Rev. C) Statistical modeling of nuclear data provides a novel approach to nuclear systematics complementary to established theoretical and phenomenological approaches based on quantum theory. More specifically, fully-connected, multilayer feedforward artificial neural network models are developed using the Levenberg-Marquardt optimization algorithm together with Bayesian regularization and cross-validation. The predictive performance of models emerging from extensive computer experiments is compared with that of traditional microscopic and phenomenological models as well as with the performance of other learning systems, including earlier neural network models as well as the support vector machines recently applied to the same problem. In discussing the results, emphasis is placed on predictions for nuclei that are far from the stability line, and especially those involved in the r-process nucleosynthesis. It is found that the new statistical models can match or even surpass the predictive performance of conventional models for beta-decay systematics and accordingly should provide a valuable additional tool for exploring the expanding nuclear landscape. I. INTRODUCTION "Numbers are the within of all things." Among nuclear physicists this need is driven both by the experimental programs of existing and future radioactive ion beam facilities and by the stresses placed on established nuclear structure theory as totally new areas of the nuclear landscape are opened for exploration. For nuclear astrophysicists, such information is intrinsic to an understanding of supernova explosions - the initialization of the explosion, the subsequent neutronization of the core material, and the strength and fate of the shock wave formed - and the nucleosynthesis of heavy elements above Fe, notably the r-process [3, 4, 5]. Both the element distribution on the r-path and the time scale of the r-process are highly sensitive to the β-decay properties of the neutron-rich nuclei involved. Except for a few key nuclei, β decay of r-process nuclei cannot be studied in terrestrial laboratories, so the required information must come from nuclear models. These include the more phenomenological treatments, such as the Gross Theory (GT), as well as microscopic approaches based on the shell model and the proton-neutron Quasiparticle Random-Phase Approximation (pnQRPA) in various versions.

Machine Learning manuscript No. (will be inserted by the editor) Abstract We present the Procrustes measure, a novel measure based on Procrustes rotation that enables quantitative comparison of the output of manifold-based embedding algorithms (such as LLE (Roweis and Saul, 2000) and Isomap (Tenenbaum et al, 2000)). The measure also serves as a natural tool when choosing dimension-reduction parameters. We also present two novel dimension-reduction techniques that attempt to minimize the suggested measure, and compare the results of these techniques to the results of existing algorithms. Finally, we suggest a simple iterative method that can be used to improve the output of existing algorithms. Keywords Dimension reducing · Manifold learning · Procrustes analysis, · Local PCA · Simulated annealing 1 Introduction Technological advances constantly improve our ability to collect and store large sets of data. The main difficulty in analyzing such high-dimensional data sets is, that the number of observations required to estimate functions at a set level of accuracy grows exponentially with the dimension. This problem, often referred to as the curse of dimensionality, has led to various techniques that attempt to reduce the dimension of the original data. Historically, the main approach to dimension reduction is the linear one. This is the approach used by principle component analysis (PCA) and factor analysis (see Mardia et al, 1979, for both).

We analyze the performance of a class of manifold-learning algorithms that find their output by minimizing a quadratic form under some normalization constraints. This class consists of Locally Linear Embedding (LLE), Laplacian Eigenmap, Local Tangent Space Alignment (LTSA), Hessian Eigenmaps (HLLE), and Diffusion maps. We present and prove conditions on the manifold that are necessary for the success of the algorithms. Both the finite sample case and the limit case are analyzed. We show that there are simple manifolds in which the necessary conditions are violated, and hence the algorithms cannot recover the underlying manifolds. Finally, we present numerical results that demonstrate our claims.

Past research on probabilistic databases has studied the problem of answering queries on a static database. Application scenarios of probabilistic databases however often involve the conditioning of a database using additional information in the form of new evidence. The conditioning problem is thus to transform a probabilistic database of priors into a posterior probabilistic database which is materialized for subsequent query processing or further refinement. It turns out that the conditioning problem is closely related to the problem of computing exact tuple confidence values. It is known that exact confidence computation is an NP-hard problem. This has led researchers to consider approximation techniques for confidence computation. However, neither conditioning nor exact confidence computation can be solved using such techniques. In this paper we present efficient techniques for both problems. We study several problem decomposition methods and heuristics that are based on the most successful search techniques from constraint satisfaction, such as the Davis-Putnam algorithm. We complement this with a thorough experimental evaluation of the algorithms proposed. Our experiments show that our exact algorithms scale well to realistic database sizes and can in some scenarios compete with the most efficient previous approximation algorithms.

Advancing AI Research and Applications by Learning from What Went Wrong and Why

We hope you are planning to join us for AAAI-08 and IAAI-08 in Chicago, Illinois, July 13-17, 2008. The AAAI-08 program will feature Eric Horvitz's cal papers will be highlighted as The program will include a research AAAI presidential address, as well as exceptional papers during the conference-wide track, industry track, invited speakers, five outstanding invited talks. Registration information invited speakers include Alexei A. Efros July 16, and another 23 short and other program details will (Carnegie Mellon University) whose papers will be presented as posters. Using Lots of Data to Infer Geometric, and awards will continue for its aiide08.php Please Photometric and Semantic Scene Properties second year with all the Hollywood send inquiries to aiide08@aaai.org

Humans learn from their mistakes. When things go badly, we notice that something is amiss, figure out what went wrong and why, and attempt to repair the problem. Artificial systems depend on their human designers to program in responses to every eventuality and therefore typically don’t even notice when things go wrong, following their programming over the proverbial, and in some cases literal, cliff. This article describes our past and current work on the Meta-Cognitive Loop, a domain-general approach to giving artificial systems the ability to notice, assess, and repair problems. The goal is to make artificial systems more robust and less dependent on their human designers.

Extempo Systems, Inc. was founded in 1995 to commercialize intelligent characters. Our team built innovative software and novel applications for several markets. We had some early-adopting customers during the Internet boom, but the company was not quite able to survive the significant downturn in corporate IT spending when the bubble burst. In 2004, Extempo ceased operations and was formally liquidated. Although our commercial venture failed, we learned a lot, had fun, and are trying again with a new company. To others who aspire to commercialize their AI technology, I say: ";;Take a chance!";;

In fact, DARPA, which funded the project, ways. Elves) (Scerri, Pynadath, and Tambe 2002; Finally, we will present some lessons Pynadath and Tambe 2003) and required learned and recent research that was motivated detailed information about the calendars by our experiences in deploying the of people using the system. Thus, we decided to deploy a new application of the Electric The Travel Elves introduced two major Elves, called the Travel Elves. This application advantages over traditional approaches to appeared to be ideal for wider deployment travel planning. First, the Travel Elves provided since it could be hosted entirely outside an interactive approach to making an organization and communication travel plans in which all of the data could be performed over wireless devices, required to make informed choices is such as cellular telephones. For example, when The mission of the Travel Elves (Ambite deciding whether to park at the airport or et al. 2002, Knoblock 2004) was to facilitate take a taxi, the system compares the cost planning a trip and to ensure that the of parking and the cost of a taxi given other resulting travel plan would execute selections, such as the airport, the specific smoothly. Initial deployment of the Travel parking lot, and the starting location Elves at DARPA went smoothly.

On March 27, 2006, I gave a light-hearted and occasionally bittersweet presentation on “Whatever Happened to AI?” at the Stanford Spring Symposium presentation – to a lively audience of active AI researchers and formerly-active ones (whose current inaction could be variously ascribed to their having aged, reformed, given up, redefined the problem, etc.)  This article is a brief chronicling of that talk, and I entreat the reader to take it in that spirit: a textual snapshot of a discussion with friends and colleagues, rather than a scholarly article. I begin by whining about the Turing Test, but only for a thankfully brief bit, and then get down to my top-10 list of factors that have retarded progress in our field, that have delayed the emergence of a true strong AI.