Generation and selection of likely interpretations during plan recognition in task-oriented consultation systems

This paper presents a mechanism which infers a user's plans from his/her utterances by directing the inference process towards the more likely interpretations of a speaker's statements among many possible interpretations. Our mechanism uses Bayesian theory of probability to assess the likelihood of an interpretation, and it complements this assessment by taking into consideration two aspects of an interpretation: its coherence and its information content. The coherence of an interpretation is determined by the relationships between the different statements in the discourse. The information content of an interpretation is a measure of how well defined the interpretation is in terms of the actions to be performed on the basis of this interpretation. This measure is used to guide the inference process towards interpretations with higher information content. The information content of an interpretation depends on the specificity and the certainty of the inferences in it, where the certainty of an inference depends on the knowledge on which the inference is based. Our mechanism has been developed for use in task-oriented consultation systems. The particular domain that we have chosen for exploration is that of travel booking.     

On teletraffic applications to OBS

We provide teletraffic models for loss probability evaluation of optical burst switching (OBS). We show that the popular Engset formula is not exact for OBS modeling and demonstrate that in certain cases it is not appropriate. A new exact model is provided. The various models are compared using numerical results for various OBS alternatives with and without burst segmentation.     

A branch and cut algorithm for the VRP with satellite facilities

An important aspect of the vehicle routing problem (VRP) that has been largely overlooked is the use of satellite facilities to replenish vehicles during a route. When possible, satellite replenishment allows the drivers to continue making deliveries until the close of their shift without necessarily returning to the central depot. This situation arises primarily in the distribution of fuels and certain retail items. When demand is random, optimizing customer routes a priori may result in significant additional costs for a particular realization of demand. Satellite facilities are one way of safeguarding against unexpected demand. This paper presents a branch and cut methodology for solving the VRP with satellite facilities subject to capacity and route time constraints. We begin with a mixed-integer linear programming formulation and then describe a series of valid inequalities that can be used to cut off solutions to the linear programming relaxation. Several separation heuristics are then outlined that are used to generate the cuts. Embedded in the methodology is a VRP heuristic for finding good feasible solutions at each stage of the computations. Results are presented for a set of problems derived from our experience with a leading propane distributor.     

Energy Trapping in Dications

The energy interaction curves of a number of diatomic and polyatomic dication systems were calculated in order to study their energy-trapping properties. Generally, the ab initio complete active space multiconfiguration self-consistent field method was used in an extended valence + polarization basis set, with compact effective potentials replacing the core electrons. The diatomic dications include all ten possible binary combinations of oxygen, sulphur, selenium, and tellurium. O₂²⁺ shows the largest exothermicity, measured from equilibrium to the monocation combination asymptote, and highest barrier to dissociation. The calculated equilibrium bond length and harmonic vibrational frequency agree very well with experiment. The O₂²⁺, SO²⁺, SeO²⁺, and TeO²⁺ series show progressively decreasing exothermicities but similar barrier heights. The non-oxides, in contrast, show similar exothermicities but decreasing barriers with increasing size of the atom constituents. These trends are interpreted in terms of both valence bond curve-crossing and molecular orbital bonding models. The ozone dication, O₃²⁺, is found to have a number of low-lying singlet and triplet stationary state structures spanning near-linear to D_3h2+ symmetries. Although the calculated exothermicity is even larger than for O₂²⁺, the barrier to O₂⁺ + O⁺ dissociation is predicted to be low in each case. O₂²⁺ surrounded by six argon atoms to model an isolating environment shows increased equilibrium O–O bond length, decreased exothermicity, and increased barrier to dissociation, relative to the bare dication. O₂²⁺ flanked at each end by a perpendicularly oriented H₂ molecule in a staggered conformation is obstructed from direct conversion to the water dimer dication by a high barrier. However, [(H₂O)₂]²⁺ dissociates smoothly from equilibrium to two water monocations with a large exothermicity but a small barrier.     

Estimating frequency of occurrence.

Various models have been proposed that attribute judgments of frequency of occurrence either to the direct coding of frequency information or to the estimation of frequency from characteristics of memory traces that are not direct frequency codes. We present three experiments that replicate and further explore a phenomenon that is significant in the distinction between direct and indirect models of frequency coding. The phenomenon is that deeper processing of stimulus material results in superior judgments of frequency for that material than does shallower processing. This effect is at least partly attributable to the number of associations that are generated by deep analysis. The results of the present experiments thereby implicate an indirect coding mechanism underlying frequency judgments. However, we also show that there is ample reason to suppose a contribution of a direct coding mechanism as well. The most reasonable view, therefore, is that multiple mechanisms are involved in the registration of frequency of occurrence. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Sirens, seals, and containers: Reflections on psychotherapy during the 1991 Gulf War.

Presents clinical observations of the impact of the Persian Gulf War on the themes and processes of psychotherapy among a group of Israeli patients. Metaphors of the war period were analyzed in light of contemporary psychoanalytic thinking, with a particular focus on the significance of the sealed room. Issues of the psychotherapeutic technical stance and resistance vs breach in the frame were also addressed, and 2 case vignettes are included. It is concluded that the therapeutic framework is a representational constituent of early experiences pertaining to boundaries, containment, transitionality, and psychic enclosure, and remains sensitive throughout the treatment to vicissitudes of transference or to the reapportionment of psychic space. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Multiplicative Operator Splittings in Nonlinear Diffusion: From Spatial Splitting to Multiple Timesteps

Operator splitting is a powerful concept used in many diversed fields of applied mathematics for the design of effective numerical schemes. Following the success of the additive operator splitting (AOS) in performing an efficient nonlinear diffusion filtering on digital images, we analyze the possibility of using multiplicative operator splittings to process images from different perspectives.We start by examining the potential of using fractional step methods to design a multiplicative operator splitting as an alternative to AOS schemes. By means of a Strang splitting, we attempt to use numerical schemes that are known to be more accurate in linear diffusion processes and apply them on images. Initially we implement the Crank-Nicolson and DuFort-Frankel schemes to diffuse noisy signals in one dimension and devise a simple extrapolation that enables the Crank-Nicolson to be used with high accuracy on these signals. We then combine the Crank-Nicolson in 1D with various multiplicative operator splittings to process images. Based on these ideas we obtain some interesting results. However, from the practical standpoint, due to the computational expenses associated with these schemes and the questionable benefits in applying them to perform nonlinear diffusion filtering when using long timesteps, we conclude that AOS schemes are simple and efficient compared to these alternatives.We then examine the potential utility of using multiple timestep methods combined with AOS schemes, as means to expedite the diffusion process. These methods were developed for molecular dynamics applications and are used efficiently in biomolecular simulations. The idea is to split the forces exerted on atoms into different classes according to their behavior in time, and assign longer timesteps to nonlocal, slowly-varying forces such as the Coulomb and van der Waals interactions, whereas the local forces like bond and angle are treated with smaller timesteps. Multiple timestep integrators can be derived from the Trotter factorization, a decomposition that bears a strong resemblance to a Strang splitting. Both formulations decompose the time propagator into trilateral products to construct multiplicative operator splittings which are second order in time, with the possibility of extending the factorization to higher order expansions. While a Strang splitting is a decomposition across spatial dimensions, where each dimension is subsequently treated with a fractional step, the multiple timestep method is a decomposition across scales. Thus, multiple timestep methods are a realization of the multiplicative operator splitting idea. For certain nonlinear diffusion coefficients with favorable properties, we show that a simple multiple timestep method can improve the diffusion process.     

THE IMPORTANCE OF NEUTRAL EXAMPLES FOR LEARNING SENTIMENT

Most research on learning to identify sentiment ignores "neutral" examples, learning only from examples of significant (positive or negative) polarity. We show that it is crucial to use neutral examples in learning polarity for a variety of reasons. Learning from negative and positive examples alone will not permit accurate classification of neutral examples. Moreover, the use of neutral training examples in learning facilitates better distinction between positive and negative examples.