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Correction to: Non-extensitivity and criticality of atomic hydropathicity around a voltage-gated sodium channel's pore: a modeling study

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Journal of biological physics 2022年第4期48卷 477-478页

作者： Markos N Xenakis Dimos Kapetis Yang Yang Jordi Heijman Stephen G Waxman Giuseppe Lauria Catharina G Faber Hubert J Smeets Patrick J Lindsey Ronald L Westra Department of Toxicogenomics Section Clinical Genomics Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. mrksxenakis@***. Research School for Mental Health and Neuroscience (MHeNS) Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. mrksxenakis@***. Neuroalgology Unit Fondazione IRCCS Istituto Neurologico "Carlo Besta" Via Celoria 11 20133 Milan Italy. Department of Medicinal Chemistry and Molecular Pharmacology Purdue University College of Pharmacy IN 47907 West Lafayette USA. Purdue Institute for Integrative Neuroscience IN 47907 West Lafayette USA. Department of Cardiology CARIM School for Cardiovascular Diseases Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. Department of Neurology and Center for Neuroscience and Regeneration Research Yale University School of Medicine New Haven CT 06510 USA. Rehabilitation Research Center Veterans Affairs Connecticut Healthcare System West Haven CT 06516 USA. Department of Biomedical and Clinical Sciences "Luigi Sacco" University of Milan Via G.B. Grassi 74 20157 Milan Italy. Department of Neurology Maastricht University Medical Center PO Box 5800 6202 AZ Maastricht The Netherlands. Department of Toxicogenomics Section Clinical Genomics Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. Research School for Mental Health and Neuroscience (MHeNS) Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. Research School for Oncology and Developmental Biology (GROW) Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. Department of Data Science and Knowledge Engineering Maastricht University PO Box 616 6200 MD Maastricht The Netherlands.

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CHAOS Challenge - Combined (CT-MR) healthy abdominal organ segmentation

arXiv

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arXiv 2020年

作者： Kavur, A. Emre Gezer, N. Sinem Barış, Mustafa Conze, Pierre-Henri Groza, Vladimir Pham, Duc Duy Chatterjee, Soumick Ernst, Philipp Özkan, Savaş Baydar, Bora Lachinov, Dmitry Han, Shuo Pauli, Josef Isensee, Fabian Perkonigg, Matthias Sathish, Rachana Rajan, Ronnie Aslan, Sinem Sheet, Debdoot Dovletov, Gurbandurdy Speck, Oliver Nürnberger, Andreas Maier-Hein, Klaus H. Akar, Gözde Bozdagı Ünal, Gözde Dicle, Oguz Selver, M. Alper Graduate School of Natural and Applied Sciences Dokuz Eylul University Izmir Turkey Department of Radiology Faculty Of Medicine Dokuz Eylul University Izmir Turkey Dept. of Ophthalmology and Optometry Medical Uni. of Vienna Austria Intelligent Systems Faculty of Engineering University of Duisburg-Essen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany CIR Lab Dept of Biomedical Imaging and Image-guided Therapy Medical Uni. of Vienna Austria IMT Atlantique LaTIM UMR 1101 Brest France Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Department of Electrical Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India Ca’ Foscari University of Venice ECLT DAIS Venice Italy Ege University International Computer Institute Izmir Turkey Johns Hopkins University Baltimore United States Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Magnetic Resonance Otto von Guericke University Magdeburg Germany Median Technologies Valbonne France Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Dokuz Eylul University Izmir Turkey

—Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance is hard to interpret. This makes comparative analysis a necessary tool to achieve explainable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal tasks have been rarely discussed. In order to expand the knowledge in these topics, CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge has been organized in the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Despite a large number of the previous abdomen related challenges, the majority of which are focused on tumor/lesion detection and/or classification with a single modality, CHAOS providesboth abdominal CT and MR data from healthy subjects. Five different and complementary tasks have been designed to analyze the capabilities of the current approaches from multiple perspectives. The results are investigated thoroughly, compared with manual annotations and interactive methods. The outcomes are reported in detail to reflect the latest advancements in the field. CHAOS challenge and data will be available online to provide a continuous benchmark resource for segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Medical imaging

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Partitioning vectors into quadruples: Worst-case analysis of a matching-based algorithm

arXiv

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arXiv 2018年

作者： Ficker, Annette M.C. Erlebach, Thomas Mihalák, Matúš Spieksma, Frits C.R. Faculty of Economics and Business KU Leuven Leuven Belgium Department of Informatics University of Leicester Leicester United Kingdom Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands

Consider a problem where 4k given vectors need to be partitioned into k clusters of four vectors each. A cluster of four vectors is called a quad, and the cost of a quad is the sum of the component-wise maxima of the four vectors in the quad. The problem is to partition the given 4k vectors into k quads with minimum total cost. We analyze a straightforward matching-based algorithm, and prove that this algorithm is a 23-approximation algorithm for this problem. We further analyze the performance of this algorithm on a hierarchy of special cases of the problem, and prove that, in one particular case, the algorithm is a 54-approximation algorithm. Our analysis is tight in all cases except *** codes 68Q25 Copyright © 2018, The Authors. All rights reserved.

关键词： Vectors

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Set matching: An enhancement of the Hales-Jewett pairing strategy

arXiv

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arXiv 2017年

作者： Uiterwijk, Jos W.H.M. Department of Data Science and Knowledge Engineering Maastricht University Netherlands

When solving k-in-a-Row games, the Hales-Jewett pairing strategy [4] is a well-known strategy to prove that specific positions are (at most) a draw. It requires two empty squares per possible winning line (group) to be marked, i.e., with a coverage ratio of 2.0. In this paper we present a new strategy, called Set Matching. A matching set consists of a set of nodes (the markers), a set of possible winning lines (the groups), and a coverage set indicating how all groups are covered after every first initial move. This strategy needs less than two markers per group. As such it is able to prove positions in k-in-a-Row games to be draws, which cannot be proven using the Hales-Jewett pairing strategy. We show several efficient configurations with their matching sets. These include Cycle Configurations, BiCycle Configurations, and PolyCycle Configurations involving more than two cycles. Depending on configuration, the coverage ratio can be reduced to 1.14. Many examples in the domain of solving k-in-a-Row games are given, including the direct proof (without further investigation) that the empty 4 × 4 board is a draw for 4-in-a-Row. Copyright © 2017, The Authors. All rights reserved.

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SEED: Entity oriented information search and exploration 22

SEED: Entity oriented information search and exploration

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22nd International Conference on Intelligent User Interfaces, IUI 2017

作者： Chen, Jun Jacucci, Giulio Chen, Yueguo Ruotsalo, Tuukka School of Information Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Helsinki Institute for Information Technology HIIT Department of Computer Science University of Helsinki Finland

ISBN: (纸本)9781450348935

Entity search and exploration can enrich search user interfaces by presenting relevant information instantly and offering relevant exploration pointers to users. Previous research has demonstrated that large knowledge Graphs allow exploitation and recommendation of explicit links between the entities and other information to improve information access and ranking. However, less attention has been devoted to user interfaces for effectively presenting results, recommending related entities and explaining relations between entities. We introduce a system called SEED which is designed to support entity search and exploration in large knowledge Graphs. We demonstrate SEED using a dataset of hundreds of thousands of movie related entities from the DBpedia knowledge Graph. The system utilizes a graph embedding model for ranking entities and their relations, recommending related entities, and explaining their interrelations. Copyright is held by the author/owner(s).

关键词： knowledge graph

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Automatic Chinese Multiple-Choice Question Generation for Human Resource Performance Appraisal

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Procedia Computer science 2018年 139卷 165-172页

作者： Pei Quan Yong Shi Lingfeng Niu Ying Liu Tianlin Zhang School of Computer and Control Engineering University of Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA

Performance appraisal has always been an important research topic in human resource management. A reasonable performance appraisal plan lays a solid foundation for the development of an enterprise. Especially as globalization and technology advance, in order to meet the fast-changing strategic goals and increasing cross-functional tasks, enterprises face new challenges in performance appraisal. How to improve employees’ ability to accept new knowledge efficiently and constantly has been an urgent problem for enterprises. In this paper, we propose an automatic method which generation multiple-choice questions by utilizing the relations between different terminology. Graphical model is used to extract core concept from different corpus while word embedding technology is used to indicate the relevant relations. Experimental results demonstrate that the proposed question generation method outperforms the traditional manual method in both efficiency and confusion.

关键词： Human resource management multiple-choice question generation natural language processing word embedding

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High-performance and lightweight real-time deep face emotion recognition

High-performance and lightweight real-time deep face emotion...

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International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Justus Schwan Esam Ghaleb Enrique Hortal Stylianos Asteriadis Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

ISBN: (纸本)9781538607572

Deep learning is used for all kinds of tasks which require human-like performance, such as voice and image recognition in smartphones, smart home technology, and self-driving cars. While great advances have been made in the field, results are often not satisfactory when compared to human performance. In the field of facial emotion recognition, especially in the wild, Convolutional Neural Networks (CNN) are employed because of their excellent generalization properties. However, while CNNs can learn a representation for certain object classes, an amount of (annotated) training data roughly proportional to the class's complexity is needed and seldom available. This work describes an advanced pre-processing algorithm for facial images and a transfer learning mechanism, two potential candidates for relaxing this requirement. Using these algorithms, a lightweight face emotion recognition application for Human-Computer Interaction with TurtleBot units was developed.

关键词： Face Training Feature extraction Real-time systems Emotion recognition Training data Face detection

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A Retrieval-Based Dialogue System Utilizing Utterance and Context Embeddings

A Retrieval-Based Dialogue System Utilizing Utterance and Co...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Alexander Bartl Gerasimos Spanakis Department of Data Science and Knowledge Engineering Maastricht University MD Netherlands

Finding semantically rich and computer-understandable representations for textual dialogues, utterances and words is crucial for dialogue systems (or conversational agents), as their performance mostly depends on understanding the context of conversations. In recent research approaches, responses have been generated utilizing a decoder architecture, given the distributed vector representation (embedding) of the current conversation. In this paper, the utilization of embeddings for answer retrieval is explored by using Locality-Sensitive Hashing Forest (LSH Forest), an Approximate Nearest Neighbor (ANN) model, to find similar conversations in a corpus and rank possible candidates. Experimental results on the well-known Ubuntu Corpus (in English) and a customer service chat dataset (in Dutch) show that, in combination with a candidate selection method, retrieval-based approaches outperform generative ones and reveal promising future research directions towards the usability of such a system.

关键词： Decoding Logic gates Context modeling Encoding Pipelines Training databases

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Social emotion mining techniques for facebook posts reaction prediction

arXiv

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arXiv 2017年

作者： Krebs, Florian Lubascher, Bruno Moers, Tobias Schaap, Pieter Spanakis, Gerasimos Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

As of February 2016 Facebook allows users to express their experienced emotions about a post by using five so-called 'reactions'. This research paper proposes and evaluates alternative methods for predicting these reactions to user posts on public pages of firms/companies (like supermarket chains). For this purpose, we collected posts (and their reactions) from Facebook pages of large supermarket chains and constructed a dataset which is available for other researches. In order to predict the distribution of reactions of a new post, neural network architectures (convolutional and recurrent neural networks) were tested using pretrained word embeddings. Results of the neural networks were improved by introducing a bootstrapping approach for sentiment and emotion mining on the comments for each post. The final model (a combination of neural network and a baseline emotion miner) is able to predict the reaction distribution on Facebook posts with a mean squared error (or misclassification rate) of 0.135. Copyright © 2017, The Authors. All rights reserved.

关键词： Social networking (online)

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Has “Intelligent Manufacturing” Promoted the Productivity of Manufacturing Sector?--Evidence from China’s Listed Firms

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Procedia Computer science 2018年 139卷 299-305页

作者： Yi Qu Yong Shi Kun Guo Yuanchun Zheng School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA School of Computing and Control Engineering University of Chinese Academy of Sciences Beijing 100190 China

Intelligent Manufacturing has attracted global and continuous attention recent years, with more and more intelligent devices and systems applied in production. In this paper, we take China’s manufacturing listed firms to investigate the productivity difference between intelligent and general manufacturing firms. By the Cobb-Douglas production function, we built a Coefficient-varying Model and used Seemingly Unrelated Regression (SUR) to estimate the time-varying trend of productivity from 2011 to 2017. The empirical results show that “Intelligent Manufacturing” has obviously promoted the Labor factor utilization efficiency and the Total Factor Productivity (TFP) through the advances in technology. But it doesn’t have universally enhancing effect of all industries. The impact of “Intelligent Manufacturing” still remains to be observed overtime.

关键词： Intelligent Manufacturing Cobb-Douglas Production Function Total Factor Productivity (TFP) Coefficient-varying Model Seemingly Unrelated Regression (SUR)

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