For simplified cases the model is solved analytically . We also present a numerical approach to the solution in the general case . The extension to the transient spatial model is relatively straightforward . A behaviorally induced , system optimal travel demand management model is presented to achieve this goal through incremental routing . Both analytical derivation and numerical analysis have been conducted on Tucson network in Arizona , as well as on the Capital Area Metropolitan Planning Organization ( CAMPO ) network in Austin , TX . The outcomes of both studies show that our proposed modeling framework is promising for improving network traffic conditions toward SO , and results in substantial economic savings . The method of adjustment of the resources allocated to processing elements to the information workload of the one-shot skip-level reporting structures of information processing is presented , and the efficiency frontier is characterized . Furthermore , the optimal organization of numerical data processing in enterprises is analyzed on the example of predicting demand in the firm . Monte Carlo simulations are then carried out to examine the performance of the proposed estimators . Finally , a numerical data analysis is performed in order to illustrate all the methods of inference discussed here . VisAD is a Java class library for interactive and collaborative visualization and analysis of numerical data . It is designed to support distributed computing and data sharing on the Internet through the use of distributed objects and a very general numerical data model . The data model integrates metadata for data organization , units , coordinate systems , sampling geometries and topoligies , missing data indicators , and error estimates . The rapidly increasing power and availability of small computers have made sophisticated numerical analysis of data a routine feature of modem chemistry . Analytical computations that would have been too time consuming on a mainframe computer of 20 years ago have become commonplace on the laboratory microcomputers of today . The purpose of the third module , entitled Model Construction , is the construction of trend predictive numerical models . The fourth and last module , entitled Backtesting and Numerical Results , evaluates the accuracy of the trend predictive numerical models over a " significant " test set via two generic backtesting plans . The first plan computes recognition rates of upward and downward trends . A fundamentally new data compression approach , which is based on the optimizing properties of Fibonacci numbers lies in the fact that the figures are considered as positive whole numbers and presented by a linear Fibonacci form , was investigated . Formation features of the numerical data source model were examined . The effect of the length of data blocks of the compressed file on the compression ratio was studied . Theoretically , the number of non - zero singular values of a matrix is equal to its rank . However , performing singular value decomposition of real valued matrices using numerical approaches often encounter roundoff errors . Hence , we adopt the expected roundoff error suggested by press2007numerical when estimating the ranks of , and . Although the strict mathematical proof requires very strong conditions , the fact that this statement resonates with the well known Hebbian principle – neurons that fire together , wire together – suggests that the underlying idea is applicable even under less strict conditions , in practice . On the downside , todayâs computing infrastructures are very inefficient when it comes to numerical calculation on non - uniform sparse data structures . Even if the number of arithmetic operations is reduced by , the overhead of lookups and cache misses is so dominant that switching to sparse matrices would not pay off . However , in the small sample size case , we have ; as a result , is singular . FST thus runs in numerical problems and common solutions include reducing by PCA or adding a regularisation term to . In , a more principled way to overcome the SSS problem in FST is proposed , termed as Null Foley - Sammon transform ( NFST ) . This volume contains 37 papers , selected out of 133 submitted papers ; seven invited papers ( four invited talks and three tutorials ) are also included . The selected papers are grouped into sessions on Process Algebras , Categorical Approaches , The pi-Calculus , Decidability & Complexity , Probability , Functional & Constraint Programming , Petri Nets , Verification , Automata & Causality , Practical Models , Shared Memory Systems . Our results confirm that the mid-level visual features preserved in the scrambled objects contribute to animacy decoding , but also demonstrate that the dynamics vary markedly for intact versus scrambled objects . Our findings suggest a complex interplay between visual feature coding and categorical representations that is mediated by the visual system 's capacity to use image features to resolve a recognisable object . It relies on retrieving optimal particle positions in the search space that correspond to the centroids of classes . When evaluating the fitness function , we use different mechanisms to interpret the particle positions in the description space , based on data type ; as will be described , rounding is used for integer attributes while a frequency measure is used for categorical descriptors . An experimental set up was realized and tested on the Adult database , leading to recognition accuracies that were better than those obtained with well known classifiers . Timeliner is a browser for long audio recordings and features that it derives from such recordings . Features can be either signal-based , like spectrograms , or model-based , like categorical classifiers . Unlike conventional audio editors , Timeliner pans and zooms smoothly across many orders of magnitude , from days-long overviews to millisecond-scale details , with zero latency , zero flicker , and low CPU load . : This is a problematic task , since models of classical logic easily “ collapse ” by equating any two proofs of the same statement . Furthermore , existing models ( in particular categorical ones ) are often too “ coarse-grained ” insofar as they impose various equalities on proofs which are , at the least , debatable . Approach of this Thesis : In this thesis , a model based on weak 2-categories ( with additional structure ) is defined . However , new hypotheses need to be further investigated . Great efforts should be made to RP research in terms of varies of materials , combination with other methods , learning mechanism of categorical feature processing . This is a challenging task , mainly due to the absence of class labels to guide the search for relevant features . Categorical feature selection for clustering has rarely been addressed in the literature , with most of the proposed approaches having focused on numerical data . In this work , we propose an approach to simultaneously cluster categorical data and select a subset of relevant features . We use a Bayesian approach , which assigns a probability of having ( or not having ) a particular function to each protein , given the various sources of evidence . In our method , each data source is represented by either a functional linkage graph or a categorical feature vector . RESULTS AND CONCLUSIONS The methods are tested on Plasmodium falciparum , the species responsible for the deadliest malaria infections . Thus , in this thesis , the concept of attribute relative which is based on the theory of soft-set is discussed and consequently introduces an alternative technique to the partition attribute selection approach for the used in the categorical data clustering . A technique which called Maximum Total Attribute Relative ( MTAR ) is able to determine the partition attribute of the categorical information system at the category level without compromising the computational cost and at the same time enhance the legitimacy of the resulting clusters . Experiments on sixteen ( 16 ) UCI-MLR benchmark datasets demonstrate the potentials of MTAR to achieved lower computational time with the improvements up to 90 % as compared to TR , MMR , MDA and NSS . This paper introduces a categorical data analysis technique called Hildebrand 's del . The advantages of del are ; it allows for the testing of customized prediction rules , it provides a strength of a ... Тhis paper presents English acronyms as a way of linguistic retrenchment in the aspect of their differentiation according to their species , word-building model , acoustic form , usage area . Basic methods of the research : categorical analysis , determining of statistic parameters of word-forming models . In the process of the research , the following types of acronyms were defined : 1 ) terms , 2 ) acronyms pronounced as individual letters , considered as complete words . Unfortunately , the statistical training of most psychologists emphasizes data models and methods geared toward interval and ratio scale data . While some statistic courses give cursory coverage of contingency tables and a few nonparametric techniques , most psychologists are unaware of the rich possibilities that other research fields have enjoyed through the use of categorical data method . Advances in both statistical theory and software development can deliver the promises of strong categorical analyses The symposium will show participants how to gain the most information from two-way categorical contingency table analyses and will then provide glimpses and overviews of advanced categorical procedures such as loglinear modeling , categorical logistic regression , configural frequency analysis , correspondence analysis , categorical regression trees , and latent class analyses . The traditional centroid-based classifiers can not be directly applied to categorical data classification due to the undefined concept of centroid for a categorical class , and the lack of an effective distance measure for categorical objects . In this paper , two centroid-based classifiers are proposed for categorical data classification . We propose a new formulation for the centroid of categorical classes to address the first problem , while two weighted distance measures are defined for the second problem . For that purpose we compared 3B42 against 205 quality-controlled rain gauge measurements for the period from March 1998 to July 2013 , with the aim to know whether 3B42 is reliable for climate studies . Analysis of quantitative ( Bias , Relative RMSE ) and categorical statistics ( POD , FAR ) for the whole period show a more accurate spatial distribution of mean daily rainfall estimations in the lowlands than in the Andean regions . In the latter , the location of a rain gauge and its exposure seem to be more relevant to explain mismatches with 3B42 rather than its elevation . An ELM-aware and a SVM-aware representation methods have been instantiated under this framework . Extensive experiments of classification with the embedded representation on 17 data sets demonstrate that the proposed framework can significantly improve the categorical data representation performance compared with state-of-the-art competitors . What are categorical data types ? The categorical data type approach is an extension of the abstract data type approach in a way that seems particularly useful for parallel computation . It is more than a programming language , however . Other modified ANOVA models , response error models in sample surveys , and related multivariate extensions are discussed . For the categorical data case , special attention is given to measures of inter- , observer agreement and tests of hypotheses concerning inter-observer bias . In particular , similarities between-the dichotomous and quantitative data cases are presented in terms of intraclass correlation coefficients . We experiment with two settings of the distribution : a categorical distribution across each channel PixelRNN and a mixture of discretized logistics over three channels PixelCNNpp . The categorical distribution ( cat ) captures each intensity value as a discrete outcome and factorizes across channels . In total , there are parameters for each pixel ; for images , the network outputs dimensions . section : Inference Across all of the presented experiments , we use categorical sampling during decoding with a tempered PixelRecursiveSuperResolution . We adjust the concentration of the distribution we sample from with a temperature by which we divide the logits for the channel intensities . Subsequently , hessel2018rainbow combined C51 with enhancements such as prioritized experience replay , - step updates , and the dueling architecture , leading to the Rainbow agent , current state - of - the - art in Atari - 57 . The categorical parameterization , using the projected KL loss , has also been used in recent work to improve the critic of a policy gradient algorithm , D4PG , achieving significantly improved robustness and state - of - the - art performance across a variety of continuous control tasks . subsection : - Wasserstein Metric First and most relevant to this work , dabney2017qr proposed the use of quantile regression for distributional RL and showed that by choosing the quantile targets suitably the resulting projected distributional Bellman operator is a contraction in the - Wasserstein metric . Concurrently , rowland2018analysis showed the original class of categorical algorithms are a contraction in the Cramér distance , the metric on cumulative distribution functions . By estimating the quantile function at precisely chosen points , QR - DQN minimizes the Wasserstein distance to the distributional Bellman target . Indeed , in order to achieve the discrete vs. continuous decomposition , we have to use very deep and powerful PixelCNN decoders ( up to 20 residual blocks ) to capture both the global and local statistics of the style by the PixelCNN while the discrete content of the image is captured by the categorical latent variable . subsection : PixelGAN Autoencoders with Categorical Priors In this section , we present an architecture of the PixelGAN autoencoder that can separate the discrete information ( e.g. , class label ) from the continuous information ( e.g. , style information ) in the images . Here , a concept of natural language processing in the form of word2vec is used to represent the motif of the target class automatically . These motifs ( numerical descriptors ) are automatically targeted from the human genome without any clear definition . Further , the extracted feature space is then forwarded to the convolution neural network model as input for prediction . Traditional feature extraction methods can not address this issue very well because of inherent characteristics . Aiming at this problem , a novel numerical feature extraction approach EPLS is proposed . It is an integration of the Ensemble Empirical Mode ( EEMD ) , Principal Component Analysis ( PCA ) and Least Square ( LS ) method . Behavior analysis using trajectory data presents a practical and interesting challenge for KDD . Conventional analyses address discriminative tasks of behaviors , e.g. , classification and clustering typically using the subsequences extracted from the trajectory of an object as a numerical feature representation . In this paper , we explore further to identify the difference in the high-level semantics of behaviors such as roles and address the task in a cross-domain learning approach . It has a simple introduction of sea surface modeling based on PM power spectrum theory first . Because sea surface propagation causes multipath effect , it 's necessary to study the multipath ray model and the numerical characteristics . A multipath propagation channel model is established by utilizing the stochastic bridge process modeling approach . A forward greedy search algorithm for numerical feature selection is constructed with the proposed measure . Experimental results show that the proposed method presents an efficient and effective solution for numerical feature analysis . Summary : Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural , functional , expression and interaction profiles of proteins and peptides as well as DNAs/RNAs . Here , we present iFeature , a versatile Python‐based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences . iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors . We show the method outputs assignments ( classification results ) which are near perfectly correlated with the clinical diagnosis of several lesion types ' lesions utilizing a database of 94 patients in total . Experimental comparisons also show the new method can significantly outperform standard numerical feature-type methods in terms of agreement with the clinical diagnosis gold standard . In addition , the new method could potentially be used to derive insights into biologically meaningful nuclear morphology differences in these lesions . In this paper we investigate on how the choice of similarity measures affects the performance of content-based 3D model retrieval ( CB3DR ) algorithms . In CB3DR , shape descriptors are used to provide a numerical representation of the salient features of the data , while similarity functions capture the high level semantic concepts . In the first part of the paper , we demonstrate experimentally that the Euclidean distance is not the optimal similarity function for 3D model classification and retrieval . The MPEG-7 uses an extended XML standard for defining structural relation between descriptors allowing creation and modification of description schemes . MPEG-7 visual descriptors are numerical representations of features - such as : texture , shape and color - extracted from an image . In this paper , the MPEG-7 is conceived as a set of services for extracting and storing visual descriptors . We describe our approach on voice seach in a mobile context by a TV guide search app that integrates linked open data to identify movies from a search query . A text parser to match keywords against vocabularies and numerical value descriptors is introduced . Due to this property , the convolutions are designed as temporal convolutions . Furthermore , it is necessary to generate a numerical representation from the text so the network can be trained using this representation . This representation , namely embeddings , is usually obtained through the application of a lookup table , generated from a given dictionary . However , present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks . Here we propose node2vec , an algorithmic framework for learning continuous feature representations for nodes in networks . In node2vec , we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes . Of particular interest is the error produced due to mismatch between training speaker recognition models with neutral speech and testing them with expressive speech . While previous studies have considered categorical emotions , expressive speech during human interaction conveys subtle behaviors that are better characterized with continuous descriptors ( e.g. , attributes such as arousal , valence , dominance ) . As the emotion becomes more intense , we expect the performance of speaker recognition tasks to drop . Image and video retrieval experiments are performed on two standard datasets ( a Corel dataset and a TRECVID3 dataset ) which consist of 4,500 images and about 44,100 keyframes respectively . The results show that based on a large visual vocabulary the model runs extremely fast on even very large datasets while having comparable retrieval performance to the best performing ( continuous feature ) models . The head of the user is localized via a combination of colorand shape-based segmentation . For a continuous feature extraction , we use a template matching of the nose bridge in combination with selected features derived from the optical flow . The core classification unit consists of discrete Hidden Markov Models ( DHMMs ) . A prediction method ( GA-CNN-LSTM ) which combines convolutional neural network ( CNN ) and long-short-term memory network ( LSTM ) and optimized by genetic algorithm ( GA ) is established . First , network search data , meteorological data , and other data are constructed into continuous feature maps . Then , feature vectors are extracted by convolutional neural network ( CNN ) . Abstract This paper proposes a unified approach to learning from constraints , which integrates the ability of classical machine learning techniques to learn from continuous feature-based representations with the ability of reasoning using higher-level semantic knowledge typical of Statistical Relational Learning . Learning tasks are modeled in the general framework of multi-objective optimization , where a set of constraints must be satisfied in addition to the traditional smoothness regularization term . This article presents an alternative approach ( the continuous FITA ) that addresses these complications effectively by representing continuous features in a more natural way . It is shown that this approach is well-suited to continuous feature information analysis and , furthermore , can be used to estimate redundancy in multiple features and information transmitted by combinations of features . The continuous FITA can quantify information in extracted features before an identification experiment has been conducted . Those enhance the aggregation of protocol features . Continuous feature set and content monitoring of real-time monitoring traffic are designed . Associating spatial information features is enhances the adaptive ability of traffic feature set . Smart systems with related word prediction have turned out to be extremely prevalent for English language but there is no such big efforts for Hindi language . The main goal of this dissertation work is to provide syntactically and semantically related words based on continuous feature vector representation . Continuous Bag of Words ( CBOW ) language model is used to get the feature vector representation of each word in training set . Future extensions of node2vec could involve networks with special structure such as heterogeneous information networks , networks with explicit domain features for nodes and edges and signed - edge networks . Continuous feature representations are the backbone of many deep learning algorithms , and it would be interesting to use node2vec representations as building blocks for end - to - end deep learning on graphs . Acknowledgements . In the literature it is currently best practice to add real - valued noise to the pixel values to dequantize the data when using density functions . When uniform noise is added ( with values in the interval [ 0 , 1 ] ) , then the log - likelihoods of continuous and discrete models are directly comparable . In our case , we can use the values from the discrete distribution as a piecewise - uniform continuous function that has a constant value for every interval . Most competitive neural sequence transduction models have an encoder - decoder structure cho2014learning , bahdanau2014neural , sutskever14 . Here , the encoder maps an input sequence of symbol representations to a sequence of continuous representations . Given , the decoder then generates an output sequence of symbols one element at a time . In this work , we propose a long dependency aware deep architecture for joint S&T task . Specifically , to simulate the feature templates of traditional discrete feature based models , we use different filters to model the complex compositional features with convolutional and pooling layer , and then utilize long distance dependency information with recurrent layer . Experiment results on five different datasets show the effectiveness of our proposed model . Spam is unsolicited bulk email . Most of the popular spam filters work depending on individual training corpus or discrete feature extraction , and these filters have n't considered spam 's bulk feature . The better method to stop spam may be the collaborative approach that does not only consider the content of the email but depends on the collaboration of groups of users who share information about spam . Visual search is the task of finding objects in an image which are described in a high dimensional space , spanned by preattentive features , e.g. orientation , scale , and colour . By using steerable filters this search space may be scanned continouosly , though spanned by discrete feature detectors . Based on this idea , we will show a method for detecting arbitrarily oriented bars from steerable filter responses . If we compare the widely used Conditional Random Fields ( CRF ) with newly proposed “ deep architecture ” sequence models ( Collobert et al. , 2011 ) , there are two things changing : from linear architecture to non-linear , and from discrete feature representation to distributional . It is unclear , however , what utility nonlinearity offers in conventional featurebased models . In this chapter , we propose a novel framework for image classification and automatic annotation . First , feature selection strategy is explicitly introduced , including discrete feature method and continuous feature method . In both methods , the salient patches are detected and quantized for every image category . Abstract Confronting low data reduction typologies , as established by using data from parallel texts , with the high data reduction typologies of WALS reveals a systematic bias of WALS typologies toward highly bimodal distribution . Properties with a distribution supporting a discrete feature analysis in many languages are likelier to be represented in WALS and to be represented accurately . This bias has important consequences when WALS typologies are interpreted theoretically or further processed statistically . Abstract This paper deals with the challenging task of acquiring stable image features in a sequence of images of the same scene taken under different viewing positions by a digital still camera . Two popular contemporary algorithms for discrete feature detection : SIFT and SURF are regarded . The results of the timing performance analysis of their sequential implementations are presented and discussed . In this paper , a two-tier intelligent automated predictor has been developed called iMem-2LSAAC , which classifies protein sequence as membrane or non-membrane in first-tier ( phase1 ) and in case of membrane the second-tier ( phase2 ) identifies functional types of membrane protein . Quantitative attributes were extracted from protein sequences by applying three discrete features extraction schemes namely amino acid composition , pseudo amino acid composition and split amino acid composition ( SAAC ) . Various learning algorithms were investigated by using jackknife test to select the best one for predictor . Also , we model the geometric distortions caused by page curl and perspective view as the generalized cylindrical surfaces and camera rotation respectively . With these distortion models and the discrete representation of the features , we design a cost function whose minimization yields the parameters of the distortion model . In the cost function , we encode the properties of the pages such as text-block alignment , line-spacing , and the straightness of text-lines . In the literature it is currently best practice to add real - valued noise to the pixel values to dequantize the data when using density functions . When uniform noise is added ( with values in the interval [ 0 , 1 ] ) , then the log - likelihoods of continuous and discrete models are directly comparable . In our case , we can use the values from the discrete distribution as a piecewise - uniform continuous function that has a constant value for every interval . However , little progress has been made in applying GANs to sequence discrete data generation problems , e.g. natural language generation . This is due to the generator network in GAN is designed to be able to adjust the output continuously , which does not work on discrete data generation . On the other hand , a lot of efforts have been made to generate structured sequences . Prior work has suggested a variety of different novelty functions e.g. , based on state visitation frequency . While such methods offer a number of appealing guarantees , such as near - Bayesian exploration in polynomial time , they require a concise , often discrete representation of the agent ’s state - action space to measure state visitation frequencies . In our approach , we will employ function approximation and representation learning to devise an alternative to these requirements .
For simplified cases the model is solved analytically . We also present a numerical approach to the solution in the general case . The extension to the transient spatial model is relatively straightforward .
A behaviorally induced , system optimal travel demand management model is presented to achieve this goal through incremental routing . Both analytical derivation and numerical analysis have been conducted on Tucson network in Arizona , as well as on the Capital Area Metropolitan Planning Organization ( CAMPO ) network in Austin , TX . The outcomes of both studies show that our proposed modeling framework is promising for improving network traffic conditions toward SO , and results in substantial economic savings .
The method of adjustment of the resources allocated to processing elements to the information workload of the one-shot skip-level reporting structures of information processing is presented , and the efficiency frontier is characterized . Furthermore , the optimal organization of numerical data processing in enterprises is analyzed on the example of predicting demand in the firm .
Monte Carlo simulations are then carried out to examine the performance of the proposed estimators . Finally , a numerical data analysis is performed in order to illustrate all the methods of inference discussed here .
VisAD is a Java class library for interactive and collaborative visualization and analysis of numerical data . It is designed to support distributed computing and data sharing on the Internet through the use of distributed objects and a very general numerical data model . The data model integrates metadata for data organization , units , coordinate systems , sampling geometries and topoligies , missing data indicators , and error estimates .
The rapidly increasing power and availability of small computers have made sophisticated numerical analysis of data a routine feature of modem chemistry . Analytical computations that would have been too time consuming on a mainframe computer of 20 years ago have become commonplace on the laboratory microcomputers of today .
The purpose of the third module , entitled Model Construction , is the construction of trend predictive numerical models . The fourth and last module , entitled Backtesting and Numerical Results , evaluates the accuracy of the trend predictive numerical models over a " significant " test set via two generic backtesting plans . The first plan computes recognition rates of upward and downward trends .
A fundamentally new data compression approach , which is based on the optimizing properties of Fibonacci numbers lies in the fact that the figures are considered as positive whole numbers and presented by a linear Fibonacci form , was investigated . Formation features of the numerical data source model were examined . The effect of the length of data blocks of the compressed file on the compression ratio was studied .
Theoretically , the number of non - zero singular values of a matrix is equal to its rank . However , performing singular value decomposition of real valued matrices using numerical approaches often encounter roundoff errors . Hence , we adopt the expected roundoff error suggested by press2007numerical when estimating the ranks of , and .
Although the strict mathematical proof requires very strong conditions , the fact that this statement resonates with the well known Hebbian principle – neurons that fire together , wire together – suggests that the underlying idea is applicable even under less strict conditions , in practice . On the downside , todayâs computing infrastructures are very inefficient when it comes to numerical calculation on non - uniform sparse data structures . Even if the number of arithmetic operations is reduced by , the overhead of lookups and cache misses is so dominant that switching to sparse matrices would not pay off .
However , in the small sample size case , we have ; as a result , is singular . FST thus runs in numerical problems and common solutions include reducing by PCA or adding a regularisation term to . In , a more principled way to overcome the SSS problem in FST is proposed , termed as Null Foley - Sammon transform ( NFST ) .
This volume contains 37 papers , selected out of 133 submitted papers ; seven invited papers ( four invited talks and three tutorials ) are also included . The selected papers are grouped into sessions on Process Algebras , Categorical Approaches , The pi-Calculus , Decidability & Complexity , Probability , Functional & Constraint Programming , Petri Nets , Verification , Automata & Causality , Practical Models , Shared Memory Systems .
Our results confirm that the mid-level visual features preserved in the scrambled objects contribute to animacy decoding , but also demonstrate that the dynamics vary markedly for intact versus scrambled objects . Our findings suggest a complex interplay between visual feature coding and categorical representations that is mediated by the visual system 's capacity to use image features to resolve a recognisable object .
It relies on retrieving optimal particle positions in the search space that correspond to the centroids of classes . When evaluating the fitness function , we use different mechanisms to interpret the particle positions in the description space , based on data type ; as will be described , rounding is used for integer attributes while a frequency measure is used for categorical descriptors . An experimental set up was realized and tested on the Adult database , leading to recognition accuracies that were better than those obtained with well known classifiers .
Timeliner is a browser for long audio recordings and features that it derives from such recordings . Features can be either signal-based , like spectrograms , or model-based , like categorical classifiers . Unlike conventional audio editors , Timeliner pans and zooms smoothly across many orders of magnitude , from days-long overviews to millisecond-scale details , with zero latency , zero flicker , and low CPU load .
: This is a problematic task , since models of classical logic easily “ collapse ” by equating any two proofs of the same statement . Furthermore , existing models ( in particular categorical ones ) are often too “ coarse-grained ” insofar as they impose various equalities on proofs which are , at the least , debatable . Approach of this Thesis : In this thesis , a model based on weak 2-categories ( with additional structure ) is defined .
However , new hypotheses need to be further investigated . Great efforts should be made to RP research in terms of varies of materials , combination with other methods , learning mechanism of categorical feature processing .
This is a challenging task , mainly due to the absence of class labels to guide the search for relevant features . Categorical feature selection for clustering has rarely been addressed in the literature , with most of the proposed approaches having focused on numerical data . In this work , we propose an approach to simultaneously cluster categorical data and select a subset of relevant features .
We use a Bayesian approach , which assigns a probability of having ( or not having ) a particular function to each protein , given the various sources of evidence . In our method , each data source is represented by either a functional linkage graph or a categorical feature vector . RESULTS AND CONCLUSIONS The methods are tested on Plasmodium falciparum , the species responsible for the deadliest malaria infections .
Thus , in this thesis , the concept of attribute relative which is based on the theory of soft-set is discussed and consequently introduces an alternative technique to the partition attribute selection approach for the used in the categorical data clustering . A technique which called Maximum Total Attribute Relative ( MTAR ) is able to determine the partition attribute of the categorical information system at the category level without compromising the computational cost and at the same time enhance the legitimacy of the resulting clusters . Experiments on sixteen ( 16 ) UCI-MLR benchmark datasets demonstrate the potentials of MTAR to achieved lower computational time with the improvements up to 90 % as compared to TR , MMR , MDA and NSS .
This paper introduces a categorical data analysis technique called Hildebrand 's del . The advantages of del are ; it allows for the testing of customized prediction rules , it provides a strength of a ...
Тhis paper presents English acronyms as a way of linguistic retrenchment in the aspect of their differentiation according to their species , word-building model , acoustic form , usage area . Basic methods of the research : categorical analysis , determining of statistic parameters of word-forming models . In the process of the research , the following types of acronyms were defined : 1 ) terms , 2 ) acronyms pronounced as individual letters , considered as complete words .
Unfortunately , the statistical training of most psychologists emphasizes data models and methods geared toward interval and ratio scale data . While some statistic courses give cursory coverage of contingency tables and a few nonparametric techniques , most psychologists are unaware of the rich possibilities that other research fields have enjoyed through the use of categorical data method . Advances in both statistical theory and software development can deliver the promises of strong categorical analyses The symposium will show participants how to gain the most information from two-way categorical contingency table analyses and will then provide glimpses and overviews of advanced categorical procedures such as loglinear modeling , categorical logistic regression , configural frequency analysis , correspondence analysis , categorical regression trees , and latent class analyses .
The traditional centroid-based classifiers can not be directly applied to categorical data classification due to the undefined concept of centroid for a categorical class , and the lack of an effective distance measure for categorical objects . In this paper , two centroid-based classifiers are proposed for categorical data classification . We propose a new formulation for the centroid of categorical classes to address the first problem , while two weighted distance measures are defined for the second problem .
For that purpose we compared 3B42 against 205 quality-controlled rain gauge measurements for the period from March 1998 to July 2013 , with the aim to know whether 3B42 is reliable for climate studies . Analysis of quantitative ( Bias , Relative RMSE ) and categorical statistics ( POD , FAR ) for the whole period show a more accurate spatial distribution of mean daily rainfall estimations in the lowlands than in the Andean regions . In the latter , the location of a rain gauge and its exposure seem to be more relevant to explain mismatches with 3B42 rather than its elevation .
An ELM-aware and a SVM-aware representation methods have been instantiated under this framework . Extensive experiments of classification with the embedded representation on 17 data sets demonstrate that the proposed framework can significantly improve the categorical data representation performance compared with state-of-the-art competitors .
What are categorical data types ? The categorical data type approach is an extension of the abstract data type approach in a way that seems particularly useful for parallel computation . It is more than a programming language , however .
Other modified ANOVA models , response error models in sample surveys , and related multivariate extensions are discussed . For the categorical data case , special attention is given to measures of inter- , observer agreement and tests of hypotheses concerning inter-observer bias . In particular , similarities between-the dichotomous and quantitative data cases are presented in terms of intraclass correlation coefficients .
We experiment with two settings of the distribution : a categorical distribution across each channel PixelRNN and a mixture of discretized logistics over three channels PixelCNNpp . The categorical distribution ( cat ) captures each intensity value as a discrete outcome and factorizes across channels . In total , there are parameters for each pixel ; for images , the network outputs dimensions .
section : Inference Across all of the presented experiments , we use categorical sampling during decoding with a tempered PixelRecursiveSuperResolution . We adjust the concentration of the distribution we sample from with a temperature by which we divide the logits for the channel intensities .
Subsequently , hessel2018rainbow combined C51 with enhancements such as prioritized experience replay , - step updates , and the dueling architecture , leading to the Rainbow agent , current state - of - the - art in Atari - 57 . The categorical parameterization , using the projected KL loss , has also been used in recent work to improve the critic of a policy gradient algorithm , D4PG , achieving significantly improved robustness and state - of - the - art performance across a variety of continuous control tasks . subsection : - Wasserstein Metric
First and most relevant to this work , dabney2017qr proposed the use of quantile regression for distributional RL and showed that by choosing the quantile targets suitably the resulting projected distributional Bellman operator is a contraction in the - Wasserstein metric . Concurrently , rowland2018analysis showed the original class of categorical algorithms are a contraction in the Cramér distance , the metric on cumulative distribution functions . By estimating the quantile function at precisely chosen points , QR - DQN minimizes the Wasserstein distance to the distributional Bellman target .
Indeed , in order to achieve the discrete vs. continuous decomposition , we have to use very deep and powerful PixelCNN decoders ( up to 20 residual blocks ) to capture both the global and local statistics of the style by the PixelCNN while the discrete content of the image is captured by the categorical latent variable . subsection : PixelGAN Autoencoders with Categorical Priors In this section , we present an architecture of the PixelGAN autoencoder that can separate the discrete information ( e.g. , class label ) from the continuous information ( e.g. , style information ) in the images .
Here , a concept of natural language processing in the form of word2vec is used to represent the motif of the target class automatically . These motifs ( numerical descriptors ) are automatically targeted from the human genome without any clear definition . Further , the extracted feature space is then forwarded to the convolution neural network model as input for prediction .
Traditional feature extraction methods can not address this issue very well because of inherent characteristics . Aiming at this problem , a novel numerical feature extraction approach EPLS is proposed . It is an integration of the Ensemble Empirical Mode ( EEMD ) , Principal Component Analysis ( PCA ) and Least Square ( LS ) method .
Behavior analysis using trajectory data presents a practical and interesting challenge for KDD . Conventional analyses address discriminative tasks of behaviors , e.g. , classification and clustering typically using the subsequences extracted from the trajectory of an object as a numerical feature representation . In this paper , we explore further to identify the difference in the high-level semantics of behaviors such as roles and address the task in a cross-domain learning approach .
It has a simple introduction of sea surface modeling based on PM power spectrum theory first . Because sea surface propagation causes multipath effect , it 's necessary to study the multipath ray model and the numerical characteristics . A multipath propagation channel model is established by utilizing the stochastic bridge process modeling approach .
A forward greedy search algorithm for numerical feature selection is constructed with the proposed measure . Experimental results show that the proposed method presents an efficient and effective solution for numerical feature analysis .
Summary : Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural , functional , expression and interaction profiles of proteins and peptides as well as DNAs/RNAs . Here , we present iFeature , a versatile Python‐based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences . iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors .
We show the method outputs assignments ( classification results ) which are near perfectly correlated with the clinical diagnosis of several lesion types ' lesions utilizing a database of 94 patients in total . Experimental comparisons also show the new method can significantly outperform standard numerical feature-type methods in terms of agreement with the clinical diagnosis gold standard . In addition , the new method could potentially be used to derive insights into biologically meaningful nuclear morphology differences in these lesions .
In this paper we investigate on how the choice of similarity measures affects the performance of content-based 3D model retrieval ( CB3DR ) algorithms . In CB3DR , shape descriptors are used to provide a numerical representation of the salient features of the data , while similarity functions capture the high level semantic concepts . In the first part of the paper , we demonstrate experimentally that the Euclidean distance is not the optimal similarity function for 3D model classification and retrieval .
The MPEG-7 uses an extended XML standard for defining structural relation between descriptors allowing creation and modification of description schemes . MPEG-7 visual descriptors are numerical representations of features - such as : texture , shape and color - extracted from an image . In this paper , the MPEG-7 is conceived as a set of services for extracting and storing visual descriptors .
We describe our approach on voice seach in a mobile context by a TV guide search app that integrates linked open data to identify movies from a search query . A text parser to match keywords against vocabularies and numerical value descriptors is introduced .
Due to this property , the convolutions are designed as temporal convolutions . Furthermore , it is necessary to generate a numerical representation from the text so the network can be trained using this representation . This representation , namely embeddings , is usually obtained through the application of a lookup table , generated from a given dictionary .
However , present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks . Here we propose node2vec , an algorithmic framework for learning continuous feature representations for nodes in networks . In node2vec , we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes .
Of particular interest is the error produced due to mismatch between training speaker recognition models with neutral speech and testing them with expressive speech . While previous studies have considered categorical emotions , expressive speech during human interaction conveys subtle behaviors that are better characterized with continuous descriptors ( e.g. , attributes such as arousal , valence , dominance ) . As the emotion becomes more intense , we expect the performance of speaker recognition tasks to drop .
Image and video retrieval experiments are performed on two standard datasets ( a Corel dataset and a TRECVID3 dataset ) which consist of 4,500 images and about 44,100 keyframes respectively . The results show that based on a large visual vocabulary the model runs extremely fast on even very large datasets while having comparable retrieval performance to the best performing ( continuous feature ) models .
The head of the user is localized via a combination of colorand shape-based segmentation . For a continuous feature extraction , we use a template matching of the nose bridge in combination with selected features derived from the optical flow . The core classification unit consists of discrete Hidden Markov Models ( DHMMs ) .
A prediction method ( GA-CNN-LSTM ) which combines convolutional neural network ( CNN ) and long-short-term memory network ( LSTM ) and optimized by genetic algorithm ( GA ) is established . First , network search data , meteorological data , and other data are constructed into continuous feature maps . Then , feature vectors are extracted by convolutional neural network ( CNN ) .
Abstract This paper proposes a unified approach to learning from constraints , which integrates the ability of classical machine learning techniques to learn from continuous feature-based representations with the ability of reasoning using higher-level semantic knowledge typical of Statistical Relational Learning . Learning tasks are modeled in the general framework of multi-objective optimization , where a set of constraints must be satisfied in addition to the traditional smoothness regularization term .
This article presents an alternative approach ( the continuous FITA ) that addresses these complications effectively by representing continuous features in a more natural way . It is shown that this approach is well-suited to continuous feature information analysis and , furthermore , can be used to estimate redundancy in multiple features and information transmitted by combinations of features . The continuous FITA can quantify information in extracted features before an identification experiment has been conducted .
Those enhance the aggregation of protocol features . Continuous feature set and content monitoring of real-time monitoring traffic are designed . Associating spatial information features is enhances the adaptive ability of traffic feature set .
Smart systems with related word prediction have turned out to be extremely prevalent for English language but there is no such big efforts for Hindi language . The main goal of this dissertation work is to provide syntactically and semantically related words based on continuous feature vector representation . Continuous Bag of Words ( CBOW ) language model is used to get the feature vector representation of each word in training set .
Future extensions of node2vec could involve networks with special structure such as heterogeneous information networks , networks with explicit domain features for nodes and edges and signed - edge networks . Continuous feature representations are the backbone of many deep learning algorithms , and it would be interesting to use node2vec representations as building blocks for end - to - end deep learning on graphs . Acknowledgements .
In the literature it is currently best practice to add real - valued noise to the pixel values to dequantize the data when using density functions . When uniform noise is added ( with values in the interval [ 0 , 1 ] ) , then the log - likelihoods of continuous and discrete models are directly comparable . In our case , we can use the values from the discrete distribution as a piecewise - uniform continuous function that has a constant value for every interval .
Most competitive neural sequence transduction models have an encoder - decoder structure cho2014learning , bahdanau2014neural , sutskever14 . Here , the encoder maps an input sequence of symbol representations to a sequence of continuous representations . Given , the decoder then generates an output sequence of symbols one element at a time .
In this work , we propose a long dependency aware deep architecture for joint S&T task . Specifically , to simulate the feature templates of traditional discrete feature based models , we use different filters to model the complex compositional features with convolutional and pooling layer , and then utilize long distance dependency information with recurrent layer . Experiment results on five different datasets show the effectiveness of our proposed model .
Spam is unsolicited bulk email . Most of the popular spam filters work depending on individual training corpus or discrete feature extraction , and these filters have n't considered spam 's bulk feature . The better method to stop spam may be the collaborative approach that does not only consider the content of the email but depends on the collaboration of groups of users who share information about spam .
Visual search is the task of finding objects in an image which are described in a high dimensional space , spanned by preattentive features , e.g. orientation , scale , and colour . By using steerable filters this search space may be scanned continouosly , though spanned by discrete feature detectors . Based on this idea , we will show a method for detecting arbitrarily oriented bars from steerable filter responses .
If we compare the widely used Conditional Random Fields ( CRF ) with newly proposed “ deep architecture ” sequence models ( Collobert et al. , 2011 ) , there are two things changing : from linear architecture to non-linear , and from discrete feature representation to distributional . It is unclear , however , what utility nonlinearity offers in conventional featurebased models .
In this chapter , we propose a novel framework for image classification and automatic annotation . First , feature selection strategy is explicitly introduced , including discrete feature method and continuous feature method . In both methods , the salient patches are detected and quantized for every image category .
Abstract Confronting low data reduction typologies , as established by using data from parallel texts , with the high data reduction typologies of WALS reveals a systematic bias of WALS typologies toward highly bimodal distribution . Properties with a distribution supporting a discrete feature analysis in many languages are likelier to be represented in WALS and to be represented accurately . This bias has important consequences when WALS typologies are interpreted theoretically or further processed statistically .
Abstract This paper deals with the challenging task of acquiring stable image features in a sequence of images of the same scene taken under different viewing positions by a digital still camera . Two popular contemporary algorithms for discrete feature detection : SIFT and SURF are regarded . The results of the timing performance analysis of their sequential implementations are presented and discussed .
In this paper , a two-tier intelligent automated predictor has been developed called iMem-2LSAAC , which classifies protein sequence as membrane or non-membrane in first-tier ( phase1 ) and in case of membrane the second-tier ( phase2 ) identifies functional types of membrane protein . Quantitative attributes were extracted from protein sequences by applying three discrete features extraction schemes namely amino acid composition , pseudo amino acid composition and split amino acid composition ( SAAC ) . Various learning algorithms were investigated by using jackknife test to select the best one for predictor .
Also , we model the geometric distortions caused by page curl and perspective view as the generalized cylindrical surfaces and camera rotation respectively . With these distortion models and the discrete representation of the features , we design a cost function whose minimization yields the parameters of the distortion model . In the cost function , we encode the properties of the pages such as text-block alignment , line-spacing , and the straightness of text-lines .
In the literature it is currently best practice to add real - valued noise to the pixel values to dequantize the data when using density functions . When uniform noise is added ( with values in the interval [ 0 , 1 ] ) , then the log - likelihoods of continuous and discrete models are directly comparable . In our case , we can use the values from the discrete distribution as a piecewise - uniform continuous function that has a constant value for every interval .
However , little progress has been made in applying GANs to sequence discrete data generation problems , e.g. natural language generation . This is due to the generator network in GAN is designed to be able to adjust the output continuously , which does not work on discrete data generation . On the other hand , a lot of efforts have been made to generate structured sequences .
Prior work has suggested a variety of different novelty functions e.g. , based on state visitation frequency . While such methods offer a number of appealing guarantees , such as near - Bayesian exploration in polynomial time , they require a concise , often discrete representation of the agent ’s state - action space to measure state visitation frequencies . In our approach , we will employ function approximation and representation learning to devise an alternative to these requirements .
A mathematical model for pollution in a river and its remediation by aeration Development of a behaviorally induced system optimal travel demand management system An Economic Analysis of Numerical Data Processing in the Firm Bivariate Birnbaum-Saunders distribution and associated inference The VisAD Java Class Library for Scientific Data and Visualization Analysis of Chemical Data by Computer Modeling. Machine learning methods for discrete multi-scale fows : application to finance РОЗРОБКА ТА ДОСЛіДЖЕННЯ МЕТОДіВ АДАПТИВНОГО УЩіЛЬНЕННЯ ДАНИХ НА ОСНОВі ЛіНіЙНОї ФОРМИФіБОНАЧЧі Breaking the Softmax Bottleneck: A High-Rank RNN Language Model Going deeper with convolutions Learning a Discriminative Null Space for Person Re-identification Lecture Notes in Computer Science. Concur '96: Concurrency Theory, Untangling featural and conceptual object representations A Particle Swarm Optimization approach to mixed attribute data-set classification Effective browsing of long audio recordings A 2-categorical approach to proof-comparison Analysis on Cognitive Function of Recognition Potential Feature selection for clustering categorical data with an embedded modelling approach Predicting malaria interactome classifications from time-course transcriptomic data along the intraerythrocytic developmental cycle Maximum total attribute relative of soft-set theory for efficient categorical data clustering The Analysis of Cross-Classification Data: A Prediction Approach. English Acronyms As A Way Of Linguistic Retrenchment The Strength of Categorical Data Analysis Centroid-Based Classification of Categorical Data Performance of TRMM TMPA 3B42 V7 in Replicating Daily Rainfall and Regional Rainfall Regimes in the Amazon Basin (1998-2013) Model-aware categorical data embedding: a data-driven approach Questions and Answers About Categorical Data Types A general methodology for the measurement of observer agreement when the data are categorical Image Transformer Implicit Quantile Networks for Distributional Reinforcement Learning PixelGAN Autoencoders Prediction of N6-methyladenosine sites using convolution neural network model based on distributed feature representations. EPLS: A novel feature extraction method for migration data clustering Role-Behavior Analysis from Trajectory Data by Cross-Domain Learning Simulation analysis of microwave propagation channel based on stochastic modeling in sea environment Uncertainty measures for fuzzy relations and their applications iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences Accurate diagnosis of thyroid follicular lesions from nuclear morphology using supervised learning Supervised Learning of Similarity Measures for Content-Based 3D Model Retrieval MPEG-7 service oriented system — MPEG-7 SOS Voice search in mobile applications and the use of linked open data Squeezed Very Deep Convolutional Neural Networks for Text Classification Beyond nodes and edges: multiresolution algorithms for network data Predicting speaker recognition reliability by considering emotional content A discrete direct retrieval model for image and video retrieval A Real-Time Demonstrator for Video-Based Recognition of Dynamic Head Gestures, Using Discrete Hidden Markov Models A Method Based on GA-CNN-LSTM for Daily Tourist Flow Prediction at Scenic Spots Semantic-based regularization for learning and inference Information transmission analysis for continuous speech features Discrete Noetherian ring variational pattern feature sets for space-air-ground network protocol monitoring An Approach for Predicting Related Word for the Hindi Language node2vec: Scalable Feature Learning for Networks Pixel Recurrent Neural Networks Attention is All you Need A Long Dependency Aware Deep Architecture for Joint Chinese Word Segmentation and POS Tagging A spam filtering technique based on feature fingerprint Preattentive Colour Features by Steerable Filters Effect of Non-linear Deep Architecture in Sequence Labeling A Novel Framework for Image Categorization and Automatic Annotation Data reduction typology and the bimodal distribution bias Performance Analysis of Robust Image Features Detection Algorithms iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou's pseudo amino acid composition. Document dewarping via text-line based optimization Pixel Recurrent Neural Networks SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models
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