We present an efficient algorithm for the inference of stochastic block models in large networks. The algorithm can be used as an optimized Markov chain Monte Carlo (MCMC) method, with a fast mixing time and a much reduced susceptibility to getting trapped in metastable states, or as a greedy agglomerative heuristic, with an almost linear $O(N\ln^2N)$ complexity, where $N$ is the number of nodes in the network, independent on the number of blocks being inferred. We show that the heuristic is capable of delivering results which are indistinguishable from the more exact and numerically expensive MCMC method in many artificial and empirical networks, despite being much faster. The method is entirely unbiased towards any specific mixing pattern, and in particular it does not favor assortative community structures.

Recently, a number of mostly $\ell_1$-norm regularized least squares type deterministic algorithms have been proposed to address the problem of \emph{sparse} adaptive signal estimation and system identification. From a Bayesian perspective, this task is equivalent to maximum a posteriori probability estimation under a sparsity promoting heavy-tailed prior for the parameters of interest. Following a different approach, this paper develops a unifying framework of sparse \emph{variational Bayes} algorithms that employ heavy-tailed priors in conjugate hierarchical form to facilitate posterior inference. The resulting fully automated variational schemes are first presented in a batch iterative form. Then it is shown that by properly exploiting the structure of the batch estimation task, new sparse adaptive variational Bayes algorithms can be derived, which have the ability to impose and track sparsity during real-time processing in a time-varying environment. The most important feature of the proposed algorithms is that they completely eliminate the need for computationally costly parameter fine-tuning, a necessary ingredient of sparse adaptive deterministic algorithms. Extensive simulation results are provided to demonstrate the effectiveness of the new sparse variational Bayes algorithms against state-of-the-art deterministic techniques for adaptive channel estimation. The results show that the proposed algorithms are numerically robust and exhibit in general superior estimation performance compared to their deterministic counterparts.

We assume data sampled from a mixture of d-dimensional linear subspaces with spherically symmetric distributions within each subspace and an additional outlier component with spherically symmetric distribution within the ambient space (for simplicity we may assume that all distributions are uniform on their corresponding unit spheres). We also assume mixture weights for the different components. We say that one of the underlying subspaces of the model is most significant if its mixture weight is higher than the sum of the mixture weights of all other subspaces. We study the recovery of the most significant subspace by minimizing the lp-averaged distances of data points from d-dimensional subspaces, where p>0. Unlike other lp minimization problems, this minimization is non-convex for all p>0 and thus requires different methods for its analysis. We show that if 01 and there is more than one underlying subspace, then with overwhelming probability the most significant subspace cannot be recovered or nearly recovered. This last result does not require spherically symmetric outliers.

Graphical modelling has a long history in statistics as a tool for the analysis of multivariate data, starting from Wright's path analysis and Gibbs' applications to statistical physics at the beginning of the last century. In its modern form, it was pioneered by Lauritzen and Wermuth and Pearl in the 1980s, and has since found applications in fields as diverse as bioinformatics, customer satisfaction surveys and weather forecasts. Genetics and systems biology are unique among these fields in the dimension of the data sets they study, which often contain several hundreds of variables and only a few tens or hundreds of observations. This raises problems in both computational complexity and the statistical significance of the resulting networks, collectively known as the "curse of dimensionality". Furthermore, the data themselves are difficult to model correctly due to the limited understanding of the underlying mechanisms. In the following, we will illustrate how such challenges affect practical graphical modelling and some possible solutions.

Accurate time series prediction over long future horizons is challenging and of great interest to both practitioners and academics. As a well-known intelligent algorithm, the standard formulation of Support Vector Regression (SVR) could be taken for multi-step-ahead time series prediction, only relying either on iterated strategy or direct strategy. This study proposes a novel multiple-step-ahead time series prediction approach which employs multiple-output support vector regression (M-SVR) with multiple-input multiple-output (MIMO) prediction strategy. In addition, the rank of three leading prediction strategies with SVR is comparatively examined, providing practical implications on the selection of the prediction strategy for multi-step-ahead forecasting while taking SVR as modeling technique. The proposed approach is validated with the simulated and real datasets. The quantitative and comprehensive assessments are performed on the basis of the prediction accuracy and computational cost. The results indicate that: 1) the M-SVR using MIMO strategy achieves the best accurate forecasts with accredited computational load, 2) the standard SVR using direct strategy achieves the second best accurate forecasts, but with the most expensive computational cost, and 3) the standard SVR using iterated strategy is the worst in terms of prediction accuracy, but with the least computational cost.

Motivated by the original idea of artificial intelligence in the 1950s and 1960s, there has been a revival of research in general intelligence during the last years. The annual AGI conference series, which is the major event in this area, has been held in cooperation with AAAI since 2008. The sixth conference on AGI was held at Peking University, Beijing, from July 31 to August 3, 2013. AGI-13 was collocated with the International Joint Conference on Artificial Intelligence (IJCAI 2013), the major international AI conference. This was the first time an AGI conference took place in Asia.

Springs, NY. ICCBR is the annual meeting of the CBR community and the ICCBR also featured a workshop program consisting of three workshops. The main conference track featured 16 research paper presentations, nine posters, and two invited speakers. The papers and posters reflected the state of the art of case-based reasoning, dealing both with open problems at the core of CBR (especially in similarity assessment, case adaptation, and case-based maintenance), as well as trending applications of CBR (especially recommender systems and computer games) and the intersections of CBR with other areas such as multiagent systems. The first invited speaker, Igor Jurisica from the Ontario Cancer Institute and the University of Toronto, spoke about how to scale up case-based reasoning for "big data" applications. The Case-Based Reasoning in Health Sciences workshop, organized by Isabelle Bichindaritz, Cindy Marling, and Stefania Montani, and the EXPPORT workshop (Experience Reuse: Provenance, Process-Orientation and Traces), organized by David Leake, Béatrice Fuchs, Juan A. Recio Garcia, and Stefania Montani, were held jointly and dealt with how to deal with data represented CDPHP, was the local chair; William E. University, and Jonathan Rubin, from Registration information is available at www.aaai.org/Symposia/ the Palo Alto Research Center, were the Spring/ sss14.php.

Under the auspices of the Humaine Association (now called the Association for the Advancement of Affective Computing, AAAC), the ACII conference series has become an important international forum for research on affective human-machine interaction and intelligent affective systems. Affect is a phenomenon of substantial importance in most if not all of human activities. This ACII conference therefore strived to emphasize the humanistic side of affective computing by promoting research at the crossroads between engineering and human sciences, including biological, social, and cultural aspects of human life. This has been exemplified by conference topics as varied as computerized psychological emotional modeling; art and cinema studies; gaming; learning; depression, stress, and anxiety management; robots, avatars, and virtual worlds; social media analysis; pattern recognition, classification, and data mining; real-time and embedded affective systems; and others. All have in common affect and emotions, with an emphasis on a computational view of emotion.

Articulating thought in computer-based media is a powerful means for humans to develop their understanding of phenomena. We have created DynaLearn, an Intelligent Learning Environment that allows learners to acquire conceptual knowledge by constructing and simulating qualitative models of how systems behave. DynaLearn uses diagrammatic representations for learners to express their ideas. The environment is equipped with semantic technology components capable of generating knowledge-based feedback, and virtual characters enhancing the interaction with learners. Teachers have created course material, and successful evaluation studies have been performed. This article presents an overview of the DynaLearn system.

The Twenty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-14) will focus on successful applications of AI technology.