Mutual exclusion in partitioned distributed systems

A network partition can break a distributed computing system into groups of isolated nodes. When this occurs, a mutual exclusion mechanism may be required to ensure that isolated groups do not concurrently perform conflicting operations. We study and formalize these mechanisms in three basic scenarios: where there is a single conflicting type of action; where there are two conflicting types, but operations of the same type do not conflict; and where there are two conflicting types, but operations of one type do not conflict among themselves. For each scenario, we present applications that require mutual exclusion (e.g., name servers, termination protocols, concurrency control). In each case, we also present mutual exclusion mechanisms that are more general and that may provide higher reliability than the voting mechanisms that have been proposed as solutions to this problem. Daniel Barbara is a graduate student in the Computer Science Department at Princeton University and expects to receive his Ph.D. Degree by July 1985. He obtained his BS in Electrical Engineering at the Universidad Metropolitana, Caracas, Venezuela in 1975. His research interests are Distributed Systems, Databases and Computer Networks. He is a member of IEEE and ACM. Hector Garcia-Molina is associate professor in the Department of Computer Science at Princeton University, Princeton, New Jersey. His research interests include distributed computing systems and database systems. He received a BS in electrical engineering from the Instituto Tecnologico de Monterrey. Mexico, in 1974. From Stanford University, Stanford, California, he received in 1975 a MS in electrical engineering and a PhD in computer science in 1979. Garcia-Molina is a member of the ACM and IEEE.     

The TSIMMIS Approach to Mediation: Data Models and Languages

TSIMMIS—The Stanford-IBM Manager of Multiple InformationSources—is a system for integrating information. It offers a datamodel and a common query language that are designed to support thecombining of information from many different sources. It also offerstools for generating automatically the components that are needed tobuild systems for integrating information. In this paper we shalldiscuss the principal architectural features and their rationale.     

Parametric and semi-parametric approaches in the analysis of short-term effects of air pollution on health

Generalized additive models (GAMs) have become the standard tool for the analysis of short-term effects of air pollution on human health. Usually, the confounding effect of seasonality and long-term trend is described by flexible parametric or non-parametric functions of calendar time. Two different modeling strategies, i.e. GAM with penalized regression splines and GAM with regression splines, were compared by means of a simulation study, addressing attention to the inference on air pollutant effect. Simulation results indicated that GAM with regression splines provides negligibly biased estimates of air pollutant effect and it is robust to misspecification of the degrees of freedom of the spline. GAM with penalized regression splines requires a certain amount of undersmoothing in order to reduce the bias of the estimates and to improve the coverage of confidence intervals. These findings agree with asymptotic results developed in the context of partially splined models.     

A Parallel Implementation of Multiple Secret Image Sharing Based on Cellular Automata

Recently, a multisecret sharing scheme for secret color images among a set of users was proposed, which allows that each participant to share secret color images with the rest of participants in such way that all of them can recover all secret color images only if all participants pool their shares. In this work a parallel implementation of the cellular automata-based multisecret sharing scheme is proposed, in which the technology of CUDA （Compute Unified Device Architecture） is used in parallelization, taking advantage that each cell of cellular automata can be processed independently. The processing time of the proposed scheme is analyzed and it is proved that the proposed parallel algorithm using the CUDA structure is more than 12 times faster than the conventional sequential algorithm. This reduction of temporal complexity allows the practical use of the secret sharing scheme in many information security fields.     

Antiferromagnetism in the semiconducting lanthanide cuprates Ln2CuO4

The semiconducting lanthanide cuprates Pr₂CuO₄, Nd₂CuO₄, and Gd₂CuO₄ have been investigated using X-ray diffraction, magnetic susceptibility, and electron paramagnetic resonance techniques. The susceptibility and resonance studies indicate that the Cu²⁺ ions are ordered antiferromagnetically and that the exchange interactions involving the Ln³⁺ ions are relatively weak. The deviations from the Curie-Weiss law in Pr₂CuO₄ and Nd₂CuO₄ are in qualitative agreement with the predicted behavior for isolated Ln³⁺ ions under the influence of a crystal field. Hence these compounds provide interesting examples of planar Cu²⁺-ion antiferromagnets.     

Structural optimal design of systems with imprecise properties: a possibilistic approach

The optimization problem of structural systems with imprecise properties on the basis of a possibilistic approach is considered. System imprecisions are defined by fuzzy numbers and characterized by membership functions. A methodology for the efficient solution of the optimization process is presented. A two-step method is used to include the imprecision within the optimization, where high quality approximations are used for the evaluation of structural responses. The approximations are constructed using concepts of intermediate response quantities and intermediate variables. The approach is basically an algebraic process which can be implemented very efficiently for the optimal design of general structural systems with imprecise parameters. The method provides more information to the designer than is available using conventional design tools. The effectiveness of the methodology and the interpretation of the results are illustrated by the solution of two example problems.     

Daily living activity recognition based on statistical feature quality group selection

The benefits arising from proactive conduct and subject-specialized healthcare have driven e-health and e-monitoring into the forefront of research, in which the recognition of motion, postures and physical exercise is one of the main subjects. We propose here a multidisciplinary method for the recognition of physical activity with the emphasis on feature extraction and selection processes, which are considered to be the most critical stages in identifying the main unknown activity discriminant elements. Efficient feature selection processes are particularly necessary when dealing with huge training datasets in a multidimensional space, where conventional feature selection procedures based on wrapper methods or ‘branch and bound’ are highly expensive in computational terms. We propose an alternative filter method using a feature quality group ranking via a couple of two statistical criteria. Satisfactory results are achieved in both laboratory and semi-naturalistic activity living datasets for real problems using several classification models, thus proving that any body sensor location can be suitable to define a simple one-feature-based recognition system, with particularly remarkable accuracy and applicability in the case of the wrist.     

Global stabilization of linear discrete-time systems with bounded feedback

This paper deals with the problem of global stabilization of linear discrete time systems by means of bounded feedback laws. The main result proved is an analog of one proved for the continuous time case by the authors, and shows that such stabilization is possible if and only if the system is stabilizable with arbitrary controls and the transition matrix has spectral radius less than or equal to one. The proof provides in principle an algorithm for the construction of such feedback laws, which can be implemented either as cascades or as parallel connections (“single hidden layer neural networks”) of simple saturation functions.