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arXiv 2018年

作者： Yao, Yu-Xiang Bing, Zhi-Tong Huang, Liang Huang, Zi-Gang Lai, Ying-Cheng School of Physical Science and Technology Lanzhou University Lanzhou730000 China Evidence Based Medicine Center School of Basic Medical Science of Lanzhou University Lanzhou730000 China Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province Lanzhou730000 China Department of Computational Physics Institute of Modern Physics Chinese Academy of Sciences Lanzhou730000 China Key Laboratory of Biomedical Information Engineering of Ministry of Education National Engineering Research Center of Health Care and Medical Devices Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Institute of Health and Rehabilitation Science School of Life Science and Technology Xi’an Jiaotong University Xi’an710049 China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Department of Physics Arizona State University TempeAZ85287 United States

Radiotherapy plays a vital role in cancer treatment, for which accurate prognosis is important for guiding sequential treatment and improving the curative effect for patients. An issue of great significance in radiotherapy is to assess tumor radiosensitivity for devising the optimal treatment strategy. Previous studies focused on gene expression in cells closely associated with radiosensitivity, but factors such as the response of a cancer patient to irradiation and the patient survival time are largely ignored. For clinical cancer treatment, a specific pre-treatment indicator taking into account cancer cell type and patient radiosensitivity is of great value but it has been missing. Here, we propose an effective indicator for radiosensitivity: radiosensitive gene group centrality (RSGGC), which characterizes the importance of the group of genes that are radiosensitive in the whole gene correlation network. We demonstrate, using both clinical patient data and experimental cancer cell lines, which RSGGC can provide a quantitative estimate of the effect of radiotherapy, with factors such as the patient survival time and the survived fraction of cancer cell lines under radiotherapy fully taken into account. Our main finding is that, for patients with a higher RSGGC score before radiotherapy, cancer treatment tends tobe more effective. The RSGGC can have significant applications in clinical prognosis, serving as a key measure to classifying radiosensitive and radioresistant patients. Copyright © 2018, The Authors. All rights reserved.

关键词： Radiotherapy

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Web science 2024: Conference Chairs' Welcome Message

Proceedings of the 16th ACM Web Science Conference, WebSci 2...

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Proceedings of the 16th ACM Web science Conference, WebSci 2024 2024年 III-X页

作者： Staab, Steffen Aiello, Luca Maria Mejova, Yelena Seneviratne, Oshani Sindermann, Cornelia Hagendorff, Thilo Brandes, Simone Rohlmann, Juliane Spaniol, Marc Kinder-Kurlanda, Katharina Faleńska, Agnieszka Heiberger, Raphael Sun, Jun Kaiser, Sierra De Choudhury, Munmun Weber, Matthew De Francisci Morales, Gianmarco Dunn, Adam Gadiraju, Ujwal Gaito, Sabrina Hooper, Silas Javarone, Marco Alberto Kalimeri, Kyriaki Kwak, Haewoon Maity, Suman Kalyan Mashhadi, Afra Mustafaraj, Eni Purohit, Hemant Rossetti, Giulio Sastry, Nishanth Srinivasa, Srinath Vaz-De-Melo, Pedro Weal, Mark Weber, Ingmar Weninger, Tim Withing, Mark Zubiaga, Arkaitz Ahlers, Dirk Tamime, Reham Al Alam, Sawood Alhoori, Hamed Andriotis, Panagiotis Bär, Dominik Bernstein, Mark Boratto, Ludovico Camargo, Chico Capozzi, Arthur Chelmis, Charalampos Vieira, Carolina Coimbra De Meo, Pasquale Dinh, Ly Dividino, Renata Doerfel, Stephan Farahbakhsh, Reza Ferreira, Carlos H.G. Flores-Saviaga, Claudia Florio, Komal Galdeman, Alessia Guo, Cheng Hakimov, Sherzod Rad, Radin Hamidi Helic, Denis Hohmann, Marilena Horawalavithana, Sameera Horne, Benjamin Ibanez-Gonzalez, Luis Jiang, Julie Jones, Shawn Kamdar, Maulik R. Kao, Hsien-Te Kassa, Yonas Mitike Kastrin, Andrej Kazama, Kazuhiro Kelly, Mat Khanday, Akib Khatua, Aparup Khudabukhsh, Ashiqur Krüger, Frank Kumari, Neha Lai, Mirko Lamba, Hemank Lee, Roy Ka-Wei Lee, Eun Liao, Yiming Lin, Wenqing Magelinski, Thomas Magnani, Matteo Hill, Benjamin Mako Manikonda, Lydia Masud, Sarah Mekouar, Soufiana Møller, Anders Giovanni Muthiah, Sakthi Ni, Congning Nishimura, Joel Nwala, Alexander Pera, Arianna Piao, Guangyuan Piccardi, Tiziano Pierri, Francesco Porcaro, Lorenzo Ramasco, Jose J. Reelfs, Jens Rezapour, Rezvaneh Salminen, Joni Šćepanović, Sanja Shahi, Gautam Kishore Sinha, Priyanka Smaragdakis, Georgios Smirnova, Inna Song, Melodie Yun-Ju Sosnovik, Vera Truong, Bao Tran Tsugawa, Sho Vasarhelyi, Orsolya D'Aurelio, Davide Vega Vekaria, Yash Vilella, Salvatore Viviani, Marco Wang, Yichen Wu, Siqi Yang, Kaicheng Zeng, Yilei Zha University of Stuttgart Germany IT University of Copenhagen Denmark ISI Foundation Italy Rensselaer Polytechnic Institute United States Université de Caen Normandie France University of Klagenfurt Austria University of Washington United States GESIS Germany Georgia Tech United States IIIT Bangalore India Rutgers University United States CENTAI Italy University of Sydney Australia Delft University of Technology Netherlands University of Milan Italy Canada Centro Ricerche Enrico Fermi UCL CBT Italy Indiana University Bloomington United States Northwestern University United States UW United States Wellesley College United States George Mason University United States CNRS France King's College London United Kingdom International Institute of Information Technology Bangalore India UFMG Brazil University of Southampton United Kingdom Saarland University Germany University of Notre Dame United States UPenn United States Queen Mary University of London United Kingdom Norwegian University of Science and Technology Norway Internet Archive United States Northern Illinois University United States University of the West of England United Kingdom LMU Munich Germany Eastgate Systems Inc. United States University of Cagliari Italy University of Exeter United Kingdom University of Turin Italy University at Albany State University of New York United States Max Planck Institute for Demographic Research Germany Vrije Universiteit Amsterdam Netherlands University of South Florida United States Brock University Canada Kiel University of Applied Sciences Germany Institut Mines-Télécom Télécom SudParis France Universidade Federal de Ouro Preto Brazil Northeastern University United States Università di Torino Dipartimento di Informatica Italy Clemson University Google United States TIB - Leibniz Information Centre for Science and Technology Germany Ryerson University Canada Graz University of Technology Austria University of Copenhagen Denmark Pacific Northwest National La

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Aadhaar Card: Challenges and Impact on Digital Transformation

arXiv

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arXiv 2017年

作者： Raju, Raja Siddharth Singh, Sukhdev Khatter, Kiran Department of Computer Science Engineering Manav Rachna International University FaridabadHaryana121004 India Accendere Knowledge Management Services Pvt. Ltd Chennai600101 India

Objectives: This paper presents a brief review on Aadhaar card, and discusses the scope and advantages of linking Aadhaar card to various systems. Further we present various cases in which Aadhaar card may pose security threats. The observations of Supreme Court of India are also presented in this paper followed by a discussion on the loopholes in the existing system. Methods: We conducted literature survey based on the various research articles, leading newspapers, case studies and the observations of Supreme Court of India, and categorized the various cases into three categories. Findings: Aadhaar project is one of the significant projects in India to bring the universal trend of digital innovation. The launch of this project was focused on the inter-operability of various e-governance functionalities to ensure the optimal utilization of Information, Communication and Technology Infrastructure. Towards this Government of India has recently made Aadhaar card mandatory for many government applications, and also has promoted Aadhaar enabled transactions. Improvements: There are many issues related to security and privacy of the Aadhaar data need to be addressed. This paper highlights such cases. Copyright © 2017, The Authors. All rights reserved.

关键词： Data privacy

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LifeTime and improving European healthcare through cell-based interceptive medicine (vol 587, pg 377, 2020)

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NATURE 2021年第7852期592卷 E8-E8页

作者： Rajewsky, Nikolaus Almouzni, Genevieve Gorski, Stanislaw A. Aerts, Stein Amit, Ido Bertero, Michela G. Bock, Christoph Bredenoord, Annelien L. Cavalli, Giacomo Chiocca, Susanna Clevers, Hans De Strooper, Bart Eggert, Angelika Ellenberg, Jan Fernandez, Xose M. Figlerowicz, Marek Gasser, Susan M. Hubner, Norbert Kjems, Jorgen Knoblich, Jurgen A. Krabbe, Grietje Lichter, Peter Linnarsson, Sten Marine, Jean-Christophe Marioni, John C. Marti-Renom, Marc A. Netea, Mihai G. Nickel, Dorthe Nollmann, Marcelo Novak, Halina R. Parkinson, Helen Piccolo, Stefano Pinheiro, Ines Pombo, Ana Popp, Christian Reik, Wolf Roman-Roman, Sergio Rosenstiel, Philip Schultze, Joachim L. Stegle, Oliver Tanay, Amos Testa, Giuseppe Thanos, Dimitris Theis, Fabian J. Torres-Padilla, Maria-Elena Valencia, Alfonso Vallot, Celine van Oudenaarden, Alexander Vidal, Marie Voet, Thierry Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine in the Helmholtz Association Berlin Germany Charité-Universitätsmedizin Berlin Germany Berlin Institute of Health (BIH) Berlin Germany German Center for Cardiovascular Research (DZHK) Partner Site Berlin Berlin Germany Institute for Biology Humboldt University of Berlin Berlin Germany Cardiovascular and Metabolic Sciences Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany Department of Pediatric Oncology/Hematology Charité-Universitätsmedizin Berlin Berlin Germany Institut Curie CNRS PSL Research University Sorbonne Université Nuclear Dynamics Unit Equipe Labellisée Ligue contre le cancer Paris France VIB Center for Brain and Disease Research Leuven Belgium Department of Human Genetics KU Leuven Leuven Belgium Department of Neurosciences KU Leuven Leuven Belgium UK Dementia Research Institute at UCL University College London London UK Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK Department of Immunology Weizmann Institute of Science Rehovot Israel Centre for Genomic Regulation (CRG) Barcelona Institute of Science and Technology Barcelona Spain CNAG-CRG Centre for Genomic Regulation Barcelona Institute of Science and Technology Barcelona Spain Universitat Pompeu Fabra Barcelona Spain ICREA Barcelona Spain CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria Department of Laboratory Medicine Medical University of Vienna Vienna Austria Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases Vienna Austria Department of Medical Humanities Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht The Netherlands Institute of Human Genetics UMR 9002 CNRS and University of Montpellier Montpellier France Department of Experimental Oncology IEO European Institute of Oncology IRCCS Milan Italy Department of Oncology and Hemato

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Inverse extreme learning machine for learning with label proportions

Inverse extreme learning machine for learning with label pro...

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IEEE International Conference on Big Data

作者： Limeng Cui Jiawei Zhang Zhensong Chen Yong Shi Philip S. Yu School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Department of Computer Science Florida State University FL USA Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Beijing China Department of Computer Science University of Illinois at Chicago IL USA

In large-scale learning problem, the scalability of learning algorithms is usually the key factor affecting the algorithm practical performance, which is determined by both the time complexity of the learning algorithms and the amount of supervision information (i.e., labeled data). Learning with label proportions (LLP) is a new kind of machine learning problem which has drawn much attention in recent years. Different from the well-known supervised learning, LLP can estimate a classifier from groups of weakly labeled data, where only the positive/negative class proportions of each group are known. Due to its weak requirements for the input data, LLP presents a variety of real-world applications in almost all the fields involving anonymous data, like computer vision, fraud detection and spam filtering. However, even through the required labeled data is of a very small amount, LLP still suffers from the long execution time a lot due to the high time complexity of the learning algorithm itself. In this paper, we propose a very fast learning method based on inversing output scaling process and extreme learning machine, namely Inverse Extreme Learning Machine (IELM), to address the above issues. IELM can speed up the training process by order of magnitudes for large datasets, while achieving highly competitive classification accuracy with the existing methods at the same time. Extensive experiments demonstrate the significant speedup of the proposed method. We also demonstrate the feasibility of IELM with a case study in real-world setting: modeling image attributes based on ImageNet Object Attributes dataset.

关键词： Training Calibration Support vector machines Supervised learning Visualization Optimization

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A comparison study between Doane's and Freedman-Diaconis' binning rule in characterizing potential water resources availability

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Journal of Physics: Conference Series 2019年第1期1366卷

作者： Zun Liang Chuan Wan Nur Syahidah Wan Yusoff Mohd Khairul Bazli Mohd Aziz Azlyna Senawi Tan Lit Ken Centre for Mathematical Sciences Universiti Malaysia Pahang Lebuhraya Tun Razak 26300 Gambang Kuantan Pahang Malaysia Takasago Thermal/Environmental Systems Laboratory Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Computer Science Faculty of Knowledge Engineering Tokyo City University 1-28-1 Tamazutsumi Setagaya-ku Tokyo 158-8557 Japan

One of the primary constraints for development and management of water resources is the spatial and temporal uncertainty of rainfall. This is due to the stability and reliability of water supply is dynamically associated with the spatial and temporal uncertainty of rainfall. However, this spatial and temporal uncertainty can be assessed using the intensity entropy (IE) and apportionment entropy (AE). The main objective of this study is to investigate the implications of the use of Doane's and Freedman-Diaconis' binning rule in characterizing potential water resource availability (PWRA), which the PWRA is assessed via the standardized intensity entropy (IE') against the standardized apportionment entropy (AE') scatter diagram. To pursue the objective of this study, the daily rainfall data recorded ranging from January 2008 to December 2016 at four rainfall monitoring stations located Coastal region of Kuantan District Pahang are analyzed. The analysis results illustrated that the use of Doane's binning rule is more appropriate than Freedman-Diaconis' binning rule. This is due to the resulted PWRA characteristics using Doane's binning rule is relatively consistent with practical climate such that the study region is experiencing poor-in-water zone with less amount and high uncertainty of rainfall during the Southwest Monsoon, while abundant and perennial rainfall during the Northeast Monsoon. Furthermore, the use of Doane's binning rule is more advantages compared to the Freedman-Diaconis' binning rule with the abstraction of computational cost and time.

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A Comparative Study on Latest Substring Association Rule Mining and Hidden Markov Model

A Comparative Study on Latest Substring Association Rule Min...

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International Conference on computer, Electrical & Communication engineering (ICCECE)

作者： Rudra Prasad Chatterjee Chhanda Ray Rajib Bag Department of Computer Science & Engineering Supreme Knowledge Foundation Group Of Institutions Mankundu West Bengal INDIA State Council of Educational Research Training Govt. of West Bengal Kolkata INDIA

The Web usage mining techniques are used to scrutinize the web usage patterns for a web site. Web page prediction plays a vital role by predicting next set of web pages that a user may visit based on the knowledge of the previously visited pages. Web page prediction is the focus of attention of many researchers in recent times and different web page prediction frameworks have been proposed. In this paper, a comparative analysis between two different approaches of web page prediction, namely, Latest Substring Association Rule mining (LSA) and Hidden Markov Model (HMM) has been represented. Web page prediction is implemented by using both the approaches and the experimental results are provided. Finally, an improved approach for web page prediction is proposed at the end of the paper.

关键词： Web pages Hidden Markov models Predictive models Markov processes Clustering algorithms Prediction algorithms Data mining

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Relation extraction from clinical texts using domain invariant convolutional neural network 15

Relation extraction from clinical texts using domain invaria...

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15th Workshop on Biomedical Natural Language Processing, BioNLP 2016

作者： Sahu, Sunil Kumar Anand, Ashish Oruganty, Krishnadev Gattu, Mahanandeeshwar Department of Computer Science and Engineering Iit Guwahati Assam India Excelra Knowledge Solutions Pvt Ltd Telangana Hyderabad India

ISBN: (纸本)9781945626128

In recent years extracting relevant information from biomedical and clinical texts such as research articles, discharge summaries, or electronic health records have been a subject of many research efforts and shared challenges. Relation extraction is the process of detecting and classifying the semantic relation among entities in a given piece of texts. Existing models for this task in biomedical domain use either manually engineered features or kernel methods to create feature vector. These features are then fed to classifier for the prediction of the correct class. It turns out that the results of these methods are highly dependent on quality of user designed features and also suffer from curse of dimensionality. In this work we focus on extracting relations from clinical discharge summaries. Our main objective is to exploit the power of convolution neural network (CNN) to learn features automatically and thus reduce the dependency on manual feature engineering. We evaluate performance of the proposed model on i2b2-2010 clinical relation extraction challenge dataset. Our results indicate that convolution neural network can be a good model for relation exaction in clinical text without being dependent on expert's knowledge on defining quality features. © BioNLP 2016. All rights reserved.

关键词： Extraction

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Human behavior analysis for library and information science

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Library Hi Tech 2017年第4期35卷 442-444页

作者： Mu-Yen Chen Edwin David Lughofer Neil Y. Yen Chia-Chen Chen Department of Information Management National Taichung University of Science and Technology Taichung Taiwan Department of Knowledge-Based Mathematical Systems Johannes Kepler University Linz Linz Austria School of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Japan Department of Management Information Systems National Chung Hsing University Taichung City Taiwan

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Semantics and computation of the evolution of hybrid systems with ariadne 14

Semantics and computation of the evolution of hybrid systems...

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14th International Conference on Computability and Complexity in Analysis, CCA 2017

作者： Collins, Pieter Bresolin, Davide Gonzalez, Sanja Živanovic Geretti, Luca Villa, Tiziano Department of Data Science and Knowledge Engineering Maastricht University Bouillonstraat 8-10 Postbus 616 6200MD Netherlands Dipartimento di Informatica Università di Bologna Mura Anteo Zamboni 7 Bologna40126 Italy Department of Mathematics and Computer Science Barry University 11300 NE 2nd Avenue Miami ShoresFL33161-6695 United States Dipartimento di Informatica Università Degli Studi di Verona Strada le Grazie 15 Verona37134 Italy

In this talk we will present material on the semantics, computability, and algorithms for the evolution of hybrid dynamical systems, and an overview of the tool Ariadne for verification of hybrid systems [1]. Hybrid systems are characterised by undergoing continuous evolution interspersed by discrete jumps. They exhibit all the complexities of finite automata, nonlinear dynamic systems and differential equations, and are extremely difficult to analyze. We will consider hybrid systems in which the continuous dynamics is given by a differential equation x = f(x), with discrete jumps x' = ri(x) which occur as soon as a guard condition gi(x) ≥ 0 is activated. It is clear that the evolution of a hybrid system undergoes discontinuities, but since only continuous functions are computable, it is not clear to what extent, if any, it is possible to perform a rigorous analysis of a hybrid system. We will first show that we can define lower and upper semantics of evolution under which it is possible to compute reachable sets, and that away from discontinuity points (such as grazing or corner collision points), these semantics agree [2]. In order to perform reachability analysis, it is necessary to define the evolution over bounded initial sets of states. We show that this can be done using the operations of range, compose, flow and solve operations on functions. We will see that constrained image sets of the form {f(x) | x ∈ D | g(x) ∈ C}, are sufficient to express the evolution exactly, except for the case of degenerate (non-transverse) cross- ings [3]. The flow operation is the most computationally demanding, and we will give some details of the implementation and efficiency considerations [4]. We will give examples of reachability analysis in Ariadne, including electrical power converters and heating systems. Finally, we will outline some areas of active research, including differential inclusions [5] and modular reasoning. © 2017 14th International Conference on Computabili

关键词： Hybrid systems

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