This paper describes a system capable of semi-automatically filling an XML template from free texts in the clinical domain (practice guidelines). The XML template includes semantic information not explicitly encoded in the text (pairs of conditions and actions/recommendations). Therefore, there is a need to compute the exact scope of conditions over text sequences expressing the required actions. We present a system developed for this task. We show that it yields good performance when applied to the analysis of French practice guidelines.

In the process of training Support Vector Machines (SVMs) by decomposition methods, working set selection is an important technique, and some exciting schemes were employed into this field. To improve working set selection, we propose a new model for working set selection in sequential minimal optimization (SMO) decomposition methods. In this model, it selects B as working set without reselection. Some properties are given by simple proof, and experiments demonstrate that the proposed method is in general faster than existing methods.

In anaphora resolution for English, animacy identification can play an integral role in the application of agreement restrictions between pronouns and candidates, and as a result, can improve the accuracy of anaphora resolution systems. In this paper, two methods for animacy identification are proposed and evaluated using intrinsic and extrinsic measures. The first method is a rule-based one which uses information about the unique beginners in WordNet to classify NPs on the basis of their animacy. The second method relies on a machine learning algorithm which exploits a WordNet enriched with animacy information for each sense. The effect of word sense disambiguation on the two methods is also assessed. The intrinsic evaluation reveals that the machine learning method reaches human levels of performance. The extrinsic evaluation demonstrates that animacy identification can be beneficial in anaphora resolution, especially in the cases where animate entities are identified with high precision.

The SEMATECH sponsored J-88-E project teaming Texas Instruments with NeuroDyne (et al.) focused on Fault Detection and Classification (FDC) on a Lam 9600 aluminum plasma etch reactor, used in the process of semiconductor fabrication. Fault classification was accomplished by implementing a series of virtual sensor models which used data from real sensors (Lam Station sensors, Optical Emission Spectroscopy, and RF Monitoring) to predict recipe setpoints and wafer state characteristics. Fault detection and classification were performed by comparing predicted recipe and wafer state values with expected values. Models utilized include linear PLS, Polynomial PLS, and Neural Network PLS. Prediction of recipe setpoints based upon sensor data provides a capability for cross-checking that the machine is maintaining the desired setpoints. Wafer state characteristics such as Line Width Reduction and Remaining Oxide were estimated on-line using these same process sensors (Lam, OES, RFM). Wafer-to-wafer measurement of these characteristics in a production setting (where typically this information may be only sparsely available, if at all, after batch processing runs with numerous wafers have been completed) would provide important information to the operator that the process is or is not producing wafers within acceptable bounds of product quality. Production yield is increased, and correspondingly per unit cost is reduced, by providing the operator with the opportunity to adjust the process or machine before etching more wafers.

In this paper we derive the equations for Loop Corrected Belief Propagation on a continuous variable Gaussian model. Using the exactness of the averages for belief propagation for Gaussian models, a different way of obtaining the covariances is found, based on Belief Propagation on cavity graphs. We discuss the relation of this loop correction algorithm to Expectation Propagation algorithms for the case in which the model is no longer Gaussian, but slightly perturbed by nonlinear terms.

A major achievement of mechanism design theory is a general method for the construction of truthful mechanisms called VCG (Vickrey, Clarke, Groves). When applying this method to complex problems such as combinatorial auctions, a difficulty arises: VCG mechanisms are required to compute optimal outcomes and are, therefore, computationally infeasible. However, if the optimal outcome is replaced by the results of a sub-optimal algorithm, the resulting mechanism (termed VCG-based) is no longer necessarily truthful. The first part of this paper studies this phenomenon in depth and shows that it is near universal. Specifically, we prove that essentially all reasonable approximations or heuristics for combinatorial auctions as well as a wide class of cost minimization problems yield non-truthful VCG-based mechanisms. We generalize these results for affine maximizers. The second part of this paper proposes a general method for circumventing the above problem. We introduce a modification of VCG-based mechanisms in which the agents are given a chance to improve the output of the underlying algorithm. When the agents behave truthfully, the welfare obtained by the mechanism is at least as good as the one obtained by the algorithm's output. We provide a strong rationale for truth-telling behavior. Our method satisfies individual rationality as well.

Solution-Guided Multi-Point Constructive Search (SGMPCS) is a novel constructive search technique that performs a series of resource-limited tree searches where each search begins either from an empty solution (as in randomized restart) or from a solution that has been encountered during the search. A small number of these "elite'' solutions is maintained during the search. We introduce the technique and perform three sets of experiments on the job shop scheduling problem. First, a systematic, fully crossed study of SGMPCS is carried out to evaluate the performance impact of various parameter settings. Second, we inquire into the diversity of the elite solution set, showing, contrary to expectations, that a less diverse set leads to stronger performance. Finally, we compare the best parameter setting of SGMPCS from the first two experiments to chronological backtracking, limited discrepancy search, randomized restart, and a sophisticated tabu search algorithm on a set of well-known benchmark problems. Results demonstrate that SGMPCS is significantly better than the other constructive techniques tested, though lags behind the tabu search.

Some games, such as The Sims, encourage mods (including virtual plagues) and the game has a huge community of modders. In one famous case, the guinea pig mod [15]; one of the first recorded virtual plagues, the culprit turned out to be the chief game developer Mr. Will Wright himself [10]. This opened for a discussion about the responsibility from the game developer side as to acknowledging the time and effort put in by people into their game characters, including not only virtual plagues but also, e.g., keeping servers alive in spite of declining numbers of players. The very first mods were made by Wizards (and players with access to the underlying text databases) of Multi-User Dungeon (MUD) in the late 1980s [11]. The most famous example of virtual plague initiation from the developer side is perhaps Blizzard's introduction of the Corrupted Blood debuff in World of Warcraft. This debuff was originally conceived of as spreading from monster to player only, although Blizzard let it transmit from player to player if the players were standing close to each other. The idea was to let one boss monster issue the debuff, which would then be allowed to affect all players fighting that particular monster (and ultimately help kill off another boss monster in the same area). What was unexpected, and seemingly a surprise to Blizzard, was that players passed the debuff on to their pets, dismissed the pets, and then resummoned them later, in much more populated areas. This led to tens of thousands of deaths on at least three servers, and caused Blizzard to try to correct the problem through various quick fixes (cf.

Semantic network research has seen a resurgence from its early history in the cognitive sciences with the inception of the Semantic Web initiative. The Semantic Web effort has brought forth an array of technologies that support the encoding, storage, and querying of the semantic network data structure at the world stage. Currently, the popular conception of the Semantic Web is that of a data modeling medium where real and conceptual entities are related in semantically meaningful ways. However, new models have emerged that explicitly encode procedural information within the semantic network substrate. With these new technologies, the Semantic Web has evolved from a data modeling medium to a computational medium. This article provides a classification of existing computational modeling efforts and the requirements of supporting technologies that will aid in the further growth of this burgeoning domain.

Observations consisting of measurements on relationships for pairs of objects arise in many settings, such as protein interaction and gene regulatory networks, collections of author-recipient email, and social networks. Analyzing such data with probabilisic models can be delicate because the simple exchangeability assumptions underlying many boilerplate models no longer hold. In this paper, we describe a latent variable model of such data called the mixed membership stochastic blockmodel. This model extends blockmodels for relational data to ones which capture mixed membership latent relational structure, thus providing an object-specific low-dimensional representation. We develop a general variational inference algorithm for fast approximate posterior inference. We explore applications to social and protein interaction networks.