Covariance graphical lasso applies a lasso penalty on the elements of the covariance matrix. This method is useful because it not only produces sparse estimation of covariance matrix but also discovers marginal independence structures by generating zeros in the covariance matrix. We propose and explore two new algorithms for solving the covariance graphical lasso problem. Our new algorithms are based on coordinate descent and ECM. We show that these two algorithms are more attractive than the only existing competing algorithm of Bien and Tibshirani (2011) in terms of simplicity, speed and stability. We also discuss convergence properties of our algorithms.

For successful experimental implementation of any quantum protocol, the quantum states and operations involved must be confirmed to be sufficiently closed to their theoretical targets. One way to obtain such a confirmation is to perform another experiment and from the obtained data make an estimate of the quantum operator involved. Statistically, this is a constrained multiparameter estimation problem - the quantum estimation problem - where we assume we are given a finite number of identical copies of a quantum state or operation, we perform measurements whose mathematical description is assumed to be known, and from the outcome statistics we make our estimate. Due to the probabilistic behavior of the measurement outcomes and the finiteness of the number of measurement trials, there always exist statistical errors in any quantum estimate. The size of the error depends on the choice of measurements and the estimation procedure. In statistics, the former is called an experimental design, while the latter is called an estimator. It is, therefore, a key aim of both classical and quantum estimation theory to find a combination of experimental design and estimator which gives us more precise estimation results using fewer measurement trials. A standard combination in quantum information experiments is that of quantum tomography and maximum likelihood estimator. Although the term "quantum tomography" can be used in several different contexts, we use it to mean an experimental design in which an independently and identically prepared set of measurements are used throughout the entire experiment [1].

The free energy functional has recently been proposed as a variational principle for bounded rational decision-making, since it instantiates a natural trade-off between utility gains and information processing costs that can be axiomatically derived. Here we apply the free energy principle to general decision trees that include both adversarial and stochastic environments. We derive generalized sequential optimality equations that not only include the Bellman optimality equations as a limit case, but also lead to well-known decision-rules such as Expectimax, Minimax and Expectiminimax. We show how these decision-rules can be derived from a single free energy principle that assigns a resource parameter to each node in the decision tree. These resource parameters express a concrete computational cost that can be measured as the amount of samples that are needed from the distribution that belongs to each node. The free energy principle therefore provides the normative basis for generalized optimality equations that account for both adversarial and stochastic environments.

This research is about the distribution of processing involved in the search for the best product-unit neural network (PUNN) models [Durbin, 1990] [Martínez-Estud illo, 2006A], using evolutionary algorithms, EAs. A cluster of computers [Buyya, 1999] will be used to carry out the distribution of this processing. Many different types of neural network architectures have been used, but the most popular one has been the single-hidden-layer feedforward network. Amongst the numerous approaches that use neural networks in classification problems, we focus our attention on ev olutionary artificial neural networks (EANNs). EANNs have been a key research area in the past decade pr oviding an improved platfo rm for optimizing network performance and architecture (number of hidden nodes and number of connections) simultaneously.

ICINCO 2012 will be held July 28-31, 2012 in Rome, Italy This page includes forthcoming AAAI sponsored conferences, conferences presented Sixth International RuleML Symposium by AAAI Affiliates, and conferences held in cooperation with AAAI. RuleML-2012 will be Magazine also maintains a calendar listing that includes nonaffiliated conferences held August 27-31, 2012 in Montpellier, at www.aaai.org/Magazine/calendar.php. Knowledge Engineering and Knowledge ICWSM-12 will be held June 4-7 at Flairs-2012 will be held May 23-25, Management. AAAI-12 will be Representation and Reasoning. Twenty-Fourth Innovative Applications Twenty-Second International Conference of Artificial Intelligence Conference. on Automated Planning and IAAI-12 will be held July Scheduling.

The Seventh Artificial Intelligence for Interactive Digital Entertainment Conference (AIIDE-11) was held October 11–14, 2011 at Stanford University, Stanford, California. Two one-day workshops were held on October 11: Artificial Intelligence in the Game Design Process, and Intelligent Narrative Technologies. The highlights of each workshop are presented in this report.

The Association for the Advancement of e are pleased to introduce Worldwide AI -- a new column in AI Magazine Artificial Intelligence now serves a global audience, and our members, meeting participants, councilors, and officers reside in countries throughout the world. Worldwide AI is designed to meet our expanded audience's interests. In the columns that will appear in this and forthcoming issues, readers will find a continuing source of news and information on significant research projects and accomplishments, academic and community events, and experiences fielding notable applications of AI. We expect that increased awareness about AI activities around the world will fuel new opportunities for communication and collaboration. The inaugural columns in this issue of Worldwide AI describe artificial intelligence trends in India and South Africa.

According to a 2008 OECD review of national policies for education in South Africa, typically only 15 percent to 18 percent of secondary school students who sit for their final year exams every year qualify automatically for university-level education; and this number seems to be decreasing as more students choose to complete subjects on so-called standard grade instead of higher grade, a trend that is especially apparent for mathematics and science, the two fields with critical skills shortages in the country. The South African tertiary education sector is quite small for a country with a population of around 50 million, with 11 "traditional" universities, 6 technical universities, and 6 comprehensive universities. The latter university types focus on more technical or vocational education. The public sector also funds 16 research institutions. In spite of these obstacles, South African universities participate in world-class research activities in many fields and range among the best on the African continent.

The second was the propensity of the computing industry toward more lucrative assignments in the service sector. Both these factors are changing, not least because leading international software companies have set up research and development centers in the country. Computer science education established itself in India in the early 1980s when the Indian Institutes of Technology (IITs) set up computer science departments and started offering undergraduate programs in the discipline. Research in artificial intelligence took off soon afterward when the government of India launched the Knowledge Based Computing Systems (KBCS) program in conjunction with the United Nations Development Program (Saint-Dizier 1991). A number of nodal centers were set up to focus on different areas of research including expert systems (IIT Madras), speech processing (Tata Institue of Fundamental Research), parallel processing (Indian Institute for Science), image processing (Indian Statistical Institute), and natural language processing (Center for Development of Advanced Computing).

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