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5th international conference on Scale Space and Variational methods in Computer Vision, SSVM 2015

作者： Raad, Lara Desolneux, Agnès Morel, Jean-Michel CMLA Ecole Normale Supérieure de Cachan Cachan France

ISBN: (纸本)9783319184609

An ideal exemplar-based texture synthesis algorithm should create a new texture that is perceptually equivalent to its texture example. To this goal it should respect the statistics of the example and avoid proceeding to a "copy-paste" process, which is the main drawback of the non-parametric approaches. In a previous work we modeled textures as a locally Gaussian patch model. this model was estimated for each patch before stitching it to the preceding ones. In the present work, we extend this model to a local conditional Gaussian patch distribution. the condition is taken over the already computed values. Our experiments here show that the conditional model reproduces well periodic and pseudoperiodic textures without requiring the use of any stitching technique. the experiments put also in evidence the importance of the right choice for the patch size. We conclude by pointing out the remaining limitations of the approach and the necessity of a multiscale approach. © Springer international Publishing Switzerland 2015.

关键词： Gaussian distribution

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How to increase the effectiveness of the hepatitis diagnostics by means of appropriate machine learning methods 6th

How to increase the effectiveness of the hepatitis diagnosti...

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6th international conference on Information Technology in Bio- and Medical Informatics, ITBAM 2015

作者： Lukáčová, Alexandra Babič, František Paraličová, Zuzana Paralič, Ján Department of Cybernetics and Artificial Intelligence Technical University of Košice Letná 9/B Košice042 00 Slovakia University of Pavol Jozef Šafárik Tr. SNP 1 Košice Slovakia

ISBN: (纸本)9783319227405

this paper presents how to improve the diagnostic process of hepatitis B and C based on collected questionnaires from patients hospitalized in all regional departments of infectology in Slovakia. Performed experiments were oriented in two directions: economic demands of the recommended treatment based on realized diagnostics and possible improvement of hepatitis diagnostics by means of exploratory and predictive analysis of additional information provided by patients. Exploratory data analysis was used to confirm or to reject some expected relationships between input attributes (e.g. ager or gender) and target diagnosis. Also, predictive mining resulted into interesting decision rules that can be used in medical practice as supporting information at an early stage of the diagnostic process. Finally, analysis of the treatment economic demands based on the estimated costs showed the need for timely and quality diagnostics to minimize the percentage of patients for which was hepatitis diagnosed late. © Springer international Publishing Switzerland 2015.

关键词： Diagnosis

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Does the Inclusion of Data Sampling Improve the Performance of Boosting algorithms on Imbalanced bioinformatics Data?

Does the Inclusion of Data Sampling Improve the Performance ...

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international conference on Machine Learning and Applications (ICMLA)

作者： Alireza Fazelpour Taghi M. Khoshgoftaar David J. Dittman Amri Napolitano Florida Atlantic University Boca Raton FL

bioinformatics datasets contain many challenging characteristics, such as class imbalance, which adversely impacts the performance of supervised classification models built on these datasets. Techniques such as ensemble learning and data sampling from the domain of data mining can be deployed to alleviate the problem and to improve the classification performance. In this study, we sought to seek whether inclusion of data sampling within the ensemble framework can further improve the performance of classification models. To this end, we performed an experimental study using two newly hybrid ensemble techniques, one integrates feature selection within the boosting process and the other incorporates random under-sampling followed by feature selection within the boosting framework, two learners, three forms of feature rankers, and four feature subset sizes on 15 highly imbalanced bioinformatics datasets. Our results and statistical analysis demonstrate that the difference between the two boosting methods is statistically insignificant. therefore, as the inclusion of data sampling has no significant positive effect on the performance of ensemble classifiers, it is not required to achieve maximum classification performance. To our knowledge, this is the first empirical study that examined the effects of data sampling, random under-sampling, to enhance classification performance of boosting algorithm for highly imbalanced bioinformatics data.

关键词： Boosting bioinformatics Data models Buildings Data mining Training Measurement

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14th international conference of the Italian Association for Artificial Intelligence, AIIA 2015

14th International Conference of the Italian Association for...

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14th international conference of the Italian Association for Artificial Intelligence, 2015

ISBN: (纸本)9783319243085

the proceedings contain 35 papers. the special focus in this conference is on Swarm Intelligence;Genetic algorithms;Computer Vision;Multi Agent Systems;Knowledge Representation;Reasoning;Machine Learning and Natural Language. the topics include: Collective self-awareness and self-expression for efficient network exploration;swarm-based controller for traffic lights management;path relinking for a constrained simulation-optimization team scheduling problem arising in hydroinformatics;adaptive tactical decisions in pedestrian simulation;using stochastic optimization to improve the detection of small checkerboards;empowering agent coordination with social engagement;anticipatory coordination in socio-technical knowledge-intensive environments;a kinetic study of opinion dynamics in multi-agent systems;cooperating with trusted parties would make life easier;agent based simulation of incentive mechanisms on photovoltaic adoption;feature-based modelling and information systems for engineering;a multi-engine theorem prover for a description logic of typicality;defeasible logic programming in satisfiability modulo CHR;abstract solvers for quantified Boolean formulas and their applications;learning accurate cutset networks by exploiting decomposability;common-sense knowledge for natural language understanding;testing a learn-verify-repair approach for safe human-robot interaction;empowered negative specialization in inductive logic programming;open data integration using SPARQL and SPIN;bootstrapping large scale polarity lexicons through advanced distributional methods;using semantic models for robust natural language human robot interaction;automatic identification and disambiguation of concepts and named entities in the multilingual Wikipedia and a logic-based approach to named-entity disambiguation in the web of data.

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Parallel metaheuristics in computational biology: an asynchronous cooperative enhanced Scatter Search method

Parallel metaheuristics in computational biology: an asynchr...

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15th Annual international conference on Computational Science (ICCS)

作者： Penas, David R. Gonzalez, Patricia Egea, Jose A. Banga, Julio R. Doallo, Ramon IIM CSIC Inst Invest Marinas BioProc Engn Grp C Eduardo Cabello 6 E-36208 Vigo Spain Univ A Coruna Comp Architecture Grp La Coruna 15071 Spain Univ Politecn Cartagena Dept Appl Math & Stat Cartagena 30202 Spain

Metaheuristics are gaining increased attention as efficient solvers for hard global optimization problems arising in bioinformatics and computational systems biology. Scatter Search (SS) is one of the recent outstanding algorithms in that class. However, its application to very hard problems, like those considering parameter estimation in dynamic models of systems biology, still results in excessive computation times. In order to reduce the computational cost of the SS and improve its success, several research efforts have been made to propose different variants of the algorithm, including parallel approaches. this work presents an asynchronous Cooperative enhanced Scatter Search (aCeSS) based on the parallel execution of different enhanced Scatter Search threads and the cooperation between them. the main features of the proposed solution are: low overhead in the cooperation step, by means of an asynchronous protocol to exchange information between processes;more effectiveness of the cooperation step, since the exchange of information is driven by quality of the solution obtained in each process, rather than by a time elapsed;optimal use of available resources, thanks to a complete distributed approach that avoids idle processes at any moment. Several challenging parameter estimation problems from the domain of computational systems biology are used to assess the efficiency of the proposal and evaluate its scalability in a parallel environment.

关键词： Computational Systems Biology Parallel Metaheuristics Enhanced Scatter Search

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Craniomaxillofacial deformity correction via sparse representation in coherent space 1

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6th international Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015

作者： Li, Zuoyong An, Le Zhang, Jun Wang, Li Xia, James J. Shen, Dinggang Department of Computer Science Minjiang University Fuzhou China Department of Radiology and BRIC University of North Carolina at Chapel Hill Chapel Hill United States Department of Oral and Maxillofacial Surgery Houston Methodist Research Institute Weill Medical College of Cornell University New York United States Department of Oral and Craniomaxillofacial Science Shanghai Ninth Hospital Shanghai Jiao Tong University School of Medicine Shanghai China

ISBN: (数字)9783319248882

ISBN: (纸本)9783319248875

Orthognathic surgery is popular for patients with craniomaxillofacial (CMF) deformity. For orthognathic surgical planning, it is critical to have a patient-specific jaw reference model as guidance. One way is to estimate a normal jaw shape for the patient, by first searching for a normal subject with similar midface and then borrowing his/her (normal) jaw shape as reference. Intuitively, we can search for multiple normal subjects with similar midface and then linearly combine them as final reference. the respective coefficients for linear combination can be estimated, i.e., by sparse representation of patient’s midface by midfaces of all training normal subjects. However, this approach implicitly assumes that the representation of midface shapes is strongly correlated with the representation of jaw shapes, which is unfortunately difficult to meet in practice due to generally different data distributions of shapes of midfaces and jaws. To address this limitation, we propose to estimate the patient-specific jaw reference model in a coherent space. Specifically, we first employ canonical correlation analysis (CCA) to map the midface and jaw landmarks of training normal subjects into a coherent space, in which their correlation is maximized. then, in the coherent space, the mapped midface landmarks of patient can be sparsely represented by the mapped midface landmarks of training normal subjects. those learned sparse coefficients can now be used to combine the jaw landmarks of training normal subjects for estimating the normal jaw landmarks for patient and then building normal jaw shape reference model. Moreover, we also iteratively maximize the correlation between the midface and the jaw shapes in the new coherent space with a multi-layer mapping and refinement (MMR) process. Experimental results on real clinical data show that the proposed method can more accurately reconstruct the normal jaw shape for patient than the competing methods. © Springer international Publi

关键词： Iterative methods

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6th international Workshop on New Computational methods for Inverse Problems

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Journal of Physics: conference Series 2016年第1期756卷

Foreword this volume of Journal of Physics: conference Series is dedicated to the scientific contributions presented during the 6th international Workshop on New Computational methods for Inverse Problems, NCMIP 2016 (http://***/NCMIP ***). this workshop took place at Ecole Normale Supérieure de Cachan, on May 20, 2016. the prior editions of NCMIP also took place in Cachan, France, firstly within the scope of ValueTools conference, in May 2011, and secondly at the initiative of Institut Farman, in May 2012, May 2013, May 2014 and May 2015. the New Computational methods for Inverse Problems (NCMIP) workshop focused on recent advances in the resolution of inverse problems. Indeed, inverse problems appear in numerous scientific areas such as geophysics, biological and medical imaging, material and structure characterization, electrical, mechanical and civil engineering, and finances. the resolution of inverse problems consists in estimating the parameters of the observed system or structure from data collected by an instrumental sensing or imaging device. Its success firstly requires the collection of relevant observation data. It also requires accurate models describing the physical interactions between the instrumental device and the observed system, as well as the intrinsic properties of the solution itself. Finally, it requires the design of robust, accurate and efficient inversion algorithms. Advanced sensor arrays and imaging devices provide high rate and high volume data; in this context, the efficient resolution of the inverse problem requires the joint development of new models and inversion methods, taking computational and implementation aspects into account. During this one- day workshop, researchers had the opportunity to bring to light and share new techniques and results in the field of inverse problems. the topics of the workshop were: algorithms and computational aspects of inversion, Bayesian estimation, Kernel method

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A web text classification technique for unlabeled training samples

A web text classification technique for unlabeled training s...

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IEEE international conference on Software Engineering and Service Sciences (ICSESS)

作者： Francois Tchiegue Rui Li Shilong Ma State Key Lab. Of Software Development Environment Beihang University Beijing China

the common classification is conducted under the supervised learning algorithms, which design classifiers through learning the labeled training samples. However, in actual situations, it is very costly to acquire class-labeled samples, because manually labeling documents requires a lot of time and efforts from experts. therefore, it restrains the text classification to a great extent. To solve the issue that labeled texts are hard to retrieve from the Internet, this paper has proposed the text classification method combining Fuzzy Partition Clustering Method (FPCM) and Naive Bayesian Augment Learning to integrate the unsupervision of the clustering with the prior knowledge of the sample, which has solved the bottleneck problem of unlabeled training set in the text classification, further improved the classification performance by estimating the classification error loss to balance the sample selection, and constructed superior classification learning method.

关键词： Training Clustering methods Bayes methods Text categorization Clustering algorithms Classification algorithms Data models

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Using small checkerboards as size reference: A model-based approach

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics) 2015年 9281卷 393-400页

作者： Hassannejad, Hamid Matrella, Guido Mordonini, Monica Cagnoni, Stefano Dipartimento di Ingegneria Dell’Informazione Università degli Studi di Parma Parma Italy

ISBN: (纸本)9783319232218

Monitoring diet is crucial for preventing or dealing with many chronic diseases. therefore, plenty of different methods have been developed to serve this purpose. Among these, automatic diet monitoring based on mobile devices are of particular interest. An automatic system is supposed to be able to detect type and amount of food intake. this work suggests using a small checkerboard in food images as size reference as an aid for estimating food amount. Although checkerboard is a simple pattern, most of the off-the-shelf algorithms do not perform well in detecting small checkerboards. this paper extends a previous work presenting a new stochastic model-based algorithm to detect small checkerboards. the algorithm first locates the checkerboard in the food image and then applies a customized corner detection algorithm to the located region. Experimental results show notably better performance in comparison to basic methods and to the previous version of the method. © Springer international Publishing Switzerland 2015.

关键词： Stochastic models

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Detection of differentially methylated regions from bisulfite-seq data by hidden Markov models incorporating genome-wide methylation level distributions

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BMC GENOMICS 2015年第Sup12期16卷 S3-S3页

作者： Saito, Yutaka Mituyama, Toutai Natl Inst Adv Ind Sci & Technol Biotechnol Res Inst Drug Discovery Koto Ku 2-4-7 Aomi Tokyo 1350064 Japan Japan Sci & Technol Agcy JST Core Res Evolut Sci & Technol Kawaguchi Saitama 3320012 Japan

Background: Detection of differential methylation between biological samples is an important task in bisulfite-seq data analysis. Several studies have attempted de novo finding of differentially methylated regions (DMRs) using hidden Markov models (HMMs). However, there is room for improvement in the design of HMMs, especially on emission functions that evaluate the likelihood of differential methylation at each cytosine site. Results: We describe a new HMM for DMR detection from bisulfite-seq data. Our method utilizes emission functions that combine binomial models for aligned read counts, and beta mixtures for incorporating genome-wide methylation level distributions. We also develop unsupervised learning algorithms to adjust parameters of the beta-binomial models depending on differential methylation types (up, down, and not changed). In experiments on both simulated and real datasets, the new HMM improves DMR detection accuracy compared with HMMs in our previous study. Furthermore, our method achieves better accuracy than other methods using Fisher's exact test and methylation level smoothing. Conclusions: Our method enables accurate DMR detection from bisulfite-seq data. the implementation of our method is named ComMet, and distributed as a part of Bisulfighter package, which is available at http://***/bisulfighter.

关键词： epigenomics DNA methylation differentially methylated region (DMR) bisulfite sequencing hidden Markov model (HMM) beta mixture

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