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Jean Vaillancourt

Publications and source records attributed to Jean Vaillancourt.

12 recordsLinked to original sources
arXiv2026

Central limit theorems for martingales-II: convergence in the weak dual topology, with corrections

A convergence theorem for martingales with càdlàg trajectories (right continuous with left limits everywhere) is obtained in the sense of the weak dual topology on Hilbert space, under conditions that are much weaker than those required for any of the usual Skorohod topologies. Examples are provided to show that these conditions are also very easy to check and yield useful asymptotic results, especially when the limit is a mixture of stochastic processes with discontinuities.

math.PR↗
arXiv2025

CLT for martingales-III: discontinuous compensators

We propose a new weak convergence theorem for martingales, under gentler conditions than the usual convergence in probability of the sequence of associated quadratic variations. Its proof requires the combined use of Skorohod's $\cJ_1$-topology and $\cM_1$-topology on the space of càdlàg trajectories. The emphasis is on those instances where the sequence of martingales or its limit is a mixture of stochastic processes with discontinuities. Alternative conditions are set forth in the special cases of arrays of discrete time martingale differences and martingale transforms. Examples of applications are provided, notably when the limiting process is a Brownian subordinator.

math.PR↗
arXiv2024

Central limit theorems for martingales-I : continuous limits

When the limiting compensator of a sequence of martingales is continuous, we obtain a weak convergence theorem for the martingales; the limiting process can be written as a Brownian motion evaluated at the compensator and we find sufficient conditions for both processes to be independent. Examples of applications are provided, notably for occupation time processes and statistical estimators of financial volatility measures.

math.PR↗
arXiv2021

Detecting Important Patterns Using Conceptual Relevance Interestingness Measure

Discovering meaningful conceptual structures is a substantial task in data mining and knowledge discovery applications. While off-the-shelf interestingness indices defined in Formal Concept Analysis may provide an effective relevance evaluation in several situations, they frequently give inadequate results when faced with massive formal contexts (and concept lattices), and in the presence of irrelevant concepts. In this paper, we introduce the Conceptual Relevance (CR) score, a new scalable interestingness measurement for the identification of actionable concepts. From a conceptual perspective, the minimal generators provide key information about their associated concept intent. Furthermore, the relevant attributes of a concept are those that maintain the satisfaction of its closure condition. Thus, the guiding idea of CR exploits the fact that minimal generators and relevant attributes can be efficiently used to assess concept relevance. As such, the CR index quantifies both the amount of conceptually relevant attributes and the number of the minimal generators per concept intent. Our experiments on synthetic and real-world datasets show the efficiency of this measure over the well-known stability index.

cs.AI↗
arXiv2021

Identifying Influential Nodes in Two-mode Data Networks using Formal Concept Analysis

Identifying important actors (or nodes) in a two-mode network often remains a crucial challenge in mining, analyzing, and interpreting real-world networks. While traditional bipartite centrality indices are often used to recognize key nodes that influence the network information flow, they frequently produce poor results in intricate situations such as massive networks with complex local structures or a lack of complete knowledge about the network topology and certain properties. In this paper, we introduce Bi-face (BF), a new bipartite centrality measurement for identifying important nodes in two-mode networks. Using the powerful mathematical formalism of Formal Concept Analysis, the BF measure exploits the faces of concept intents to identify nodes that have influential bicliques connectivity and are not located in irrelevant bridges. Unlike off-the shelf centrality indices, it quantifies how a node has a cohesive-substructure influence on its neighbour nodes via bicliques while not being in network core-peripheral ones through its absence from non-influential bridges. Our experiments on several real-world and synthetic networks show the efficiency of BF over existing prominent bipartite centrality measures such as betweenness, closeness, eigenvector, and vote-rank among others.

cs.SI↗
arXiv2017

Combining losing games into a winning game

Parrondo's paradox is extended to regime switching random walks in random environments. The paradoxical behavior of the resulting random walk is explained by the effect of the random environment. Full characterization of the asymptotic behavior is achieved in terms of the dimensions of some random subspaces occurring in Oseledec's theorem. The regime switching mechanism gives our models a richer and more complex asymptotic behavior than the simple random walks in random environments appearing in the literature, in terms of transience and recurrence.

math.PR↗
arXiv2013

On signed measure valued solutions of stochastic evolution equations

We study existence, uniqueness and mass conservation of signed measure valued solutions of a class of stochastic evolution equations with respect to the Wiener sheet, including as particular cases the stochastic versions of the regularized two-dimensional Navier-Stokes equations in vorticity form introduced by Kotelenez.

math.PR↗
arXiv2009

Mining Generalized Patterns from Large Databases using Ontologies

Formal Concept Analysis (FCA) is a mathematical theory based on the formalization of the notions of concept and concept hierarchies. It has been successfully applied to several Computer Science fields such as data mining,software engineering, and knowledge engineering, and in many domains like medicine, psychology, linguistics and ecology. For instance, it has been exploited for the design, mapping and refinement of ontologies. In this paper, we show how FCA can benefit from a given domain ontology by analyzing the impact of a taxonomy (on objects and/or attributes) on the resulting concept lattice. We willmainly concentrate on the usage of a taxonomy to extract generalized patterns (i.e., knowledge generated from data when elements of a given domain ontology are used) in the form of concepts and rules, and improve navigation through these patterns. To that end, we analyze three generalization cases and show their impact on the size of the generalized pattern set. Different scenarios of simultaneous generalizations on both objects and attributes are also discussed

cs.AI↗
arXiv2009

Algebraic operators for querying pattern bases

The objectives of this research work which is intimately related to pattern discovery and management are threefold: (i) handle the problem of pattern manipulation by defining operations on patterns, (ii) study the problem of enriching and updating a pattern set (e.g., concepts, rules) when changes occur in the user's needs and the input data (e.g., object/attribute insertion or elimination, taxonomy utilization), and (iii) approximate a "presumed" concept using a related pattern space so that patterns can augment data with knowledge. To conduct our work, we use formal concept analysis (FCA) as a framework for pattern discovery and management and we take a joint database-FCA perspective by defining operators similar in spirit to relational algebra operators, investigating approximation in concept lattices and exploiting existing work related to operations on contexts and lattices to formalize such operators.

cs.DB↗
arXiv2008

Reversibility of Interacting Fleming-Viot Processes with Mutation, Selection, and Recombination

Reversibility of the Fleming-Viot process with mutation, selection, and recombination is well understood. In this paper, we study the reversibility of a system of Fleming-Viot processes that live on a countable number of colonies interacting with each other through migrations between the colonies. It is shown that reversibility fails when both migration and mutation are non-trivial.

math.PR↗