Implicit Leadership Theories in Applied Settings: Factor Structure, Generalizability, and Stability Over Time.

The present empirical investigation had a 3-fold purpose: (a) to cross-validate L. R. Offermann, J. K. Kennedy, and P. W. Wirtz's (1994) scale of Implicit Leadership Theories (ILTs) in several organizational settings and to further provide a shorter scale of ILTs in organizations; (b) to assess the generalizability of ILTs across different employee groups, and (c) to evaluate ILTs' change over time. Two independent samples were used for the scale validation (N1 = 500 and N2 = 439). A 6-factor structure (Sensitivity, Intelligence, Dedication, Dynamism, Tyranny, and Masculinity) was found to most accurately represent ELTs in organizational settings. Regarding the generalizability of ILTs, although the 6-factor structure was consistent across different employee groups, there was only partial support for total factorial invariance. Finally, evaluation of gamma, beta, and alpha change provided support for ILTs' stability over time. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Branching-time logics with path relativisation

We define extensions of the full branching-time temporal logic CTL^? in which the path quantifiers are relativised by formal languages of infinite words, and consider its natural fragments obtained by extending the logics CTL and CTL⁺ in the same way. This yields a small and two-dimensional hierarchy of temporal logics parametrised by the class of languages used for the path restriction on one hand, and the use of temporal operators on the other. We motivate the study of such logics through two application scenarios: in abstraction and refinement they offer more precise means for the exclusion of spurious traces; and they may be useful in software synthesis where decidable logics without the finite model property are required. We study the relative expressive power of these logics as well as the complexities of their satisfiability and model-checking problems.     

Adding cardinality constraints to integer programs with applications to maximum satisfiability

Max-SAT-CC is the following optimization problem: Given a formula in CNF and a bound k, find an assignment with at most k variables being set to true that maximizes the number of satisfied clauses among all such assignments. If each clause is restricted to have at most ? literals, we obtain the problem Max-?SAT-CC. Sviridenko [Algorithmica 30 (3) (2001) 398-405] designed a (1−e⁻¹)-approximation algorithm for Max-SAT-CC. This result is tight unless P=NP [U. Feige, J. ACM 45 (4) (1998) 634-652]. Sviridenko asked if it is possible to achieve a better approximation ratio in the case of Max-?SAT-CC. We answer this question in the affirmative by presenting a randomized approximation algorithm whose approximation ratio is . To do this, we develop a general technique for adding a cardinality constraint to certain integer programs. Our algorithm can be derandomized using pairwise independent random variables with small probability space.     

Matrix Interpretations for Proving Termination of Term Rewriting

We present a new method for automatically proving termination of term rewriting. It is based on the well-known idea of interpretation of terms where every rewrite step causes a decrease, but instead of the usual natural numbers we use vectors of natural numbers, ordered by a particular nontotal well-founded ordering. Function symbols are interpreted by linear mappings represented by matrices. This method allows us to prove termination and relative termination. A modification of the latter, in which strict steps are only allowed at the top, turns out to be helpful in combination with the dependency pair transformation. By bounding the dimension and the matrix coefficients, the search problem becomes finite. Our implementation transforms it to a Boolean satisfiability problem (SAT), to be solved by a state-of-the-art SAT solver.     

Combination of convex theories: Modularity,deduction completeness,and explanation

Decision procedures are key components of theorem provers and constraint satisfaction systems. Their modular combination is of prime interest for building efficient systems, but their effective use is often limited by poor interface capabilities, when such procedures only provide a simple “sat/unsat” answer. In this paper, we develop a framework to design cooperation schemas between such procedures while maintaining modularity of their interfaces. First, we use the framework to specify and prove the correctness of classic combination schemas by Nelson–Oppen and Shostak. Second, we introduce the concept of deduction complete satisfiability procedures, we show how to build them for large classes of theories, then we provide a schema to modularly combine them. Third, we consider the problem of modularly constructing explanations for combinations by re-using available proof-producing procedures for the component theories.     

命题逻辑公式中的冗余子句及冗余文字

主要研究命题逻辑公式中的冗余子句和冗余文字。针对子句集中必需的、有用的、无用的子句,分别给出了一些等价描述方法,进而讨论子句集的无冗余等价子集。另外,得到了子句集中冗余文字的判别方法,借助可满足性给出了冗余子句的一种等价条件。上述结果为命题逻辑公式的化简奠定了一些理论基础。     

Reconstructing (h,v)-convex 2-dimensional patterns of objects from approximate horizontal and vertical projections

The problem of reconstructing a pattern of an object from its approximate discrete orthogonal projections in a 2-dimensional grid, may have no solution because the inaccuracy in the measurements of the projections may generate an inconsistent problem. To attempt to overcome this difficulty, one seeks to reconstruct a pattern with projection values having possibly some bounded differences with the given projection values and minimizing the sum of the absolute differences.

This paper addresses the problem of reconstructing a pattern with a difference at most equal to +1 or −1 between each of its projection values and the corresponding given projection value. We deal with the case of patterns which have to be horizontally and vertically convex and the case of patterns which have to be moreover connected, the so-called convex polyominoes. We show that in both cases, the problem of reconstructing a pattern can be transformed into a Satisfiability (SAT) Problem. This is done in order to take advantage of the recent advances in the design of solvers for the SAT Problem. We show, experimentally, that by adding two important features to CSAT (an efficient SAT solver), optimal patterns can be found if there exist feasible ones. These two features are: first, a method that extracts in linear time an optimal pattern from a set of feasible patterns grouped in a generic pattern (obtaining a generic pattern may be exponential in the worst case) and second, a method that computes actively a lower bound of the sum of absolute differences that can be obtained from a partially defined pattern. This allows to prune the search tree if this lower bound exceeds the best sum of absolute differences found so far.     

Algorithms for four variants of the exact satisfiability problem

We present four polynomial space and exponential time algorithms for variants of the E S problem. First, an O(1.1120ⁿ) (where n is the number of variables) time algorithm for the NP-complete decision problem of E 3-S , and then an O(1.1907ⁿ) time algorithm for the general decision problem of E S . The best previous algorithms run in O(1.1193ⁿ) and O(1.2299ⁿ) time, respectively. For the #P-complete problem of counting the number of models for E 3-S we present an O(1.1487ⁿ) time algorithm. We also present an O(1.2190ⁿ) time algorithm for the general problem of counting the number of models for E S ; presenting a simple reduction, we show how this algorithm can be used for computing the permanent of a 0/1 matrix.     

Semantic email: theory and applications

This paper investigates how the vision of the Semantic Web can be carried over to the realm of email. We introduce a general notion of semantic email, in which an email message consists of a structured query or update coupled with corresponding explanatory text. Semantic email opens the door to a wide range of automated, email-mediated applications with formally guaranteed properties. In particular, this paper introduces a broad class of semantic email processes. For example, consider the process of sending an email to a program committee, asking who will attend the PC dinner, automatically collecting the responses, and tallying them up. We define both logical and decision-theoretic models where an email process is modeled as a set of updates to a data set on which we specify goals via certain constraints or utilities. We then describe a set of inference problems that arise while trying to satisfy these goals and analyze their computational tractability. In particular, we show that for the logical model it is possible to automatically infer which email responses are acceptable w.r.t. a set of constraints in polynomial time, and for the decision-theoretic model it is possible to compute the optimal message-handling policy in polynomial time. In addition, we show how to automatically generate explanations for a process's actions, and identify cases where such explanations can be generated in polynomial time. Finally, we discuss our publicly available implementation of semantic email and outline research challenges in this realm.¹     

A cluster-DEE-based strategy to empower protein design

The Medical and Pharmaceutical industries have shown high interest in the precise engineering of protein hormones and enzymes that perform existing functions under a wide range of conditions. Proteins are responsible for the execution of different functions in the cell: catalysis in chemical reactions, transport and storage, regulation and recognition control. Computational Protein Design (CPD) investigates the relationship between 3-D structures of proteins and amino acid sequences and looks for all sequences that will fold into such 3-D structure. Many computational methods and algorithms have been proposed over the last years, but the problem still remains a challenge for Mathematicians, Computer Scientists, Bioinformaticians and Structural Biologists. In this article we present a new method for the protein design problem. Clustering techniques and a Dead-End-Elimination algorithm are combined with a SAT problem representation of the CPD problem in order to design the amino acid sequences. The obtained results illustrate the accuracy of the proposed method, suggesting that integrated Artificial Intelligence techniques are useful tools to solve such an intricate problem.