A Multi-Dimensional Trust Model for Heterogeneous Contract Observations

In this paper we develop a novel probabilistic model of computational trust that allows agents to exchange and combine reputation reports over heterogeneous, correlated multi-dimensional contracts. We consider the specific case of an agent attempting to procure a bundle of services that are subject to correlated quality of service failures (e.g. due to use of shared resources or infrastructure), and where the direct experience of other agents within the system consists of contracts over different combinations of these services. To this end, we present a formalism based on the Kalman filter that represents trust as a vector estimate of the probability that each service will be successfully delivered, and a covariance matrix that describes the uncertainty and correlations between these probabilities. We describe how the agents’ direct experiences of contract outcomes can be represented and combined within this formalism, and we empirically demonstrate that our formalism provides significantly better trustworthiness estimates than the alternative of using separate single-dimensional trust models for each separate service (where information regarding the correlations between each estimate is lost)

Mock Catalogs for UHECR Studies

We provide realistic mock-catalogs of cosmic rays above 40 EeV, for a pure proton composition, assuming their sources are a random subset of ordinary galaxies in a simulated, volume-limited survey, for various choices of source density: 10^-3.5 Mpc^-3, 10^-4.0 Mpc^-3 and 10^-4.5 Mpc^-3. The spectrum at the source is taken to be E^-2.3 and the effects of cosmological redshifting as well as photo-pion and e^+ e^- energy losses are included.Comment: 7 pages, 4 figure

Coupled thermomechanical dynamics of phase transitions in shape memory alloys and related hysteresis phenomena

In this paper the nonlinear dynamics of shape memory alloy phase transformations is studied with thermomechanical models based on coupled systems of partial differential equations by using computer algebra tools. The reduction procedures of the original model to a system of differential-algebraic equations and its solution are based on the general methodology developed by the authors for the analysis of phase transformations in shape memory materials with low dimensional approximations derived from center manifold theory. Results of computational experiments revealing the martensitic-austenitic phase transition mechanism in a shape-memory-alloy rod are presented. Several groups of computational experiments are reported. They include results on stress- and temperature-induced phase transformations as well as the analysis of the hysteresis phenomenon. All computational experiments are presented for Cu-based structures

The use of high altitude, color and spectrozonal imagery for the inventory of wildland resources. Volume II - The range resource Annual progress report

High altitude multispectral imagery for identifying and mapping rangeland characteristic

Research into fundamental phenomena associated with spacecraft electrochemical devices, calorimetry of nickel-cadmium cells Progress report, 1 Oct. - 31 Dec. 1967

Calorimetry of nickel cadmium cells for spacecraft electrochemical system

Information Agents for Pervasive Sensor Networks

In this paper, we describe an information agent, that resides on a mobile computer or personal digital assistant (PDA), that can autonomously acquire sensor readings from pervasive sensor networks (deciding when and which sensor to acquire readings from at any time). Moreover, it can perform a range of information processing tasks including modelling the accuracy of the sensor readings, predicting the value of missing sensor readings, and predicting how the monitored environmental parameters will evolve into the future. Our motivating scenario is the need to provide situational awareness support to first responders at the scene of a large scale incident, and we describe how we use an iterative formulation of a multi-output Gaussian process to build a probabilistic model of the environmental parameters being measured by local sensors, and the correlations and delays that exist between them. We validate our approach using data collected from a network of weather sensors located on the south coast of England

Towards Real-Time Information Processing of Sensor Network Data using Computationally Efficient Multi-output Gaussian Processes

In this paper, we describe a novel, computationally efficient algorithm that facilitates the autonomous acquisition of readings from sensor networks (deciding when and which sensor to acquire readings from at any time), and which can, with minimal domain knowledge, perform a range of information processing tasks including modelling the accuracy of the sensor readings, predicting the value of missing sensor readings, and predicting how the monitored environmental variables will evolve into the future. Our motivating scenario is the need to provide situational awareness support to first responders at the scene of a large scale incident, and to this end, we describe a novel iterative formulation of a multi-output Gaussian process that can build and exploit a probabilistic model of the environmental variables being measured (including the correlations and delays that exist between them). We validate our approach using data collected from a network of weather sensors located on the south coast of England

Theoretical aspects of high--Q^2 deep inelastic scattering

We present an overview of the theory of high--Q^2 deep inelastic scattering. We focus in particular on the theoretical uncertainties in the predictions for neutral and charged current cross sections obtained by extrapolating from lower Q^2.Comment: 10 (Latex) pages, including 6 embedded figures, uses epsfig.sty, ioplppt.sty and iopl12.sty; Plenary talk presented at the 3rd UK Phenomenology Workshop on HERA Physics, Durham, September 1998, to be published in the Proceeding