e denote the figure-eight knot 41 .	preliminaries let v denote the twisted heisenberg-virasoro lie algebra .
in it has been shown that the 3-manifolds represented by these diagrams are cyclic coverings of lens spaces , branched over -knots .	as proved in , all these manifolds turn out to be strongly-cyclic coverings of lens spaces , branched over -knots .
anomaly detection is an important problem with various applications , such as cybersecurity , quality control , fraud detection , fault detection , and health care .	anomaly detection is an important problem that has been studied in a variety of areas and used in diverse applications including intrusion detection , fraud detection , and image processing .
there is no frame stacking , and the output senone label is delayed by 5 frames as in .	there is no frame stacking , and the output hmm state label is delayed by 5 frames as in .
recent rapid advances in deep learning are allowing for the learning of complex functions through convolutional neural networks , which have achieved stateof-the-art performances in a plethora of computer vision tasks .	mobile and internet of things devices are increasingly relying on deep neural networks to provide state-of-the-art performance in various intelligent applications .
deep neural networks achieved remarkable accuracy in many application domains , such as , image classification , object detection , semantic segmentation , speech recognition .	deep neural networks perform impressively well in classic machine learning areas such as image classification , segmentation , speech recognition and language translation .
these measures have found recent applications in the analysis and design of networked control systems .	these are commonly adopted simplifying assumptions in sensor and actuator placement problems for large scale networked control systems .
deep convolutional neural networks have been successful in many computer vision tasks including image classification .	deep convolutional neural networks achieve state-of-the-art performance for a wide variety of tasks in computer vision , such as image classification and segmentation .
therefore , the axion is a pseudo goldstone boson .	if an axion is a boundary field confined in 4 dimensions , the pq scale fp q is bounded by ms .
a homomorphism from a substructure of a to b is called a partial homomorphism from a to b .	the homomorphism is a cyclic subgroup of r .
furthermore , this adjunction is a quillen adjunction when alg is endowed with its trivial model structure .	by adjunction , that is a curve of genus at least 2 .
reinforcement learning represents a computational framework for modelling complex reward-driven behavior in both artificial and biological agents .	reinforcement learning is a widely researched approach for solving tasks formulated as markov decision processes .
we optimize the objective loss using adam , no mini-batches and run for 100 epochs .	we optimize the model with the adam optimizer with a batch size of 128 for 30 epochs .
the interspeech 2013 compare vocalization challenge feature set configuration is used in opensmile toolkit to extract low-level descriptors of 141d using 250ms frame size and 10ms step size .	the interspeech 2013 compare vocalization challenge feature set is used to extract llds of 141d using 250ms frame size and 10ms step size .
deep neural networks have demonstrated their success in many machine learning and computer vision applications , including image classification .	deep neural networks have been very successful in large-scale recognition and classification tasks , some even surpassing human-level accuracy .

under a priori condition that the sound-soft scatterer is a ball or disk , it was proved in that the radius of the scatterer can be uniquely determined by a single phaseless far-field datum .

under the condition that the scatterer is a small sound-soft ball , liu and zhang proved that it can be uniquely determined by the modulus of the far-field datum measured at a fixed observation corresponding to a single incident plane wave .

deep learning has shown its effectiveness in many computer vision tasks , such as object detection .

deep learning has been successful in various high-level computer vision tasks .

quantum computers for instance can search faster and simulate better than their classical counterparts , and factor large numbers efficiently .

for instance , quantum computers can factor integers exponentially faster than the best known classical algorithms .

on the other hand , it is shown that on some of the datasets , the performance after pca w substantially drops compared with the raw descriptors .

on the other hand , it is shown that on some datasets , the performance after pca w substantially drops compared to the raw descriptors .

korniss , in the monte carlo method in the physical sciences , edited by j .

sloot , in parallel and distributed discrete event simulation , edited by c .

multitask learning is a widely used learning framework where similar tasks are considered jointly for the purpose of improving performance compared to learning the tasks separately .

multi-task learning is a learning paradigm which seeks to improve the generalization performance of a learning task with the help of some other related tasks .

by training the fed on a simulated faint-edges dataset , we developed a novel approach for edge detection in noisy images .

by training the u-net on a simulated faintedges dataset , we developed a new algorithms that deal with noise .

although in some particular cases chance constraints problems are convex , in general , these problems are not convex .

although , there are instances of convex chancecontrained problems , generally such problems are not convex .

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

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

we modify resnet-18 to generate a mask image as output .

we apply resnet-152 to extract the 2,048-dimensional image features .

we reduced the data using the miriad software package .

the data were reduced using the miriad package .

wengler , for the lep collaborations , these proceedings .

calderini , for the babar collaboration , these proceedings .

to automatically learn such patterns , we use the autoslog-ts weakly-supervised extraction pattern learner .

to bootstrap , we apply autoslog-ts , a weakly supervised pattern learner that only requires training sets os stories labeled broadly as positive or negative .

for infinite stream data publication , kellaris et al proposed a w-event privacy framework , protecting any event sequence occurring within any sliding window of w time stamps .

recently , kellaris et al tackled the limitations of event-level dp and user-level dp , and introduced the notion of w-event privacy where a sliding window methodology is applied .

the weights in each stream are initialized by pre-training on the imagenet dataset .

the initial weights of these network are pre-trained on the imagenet dataset .

deep learning and especially convolutional neural networks have revolutionized image-based tasks , eg , image classification .

deep neural networks have contributed to tremendous advances in computer vision during recent times .

this method utilizes the techniques used in generative adversarial networks .

the transformation model is trained using a generative adversarial network .

attention mechanism has been successfully applied in image captioning .

attention mechanisms have shown its efficiency in various tasks such as image captioning .

generally , the free energy is a difficult quantity to evaluate .

the free energy consists of the two main contributions , the interaction energy of the collapsed h-blocks do not contribute to the total elastic energy .

the imaging data were processed through a series of pipelines that perform astrometric calibration , photometric reduction , and photometric calibration , .

the data was passed through pipelines designed to perform astrometric calibration , photometric reduction , and photometric calibration .

in physics this map is called a gauge transformation .

transformation from a point in the phase space to the physically equivalent point is called gauge transformation .

this is due to the reductionist effect of weight distribution of highly connected nodes in these networks .

this paralellism is the result of both randomness of weight distribution and the lack of triangles in these networks .

a new polynomial-time algorithm for linear programming .

a polynomial algorithm in linear programming .

the metallicity is the best indicator for finding out which of these processes are most important .

the metallicity is the log of the ratio of the amount of iron to hydrogen in the stars relative to the sun .

a boolean satisfiability approach to the resource-constrained project scheduling problem .

an evolutionary algorithm for the resource-constrained project scheduling problem with minimum and maximum time lags .

sharp boundedness and regularizing effects of the integral menger curvature for submanifolds .

integral menger curvature for sets of arbitrary dimension and codi mension .

thus , it suffices to show that there is a unique equivalence class of proper up-to-center-epimorphisms .

it suffices to prove that m is a cauchy sequence .

the invariants here are where m is the absolute magnitude .

properties that remain the same up to isomorphism of graphs are called invariants .

both white gaussian noise and white poisson noise can be recovered from the dichotomous noise by taking suitable limits , so the results obtained here are quite generic .

both white gaussian noise and white poisson noise can be recovered from the dichotomous noise by taking suitable limits , so the results obtained are quite generic .

recently , convolutional neural networks achieve remarkable progresses in a variety of computer vision tasks , such as image classification .

convolutional neural networks have been proven to achieve astonishing results in different research areas such as face recognition .

the coproduct denotes concatenation of signatures .

the coproduct is a very important operation for our coarse-graining .

the superfluid phase is a condensate of singlet pairs , in which tunneling of individual atoms between the wells is suppressed and only singlet pairs are delocalized .

thus , this superfluid phase is a very useful system to examine how the detailed excitation spectra of collective excitations affect their tunneling properties .

deep learning has brought significant breakthroughs in many computer vision tasks , including object detection .

deep neural networks have significantly advanced the state-of-the-art performance for various machine learning problems .

our analysis uses several ideas introduced by kolipaka and szegedy .

we note that this concept originated in the work of kolipaka and szegedy .

for the stn network , we adopt the resnet-18 as our localization network .

for the image encoder , we use a resnet-18 architecture , pretrained on imagenet .

let sp σ be the category of symmetric spectra of simplicial sets as introduced in .

let denote a symmetric monoidal category of spectra , such as those given by .

since these conditions are obviously first order , transfer proves the object part of the proposition .

since these conditions are first order , the object part of the proposition follows .

the vgg-16 network consists of 16 layers and over 130 million weight parameters .

the classification network is constructed based on vgg 16-layer net .

adam was used as the optimizer to verify the validity of the proposed technique for different learning methods .

adam optimizer was employed as the optimizer with a initial learning rate of 1e-4 .

and asterisks denote orientifold image d6-branes .

the asterisks denote the nearby single-β clusters .

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

deep neural networks have demonstrated significant performance improvements in a wide range of computer vision tasks .

deep learning methods have achieved state of the art results on computer vision problems with supervision using large amounts of data .

in recent years , deep learning has demonstrated strong model capabilities and obtains very promising performances in many computer vision tasks .

deep learning approaches , in particularly deep convolutional neural networks , have achieved tremendous successes in various visual recognition tasks .

convolutional neural networks have gained remarkable success on a variety of visual recognition tasks .

therefore , the tetrahedron property holds for this system .

in this case we suppose that the tetrahedron property holds .

the conjecture was proved for log canonical thresholds on smooth varieties in .

this conjecture was eventually proved for the smooth case in .

this then enables us to characterize the k-addable corners and consequently , the saturated chains in the k-young lattice .

this enables us to show that the k-young lattice is isomorphic to the weak order on this quotient .

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

deep neural networks have been evolved to powerful predictive models with remarkable performance on computer vision tasks .

we initialize the weights as in and train googlenet from scratch .

we initialize the weights as in and train resnext 50 from scratch .

the geometric observable v0 is called euler characteristic .

but the euler characteristic is the same .

the degeneracy is the number of partitions of the level number into positive integers .

this degeneracy is the result of similar effects upon the relative heights of the first and second peak and is only broken by the presence of the third peak 13 in the spectrum .

we have found that in the large-n limit at weak nonhermiticity the two partition functions agree for an arbitrary and different number of quarks and conjugate anti-quarks , showing that they both belong to the same universality class .

at strong nonhermiticity we found that the partition functions of the two models can be mapped onto each other , provided we consider an equal number of quarks and conjugate anti-quarks .

however , if the gravitino is the lsp , the next-to-lightest supersymmetric particle decays to its standard model partner and a gravitino .

for the heavier gravitino , there is a very small region which is compatible with the bounds from the stau nlsp and the muon g 2 .

in , hardware demonstration of the benefit of inter-operator spectrum sharing was demonstrated .

in , the benefit of inter-operator spectrum sharing was demonstrated .

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

the em algorithm is one of the most popular methods for maximum likelihood identification of latent variable models .

the classical morrey spaces have been introduced by morrey in to study the local behavior of solutions of second order elliptic partial differential equations .

the classical morrey spaces l p , λ were originally introduced by morrey in to study the local behavior of solutions to second order elliptic partial differential equations .

the re- call is the fraction of ground truth boundary pixels that are matched by the boundaries defined by the algorithm .

the recall is the fraction of ground truth boundary pixels that are matched by the boundaries defined by the algorithm .

in recent years , deep neural networks have demonstrated impressive performance improvements on a wide range of challenging machine learning tasks .

deep neural networks have revitalized the field of machine learning by achieving accuracy levels beyond human perception in a variety of image , video , text and speech processing tasks .

some network community detection approaches like conductance measure similarity by the number of edges linking nodes to other nodes within the same community .

some community detection approaches measure similarity by the number of edges linking nodes to other nodes within the same community .

in , the authors proved that the class of good semigroups is actually larger than the one of value semigroups .

the properties of these semigroups were already considered in that it was proved that the class of good semigroups is larger than the one of value semigroups .

the direct simulation monte carlo method has been widely used in engineering applications for gas flows beyond the continuum limit .

this has applications for the direct simulation monte carlo method employed to model various aspects of rarefied gas flows .

the dlcq description involves a quantum-mechanical sigma model , and it was derived from matrix theory in .

this theory has a matrix-like dlcq description as a quantum mechanics on the moduli space of instantons .

each summarization layer is composed of a 1d convolutional layer , with a pre-defined number of filters of adjustable kernel sizes , followed by a batch normalization layer .

each convolutional layer consists of a convolution using the specified size of filters , followed by the relu activation function and batch normalization .

in the nnlo case , it is not difficult to obtain simple low-dimensional integrals .

in the nnlo case , it is possible to arrive at answers in the form of low-dimensional absolutely convergent integrals .

however , only relatively recently have localisation models capable of learning from challenging data such as pascal voc 2007 been proposed .

however , only relatively recently have localisation models capable of learning from challenging data such as the pascal voc 2007 dataset been proposed .

generative adversarial networks are generative models known for their ability to sample from complex and intractable distributions , inherent in tasks such as realistic image generation from natural scenes .

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 .

dft calculations were performed using the plane-wave pseudopotential code vasp .

dft calculations were performed using the vasp code based on spin-polarized dft .

we compare our models with 9 other state-of-the-art algorithms , including sf .

we compare our proposed salient object detector with 16 recent state-of-the-art saliency models , including drfi .

subjects therefore did not exert any effort to maintain the position of the left reference ankle , preventing the contribution of effort cues coming from the reference ankle to the sense of position during the test .

by doing so , participants did not exert any effort to maintain the position of the left reference ankle , preventing the contribution of effort cues coming from the reference ankle to the sense of position during the test .

in recent years , convolutional neural networkshave demonstrated great efficacy on computer vision tasks such as classification .

in recent years , deep convolutional networks have achieved remarkable results in many computer vision tasks .

the generalized gradient approximation was used with the perdewburke-ernzerhof functional to describe the exchange-correlation interaction .

the perdew-burke-ernzerhof scheme of the generalized gradient approximation is adopted to describe the exchange-correlation effect among electrons .

the panels is the power spectrum of the latter in the double-logarithmic scale .

each of the panels is a different assumption for the bss formation rate , as indicated on the top of the panel .

saut et al used decomposition to plan grasps and implemented dual-arm regrasp of complicated models .

saut et al used decomposition to plan grasps and implemented dual-arm regrasp of complicated mesh models .

it was recently studied by and song et al by using robust regression mixture models based on , respectively , the t distribution and the laplace distribution .

it was recently studied by bai et al and song et al by using robust regression mixture models based on , respectively , the t distribution and the laplace distribution .

elements of the two-dimensional integer lattice z2 are called sites .

together with a , these sites comprise another cell which tiles the remaining part of the lattice .

in , an optimal load control problem is formulated and a primary load-side control is derived as a partial primal-dual gradient algorithm for solving the olc problem .

in , an optimal load control problem is formulated and a ubiquitous primary load-side control is derived as a partial primal-dual gradient algorithm for solving the olc problem .

overlaid is the simple line fit discussed in the text .

overlaid is the measurement of the hmf from the 2dfgrs 2pigg catalogue and our expected constraint from the final gama survey .

inside each residual block , batch normalisation is applied on the output of the first convolutional layer , followed by a rectified linear activation function .

each residual block first applies a convolutional layer on the input , followed by batch normalisation and a rectified linear activation function .

quantum mechanics is a one dimensional field theory .

this is why they are called discrete quantum mechanics .

as well as giving properties of these operators , we also considered their complexity .

as well as giving properties of these operators , we also considered complexity questions .

convolutional neural networks have been extensively studied in the computer vision literature to tackle a variety of tasks , such as image classification .

convolutional neural networks , as one of the widely used deep learning methods , have been proven to be very successful for object recognition in images .

similarly we can define smarandache right ideal .

similarly we can define quasi minimal bi-ideal .

the runge-kutta methods are well-known examples of such schemes widely used in modern computational practice .

the rungekutta methods are well-known examples of such schemes widely used in modern computing practice .

we use the resnet50 architecture and its weights for initialization .

we use the 34 layers residual network to perform the classification task .

as an application we determine some characteristic numbers of del pezzo surfaces .

we will show that these surfaces are limits of del pezzo surfaces .

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

with the development of convolutional neural networks , deep learning is widely used in saliency detection tasks .

preliminaries let e denote the figure-eight knot 41 .

preliminaries let v denote the twisted heisenberg-virasoro lie algebra .

in it has been shown that the 3-manifolds represented by these diagrams are cyclic coverings of lens spaces , branched over -knots .

as proved in , all these manifolds turn out to be strongly-cyclic coverings of lens spaces , branched over -knots .

anomaly detection is an important problem with various applications , such as cybersecurity , quality control , fraud detection , fault detection , and health care .

anomaly detection is an important problem that has been studied in a variety of areas and used in diverse applications including intrusion detection , fraud detection , and image processing .

there is no frame stacking , and the output senone label is delayed by 5 frames as in .

there is no frame stacking , and the output hmm state label is delayed by 5 frames as in .

recent rapid advances in deep learning are allowing for the learning of complex functions through convolutional neural networks , which have achieved stateof-the-art performances in a plethora of computer vision tasks .

mobile and internet of things devices are increasingly relying on deep neural networks to provide state-of-the-art performance in various intelligent applications .

deep neural networks achieved remarkable accuracy in many application domains , such as , image classification , object detection , semantic segmentation , speech recognition .

deep neural networks perform impressively well in classic machine learning areas such as image classification , segmentation , speech recognition and language translation .

these measures have found recent applications in the analysis and design of networked control systems .

these are commonly adopted simplifying assumptions in sensor and actuator placement problems for large scale networked control systems .

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

deep convolutional neural networks achieve state-of-the-art performance for a wide variety of tasks in computer vision , such as image classification and segmentation .

therefore , the axion is a pseudo goldstone boson .

if an axion is a boundary field confined in 4 dimensions , the pq scale fp q is bounded by ms .

a homomorphism from a substructure of a to b is called a partial homomorphism from a to b .

the homomorphism is a cyclic subgroup of r .

furthermore , this adjunction is a quillen adjunction when alg is endowed with its trivial model structure .

by adjunction , that is a curve of genus at least 2 .

reinforcement learning represents a computational framework for modelling complex reward-driven behavior in both artificial and biological agents .

reinforcement learning is a widely researched approach for solving tasks formulated as markov decision processes .

we optimize the objective loss using adam , no mini-batches and run for 100 epochs .

we optimize the model with the adam optimizer with a batch size of 128 for 30 epochs .

the interspeech 2013 compare vocalization challenge feature set configuration is used in opensmile toolkit to extract low-level descriptors of 141d using 250ms frame size and 10ms step size .

the interspeech 2013 compare vocalization challenge feature set is used to extract llds of 141d using 250ms frame size and 10ms step size .

deep neural networks have demonstrated their success in many machine learning and computer vision applications , including image classification .

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