Can one still query into the dataset in an online fashion and be provided data that is always sound ? In other words , can one design a filter which , when given a query to any item I in the dataset , returns a sound item J that , although not necessarily in the dataset , differs from I as infrequently as possible . No preprocessing should be allowed and queries should be answered online . The impulse response of a system is estimated and multi-channel echo cancelling is performed employing multirate operations through decimation and interpolation . The presented approach is expected to yield high echo suppression and fast convergence for the filter weight adaptation by means of multirate parallel processing of the filter bank . In Part V , the detection of transient signals using filter banks and higher-order statistics ( cumulants ) is studied . In addition we also achieve a reduction of both dynamic and fixed pattern noise which leads to an overall increase in dynamic range . The digital ND filter can be used with regular image sensors and does not require hardware modifications . In order to resolve the problem incurred by low efficient manual classification of tremendous aurora images , an automatic aurora images classification system for huge dataset application is proposed The boundary based image segmentation and representation system takes a thinned edge image and produces a unique hierarchical representation using nearest mean classifier . In the proposed work segmentation using K-means clustering of feature vectors and denoising techniques such as Gaussian noise method , Weiner filter methods are used to improve the resolution of the images . The experimental results which prove that the hierarchical representation supports the fast and reliable computation of several geometric features which recognizes the aurora images are presented . In this paper , we presented a visual tracking algorithm which combines the particle filter framework with memory strategies to handle occlusions , called as memory-based particle filter ( MbPF ) . The proposed algorithm follows the classical particle filter stages when a confidence measurement can be obtained from the system . Otherwise , a memory-based module try to estimate the hidden target state and to predict its future states using the process history . A real time image dehazing method using bilateral filter is proposed to eliminate ill effects of haze or smoke for outdoor machine vision system . Airlight vector was estimated based on statistics of dark channel prior at first , and then the atmospheric veil was inferred by the estimation of fast bilateral filter . The ideal scene radiance could be recovered by solve imaging physical equation of hazing scene . Given a convolution kernel of limited size , as is often available on pipeline image processors , it is shown ! hat rhe hesr filter for image segmentation can be found using a least squares fit between rhe input image and a user-dejned ideal image . TWO versions , a linear image segmentation model , minimizing error between ideal and input images , and a nonlinear image segmentation model , minimizing error from minimum objectbackground contrast , were implemented . Meanwhile , put forward an iris normalization method which is based on line extraction . In the feature extraction , iris feature information is extracted by the method of 2D Log-Gabo filter which can generate multi-channel filter though multi-scale and multiple directions , the method also can reduce the length of the iris code and the time of feature matching , improve the feature matching accuracy , can effectively improve the accuracy of iris recognition . Using a stochastic parameter-dependent approach , sufficient conditions for the addressed filtering problem are obtained . By solving the proposed conditions , a desired l2 - l∞ filter can be constructed for any admissible random mismatch . Moreover , a key relationship of l2 - l∞ performance between the presented results and the classical mode-dependent , mode-independent filtering is demonstrated . In this paper we discuss the transform filter ta nk system based on discrete positive frequancy transform . the iterative match ide al filter bank has simple structurk , convenent application and ideal frequency splitting . It can be used to image decomposition and coding . Similar to object detection frameworks , we run CNN classifiers on the selected boxes . The proposal step also serves as a filter whose goal is to preserve the object boxes with high recall rate , while removing the easy negatives . The verification classifiers then address a more focused problem on a smaller set of instances . To better understand the effect of 2D convolution and 2D max pooling operation , this paper conducts experiments on Stanford Sentiment Treebank fine - grained task . It first depicts the performance of the proposed models on different length of sentences , and then conducts a sensitivity analysis of 2D filter and max pooling size . The remainder of the paper is organized as follows . Traditional flow methods linearize the brightness constancy equation resulting in an optical flow constraint equation implemented with spatial and temporal derivative filters . Sometimes methods adopt a more generic filter constancy assumption . Our filters are somewhat different . The input resolution is normalized to 256 256 . Before the DU - Net , a Conv ( ) filter with stride 2 and a max pooling would produce 128 features with resolution 64 64 . Hence , the maximum resolution of DU - Net is 64 64 . In this section , we show our evaluation results on sentiment classification and question type classification tasks . Moreover , we give some model analysis on the filter size configuration . subsection : Sentiment Classification To find the 20 most representative images , changes which occur in a small timeframe , should only be represented by a single image . This was achieved by iterating over the differences using a maximum filter with decreasing filter size until 20 frames have been found . In total 3740 images have been extracted . We view this as a notable advantage over other CNN - based models which simply perform image - to - image regression . Unlike activations of intermediate layers of a CNN , linear filter weights have a well defined semantics that can be visualized and analyzed using well developed tools of linear signal processing . section : Conclusion and Future Work The next step of the mean - field iteration is taking a weighted sum of the M filter outputs from the previous step , for each class label l. When each class label is considered individually , this can be viewed as usual convolution with a 1 × 1 filter with M input channels , and one output channel . Since both inputs and the outputs to this step are known during back - propagation , the error derivative w.r.t . For a network with layers , we learn upscaling filters for the feature maps as opposed to one upscaling filter for the input image . In addition , not using an explicit interpolation filter means that the network implicitly learns the processing necessary for SR . Thus , the network is capable of learning a better and more complex LR to HR mapping compared to a single fixed filter upscaling at the first layer . . , ŷ ll , n ) is a diagonal matrix of spectral multipliers representing a learnable filter in the spectral domain , and ξ is a nonlinearity ( e.g. ReLU ) applied on the vertex - wise function values . Unlike classical convolutions carried out efficiently in the spectral domain using FFT , the computations of the forward and inverse graph This thesis presents a design for a portable , flexible high-security architecture based on a traditional computer security model . Behind the API it consists of a kernel implementing a reference monitor which controls access to security-relevant objects and attributes based on a configurable security policy . Layered over the kernel are various objects which abstract core functionality such as encryption and digital signature capabilities , certificate management , and secure sessions and data enveloping ( email encryption ) . Comparing with the numerical methods based on mesh , the reproducing kernel particle method only needs the scattered nodes instead of meshing the domain of the problem . The reproducing kernel particle method is an efficient mesh free technique for the numerical solution of partial differential equations . The result of numerical example is presented . random variables . Abstract Let U n be U -statistics based on a symmetric kernel h ( x , y ) and i.i.d . samples { X n ; n ≥ 1 } . This paper aims at establishing through a detailed comparison the spatial points pattern fitting the best with BSs real deployment data , for different frequency ranges and different operators . The candidate models belong to the Neyman-Scott Process ( NSP ) and results show that the NSP with variance gamma kernel is the most accurate for matching the microcells distribution for different scenarios . The considered measure of performance is the Normalized Means Square Error ( NMSE ) . The proposed online kernel method is evaluated by handling the Cascades Tanks system . In the constant bit rate encoder , the kernel MPEG encoder circuit is followed by a buffer having a certain actual capacity . This buffer is used to smooth the core of the MPEG encoder output variable bit rate . Kernel online convex optimization ( KOCO ) is a framework combining the expressiveness of non-parametric kernel models with the regret guarantees of online learning . First-order KOCO methods such as functional gradient descent require only $ O(t)$ time and space per iteration , and , when the only information on the losses is their convexity , achieve a minimax optimal $ O(\sqrt{T})$ regret . Parameter is the number k of r-stars . Linear-vertex kernel . Conjecture for the general case . Iris location is a kernel procession in an iris recognition system . The performance of the iris recognition system is determined by the speed and accuracy of the iris location decide . Our first contribution is to characterize both the feature spaces and spectral properties associated with two kernels for rankings , the Kendall and Mallows kernels . Using tools from representation theory , we explain the limited expressive power of the Kendall kernel by characterizing its degenerate spectrum , and in sharp contrast , we prove that Mallows ' kernel is universal and characteristic . We also introduce families of polynomial kernels that interpolate between the Kendall ( degree one ) and Mallows ' ( infinite degree ) kernels . The second one is sampled from and serves as input to the generator along with a latent vector to generate an image , and it can be sampled using different approaches : Kernel density estimation : also known as Parzen window estimation , it consists in randomly sampling from a kernel ( e.g. Gaussian kernel with a cross - validated ) . Direct interpolation : interpolate between label vectors from the training set Reed2016 . The Doubly Stochastic Gradients method of showed that effective use of randomization can allow kernel methods to scale to extremely large data sets . However , the approach used fixed convolutional features , and can not jointly learn the kernel classifier and convolutional filters . showed how to learn a kernel function in an unsupervised manner . Our experiments have also cast some light on the issue of random versus adaptive weights . The structured random transformations developed in the kernel literature perform very well on MNIST without any learning ; however , when moving to ImageNet , the benefit of adaptation becomes clear , as it allows us to achieve substantially better performance . This is an important point which illustrates the importance of learning which would not have been visible from experiments only on small data sets . In computer vision , domain adaptation has been widely studied as an image classification problem in computer vision . Conventional methods include asymmetric metric learning , subspace interpolation , geodesic flow kernel , subspace alignment , covariance matrix alignment , . Recent works aim to improve the domain adaptability of deep neural networks , including . Kernels with invariance properties for visual recognition have been proposed in . Such kernels are built with a parameterized “ neural response ” function , which consists in computing the maximal response of a base kernel over a local neighborhood . Multiple layers are then built by iteratively renormalizing the response kernels and pooling using neural response functions . For Weisfeiler - Lehman ( WL ) kernel , we chose height of subtree kernel from . For graphlet kernel ( GK ) , we chose graphlets size and for deep graph kernels ( DGK ) , we report the best classification accuracy obtained among : deep graphlet kernel , deep shortest path kernel and deep Weisfeiler - Lehman kernel . For Multiscale Laplacian Graph ( MLG ) kernel , we chose and parameter of the algorithm from , radius size from , and level number from . paragraph : Blur kernel . Different from image deblurring , the blur kernel setting of SISR is usually simple . The most popular choice is isotropic Gaussian blur kernel parameterized by standard deviation or kernel width . The grid defines the receptive field size and dilation . For example , defines a kernel with dilation . For each location on the output feature map , we have where enumerates the locations in . Denote ; , ; , where and are the losses for and respectively . Assume an isotropic stationary kernel ( ) is used in MMD . We first show : The MLP we use for predicting the mask and score is fairly simple . We apply a 5×5 kernel with 512 outputs , followed by sibling fully connected layers to predict a 14×14 mask ( 14 2 outputs ) and object score ( 1 output ) . The model is implemented in a fully convolutional manner ( using 1×1 convolutions in place of fully connected layers ) . This thesis addresses TCP/IP 's application to wireless and secure area to discuss , it analyzes several problems about a few of critical parts within TCP/IP in details , simulaneously delves into slow start algorithm , initial window size , increasing policy of ini- tial window , RTO estimator , delay acknowledgement , deficiency of kernel implementation and security leakage and gives some constructive solutions in order to further research to pave a basis . An abstract mathematical model of VMDFS is constructed and associated properties are evaluated . The kernel-level implementation framework of VMDFS is illustrated based on Linux 2.4.22 . The network paging latencies for LAN , Wireless VPN and 2.5 G GPRS are measured experimentally . We evaluate the accuracy and timeliness of the performance estimates and provide guidelines for tuning up the parameters . From the implementation perspective , we show that using application level measurements is highly CPU intensive , while a kernel based implementation has comparably a very low CPU usage . The Linux kernel implementation results in an efficient use of batteries in Mobile Hosts and intermediate Mobility Management Nodes can scale up to monitoring thousands of flows . In the past , several user-mode implementations of this idea were realized without any kernel modification . This paper describes a kernel-mode implementation and compares two different design alternatives . Experiences of using the Windows Research Kernel ( WRK ) for experiments with the scheduler are given . In this paper , web techniques based on the structure of B/S , especially for Sybase are discussed and analyzed . The principle and kernel implementing technology of Sybase web solution are introduced in detail . As an example , the implementing method of the application to one Entrust and Investment Company is presented . Component hardware modules will be developed with the accumulation of implementation techniques such as appropriate hardware building blocks and common software tools . Realistic software research environments will be provided by connecting PSIs to encourage kernel language implementation and parallel operating system development . Efforts to integrate them into a total PIM system will start around the middle of the intermediate stage . The work of lei - EtAl:2014:P14 - 1 suggests a low - rank tensor representation to automatically find good feature combinations . taubtabib2015template suggest a kernel - based approach to implicitly consider all possible feature combinations over sets of core - features . The recent popularity of neural networks prompted a move from templates of sparse , binary indicator features to dense core feature encodings fed into non - linear classifiers . To take advantage of the popular well - optimized implementations such as cuda - convnet [ reference ] , we exclude this " layer " from learning . a kernel size c × f 1 × [ reference ] essentially describes the output for a new class as a linear combination of the labels in the support set . Where the attention mechanism is a kernel on , then ( [ reference ] ) is akin to a kernel density estimator . Where the attention mechanism is zero for the furthest from according to some distance metric and an appropriate constant otherwise , then ( [ reference ] ) is equivalent to ‘ ’ - nearest neighbours ( although this requires an extension to the attention mechanism that we describe in Section [ reference ] ) . Importantly , it has a closed - form solution , no parameter to tune , requires no pre - precessing steps to reduce the feature dimension , and can be computed efficiently . Furthermore , to deal with the non - linearity of the person ’s appearance , a kernel version can be developed easily to further boost the matching performance within the null space . It therefore offers a perfect solution to the challenging person re - i d problem . The NFST model is a linear model . It has been demonstrated that many distance metric learning or discriminative subspace based methods for person re - i d benefit from kernelisation because of the non - linearity in person ’s appearance . In the following we describe how the discriminative null space can be kernelised . Although the antennal genetic circadian clock and the azimuth of the sun are instrumental for proper function of the compass , it is unclear how these signals are represented on a neuronal level and how they are integrated to produce flight control . To address these questions , we constructed a receptive field model of the compound eye that encodes the solar azimuth . We then derived a neural circuit model that integrates azimuthal and circadian signals to correct flight direction . Some recent studies have approached this task by asking first how the system deals with information from a single retinal point . This requires that the traditional question of receptive-field analysis be turned on its head : instead of asking ‘ Here is a cell ; where are the points that it sees ? ’ , the question becomes , ‘ Here is a point ; where are the cells that see it ? The transform captures the image data in that it is injective . Using this starting point , we present a continuous analog of the four stage edge detection breakdown by Bezdek et al. , and arrive at a framework for casting many kinds of image processing algorithms in a biologically plausible manner : as feed forward , receptive field based algorithms using known operations . We show how this leads to an optimization scheme for Radon transform based algorithms and show the results of applying this theory to biologically plausible algorithms for motion and color processing . An overview of the history of receptive field mapping of mammalian retinal ganglion cells is given in this article . The receptive field concept is described , along with how this concept has been applied to retinal ganglion cells . A brief description of different types of retinal ganglion cell receptive fields is given and an outline provided of widely accepted receptive field models . However , the gross organization of spectrotemporal processing of speech within the STG is not well characterized . Here we use natural speech stimuli and advanced receptive field characterization methods to show that spectrotemporal features within speech are well organized along the posterior-to-anterior axis of the human STG . These findings demonstrate robust functional organization based on spectrotemporal modulation content , and illustrate that much of the encoded information in the STG represents the physical acoustic properties of speech stimuli . A recent effort using more complex pooling operations , spatial pyramid pooling , is mainly designed to deal with images of varying size , rather than delving in to different pooling functions or incorporating learning . Learning pooling functions is analogous to receptive field learning . However methods like lead to a more difficult learning procedure that in turn leads to a less competitive result , e.g. an error rate of on unaugmented CIFAR10 . [ t ! ] ReceptiveField : Create Receptive Field { algorithmic} [ 1 ] vertex v , graph labeling ℓ , receptive field size k Gnorm We have the following result concerning the complexity of the optimal normalization problem . In this article , we first evaluated the effects of leaf inclination on the most commonly used kernel-driven RossThick-LiSparse-Reciprocal ( RTLSR ) model by using the reflectance benchmark simulated by the mature PROSAIL ( PROSAIL+SAIL ) radiative transfer model . Subsequently , we improved the RTLSR model into a four-parameter version ( RTLSRV4p ) with a new volumetric scattering kernel derived from the assumption of vertical leaf inclination . Finally , the proposed RTLSRV4p model was validated by PROSAIL canopy BRF simulations , in situ canopy BRF measurements , and wide-angle infrared dual-mode line/area Aarray scanner ( WIDAS ) airborne observations . Finally , we propose a simple criterion to select the time value that provides the best compromise between approximation accuracy and smoothness of the solution . Graphical abstractDisplay Omitted HighlightsDiscretization and spectrum-free computation of the volumetric heat kernel . Simple scale selection as compromise between approximation accuracy and smoothness . Each stage comprises one to three convolutional layers . The convolutions performed in each stage use volumetric kernels having size 5 × 5 × 5 voxels . As the data proceeds through different stages along the compression path , its resolution is reduced . In this paper we propose novel effective approaches to hardware prefetching to be used in image processing programs for multimedia . Experimental results are reported for a suite of multimedia image processing programs including convolutions with kernels , MPEG-2 decoding , and edge chain coding . Presented algorithms are in the form of filter bank structure . They can be used for highest order Volterra kernel realization as well . Finite-length Volterra filters are known to be able to model a wide range of real world nonlinear systems . In this paper , we present an algorithm that allows for different memory lengths of the linear and quadratic Volterra kernel while preserving the advantages of fast convolution techniques in the frequency domain for a second-order Volterra filter . This is achieved by extending partitioned block methods to second-order Volterra filters . Further , the phase based motion detection model is shown to be equivalent to a second-order Volterra kernel acting on two normalized inputs . Subsequently , a general model that computes the ratio of two non-linear functionals , each comprising linear ( first order Volterra kernel ) and quadratic ( second-order Volterra kernel ) filters , is proposed . It is shown that , even under these highly non-linear operations , a population of cells can encode stimuli faithfully using a number of measurements that are proportional to the bandwidth of the input stimulus . The method of a black-box diagnostics , founded on nonparametric identification of objects using integro-power Volterra series is offered . It provides a set of diagnostic features formed on base of multidimensional Volterra kernels : discrete values of Volterra kernels , heuristic features , moments and wavelet transform coefficients . It is researched a self-descriptiveness of provided features using classifier on base of neural nets . So we learn the compact RGB-D kernel descriptors using the methods of downsampling and PCA . The learned kernel features include : Spatio_dim kernel feature , Grad_nor kernel feature , Volum_shape kernel feature , and KPCA kernel feature . All of them can not replace each other , but complete each other . The fully connected layers of these nets have fixed dimensions and throw away spatial coordinates . However , fully connected layers can also be viewed as convolutions with kernels that cover their entire input regions . Doing so casts these nets into fully convolutional networks that take input of any size and make spatial output maps . As confirmed by our empirical observations , this architecture ensures convergence in a fraction of the time required by a similar network that does not learn residual functions . The convolutions performed in each stage use volumetric kernels having size voxels . As the data proceeds through different stages along the compression path , its resolution is reduced . As the data proceeds through different stages along the compression path , its resolution is reduced . This is performed through convolution with voxels wide kernels applied with stride ( Figure [ reference ] ) . Since the second operation extracts features by considering only non overlapping volume patches , the size of the resulting feature maps is halved .
Can one still query into the dataset in an online fashion and be provided data that is always sound ? In other words , can one design a filter which , when given a query to any item I in the dataset , returns a sound item J that , although not necessarily in the dataset , differs from I as infrequently as possible . No preprocessing should be allowed and queries should be answered online .
The impulse response of a system is estimated and multi-channel echo cancelling is performed employing multirate operations through decimation and interpolation . The presented approach is expected to yield high echo suppression and fast convergence for the filter weight adaptation by means of multirate parallel processing of the filter bank . In Part V , the detection of transient signals using filter banks and higher-order statistics ( cumulants ) is studied .
In addition we also achieve a reduction of both dynamic and fixed pattern noise which leads to an overall increase in dynamic range . The digital ND filter can be used with regular image sensors and does not require hardware modifications .
In order to resolve the problem incurred by low efficient manual classification of tremendous aurora images , an automatic aurora images classification system for huge dataset application is proposed The boundary based image segmentation and representation system takes a thinned edge image and produces a unique hierarchical representation using nearest mean classifier . In the proposed work segmentation using K-means clustering of feature vectors and denoising techniques such as Gaussian noise method , Weiner filter methods are used to improve the resolution of the images . The experimental results which prove that the hierarchical representation supports the fast and reliable computation of several geometric features which recognizes the aurora images are presented .
In this paper , we presented a visual tracking algorithm which combines the particle filter framework with memory strategies to handle occlusions , called as memory-based particle filter ( MbPF ) . The proposed algorithm follows the classical particle filter stages when a confidence measurement can be obtained from the system . Otherwise , a memory-based module try to estimate the hidden target state and to predict its future states using the process history .
A real time image dehazing method using bilateral filter is proposed to eliminate ill effects of haze or smoke for outdoor machine vision system . Airlight vector was estimated based on statistics of dark channel prior at first , and then the atmospheric veil was inferred by the estimation of fast bilateral filter . The ideal scene radiance could be recovered by solve imaging physical equation of hazing scene .
Given a convolution kernel of limited size , as is often available on pipeline image processors , it is shown ! hat rhe hesr filter for image segmentation can be found using a least squares fit between rhe input image and a user-dejned ideal image . TWO versions , a linear image segmentation model , minimizing error between ideal and input images , and a nonlinear image segmentation model , minimizing error from minimum objectbackground contrast , were implemented .
Meanwhile , put forward an iris normalization method which is based on line extraction . In the feature extraction , iris feature information is extracted by the method of 2D Log-Gabo filter which can generate multi-channel filter though multi-scale and multiple directions , the method also can reduce the length of the iris code and the time of feature matching , improve the feature matching accuracy , can effectively improve the accuracy of iris recognition .
Using a stochastic parameter-dependent approach , sufficient conditions for the addressed filtering problem are obtained . By solving the proposed conditions , a desired l2 - l∞ filter can be constructed for any admissible random mismatch . Moreover , a key relationship of l2 - l∞ performance between the presented results and the classical mode-dependent , mode-independent filtering is demonstrated .
In this paper we discuss the transform filter ta nk system based on discrete positive frequancy transform . the iterative match ide al filter bank has simple structurk , convenent application and ideal frequency splitting . It can be used to image decomposition and coding .
Similar to object detection frameworks , we run CNN classifiers on the selected boxes . The proposal step also serves as a filter whose goal is to preserve the object boxes with high recall rate , while removing the easy negatives . The verification classifiers then address a more focused problem on a smaller set of instances .
To better understand the effect of 2D convolution and 2D max pooling operation , this paper conducts experiments on Stanford Sentiment Treebank fine - grained task . It first depicts the performance of the proposed models on different length of sentences , and then conducts a sensitivity analysis of 2D filter and max pooling size . The remainder of the paper is organized as follows .
Traditional flow methods linearize the brightness constancy equation resulting in an optical flow constraint equation implemented with spatial and temporal derivative filters . Sometimes methods adopt a more generic filter constancy assumption . Our filters are somewhat different .
The input resolution is normalized to 256 256 . Before the DU - Net , a Conv ( ) filter with stride 2 and a max pooling would produce 128 features with resolution 64 64 . Hence , the maximum resolution of DU - Net is 64 64 .
In this section , we show our evaluation results on sentiment classification and question type classification tasks . Moreover , we give some model analysis on the filter size configuration . subsection : Sentiment Classification
To find the 20 most representative images , changes which occur in a small timeframe , should only be represented by a single image . This was achieved by iterating over the differences using a maximum filter with decreasing filter size until 20 frames have been found . In total 3740 images have been extracted .
We view this as a notable advantage over other CNN - based models which simply perform image - to - image regression . Unlike activations of intermediate layers of a CNN , linear filter weights have a well defined semantics that can be visualized and analyzed using well developed tools of linear signal processing . section : Conclusion and Future Work
The next step of the mean - field iteration is taking a weighted sum of the M filter outputs from the previous step , for each class label l. When each class label is considered individually , this can be viewed as usual convolution with a 1 × 1 filter with M input channels , and one output channel . Since both inputs and the outputs to this step are known during back - propagation , the error derivative w.r.t .
For a network with layers , we learn upscaling filters for the feature maps as opposed to one upscaling filter for the input image . In addition , not using an explicit interpolation filter means that the network implicitly learns the processing necessary for SR . Thus , the network is capable of learning a better and more complex LR to HR mapping compared to a single fixed filter upscaling at the first layer .
. , ŷ ll , n ) is a diagonal matrix of spectral multipliers representing a learnable filter in the spectral domain , and ξ is a nonlinearity ( e.g. ReLU ) applied on the vertex - wise function values . Unlike classical convolutions carried out efficiently in the spectral domain using FFT , the computations of the forward and inverse graph
This thesis presents a design for a portable , flexible high-security architecture based on a traditional computer security model . Behind the API it consists of a kernel implementing a reference monitor which controls access to security-relevant objects and attributes based on a configurable security policy . Layered over the kernel are various objects which abstract core functionality such as encryption and digital signature capabilities , certificate management , and secure sessions and data enveloping ( email encryption ) .
Comparing with the numerical methods based on mesh , the reproducing kernel particle method only needs the scattered nodes instead of meshing the domain of the problem . The reproducing kernel particle method is an efficient mesh free technique for the numerical solution of partial differential equations . The result of numerical example is presented .
random variables . Abstract Let U n be U -statistics based on a symmetric kernel h ( x , y ) and i.i.d . samples { X n ; n ≥ 1 } .
This paper aims at establishing through a detailed comparison the spatial points pattern fitting the best with BSs real deployment data , for different frequency ranges and different operators . The candidate models belong to the Neyman-Scott Process ( NSP ) and results show that the NSP with variance gamma kernel is the most accurate for matching the microcells distribution for different scenarios .
The considered measure of performance is the Normalized Means Square Error ( NMSE ) . The proposed online kernel method is evaluated by handling the Cascades Tanks system .
In the constant bit rate encoder , the kernel MPEG encoder circuit is followed by a buffer having a certain actual capacity . This buffer is used to smooth the core of the MPEG encoder output variable bit rate .
Kernel online convex optimization ( KOCO ) is a framework combining the expressiveness of non-parametric kernel models with the regret guarantees of online learning . First-order KOCO methods such as functional gradient descent require only $ O(t)$ time and space per iteration , and , when the only information on the losses is their convexity , achieve a minimax optimal $ O(\sqrt{T})$ regret .
Parameter is the number k of r-stars . Linear-vertex kernel . Conjecture for the general case .
Iris location is a kernel procession in an iris recognition system . The performance of the iris recognition system is determined by the speed and accuracy of the iris location decide .
Our first contribution is to characterize both the feature spaces and spectral properties associated with two kernels for rankings , the Kendall and Mallows kernels . Using tools from representation theory , we explain the limited expressive power of the Kendall kernel by characterizing its degenerate spectrum , and in sharp contrast , we prove that Mallows ' kernel is universal and characteristic . We also introduce families of polynomial kernels that interpolate between the Kendall ( degree one ) and Mallows ' ( infinite degree ) kernels .
The second one is sampled from and serves as input to the generator along with a latent vector to generate an image , and it can be sampled using different approaches : Kernel density estimation : also known as Parzen window estimation , it consists in randomly sampling from a kernel ( e.g. Gaussian kernel with a cross - validated ) . Direct interpolation : interpolate between label vectors from the training set Reed2016 .
The Doubly Stochastic Gradients method of showed that effective use of randomization can allow kernel methods to scale to extremely large data sets . However , the approach used fixed convolutional features , and can not jointly learn the kernel classifier and convolutional filters . showed how to learn a kernel function in an unsupervised manner .
Our experiments have also cast some light on the issue of random versus adaptive weights . The structured random transformations developed in the kernel literature perform very well on MNIST without any learning ; however , when moving to ImageNet , the benefit of adaptation becomes clear , as it allows us to achieve substantially better performance . This is an important point which illustrates the importance of learning which would not have been visible from experiments only on small data sets .
In computer vision , domain adaptation has been widely studied as an image classification problem in computer vision . Conventional methods include asymmetric metric learning , subspace interpolation , geodesic flow kernel , subspace alignment , covariance matrix alignment , . Recent works aim to improve the domain adaptability of deep neural networks , including .
Kernels with invariance properties for visual recognition have been proposed in . Such kernels are built with a parameterized “ neural response ” function , which consists in computing the maximal response of a base kernel over a local neighborhood . Multiple layers are then built by iteratively renormalizing the response kernels and pooling using neural response functions .
For Weisfeiler - Lehman ( WL ) kernel , we chose height of subtree kernel from . For graphlet kernel ( GK ) , we chose graphlets size and for deep graph kernels ( DGK ) , we report the best classification accuracy obtained among : deep graphlet kernel , deep shortest path kernel and deep Weisfeiler - Lehman kernel . For Multiscale Laplacian Graph ( MLG ) kernel , we chose and parameter of the algorithm from , radius size from , and level number from .
paragraph : Blur kernel . Different from image deblurring , the blur kernel setting of SISR is usually simple . The most popular choice is isotropic Gaussian blur kernel parameterized by standard deviation or kernel width .
The grid defines the receptive field size and dilation . For example , defines a kernel with dilation . For each location on the output feature map , we have where enumerates the locations in .
Denote ; , ; , where and are the losses for and respectively . Assume an isotropic stationary kernel ( ) is used in MMD . We first show :
The MLP we use for predicting the mask and score is fairly simple . We apply a 5×5 kernel with 512 outputs , followed by sibling fully connected layers to predict a 14×14 mask ( 14 2 outputs ) and object score ( 1 output ) . The model is implemented in a fully convolutional manner ( using 1×1 convolutions in place of fully connected layers ) .
This thesis addresses TCP/IP 's application to wireless and secure area to discuss , it analyzes several problems about a few of critical parts within TCP/IP in details , simulaneously delves into slow start algorithm , initial window size , increasing policy of ini- tial window , RTO estimator , delay acknowledgement , deficiency of kernel implementation and security leakage and gives some constructive solutions in order to further research to pave a basis .
An abstract mathematical model of VMDFS is constructed and associated properties are evaluated . The kernel-level implementation framework of VMDFS is illustrated based on Linux 2.4.22 . The network paging latencies for LAN , Wireless VPN and 2.5 G GPRS are measured experimentally .
We evaluate the accuracy and timeliness of the performance estimates and provide guidelines for tuning up the parameters . From the implementation perspective , we show that using application level measurements is highly CPU intensive , while a kernel based implementation has comparably a very low CPU usage . The Linux kernel implementation results in an efficient use of batteries in Mobile Hosts and intermediate Mobility Management Nodes can scale up to monitoring thousands of flows .
In the past , several user-mode implementations of this idea were realized without any kernel modification . This paper describes a kernel-mode implementation and compares two different design alternatives . Experiences of using the Windows Research Kernel ( WRK ) for experiments with the scheduler are given .
In this paper , web techniques based on the structure of B/S , especially for Sybase are discussed and analyzed . The principle and kernel implementing technology of Sybase web solution are introduced in detail . As an example , the implementing method of the application to one Entrust and Investment Company is presented .
Component hardware modules will be developed with the accumulation of implementation techniques such as appropriate hardware building blocks and common software tools . Realistic software research environments will be provided by connecting PSIs to encourage kernel language implementation and parallel operating system development . Efforts to integrate them into a total PIM system will start around the middle of the intermediate stage .
The work of lei - EtAl:2014:P14 - 1 suggests a low - rank tensor representation to automatically find good feature combinations . taubtabib2015template suggest a kernel - based approach to implicitly consider all possible feature combinations over sets of core - features . The recent popularity of neural networks prompted a move from templates of sparse , binary indicator features to dense core feature encodings fed into non - linear classifiers .
To take advantage of the popular well - optimized implementations such as cuda - convnet [ reference ] , we exclude this " layer " from learning . a kernel size c × f 1 ×
[ reference ] essentially describes the output for a new class as a linear combination of the labels in the support set . Where the attention mechanism is a kernel on , then ( [ reference ] ) is akin to a kernel density estimator . Where the attention mechanism is zero for the furthest from according to some distance metric and an appropriate constant otherwise , then ( [ reference ] ) is equivalent to ‘ ’ - nearest neighbours ( although this requires an extension to the attention mechanism that we describe in Section [ reference ] ) .
Importantly , it has a closed - form solution , no parameter to tune , requires no pre - precessing steps to reduce the feature dimension , and can be computed efficiently . Furthermore , to deal with the non - linearity of the person ’s appearance , a kernel version can be developed easily to further boost the matching performance within the null space . It therefore offers a perfect solution to the challenging person re - i d problem .
The NFST model is a linear model . It has been demonstrated that many distance metric learning or discriminative subspace based methods for person re - i d benefit from kernelisation because of the non - linearity in person ’s appearance . In the following we describe how the discriminative null space can be kernelised .
Although the antennal genetic circadian clock and the azimuth of the sun are instrumental for proper function of the compass , it is unclear how these signals are represented on a neuronal level and how they are integrated to produce flight control . To address these questions , we constructed a receptive field model of the compound eye that encodes the solar azimuth . We then derived a neural circuit model that integrates azimuthal and circadian signals to correct flight direction .
Some recent studies have approached this task by asking first how the system deals with information from a single retinal point . This requires that the traditional question of receptive-field analysis be turned on its head : instead of asking ‘ Here is a cell ; where are the points that it sees ? ’ , the question becomes , ‘ Here is a point ; where are the cells that see it ?
The transform captures the image data in that it is injective . Using this starting point , we present a continuous analog of the four stage edge detection breakdown by Bezdek et al. , and arrive at a framework for casting many kinds of image processing algorithms in a biologically plausible manner : as feed forward , receptive field based algorithms using known operations . We show how this leads to an optimization scheme for Radon transform based algorithms and show the results of applying this theory to biologically plausible algorithms for motion and color processing .
An overview of the history of receptive field mapping of mammalian retinal ganglion cells is given in this article . The receptive field concept is described , along with how this concept has been applied to retinal ganglion cells . A brief description of different types of retinal ganglion cell receptive fields is given and an outline provided of widely accepted receptive field models .
However , the gross organization of spectrotemporal processing of speech within the STG is not well characterized . Here we use natural speech stimuli and advanced receptive field characterization methods to show that spectrotemporal features within speech are well organized along the posterior-to-anterior axis of the human STG . These findings demonstrate robust functional organization based on spectrotemporal modulation content , and illustrate that much of the encoded information in the STG represents the physical acoustic properties of speech stimuli .
A recent effort using more complex pooling operations , spatial pyramid pooling , is mainly designed to deal with images of varying size , rather than delving in to different pooling functions or incorporating learning . Learning pooling functions is analogous to receptive field learning . However methods like lead to a more difficult learning procedure that in turn leads to a less competitive result , e.g. an error rate of on unaugmented CIFAR10 .
[ t ! ] ReceptiveField : Create Receptive Field { algorithmic} [ 1 ] vertex v , graph labeling ℓ , receptive field size k Gnorm We have the following result concerning the complexity of the optimal normalization problem .
In this article , we first evaluated the effects of leaf inclination on the most commonly used kernel-driven RossThick-LiSparse-Reciprocal ( RTLSR ) model by using the reflectance benchmark simulated by the mature PROSAIL ( PROSAIL+SAIL ) radiative transfer model . Subsequently , we improved the RTLSR model into a four-parameter version ( RTLSRV4p ) with a new volumetric scattering kernel derived from the assumption of vertical leaf inclination . Finally , the proposed RTLSRV4p model was validated by PROSAIL canopy BRF simulations , in situ canopy BRF measurements , and wide-angle infrared dual-mode line/area Aarray scanner ( WIDAS ) airborne observations .
Finally , we propose a simple criterion to select the time value that provides the best compromise between approximation accuracy and smoothness of the solution . Graphical abstractDisplay Omitted HighlightsDiscretization and spectrum-free computation of the volumetric heat kernel . Simple scale selection as compromise between approximation accuracy and smoothness .
Each stage comprises one to three convolutional layers . The convolutions performed in each stage use volumetric kernels having size 5 × 5 × 5 voxels . As the data proceeds through different stages along the compression path , its resolution is reduced .
In this paper we propose novel effective approaches to hardware prefetching to be used in image processing programs for multimedia . Experimental results are reported for a suite of multimedia image processing programs including convolutions with kernels , MPEG-2 decoding , and edge chain coding .
Presented algorithms are in the form of filter bank structure . They can be used for highest order Volterra kernel realization as well .
Finite-length Volterra filters are known to be able to model a wide range of real world nonlinear systems . In this paper , we present an algorithm that allows for different memory lengths of the linear and quadratic Volterra kernel while preserving the advantages of fast convolution techniques in the frequency domain for a second-order Volterra filter . This is achieved by extending partitioned block methods to second-order Volterra filters .
Further , the phase based motion detection model is shown to be equivalent to a second-order Volterra kernel acting on two normalized inputs . Subsequently , a general model that computes the ratio of two non-linear functionals , each comprising linear ( first order Volterra kernel ) and quadratic ( second-order Volterra kernel ) filters , is proposed . It is shown that , even under these highly non-linear operations , a population of cells can encode stimuli faithfully using a number of measurements that are proportional to the bandwidth of the input stimulus .
The method of a black-box diagnostics , founded on nonparametric identification of objects using integro-power Volterra series is offered . It provides a set of diagnostic features formed on base of multidimensional Volterra kernels : discrete values of Volterra kernels , heuristic features , moments and wavelet transform coefficients . It is researched a self-descriptiveness of provided features using classifier on base of neural nets .
So we learn the compact RGB-D kernel descriptors using the methods of downsampling and PCA . The learned kernel features include : Spatio_dim kernel feature , Grad_nor kernel feature , Volum_shape kernel feature , and KPCA kernel feature . All of them can not replace each other , but complete each other .
The fully connected layers of these nets have fixed dimensions and throw away spatial coordinates . However , fully connected layers can also be viewed as convolutions with kernels that cover their entire input regions . Doing so casts these nets into fully convolutional networks that take input of any size and make spatial output maps .
As confirmed by our empirical observations , this architecture ensures convergence in a fraction of the time required by a similar network that does not learn residual functions . The convolutions performed in each stage use volumetric kernels having size voxels . As the data proceeds through different stages along the compression path , its resolution is reduced .
As the data proceeds through different stages along the compression path , its resolution is reduced . This is performed through convolution with voxels wide kernels applied with stride ( Figure [ reference ] ) . Since the second operation extracts features by considering only non overlapping volume patches , the size of the resulting feature maps is halved .
Property-Preserving Data Reconstruction Filter Bank Techniques for Signal and Image Processing Digital neutral density filter for moving picture cameras An Innovative Approach for Aurora Recognition A Memory-Based Particle Filter for Visual Tracking through Occlusions Real Time Image Haze Removal Using Bilateral Filter Optimal filters for segmentation of microscope images Research on Application of Iris Recognition Based on Improved Algorithm Asynchronous l2 − l∞ filtering for Markov jump systems Discrete Positive-Freguency Transtorm and itsApplication to Transform Filter Bank System ProNet: Learning to Propose Object-Specific Boxes for Cascaded Neural Networks Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling Optical Flow Estimation Using a Spatial Pyramid Network Quantized Densely Connected U-Nets for Efficient Landmark Localization A C-LSTM Neural Network for Text Classification DeXpression: Deep Convolutional Neural Network for Expression Recognition Image Reconstruction with Predictive Filter Flow Conditional Random Fields as Recurrent Neural Networks Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks The design and verification of a cryptographic security architecture The meshless method for a two-dimensional parabolic problem with a source parameter Precise asymptotics for complete moment convergence of U-statistics of i.i.d. random variables Distribution of Microcells for Cellular Mobile Networks Online identification of nonlinear system using a new kernel algorithm Method and apparatus for encoding or decoding digital video data Second-Order Kernel Online Convex Optimization with Adaptive Sketching Linear-vertex kernel for the problem of packing r-stars into a graph without long induced paths An Iris Location Method Based on Gray Distribution Features of Eyes On kernel methods for covariates that are rankings Invertible Conditional GANs for image editing Deep Fried Convnets ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes Convolutional Kernel Networks Graph Capsule Convolutional Neural Networks Learning a Single Convolutional Super-Resolution Network for Multiple Degradations Deformable Convolutional Networks Improving MMD-GAN Training with Repulsive Loss Function Feature Pyramid Networks for Object Detection Exhaustive Analysis of TCP/IP Protocol's Deficiency VMDFS: Virtual Memory based Mobile Distributed File System Accurate and Efficient Measurements of IP Level Performance to Drive Interface Selection in Heterogeneous Wireless Networks Kernel-mode scheduling server for CPU partitioning: a case study using the Windows research kernel THE DISCUSSION AND APPLICATION OF THE THREE LAYEREDB/S MODEL BASED ON WEB Towards a High Performance Parallel Inference Machine - The Intermediate Stage Plan of PIM Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations Image Super-Resolution Using Deep Convolutional Networks Matching Networks for One Shot Learning Learning a Discriminative Null Space for Person Re-identification Neural Integration Underlying a Time-Compensated Sun Compass in the Migratory Monarch Butterfly. Point images in the visual system: new interest in an old idea Deriving and combining biologically plausible visual processes with the windowed Radon transform Retinal Ganglion Cells: Receptive Fields Human Superior Temporal Gyrus Organization of Spectrotemporal Modulation Tuning Derived from Speech Stimuli Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree Learning Convolutional Neural Networks for Graphs Improving Kernel-Driven BRDF Model for Capturing Vegetation Canopy Reflectance With Large Leaf Inclinations Volumetric heat Kernel: Padé-Chebyshev approximation, convergence, and computation Kidney Segmentation Framework using 3D CNN Hardware prefetching techniques for cache memories in multimedia applications Filter bank realizations of Volterra kernels Partitioned block frequency-domain adaptive second-order Volterra filter Sparse algorithms for decoding and identification of neural circuits Technology for data acquisition in diagnosis processes by means of the identification using Volterra models New RGB-D features for object recognition on kernel view Fully Convolutional Networks for Semantic Segmentation V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
ca9de54533279f8733f8fa2c538ec92861b70756 41d460e640a014213af4e7552415c632128a9636 a460952a7a36bc79cde2b37749a205d4ac6bace3 dfb0d4f8a2404782387441aadc766a12c42c9171 e2a34faf73d810cd1965d5ece255ca9a63ea3f11 48858096242a74e9157c711df018ade6dad68f47 f60a827fabc597756b44c708664f54e6753f2037 2516c24406b52e61b1dc84a45994a0275bafeb4f be19a173d974cc57c9048f944b763d6c9a059b82 f1fdbcc009de9c7ae657dbeab7ec8b95c9607124 448c6f10d087d04d5802f7d562dfaf59323bd56d 27de40f7263a72af8822353fb2b7ad620da300f8 284b91cd35aa71a5a29f85a1bb0468b7c7cf58f5 6c490eea4eab76f390203b8cd8435e29738d819f 9b0b88432babefd83aae4a65b6e33d4b24c93b46 1d86f33e617459c1750fe845af4803ed7d727cce dab9d16c1f6c7c333f2718ce69f0a182761352b3 8e8613b3bc9444bf33b0a9d48c554caae1d48b6c dadbf088bf0f8b7ff24907ae315f811fb692e110 84031c4d924a8f7c9f031ece9183015ec1c1474c 6590c7271072e0501a10800ba47649a3924afd29 7558e6bbacdb305f498ff68c4cc0d63a3c6d549b 1f4869c47174154746ddaccf22e9fe5495d8329c a1491a51fe9f04a9b508dd360d083b0ac72f2955 16a7168067710d7059529f3432ceea6e4da8dfe1 db776d7baff4f1d6bab95d27eb62e281d95ef0d5 5acce66990d6107b6dd16a3871583b0375043295 26d8b49a450f973120370ae6b35308617f178363 803b5b0673411ec2b4760ef051ada350db7f072a 4cc63c50662b7b61ab7d4808bef5bdfe41f08e20 6a39110d032b2f0935207cf00c9aee896c3a3dad 2ae92f6eda37334f7cea464e0f88927027d015e1 30a3d33e7a2f383f6399c1018b0d5d5ba455fe2b b844e4b466edeff328153c8dfdc8329e807a82e3 4f7a2ef57fdf080d5f75ecce16bc096649ae4b92 15df77eacaa13d9973ba31ae472cd4ad8a9cc243 3df4631062336cbb549265ff2f0e66d229567a7f 83e5fe5f578175ad778f08acf5dfdaac1e7b20fc 738627f3ca35837ae883cbbfba0232921be5b078 4c5dc3d933110d138543ed9bac70f7f4f24b9848
["Computer Science","Mathematics"] ["Computer Science"] ["Engineering","Physics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Biology","Medicine"] ["Psychology"] ["Engineering","Computer Science","Physics"] ["Computer Science"] ["Medicine","Biology"] ["Mathematics","Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"]
2004 1996 2010 2012 2009 2011 1993 2013 2013 2000 2016 2016 2017 2018 2015 2015 2018 2015 2016 2017 2008 2012 2016 2015 1997 2017 2015 2010 2016 2015 2018 2014 2018 2018 2017 2019 2017 2001 2011 2016 2008 2004 1986 2016 2016 2016 2016 2016 1986 1998 2009 2016 2016 2016 2020 2015 2019 2000 2001 2005 2018 2009 2014 2017 2016 2016
Computational Imaging IWINAC (2) AuCC 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) COLING 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV ArXiv ArXiv ArXiv 2015 IEEE International Conference on Computer Vision (ICCV) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS VTC Spring ArXiv 2015 IEEE International Conference on Computer Vision (ICCV) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition NIPS ArXiv 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE International Conference on Computer Vision (ICCV) ICLR 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) SAC Future Parallel Computers Transactions of the Association for Computational Linguistics IEEE Transactions on Pattern Analysis and Machine Intelligence NIPS 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Human Vision and Electronic Imaging AISTATS ICML CAMP MAVEBA IEEE Transactions on Pattern Analysis and Machine Intelligence 2016 Fourth International Conference on 3D Vision (3DV) 2016 Fourth International Conference on 3D Vision (3DV)
https://api.semanticscholar.org/ca9de54533279f8733f8fa2c538ec92861b70756 https://api.semanticscholar.org/41d460e640a014213af4e7552415c632128a9636 https://api.semanticscholar.org/a460952a7a36bc79cde2b37749a205d4ac6bace3 https://api.semanticscholar.org/dfb0d4f8a2404782387441aadc766a12c42c9171 https://api.semanticscholar.org/e2a34faf73d810cd1965d5ece255ca9a63ea3f11 https://api.semanticscholar.org/48858096242a74e9157c711df018ade6dad68f47 https://api.semanticscholar.org/f60a827fabc597756b44c708664f54e6753f2037 https://api.semanticscholar.org/2516c24406b52e61b1dc84a45994a0275bafeb4f https://api.semanticscholar.org/be19a173d974cc57c9048f944b763d6c9a059b82 https://api.semanticscholar.org/f1fdbcc009de9c7ae657dbeab7ec8b95c9607124 https://www.semanticscholar.org/paper/5c6e7cf3ee21d1e53363c716055c6d68cbde6ae7 https://www.semanticscholar.org/paper/786f95cada23d4639aa1a8b922cdb9fb9a9c03fa https://www.semanticscholar.org/paper/a93d81c7f033f1e2b54d3288b60d214f55ccc010 https://www.semanticscholar.org/paper/4543052aeaf52fdb01fced9b3ccf97827582cef5 https://www.semanticscholar.org/paper/10f62af29c3fc5e2572baddca559ffbfd6be8787 https://www.semanticscholar.org/paper/8cea404e8a5c4c11064923e5a6c023a0ae594a5a https://www.semanticscholar.org/paper/15922c4cb9468077050f114c759477e286f28fc1 https://www.semanticscholar.org/paper/ca5c766b2d31a1f5ce8896a0a42b40a2bff9323a https://www.semanticscholar.org/paper/03a5b2aac53443e6078f0f63b35d4f95d6d54c5d https://www.semanticscholar.org/paper/137bbe604334584fd4a1d6eb9218a588ae3dda3e https://api.semanticscholar.org/448c6f10d087d04d5802f7d562dfaf59323bd56d https://api.semanticscholar.org/27de40f7263a72af8822353fb2b7ad620da300f8 https://api.semanticscholar.org/284b91cd35aa71a5a29f85a1bb0468b7c7cf58f5 https://api.semanticscholar.org/6c490eea4eab76f390203b8cd8435e29738d819f https://api.semanticscholar.org/9b0b88432babefd83aae4a65b6e33d4b24c93b46 https://api.semanticscholar.org/1d86f33e617459c1750fe845af4803ed7d727cce https://api.semanticscholar.org/dab9d16c1f6c7c333f2718ce69f0a182761352b3 https://api.semanticscholar.org/8e8613b3bc9444bf33b0a9d48c554caae1d48b6c https://api.semanticscholar.org/dadbf088bf0f8b7ff24907ae315f811fb692e110 https://api.semanticscholar.org/84031c4d924a8f7c9f031ece9183015ec1c1474c https://www.semanticscholar.org/paper/29afab1cb2d8ad8d37bd6f4a1adec42e131799fe https://www.semanticscholar.org/paper/27a99c21a1324f087b2f144adc119f04137dfd87 https://www.semanticscholar.org/paper/0301963f73e8ace21653c0bed0e5feee128f140e https://www.semanticscholar.org/paper/6eee8041415facc9823d6b93ddb67a3773a3e1e3 https://www.semanticscholar.org/paper/1a8ee9382e79b92986f9d780a929fc2d2be2f47b https://www.semanticscholar.org/paper/b26b4c9da872d3c6122dbf23b9d6e063dc6456b5 https://www.semanticscholar.org/paper/4a73a1840945e87583d89ca0216a2c449d50a4a3 https://www.semanticscholar.org/paper/15cc54ed7b1582b2efd71bedf28b23634d82991b https://www.semanticscholar.org/paper/b9b4e05faa194e5022edd9eb9dd07e3d675c2b36 https://api.semanticscholar.org/6590c7271072e0501a10800ba47649a3924afd29 https://api.semanticscholar.org/7558e6bbacdb305f498ff68c4cc0d63a3c6d549b https://api.semanticscholar.org/1f4869c47174154746ddaccf22e9fe5495d8329c https://api.semanticscholar.org/a1491a51fe9f04a9b508dd360d083b0ac72f2955 https://api.semanticscholar.org/16a7168067710d7059529f3432ceea6e4da8dfe1 https://api.semanticscholar.org/db776d7baff4f1d6bab95d27eb62e281d95ef0d5 https://www.semanticscholar.org/paper/eec3a236ecd185712ce65fb336141f8656eea13d https://www.semanticscholar.org/paper/66e9dc728b5041271bff0cd6ac0d7eadcd88442f https://www.semanticscholar.org/paper/be1bb4e4aa1fcf70281b4bd24d8cd31c04864bb6 https://www.semanticscholar.org/paper/bb713964d37530310940aa5814cfb9b44ffad8b3 https://api.semanticscholar.org/5acce66990d6107b6dd16a3871583b0375043295 https://api.semanticscholar.org/26d8b49a450f973120370ae6b35308617f178363 https://api.semanticscholar.org/803b5b0673411ec2b4760ef051ada350db7f072a https://api.semanticscholar.org/4cc63c50662b7b61ab7d4808bef5bdfe41f08e20 https://api.semanticscholar.org/6a39110d032b2f0935207cf00c9aee896c3a3dad https://www.semanticscholar.org/paper/4b88e948121a87f00fb5aa0081d2044dde51ee36 https://www.semanticscholar.org/paper/7c6de5a9e02a779e24504619050c6118f4eac181 https://api.semanticscholar.org/2ae92f6eda37334f7cea464e0f88927027d015e1 https://api.semanticscholar.org/30a3d33e7a2f383f6399c1018b0d5d5ba455fe2b https://api.semanticscholar.org/b844e4b466edeff328153c8dfdc8329e807a82e3 https://api.semanticscholar.org/4f7a2ef57fdf080d5f75ecce16bc096649ae4b92 https://api.semanticscholar.org/15df77eacaa13d9973ba31ae472cd4ad8a9cc243 https://api.semanticscholar.org/3df4631062336cbb549265ff2f0e66d229567a7f https://api.semanticscholar.org/83e5fe5f578175ad778f08acf5dfdaac1e7b20fc https://api.semanticscholar.org/738627f3ca35837ae883cbbfba0232921be5b078 https://api.semanticscholar.org/4c5dc3d933110d138543ed9bac70f7f4f24b9848 https://www.semanticscholar.org/paper/317aee7fc081f2b137a85c4f20129007fd8e717e https://www.semanticscholar.org/paper/50004c086ffd6a201a4b782281aaa930fbfe6ecf https://www.semanticscholar.org/paper/50004c086ffd6a201a4b782281aaa930fbfe6ecf