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2016 IEEE ANDESCON, ANDESCON 2016

作者： Tupac, Yvan Talavera, Alvaro Rivero, Cristian Rodriguez Computer Science School Universidad Católica San Pablo Arequipa Peru Department of Engineering Universidad Del Pacífico Lima Peru Math Research Laboratory Applied to Control Universidad Nacional de Córdoba Córdoba Argentina

ISBN: (纸本)9781509025312

In this work, a reservoir simulation approximation model (proxy) based on recurrent artificial neural networks is proposed. This model is intended to obtain rates of oil, gas and water production at time t+1 from the respective production rates, average pressure and water cut at t time and the well operation points to be applied in t + 1. Also, this model is able to follow the dynamics of the reservoir system applying online learning from real production observed values. Also, this model allows perform fast and accurate production forecasting for several steps using a recursive mechanism. This model will be inserted into an oil-production control tool to find the optimal operation conditions within a forecast horizon. The obtained outcomes over the approximation tests indicate the methodology is adequate to perform production forecasts. © 2016 IEEE.

关键词： Production control

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Max-Information, Differential Privacy, and Post-selection Hypothesis Testing

Max-Information, Differential Privacy, and Post-selection Hy...

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Annual IEEE Symposium on Foundations of computer science

作者： Ryan Rogers Aaron Roth Adam Smith Om Thakkar Applied Math and Computational Sciences University of Pennsylvania Philadelphia PA USA Computer and Information Science University of Pennsylvania Philadelphia PA USA Computer Science and Engineering Department Pennsylvania State University University Park PA USA

ISBN: (纸本)9781509039340

In this paper, we initiate a principled study of how the generalization properties of approximate differential privacy can be used to perform adaptive hypothesis testing, while giving statistically valid p-value corrections. We do this by observing that the guarantees of algorithms with bounded approximate max-information are sufficient to correct the p-values of adaptively chosen hypotheses, and then by proving that algorithms that satisfy (∈,δ)-differential privacy have bounded approximate max information when their inputs are drawn from a product distribution. This substantially extends the known connection between differential privacy and max-information, which previously was only known to hold for (pure) (∈,0)-differential privacy. It also extends our understanding of max-information as a partially unifying measure controlling the generalization properties of adaptive data analyses. We also show a lower bound, proving that (despite the strong composition properties of max-information), when data is drawn from a product distribution, (∈,δ)-differentially private algorithms can come first in a composition with other algorithms satisfying max-information bounds, but not necessarily second if the composition is required to itself satisfy a nontrivial max-information bound. This, in particular, implies that the connection between (∈,δ)-differential privacy and max-information holds only for inputs drawn from product distributions, unlike the connection between (∈,0)-differential privacy and max-information.

关键词： Privacy Algorithm design and analysis Testing Data analysis Probability Data privacy computer science

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ASYMPTOTIC BEHAVIOR OF GLOBAL SMOOTH SOLUTIONS FOR BIPOLAR COMPRESSIBLE NAVIER-STOKES-MAXWELL SYSTEM FROM PLASMAS

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Acta mathematica Scientia 2015年第5期35卷 955-969页

作者：冯跃红王术李新 College of Applied Sciences Beijing University of Technology Laboratoire de Mathématiques Université Blaise Pascal Department of Mathematics and Computer Science Xinyang Vocational and Technical College

This paper is concerned with the bipolar compressible Navier-Stokes-Maxwell system for plasmas. We investigated, by means of the techniques of symmetrizer and elaborate energy method, the Cauchy problem in R^3. Under the assumption that the initial values are close to a equilibrium solutions, we prove that the smooth solutions of this problem converge to a steady state as the time goes to the infinity. It is shown that the difference of densities of two carriers converge to the equilibrium states with the norm ｜｜·｜｜H^s-1, while the velocities and the electromagnetic fields converge to the equilibrium states with weaker norms than ｜｜·｜｜H^s-1. This phenomenon on the charge transport shows the essential difference between the unipolar Navier-Stokes-Maxwell and the bipolar Navier-Stokes-Maxwell system.

关键词： bipolar compressible Navier-Stokes-Maxwell system plasmas global smooth solutions energy estimates large-time behavior

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DOT-K: A distributed online top-K elements algorithm using extreme value statistics

DOT-K: A distributed online top-K elements algorithm using e...

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IEEE International Conference on e-science and Grid Computing

作者： Nicholas Carey Tamás Budavári Yanif Ahmad Alexander Szalay Department of Computer Science Johns Hopkins University Baltimore Maryland Department of Applied Math and Statistics Johns Hopkins University Baltimore Maryland Department of Physics and Astronomy Johns Hopkins University Baltimore Maryland

Extremely large (peta-scale) data collections are generally partitioned into millions of containers (disks/volumes/files) which are essentially unmovable due to their aggregate size. They are stored over a large distributed cloud of machines, with computing co-located with the data. Given this data layout, even simple tasks are difficult to perform and naive algorithms can easily become quite expensive. We present a one pass, communications-efficient technique useful for both estimating upper order quantiles and selecting the largest k elements across a highly distributed dataset or stream. Our novel approach draws its foundations from Extreme Value Statistics (EVS) to reason about the statistical relationships between the tail distributions of dataset partitions. The tail of each partition is fitted by the Generalized Pareto Distribution, which captures threshold exceedances. The obtained parameters are communicated to a central coordinator and used to estimate quantiles, or solve for a threshold above which there are approximately k elements. We discuss the computational and bandwidth costs of the algorithm, and demonstrate the accuracy of the method on both a variety of synthetic datasets and a PageRank dataset.

关键词： Partitioning algorithms Distributed databases Estimation mathematical model Parameter estimation Random variables computer science

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Inducing approximately optimal flow using truthful mediators 15

Inducing approximately optimal flow using truthful mediators

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16th ACM Conference on Economics and Computation, EC 2015

作者： Rogers, Ryan Roth, Aaron Ullman, Jonathan Wu, Zhiwei Steven Applied Math and Computational Science Department University of Pennsylvania United States Department of Computer Science Columbia University United States

ISBN: (纸本)9781450334105

We revisit a classic coordination problem from the perspective of mechanism design: how can we coordinate a social welfare maximizing flow in a network congestion game with selfish players? The classical approach, which computes tolls as a function of known demands, fails when the demands are unknown to the mechanism designer, and naively eliciting them does not necessarily yield a truthful mechanism. Instead, we introduce a weak mediator that can provide suggested routes to players and set tolls as a function of reported demands. However, players can choose to ignore or misreport their type to this mediator. Using techniques from differential privacy, we show how to design a weak mediator such that it is an asymptotic ex-post Nash equilibrium for all players to truthfully report their types to the mediator and faithfully follow its suggestion, and that when they do, they end up playing a nearly optimal flow. Notably, our solution works in settings of incomplete information even in the absence of a prior distribution on player types. Along the way, we develop new techniques for privately solving convex programs which may be of independent interest.

关键词： algorithms differential privacy mechanism design

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Private pareto optimal exchange 15

Private pareto optimal exchange

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16th ACM Conference on Economics and Computation, EC 2015

作者： Kannan, Sampath Morgenstern, Jamie Rogers, Ryan Roth, Aaron Applied Math and Computational Sciences Department University of Pennsylvania United States Carnegie Mellon University Computer Science Department United States

ISBN: (纸本)9781450334105

We consider the problem of implementing an individually rational, asymptotically Pareto optimal allocation in a barter-exchange economy where agents are endowed with goods and preferences over the goods of others, but may not use money as a medium of exchange. Because one of the most important instantiations of such economies is kidney exchange - where the "input" to the problem consists of sensitive patient medical records - we ask to what extent such exchanges can be carried out while providing formal privacy guarantees to the participants. We show that individually rational allocations cannot achieve any non-trivial approximation to Pareto optimality if carried out under the constraint of differential privacy - or even the relaxation of joint-differential privacy, under which it is known that asymptotically optimal allocations can be computed in two sided markets [Hsu et al. STOC 2014]. We therefore consider a further relaxation that we call marginal-differential privacy - which promises, informally, that the privacy of every agent i is protected from every other agent j 6 ≡ i so long as j does not collude or share allocation information with other agents. We show that under marginal differential privacy, it is possible to compute an individually rational and asymptotically Pareto optimal allocation in such exchange economies.

关键词： algorithms differential privacy mechanism design

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Shortest Path to a Segment and Quickest Visibility Queries 31

Shortest Path to a Segment and Quickest Visibility Queries

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31st International Symposium on Computational Geometry, SoCG 2015

作者： Arkin, Esther M. Efrat, Alon Knauer, Christian Mitchell, Joseph S.B. Polishchuk, Valentin Rote, Günter Schlipf, Lena Talvitie, Topi Department of Applied Math and Statistics Stony Brook University United States Department of Computer Science University of Arizona United States Institute of Computer Science Universität Bayreuth Germany Communications and Transport Systems ITN Linköping University Sweden Institute of Computer Science Freie Universität Berlin Germany Department of Computer Science University of Helsinki Finland

ISBN: (纸本)9783939897835

We show how to preprocess a polygonal domain with a fixed starting point s in order to answer efficiently the following queries: Given a point q, how should one move from s in order to see q as soon as possible? This query resembles the well-known shortest-path-to-a-point query, except that the latter asks for the fastest way to reach q, instead of seeing it. Our solution methods include a data structure for a different generalization of shortest-path-to-a-point queries, which may be of independent interest: to report efficiently a shortest path from s to a query segment in the domain.

关键词： Visibility

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Intelligent model for measuring learners' satisfaction towards new contents 15

Intelligent model for measuring learners' satisfaction towar...

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15th International Conference on Information Technology Based Higher Education and Training, ITHET 2016

作者： Elghibari, Fatiha Elouahbi, Rachid El Khoukhi, Fatima Ezzahri, Layla Macs, Labo Amali, Said Department of Mathematics and Computer Science Moulay Ismail University Faculty of Sciences Meknes Morocco Team Modeling Applied Informatics in Humanities MIASH Moulay Ismail University Faculty of Humanities Meknes Morocco Team Théorie des Systèmes et Informatique TSI Moulay Ismail University Faculty of Sciences Meknes Morocco Team Outils Mathématiques en Economie Gestion et Actuariat OMEGA Moulay Ismail University Faculty of Juridical Economic and Social Sciences Meknes Morocco

ISBN: (纸本)9781509007783

Learner satisfaction is an important factor for a successful e-Learning. It allows learners to continue learning and provide their conveniences to facilitate the knowledge. That is why, the main purpose of this study is to enhance learning services in online environments, in order to keep learners' motivation and track their cognition' evolution. Namely, identify their satisfaction toward new inserted contents. In this paper, we present a learning model that detects satisfaction' degree of learners at two levels;during and after completing the course. During learning by using satisfaction agent (SA) that collects data and calculates satisfaction level towards each content' unit. This process allows to integrate improvement if we have a low levels of satisfaction. And after completing course in order to evaluate the evolution of learners' knowledge, and test the effectiveness of the methods and tools used in. © 2016 IEEE.

关键词： Motivation

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Intelligent model for measuring learners' satisfaction towards new contents

Intelligent model for measuring learners' satisfaction towar...

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International Conference on Information Technology Based Higher Education and Training, ITHET

作者： Fatiha Elghibari Rachid Elouahbi Fatima El Khoukhi Layla Ezzahri Said Amali Department of mathematics and computer Science Moulay Ismail University Meknes Morocco Team Modeling Applied Informatics in Humanities MIASH Moulay Ismail University Meknes Morocco Faculty of Sciences Moulay Ismail University Meknes Morocco Team Outils Mathématiques en Economie Moulay Ismail University Meknes Morocco

ISBN: (纸本)9781509007790

Learner satisfaction is an important factor for a successful e-Learning. It allows learners to continue learning and provide their conveniences to facilitate the knowledge. That is why, the main purpose of this study is to enhance learning services in online environments, in order to keep learners' motivation and track their cognition' evolution. Namely, identify their satisfaction toward new inserted contents. In this paper, we present a learning model that detects satisfaction' degree of learners at two levels; during and after completing the course. During learning by using satisfaction agent (SA) that collects data and calculates satisfaction level towards each content' unit. This process allows to integrate improvement if we have a low levels of satisfaction. And after completing course in order to evaluate the evolution of learners' knowledge, and test the effectiveness of the methods and tools used in.

关键词： Electronic learning Training Computational modeling mathematical model computer science

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On the consistency of the likelihood maximization vertex nomination scheme: bridging the gap between maximum likelihood estimation and graph matching

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Vince Lyzinski Keith Levin Donniell E. Fishkind Carey E. Priebe Google MPI for Intelligent Systems Department of Applied Math and Statistics Johns Hopkins University Baltimore MD Department of Computer Science Johns Hopkins University Baltimore MD

Given a graph in which a few vertices are deemed interesting a priori, the vertex nomination task is to order the remaining vertices into a nomination list such that there is a concentration of interesting vertices at the top of the list. Previous work has yielded several approaches to this problem, with theoretical results in the setting where the graph is drawn from a stochastic block model (SBM), including a vertex nomination analogue of the Bayes optimal classifier. In this paper, we prove that maximum likelihood (ML)-based vertex nomination is consistent, in the sense that the performance of the ML-based scheme asymptotically matches that of the Bayes optimal scheme. We prove theorems of this form both when model parameters are known and unknown. Additionally, we introduce and prove consistency of a related, more scalable restricted-focus ML vertex nomination scheme. Finally, we incorporate vertex and edge features into ML-based vertex nomination and briey explore the empirical effectiveness of this approach.

关键词： graph inference graph matching graph mining stochastic block model vertex nomination

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