We evaluate system dependability for given two models . Stochastic Reward Nets ( SRN ) are used to model the system availability . We describe our SRN modeling using Stochastic Petri Net Package , and compute several interesting results that characterize HA-OSCAR availability . We first show that the dendrogram depicting a lexical hierarchy among words that Elman obtained by training an SRN ( simple recurrent networks ) is in fact obtained without training the SRN . We then show that the reason why training was not required is that the SRN itself ( 1 ) assigns a random code , which is in fact a set of weights of the SRN , to each word and ( 2 ) assigns a composite code reflecting contexts ( a set of words ) of a word , which is in fact a vector of hidden unit activations of the SRN , to the word , since a lexical hierarchy among words can be built upon the similarity of their contexts . We ascertained that the above scheme is valid although the codes are skewed by non-linear output function ( the standard sigmoidal function ) . On one side , FPTIME can be captured by Intuitionistic Light Affine Logic ( ILAL ) , a logic derived from Linear Logic , characterized by the structural invariant Stratification . On the other side , FPTIME can be captured by Safe Recursion on Notation ( SRN ) , an algebra of functions based on Predicative Recursion , a restriction of the standard recursion schema used to defiine primitive recursive functions . Stratifiication and Predicative Recursion seem to share common underlying principles , whose study is the main subject of this work . In this work , the recurrent neural networks ( RNNs ) for medical examination data prediction with missing information are proposed . Simple recurrent network ( SRN ) , long short-term memory ( LSTM ) and gated recurrent unit ( GRU ) are selected among many variations of RNNs for the missing information imputation while they are also used to predict the future medical examination data . Besides , the missing information imputation based on bidirectional LSTM is also proposed to consider past information as well as the future information in the imputation process , while the traditional RNNs can only consider the past information during the imputation . Based on SRN network , we can get rid of the limitation of the traditional feature-based approach and carry out more in-depth video semantic analysis . By analyzing the characters in the SRN network , a community identification method based on the core characters is proposed , which can automatically confirm the core characters in the video and dig out the communities around the core characters . In this paper , lots of movie videos are used to experiment , and experimental results show the effectiveness of SRN model and community identification method . In this paper , a stochastic reward net ( SRN ) model is constructed to analyze the performance of low Earth orbit ( LEO ) satellite networks in the case of packet loss . A lemma is proved to calculate the packet loss probability . We then apply this modeling technique to the study of operating systems with dynamic priorities . In particular , we investigate the performance of the BS2000 operating system , which implements a dynamic priority mechanism , by means of a stochastic reward net ( SRN ) model . The results are then compared against an approximate model in which a static priority mechanism is assumed . We also compared the performance of the different feature selection methods using the receiver operating characteristic analysis and the areas under the curve ( AUCs ) . The AUCs for the F-score , Relief , SVM-RFE , SVM-RFE ( mRMR ) , and SRN methods are 0.9014 , 0.8916 , 0.9121 , 0.9236 , and 0.9439 , respectively , with a tenfold cross-validation procedure , and are 0.9312 , 0.9178 , 0.9324 , 0.9413 , and 0.9615 , respectively , with a LOO scheme . Both the accuracy and AUC values show that the proposed SRN feature selection method has the best performance . We derive a gradient-based algorithm for finding the parameters of the network that maximize the likelihood of the training sequences . Finally , we show that if the target structure generating the training sequences is unifilar , then the trained stochastic SRN behaves deterministically . Many real world problems require the handling of temporally correlated events Recently some discrete time recurrent neural network models have been proposed to solve various temporal sequence processing problems One of the most popular models is Elman s simple recurrent network SRN which has been used for grammatical inference or formal language learning problems In this paper some major drawbacks of SRN and its variants are iden ti ed and investigated in detail Based on these observations a new model called ASCOC which stands for auto associative SRN with context to output connections is proposed to overcome the problems encountered by SRN Two experiments in using the ASCOC to learn regular grammars are reported with detailed analysis of the hidden layer patterns learned by the network Not only is it easier to train and use ASCOC than SRN the new model is also more powerful in that it can remember more nonlocal information that is relevant to correct learning of grammars with embedded structures Possible extension to domains beyond formal language learning is discussed However , their ability to handle long-term dependencies between sentence constituents is somewhat limited . NARX networks have recently been shown to outperform SRN sb y preserving past information in explicit delays from the network ’s prior output . However , it is unclear how the number of delays should be determined . We then compute word surprisal values for stimuli used in self-paced reading , eye-tracking , and electroencephalography experiments , and quantify the surprisal values ’ fit to experimental measures that indicate human sentence reading effort . While the gated networks provide better language models , they do not outperform their SRN counterpart as cognitive models when language model quality is equal across network types . Our results suggest that the different architectures are equally valid as models of human sentence processing . The probability distribution describing the state of a Stochastic Reaction Network ( SRN ) evolves according to the Chemical Master Equation ( CME ) . It is common to estimate its solution using Monte Carlo methods such as the Stochastic Simulation Algorithm ( SSA ) . This leads to improved skeleton localization , scale prediction , and better overall performance . Ke et al . present a side - output residual network ( SRN ) , which leverages the output residual units to fit the errors between the ground - truth and the side - outputs . By cascading residual units in a deep - to - shallow manner , SRN can effectively detect the skeleton at different scales . Liu et at . develop a two - stream network that combines image and segmentation cues to capture complementary information for skeleton localization . The self-attention solves the long distance reliance of image local-region features when generate image . With spectral normalization , the training of GANs become more stable than traditional GANs , which conduces to avoid model collapse and gradient vanishing or explosion . We conduct experiments on widely used Oxford-102 flower dataset and CUB bird dataset to validate our proposed method . Two neural network structures , DeblurRNN and DeblurMerger , are presented to exploit the pair of images in a sequential manner or parallel manner . To boost the training , gradient loss , adversarial loss and spectral normalization are leveraged . The training dataset that consists of pairs of noisy/blurry images and the corresponding ground truth sharp image is synthesized based on the benchmark dataset GOPRO . Of key importance was the creation of a methodology that was accurate and repeatable and , as such , could be used to create earlier , comparable land-cover classifications in the future for the same region . We examined the effects of band normalization and different time-series image compositing methods on classification accuracy . Both top of atmosphere and surface reflectance products from the Landsat 8 Operational Land Imager ( OLI ) were tested for single-time classification independently , where the latter resulted in 1.1 % greater classification overall accuracy . Since most of the content or energy of natural images resides in the low-frequency spectrum , their transformed coefficients in the frequency domain are highly imbalanced . To address this issue , we present a band normalization module ( BNM ) to normalize the coefficients from different parts of the frequency spectrum . Moreover , we employ a band discriminative training ( BDT ) criterion to enhance the model regression . In order to GLCM features still has difference range for classification techniques , therefore , the GLCM feature will be normalized range entering to classify the image skin lesion image . The problem is normalization belonging the same class called Local-GLCM normalization which used maximum and minimum values within the same class for unseen data . This paper aims to improve and classify type of class for finding out the maximum and minimum values within the same class for process of normalization using clustering techniques . The unseen data are needed to use maximum and minimum value to enhance Local-GLCM normalization . This preprocessing data given highest accuracy value compared with non-normalization by classification techniques . We are able to demonstrate differences in transferability properties from those observed in computer vision . We show that dataset normalization techniques such as z-score normalization does not affect the transferability of adversarial attacks and we show that techniques such as knowledge distillation do not increase the transferability of attacks . In general , we find that FastICA ( Fast Independent Component Analysis ) , SIMPLISMA ( SIMPLe-to-use-Interactive Self-modeling Mixture Analysis ) , and NNMF ( Non-Negative Matrix Factorization ) are top-performing techniques . We demonstrate that dataset normalization approaches prior to blind source separation do not considerably improve outcomes . Within the range of noise levels studied , we did not find drastic changes to the ranking of techniques . To mitigate this gap , we propose a 3D-Aided Deep Pose-Invariant Face Recognition Model ( 3D-PIM ) , which automatically recovers realistic frontal faces from arbitrary poses through a 3D face model in a novel way . Specifically , 3D-PIM incorporates a simulator with the aid of a 3D Morphable Model ( 3D MM ) to obtain shape and appearance prior for accelerating face normalization learning , requiring less training data . It further leverages a globallocal Generative Adversarial Network ( GAN ) with multiple critical improvements as a refiner to enhance the realism of both global structures and local details of the face simulator ’s output using unlabelled real data only , while preserving the identity information . This project attempt to develop an efficient chart parser for Analysing Modern Standard Arabic ( MSA ) sentence with using natural language processing techniques . Firstly , Aljlayl & Frieder Normalization Algorithm is used to normalization text . Secondly , depending on the affixes list , Root-based and rule-based stemmers algorithms are used to catch each word stem . Current inter-chip normalization methods rely on some assumptions , which can rarely be verified by experiments , such as the invariant expression level of housekeeping genes or the stability of statistical data distribution . In this paper , we present a highly verifiable inter-chip normalization method using genetic optimization algorithm , which depends only on a small number of housekeeping genes , and therefore facilitates experimental validation without any accuracy lost . Nonnative speakers typically speak more slowly than natives , an experience that listeners take into account when explicitly judging the rate of nonnative speech . The present study investigated whether this is also reflected in implicit rate normalization . Results indicate that nonnative speech is implicitly perceived as faster than temporally-matched native speech , suggesting that the additional cognitive load of listening to an accent speeds up rate perception . While general structures to implement convolution or fully-connected layers in RCA-based BNN hardware were actively studied in previous works , Batch-Normalization ( BN ) layer , which is another key layer of BNN , has not been discussed in depth yet . In this work , we propose in-memory batch-normalization schemes which integrate BN layers on RCA so that area/energy-efficiency of the BNN accelerators can be maximized . In addition , we also show that sense amp error due to device mismatch can be suppressed using the proposed in-memory BN design . section : Batch Normalization for Domain Adaptation Batch normalization ( BN ) [ reference ] , which whitens the output of the hidden layer in a CNN , is an effective technique to accelerate training speed and enhance the accuracy of the model . In addition , in domain adaptation , whitening the hidden layer 's output is effective for improving the performance , which make the distribution in different domains similar [ reference ][ reference ] . We can see that pre - normalization by trace produces 0.3 % lower error rate than that by Frobenius norm , while taking similar time with the latter . Hence , in all the remaining experiments , we adopt trace based pre - normalization method . Comparison with Other Covariance Pooling Methods In this article , we discuss the impact of these effects on the TREC 2007 data sets . We show that the use of simple normalization methods significantly outperforms cosine normalization in the legal domain . Experiments show that the new scheme is less sensitive to OCR errors and facilitates use of more diverse basic weighting schemes . It also yields significant improvements in retrieval effectiveness over cosine normalization . In this paper , we propose a new type of normalization called term-length normalization which considers term distribution in a class . The performance of this normalization is investigated in three environments of a standard centroid-based classifier ( TFIDF ) : ( 1 ) without class-length normalization , ( 2 ) with cosine class-length normalization and ( 3 ) with summing weight normalization . The results suggest that our term-length normalization is useful for improving classification accuracy in all cases . The paper shows that the new probabilistic interpretation of tf × idf term weighting might lead to better understanding of statistical ranking mechanisms , for example by explaining how they relate to coordination level ranking . A pilot experiment on the Cranfield test collection indicates that the presented model outperforms the vector space model with classical tf × idf and cosine length normalisation . Training pivoted normalization on one collection , we can successfully use it on other ( new ) text collections , yielding a robust , collectzorz independent normalization technique . We use the idea of pivoting with the well known cosine normalization function . We point out some shortcomings of the cosine function andpresent two new normalization functions -- pivoted unique normalization and piuotert byte size normalization . After reviewing existing edge and gradient based descriptors , we show experimentally that grids of histograms of oriented gradient ( HOG ) descriptors significantly outperform existing feature sets for human detection . We study the influence of each stage of the computation on performance , concluding that fine-scale gradients , fine orientation binning , relatively coarse spatial binning , and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results . The new approach gives near-perfect separation on the original MIT pedestrian database , so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds . We present a new image‐based , post‐processing antialiasing technique , which offers practical solutions to the common , open problems of existing filter‐based real‐time antialiasing algorithms . Some of the new features include local contrast analysis for more reliable edge detection , and a simple and effective way to handle sharp geometric features and diagonal lines . This , along with our accelerated and accurate pattern classification allows for a better reconstruction of silhouettes . 5 should be : 5L . Drever , D. M. Robinson , A. McEwan , and W. Roa , “ A local contrast based approach to threshold segmentation for PET target volume delineation , ” Med . Phys . 33 , 1583–1594 ( 2006 ) . It is believed that computer-aided detection ( CAD ) schemes could ultimately provide a useful “ second option ” for radiologists and potentially improve their diagnostic accuracy . The proposed detection process bases on local contrast thresholding and rule-based classification which is performed over the preprocessed and segmented mammograms . A relatively high detection rate of suspicious abnormal regions ( mass and/or microcalcification ) on the testing set of mammograms from Mini Mias Database implies that the proposed method can assist technologists in more efficiently and accurately locating the exact areas for subsequent exams . Microaneurysms ( MAs ) are the earliest sign of this disease and so are very important for classifying whether images show signs of retinopathy . This paper describes automatic methods for MA detection and shows how image contrast normalization can improve the ability to distinguish between MAs and other dots that occur on the retina . Various methods for contrast normalization are compared . Then the detected text region is binarized by a combined thresholding technique . After that , a local contrast average method is introduced to extract the RMB characters from the binarization result . The experiments demonstrate that the proposed binarization method outperforms other well-known methods . Firstly , saliency detection method is utilized to get training samples for unsupervised feature learning . Next , these samples are sent to the denoising sparse autoencoder ( DSAE ) , followed by convolutional layer and local contrast normalization layer . Generally , prior in a specific task is helpful for the task solution . The main outputs are percentage ( % ) neurite density and % myelination . % neurite density is calculated using the normalize local contrast algorithm , followed by thresholding , to adjust for differences in intensity . For % myelination the myelin sheaths are selected using the Frangi vesselness algorithm , in conjunction with a grey scale morphology filter and the removal of cell bodies using a high intensity mask . In this study , we consider two specific factors . First , flankers could diminish the sensory response to the target via spatial contrast normalization in early visual processing . Second , rats may treat the sensory signal caused by the flankers as if it belonged to the target . Each tile ’s contrast is enhanced such that its histogram becomes roughly uniform . Contrast Normalization ( Conorm ) enhances edges , filtering the input image by a difference of Gaussians , using a filter size of 5x5 pixels . Note that the above normalizations , except Conorm , are performed in a color space with image intensity as one of its components . section : Related Work Starting with LeNet - 5 , convolutional neural networks ( CNN ) have typically had a standard structure – stacked convolutional layers ( optionally followed by contrast normalization and max - pooling ) are followed by one or more fully - connected layers . Variants of this basic design are prevalent in the image classification literature and have yielded the best results to - date on MNIST , CIFAR and most notably on the ImageNet classification challenge . A total number of 60 , 000 images are split into 50 , 000 training and 10 , 000 testing images . The dataset is preprocessed by global contrast normalization . To compare our results with the previous state - of - the - art , in this case , we also augmented the dataset by zero padding 4 pixels on each side , then do corner cropping and random flipping on the fly during training . = 1 and λL2 = 0.005 . The Maxout network , M , was trained with data augmentation following global contrast normalization and ZCA normalization . The model consists of three convolutional maxout layers followed by a fully connected maxout and softmax outputs . The momentum term learning rate was fixed to 0.9 . The dataset was preprocessed as described in with global contrast normalization and ZCA whitening . Additionally , the images were padded with four zero pixels at all borders . This leads to an interesting question of whether controlling these quantities might improve the generalization behaviour of NNs . To this end , we propose stable rank normalization ( SRN ) , a novel , optimal , and computationally efficient weight-normalization scheme which minimizes the stable rank of a linear operator . Surprisingly we find that SRN , inspite of being non-convex problem , can be shown to have a unique optimal solution .
We evaluate system dependability for given two models . Stochastic Reward Nets ( SRN ) are used to model the system availability . We describe our SRN modeling using Stochastic Petri Net Package , and compute several interesting results that characterize HA-OSCAR availability .
We first show that the dendrogram depicting a lexical hierarchy among words that Elman obtained by training an SRN ( simple recurrent networks ) is in fact obtained without training the SRN . We then show that the reason why training was not required is that the SRN itself ( 1 ) assigns a random code , which is in fact a set of weights of the SRN , to each word and ( 2 ) assigns a composite code reflecting contexts ( a set of words ) of a word , which is in fact a vector of hidden unit activations of the SRN , to the word , since a lexical hierarchy among words can be built upon the similarity of their contexts . We ascertained that the above scheme is valid although the codes are skewed by non-linear output function ( the standard sigmoidal function ) .
On one side , FPTIME can be captured by Intuitionistic Light Affine Logic ( ILAL ) , a logic derived from Linear Logic , characterized by the structural invariant Stratification . On the other side , FPTIME can be captured by Safe Recursion on Notation ( SRN ) , an algebra of functions based on Predicative Recursion , a restriction of the standard recursion schema used to defiine primitive recursive functions . Stratifiication and Predicative Recursion seem to share common underlying principles , whose study is the main subject of this work .
In this work , the recurrent neural networks ( RNNs ) for medical examination data prediction with missing information are proposed . Simple recurrent network ( SRN ) , long short-term memory ( LSTM ) and gated recurrent unit ( GRU ) are selected among many variations of RNNs for the missing information imputation while they are also used to predict the future medical examination data . Besides , the missing information imputation based on bidirectional LSTM is also proposed to consider past information as well as the future information in the imputation process , while the traditional RNNs can only consider the past information during the imputation .
Based on SRN network , we can get rid of the limitation of the traditional feature-based approach and carry out more in-depth video semantic analysis . By analyzing the characters in the SRN network , a community identification method based on the core characters is proposed , which can automatically confirm the core characters in the video and dig out the communities around the core characters . In this paper , lots of movie videos are used to experiment , and experimental results show the effectiveness of SRN model and community identification method .
In this paper , a stochastic reward net ( SRN ) model is constructed to analyze the performance of low Earth orbit ( LEO ) satellite networks in the case of packet loss . A lemma is proved to calculate the packet loss probability .
We then apply this modeling technique to the study of operating systems with dynamic priorities . In particular , we investigate the performance of the BS2000 operating system , which implements a dynamic priority mechanism , by means of a stochastic reward net ( SRN ) model . The results are then compared against an approximate model in which a static priority mechanism is assumed .
We also compared the performance of the different feature selection methods using the receiver operating characteristic analysis and the areas under the curve ( AUCs ) . The AUCs for the F-score , Relief , SVM-RFE , SVM-RFE ( mRMR ) , and SRN methods are 0.9014 , 0.8916 , 0.9121 , 0.9236 , and 0.9439 , respectively , with a tenfold cross-validation procedure , and are 0.9312 , 0.9178 , 0.9324 , 0.9413 , and 0.9615 , respectively , with a LOO scheme . Both the accuracy and AUC values show that the proposed SRN feature selection method has the best performance .
We derive a gradient-based algorithm for finding the parameters of the network that maximize the likelihood of the training sequences . Finally , we show that if the target structure generating the training sequences is unifilar , then the trained stochastic SRN behaves deterministically .
Many real world problems require the handling of temporally correlated events Recently some discrete time recurrent neural network models have been proposed to solve various temporal sequence processing problems One of the most popular models is Elman s simple recurrent network SRN which has been used for grammatical inference or formal language learning problems In this paper some major drawbacks of SRN and its variants are iden ti ed and investigated in detail Based on these observations a new model called ASCOC which stands for auto associative SRN with context to output connections is proposed to overcome the problems encountered by SRN Two experiments in using the ASCOC to learn regular grammars are reported with detailed analysis of the hidden layer patterns learned by the network Not only is it easier to train and use ASCOC than SRN the new model is also more powerful in that it can remember more nonlocal information that is relevant to correct learning of grammars with embedded structures Possible extension to domains beyond formal language learning is discussed
However , their ability to handle long-term dependencies between sentence constituents is somewhat limited . NARX networks have recently been shown to outperform SRN sb y preserving past information in explicit delays from the network ’s prior output . However , it is unclear how the number of delays should be determined .
We then compute word surprisal values for stimuli used in self-paced reading , eye-tracking , and electroencephalography experiments , and quantify the surprisal values ’ fit to experimental measures that indicate human sentence reading effort . While the gated networks provide better language models , they do not outperform their SRN counterpart as cognitive models when language model quality is equal across network types . Our results suggest that the different architectures are equally valid as models of human sentence processing .
The probability distribution describing the state of a Stochastic Reaction Network ( SRN ) evolves according to the Chemical Master Equation ( CME ) . It is common to estimate its solution using Monte Carlo methods such as the Stochastic Simulation Algorithm ( SSA ) .
This leads to improved skeleton localization , scale prediction , and better overall performance . Ke et al . present a side - output residual network ( SRN ) , which leverages the output residual units to fit the errors between the ground - truth and the side - outputs . By cascading residual units in a deep - to - shallow manner , SRN can effectively detect the skeleton at different scales . Liu et at . develop a two - stream network that combines image and segmentation cues to capture complementary information for skeleton localization .
The self-attention solves the long distance reliance of image local-region features when generate image . With spectral normalization , the training of GANs become more stable than traditional GANs , which conduces to avoid model collapse and gradient vanishing or explosion . We conduct experiments on widely used Oxford-102 flower dataset and CUB bird dataset to validate our proposed method .
Two neural network structures , DeblurRNN and DeblurMerger , are presented to exploit the pair of images in a sequential manner or parallel manner . To boost the training , gradient loss , adversarial loss and spectral normalization are leveraged . The training dataset that consists of pairs of noisy/blurry images and the corresponding ground truth sharp image is synthesized based on the benchmark dataset GOPRO .
Of key importance was the creation of a methodology that was accurate and repeatable and , as such , could be used to create earlier , comparable land-cover classifications in the future for the same region . We examined the effects of band normalization and different time-series image compositing methods on classification accuracy . Both top of atmosphere and surface reflectance products from the Landsat 8 Operational Land Imager ( OLI ) were tested for single-time classification independently , where the latter resulted in 1.1 % greater classification overall accuracy .
Since most of the content or energy of natural images resides in the low-frequency spectrum , their transformed coefficients in the frequency domain are highly imbalanced . To address this issue , we present a band normalization module ( BNM ) to normalize the coefficients from different parts of the frequency spectrum . Moreover , we employ a band discriminative training ( BDT ) criterion to enhance the model regression .
In order to GLCM features still has difference range for classification techniques , therefore , the GLCM feature will be normalized range entering to classify the image skin lesion image . The problem is normalization belonging the same class called Local-GLCM normalization which used maximum and minimum values within the same class for unseen data . This paper aims to improve and classify type of class for finding out the maximum and minimum values within the same class for process of normalization using clustering techniques . The unseen data are needed to use maximum and minimum value to enhance Local-GLCM normalization . This preprocessing data given highest accuracy value compared with non-normalization by classification techniques .
We are able to demonstrate differences in transferability properties from those observed in computer vision . We show that dataset normalization techniques such as z-score normalization does not affect the transferability of adversarial attacks and we show that techniques such as knowledge distillation do not increase the transferability of attacks .
In general , we find that FastICA ( Fast Independent Component Analysis ) , SIMPLISMA ( SIMPLe-to-use-Interactive Self-modeling Mixture Analysis ) , and NNMF ( Non-Negative Matrix Factorization ) are top-performing techniques . We demonstrate that dataset normalization approaches prior to blind source separation do not considerably improve outcomes . Within the range of noise levels studied , we did not find drastic changes to the ranking of techniques .
To mitigate this gap , we propose a 3D-Aided Deep Pose-Invariant Face Recognition Model ( 3D-PIM ) , which automatically recovers realistic frontal faces from arbitrary poses through a 3D face model in a novel way . Specifically , 3D-PIM incorporates a simulator with the aid of a 3D Morphable Model ( 3D MM ) to obtain shape and appearance prior for accelerating face normalization learning , requiring less training data . It further leverages a globallocal Generative Adversarial Network ( GAN ) with multiple critical improvements as a refiner to enhance the realism of both global structures and local details of the face simulator ’s output using unlabelled real data only , while preserving the identity information .
This project attempt to develop an efficient chart parser for Analysing Modern Standard Arabic ( MSA ) sentence with using natural language processing techniques . Firstly , Aljlayl & Frieder Normalization Algorithm is used to normalization text . Secondly , depending on the affixes list , Root-based and rule-based stemmers algorithms are used to catch each word stem .
Current inter-chip normalization methods rely on some assumptions , which can rarely be verified by experiments , such as the invariant expression level of housekeeping genes or the stability of statistical data distribution . In this paper , we present a highly verifiable inter-chip normalization method using genetic optimization algorithm , which depends only on a small number of housekeeping genes , and therefore facilitates experimental validation without any accuracy lost .
Nonnative speakers typically speak more slowly than natives , an experience that listeners take into account when explicitly judging the rate of nonnative speech . The present study investigated whether this is also reflected in implicit rate normalization . Results indicate that nonnative speech is implicitly perceived as faster than temporally-matched native speech , suggesting that the additional cognitive load of listening to an accent speeds up rate perception .
While general structures to implement convolution or fully-connected layers in RCA-based BNN hardware were actively studied in previous works , Batch-Normalization ( BN ) layer , which is another key layer of BNN , has not been discussed in depth yet . In this work , we propose in-memory batch-normalization schemes which integrate BN layers on RCA so that area/energy-efficiency of the BNN accelerators can be maximized . In addition , we also show that sense amp error due to device mismatch can be suppressed using the proposed in-memory BN design .
section : Batch Normalization for Domain Adaptation Batch normalization ( BN ) [ reference ] , which whitens the output of the hidden layer in a CNN , is an effective technique to accelerate training speed and enhance the accuracy of the model . In addition , in domain adaptation , whitening the hidden layer 's output is effective for improving the performance , which make the distribution in different domains similar [ reference ][ reference ] .
We can see that pre - normalization by trace produces 0.3 % lower error rate than that by Frobenius norm , while taking similar time with the latter . Hence , in all the remaining experiments , we adopt trace based pre - normalization method . Comparison with Other Covariance Pooling Methods
In this article , we discuss the impact of these effects on the TREC 2007 data sets . We show that the use of simple normalization methods significantly outperforms cosine normalization in the legal domain .
Experiments show that the new scheme is less sensitive to OCR errors and facilitates use of more diverse basic weighting schemes . It also yields significant improvements in retrieval effectiveness over cosine normalization .
In this paper , we propose a new type of normalization called term-length normalization which considers term distribution in a class . The performance of this normalization is investigated in three environments of a standard centroid-based classifier ( TFIDF ) : ( 1 ) without class-length normalization , ( 2 ) with cosine class-length normalization and ( 3 ) with summing weight normalization . The results suggest that our term-length normalization is useful for improving classification accuracy in all cases .
The paper shows that the new probabilistic interpretation of tf × idf term weighting might lead to better understanding of statistical ranking mechanisms , for example by explaining how they relate to coordination level ranking . A pilot experiment on the Cranfield test collection indicates that the presented model outperforms the vector space model with classical tf × idf and cosine length normalisation .
Training pivoted normalization on one collection , we can successfully use it on other ( new ) text collections , yielding a robust , collectzorz independent normalization technique . We use the idea of pivoting with the well known cosine normalization function . We point out some shortcomings of the cosine function andpresent two new normalization functions -- pivoted unique normalization and piuotert byte size normalization .
After reviewing existing edge and gradient based descriptors , we show experimentally that grids of histograms of oriented gradient ( HOG ) descriptors significantly outperform existing feature sets for human detection . We study the influence of each stage of the computation on performance , concluding that fine-scale gradients , fine orientation binning , relatively coarse spatial binning , and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results . The new approach gives near-perfect separation on the original MIT pedestrian database , so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds .
We present a new image‐based , post‐processing antialiasing technique , which offers practical solutions to the common , open problems of existing filter‐based real‐time antialiasing algorithms . Some of the new features include local contrast analysis for more reliable edge detection , and a simple and effective way to handle sharp geometric features and diagonal lines . This , along with our accelerated and accurate pattern classification allows for a better reconstruction of silhouettes .
5 should be : 5L . Drever , D. M. Robinson , A. McEwan , and W. Roa , “ A local contrast based approach to threshold segmentation for PET target volume delineation , ” Med . Phys . 33 , 1583–1594 ( 2006 ) .
It is believed that computer-aided detection ( CAD ) schemes could ultimately provide a useful “ second option ” for radiologists and potentially improve their diagnostic accuracy . The proposed detection process bases on local contrast thresholding and rule-based classification which is performed over the preprocessed and segmented mammograms . A relatively high detection rate of suspicious abnormal regions ( mass and/or microcalcification ) on the testing set of mammograms from Mini Mias Database implies that the proposed method can assist technologists in more efficiently and accurately locating the exact areas for subsequent exams .
Microaneurysms ( MAs ) are the earliest sign of this disease and so are very important for classifying whether images show signs of retinopathy . This paper describes automatic methods for MA detection and shows how image contrast normalization can improve the ability to distinguish between MAs and other dots that occur on the retina . Various methods for contrast normalization are compared .
Then the detected text region is binarized by a combined thresholding technique . After that , a local contrast average method is introduced to extract the RMB characters from the binarization result . The experiments demonstrate that the proposed binarization method outperforms other well-known methods .
Firstly , saliency detection method is utilized to get training samples for unsupervised feature learning . Next , these samples are sent to the denoising sparse autoencoder ( DSAE ) , followed by convolutional layer and local contrast normalization layer . Generally , prior in a specific task is helpful for the task solution .
The main outputs are percentage ( % ) neurite density and % myelination . % neurite density is calculated using the normalize local contrast algorithm , followed by thresholding , to adjust for differences in intensity . For % myelination the myelin sheaths are selected using the Frangi vesselness algorithm , in conjunction with a grey scale morphology filter and the removal of cell bodies using a high intensity mask .
In this study , we consider two specific factors . First , flankers could diminish the sensory response to the target via spatial contrast normalization in early visual processing . Second , rats may treat the sensory signal caused by the flankers as if it belonged to the target .
Each tile ’s contrast is enhanced such that its histogram becomes roughly uniform . Contrast Normalization ( Conorm ) enhances edges , filtering the input image by a difference of Gaussians , using a filter size of 5x5 pixels . Note that the above normalizations , except Conorm , are performed in a color space with image intensity as one of its components .
section : Related Work Starting with LeNet - 5 , convolutional neural networks ( CNN ) have typically had a standard structure – stacked convolutional layers ( optionally followed by contrast normalization and max - pooling ) are followed by one or more fully - connected layers . Variants of this basic design are prevalent in the image classification literature and have yielded the best results to - date on MNIST , CIFAR and most notably on the ImageNet classification challenge .
A total number of 60 , 000 images are split into 50 , 000 training and 10 , 000 testing images . The dataset is preprocessed by global contrast normalization . To compare our results with the previous state - of - the - art , in this case , we also augmented the dataset by zero padding 4 pixels on each side , then do corner cropping and random flipping on the fly during training .
= 1 and λL2 = 0.005 . The Maxout network , M , was trained with data augmentation following global contrast normalization and ZCA normalization . The model consists of three convolutional maxout layers followed by a fully connected maxout and softmax outputs .
The momentum term learning rate was fixed to 0.9 . The dataset was preprocessed as described in with global contrast normalization and ZCA whitening . Additionally , the images were padded with four zero pixels at all borders .
This leads to an interesting question of whether controlling these quantities might improve the generalization behaviour of NNs . To this end , we propose stable rank normalization ( SRN ) , a novel , optimal , and computationally efficient weight-normalization scheme which minimizes the stable rank of a linear operator . Surprisingly we find that SRN , inspite of being non-convex problem , can be shown to have a unique optimal solution .
Achieving high availability and performance computing with an HA-OSCAR cluster Simple recurrent networks and random indexing On the complexity of stratified logics Medical examination data prediction with missing information imputation based on recurrent neural networks SRN: The Movie Character Relationship Analysis via Social Network A Stochastic Reward Net-Based Performance Analysis of LEO Satellite Networks Performance evaluation of dynamic priority operating systems Mass Classification in Mammograms Using Selected Geometry and Texture Features, and a New SVM-Based Feature Selection Method Stochastic simple recurrent neural networks ASCOC: a recurrent neural network model for grammatical inference Using a Sequential SOM to Parse Long-term Dependencies Comparing Gated and Simple Recurrent Neural Network Architectures as Models of Human Sentence Processing Adaptive hybrid simulations for multiscale stochastic reaction networks. DeepFlux for Skeletons in the Wild Hybrid Attention Driven Text-to-Image Synthesis via Generative Adversarial Networks Deep Motion Blur Removal Using Noisy/Blurry Image Pairs Operational Large-Area Land-Cover Mapping: An Ethiopia Case Study Enhancement of a CNN-Based Denoiser Based on Spatial and Spectral Analysis Improved Skin Lesion Image Classification Using Clustering with Local-GLCM Normalization A Study on the Transferability of Adversarial Attacks in Sound Event Classification Blind Source Separation for NMR Spectra with Negative Intensity 3D-Aided Deep Pose-Invariant Face Recognition Development of Arabic parser based on Arabic grammar rules Optimizing Reference Gene Set for Inter-Chip Normalization Using Genetic Algorithm Normalization for speech rate in native and non-native speech In-memory batch-normalization for resistive memory based binary neural network hardware Asymmetric Tri-training for Unsupervised Domain Adaptation Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization On Retrieving Legal Files: Shortening Documents and Weeding Out Garbage Length Normalization in Degraded Text Collections Term-length Normalization for Centroid-based Text Categorization A Linguistically Motivated Probabilistic Model of Information Retrieval Pivoted document length normalization Histograms of oriented gradients for human detection SMAA: Enhanced Subpixel Morphological Antialiasing Erratum: "A novel PET tumor delineation method based on adaptive region-growing and dual-front active contours" [Med. Phys., - (2008)]. Detect abnormalities in mammograms by local contrast thresholding and rule-based classification Automated microaneurysm detection using local contrast normalization and local vessel detection Extraction of Serial Numbers on Bank Notes Image Classification Based on Convolutional Denoising Sparse Autoencoder MyelinJ: an ImageJ macro for high throughput analysis of myelinating cultures Rat performance on visual detection task modeled with divisive normalization and adaptive decision thresholds. Multi-column deep neural networks for image classification Going deeper with convolutions Deeply-Supervised Nets Deep Networks with Internal Selective Attention through Feedback Connections Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) Stable Rank Normalization for Improved Generalization in Neural Networks and GANs
166ff4d146edb85ac04fd91695522e92bfe55058 c685efe906f2c39e6f762c205b7d24ece6f95dbd 4e5f28fb8bc89f1ce2cf618b0d2fe3a4c122e8b5 de16693899cc929fa55784f34bfb0879cbfe777f b5609ed700d4309fd75eaef5f66f2bca327f0438 82c3bac9588ccf267ccdfbac43835322fda3e24c 55499f0b7c66f304c60c53396adb0e1fcbce95f9 c872bb34df96a496c80a8031c17b6a563816c1bd 45ea30887116fe74ef6ff4b268cf641b5e4c2a36 8ce41205112d2b9ad729bfaf7c0760d79cce63fb 8f424631fc0e628b30fe359e92692ae8c59d5f3b a7f4a47a19b30791c79c07cc4904796e485a8301 6280d3e55ce43139e8294105e85e8ebd011502ad aff93cf7b4a70edc4565ebe1688931b0b38fda83 a2e1bf0aadd18eb4941fefda626681383841e7a6 01f440d30c7a2fe63bab95b13f5e6a1dad8403ee 42956b7174909274e39dfc3624f9df6bb743d6db 008a391f1524b75c14a1f17157092d146bebbaec 4eb230360f05d528b865832d3cbe641516bbe0ca 3b53508d8ba8ae9c47e6ae11a517587a735a31fe a3f69a073dcfb6da8038607a9f14eb28b5dab2db 8bd216549d7e78f2fe9b00fe3d0fd4dad34c8cb2 086cae4aeea5438a35e20670a5c9f9f089b8630c 3beffd0b1bca3262c49dc5a8d0db07a187d5595a d30c972325a621a4fb839437dafcba9f561fc628 2c77140d550ec078309031a00cdfe64508bf2257 1713b37666ebab21fbd675160a10f7fa8d9af80b d8aa2c8592f157aeca82c216b2057ad0898e8f2a 7eb4731df814c5e7dc40dbeec2a549e3737dcd7c 2dea7a926fa54decb993d3c23e0140f45e15c4d8 cec734d7097ab6b1e60d95228ffd64248eb89d66 cfe88d1713bbe634efae3ca7aeaf210531586e58 2b4a709eab6c9edbd4dac2c2faea8282f129fbed 9b711e238815d3a82db4d452de864e370585a56f 906d00fef675d0ceec4ec66353e3a305f913043b fd3f6afcaa9b3a3f206a110426d4cac0b448a5ba b22d1ef896a034c080ae3ce44eb78393f2156396 867abd6a3388de93a22e5a75e0402ffbe83ff856 dd78b5392fce3b64005fbd8d8f6bbd0012d43b20 2ac189d66e5b44d5992c549c22565029e875708a
["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Psychology"] ["Medicine","Chemistry","Biology","Mathematics","Computer Science"] ["Computer Science"] ["Computer Science","Engineering"] ["Geology","Computer Science"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science"] ["Engineering","Computer Science","Physics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Physics","Medicine"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Psychology","Medicine"] ["Computer Science","Mathematics"]
2005 2002 2010 2017 2018 2006 1995 2014 1996 1994 1999 2019 2015 2019 2019 2019 2020 2019 2018 2020 2020 2018 2011 2009 2015 2019 2017 2018 2007 1995 2003 1998 1996 2005 2012 2008 2010 2006 2013 2017 2019 2011 2012 2015 2015 2014 2016 2020
MMM (2) PNPM ICGI CogSci 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ICANN (Workshop) ICIP EECS ICASSP IJCAI BMEI ICPhS ASP-DAC ICML 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition TREC KES ECDL SIGIR CVPR (1) ICDAR 2012 IEEE Conference on Computer Vision and Pattern Recognition 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) AISTATS NIPS ICLR ICLR
https://api.semanticscholar.org/166ff4d146edb85ac04fd91695522e92bfe55058 https://api.semanticscholar.org/c685efe906f2c39e6f762c205b7d24ece6f95dbd https://api.semanticscholar.org/4e5f28fb8bc89f1ce2cf618b0d2fe3a4c122e8b5 https://api.semanticscholar.org/de16693899cc929fa55784f34bfb0879cbfe777f https://api.semanticscholar.org/b5609ed700d4309fd75eaef5f66f2bca327f0438 https://api.semanticscholar.org/82c3bac9588ccf267ccdfbac43835322fda3e24c https://api.semanticscholar.org/55499f0b7c66f304c60c53396adb0e1fcbce95f9 https://api.semanticscholar.org/c872bb34df96a496c80a8031c17b6a563816c1bd https://api.semanticscholar.org/45ea30887116fe74ef6ff4b268cf641b5e4c2a36 https://api.semanticscholar.org/8ce41205112d2b9ad729bfaf7c0760d79cce63fb https://api.semanticscholar.org/8f424631fc0e628b30fe359e92692ae8c59d5f3b https://api.semanticscholar.org/a7f4a47a19b30791c79c07cc4904796e485a8301 https://api.semanticscholar.org/6280d3e55ce43139e8294105e85e8ebd011502ad https://www.semanticscholar.org/paper/9431bd6b37b3aced0b4bddaa1800a3341ccf47f1 https://api.semanticscholar.org/aff93cf7b4a70edc4565ebe1688931b0b38fda83 https://api.semanticscholar.org/a2e1bf0aadd18eb4941fefda626681383841e7a6 https://api.semanticscholar.org/01f440d30c7a2fe63bab95b13f5e6a1dad8403ee https://api.semanticscholar.org/42956b7174909274e39dfc3624f9df6bb743d6db https://api.semanticscholar.org/008a391f1524b75c14a1f17157092d146bebbaec https://api.semanticscholar.org/4eb230360f05d528b865832d3cbe641516bbe0ca https://api.semanticscholar.org/3b53508d8ba8ae9c47e6ae11a517587a735a31fe https://api.semanticscholar.org/a3f69a073dcfb6da8038607a9f14eb28b5dab2db https://api.semanticscholar.org/8bd216549d7e78f2fe9b00fe3d0fd4dad34c8cb2 https://api.semanticscholar.org/086cae4aeea5438a35e20670a5c9f9f089b8630c https://api.semanticscholar.org/3beffd0b1bca3262c49dc5a8d0db07a187d5595a https://api.semanticscholar.org/d30c972325a621a4fb839437dafcba9f561fc628 https://www.semanticscholar.org/paper/3a331607be6e10e6ab11d0402d7c1ce86c14ace4 https://www.semanticscholar.org/paper/2451db113552afb6d9ad15ef4009ec4133d28f74 https://api.semanticscholar.org/2c77140d550ec078309031a00cdfe64508bf2257 https://api.semanticscholar.org/1713b37666ebab21fbd675160a10f7fa8d9af80b https://api.semanticscholar.org/d8aa2c8592f157aeca82c216b2057ad0898e8f2a https://api.semanticscholar.org/7eb4731df814c5e7dc40dbeec2a549e3737dcd7c https://api.semanticscholar.org/2dea7a926fa54decb993d3c23e0140f45e15c4d8 https://api.semanticscholar.org/cec734d7097ab6b1e60d95228ffd64248eb89d66 https://api.semanticscholar.org/cfe88d1713bbe634efae3ca7aeaf210531586e58 https://api.semanticscholar.org/2b4a709eab6c9edbd4dac2c2faea8282f129fbed https://api.semanticscholar.org/9b711e238815d3a82db4d452de864e370585a56f https://api.semanticscholar.org/906d00fef675d0ceec4ec66353e3a305f913043b https://api.semanticscholar.org/fd3f6afcaa9b3a3f206a110426d4cac0b448a5ba https://api.semanticscholar.org/b22d1ef896a034c080ae3ce44eb78393f2156396 https://api.semanticscholar.org/867abd6a3388de93a22e5a75e0402ffbe83ff856 https://api.semanticscholar.org/dd78b5392fce3b64005fbd8d8f6bbd0012d43b20 https://www.semanticscholar.org/paper/398c296d0cc7f9d180f84969f8937e6d3a413796 https://www.semanticscholar.org/paper/e15cf50aa89fee8535703b9f9512fca5bfc43327 https://www.semanticscholar.org/paper/fb91db6aa4f710814f8aec28a7f3ecbc4e5ad4fd https://www.semanticscholar.org/paper/70155488a49d51755c1dfea728e03a6dd72703a1 https://www.semanticscholar.org/paper/f63e917638553414526a0cc8550de4ad2d83fe7a https://api.semanticscholar.org/2ac189d66e5b44d5992c549c22565029e875708a