The typical processing paradigm in natural language processing is the "pipeline" approach, where learners are being used at one level, their outcomes are being used as features for a second level of predictions and so one. In addition to accumulating errors, it is clear that the sequential processing is a crude approximation to a process in which interactions occur across levels and down stream decisions often interact with previous decisions. This work develops a general...

The nonholonomic nature of rolling between rigid bodies can be exploited to achieve dextrous manipulation of industrial parts with minimally complex robotic effectors. While for parts with smooth surfaces a relatively well developed theory exists, planning for parts with only piece-wise smooth surfaces is largely an open problem. The problem of arbitrarily displacing and reorienting a polyhedron by means of rotations about edges belonging to a fixed plane is considered. Relevant theoretical results are reviewed, and a polynomial time algorithm is proposed that allows planning such motions. The effects of finite accuracy in representing problem data, as well as the operational and computational complexity of the method are considered in detail.

We consider probabilistic modal logic, graded modal logic and stochastic modal logic, where linear inequalities may be used to express numerical constraints between quantities. For each of the logics, we construct a cut-free sequent calculus and show soundness with respect to a natural class of models. The completeness of the associated sequent calculi is then established with the help of coalgebraic semantics which gives completeness over a (typically much smaller) class of models. With respect to either semantics, it follows that the satisfiability problem of each of these logics is decidable in polynomial space. Keywords: Probabilistic modal logic, graded modal logic, linear inequalities

We present a formal method for the verification of regular VLSI architectures. In our method, the behavioral specification of the chip and its implementation are first expressed in Alpha, a language for the design of regular synchronous architectures. The behavioral specification is refined down to an abstract architecture description, while the implementation is simplified by induction techniques up to the same abstract architecture level. Verification is then done by matching both descriptions. This method has been successfully applied to check the correctness of a 300.000 transistor VLSI systolic chip named Api69 for sequence comparison. Proc. Int. Conf. on Application Specific Array Processors, San Francisco, IEEE Computer Society Press, August 1994, pp. 164--176 1: Introduction Traditionally, hardware systems have been validated by means of simulation. This method is limited, as it is difficult to achieve 100% fault coverage. This is the reason why formal verification is being ...

Traditional information retrieval models treat the query as a bag of words, assuming that the occurrence of each query term is independent of the positions and occurrences of others. Several of these traditional models have been extended to incorporate positional information, most often through the inclusion of phrases. This has shown improvements in effectiveness on large, modern test collections. The language modeling approach to information retrieval is attractive because it provides a well-studied theoretical framework that has been successful in other fields. Incorporating positional information into language models is intuitive and has shown significant improvements in several language-modeling applications. However, attempts to integrate positional information into the language-modeling approach to IR have not shown consistent significant improvements. This paper provides a broader exploration of this problem. We apply the backoff technique to incorporate a bigram phrase language model with the traditional unigram one and compare its performance to an interpolation of a conditional bigram model with the unigram model. While this novel application of backoff does not improve effectiveness, we find that our formula for interpolating a conditional bigram model with the unigram model yields significantly different results from prior work. Namely, it shows an 11 % relative improvement in average precision on one query set, while yielding no improvement on the other two. 1.

Natural language decisions often involve assigning values to sets of variables, representing low level decisions and context dependent disambiguation. In most cases there are complex relationships among these variables representing dependencies that range from simple statistical correlations to those that are constrained by deeper structural, relational and semantic properties of the text. In this work we study a specific instantiation of this problem in the context of identifying named entities and relations between them in free form text. Given a collection of discrete random variables representing outcomes of learned local predictors for entities and relations, we seek an optimal global assignment to the variables that respects multiple constraints, including constraints on the type of arguments a relation can take, and the mutual activity of different relations. We develop a linear programming formulation to address this global inference problem and evaluate it in the context of simultaneously learning named entities and relations. We show that global inference improves stand-alone learning; in addition, our approach allows us to efficiently incorporate expressive domain and task specific constraints at decision time, resulting, beyond significant improvements in the accuracy, in “coherent ” quality of the inference. 2 Global Inference for Entity and Relation Identification via a Linear Programming Formulation 1.1

The Parametric Integer Programming (PIP) algorithm due to Feautrier is highly suitable for solving linear programming problems arising in program parallelization and synthesis. In this report, we describe the interface allowing to use the arbitrary-precision arithmetic implementation of PIP as an external problem solver in Mathematica, thus providing a convenient means for fast experimentation with algorithms which use PIP as a building block. The first part of the report offers a short overview of PIP, followed by an example-based user's guide to MathPIP. The second part is a reference manual describing in detail each user-visible component of MathPIP. Copyright c fl1994. All rights reserved. Reproduction of all or part of this work is permitted for educational or research purposes on condition that (1) this copyright notice is included, (2) proper attribution to the author or authors is made and (3) no commercial gain is involved. Recent technical reports issued by the Depa...

Abstract not found

In this paper, we describe the realization and control of robotic end-effectors that are designed to achieve high operational versatility with limited constructive complexity. The design of such endeffectors, which can be regarded either as low-complexity robot hands or as highly versatile robot grippers, is based on the intentional exploitation of nonholonomic effects that occur in rolling. While the potential usefulness of manipulation by rolling has been theoretically established in the literature, several problems in the practical implementation of the concept remained open. In particular, manipulation of parts of complex, and a priori unknown, shape is considered in this paper. Experimental low-complexity grippers that realize dexterous manipulation by rolling are also described.

Information extraction is a process that extracts limited semantic concepts from text documents and presents them in an organized way. Unlike several other natural language tasks, information extraction has a direct impact on end-user applications. Despite its importance, information extraction is still a difficult task due to the inherent complexity and ambiguity of human languages. Moreover, mutual dependencies between local predictions of the target concepts further increase difficulty of the task. In order to enhance information extraction technologies, we develop general approaches for two aspects – relational feature generation and global inference with classifiers. It has been quite convincingly argued that relational learning is suitable in training a complicated natural language system. We propose a relational feature generation approach that facilitates relational learning through propositional learning algorithms. In particular, we develop a relational representation language to produce features in a data driven way. The resulting features capture the relational structures of a given domain, and therefore allow the learning algorithms to effectively learn the relational definitions of target concepts. Although the learned classifier can be used to directly predict the target concepts, conflicts between the labels of different target variables often occur due to imperfect classifiers. We propose