snu-aidas/HVMap-dataset huggingface dataset:
Percutaneous left atrial appendage closure vs warfarin for atrial fibrillation: a randomized clinical trial.

IMPORTANCE
While effective in preventing stroke in patients with atrial fibrillation (AF), warfarin is limited by a narrow therapeutic profile, a need for lifelong coagulation monitoring, and multiple drug and diet interactions.


OBJECTIVE
To determine whether a local strategy of mechanical left atrial appendage (LAA) closure was noninferior to warfarin.


DESIGN, SETTING, AND PARTICIPANTS
PROTECT AF was a multicenter, randomized (2:1), unblinded, Bayesian-designed study conducted at 59 hospitals of 707 patients with nonvalvular AF and at least 1 additional stroke risk factor (CHADS2 score ≥1). Enrollment occurred between February 2005 and June 2008 and included 4-year follow-up through October 2012. Noninferiority required a posterior probability greater than 97.5% and superiority a probability of 95% or greater; the noninferiority margin was a rate ratio of 2.0 comparing event rates between treatment groups.


INTERVENTIONS
Left atrial appendage closure with the device (n = 463) or warfarin (n = 244; target international normalized ratio, 2-3).


MAIN OUTCOMES AND MEASURES
A composite efficacy end point including stroke, systemic embolism, and cardiovascular/unexplained death, analyzed by intention-to-treat.


RESULTS
At a mean (SD) follow-up of 3.8 (1.7) years (2621 patient-years), there were 39 events among 463 patients (8.4%) in the device group for a primary event rate of 2.3 events per 100 patient-years, compared with 34 events among 244 patients (13.9%) for a primary event rate of 3.8 events per 100 patient-years with warfarin (rate ratio, 0.60; 95% credible interval, 0.41-1.05), meeting prespecified criteria for both noninferiority (posterior probability, >99.9%) and superiority (posterior probability, 96.0%). Patients in the device group demonstrated lower rates of both cardiovascular mortality (1.0 events per 100 patient-years for the device group [17/463 patients, 3.7%] vs 2.4 events per 100 patient-years with warfarin [22/244 patients, 9.0%]; hazard ratio [HR], 0.40; 95% CI, 0.21-0.75; P = .005) and all-cause mortality (3.2 events per 100 patient-years for the device group [57/466 patients, 12.3%] vs 4.8 events per 100 patient-years with warfarin [44/244 patients, 18.0%]; HR, 0.66; 95% CI, 0.45-0.98; P = .04).


CONCLUSIONS AND RELEVANCE
After 3.8 years of follow-up among patients with nonvalvular AF at elevated risk for stroke, percutaneous LAA closure met criteria for both noninferiority and superiority, compared with warfarin, for preventing the combined outcome of stroke, systemic embolism, and cardiovascular death, as well as superiority for cardiovascular and all-cause mortality.


TRIAL REGISTRATION
clinicaltrials.gov Identifier: NCT00129545.
Parameter estimation for text analysis

Presents parameter estimation methods common with discrete probability distributions, which is of particular interest in text modeling. Starting with maximum likelihood, a posteriori and Bayesian estimation, central concepts like conjugate distributions and Bayesian networks are reviewed. As an application, the model of latent Dirichlet allocation (LDA) is explained in detail with a full derivation of an approximate inference algorithm based on Gibbs sampling, including a discussion of Dirichlet hyperparameter estimation. History: version 1: May 2005, version 2.4: August 2008.
A COEFFICIENT OF AGREEMENT FOR NOMINAL SCALES 1
A review of essential standards and patent landscapes for the Internet of Things: A key enabler for Industry 4.0

This paper is a formal overview of standards and patents for Internet of Things (IoT) as a key enabler for the next generation advanced manufacturing, referred as Industry 4.0 (I 4.0). IoT at the fundamental level is a means of connecting physical objects to the Internet as a ubiquitous network that enables objects to collect and exchange information. The manufacturing industry is seeking versatile manufacturing service provisions to overcome shortened product life cycles, increased labor costs, and fluctuating customer needs for competitive marketplaces. This paper depicts a systematic approach to review IoT technology standards and patents. The thorough analysis and overview include the essential standard landscape and the patent landscape based on the governing standards organizations for America, Europe and China where most global manufacturing bases are located. The literature of emerging IoT standards from the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC) and the Guobiao standards (GB), and global patents issued in US, Europe, China and World Intellectual Property Organization (WIPO) are systematically presented in this study. 2016 Elsevier Ltd. All rights reserved.
Exploiting Topic based Twitter Sentiment for Stock Prediction

*This work is supported by a grant from National Science Foundation (IIS-1111092) and a strategic research grant from City University of Hong Kong (7002770) *The work was done when the first author was visiting University of Illinois at Chicago. Abstract • The Web has seen a tremendous rise in social media. • People’s experiences and opinions expressed in Social Media (e.g., Twitter, Facebook) contain rich social signals (e.g., about consumer sentiment, stock market, economic conditions, etc.). • The goal of this paper is to exploit public sentiment for stock market prediction. • Topic based sentiment is an important factor in stock prediction because it reflects public opinions about current issues/topics. • We employ a Bayesian non-parametric topic-based sentiment time series approach learned from streaming tweets to predict the S&P100 Stock Index.
Collaboration, creativity and the co-construction of oral and written texts

The Open University's repository of research publications and other research outputs Collaboration, creativity and the co-construction of oral and written texts Journal Article Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyright owners. For more information on Open Research Online's data policy on reuse of materials please consult the policies page. (To be included in a special issue of Thinking Skills and Creativity, on " Collaborative Abstract In this paper we explore how primary school children 'learn to collaborate' and 'collaborate to learn' on creative writing projects by using diverse cultural artefacts-including oracy, literacy and ICT. We begin by reviewing some key socio-cultural concepts which serve as a theoretical framework for the research reported. Secondly, we describe the context in which the children talked and worked together to create their projects. This context is a 'learning community' developed as part of an innovative educational programme with the aim of promoting the social construction of knowledge among all participants. We then present microgenetic analyses of the quality of the interaction and dialogues taking place as peers worked together on their projects, and how these collaborative processes and uses of the mediational artefacts were taken up by the children. In order to exemplify these processes, our analyses centre on a selection of examples of dialogues, texts and multimedia products of stories created by groups of 4th grade (9-10 year-old) children. Overall, the work reveals the dynamic functioning in educational settings of some central socio-cultural concepts. These include: co-construction; intertextuality and intercontextuality amongst oracy, literacy and uses of ICT; collaborative creativity; development of dialogical and text production strategies and appropriation of diverse cultural artefacts for knowledge construction.
Less is more ? How demographic sample weights can improve public opinion estimates based on Twitter data

Twitter data is widely acknowledged to hold great promise for the study of political behavior and public opinion. However, a key limitation in previous studies is the lack of information about the sociodemographic characteristics of individual users, which raises concerns about the validity of inferences based on this source of data. This paper addresses this challenge by employing supervised machine learning methods to estimate the age, gender, race, party affiliation, propensity to vote, and income of any Twitter user in the U.S. The training dataset for these classifiers was obtained by matching a large dataset of 1 billion geolocated Twitter messages with voting registration records and estimates of home values across 15 different states, resulting in a sample of nearly 250,000 Twitter users whose sociodemographic traits are known. To illustrate the value of this approach, I offer three applications that use information about the predicted demographic composition of a random sample of 500,000 U.S. Twitter users. First, I explore how attention to politics varies across demographics groups. Then, I apply multilevel regression and postratification methods to recover valid estimate of presidential and candidate approval that can serve as early indicators of public opinion changes and thus complement traditional surveys. Finally, I demonstrate the value of Twitter data to study questions that may suffer from social desirability bias. Working paper. This version: September 21, 2016. I would like to thank John Ahlquist, Drew Dimmery, Pat Egan, Martin Elff, Mik Laver, Jeff Lewis, Thomas Leeper, Jonathan Nagler, Molly Roberts, Arthur Spirling, David Sontag for helpful comments and discussions. I also gratefully acknowledges financial support from the Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation.
A Survey on Preprocessing Methods for Web Usage Data

World Wide Web is a huge repository of web pages and links. It provides abundance of information for the Internet users. The growth of web is tremendous as approximately one million pages are added daily. Users’ accesses are recorded in web logs. Because of the tremendous usage of web, the web log files are growing at a faster rate and the size is becoming huge. Web data mining is the application of data mining techniques in web data. Web Usage Mining applies mining techniques in log data to extract the behavior of users which is used in various applications like personalized services, adaptive web sites, customer profiling, prefetching, creating attractive web sites etc., Web usage mining consists of three phases preprocessing, pattern discovery and pattern analysis. Web log data is usually noisy and ambiguous and preprocessing is an important process before mining. For discovering patterns sessions are to be constructed efficiently. This paper reviews existing work done in the preprocessing stage. A brief overview of various data mining techniques for discovering patterns, and pattern analysis are discussed. Finally a glimpse of various applications of web usage mining is also presented. KeywordsData Cleaning, Path Completion, Session Identification , User Identification, Web Log Mining
Exploration-exploitation tradeoff using variance estimates in multi-armed bandits

Algorithms based on upper confidence bounds for balancing exploration and exploitation are gaining popularity since they are easy to implement, efficient and effective. This paper considers a variant of the basic algorithm for the stochastic, multi-armed bandit problem that takes into account the empirical variance of the different arms. In earlier experimental works, such algorithms were found to outperform the competing algorithms. We provide the first analysis of the expected regret for such algorithms. As expected, our results show that the algorithm that uses the variance estimates has a major advantage over its alternatives that do not use such estimates provided that the variances of the payoffs of the suboptimal arms are low. We also prove that the regret concentrates only at a polynomial rate. This holds for all the upper confidence bound based algorithms and for all bandit problems except those special ones where with probability one the payoff obtained by pulling the optimal arm is IThis research was funded in part by the National Science and Engineering Research Council (NSERC), iCore and the Alberta Ingenuity Fund. ∗Corresponding author. Email addresses: audibert@certis.enpc.fr (Jean Yves Audibert), remi.munos@inria.fr (Rémi Munos), szepesva@cs.ualberta.ca (Csaba Szepesvári) 1Csaba Szepesvári is on leave from MTA SZTAKI, Budapest, Hungary. Preprint submitted to Theoretical Computer Science October 1, 2008 larger than the expected payoff for the second best arm. Hence, although upper confidence bound bandit algorithms achieve logarithmic expected regret rates, they might not be suitable for a risk-averse decision maker. We illustrate some of the results by computer simulations.
Traffic engineering techniques for data center networks

Traffic engineering consists in improving the performance of the teleco-munication networks which is evaluated by a large number of criteria. The ultimate objective is to avoid congestion in the network by keeping its links from being overloaded. In large Ethernet networks with thousands of servers, such as data centers, improving the performance of the traditional switching protocols is a crucial but very challenging task due to an exploration in the size of solution space and the complexity. Thus, exact methods are inappropriate even for reasonable size networks. Local Search (LS) is a powerful method for solving computational optimization problems such as the Vertex Cover, Traveling Salesman, or Boolean Satisfiability. The advantage of LS for these problems is its ability to find an intelligent path from a low quality solution to a high quality one in a huge search space. In this thesis, we propose different approximate methods based on Local Search for solving the class of traffic engineering problems in data center networks that implement Spanning Tree Protocol and Multiple Spanning Tree Protocol. First, we tackle the minimization of the maximal link utilization in the Ethernet networks with one spanning tree. Next, we cope with data center networks containing many spanning trees. We then deal with the minimization of service disruption and the worst-case maximal link utilization in data center networks with many spanning trees. Last, we develop a novel design of multi-objective algorithms for solving the traffic engineering problems in large data centers by taking into account three objectives to be minimized: maximal link utilization, network total load and number of used links. Our schemes reduce significantly the size of the search space by releasing the dependence of the solutions from the link cost computation iii iv in order to obtain an intended spanning tree. We propose efficient in-cremental techniques to speed up the computation of objective values. Furthermore, our approaches show good results on the credible data sets and are evaluated by the strong assessment methods. ACKNOWLEDGEMENTS First of all, I would like to thank Yves Deville and Olivier Bonaventure for the four years they spent helping me. I am very lucky to have two great advisors of two different promoting styles. Yves Deville is a very demanding supervisor who wants every smallest detail to be clear and intuitive. His ideas and his criticisms sometimes made me nervous or even angry but I could not deny the truth …
Optimal Expectations and the Welfare Cost of Climate Variability

Uncertainty about the future is an important determinant of well-being, especially in developing countries where financial markets and other market failures result in ineffective insurance mechanisms. However, separating the effects of future uncertainty from realised events, and then measuring the impact of uncertainty on utility, presents a number of empirical challenges. This paper aims to address these issues and provides supporting evidence to show that increased climate variability (a proxy for future income uncertainty) reduces farmers’ subjective well-being, consistent with the theory of optimal expectations (Brunnermeier & Parker, 2005 AER), using panel data from rural Ethiopia and a new data set containing daily atmospheric parameters. The magnitude of our result indicates that a one standard deviation (7%) increase in climate variability has an equivalent effect on life satisfaction to a two standard deviation (1-2%) decrease in consumption. This effect is one of the largest determinants of life satisfaction in rural Ethiopia. (JEL: C25, D60, I31.) ∗Alem – Department of Economics, University of Gothenburg, Sweden and Department of Agricultural and Resource Economics, UC, Berkeley, USA. E-mail: yonas.alem@economics.gu.se. Colmer – The Grantham Research Institute and Centre for Economic Performance, London School of Economics, UK. E-mail: j.m.colmer@lse.ac.uk. We are grateful to Allen Blackman, Jeffery Bookwalter, Gharad Bryan, Douglas Dalenberg, Paul Dolan, John Feddersen, Greer Gosnell, Cameron Hepburn, Derek Kellenberg, Peter Martinsson, Kyle Meng, Rob Metcalfe, Eric Neumayer, Jonathan Parker, Hendrik Wolff and seminar participants at the EfD 6th Annual Meeting, EAERE, the University of Oxford, London School of Economics, University of Montana, and Resources for the Future for helpful thoughts, comments and discussions. The first author would like to thank the Swedish International Development Agency (Sida) through the Environment for Development Initiative (EfD) at the University of Gothenburg, and the Swedish Research Council Formas through the Human Cooperation to Manage Natural Resources (COMMONS) programme for financial support. Part of this research was done while Alem was a Visiting Scholar at the Department of Agricultural and Resource Economics, University of California Berkeley. The second author would like to thank the ESRC Centre for Climate Change Economics and Policy and the Grantham Foundation for financial support. The data used in this article were collected by the University of Addis Ababa, the International Food Policy Research Institute (IFPRI), and the Centre for the Study of African Economies (CSAE). Funding for the ERHS survey was provided by the Economic and Social Research Council (ESRC), the Swedish International Development Agency (SIDA) and the United States Agency for International Development (USAID). All errors and omissions are our own.
Building a Large-Scale Knowledge Base for Machine Translation

Introduction Linguistic Resources Knowledge-based machine translation (KBMT) systems have achieved excellent results in constrained domains, but have not yet scaled up to newspaper text. The reason is that knowledge resources (lexicons, grammar rules, world models) must be painstakingly handcrafted from scratch. One of the hypotheses being tested in the PANGLOSS machine translation project is whether or not these resources can be semi-automatically acquired on a very large scale. This paper focuses on the construction of a large ontology (or knowledge base, or world model) for supporting KBMT. It contains representations for some 70,000 commonly encountered objects, processes, qualities, and relations. The ontology was constructed by merging various online dictionaries, semantic networks, and bilingual resources, through semi-automatic methods. Some of these methods (e.g., conceptual matching of semantic taxonomies) are broadly applicable to problems of importing/exporting knowledge from one KB to another. Other methods (e.g., bilingual matching) allow a knowledge engineer to build up an index to a KB in a second language, such as Spanish or Japanese. USC/Information Sciences Institute 4676 Admiralty Way Marina del Rey, CA 90292 knight,luk @isi.edu
Panelrama : A Framework for Cross-Device Applications

We introduce Panelrama, a web framework for the construction of distributed user interfaces (DUI). Our implementation provides developers with low migration costs through built-in mechanisms for the synchronization of UI state and minimal changes to existing languages. Additionally, we describe a solution to categorize device characteristics and dynamically change UI allocation to best-fit devices. Finally, we illustrate the use of Panelrama through a sample application which demonstrates multidevice interaction techniques and collaborative potential. Author
Multi-target tracking by learning local-to-global trajectory models

The multi-target tracking problem is challenging when there exist occlusions, tracking failures of the detector and severe interferences between detections. In this paper, we propose a novel detection based tracking method that links detections into tracklets and further forms long trajectories. Unlike many previous hierarchical frameworks which split the data association into two separate optimization problems (linking detections locally and linking tracklets globally), we introduce a unified algorithm that can automatically relearn the trajectory models from the local and global information for finding the joint optimal assignment. In each temporal window, the trajectory models are initialized by the local information to link those easy-to-connect detections into a set of tracklets. Then the trajectory models are updated by the reliable tracklets and reused to link separated tracklets into long trajectories. We iteratively update the trajectory models by more information from more frames until the result converges. The iterative process gradually improves the accuracy of the trajectory models, which in turn improves the target ID inferences for all detections by the MRF model. Experiment results revealed that our proposed method achieved state-of-the-art multi-target tracking performance. Crown Copyright & 2014 Published by Elsevier Ltd. All rights reserved.
On-line Q-learning using connectionist systems

Reinforcement learning algorithms are a powerful machine learning technique. However, much of the work on these algorithms has been developed with regard to discrete nite-state Markovian problems, which is too restrictive for many real-world environments. Therefore, it is desirable to extend these methods to high dimensional continuous state-spaces, which requires the use of function approximation to generalise the information learnt by the system. In this report, the use of back-propagation neural networks (Rumelhart, Hinton and Williams 1986) is considered in this context. We consider a number of di erent algorithms based around Q-Learning (Watkins 1989) combined with the Temporal Di erence algorithm (Sutton 1988), including a new algorithm (Modi ed Connectionist Q-Learning), and Q( ) (Peng and Williams 1994). In addition, we present algorithms for applying these updates on-line during trials, unlike backward replay used by Lin (1993) that requires waiting until the end of each trial before updating can occur. On-line updating is found to be more robust to the choice of training parameters than backward replay, and also enables the algorithms to be used in continuously operating systems where no end of trial conditions occur. We compare the performance of these algorithms on a realistic robot navigation problem, where a simulated mobile robot is trained to guide itself to a goal position in the presence of obstacles. The robot must rely on limited sensory feedback from its surroundings, and make decisions that can be generalised to arbitrary layouts of obstacles. These simulations show that on-line learning algorithms are less sensitive to the choice of training parameters than backward replay, and that the alternative update rules of MCQ-L and Q( ) are more robust than standard Q-learning updates. 1