Learning parities in the mistake-bound model

We study the problem of learning parity functions that depend on at most k variables (k-parities) attribute-efficiently in the mistake-bound model. We design a simple, deterministic, polynomial-time algorithm for learning k-parities with mistake bound . This is the first polynomial-time algorithm to learn ω(1)-parities in the mistake-bound model with mistake bound o(n).Using the standard conversion techniques from the mistake-bound model to the PAC model, our algorithm can also be used for learning k-parities in the PAC model. In particular, this implies a slight improvement over the results of Klivans and Servedio (2004) [1] for learning k-parities in the PAC model.We also show that the time algorithm from Klivans and Servedio (2004) [1] that PAC-learns k-parities with sample complexity can be extended to the mistake-bound model.     

Algorithms to Separate $\{0,\frac{1}{2}\}$ -Chvátal-Gomory Cuts

Chvátal-Gomory cuts are among the most well-known classes of cutting planes for general integer linear programs (ILPs). In case the constraint multipliers are either 0 or , such cuts are known as -cuts. It has been proven by Caprara and Fischetti (Math. Program. 74:221–235, 1996) that separation of -cuts is -hard. In this paper, we study ways to separate -cuts effectively in practice. We propose a range of preprocessing rules to reduce the size of the separation problem. The core of the preprocessing builds a Gaussian elimination-like procedure. To separate the most violated -cut, we formulate the (reduced) problem as integer linear program. Some simple heuristic separation routines complete the algorithmic framework. Computational experiments on benchmark instances show that the combination of preprocessing with exact and/or heuristic separation is a very vital idea to generate strong generic cutting planes for integer linear programs and to reduce the overall computation times of state-of-the-art ILP-solvers.     

Visual simulation of thermal fluid dynamics in a pressurized water reactor

We present a simulation and visualization system for a critical application—analysis of the thermal fluid dynamics inside a pressurized water reactor of a nuclear power plant when cold water is injected into the reactor vessel. We employ a hybrid thermal lattice Boltzmann method (HTLBM), which has the advantages of ease of parallelization and ease of handling complex simulation boundaries. For efficient computation and storage of the irregular-shaped simulation domain, we classify the domain into nonempty and empty cells and apply a novel packing technique to organize the nonempty cells. This method is implemented on a GPU cluster for acceleration. We demonstrate the formation of cold-water plumes in the reactor vessel. A set of interactive visualization tools, such as side-view slices, 3D volume rendering, thermal layers rendering, and panorama rendering, are provided to collectively visualize the structure and dynamics of the temperature field in the vessel. To the best of our knowledge, this is the first system that combines 3D simulation and visualization for analyzing thermal shock risk in a pressurized water reactor.     

Fuzzy geographically weighted clustering using artificial bee colony: An efficient geo-demographic analysis algorithm and applications to the analysis of crime behavior in population

Geo-demographic analysis is an essential part of a geographical information system (GIS) for predicting people’s behavior based on statistical models and their residential location. Fuzzy Geographically Weighted Clustering (FGWC) serves as one of the most efficient algorithms in geo-demographic analysis. Despite being an effective algorithm, FGWC is sensitive to initialize when the random selection of cluster centers makes the iterative process falling into the local optimal solution easily. Artificial Bee Colony (ABC), one of the most popular meta-heuristic algorithms, can be regarded as the tool to achieve global optimization solutions. This research aims to propose a novel geo-demographic analysis algorithm that integrates FGWC to the optimization scheme of ABC for improving geo-demographic clustering accuracy. Experimental results on various datasets show that the clustering quality of the proposed algorithm called FGWC-ABC is better than those of other relevant methods. The proposed algorithm is also applied to a decision-making application for analyzing crime behavior problem in the population using the US communities and crime dataset. It provides fuzzy rules to determine the violent crime rate in terms of linguistic labels from socioeconomic variables. These results are significant to make predictions of further US violent crime rate and to facilitate appropriate decisions on prevention such the situations in the future.     

B2B Procurement and Marketplace Transformation

Emerging web technologies and systems have started a revolutionary path that will have a profound impact on procurement processes and systems, especially in the area of indirect procurement and the management of operating resources. In fact, what started as more narrowly focused procurement systems only a couple of years ago, has transformed into a web of technologies, markets, and business strategies that have ramifications well beyond the purchasing function. The focus of this paper is on the marketplace structure, functionality and capabilities of systems, and key factors that buyers need to take into consideration when transforming their procurement processes. Contents management, markets, standards, and outsourcing are important problems that are discussed in this paper.     

Data structures for symbolic multi-valued model-checking

Multi-valued logics provide an interesting alternative to classical boolean logic for modeling and reasoning about systems. Such logics can be used for reasoning about partially-specified systems, effectively encode vacuity detection and query-checking problems, help in detecting inconsistencies, and many others.In our earlier work, we identified a useful family of multi-valued logics: those specified over finite distributive lattices where negation preserves involution, i.e., for every element a of the logic. Such structures are called quasi-boolean algebras, and model-checking over these not only extends the domain of applicability of automated reasoning to new problems, but can also speed up solutions to some classical verification problems.Symbolic model-checking over quasi-boolean algebras can be cast in terms of operations over multi-valued sets: sets whose membership functions are multi-valued. In this paper, we propose and empirically evaluate several choices for implementing multi-valued sets with decision diagrams. In particular, we describe two major approaches: (1) representing the multi-valued membership function canonically, using MDDs or ADDs; (2) representing multi-valued sets as a collection of classical sets, using a vector of either MBTDDs or BDDs. The naive implementation of (2) includes having a classical set for each value of the algebra. We exploit a result of lattice theory to reduce the number of such sets that need to be represented.The major contribution of this paper is the evaluation of the different implementations of multi-valued sets, done via a series of experiments and using several case studies.

Pure mode versus mixed mode interfacing between analog circuits

The concepts of transmission error and mismatch factor ^–1 are introduced to evaluate the effect of impedance mismatch on the accuracy of broadband signal transmission between two feedback amplifiers. It is shown that, in comparison with the error introduced by the feedback amplifiers, becomes negligible for pure V.M. (voltage mode) or C.M. (current mode) signal transmission; in the mixed mode, in which a V.M. output circuit feeds a C.M. input circuit, or vice versa, may become significant. Computer simulations show that the pure mode also yields reduced T.H.D., improved bandwidth and improved transient response. It is also shown that a particular combination between the kind of feedback and the active circuit to which it is applied, termed enhancing combination, further increases the accuracy of signal transmission.1. The notation (OL) relates to the open-loop value of the parameter involved.2. It can be shown that rule 2 does not apply to a low-noise preamplifier.     

Flow Events in the Negev—A Regional Quantitative Model

ABSTRACT

The provision of piped water has often been considered a major sanitation measure which will improve the general health of a given population. The associated problems of maintaining a functioning sewerage system so as to insure the quality of the piped water have received increasing recognition. From November 1978 to December 1979, and under varying meteorological conditions, water samples were drawn from ten taps of a piped water supply in an urban slum of Madras, Tamil Nadu, South India. Samples were tested for fecal coliform organisms as an indicator of fecal contamination of water at the taps. Regression analyses were performed on the data, using distance from the tap to the slum's boundary as the independent variable (x) and regressing average log concentration of fecal coliforms (y) during periods of dry and wetter weather, on x. It was found that during wet conditions there was a regression coefficient of +0.972 whereas during dry conditions the regression coefficient was +0.816. The linear regression model suggests that during wet conditions, especially, there is on-site contamination of piped water in the high population density conditions of this urban slum.