Archetypal analysis (AA) represents observations as composition of pure patterns, i.e., archetypes, or equivalently convex combinations of extreme values (Cutler and Breiman, 1994). Although AA bears resemblance with many well established prototypical analysis tools, such as principal component analysis (PCA, Mohamed et al, 2009), nonnegative matrix factorization (NMF, F evotte and Idier, 2011), probabilistic latent semantic analysis (Hofmann, 2013), andk -means (Steinley, 2006); AA is arguably unique, both conceptually and computationally . Conceptually, AA imitates the human tendency of representing a group of objects by its extreme elements (Davis and Love, 2010): this makes AA an interesting exploratory tool for applied scientists (e.g., Eugster, 2012; Seiler and Wohlrabe, 2013). Computationally, AA is data-driven, and requires the factors to be probability vectors: these make AA a computationally demanding tool, yet brings better interpretability . The concept of AA was originally formulated by Cutler and Breiman (1994).

Graphical models, such as Bayesian Networks and Markov networks play an important role in artificial intelligence and machine learning. Inference is a central problem to be solved on these networks. This, and other problems on these graph models are often known to be hard to solve in general, but tractable on graphs with bounded Treewidth. Therefore, finding or approximating the Treewidth of a graph is a fundamental problem related to inference in graphical models. In this paper, we study the approximability of a number of graph problems: Treewidth and Pathwidth of graphs, Minimum Fill-In, One-Shot Black (and Black-White) pebbling costs of directed acyclic graphs, and a variety of different graph layout problems such as Minimum Cut Linear Arrangement and Interval Graph Completion. We show that, assuming the recently introduced Small Set Expansion Conjecture, all of these problems are NP-hard to approximate to within any constant factor in polynomial time.

DISASTER ROBOTICS LOGIC IN GAMES Robin R. Murphy Johan van Benthem "A thorough overview and A comprehensive examination intellectually stimulating of the interfaces of logic, treatment of foundational and computer science, and game advanced concepts by one of theory, drawing on twenty the pioneers in the field, this years of research on logic book is an authoritative refer-and games. HCOMP 2013 to be Held in Pittsburgh! The Second AAAI Conference on Human Computation and Crowdsourcing (HCOMP-2014) will be held November 2-4 in Pittsburgh, Pennsylvania, USA. The HCOMP conference is cross-disciplinary, and invites submissions across the broad spectrum of crowdsourcing and human computation work. Human computation and crowdsourcing is unique in its direct engagement and reliance on both human-centered studies and traditional computer science.

Sparse clustering, which aims to find a proper partition of an extremely high-dimensional data set with redundant noise features, has been attracted more and more interests in recent years. The existing studies commonly solve the problem in a framework of maximizing the weighted feature contributions subject to a $\ell_2/\ell_1$ penalty. Nevertheless, this framework has two serious drawbacks: One is that the solution of the framework unavoidably involves a considerable portion of redundant noise features in many situations, and the other is that the framework neither offers intuitive explanations on why this framework can select relevant features nor leads to any theoretical guarantee for feature selection consistency. In this article, we attempt to overcome those drawbacks through developing a new sparse clustering framework which uses a $\ell_{\infty}/\ell_0$ penalty. First, we introduce new concepts on optimal partitions and noise features for the high-dimensional data clustering problems, based on which the previously known framework can be intuitively explained in principle. Then, we apply the suggested $\ell_{\infty}/\ell_0$ framework to formulate a new sparse k-means model with the $\ell_{\infty}/\ell_0$ penalty ($\ell_0$-k-means for short). We propose an efficient iterative algorithm for solving the $\ell_0$-k-means. To deeply understand the behavior of $\ell_0$-k-means, we prove that the solution yielded by the $\ell_0$-k-means algorithm has feature selection consistency whenever the data matrix is generated from a high-dimensional Gaussian mixture model. Finally, we provide experiments with both synthetic data and the Allen Developing Mouse Brain Atlas data to support that the proposed $\ell_0$-k-means exhibits better noise feature detection capacity over the previously known sparse k-means with the $\ell_2/\ell_1$ penalty ($\ell_1$-k-means for short).

This paper presents a new method, Minimax Tree Optimization (MMTO), to learn a heuristic evaluation function of a practical alpha-beta search program. The evaluation function may be a linear or non-linear combination of weighted features, and the weights are the parameters to be optimized. To control the search results so that the move decisions agree with the game records of human experts, a well-modeled objective function to be minimized is designed. Moreover, a numerical iterative method is used to nd local minima of the objective function, and more than forty million parameters are adjusted by using a small number of hyper parameters. This method was applied to shogi, a major variant of chess in which the evaluation function must handle a larger state space than in chess. Experimental results show that the large-scale optimization of the evaluation function improves the playing strength of shogi programs, and the new method performs signicantly better than other methods. Implementation of the new method in our shogi program Bonanza made substantial contributions to the program's rst-place nish in the 2013 World Computer Shogi Championship. Additionally, we present preliminary evidence of broader applicability of our method to other two-player games such as chess.

Since long, corporations are looking for knowledge sources which can provide structured description of data and can focus on meaning and shared understanding. Structures which can facilitate open world assumptions and can be flexible enough to incorporate and recognize more than one name for an entity. A source whose major purpose is to facilitate human communication and interoperability. Clearly, databases fail to provide these features and ontologies have emerged as an alternative choice, but corporations working on same domain tend to make different ontologies. The problem occurs when they want to share their data/knowledge. Thus we need tools to merge ontologies into one. This task is termed as ontology matching. This is an emerging area and still we have to go a long way in having an ideal matcher which can produce good results. In this paper we have shown a framework to matching ontologies using graphs.

The fundamental step in measuring the robustness of a system is the synthesis of the so called Process Map.This is generally based on the user raw data material.Process Maps are of fundamental importance towards the understanding of the nature of a system in that they indicate which variables are causally related and which are particularly important.This paper represent the system Map or business structure map to understand business criteria studying the various aspects of the company.The business structure map or knowledge map or Process map are used to increase the growth of the company by giving some useful measures according to the business criteria.This paper also deals with the different company strategy to reduce the risk factors.Process Map is helpful for building such knowledge successfully.Making decisions from such map in a highly complex situation requires more knowledge and resources.

Multi-view learning aims to improve classification performance by leveraging the consistency among different views of data. The incorporation of multiple views was paid little attention in the studies of domain adaptation, where the view consistency based on source data is largely violated in the target domain due to the distribution gap between different domain data. In this paper, we leverage multiple views for cross-domain document classification. The central idea is to strengthen the views' consistency on target data by identifying the associations of domain-specific features from different domains. We present an Information-theoretic Multi-view Adaptation Model (IMAM) using a multi-way clustering scheme, where word and link clusters can draw together seemingly unrelated features across domains, which boosts the consistency between document clusterings that are based on the respective word and link views. Moreover, we demonstrate that IMAM can always find the document clustering with the minimal disagreement rate to the overlap of view-based clusterings. We provide both theoretical and empirical justifications of the proposed method. Our experiments show that IMAM significantly outperforms traditional multi-view algorithm co-training, the co-training-based adaptation algorithm CODA, the single-view transfer model CoCC and the large-margin-based multi-view transfer model MVTL-LM.

Online dating sites have become popular platforms for people to look for potential romantic partners. Many online dating sites provide recommendations on compatible partners based on their proprietary matching algorithms. It is important that not only the recommended dates match the user's preference or criteria, but also the recommended users are interested in the user and likely to reciprocate when contacted. The goal of this paper is to predict whether an initial contact message from a user will be replied to by the receiver. The study is based on a large scale real-world dataset obtained from a major dating site in China with more than sixty million registered users. We formulate our reply prediction as a link prediction problem of social networks and approach it using a machine learning framework. The availability of a large amount of user profile information and the bipartite nature of the dating network present unique opportunities and challenges to the reply prediction problem. We extract user-based features from user profiles and graph-based features from the bipartite dating network, apply them in a variety of classification algorithms, and compare the utility of the features and performance of the classifiers. Our results show that the user-based and graph-based features result in similar performance, and can be used to effectively predict the reciprocal links. Only a small performance gain is achieved when both feature sets are used. Among the five classifiers we considered, random forests method outperforms the other four algorithms (naive Bayes, logistic regression, KNN, and SVM). Our methods and results can provide valuable guidelines to the design and performance of recommendation engine for online dating sites.

Deep learning has recently led to great successes in tasks such as image recognition (e.g Krizhevsky et al., 2012). However, deep networks are still outmatched by the power and versatility of the brain, perhaps in part due to the richer neuronal computations available to cortical circuits. The challenge is to identify which neuronal mechanisms are relevant, and to find suitable abstractions to model them. Here, we show how aspects of spike timing, long hypothesized to play a crucial role in cortical information processing, could be incorporated into deep networks to build richer, versatile representations. We introduce a neural network formulation based on complex-valued neuronal units that is not only biologically meaningful but also amenable to a variety of deep learning frameworks. Here, units are attributed both a firing rate and a phase, the latter indicating properties of spike timing. We show how this formulation qualitatively captures several aspects thought to be related to neuronal synchrony, including gating of information processing and dynamic binding of distributed object representations. Focusing on the latter, we demonstrate the potential of the approach in several simple experiments. Thus, neuronal synchrony could be a flexible mechanism that fulfills multiple functional roles in deep networks.