3576
Collapsibility and Consistency in Quantified Constraint Satisfaction
The concept of consistency has pervaded studies of the constraint satisfiction problem. We introduce two concepts, which are inspired by consistency, for the more general framework of the quantified constraint satisfaction problem (QCSP). We use these concepts to derive, in a uniform fashion, proofs of polynomial-time tractability and corresponding algorithms for certain cases of the QCSP where the types of allowed relations are restricted. We not only unify existing tractability results and algorithms, but also identify new classes of tractable QCSPs.
39653431
A Coalgebraic Approach to Kleene Algebra with Tests
Abstract Kleene Algebra with Tests is an extension of Kleene Algebra, the algebra of regular expressions, which can be used to reason about programs. We develop a coalgebraic theory of Kleene Algebra with Tests, along the lines of the coalgebraic theory of regular expressions based on deterministic automata. Since the known automata-theoretic presentation of Kleene Algebra with Tests does not lend itself to a coalgebraic theory, we define a new interpretation of Kleene Algebra with Tests expressions and a corresponding automata-theoretic presentation. One outcome of the theory is a coinductive proof principle, that can be used to establish equivalence of our Kleene Algebra with Tests expressions.
3121075
Parameterized Compilability
Compilability is a measure of how effectively compilation (or preprocessing) can be applied to knowledge bases specified in a particular knowledge representation formalism; the aim of compilation is to allow for efficient, on-line query processing. A theory of compilability has been established for organizing knowledge representation formalisms according to a scheme of "compilability classes", and bears strong analogies to the classical theory of complexity, which permits the organization of computational problems according to complexity classes. We develop a novel theory of compilability, called parameterized compilability, which incorporates the notion of parameterization as used in parameterized complexity and permits for refined analysis of compilability.
27175112
Learnability of Relatively Quantified Generalized Formulas
In this paper we study the learning complexity of a vast class of quantifed formulas called Relatively Quantified Generalized Formulas. This class of formulas is parameterized by a set of predicates, called a basis. We give a complete classification theorem, showing that every basis gives rise to quantified formulas that are either polynomially learnable with equivalence queries, or not polynomially predictable with membership queries under some cryptographic assumption. We also provide a simple criteria distinguishing the learnable cases from the non-learnable cases.
9351295
Looking Algebraically at Tractable Quantified Boolean Formulas
We make use of the algebraic theory that has been used to study the complexity of constraint satisfaction problems, to investigate tractable quantified boolean formulas. We present a pair of results: the first is a new and simple algebraic proof of the tractability of quantified 2-satisfiability; the second is a purely algebraic characterization of models for quantified Horn formulas that were given by Kleine Buning, Subramani, and Zhao, and described proof-theoretically.
958592
Inverse Circumscription
Inverse (or identification) problems involve deciding whether or not an explicitly given set of data points have an implicit description, for instance, in the form of a constraint network. Such problems provide insight into the relationships among various representations of knowledge, which may have differing computational properties. This paper formalizes and studies the inverse circumscription problem, which (roughly speaking) is to decide, given a set of models, if there exists a formula whose circumscription describes the input set.
16294852
Interactive volume visualization on a heterogeneous message-passing multicomputer
This paper describes VOL2, an interactive general-purpose volume renderer based on ray casting and implemented on Pixel-Planes 5, a distributed-memory, message-passing multicomputer. VOL2 is a pipelined renderer using image-space task parallelism and object-space data partitioning. We describe the parallelization and load balancing techniques used in order to achieve interactive response and near-real-time frame rates. We also present a number of applications for our system and derive some general conclusions about operation of image-order rendering algorithms on message-passing multicomputers.
12630328
Creating a large-scale content-based airphoto image digital library
This paper describes a content-based image retrieval digital library that supports geographical image retrieval over a testbed of 800 aerial photographs, each 25 megabytes in size. In addition, this paper also introduces a methodology to evaluate the performance of the algorithms in the prototype system. There are two major contributions: we suggest an approach that incorporates various image processing techniques including Gabor filters, image enhancement and image compression, as well as information analysis techniques such as the self-organizing map (SOM) into an effective large-scale geographical image retrieval system. We present two experiments that evaluate the performance of the Gabor-filter-extracted features along with the corresponding similarity measure against that of human perception, addressing the lack of studies in assessing the consistency between an image representation algorithm or an image categorization method and human mental model.
8049932
On the variable capacity property of CC/DS-CDMA systems
A complete complementary code based direct sequence code division multiple access (CC/DS-CDMA) system has been proposed recently as a potential candidate for beyond third generation (B3G) wireless communications. This paper addresses the issues that design of efficient code assignment schemes should be based on a flexible physical layer support, which is extremely important for emerging cross-layer designs in future wireless applications. The study in this paper considers a CC/DS-CDMA system with multiple time slots, three traffic classes and two dynamic code-flock assignment schemes, namely random assignment (RA) and compact assignment (CA). Simulation results show that the CC/DS-CDMA system has variable capacity property (VCP), which is sensitively affected by different code-flock assignment schemes. In general, CA can offer lower blocking probability, whereas RA can offer a larger mean system capacity and higher throughput when offered traffic is heavy
12061239
Constraint generation for separation of duty
Separation of Duty (SoD) is widely recognized to be a fundamental principle in computer security. A Static SoD (SSoD) policy states that in order to have all permissions necessary to complete a sensitive task, the cooperation of at least a certain number of users is required. In Role-Based Access Control (RBAC), Statically Mutually Exclusive Role (SMER) constraints are used to enforce SSoD policies. This paper studies the problem of generating sets of constraints that (a) enforce a set of SSoD policies, (b) are compatible with the existing role hierarchy, and (c) are minimal in the sense that there is no other constraint set that is less restrictive and satisfies (a) and (b).
2128704
Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes
BackgroundDue to the high cost and low reproducibility of many microarray experiments, it is not surprising to find a limited number of patient samples in each study, and very few common identified marker genes among different studies involving patients with the same disease. Therefore, it is of great interest and challenge to merge data sets from multiple studies to increase the sample size, which may in turn increase the power of statistical inferences. In this study, we combined two lung cancer studies using micorarray GeneChip®, employed two gene shaving methods and a two-step survival test to identify genes with expression patterns that can distinguish diseased from normal samples, and to indicate patient survival, respectively.ResultsIn addition to common data transformation and normalization procedures, we applied a distribution transformation method to integrate the two data sets. Gene shaving (GS) methods based on Random Forests (RF) and Fisher's Linear Discrimination (FLD) were then applied separately to the joint data set for cancer gene selection. The two methods discovered 13 and 10 marker genes (5 in common), respectively, with expression patterns differentiating diseased from normal samples. Among these marker genes, 8 and 7 were found to be cancer-related in other published reports. Furthermore, based on these marker genes, the classifiers we built from one data set predicted the other data set with more than 98% accuracy. Using the univariate Cox proportional hazard regression model, the expression patterns of 36 genes were found to be significantly correlated with patient survival (p < 0.05). Twenty-six of these 36 genes were reported as survival-related genes from the literature, including 7 known tumor-suppressor genes and 9 oncogenes. Additional principal component regression analysis further reduced the gene list from 36 to 16.ConclusionThis study provided a valuable method of integrating microarray data sets with different origins, and new methods of selecting a minimum number of marker genes to aid in cancer diagnosis. After careful data integration, the classification method developed from one data set can be applied to the other with high prediction accuracy.