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weak factorization systems and topological functors .
constructions of factorization systems in categories .
other interesting models are based on generative adversarial networks and variational auto- encoders .
currently , the most prominent approaches are generative adversarial networks , variational autoencoders .
moreover , svrg-sd and saga-sd achieve at least comparable performance with the best known stochastic method , katyusha , in terms of number of effective passes .
moreover , svrg-sd and saga-sd achieve at least comparable performance with the accelerated stochastic method , katyusha , in terms of number of effective passes .
deep neural networks , such as cnns , have recently achieved many successes in visual recognition tasks .
in recent years , convolutional neural networks s have emerged as the most powerful technique for image classification .
deep convolutional neural networks have been successful in many computer vision tasks including image classification .
deep convolutional neural networks have shown remarkable success for various computer vision tasks in static images , such as object detection .
deep neural networks have been significantly successful in many artificial intelligence tasks such as im- age classification .
convolutional neural networks have been widely used in various computer vision tasks , such as image classification .
hd 215733 hd 215733 is a b1 ii star located approximately 1 5 kpc below the galactic plane .
hd 37903 hd 37903 is the only sightline for which we have hst data that meets our criterion as an outlier .
we will assume that gravity is the only interaction responsible for growth of perturbations at large scales .
gravity is the dominant force at large scales and is believed to drive growth of perturbations .
we are interested here in the related notion of twisted modules , which are objects of stacks locally equivalent to stacks of modules over sheaves of rings .
finally , we recall the notion of stack of twisted modules , considering the case of modules over rings which are not necessarily commutative nor globally defined .
for imaging applications , the convolutional neural network is the most effective used network with respect to image classification .
for visual applications , the convolutional neural networks represent one of the most utilised approaches for a number of continuously increasing large-scale machine vision tasks .
cover , el gamal and salehi in made further significant progress by providing sufficient conditions for transmitting losslessly correlated observations over a mac .
cover , el gamal and salehi provided sufficient conditions for transmitting losslessly discrete correlated observations over a discrete mac .
deep generative models , such as generative adversarial networks , have recently risen to prominence due to their ability to model high-dimensional complex distributions .
generative models such as variational autoencoders and generative adversarial networks have emerged as popular techniques for unsupervised learning of intractable distributions .
especially , deep convolutional neural network based algorithms have been widely applied in image classification .
deep convolutional neural networks have achieved great success in various computer vision tasks such as image classification .
deep neural networks have transformed the machine learning field and are now widely used in many applications .
since the 21st century , deep learning has driven advances in the field of computer vision .
deep neural networks are powerful learning models which have been successfully applied to vision , speech and many other tasks .
convolutional neural networks have achieved tremendous progress on many pattern recognition tasks , especially large-scale images recognition problems .
in recent years , deep convolutional neural networks have been widely used in a variety of computer vision tasks and have achieved unprecedented progress .
models based on neural networks , especially deep convolutional neural networks and recurrent neural networks , have achieved state-of-the-art results in various computer vision tasks .
stationary probability pr for bsab variants .
stationary probability pj for various bsab variants .
a monopole is a highly coherent state of many gauge quanta and emission of a monopole by a black hole will be highly suppressed .
the monopole is a classical configuration of the magnetic field emanating from a point with nonzero magnetic charge at r0 .
in section ii , we give the discrete forms of the matched filter outputs and discuss how these may be expressed in the form of the discrete analog to generalized fourier integrals .
in section iii we derive the two-parameter fast chirp transform that can be used to evaluate the discrete matched filter expressions .
matsumoto et al recently proposed the walsh figure of merit as a computable criterion for digital nets p .
recently , matsumoto , saito , and matoba proposed the walsh figure of merit , which is a computable criterion for digital nets p .
other methods , such as generative adversarial network , jointly train generative and discriminative models .
generative adversarial network has become a dominant approach for learning generative models .
cosmic strings are topological defects which may have been formed at a high-temperature phase transition very early in the history of the universe .
cosmic strings are topologically stable gravitational defects which may have been created in the early universe after planck time by a vacuum phase transition .
the interactions also couple the renormalized energy levels .
the energies eaσ and ebσ are renormalized by the interactions .
we will see this reflected in the low-energy physics of the dual description .
it should be possible to verify these predictions directly in the low-energy dual theory .
deep residual learning has achieved great success in visual recognition .
deep learning has recently been applied very successfully in areas such as image recognition .
we have normalized all data by the structure factor measured when the to mode is fully recovered .
we have taken the harmonic structure factor to be the structure factor when the to mode is fully recovered .
the multi-head attention mechanism introduced by vaswani et al is used as a basic building block in our framework .
we use the multi-head attention module to learn the context information for word representation .
we predict the thermodynamic properties of confined hs fluids using a recent modification .
to accurately model both the structure and thermodynamics of hard-sphere fluids , we use the rosenfeld fmt approach .
in a previous paper by the first author , it was proposed that a sbh could be artificially created by firing a huge number of gamma rays from a spherically converging laser .
in a previous paper by the author , it was proposed that an abh could be artificially created by firing a huge number of gamma rays from a spherically converging laser .
similar to u-net , we use skip connections between the feature encoder and the decoder .
we use a u-net network architecture that contains an encoder-decoder structure with skip connections .
this construction has been reinterpreted recently in terms of open string quantization in the presence of a constant background b-field .
indeed , in that context , the resulting 1d topological quantum mechanics is the starting point for the zero slope limit of open string theory in the presence of a large neveu-schwarz b-field .
convolutional neural networks have significantly boosted the performance of a variety of visual analysis tasks , such as image classification in recent years due to its high capacity in learning discriminative features .
deep convolutional neural networks have already achieved tremendous success on a variety of computer vision tasks such as image classification among many others .
unfortunately , the density dependence of the symmetry energy is poorly known .
indeed , even the symmetry energy at saturation density is not well constrained experimentally .
convolutional neural networks have seen tremendous success across different problems including image classification .
deep neural networks are extremely important in various applications including computer vision , speech recognition , and natural language processing .
in recent years , deep generative models have achieved significant success , especially in generating natural images .
in recent years , deep generative models inspired by gan enable computers to imagine new samples based on the knowledge learned from the given datasets .
the vacuum is defined by is the factorizable two-particle scattering matrix of the integrable quantum field theory .
the vacuum is the quantum state which consists of the product of terms of the form .
we believe that the studies that presented unsteady solutions using direct numerical simulations , have experienced the same type of numerical oscillations because they have used a small grid mesh .
we believe that the studies that presented unsteady solutions of driven cavity flow using direct numerical simulations have experienced the same type of numerical oscillations because they have used a small grid mesh .
the lower-left panel shows the combined contribution of central and tensor correlations .
the relationship between the various kinematic quantities is shown in the bottom-right panel .
deep learning methods have been successfully applied in various computer vision tasks , including image classification , and have dramatically improved the performance of these systems , setting the new state-of-the-art .
recently , inspired by the success of deep learning in various vision tasks , deep neural networks based re-id models started to become a prevailing trend and have achieved state-of-the-art performance .
we use the pytorch 1 framework for our implementation .
our code is implemented in pytorch using the allennlp toolkit .
so two practical swipt architectures , namely , time switching and power splitting , were proposed in .
to coordinate wit and wpt at the receiver side , two practical schemes , namely , time switching and static power splitting , were proposed in .
find conditions on the group biring to be semisimple .
find conditions for semigroup binear-ring to be artinian .
this crystal structure consists of honeycomb-net planes of boron , separated by triangular planes of mg , with the center of a boron honeycomb lying both directly above and below each mg atom .
the crystal structure consists of layers of edge-sharing coo6 octahedra perpendicular to the c axis separated by na layers .
in , joint information and an beamforming at the fd-bs was investigated to guarantee the security of a single-antenna ul user and dl user .
in , the joint design of information beamforming and an generation for an fd bs was investigated to guarantee dl and ul communication security .
neural networks have become ubiquitous in applications including computer vision .
deep neural networks have been widely used in many artificial intelligence applications including computer vision .
shot noise is the non-equilibrium current fluctuation resulting from the stochastic transport of quantized charge carriers .
shot noise is a directly measurable quantity and is not affected by confusion which may be present .
deep learning has led to significant improvements in many computer vision tasks such as image classification .
deep neural networks have shown improvement in state-of-the-art in different tasks , such as image classification .
alpern et al proposed a different algorithm which runs in otime per edge insertion with δ measuring the number of edges of the minimal node subgraph that needs to be updated .
alpern et al proposed a different algorithm which runs in otime per edge insertion with δ measuring the number of edges of the minimal vertex subgraph that needs to be updated .
the tunnel ing current is shown as the dashed lines .
the tunneling current is given by the lower , dashed line .
by utilizing the pre-leader-follower decomposition , wang and xiao studied the state consensus of discrete-time multi-agent systems with bounded time-delays .
by utilizing the pre-leader-follower decomposition , wang and xiao studied the state consensus of discrete-time multi-agent systems with switching topologies and bounded time-delays .
generative adversarial networks are finding popular applications as generative models in diverse scenarios .
generative adversarial networks have been a recent breakthrough in the field of generative models .
convolutional neural networks has achieved great success in image recognition .
deep neural networks have achieved great success in cognitive applications such as image classification .
solid red is the veritas effective area with standard cuts , and the dotted blue line is the magic .
the solid like is a fit with the function explained in the text .
deep convolutional neural networks have made significant breakthroughs in many visual understanding tasks including image classification .
convolutional neural networks have enjoyed tremendous success in many computer vision applications , especially in image classification .
so we define these notions in case of s-rings .
now we proceed on to define the concept of pseudo bivector spaces .
in , different artificial noise based power allocation algorithms were proposed for the maximization of the ergodic secrecy capacity and the outage secrecy capacity , respectively .
in , power allocation algorithms were proposed for maximizing the ergodic secrecy capacity and outage secrecy capacity via artificial noise generation , respectively .
cosmic strings are one-dimensional massive objects , which may appear as topological defects at the spontaneous symmetry breaking in the early universe .
cosmic strings are linear concentrations of energy which may have formed during symmetry-breaking phase transitions in the early universe .
other confinement mechanisms were envisioned in systems far from equilibrium ande lbs were predicted in semiconductor cavities .
other confinement mechanisms were envisioned in forced dissipative system and lbs were predicted in optical parametric oscillators .
deep neural network has achieved remarkable success in classification tasks such as image classification .
convolutional neural networks have seen tremendous success across different problems including image classification .
previous work discovered that many machine learning models , including modern neural network architectures , are vulnerable to adversarial examples .
recent work on adversarial attacks has shown that neural networks are brittle and can easily be confused by small imperceptible modifications of the input .
again there is no strong correlation between the local mean field and the clump minor axis .
evidently there is no strong correlation between the mean local field and the clump minor axis .
the subspace is the destabilizing subspace .
let s be the subspace of t which is the union of all arcs joining postcritical points .
in recent years , deep neural networks have developed advanced abilities in the feature extraction and function approximation .
deep neural networks have demonstrated dramatically accurate results for challenging tasks .
similarly , we can give an upper bound to the clique cover number .
similar remarks hold for the lower bound on the chromatic and clique cover numbers .
the best known results on the k-set problem on the maximum number of incidences between a set of points and a set of lines were also established using the crossing lemma .
the best known upper bound on the k-set problem on the maximum number of incidences between a set of points and a set of lines were also established using the crossing lemma .
geelen , gerards , robertson , and whittle applied this explicitly to a submodular connectivity function .
geelen , gerards , robertson , and whittle applied this explicitly to the submodular connectivity function in matroids .
recently , deep neural networks have achieved impressive results for many image classification tasks .
recently , convolutional neural networks have achieved remarkable success on many vision tasks such as object recognition .
in both figures , calculations are performed for the model i .
in both figures , calculations are performed for the model ii .
our implementation is based on the publicly available code of matconvnet .
our implementation is based on the deep learning toolbox matconvnet and trained on a nvidia titan x gpu .
qfas were first introduced independently by moore and crutchfield .
two important models of 1qfa are mo-1qfa firstly defined by moore and crutchfield .
when quantum fluctuation effects are taken into account , the electric fluctuation induced van der waals forces dominate those induced by purely fluid mechanical motions .
when quantum fluctuation effects are taken into account , the electric fluctuation contribution to the potentials dominates the fluid mechanical contribution to the potentials as in eqs .
generative adversarial networks provide a powerful modeling framework for learning complex high-dimensional distributions .
generative adversarial networks are able to generate high-quality images based on adversarial training .
most severe is the problem of braid strands completely collapsing onto one 1the theory works equally well for braids with fixed endpoints .
the most severe one is the mass of the weinberg-salam higgs .
in this section , we study the denominator vectors introduced in in the context of monoidal categorification .
in this section , we recall definitions and basic properties of cluster algebras from .
the backbone of the visual branch is resnet-50 pre-trained on imagenet .
the backbone network is the resnet-50 model pre-trained on imagenet .
since gravity is a purely attractive force , the fluctuations had to have been - at least in the context of an eternally expanding background cosmology - very small in the early universe .
since gravity is a purely attractive force , and since the fluctuations on scales of the cmb anisotropies were small in amplitude when the anisotropies were generated , the fluctuations had to have been very small in the early universe .
the counterpart is a relatively faint be star , the northern component of a very close double .
the counterpart is a very young and perhaps also around 3 4 kev .
several studies have reported that rs-fmri can detect differences in functional connectivity between healthy controls and patients with ocd , or find correlations with treatment response to medication 18 and behavioral therapy 19 , 20 .
several studies have reported that rs-fmri can detect differences in fc between healthy controls and patients with ocd , and find correlations with treatment response to medication 19 and behavioral therapy 20 , 21 .
gravity is the driving force that at the end of the life of massive stars overcomes the pressure forces and causes the collapse of the stellar core .
gravity is the dominant force at large scales and is believed to drive growth of perturbations .
moreover , such programs can be analysed using similar coinductive methods in tm-and sld-resolution .
once again , such programs can be analysed using similar coinductive methods in tm-and sld-resolution .
moreover , as explained in the introduction , soliton solutions of field theories defined on a noncommutative space describe dp-branes .
moreover it is also well known that soliton solutions of field theories defined on a noncommutative space describe dp-branes .
the goal is to show that path-cg is much more stable than the path-sgd algorithm in neyshabur et al , implying lower generalization error of the model .
in this case study , the goal is to compare path-cg with the path-sgd algorithm neyshabur et al in terms of both accuracy and stability of the algorithm .
since the rigidity function can be directly interpreted as a density function , it can been used to define the rigidity surface of a biomolecule by taking an isovalue .
since the rigidity function can be directly interpreted as a density distribution , it can been used to define the rigidity surface of a biomolecule by taking an isovalue .
we can now go back to the case of random manifolds .
we direct now our attention to the case of random manifolds .
deep convolutional neural networks have demonstrated significant improvements over traditional approaches in many pattern recognition tasks .
large-scale deep convolutional neural networks have been successfully applied to a wide variety of applications such as image classification .
there exist various techniques to reduce the size of bitmaps , including compression .
there exists various techniques to reduce the size of bitmaps , including bitmap compression .
this analytical model permits to enlighten the behaviour of the almost adiabatic representation .
this problem is an interesting area to test the almost adiabatic formalism .
distributed dual and primal-dual methods typically converge linearly to the optimal point using a constant step-size .
dual and primal-dual methods typically converge to the optimal solution using a constant step-size .
serafini , in relativity in rotating frames , eds .
selleri , in relativity in rotating frames , eds .
in recent years , object detection greatly benefits from the rapid development of deep convolutional neural networks , whose performance is heavily dependent on a mass of labeled training images .
in the last two years , the performance of object detection has been significantly improved with the success of novel deep convolutional neural networks .
as shown in our experiments , ren significantly promotes the performance of our convnet , which outperforms all state-of-the-art methods on two challenging hand pose benchmarks .
as shown in our experiments , ren significantly promotes the performance of our convnet , which outperforms state-of-the-art methods on three challenging hand pose benchmarks .
deepwalk was the first model to learn language from a network , which adopts random walk to sample a sequence of nodes for each node , and then treats these sequences as sentences by the skip-gram mechanism .
deepwalk is the first method to leverage the skip-gram model for learning representations on networks , by extracting truncated random walks in the network and considering them as sentences .
moreover , the limit of an increasing but bounded sequence of elements always converges in the algebra .
in particular , the spectral projections of an hermitian element of the algebra often do not lie again in the algebra .
attention was further extended by vaswani et al , where the self-attentional transformer architecture achieved state-of-the-art results in machine translation .
the multi-head attention mechanism was firstly proposed in vaswani et al , where impressive performance in machine translation task has been achieved with transformer .
gans have been considerably successful as a framework for generative models in recent years .
in the recent years , gans have shown outstanding success in generating data for learning models .
let z t be the prices of the two assets with prices , represented as states e 1 , e 2 , e 3 , e 4 respectively .
let x t be the choice of weights with weights , represented as states e 1 , e 2 , e 3 respectively .
recently , deep convolutional neural networks have led to substantial improvements for numerous computer vision tasks like object detection , often achieving human-level performance .
methods based on deep neural networks have achieved stateof-the-art performance on a variety of computer vision tasks , such as scene recognition .
wavelet analysis is a natural generalization of the fourier analysis aimed at a consistent description of not only the scale but also of the spatial localization of structures .
wavelet analysis is a more appropriate tool than the traditional spectral fourier analysis .
the solutions of interest here are the flat vacuum and flat space cosmologies .
the flat space analogue of these objects are flat space cosmologies .
we evaluate our segmentation network on the part segmentation dataset shapenet parts .
we evaluate our architecture for part segmentation on shapenet-part dataset from .