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Robust principal component analysis by reverse iterative linear programming 15th

15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in databases, ECML PKDD 2016

作者： Visentin, Andrea Prestwich, Steven Armagan Tarim, S. Insight Centre for Data Analytics Department of Computer Science University College Cork Cork Ireland Department of Management Cankaya University Ankara Turkey

ISBN: (纸本)9783319462264

Principal Components Analysis (PCA) is a data analysis technique widely used in dimensionality reduction. It extracts a small number of orthonormal vectors that explain most of the variation in a dataset, which are called the Principal Components. Conventional PCA is sensitive to outliers because it is based on the L2-norm, so to improve robustness several algorithms based on the L1-norm have been introduced in the literature. We present a new algorithm for robust L1- norm PCA that computes components iteratively in reverse, using a new heuristic based on Linear Programming. This solution is focused on finding the projection that minimizes the variance of the projected points. It has only one parameter to tune, making it simple to use. On common benchmarks it performs competitively compared to other methods. The data and software related to this paper are available at https://github. com/visentin-insight/L1-PCAhp. © Springer International Publishing AG 2016.

关键词： Principal component analysis

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Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990–2019, for 204 countries and territories: the Global Burden of Diseases Study 2019

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The Lancet HIV 2021年第10期8卷 e633-e651页

作者： Jahagirdar, Deepa Walters, Magdalene K. Novotney, Amanda Brewer, Edmond D. Frank, Tahvi D. Carter, Austin Biehl, Molly H. Abbastabar, Hedayat Abhilash, E.S. Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Adekanmbi, Victor Adeyinka, Daniel Adedayo Adnani, Qorinah Estiningtyas Sakilah Afzal, Saira Aghababaei, Soodabeh Ahinkorah, Bright Opoku Ahmad, Sajjad Ahmadi, Keivan Ahmadi, Sepideh Ahmadpour, Ehsan Ahmed, Muktar Beshir Rashid, Tarik Ahmed Salih, Yusra Ahmed Aklilu, Addis Akram, Tayyaba Akunna, Chisom Joyqueenet Al Hamad, Hanadi Alahdab, Fares Alanezi, Fahad Mashhour Aleksandrova, Ekaterina A. Alene, Kefyalew Addis Ali, Liaqat Alipour, Vahid Almustanyir, Sami Alvis-Guzman, Nelson Ameyaw, Edward Kwabena Amu, Hubert Andrei, Catalina Liliana Andrei, Tudorel Anvari, Davood Arabloo, Jalal Aremu, Olatunde Arulappan, Judie Atnafu, Desta Debalkie Quintanilla, Beatriz Paulina Ayala Ayza, Muluken Altaye Azari, Samad Darshan, B.B. Banach, Maciej Bärnighausen, Till Winfried Barra, Fabio Barrow, Amadou Basu, Sanjay Bazargan-Hejazi, Shahrzad Belay, Habtamu Gebrehana Berheto, Tezera Moshago Bezabhe, Woldesellassie Mequanint Bezabih, Yihienew Mequanint Bhagavathula, Akshaya Srikanth Bhardwaj, Nikha Bhardwaj, Pankaj Bhattacharyya, Krittika Bibi, Sadia Bijani, Ali Bisignano, Catherine Bolarinwa, Obasanjo Afolabi Boloor, Archith Boltaev, Azizbek A. Briko, Nikolay Ivanovich Buonsenso, Danilo Burkart, Katrin Butt, Zahid A. Cao, Chao Charan, Jaykaran Chatterjee, Souranshu Chattu, Soosanna Kumary Chattu, Vijay Kumar Choudhari, Sonali Gajanan Chu, Dinh-Toi Couto, Rosa A.S. Cowden, Richard G. Dachew, Berihun Assefa Dadras, Omid Dagnew, Amare Belachew Dahlawi, Saad M.A. Dai, Xiaochen Dandona, Lalit Dandona, Rakhi das Neves, José Degenhardt, Louisa Demeke, Feleke Mekonnen Desta, Abebaw Alemayehu Deuba, Keshab Dhamnetiya, Deepak Dhungana, Govinda Prasad Dianatinasab, Mostafa Diaz, Daniel Djalalinia, Shirin Doan, Linh Phuong Dorostkar, Fariba Edinur, Hisham Atan Effiong, Andem Eftekharzadeh, Sahar El Sayed Zaki, Maysaa Elayedath, Rajesh Elhadi, Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Allergy and Infectious Diseases University of Washington Seattle WA United States Department of Global Health University of Washington Seattle WA United States School of Social Work University of Washington Seattle WA United States Department of Medicine University of Washington Seattle WA United States Vagelos College of Physicians and Surgeones Columbia University Medical Center New York NY United States Division of Data Analytics and Delivery for Impact WHO Geneva Switzerland Advanced Diagnostic and Interventional Radiology Research Centre Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Centre Tehran University of Medical Sciences Tehran Iran School of Public Health Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Paediatric Chronic Kidney Disease Research Centre Tehran University of Medical Sciences Tehran Iran Department of Psychiatry Tehran University of Medical Sciences Tehran Iran Metabolomics and Genomics Research Centre Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Centre Tehran University of Medical Sciences Tehran Iran Research Centre for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Non-Communicable Diseases Research Centre Tehran University of Medical Sciences Tehran Iran Department of Botany Sree Narayana Guru College Chelannur Kozhikode India Clinical Sciences Department University of Sharjah Sharjah United Arab Emirates Mass Communication Department University of Sharjah Sharjah United Arab Emirates Department of Health-Care Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Department of Population Medicine Ca

Background: The sustainable development goals (SDGs) aim to end HIV/AIDS as a public health threat by 2030. Understanding the current state of the HIV epidemic and its change over time is essential to this effort. This study assesses the current sex-specific HIV burden in 204 countries and territories and measures progress in the control of the epidemic. Methods: To estimate age-specific and sex-specific trends in 48 of 204 countries, we extended the Estimation and Projection Package Age-Sex Model to also implement the spectrum paediatric model. We used this model in cases where age and sex specific HIV-seroprevalence surveys and antenatal care-clinic sentinel surveillance data were available. For the remaining 156 of 204 locations, we developed a cohort-incidence bias adjustment to derive incidence as a function of cause-of-death data from vital registration systems. The incidence was input to a custom Spectrum model. To assess progress, we measured the percentage change in incident cases and deaths between 2010 and 2019 (threshold >75% decline), the ratio of incident cases to number of people living with HIV (incidence-to-prevalence ratio threshold <0·03), and the ratio of incident cases to deaths (incidence-to-mortality ratio threshold <1·0). Findings: In 2019, there were 36·8 million (95% uncertainty interval [UI] 35·1–38·9) people living with HIV worldwide. There were 0·84 males (95% UI 0·78–0·91) per female living with HIV in 2019, 0·99 male infections (0·91–1·10) for every female infection, and 1·02 male deaths (0·95–1·10) per female death. Global progress in incident cases and deaths between 2010 and 2019 was driven by sub-Saharan Africa (with a 28·52% decrease in incident cases, 95% UI 19·58–35·43, and a 39·66% decrease in deaths, 36·49–42·36). Elsewhere, the incidence remained stable or increased, whereas deaths generally decreased. In 2019, the global incidence-to-prevalence ratio was 0·05 (95% UI 0·05–0·06) and the global incidence-to-mortality ratio was

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Extracting key challenges in achieving sobriety through shared subspace learning 12th

Extracting key challenges in achieving sobriety through shar...

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

作者： Harikumar, Haripriya Nguyen, Thin Rana, Santu Gupta, Sunil 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

Alcohol abuse is quite common among all people without any age restrictions. The uncontrolled use of alcohol affects both the individual and society. Alcohol addiction leads to a huge increase in crime, suicide, health related problems and financial crisis. Research has shown that certain behavioral changes can be effective towards staying abstained. The analysis of behavioral changes of quitters and those who are at the beginning phase of quitting can be useful for reducing the issues related to alcohol addiction. Most of the conventional approaches are based on surveys and, therefore, expensive in both time and cost. Social media has lend itself as a source of large, diverse and unbiased data for analyzing social behaviors. Reddit is a social media platform where a large number of people communicate with each other. It has many different sub-groups called subreddits categorized based on the subject. We collected more than 40,000 self reported user’s data from a subreddit called ‘/r/stopdrinking’. We divide the data into two groups, short-term with abstinent days less than 30 and long-term abstainers with abstinent days greater than 365 based on badge days at the time of post submission. Common and discriminative topics are extracted from the data using JS-NMF, a shared subspace non-negative matrix factorization method. The validity of the extracted topics are demonstrated through predictive performance. © Springer International Publishing AG 2016.

关键词： Non-negative matrix factorization

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Understanding behavioral differences between short and long-term drinking abstainers from social media 12th

Understanding behavioral differences between short and long-...

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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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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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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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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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