How regular can maxitive measures be?

We examine domain-valued maxitive measures defined on the Borel subsets of a topological space. Several characterizations of regularity of maxitive measures are proved, depending on the structure of the topological space. Since every regular maxitive measure is completely maxitive, this yields sufficient conditions for the existence of a cardinal density. We also show that every outer-continuous maxitive measure can be decomposed as the supremum of a regular maxitive measure and a maxitive measure that vanishes on compact subsets under appropriate conditions.Comment: 24 page

Experimental Evaluation of Interchangeability in Soft CSPs

In [8], Freuder defined interchangeability for classical Constraint Satisfaction Problems (CSPs). Recently [2], we extended the definition of interchangeability to Soft CSPs and we introduced two notions of relaxation based on degradation # and on threshold # ( neighborhood interchangeability ( NI )and # neighborhood interchangeability (#NI )). In this pae

Modulus Computational Entropy

The so-called {\em leakage-chain rule} is a very important tool used in many security proofs. It gives an upper bound on the entropy loss of a random variable $X$ in case the adversary who having already learned some random variables $Z_{1},\ldots,Z_{\ell}$ correlated with $X$, obtains some further information $Z_{\ell+1}$ about $X$. Analogously to the information-theoretic case, one might expect that also for the \emph{computational} variants of entropy the loss depends only on the actual leakage, i.e. on $Z_{\ell+1}$. Surprisingly, Krenn et al.\ have shown recently that for the most commonly used definitions of computational entropy this holds only if the computational quality of the entropy deteriorates exponentially in $|(Z_{1},\ldots,Z_{\ell})|$. This means that the current standard definitions of computational entropy do not allow to fully capture leakage that occurred "in the past", which severely limits the applicability of this notion. As a remedy for this problem we propose a slightly stronger definition of the computational entropy, which we call the \emph{modulus computational entropy}, and use it as a technical tool that allows us to prove a desired chain rule that depends only on the actual leakage and not on its history. Moreover, we show that the modulus computational entropy unifies other,sometimes seemingly unrelated, notions already studied in the literature in the context of information leakage and chain rules. Our results indicate that the modulus entropy is, up to now, the weakest restriction that guarantees that the chain rule for the computational entropy works. As an example of application we demonstrate a few interesting cases where our restricted definition is fulfilled and the chain rule holds.Comment: Accepted at ICTS 201

The Tightness of the Kesten-Stigum Reconstruction Bound of Symmetric Model with Multiple Mutations

It is well known that reconstruction problems, as the interdisciplinary subject, have been studied in numerous contexts including statistical physics, information theory and computational biology, to name a few. We consider a $2q$-state symmetric model, with two categories of $q$ states in each category, and 3 transition probabilities: the probability to remain in the same state, the probability to change states but remain in the same category, and the probability to change categories. We construct a nonlinear second order dynamical system based on this model and show that the Kesten-Stigum reconstruction bound is not tight when $q \geq 4$.Comment: Accepted, to appear Journal of Statistical Physic

On the Computation of Local Interchangeability in Soft Constraint Satisfaction Problems

Freuder in (1991) de?ned interchangeability for classical Constraint Satisfaction Problems (CSPs). Recently (2002), we extended the de?nition of interchangeability to Soft CSPs and we introduced two notions of relaxations based on degradation ? and on threshold ? (?neighborhood interchangeability (?NI )and ?neighborhood interchangeability ?NI ). In this paper we study the presence of these relaxed version of interchangeability in random soft CSPs. We give a description of the implementation we used to compute interchangeabilities and to make the tests. The experiments show that there is high occurrence of ?NI and ?NI interchangeability around optimal solution in Fuzzy CSP and weighted CSPs. Thus, these algorithms can be used succesfully in solution update applications. Moreover, it is also showed that NI interchangeability can well approximate full interchangeability (FI )

Predictive performance of front-loaded experimentation strategies in pharmaceutical discovery: a Bayesian perspective

Experimentation is a significant innovation process activity and its design is fundamental to the learning and knowledge build-up process. Front-loaded experimentation is known as a strategy seeking to improve innovation process performance; by exploiting early information to spot and solve problems as upstream as possible, costly overruns in subsequent product development are avoided. Although the value of search through front-loaded experimentation in complex and novel environments is recognized, the phenomenon has not been studied in the highly relevant pharmaceutical R&D context, where typically lots of drug candidates get killed very late in the innovation process when potential problems are insufficiently anticipated upfront. In pharmaceutical research the initial problem is to discover a “drug-like” complex biological or chemical system that has the potential to affect a biological target on a disease pathway. My case study evidence found that the discovery process is managed through a front-loaded experimentation strategy. The research team gradually builds a mental model of the drug’s action in which the solution of critical design problems can be initiated at various moments in the innovation process. The purpose of this research was to evaluate the predictive performance of frontloaded experimentation strategies in the discovery process. Because predictive performance necessitates conditional probability thinking, a Bayesian methodology is proposed and a rationale is given to develop research propositions using Monte Carlo simulation. An adaptive system paradigm, then, is the basis for designing the simulation model used for top-down theory development. My simulation results indicate that front-loaded strategies in a pharmaceutical discovery context outperform other strategies on positive predictive performance. Frontloaded strategies therefore increase the odds for compounds succeeding subsequent development testing, provided they were found positive in discovery. Also, increasing the number of parallel concept explorations in discovery influences significantly the negative predictive performance of experimentation strategies, reducing the probability of missed opportunities in development. These results are shown to be robust for varying degrees of predictability of the discovery process. The counterintuitive business implication of my research findings is that the key to further reduce spend and overruns in pharmaceutical development is to be found in discovery, where efforts to better understand drug candidates lead to higher success rates later in the innovation process

A Partial Taxonomy of Substitutability and Interchangeability

Substitutability, interchangeability and related concepts in Constraint Programming were introduced approximately twenty years ago and have given rise to considerable subsequent research. We survey this work, classify, and relate the different concepts, and indicate directions for future work, in particular with respect to making connections with research into symmetry breaking. This paper is a condensed version of a larger work in progress.Comment: 18 pages, The 10th International Workshop on Symmetry in Constraint Satisfaction Problems (SymCon'10

Interchangeability with thresholds and degradation factors for Soft CSPs

Substitutability and interchangeability in constraint satisfaction problems (CSPs) have been used as a basis for search heuristics, solution adaptation and abstraction techniques. In this paper, we consider how the same concepts can be extended to soft constraint satisfaction problems (SCSPs). We introduce two notions: threshold alpha and degradation factor delta for substitutability and interchangeability, ( (alpha) substitutability/interchangeability and (delta) substitutability/interchangeabi-lity respectively). We show that they satisfy analogous theorems to the ones already known for hard constraints. In (alpha) interchangeability, values are interchangeable in any solution that is better than a threshold alpha, thus allowing to disregard differences among solutions that are not sufficiently good anyway. In (delta) interchangeability, values are interchangeable if their exchange could not degrade the solution by more than a factor of delta. We give efficient algorithms to compute ( (delta) / (alpha) )interchangeable sets of values for a large class of SCSPs, and show an example of their application. Through experimental evaluation based on random generated problem we measure first, how often neighborhood interchangeable values are occurring, second, how well they can approximate fully interchangeable ones, and third, how efficient they are when used as preprocessing techniques for branch and bound search

Compactness in Spaces of Inner Regular Measures and a General Portmanteau Lemma

This paper may be understood as a continuation of Topsøe’s seminal paper ([16]) to characterize, within an abstract setting, compact subsets of finite inner regular measures w.r.t. the weak topology. The new aspect is that neither assumptions on compactness of the inner approximating lattices nor nonsequential continuity properties for the measures will be imposed. As a providing step also a generalization of the classical Portmanteau lemma will be established. The obtained characterizations of compact subsets w.r.t. the weak topology encompass several known ones from literature. The investigations rely basically on the inner extension theory for measures which has been systemized recently by König ([8], [10],[12]).Inner Premeasures, Weak Topology, Generalized Portmanteau Lemma.

Finding robust solutions for constraint satisfaction problems with discrete and ordered domains by coverings

Constraint programming is a paradigm wherein relations between variables are stated in the form of constraints. Many real life problems come from uncertain and dynamic environments, where the initial constraints and domains may change during its execution. Thus, the solution found for the problem may become invalid. The search forrobustsolutions for constraint satisfaction problems (CSPs) has become an important issue in the ¿eld of constraint programming. In some cases, there exists knowledge about the uncertain and dynamic environment. In other cases, this information is unknown or hard to obtain. In this paper, we consider CSPs with discrete and ordered domains where changes only involve restrictions or expansions of domains or constraints. To this end, we model CSPs as weighted CSPs (WCSPs) by assigning weights to each valid tuple of the problem constraints and domains. The weight of each valid tuple is based on its distance from the borders of the space of valid tuples in the corresponding constraint/domain. This distance is estimated by a new concept introduced in this paper: coverings. Thus, the best solution for the modeled WCSP can be considered as a most robust solution for the original CSP according to these assumptionsThis work has been partially supported by the research projects TIN2010-20976-C02-01 (Min. de Ciencia e Innovacion, Spain) and P19/08 (Min. de Fomento, Spain-FEDER), and the fellowship program FPU.Climent Aunés, LI.; Wallace, RJ.; Salido Gregorio, MA.; Barber Sanchís, F. (2013). Finding robust solutions for constraint satisfaction problems with discrete and ordered domains by coverings. Artificial Intelligence Review. 1-26. https://doi.org/10.1007/s10462-013-9420-0S126Climent L, Salido M, Barber F (2011) Reformulating dynamic linear constraint satisfaction problems as weighted csps for searching robust solutions. 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