Appliances for whom? Considering place

We discuss homes as potential settings for the products of appliance design. We catalog the large international and regional differences. We look at differences in terms of infrastructure: heating, plumbing, electricity, and telephony. We examine differences in the home itself in terms of number of household members, and size of dwelling. We explore the implications of this variation for future ethnographies as well as product creation as we ask the question “appliances for whom?”     

ON THE THEORY OF BAGS

We introduce the bag structure as a set-like object in which repeated elements are significant. We discuss operations on bags such as intersection, union and addition. We introduce the operation of selecting elements from a bag based upon their membership in a set. We show the usefulness of the bag structure in relational data bases. We provide a definition for fuzzy bags. In these fuzzy bags the count of the number of elements itself becomes a crisp bags. We investigate a calculus for fuzzy bags.     

Testing Planning Domains (without Model Checkers)

We address the problem of verifying planning domains as used in model-based planning, for example in space missions. We propose a methodology for testing flight rules of planning domains which is self-contained, in the sense that flight rules are verified using a planner and no external tools are required. We review and analyse coverage conditions for requirements-based testing, and we reason in detail on "Unique First Cause" (UFC) coverage for test suites. We characterise flight rules using patterns, encoded using LTL, and we provide UFC coverage for them. We then present a translation of LTL formulae into planning goals, and illustrate our approach on a case study.     

Localization of multiple faults with group control on a discrete device

We consider the set of control points in a discrete device where testing is organized in groups with a signature analyzer. We solve the localization problem for a fault of any multiplicity located in one or different groups. For this purpose, in addition we construct various partitions of the set of control points where we organize similar group testing. We show an algorithm for constructing additional partitions. We solve the minimization problem for the testing hardware needed to implement it.     

Comparing subspace clusterings

We present the first framework for comparing subspace clusterings. We propose several distance measures for subspace clusterings, including generalizations of well-known distance measures for ordinary clusterings. We describe a set of important properties for any measure for comparing subspace clusterings and give a systematic comparison of our proposed measures in terms of these properties. We validate the usefulness of our subspace clustering distance measures by comparing clusterings produced by the algorithms FastDOC, HARP, PROCLUS, ORCLUS, and SSPC. We show that our distance measures can be also used to compare partial clusterings, overlapping clusterings, and patterns in binary data matrices.     

Planning the Resource of Information Transmission for Connection Lines of Multiservice Hierarchical Access Networks

We construct and investigate a mathematical model for joint servicing of real-time multiservice traffic in access networks with a hierarchical topology. The model takes into account how the arrival of claims in each flow depends on the number of users that form this flow of claims and that are being serviced. We define quality indicators for joint servicing of claims through the values of stationary probabilities in the model. We obtain relations between them that simplify their estimation. We develop an algorithm for calculating the characteristics of the model based on an implementation of the convolution algorithm. We propose a procedure for estimating the transmission rate of connecting lines in the access network needed to service incoming traffic with a given quality. We also give numerical examples that illustrate the characteristic features of implementing the developed computational procedures.     

The implication problem for measure-based constraints

We study the implication problem of measure-based constraints. These constraints are formulated in a framework for measures generalizing that for mathematical measures. Measures arise naturally in a wide variety of domains. We show that measure constraints, for particular measures, correspond to constraints that occur in relational databases, data mining applications, cooperative game theory, and in the Dempster–Shafer and possibility theories of reasoning about uncertainty. We prove that the implication problem for measure constraints is in general decidable. We introduce inference systems for particular classes of measure constraints and show that some of these are complete, yielding tractability for the corresponding implication problem.     

Adaptive controller for a multi-mode object

We consider an adaptive controller for a single-mode object and study the possibility of using it for a multi-mode object. We propose an enhanced adaptive control algorithm for a multi-mode object. We study the influence of the quantization effect in DA and AD converters on the results of adaptive control. We study the influence of test signal amplitudes on identification results and show the findings of experimental studies for an adaptive controller with improved algorithms.     

Eigenspaces of the discrete Walsh transform

We refine the notion of a discrete Walsh function and generalize the notion of a discrete Walsh transform, for which we propose a method for generating a corresponding W-matrix. We propose spectral decompositions of the discrete Walsh transform operators in arbitrary enumerations, as well as methods for finding bases of eigenspaces, one of them using a new direct product of matrices. We propose a notation for the fast discrete Walsh transform algorithm in the Paley enumeration. We construct Parseval frames for eigenspaces of the discrete Walsh transform in the Paley enumeration and demonstrate methods for applying them in error detection and correction.     

On the dempster-shafer framework and new combination rules

We discuss the basic concepts of the Dempster-Shafer approach, basic probability assignments, belief functions, and probability functions. We discuss how to represent various types of knowledge in this framework. We discuss measures of entropy and specificity for belief structures. We discuss the combination and extension of belief structures. We introduce some concerns associated with the Dempster rule of combination inherent in the normalization due to conflict. We introduce two alternative techniques for combining belief structures. The first uses Dempster's rule, while the second is based upon a modification of this rule. We discuss the issue of credibility of a witness.     

Adaptive estimation of variable weights in a linear adder

We consider the adaptive estimation problem for variable weights in a linear adder from known values of a scalar function. We further develop an adaptive identification algorithm for static systems. We obtain a nonlinear adaptive estimation algorithm based on correcting prior values of variable weights. In special cases, we establish conditions for the existence of a solution in the estimation problem. We demonstrate the efficiency of the proposed nonlinear adaptive estimation algorithm for variable weights in a linear adder with an example.     

Providing QOS guarantees for disk I/O

In this paper, we address the problem of providing different levels of performance guarantees or quality of service for disk I/O. We classify disk requests into three categories based on the provided level of service. We propose an integrated scheme that provides different levels of performance guarantees in a single system. We propose and evaluate a mechanism for providing deterministic service for variable-bit-rate streams at the disk. We will show that, through proper admission control and bandwidth allocation, requests in different categories can be ensured of performance guarantees without getting impacted by requests in other categories. We evaluate the impact of scheduling policy decisions on the provided service. We also quantify the improvements in stream throughput possible by using statistical guarantees instead of deterministic guarantees in the context of the proposed approach.     

Quick-VDR: out-of-core view-dependent rendering of gigantic models

We present a novel approach for interactive view-dependent rendering of massive models. Our algorithm combines view-dependent simplification, occlusion culling, and out-of-core rendering. We represent the model as a clustered hierarchy of progressive meshes (CHPM). We use the cluster hierarchy for coarse-grained selective refinement and progressive meshes for fine-grained local refinement. We present an out-of-core algorithm for computation of a CHPM that includes cluster decomposition, hierarchy generation, and simplification. We introduce novel cluster dependencies in the preprocess to generate crack-free, drastic simplifications at runtime. The clusters are used for LOD selection, occlusion culling, and out-of-core rendering. We add a frame of latency to the rendering pipeline to fetch newly visible clusters from the disk and avoid stalls. The CHPM reduces the refinement cost of view-dependent rendering by more than an order of magnitude as compared to a vertex hierarchy. We have implemented our algorithm on a desktop PC. We can render massive CAD, isosurface, and scanned models, consisting of tens or a few hundred million triangles at 15-35 frames per second with little loss in image quality.     

Analysis of integer programming algorithms with L-partition and unimodular transformations

We study algorithms for solving integer linear programming problems, in particular, set packing and knapsack problems. We pay special attention to algorithms of lexicographic enumeration of L-classes and their combinations with other approaches. We study the problems of using unimodular transformations in order to improve the structure of the problems and speed up the algorithms. We construct estimates on the number of iterations for the algorithms that take into account the specific structure of the problems in question. We also show experimental results.     

In praise of algebra

We survey the well-known algebraic laws of sequential programming, and extend them with some less familiar laws for concurrent programming. We give an algebraic definition of the Hoare triple, and algebraic proofs of all the relevant laws for concurrent separation logic. We give the provable concurrency laws for Milner transitions, for the Back/Morgan refinement calculus, and for Dijkstra’s weakest preconditions. We end with a section in praise of algebra, of which Carroll Morgan is such a master.     

A Boussinesq model for pressure and flow velocity waves in arterial segments

We derive a nonlinear model for the pressure and flow velocity wave propagation in an arterial segment. We then study the transmission and reflection of pulses at bifurcation. We observe a linear dependence of the transmitted speeds to the incoming speeds, and similarly for the reflected speeds. We propose a method for validating the numerical results obtained from this model against real data.     

Phrase-based correction model for improving handwriting recognition accuracies

We propose a method for increasing word recognition accuracies by correcting the output of a handwriting recognition system. We treat the handwriting recognizer as a black box, such that there is no access to its internals. This enables us to keep our algorithm general and independent of any particular system. We use a novel method for correcting the output based on a “phrase-based” system in contrast to traditional source-channel models. We report the accuracies of two in-house handwritten word recognizers before and after the correction. We achieve highly encouraging results for a large synthetically generated dataset. We also report results for a commercially available OCR on real data.     

Bilevel stochastic linear programming problems with quantile criterion

We propose a setting for a bilevel stochastic linear programming problem with quantile criterion. We study continuity properties of the criterial function and prove the existence theorem for a solution. We propose a deterministic equivalent of the problem for the case of a scalar random parameter. We show an equivalent problem in the form of a two-stage stochastic programming problem with equilibrium constraints and quantile criterion. For the case of a discrete distribution of random parameters, the problem reduces to a mixed linear programming problem. We show results of numerical experiments.     

Multi-Relational Learning, Text Mining, and Semi-Supervised Learning for Functional Genomics

We focus on the problem of predicting functional properties of the proteins corresponding to genes in the yeast genome. Our goal is to study the effectiveness of approaches that utilize all data sources that are available in this problem setting, including relational data, abstracts of research papers, and unlabeled data. We investigate a propositionalization approach which uses relational gene interaction data. We study the benefit of text classification and information extraction for utilizing a collection of scientific abstracts. We study transduction and co-training for using unlabeled data. We report on both, positive and negative results on the investigated approaches. The studied tasks are KDD Cup tasks of 2001 and 2002. The solutions which we describe achieved the highest score for task 2 in 2001, the fourth rank for task 3 in 2001, the highest score for one of the two subtasks and the third place for the overall task 2 in 2002.     

A framework for the design and implementation of FFT permutationalgorithms

We propose an algebraic framework for the design and implementation of a large class of data-sorting procedures, including all index-digit permutations used in FFTs. We discuss both old and new algorithms in terms of this framework. We show that the algebraic formulation of the new algorithms can be easily encoded using a functional programming language, and that the resulting code introduces no inefficiencies. We present performance results for implementations of three new algorithms for mixed-radix digit-reversal on a Cray C-90 and on a Sun Sparc 5