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Extortion provides alternative routes to the evolution of cooperation in structured populations

Physical Review E 2017年第5期95卷 052302-052302页

作者： Xiongrui Xu Zhihai Rong Zhi-Xi Wu Tao Zhou Chi Kong Tse CompleX Lab Web Sciences Center University of Electronic Science and Technology of China Chengdu 611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu 611731 China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Institute of Computational Physics and Complex Systems Lanzhou University Lanzhou Gansu 730000 People's Republic of China

In this paper, we study the evolution of cooperation in structured populations (individuals are located on either a regular lattice or a scale-free network) in the context of repeated games by involving three types of strategies, namely, unconditional cooperation, unconditional defection, and extortion. The strategy updating of the players is ruled by the replicator-like dynamics. We find that extortion strategies can act as catalysts to promote the emergence of cooperation in structured populations via different mechanisms. Specifically, on regular lattice, extortioners behave as both a shield, which can enwrap cooperators inside and keep them away from defectors, and a spear, which can defeat those surrounding defectors with the help of the neighboring cooperators. Particularly, the enhancement of cooperation displays a resonance-like behavior, suggesting the existence of optimal extortion strength mostly favoring the evolution of cooperation, which is in good agreement with the predictions from the generalized mean-field approximation theory. On scale-free network, the hubs, who are likely occupied by extortioners or defectors at the very beginning, are then prone to be conquered by cooperators on small-degree nodes as time elapses, thus establishing a bottom-up mechanism for the emergence and maintenance of cooperation.

关键词： Population dynamics Self-organized systems Social systems

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Effect of order flow imbalance on market impact across market states

Effect of order flow imbalance on market impact across marke...

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IEEE Symposium on systems and Information engineering Design, SIEDS

作者： Andrew Kromkowski Mason Montgomery Kaustav Saha Feiyin Wu Peter Beling University of Virginia Data Science Institute Department of Systems and Information Engineering University of Virginia

Every time a trader places an order to buy or sell a stock, this order causes reactions that change the market in highly complex ways. In theory these changes are always detrimental to the trader, causing the stock to become more expensive when buying and cheaper when selling. For large hedge funds, these costs have the potential to become a major obstruction to profitability when aggregated. Market impact models have been based on simple supply and demand, no arbitrage theory, and Brownian motion. These previous models have shown that the market impact of orders and trades can be largely explained using a linear model with Order Flow Imbalance (OFI), a simple measure of the buy/sell imbalance of quote orders placed to the National Best Bid and Offer (NBBO) in a window of time, as the sole predictor. By examining this relationship between OFI and price movement, previous studies have derived observable measures for the market impact of orders placed in a period of time, however these models are unsurprisingly noisy. Using a simple Brownian motion model for the movement of prices, we propose a new method for observing this price impact and compare it to previously accepted models. We then use this measure to examine how price impact varies across market states (i.e. volume, time of day, and spread).

关键词： Biological system modeling Predictive models Time measurement Mathematical model Conferences Supply and demand Linear regression

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Predicting mortality risk associated with serious treatable surgical complications at the University of Virginia health system

Predicting mortality risk associated with serious treatable ...

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IEEE Symposium on systems and Information engineering Design, SIEDS

作者： Jason Adams Sharath Pingula Akshat Verma Abigail Flower Data Science Institute University of Virginia Department of Systems and Information Engineering University of Virginia

This study focuses on predicting the risk of occurrence of serious but treatable complications and subsequent risk of mortality using a patient's preoperative conditions. Serious treatable complications include deep vein thrombosis/ pulmonary embolism, pneumonia, sepsis, and shock/cardiac arrest. These complications, if not identified and treated in time, can cause lengthened hospital stays, morbidity, and in some cases, mortality. We have modeled the risk of developing complications, and mortality due to complications, using a hierarchical prediction approach. In the first level of the hierarchy, extreme gradient boosted trees with cost sensitive weighting was used to model the risk of each complication and to identify the factors responsible for each type of complication. In the second level, similar statistical methods were used but on a smaller population set of patients, specifically those who developed one or more complications, to predict the risk of mortality. In our population of 32,202 patients, 963 developed one of the complications of interest, and of those with complications 174 died. Our predictions for sepsis, pneumonia, cardiac shock, and deep vein thrombosis/ pulmonary embolism, resulted in mean AUC values of 0.815, 0.935, 0.854, and 0.879 respectively. When making mortality predictions we achieved a mean AUC of 0.921. A propensity score analysis of patients that were predicted to be low risk but actually developed a complication was also performed. The framework proposed in this study provides hospitals with a way to more closely examine patient data regarding quality metrics by enabling them to identify patient born risks before surgical procedures are performed.

关键词： Surgery Sociology Statistics Predictive models Hospitals Lungs Diseases

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Network slicing for service-oriented networks under resource constraints

arXiv

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arXiv 2017年

作者： Zhang, Nan Liu, Ya-Feng Farmanbar, Hamid Chang, Tsung-Hui Hong, Mingyi Luo, Zhi-Quan School of Mathematical Sciences Peking University State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Huawei Canada Research Center Ottawa Canada Department of Electrical and Computer Engineering University of Minnesota United States Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China

To support multiple on-demand services over fixed communication networks, network operators must allow flexible customization and fast provision of their network resources. One effective approach to this end is network virtualization, whereby each service is mapped to a virtual subnetwork providing dedicated on-demand support to network users. In practice, each service consists of a prespecified sequence of functions, called a service function chain (SFC), while each service function in a SFC can only be provided by some given network nodes. Thus, to support a given service, we must select network function nodes according to the SFC and determine the routing strategy through the function nodes in a specified order. A crucial network slicing problem that needs to be addressed is how to optimally localize the service functions in a physical network as specified by the SFCs, subject to link and node capacity constraints. In this paper, we formulate the network slicing problem as a mixed binary linear program and establish its strong NP-hardness. Furthermore, we propose efficient penalty successive upper bound minimization (PSUM) and PSUM-R(ounding) algorithms, and two heuristic algorithms to solve the problem. Simulation results are shown to demonstrate the effectiveness of the proposed algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Network function virtualization

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A Spatial-Cue-Based Probabilistic Model for Bird Song Scene Analysis

A Spatial-Cue-Based Probabilistic Model for Bird Song Scene ...

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International Conference on data science and Advanced Analytics (DSAA)

作者： Ryosuke Kojima Osamu Sugiyama Kotaro Hoshiba Reiji Suzuki Kazuhiro Nakadai Department of Biomedical Data Intelligence Kyoto University Kyoto Japan Preemptive Medicine & Lifestyle-Related Disease Research Center Kyoto University Hospital Kyoto Japan Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo JAPAN Graduate School of Information Science Nagoya University Aichi Japan Honda Research Institute Japan Co. Ltd. Saitama JAPAN

This paper addresses bird song scene analysis focusing on location of birds and acoustic features of bird songs. Such a research area usually requires manual annotation related to positions and/or vocalization types of the target animals for a large amount of observed data. However, this manual annotation has two problems. One is that it is tough to annotate data observed in real environments because environmental noise exist and sound is reflected by trees and the ground, and also several birds at different locations may sing at the same time. The other is that it is inevitable that manual annotation produces inaccurate and inconsistent labels due to human errors and annotators' individual differences. For the first problem, we propose a Spatial-Cue-Based Probabilistic Model (SCBPM), which is a probabilistic model to estimate the maximum likelihood result for a bird song scene analysis by integrating sound source detection, localization, separation and identification based on spatial information of sound sources. For the second problem, we employ a semiautomatic annotation approach, in which a semi-supervised training method is deduced for SCBPM. This method decreases the amount of manual annotation. Preliminary experiments using recorded bird song data from the wild revealed that our system outperformed a conventional bird song scene analysis system by simply connecting sound source detection, localization, separation and identification in a cascade way in terms of identification accuracy.

关键词： Birds Acoustics Computational modeling Microphone arrays Multiple signal classification Spectrogram

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Identifying risk of progression for patients with Chronic Kidney Disease using clustering models

Identifying risk of progression for patients with Chronic Ki...

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IEEE Symposium on systems and Information engineering Design, SIEDS

作者： Michael Lenart Nikhil Mascarenhas Ruisi Xiong Abigail Flower Data Science Institute University of Virginia Faculty Department of Systems and Information Engineering University of Virginia

Chronic Kidney Disease ("CKD") and its comorbidities, diabetes, hypertension and cardiovascular disease ("CVD"), are frequently measured by routine procedures and lab tests, creating a large amount of historical data about this patient population. In this paper, we conducted a retrospective study based on Electronic Health Records ("EHR") data, in order to identify patterns in the development of CKD. In particular, we used a clustering approach to quantifiably identify diabetic patients who are at risk of progressing to advanced stages of CKD. Using values from routine measurements and lab tests such as systolic blood pressure, diastolic blood pressure, body mass index ("BMI"), Hemoglobin A1c ("HbAlc"), triglycerides and high density lipid cholesterol ("HDL cholesterol"), patients were classified into four clusters with a distinct separation between the cluster with the best value for each lab test and a cluster with the worst value for each lab test. We used lab values from each subsequent visit to calculate a progression score using the distance to the best and worst clusters, which indicated whether a patient's health was improving or deteriorating. We believe that this approach holds promise for future tools, as it is able to provide an ordered list of patients who are at greater risk of deterioration and should benefit from intervention by healthcare providers. The conclusions made in this paper are aimed at enabling timely monitoring and earlier intervention for patients that are associated with higher possibility of CKD progression.

关键词： Diseases Blood pressure Hardware design languages Lipidomics Atmospheric measurements Particle measurements

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Deep learning for multi-messenger astrophysics a gateway for discovery in the big data era

arXiv

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arXiv 2019年

作者： Allen, Gabrielle Andreoni, Igor Bachelet, Etienne Bruce Berriman, G. Bianco, Federica B. Biswas, Rahul Kind, Matias Carrasco Chard, Kyle Cho, Minsik Cowperthwaite, Philip S. Etienne, Zachariah B. George, Daniel Gibbs, Tom Graham, Matthew Gropp, William Gupta, Anushri Haas, Roland Huerta, E.A. Jennings, Elise Katz, Daniel S. Khan, Asad Kindratenko, Volodymyr Kramer, William T.C. Liu, Xin Mahabal, Ashish McHenry, Kenton Miller, J.M. Neubauer, M.S. Oberlin, Steve Olivas, Alexander R. Rosofsky, Shawn Ruiz, Milton Saxton, Aaron Schutz, Bernard Schwing, Alex Seidel, Ed Shapiro, Stuart L. Shen, Hongyu Shen, Yue Sipocz, Brigitta M. Sun, Lunan Towns, John Tsokaros, Antonios Wei, Wei Wells, Jack Williams, Timothy J. Xiong, Jinjun Zhao, Zhizhen NCSA University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Astronomy University of Illinois at Urbana-Champaign UrbanaIL61801 United States California Institute of Technology 1200 E California Blvd PasadenaCA91125 United States Las Cumbres Observatory 6740 Cortona Drive Suite 102 GoletaCA93117 United States Caltech/IPAC-NExScI 1200 E California Blvd PasadenaCA91125 United States Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Joseph R. Biden Jr . School of Public Policy and Administration University of Delaware NewarkDE19716 United States Data Science Institute University of Delaware NewarkDE19716 United States Center for Urban Science and Progress New York University 370 Jay St BrooklynNY11201 United States The Oskar Klein Centre for Cosmoparticle Physics Stockholm University AlbaNova StockholmSE-106 91 Sweden University of Chicago ChicagoIL60605 United States IBM Systems AustinTX78717 United States Observatories of the Carnegie Institution for Science 813 Santa Barbara St. PasadenaCA91101 United States Department of Mathematics West Virginia University MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95050 United States Center for Data Driven Discovery Caltech PasadenaCA91125 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Argonne National Laboratory Leadership Computing Facility LemontIL60439 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 Unit

This report provides an overview of recent work that harnesses the Big data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded "Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics;(ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data;(iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups;(iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big data Era;(v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics. Copyright © 2019, The Authors. All rights reserved.

关键词： Big data

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Named-entity recognition and data visualization techniques to communicate mission command to autonomous systems

Named-entity recognition and data visualization techniques t...

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IEEE Symposium on systems and Information engineering Design, SIEDS

作者： Donald Chesworth Nathan Harmon Leslie Tanner Stephanie Guerlain Michael Balazs Data Science Institute University of Virginia Department of Systems and Information Engineering University of Virginia The MITRE Corporation

As robotic systems are integrated into mission operations, they provide key benefits over traditional manned systems such as increased endurance, versatility, and risk reduction for personnel. In order for robotic systems to become fully integrated into United States Army missions, they will need to gain a level of autonomy that is closer to that of human personnel. Under Mission Command, the current system of issuing orders, an Operational Order (OPORD) contains the information required to execute a mission. The structure allows for authors of OPORDs to leave many details of the operation open to the discretion of the reader, making task interpretation and execution difficult for an autonomous system. The goal of this project, and one step of many in the process of automating systems to read OPORDs, is to show how OPORDs can be annotated with relevant information (such as locations, coordinates, and organizations) using the natural language processing techniques of tokenization and named-entity recognition (NER). Using A-fold cross validation of a Conditional Random Field (CRF) sequence model on 9 OPORDs containing 38,551 tokens, we were able to extract entities with an overall precision of 0.702, recall of 0.478, and F-measure of 0.569.

关键词： Organizations Robot kinematics Hidden Markov models data visualization Conferences Natural language processing

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Mechanics of jumping on water

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Physical Review Fluids 2017年第10期2卷 100505-100505页

作者： Ho-Young Kim Juliette Amauger Han-Bi Jeong Duck-Gyu Lee Eunjin Yang Piotr G. Jablonski Department of Mechanical and Aerospace Engineering Seoul National University Seoul 08826 Korea Urban Data Science Lab SNU BDI Seoul 06324 Korea Département de Physique Ecole Normale Supérieure PSL Research University 24 rue Lhomond 75005 Paris France Department of Nature-Inspired Nanoconvergence Systems Korea Institute of Machinery and Materials Daejeon 34103 Korea Korea Institute of Science and Technology Evaluation and Planning Seoul 06775 Korea School of Biological Sciences Seoul National University Seoul 08826 Korea Institute and Museum of Zoology Polish Academy of Sciences Wilcza 64 00-679 Warsaw Poland

Some species of semiaquatic arthropods including water striders and springtails can jump from the water surface to avoid sudden dangers like predator attacks. It was reported recently that the jump of medium-sized water striders is a result of surface-tension-dominated interaction of thin cylindrical legs and water, with the leg movement speed nearly optimized to achieve the maximum takeoff velocity. Here we describe the mathematical theories to analyze this exquisite feat of nature by combining the review of existing models for floating and jumping and the introduction of the hitherto neglected capillary forces at the cylinder tips. The theoretically predicted dependence of body height on time is shown to match the observations of the jumps of the water striders and springtails regardless of the length of locomotory appendages. The theoretical framework can be used to understand the design principle of small jumping animals living on water and to develop biomimetic locomotion technology in semiaquatic environments.

关键词： Biological fluid dynamics Locomotion Surface tension effects

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Determining performance boundaries on high-level system specifications 16

Determining performance boundaries on high-level system spec...

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19th International Workshop on Software and Compilers for Embedded systems, SCOPES 2016

作者： Van Teijlingen, Wouter Van Leuken, Rene Galuzzi, Carlo Kienhuis, Bart Delft University of Technology Circuits and Systems Mekelweg 4 Delft Netherlands Maastricht University Department of Data Science and Knowledge Engineering Bouillonstraat 8-11 Maastricht Netherlands Leiden University Leiden Institute of Advanced Computer Science Niels Bohrweg 1 Leiden Netherlands

ISBN: (纸本)9781450343206

We can significantly reduce the time required to realize designs if it is possible to find limits to the performance of an embedded system, solely based on high-level system specifications. For that purpose, we present in this paper the cprof profiler, which determines the number of clock cycles needed to execute a C-program in hardware. The cprof tool is based on the Clang compiler front-end to parse C-programs and to produce instrumented source code for the profiling. Using cprof, we determine a lower and upper bound limit for all 29 cases of the PolyBench/C benchmark suite. The lower and upper bound are determined using the absolute performance estimations assuming all statement are mapped onto the same processing resource and unbounded performance estimations assuming unlimited resources. We also compared the clock cycles found by cprof with RTL implementations for all 29 Polybench/C cases and found that cprof determines with 1.2% accuracy the correct number of clock cycles. It does this in a fraction of the time compared to the time needed to do a full RTL simulation. © 2016 ACM.

关键词： Program compilers

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