Papers.

Categorical perception and the convergence of social learning

Parameter convergence in systems with convex/concave parameterization

A convergent frequency estimator

Global parameter identification in systems with a sigmoidal activation function

Compression: a key for next-generation text retrieval systems

Trust Practice: MDPS: FACE THE CHALLENGE!

Chernobyl Tumor Bank

Potato-Lactulose Breath Hydrogen Testing as a Function of Gastric Motility in Diabetes Mellitus

The Future of Business School Scholarship: An Empirical Assessment of the Boyer Framework by U.S. Deans

Forces Driving Organizational Change: A Business School Perspective

The tonal phonology of Yoruba clitics

Adding Compression to Block Addressing Inverted Indexes

Structural analysis of the neuronal SNARE protein syntaxin-1A

Intracellular trafficking depends on the docking and fusion of transport vesicles with cellular membranes. Central to docking and fusion is the pairing of SNARE proteins (soluble NSF attachment protein receptors) associated with the vesicle and target membranes (v- and t-SNAREs, respectively). Here, the X-ray structure of an N-terminal conserved domain of the neuronal t-SNARE syntaxin-1A was determined to a resolution of 1.9 A using multiwavelength anomalous diffraction. This X-ray structure, which is in general agreement with an NMR structure of a similar fragment, provides new insight into the interaction surface between the N-terminal domain and the remainder of the protein. In vitro characterization of the intact cytoplasmic domain of syntaxin revealed that it forms dimers, and probably tetramers, at low micromolar concentrations, with concomitant structural changes that can be detected by limited proteolysis.

Server side web scripting in Haskell

The Common Gateway Interface (CGI) for generating dynamic documents on web servers imposes much accidental complexity on the programmer. The Haskell/CGI library documented in this paper hides all this unpleasantness by using the common sense ‘design pattern’ of separating model and presentation. Low-level query string requests are represented by association lists, and primitive HTTP responses are easily constructed using a set of HTML generating combinators. The CGI programmer only needs to write a worker function that maps an abstract request into an abstract response. A (higher-order) wrapper function then transmutes the worker into a real low-level CGI script that deals with the exact format of concrete requests and responses as required by the CGI standard.

A new universal and fault-tolerant quantum basis

Adaptive estimation of discrete-time systems with nonlinear parameterization

Binary Searching with Nonuniform Costs and Its Application to Text Retrieval

Visualization of Reusable Software Assets

Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks

Particle filters (PFs) are powerful sampling-based inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of probability distribution, nonlinearity and non-stationarity. They have appeared in several fields under such names as "condensation", "sequential Monte Carlo" and "survival of the fittest". In this paper, we show how we can exploit the structure of the DBN to increase the efficiency of particle filtering, using a technique known as Rao-Blackwellisation. Essentially, this samples some of the variables, and marginalizes out the rest exactly, using the Kalman filter, HMM filter, junction tree algorithm, or any other finite dimensional optimal filter. We show that Rao-Blackwellised particle filters (RBPFs) lead to more accurate estimates than standard PFs. We demonstrate RBPFs on two problems, namely non-stationary online regression with radial basis function networks and robot localization and map building. We also discuss other potential application areas and provide references to some finite dimensional optimal filters.

Run-time Support for Adaptive Load Balancing

A Parallel Framework for Explicit FEM

Block addressing indices for approximate text retrieval

Can biological assessments discriminate among types of stress? A case study from the Eastern Corn Belt Plains ecoregion

Using historical biological data to evaluate status and trends in the Big Darby Creek watershed (Ohio, USA)

Assessing ecological risk in watersheds: A case study of problem formulation in the Big Darby Creek watershed, Ohio, USA

A Formal Framework for Linguistic Annotation (revised version)

Inter-Scale Correlations as Measures of CMB Gaussianity

A description of CMB temperature fluctuations beyond the power spectrum is important for verifying models of structure formation, especially in view of forthcoming high-resolution observations. We argue that higher-order statistics of inter-scale correlations, because of their low cosmic variance, may be effective in detecting non-Gaussian features in the CMB. Inter-scale correlations are generically produced in defect-based models of structure formation. We analytically study properties of general higher-order cumulants of Fourier components of homogeneous random fields and design a new set of statistics suitable for small-scale data analysis. Using simulated non-Gaussian fields, we investigate the performance of the proposed statistics in presence of a Gaussian background and pixel noise, using the bispectrum as the underlying cumulant. Our numerical results suggest that detection of non-Gaussian features by our method is reliable if the power spectrum of the non-Gaussian components dominates that of the Gaussian background and noise within at least a certain range of accessible scales.

Using Names and Codes

Una arquitectura para búsqueda y visualización de resultados en Intranets

Transactional Business Process Servers: Definition and Requirements

Synchronizable Databases for the Web

SILK - a playful blend of Scheme and Java

Set Containment Joins: The Good, The Bad and The Ugly

Robustness via Run-Time Adaptation of Contingent Plans

... behavior of planetary rovers more robust for the purpose of increased productivity. Due to the inherent uncertainty in rover exploration, the traditional approach to rover control is conservative, limiting the autonomous operation of the rover and sacrificing performance for safety. Our objective is to increase the science productivity possible within a single uplink by allowing the rover's behavior to be specified with flexible, contingent plans and by employing dynamic plan adaptation during execution. We have deployed a system exhibiting flexible, contingent execution; this paper concentrates on our ongoing efforts on plan adaptation. Plans can

Remote Agent Experiment

Remote Agent (RA) is a model-based, reusable artificial intelligence (At) software system that enables goal-based spacecraft commanding and robust fault recovery. RA was flight validated during an experiment on board of DS1 between May 17th and May 21th, 1999.

Open information pools

Object-Based Adaptive Load Balancing for MPI Programs∗

New Approaches to Information Management: Attribute-Centric Data Systems (invited paper).

Iterative Combinatorial Auctions: Theory and Practice

Combinatorial auctions, which allow agents to bid directly for bundles of resources, are necessary for optimal auction-based solutions to resource allocation problems with agents that have non-additive values for resources, such as distributed scheduling and task assignment problems. We introduce iBundle, the first iterative combinatorial auction that is optimal for a reasonable agent bidding strategy, in this case myopic best-response bidding. Its optimality is proved with a novel connection to primal-dual optimization theory. We demonstrate orders of magnitude performance improvements over the only other known optimal combinatorial auction, the Generalized Vickrey Auction.

Diseñemos todo de nuevo: reflexiones sobre la computación y su enseñanza

BioCoRE: A Collaboratory for Structural Biology

Applying Aspect-Oriented Programming to Intelligent Synthesis

I discuss a component-centered, aspect-oriented system, the Object Infrastructure Framework (OIF), NASA's initiative on Intelligent Synthesis Environments (ISE), and the application of OIF to the architecture of ISE.

An analysis of the TUX web server

Algorithms for Inverse Reinforcement Learning

A Unified Approach to Model-Based Planning and Execution

Writing autonomous software is complex, requiring the coordination of functionally and technologically diverse software modules. System and mission engineers must rely on specialists familiar with the different software modules to translate requirements into application software. Also, each module often encodes the same requirement in different forms. The results are high costs and reduced reliability due to the difficulty of tracking discrepancies in these encodings. In this paper we describe a unified approach to planning and execution that we believe provides a unified representational and computational framework for an autonomous agent. We identify the four main components whose interplay provides the basis for the agent's autonomous behavior: the domain model, the plan database, the plan running module, and the planner modules. This representational and problem solving approach can be applied at all levels of the architecture of a complex agent, such as Remote Agent. In the rest of the paper we briefly describe the Remote Agent architecture. The new agent architecture proposed here aims at achieving the full Remote Agent functionality. We then give the fundamental ideas behind the new agent architecture and point out some implication of the structure of the architecture, mainly in the area of reactivity and interaction between reactive and deliberative decision making. We conclude with related work and current status.

A Terminology Server Based Tool to Support the Composition of Medical Documents

Communities in Cyberspace

Using OO design to enhance procedural software

Basic Data Structures

A Parallel Noise-Robust Algorithm to Recover Depth Information from Radial Flow Fields

A parallel algorithm operating on the units (neurons) of an artificial retina is proposed to recover depth information in a visual scene from radial flow fields induced by ego motion along a given axis. The system consists of up to 600 radii with fewer than 65 radially arranged neurons on each radius. Neurons are connected only to their nearest neighbors, and they are excited as soon as a sufficiently strong gray-level change occurs. The time difference of two subsequently activated neurons is then used by the last-excited neuron to compute the depth information. All algorithmic calculations remain strictly local, and information is exchanged only between adjacent active neurons (except for the final read-out). This, in principle, permits parallel implementation. Furthermore, it is demonstrated that the calculation of the object coordinates requires only a single multiplication with a constant, which is dependent on only the retinal position of the active neuron. The initial restriction to local operations makes the algorithm very noise sensitive. In order to solve this problem, a prediction mechanism is introduced. After an object coordinate has been determined, the active neuron computes the time when the next neuronal excitation should take place. This estimated time is transferred to the respective next neuron, which will wait for this excitation only within a certain time window. If the excitation fails to arrive within this window, the previously computed object coordinate is regarded as noisy and discarded. We will show that this predictive mechanism relies also on only a (second) single multiplication with another neuron-dependent constant. Thus, computational complexity remains low, and noisy depth coordinates are efficiently eliminated. Thus, the algorithm is very fast and operates in real time on 128128 images even in a serial implementation on a relatively slow computer. The algorithm is tested on scenes of growing complexity, and a detailed error analys