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Approximating Equilibria in Sequential Auctions with Incomplete Information and Multi-Unit Demand 12

Approximating Equilibria in Sequential Auctions with Incompl...

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Annual Conference on Neural Information Processing Systems

作者： Amy Greenwald Jiacui Li Eric Sodomka Department of Computer Science Brown University Providence RI 02912 Department of Applied Math/Economics Brown University Providence RI 02912

ISBN: (纸本)9781627480031

In many large economic markets, goods are sold through sequential auctions. Examples include eBay, online ad auctions, wireless spectrum auctions, and the Dutch flower auctions. In this paper, we combine methods from game theory and decision theory to search for approximate equilibria in sequential auction domains, in which bidders do not know their opponents' values for goods, bidders only partially observe the actions of their opponents', and bidders demand multiple goods. We restrict attention to two-phased strategies: first predict (i.e., learn); second, optimize. We use best-reply dynamics [4] for prediction (i.e., to predict other bidders' strategies), and then assuming fixed other-bidder strategies, we estimate and solve the ensuing Markov decision processes (MDP) [18] for optimization. We exploit auction properties to represent the MDP in a more compact state space, and we use Monte Carlo simulation to make estimating the MDP tractable. We show how equilibria found using our search procedure compare to known equilibria for simpler auction domains, and we approximate an equilibrium for a more complex auction domain where analytical solutions are unknown.

关键词： Markov chain cargo multicell Sequential Decision Theory Game theory state space Monte Carlo technique

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Data-intensive spatial filtering in large numerical simulation datasets 12

Data-intensive spatial filtering in large numerical simulati...

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Supercomputing Conference

作者： Kalin Kanov Randal Burns Greg Eyink Charles Meneveau Alexander Szalay Department of Computer Science Johns Hopkins University USA Department of Applied Math and Statistics Johns Hopkins University USA Department of Mechanical Engineering Johns Hopkins University USA Department of Physics and Astronomy Johns Hopkins University USA

ISBN: (纸本)9781467308045

We present a query processing framework for the efficient evaluation of spatial filters on large numerical simulation datasets stored in a data-intensive cluster. Previously, filtering of large numerical simulations stored in scientific databases has been impractical owing to the immense data requirements. Rather, filtering is done during simulation or by loading snapshots into the aggregate memory of an HPC cluster. Our system performs filtering within the database and supports large filter widths. We present two complementary methods of execution: I/O streaming computes a batch filter query in a single sequential pass using incremental evaluation of decomposable kernels, summed volumes generates an intermediate data set and evaluates each filtered value by accessing only eight points in this dataset. We dynamically choose between these methods depending upon workload characteristics. The system allows us to perform filters against large data sets with little overhead: query performance scales with the cluster's aggregate I/O throughput.

关键词： Kernel Computational modeling mathematical model Numerical simulation Spatial databases Numerical models

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Semi-supervised Graph Embedding-based Feature Extraction and Adaptive Kernel-based Classification for computer-aided Detection in CT Colonography

Semi-supervised Graph Embedding-based Feature Extraction and...

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IEEE Nuclear science Symposium and Medical Imaging Conference

作者： Lei Fan Bowen Song Xianfeng Gu Zhengrong Liang Departments of Radiology and Computer Science Stony Brook University Stony Brook NY USA Departments of Radiology and Applied Math and Statistics Stony Brook University Stony Brook NY USA Department of Computer Science Stony Brook University Stony Brook NY USA Departments of Radiology Computer Science and Biomedical Engineering Stony Brook University Stony Brook NY USA

Feature extraction and classification are two important parts in computer-aided detection (CAD) of abnormalities. For feature selection, typically we are interested in determining which, of a large number of potentially redundant or noisy features, are most discriminative for classification. Graph embedding is a relatively new category of feature selection methods. It uses a graph structure to model the geometric and discriminant structure of the data manifold. It has obvious advantages over other conventional feature selection methods like Principal Component Analysis (PCA). However, it does not properly incorporate prior labeling information like Partial Least Square (PLS) does. In this paper, we deal with feature selection for CAD of colonic polyps with graph embedding method in a semi-supervised style (SemiGE), which considers the label information as additional constraints. Specifically, we constrain the space of solutions that we explore only to contain embedding results which are consistent with the labels. Experiments were conducted on patients' datasets to compare its performance with other conventional and popular feature selection methods. The results illustrated that this presented SemiGE strategy outperforms all other exciting feature selection methods, such as PCA, PLS, etc. On the other hand, the well-known support vector machine (SVM) classifier has been widely used in CAD of colonic polyps in computed tomography conlonography (CTC). Several kernel functions have been implemented for the SVM classifier. Each kernel function affects noticeably to the classifying performance and variable performance has been observed among the kernel functions. A traditional implementation strategy and all the previous applications of SVM only preselect a kernel function (e.g., RBF, Sigmoid, .etc). This kind of pre-selection approach fails to provide a kernel function which is adaptive to the experimental data. In this paper, we introduce an improved SVM-based classifi

关键词： CAD ROC AUC

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Understanding social behavior evolutions through agent-based modeling

Understanding social behavior evolutions through agent-based...

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IEEE International Conference on Multimedia Computing and Systems (ICMCS)

作者： Mohamed Nemiche Vicent Cavero Rafael Pla Lopez Equipe de recherches Mathématiques appliquées Génie Informatique et Multimédia Ibn Zohr University Agadir Morocco Department of Computer Science University of Valencia Spain Department of Applied Mathematics University of Valencia Spain

ISBN: (纸本)9781467315180

Agent-based social simulation as a computational approach to social simulation has been largely used to explore social phenomena. The purpose of this paper is to describe a theoretical model of transmission and evolution of social behaviors in a network of artificial societies (artificial world) using agent-based modeling technology. In this model, each agent (society) is subdivided into social behaviors where individual and social learning occur. The agent-agent interactions are carried out by their social behaviors; otherwise the agent-environment interactions through consumption of ecological resources by its social behaviors in repression and satisfaction. We distinguish social behaviors by their repressive capacity and their technical satisfaction. Preliminary results of the model generate several evolutions, but we will focus on the two most important types: firstly, evolutions where the system (all living-agents) will end in a state of “globalization”; i.e. where one social behavior predominates the entire system; secondly, evolutions where an Ecological Hecatomb takes place during the globalization with the repressive social behavior. The model is implemented in java language; its simulation can help to understand the implied processes in humanity's evolution and their trajectories.

关键词： Multimedia communication Biological system modeling

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Natural image denoising: Optimality and inherent bounds

Natural image denoising: Optimality and inherent bounds

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作者： Levin, Anat Nadler, Boaz Department of Computer Science and Applied Math Weizmann Institute of Science Israel

ISBN: (纸本)9781457703942

The goal of natural image denoising is to estimate a clean version of a given noisy image, utilizing prior knowledge on the statistics of natural images. The problem has been studied intensively with considerable progress made in recent years. However, it seems that image denoising algorithms are starting to converge and recent algorithms improve over previous ones by only fractional dB values. It is thus important to understand how much more can we still improve natural image denoising algorithms and what are the inherent limits imposed by the actual statistics of the data. The challenge in evaluating such limits is that constructing proper models of natural image statistics is a long standing and yet unsolved problem. To overcome the absence of accurate image priors, this paper takes a non parametric approach and represents the distribution of natural images using a huge set of 1010 patches. We then derive a simple statistical measure which provides a lower bound on the optimal Bayesian minimum mean square error (MMSE). This imposes a limit on the best possible results of denoising algorithms which utilize a fixed support around a denoised pixel and a generic natural image prior. Our findings suggest that for small windows, state of the art denoising algorithms are approaching optimality and cannot be further improved beyond 0.1dB values. © 2011 IEEE.

关键词： Image denoising

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Sustaining economic exploitation of complex ecosystems in computational models of coupled human-natural networks 26

Sustaining economic exploitation of complex ecosystems in co...

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26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12

作者： Martinez, Neo D. Tonin, Perrine Bauer, Barbara Rael, Rosalyn C. Singh, Rahul Yoon, Sangyuk Yoon, Ilmi Dunne, Jennifer A. Pacific Ecoinformatics and Computational Ecology Lab. 1604 McGee Avenue Berkeley CA United States Center for Applied Math Ecole Polytechnique CNRS Route de Saclay 91128 Palaiseau Cedex France Düsternbrooker Weg 20 D 24105 Kiel Germany Computer Science Department San Francisco State University San Francisco CA United States Santa Fe Institute 1399 Hyde Park Road Santa Fe NM United States

ISBN: (纸本)9781577355687

Understanding ecological complexity has stymied scientists for decades. Recent elucidation of the famously coined "devious strategies for stability in enduring natural systems" has opened up a new field of computational analyses of complex ecological networks where the nonlinear dynamics of many interacting species can be more realistically modeled and understood. Here, we describe the first extension of this field to include coupled human natural systems. This extension elucidates new strategies for sustaining extraction of biomass (e.g., fish, forests, fiber) from ecosystems that account for ecological complexity and can pursue multiple goals such as maximizing economic profit, employment and carbon sequestration by ecosystems. Our more realistic modeling of ecosystems helps explain why simpler "maximum sustainable yield" bioeconomic models underpinning much natural resource extraction policy leads to less profit, biomass, and biodiversity than predicted by those simple models. Current research directions of this integrated natural and social science include applying artificial intelligence, cloud computing, and multiplayer online games. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Ecosystems

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Natural Image Denoising: Optimality and Inherent Bounds

Natural Image Denoising: Optimality and Inherent Bounds

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IEEE Conference on computer Vision and Pattern Recognition

作者： Anat Levin Boaz Nadler Department of Computer Science and Applied Math The Weizmann Institute of Science

ISBN: (纸本)9781457703942

The goal of natural image denoising is to estimate a clean version of a given noisy image, utilizing prior knowledge on the statistics of natural images. The problem has been studied intensively with considerable progress made in recent years. However, it seems that image denoising algorithms are starting to converge and recent algorithms improve over previous ones by only fractional dB values. It is thus important to understand how much more can we still improve natural image denoising algorithms and what are the inherent limits imposed by the actual statistics of the data. The challenge in evaluating such limits is that constructing proper models of natural image statistics is a long standing and yet unsolved problem. To overcome the absence of accurate image priors, this paper takes a non parametric approach and represents the distribution of natural images using a huge set of 10~(10) patches. We then derive a simple statistical measure which provides a lower bound on the optimal Bayesian minimum mean square error (MMSE). This imposes a limit on the best possible results of denoising algorithms which utilize a fixed support around a denoised pixel and a generic natural image prior. Our findings suggest that for small windows, state of the art denoising algorithms are approaching optimality and cannot be further improved beyond ～ 0.1dB values.

关键词： image denoising Noisy Image fixed pivot DATA(STATISTICS) images optimality Mean square error Converge Prior Learning Noise reduction

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New construction of ensemble classifiers for imbalanced datasets

New construction of ensemble classifiers for imbalanced data...

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2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2010

作者： Zhai, Yun Yang, Bingru Ma, Nan Ruan, Da School of Information Engineering University of Science and Technology Beijing Beijing China College of Computer Science Liaocheng University Liaocheng China College of Information Beijing Union University Beijing China Department of Applied Math and Computer Science Ghent University Krijgslaan Belgium

ISBN: (纸本)9781424467907

Learning in the presence of data imbalances presents a great challenge to machine learning. Imbalanced data sets represent a significant problem because the corresponding classifier has a tendency to ignore samples which have smaller representation in the training sets. In this paper, we propose an ensemble-based learning algorithm as a new ensemble classifier model called as SVM-C5.0 Ensemble Classifier Model, SCECM. SCECM adopts a differentiated sampling rate algorithm (DSRA) based on an improved Adaboost algorithm and further employs unique classifier-selection strategy, novel classifier integration approach and original classification decision-making method. Comparative experimental results show that the proposed approach improves performance for the minority class while preserving the ability to recognize examples from the majority classes. © 2010 IEEE.

关键词： Data mining

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Analyzing environmental samples in nuclear safeguards evaluation by a cumulative belief degrees approach 9

Analyzing environmental samples in nuclear safeguards evalua...

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Computational Intelligence Foundations and Applications - 9th International Fuzzy Logic and Intelligent Technologies in Nuclear science Conference, FLINS 2010

作者： Kabak, Özgür Ruan, Da Boeretang 200 Mol2400 Belgium Department of Industrial Engineering Istanbul Technical University Maçka Istanbul34357 Turkey Department of Applied Math. and Computer Science Ghent University Gent9000 Belgium

ISBN: (纸本)9814324698

In this study we apply a cumulative belief degrees approach to an environmental sampling example in nuclear safeguards evaluation. Transformation formulas for each indicator are defined for distinguishing anthropogenic natural uranium from mineral uranium. An order weighted averaging operator-based aggregation method is proposed to make a final decision. The methodology is illustrated by a numerical example. © 2010 by World Scientific Publishing Co. Pte. Ltd.

关键词： Fuzzy logic

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Co-clustering of image segments using convex optimization applied to EM neuronal reconstruction

Co-clustering of image segments using convex optimization ap...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Shiv N. Vitaladevuni Ronen Basri Howard Hughes Medical Institute USA Department of Computer Science and Applied Math. Weizmann Institute of Science Israel

This paper addresses the problem of jointly clustering two segmentations of closely correlated images. We focus in particular on the application of reconstructing neuronal structures in over-segmented electron microscopy images. We formulate the problem of co-clustering as a quadratic semi-assignment problem and investigate convex relaxations using semidefinite and linear programming. We further introduce a linear programming method with manageable number of constraints and present an approach for learning the cost function. Our method increases computational efficiency by orders of magnitude while maintaining accuracy, automatically finds the optimal number of clusters, and empirically tends to produce binary assignment solutions. We illustrate our approach in simulations and in experiments with real EM data.

关键词： Image segmentation Image reconstruction Linear programming Electron microscopy Cost function Biomedical imaging computer science Application software Computational efficiency Computational modeling

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