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12th International Conference on Advanced data Mining and Applications, ADMA 2016

作者： Harikumar, Haripriya Nguyen, Thin Gupta, Sunil Rana, Santu Kaimal, Ramachandra Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia Computer Science and Engineering Department Amrita University Kollam India

ISBN: (纸本)9783319495859

Drinking alcohol has high cost on society. The journey from being a regular drinker to a successful quitter may be a long and hard journey, fraught with the risk to relapse. Research has shown that certain behavioral changes can be effective towards staying abstained. Traditional way to conduct research on drinking abstainers uses questionnaire based approach to collect data from a curated group of people. However, it is an expensive approach in both cost and time and often results in small data with less diversity. Recently, social media has emerged as a rich data source. Reddit is one such social media platform that has a community (‘subreddit’) with an interest to quit drinking. The discussions among the group dates back to year 2011 and contain more than 40,000 posts. This large scale data is generated by users themselves and without being limited by any survey questionnaires. The most predictive factors from the features (unigrams, topics and LIWC) associated with short-term and long-term abstinence are identified using Lasso. It is seen that many common patterns manifest in unigrams, topics and LIWC. Whilst topics provided much richer associations between a group of words and the outcome, unigrams and LIWC are found to be good at finding highly predictive solo and psycho linguistically important words. Combining them we have found that many interesting patterns that are associated with the successful attempt made by the long-term abstainer, at the same time finding many of the common issues faced during the initial period of abstinence. © Springer International Publishing AG 2016.

关键词： Surveys

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Visual Reasoning Indexing and Retrieval Using In-Memory Computing

Visual Reasoning Indexing and Retrieval Using In-Memory Comp...

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作者： Cao, Hongfei Li, Yu Allen, Carla M. Phinney, Michael A. Shyu, Chi-Ren Computer Science Interdisciplinary Data Analytics and Search Lab University of Missouri-Columbia ColumbiaMO65211 United States Clinical and Diagnostic Science Department University of Missouri-Columbia ColumbiaMO65211 United States Computer Science Electrical and Computer Engineering Informatics Institute Interdisciplinary Data Analytics and Search Lab University of Missouri-Columbia ColumbiaMO65211 United States

Research has shown that visual information of multimedia is critical in highly-skilled applications, such as biomedicine and life sciences, and a certain visual reasoning process is essential for meaningful search in a timely manner. Relevant image characteristics are learned and verified with accumulated experiences during the reasoning processes. However, such processes are highly dynamic and elusive to computationally quantify and therefore challenging to analyze, let alone to make the knowledge sharable across users. In this paper we study real-time human visual reasoning processes with the aid of gaze tracking devices. Temporal and spatial representations are proposed for gaze modeling, and a visual reasoning retrieval system utilizing in-memory computing under Big data ecosystems is designed for real-time search of similar reasoning models. Simulated data derived from human subject experiments show that the system has a reasonably high accuracy and provides predictive estimations for hardware requirements versus data sizes for exhaustive searches. Comparison between various visual action classifiers show challenges in modeling visual actions. The proposed system provides a theoretical framework and computing platform for advancement in visual semantic computing, as well as potential applications in medicine, social science, and arts. © 2016 World Scientific Publishing Company.

关键词： Eye tracking

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The influence of pharmacological autonomic blockades on multi-scale measures of heart rate variability

The influence of pharmacological autonomic blockades on mult...

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Joint Conference of the European Medical and Biological Engineering Conference, EMBEC 2017 and Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2107

作者： Marzbanrad, Faezeh Karmakar, C.K. Khandoker, A.H. Palaniswami, M. Moritani, T. Jelinek, H.F. Department of Electrical and Computer Systems Engineering Monash University ClaytonVIC3168 Australia Centre for Research in Complex Systems and School of Community Health Charles Sturt University Albury Australia Centre of Pattern Recognition and Data Analytics Deakin University GeelongVIC Australia Khalifa University of Science Technology and Research Abu Dhabi United Arab Emirates The University of Melbourne ParkvilleVIC3010 Australia Kyoto University Kyoto Japan

ISBN: (纸本)9789811051210

Multi-scale measures have been used extensively to characterize non-linear dynamics of heart beats. However a physiological interpretation of the exponents associated with various scales of these measures and the role of the sympathetic and parasympathetic activities have not been attempted. In this study three multi-scale measures were compared at different scales in a pharmacological autonomic blockade experiment, starting with a control phase. It was then followed by injection of propranolol causing sympathetic blockade and injection of atropine resulting in the blockade of both sympathetic and parasympathetic pathways. Multiscale Entropy (MSE), Multi Fractal Detrended Fluctuation Analysis (MFDFA) and Renyi Entropy (RE) were compared using a paired test statistics. The measures performed differently depending on the scale factors and RE showed the best results indicating a good discrimination particularly at higher scale exponents. This study provides a basis for understanding the effect of autonomic activity on multi-scale measures with respect to changes in parasympathetic and sympathetic activity. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Entropy

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Highly accurate model for prediction of lung nodule malignancy with ct scans

arXiv

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arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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Towards training-free refinement for semantic indexing of visual media 22nd

Towards training-free refinement for semantic indexing of vi...

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22nd International Conference on MultiMedia Modeling, MMM 2016

作者： Wang, Peng Sun, Lifeng Yang, Shiqang Smeaton, Alan F. National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China Insight Centre for Data Analytics Dublin City University Glasnevin Dublin 9 Ireland

ISBN: (纸本)9783319276700

Indexing of visual media based on content analysis has now moved beyond using individual concept detectors and there is now a focus on combining concepts or post-processing the outputs of individual concept detection. Due to the limitations and availability of training corpora which are usually sparsely and imprecisely labeled, training-based refinement methods for semantic indexing of visual media suffer in correctly capturing relationships between concepts, including co-occurrence and ontological relationships. In contrast to training-dependent methods which dominate this field, this paper presents a training-free refinement (TFR) algorithm for enhancing semantic indexing of visual media based purely on concept detection results, making the refinement of initial concept detections based on semantic enhancement, practical and flexible. This is achieved using global and temporal neighbourhood information inferred from the original concept detections in terms of weighted nonnegative matrix factorization and neighbourhood-based graph propagation, respectively. Any available ontological concept relationships can also be integrated into this model as an additional source of external a priori knowledge. Experiments on two datasets demonstrate the efficacy of the proposed TFR solution. © Springer International Publishing Switzerland 2016.

关键词： Matrix algebra

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What are the limits to time series based recognition of semantic concepts? 22nd

What are the limits to time series based recognition of sema...

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22nd International Conference on MultiMedia Modeling, MMM 2016

作者： Wang, Peng Sun, Lifeng Yang, Shiqiang Smeaton, Alan F. National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China Insight Centre for Data Analytics Dublin City University Glasnevin Dublin 9 Ireland

ISBN: (纸本)9783319276731

Most concept recognition in visual multimedia is based on relatively simple concepts, things which are present in the image or video. These usually correspond to objects which can be identified in images or individual frames. Yet there is also a need to recognise semantic concepts which have a temporal aspect corresponding to activities or complex events. These require some form of time series for recognition and also require some individual concepts to be detected so as to utilise their time-varying features, such as co-occurrence and re-occurrence patterns. While results are reported in the literature of using concept detections which are relatively specific and static, there are research questions which remain unanswered. What concept detection accuracies are satisfactory for time series recognition? Can recognition methods perform equally well across various concept detection performances? What affecting factors need to be taken into account when building concept-based high-level event/activity recognitions? In this paper, we conducted experiments to investigate these questions. Results show that though improving concept detection accuracies can enhance the recognition of time series based concepts, they do not need to be very accurate in order to characterize the dynamic evolution of time series if appropriate methods are used. Experimental results also point out the importance of concept selection for time series recognition, which is usually ignored in the current literature. © Springer International Publishing Switzerland 2016.

关键词： Time series

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Metabolic modeling elucidates phenformin and atpenin A5 as broad-spectrum antiviral drugs against RNA viruses

Communications biology

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Communications biology 2025年第1期8卷 791页

作者： Alina Renz Mirjam Hohner Raphaël Jami Maximilian Breitenbach Jonathan Josephs-Spaulding Johanna Dürrwald Lena Best Victoria Dulière Chloé Mialon Stefanie M Bader Georgios Marinos Nantia Leonidou Filipe Cabreiro Marc Pellegrini Marcel Doerflinger Manuel Rosa-Calatrava Andrés Pizzorno Andreas Dräger Michael Schindler Christoph Kaleta Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens Institute for Bioinformatics and Medical Informatics (IBMI) Eberhard Karl University of Tübingen Tübingen Germany. Institute for Medical Virology and Epidemiology of Viral Diseases University Hospital Tübingen Tübingen Germany. German Center for Infection Research (DZIF) partner site Tübingen Germany. Research Group Medical Systems Biology Institute of Experimental Medicine Christian-Albrechts-University Kiel & University Hospital Schleswig Holstein Kiel Germany. Wolfson Wohl Cancer Research Centre School of Cancer Sciences University of Glasgow Glasgow UK. CIRI Centre International de Recherche en Infectiologie (Team VirPath) Université de Lyon INSERM U1111 Université Claude Bernard Lyon 1 CNRS UMR5308 ENS de Lyon Lyon France. VirNext Faculté de Médecine RTH Laennec Université Claude Bernard Lyon 1 Université de Lyon Lyon France. International Research Laboratory RESPIVIR France-Canada Centre Hospitalier Universitaire de Québec - Université Laval Québec Canada Centre International de Recherche en Infectiologie Faculté de Médecine RTH Laennec Université Claude Bernard Lyon 1 Université de Lyon INSERM CNRS ENS de Lyon Lyon France. Division of Infectious Diseases and Immune Defense The Walter and Eliza Hall Institute of Medical Research Melbourne VIC Australia. Department of Computer Science Eberhard Karl University of Tübingen Tübingen Germany. Cluster of Excellence 'Controlling Microbes to Fight Infections' Eberhard Karl University of Tübingen Tübingen Germany. Cologne Excellence Cluster for Cellular Stress Responses in Aging-Associated Diseases (CECAD) University of Cologne Cologne Germany. Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens Institute for Bioinformatics and Medical Informatics (IBMI) Eberhard Karl University of Tübingen Tübingen Germany. andreas.draeger@informatik.uni-halle.de. German Center for Infection Research (DZIF) par

The SARS-CoV-2 pandemic has reemphasized the urgent need for broad-spectrum antiviral therapies. We developed a computational workflow using scRNA-Seq data to assess cellular metabolism during viral infection. With this workflow we predicted the capacity of cells to sustain SARS-CoV-2 virion production in patients and found a tissue-wide induction of metabolic pathways that support viral replication. Expanding our analysis to influenza A and dengue viruses, we identified metabolic targets and inhibitors for potential broad-spectrum antiviral treatment. These targets were highly enriched for known interaction partners of all analyzed viruses. Indeed, phenformin, an NADH:ubiquinone oxidoreductase inhibitor, suppressed SARS-CoV-2 and dengue virus replication. Atpenin A5, blocking succinate dehydrogenase, inhibited SARS-CoV-2, dengue virus, respiratory syncytial virus, and influenza A virus with high selectivity indices. In vivo, phenformin showed antiviral activity against SARS-CoV-2 in a Syrian hamster model. Our work establishes host metabolism as druggable for broad-spectrum antiviral strategies, providing invaluable tools for pandemic preparedness.

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Semantically smoothed refinement for everyday concept indexing 17th

Semantically smoothed refinement for everyday concept indexi...

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17th Pacific-Rim Conference on Multimedia, PCM 2016

ISBN: (纸本)9783319488899

Instead of occurring independently, semantic concepts pairs tend to co-occur within a single image and it is intuitive that concept detection accuracy for visual concepts can be enhanced if concept correlation can be leveraged in some way. In everyday concept detection for visual lifelogging using wearable cameras to automatically record everyday activities, the captured images usually have a diversity of concepts which challenges the performance of concept detection. In this paper a semantically smoothed refinement algorithm is proposed using concept correlations which exploit topic-related concept relationships, modeled externally in a user experiment rather than extracted from training data. Results for initial concept detection are factorized based on semantic smoothness and adjusted in compliance with the extracted concept correlations. Refinement performance is demonstrated in experiments to show the effectiveness of our algorithm and the extracted correlations. © Springer International Publishing AG 2016.

关键词： Semantics

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Corrigendum to "Dissecting the genetic heterogeneity of gastric cancer"

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EBioMedicine 2023年 94卷 104709页

作者： Timo Hess Carlo Maj Jan Gehlen Oleg Borisov Stephan L Haas Ines Gockel Michael Vieth Guillaume Piessen Hakan Alakus Yogesh Vashist Carina Pereira Michael Knapp Vitalia Schüller Alexander Quaas Heike I Grabsch Jessica Trautmann Ewa Malecka-Wojciesko Anna Mokrowiecka Jan Speller Andreas Mayr Julia Schröder Axel M Hillmer Dominik Heider Florian Lordick Ángeles Pérez-Aísa Rafael Campo Jesús Espinel Fernando Geijo Concha Thomson Luis Bujanda Federico Sopeña Ángel Lanas María Pellisé Claudia Pauligk Thorsten Oliver Goetze Carolin Zelck Julian Reingruber Emadeldin Hassanin Peter Elbe Sandra Alsabeah Mats Lindblad Magnus Nilsson Nicole Kreuser René Thieme Francesca Tavano Roberta Pastorino Dario Arzani Roberto Persiani Jin-On Jung Henrik Nienhüser Katja Ott Ralf R Schumann Oliver Kumpf Susen Burock Volker Arndt Anna Jakubowska Małgorzta Ławniczak Victor Moreno Vicente Martín Manolis Kogevinas Marina Pollán Justyna Dąbrowska Antonio Salas Olivier Cussenot Anne Boland-Auge Delphine Daian Jean-Francois Deleuze Erika Salvi Maris Teder-Laving Gianluca Tomasello Margherita Ratti Chiara Senti Valli De Re Agostino Steffan Arnulf H Hölscher Katharina Messerle Christiane Josephine Bruns Armands Sīviņš Inga Bogdanova Jurgita Skieceviciene Justina Arstikyte Markus Moehler Hauke Lang Peter P Grimminger Martin Kruschewski Nikolaos Vassos Claus Schildberg Philipp Lingohr Karsten Ridwelski Hans Lippert Nadine Fricker Peter Krawitz Per Hoffmann Markus M Nöthen Lothar Veits Jakob R Izbicki Adrianna Mostowska Federico Martinón-Torres Daniele Cusi Rolf Adolfsson Geraldine Cancel-Tassin Aksana Höblinger Ernst Rodermann Monika Ludwig Gisela Keller Andres Metspalu Hermann Brenner Joerg Heller Markus Neef Michael Schepke Franz Ludwig Dumoulin Lutz Hamann Renato Cannizzaro Michele Ghidini Dominik Plaßmann Michael Geppert Peter Malfertheiner Olivier Glehen Tomasz Skoczylas Marek Majewski Jan Lubiński Orazio Palmieri Stefania Boccia Anna Latiano Nuria Aragones Thomas Schmidt Mário Dinis-Ribeiro Rui Medeiros Salah-Eddin Al-Batran Mārcis Leja Juozas Institute of Human Genetics University of Marburg Marburg Germany Institute of Human Genetics University of Bonn School of Medicine & University Hospital Bonn Bonn Germany. Institute for Genomic Statistics and Bioinformatics Medical Faculty University of Bonn Bonn Germany. Institute of Human Genetics University of Marburg Marburg Germany. Department of Upper GI Diseases Karolinska University Hospital and Unit of Gastroenterology and Rheumatology Karolinska Institutet Stockholm Sweden. Department of Visceral Transplant Thoracic and Vascular Surgery University Hospital Leipzig Leipzig Germany. Institute for Pathology Friedrich-Alexander-University Erlangen-Nuernberg Klinikum Bayreuth Bayreuth Germany. Department of Digestive and Oncological Surgery Claude Huriez Hospital CHU Lille Lille France. Department of General Visceral Cancer and Transplant Surgery University of Cologne Cologne Germany. Department of General Visceral and Thoracic Surgery University Medical Center Hamburg-Eppendorf University of Hamburg Hamburg Germany Centre for Surgical Oncology Medias Klinikum Burghausen Burghausen Germany. Molecular Oncology and Viral Pathology Group IPO Porto Research Center (CI-IPOP) Portuguese Institute of Oncology of Porto (IPO Porto) 4200-072 Porto Portugal Porto Comprehensive Cancer Center & RISE @ CI-IPO University of Porto 4200-450 Porto Portugal. Institute of Medical Biometrics Informatics and Epidemiology (IMBIE) Medical Faculty University of Bonn Bonn Germany. Institute of Pathology University Hospital Cologne Medical Faculty University of Cologne Germany. Department of Pathology GROW School for Oncology and Developmental Biology Maastricht University Medical Center Maastricht the Netherlands Pathology and Data Analytics Leeds Institute of Medical Research at St James's University of Leeds Leeds United Kingdom. Department of Digestive Tract Diseases Medical University of Lodz Lodz Poland. Department of Mathematics and Computer Science Unive

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The Temporal Bin Packing Problem: An Application to Workload Management in data Centres

The Temporal Bin Packing Problem: An Application to Workload...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Milan De Cauwer Deepak Mehta Barry O'Sullivan Department of Computer Science Insight Centre for Data Analytics Cork Ireland

ISBN: (纸本)9781509044603

This paper formalises a packing problem that emerges as a core sub-problem for managing workload consolidation in data centres. As a generalisation of the Bin Packing (BP) problem, it considers a set of tasks (items) to be assigned to a set of machines (bins) under capacity constraints (CPU usage) on each machine. Unlike classic BP settings, items have a lifespan. We define the cost of using a bin as the product of the bin's capacity and the time for which it is used. This problem will be referred to as the Temporal Bin Packing problem (TBP). We formalise the problem and present optimisation models using Mixed Integer Programming (MIP) and Constraint Programming (CP) for two contrasting but equivalent perspectives on the problem. The Packing model (PA) extends traditional BP models while the Temporal model (TP) explicitly models time with a sequence of packing problems. In addition, symmetry breaking techniques are developed. Finally, we introduce both a lower bound and an upper bound on the objective function. Our empirical results suggest that the TBP is a rather challenging problem for complete solvers to prove optimality. While breaking symmetry considerably reduces the computational effort for both PA and TP models, the Packing model using CP should be considered for larger instances.

关键词： Linear programming Computational modeling Cloud computing Resource management Biological system modeling Programming Upper bound

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