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BRANE Clust: Cluster-Assisted Gene Regulatory Network Inference Refinement
No full textInternational audienceDiscovering meaningful gene interactions is crucial for the identification of novel regulatory processes in cells. Building accurately the related graphs remains challenging due to the large number of possible solutions from available data. Nonetheless, enforcing a priori on the graph structure, such as modularity, may reduce network indeterminacy issues. BRANE Clust (Biologically-Related A priori Network Enhancement with Clustering) refines gene regulatory network (GRN) inference thanks to cluster information. It works as a post-processing tool for inference methods (i.e. CLR, GENIE3). In BRANE Clust, the clustering is based on the inversion of a linear system of equations involving a graph-Laplacian matrix promoting a modular structure. Our approach is validated on DREAM4 and DREAM5 datasets with objective measures, showing significant comparative improvements. We provide additional insights on the discovery of novel regulatory or co-expressed links in the inferred Escherichia coli network evaluated using the STRING database. The comparative pertinence of clustering is discussed computationally (SIMONE, WGCNA, X-means) and biologically (RegulonDB). BRANE Clust software is available at http://www-syscom.univ-mlv.fr/~pirayre/Codes-GRN-BRANE-clust.htm
Optimal scheduling and power allocation in wireless networks with heavy traffic: the infinite time horizon case
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Sparsity-Based Estimation Bounds With Corrupted Measurements
No full textInternational audienceIn typical Compressed Sensing operational contexts, the measurement vector y is often partially corrupted. The estimation of a sparse vector acting on the entire support set exhibits very poor estimation performance. It is crucial to estimate set I uc containing the indexes of the uncorrupted measures. As I uc and its cardinality |I uc | < N are unknown, each sample of vector y follows an i.i.d. Bernoulli prior of probability P uc , leading to a Binomial-distributed car-dinality. In this context, we derive and analyze the performance lower bound on the Bayesian Mean Square Error (BMSE) on a |S|-sparse vector where each random entry is the product of a continuous variable and a Bernoulli variable of probability P and |S| |Iuc| follows a hierarchical Binomial distribution on set {1,. .. , |I uc | − 1}. The derived lower bounds do not belong to the family of " oracle " or " genie-aided " bounds since our a priori knowledge on support I uc and its cardinality is limited to probability P uc. In this context, very compact and simple expressions of the Expected Cramer-Rao Bound (ECRB) are proposed. Finally, the proposed lower bounds are compared to standard estimation strategies robust to an impulsive (sparse) noise
Stabilization and control Lyapunov functions for language constrained discrete-time switched linear systems
No full textInternational audienceIn this paper, the stabilizability of discrete-time switched linear systems subject to constraints on the switching law is considered. The admissible switching sequences are given by the language generated by a nondeterministic finite state automaton. Constructive necessary and sufficient conditions for recurrent stabilizability are given and the exact relations with the existence of control Lyapunov functions and with general stabilizability are provided. The dependence of stabilizability on the automaton initial state is also proved
Monotone Simultaneous Paths Embeddings in
No full textInternational audienceWe study the following problem: Given paths that share the same vertex set, is there a simultaneous geometric embedding of these paths such that each individual drawing is monotone in some direction? We prove that for any dimension , there is a set of paths that does not admit a monotone simultaneous geometric embedding
Proximity operators of discrete information divergences
No full textInternational audienceWhile ϕ-divergences have been extensively studied in convex analysis, their use in optimization problems often remains challenging. In this regard, one of the main shortcomings of existing methods is that the minimization of ϕ-divergences is usually performed with respect to one of their arguments, possibly within alternating optimization techniques. In this paper, we overcome this limitation by deriving new closed-form expressions for the proximity operator of such two-variable functions. This makes it possible to employ standard proximal methods for efficiently solving a wide range of convex optimization problems involving ϕ-divergences. In addition, we show that these proximity operators are useful to compute the epigraphical projection of several functions. The proposed proximal tools are numerically validated in the context of optimal query execution within database management systems, where the problem of selectivity estimation plays a central role. Experiments are carried out on small to large scale scenarios
Compositional Abstraction and Safety Synthesis using Overlapping Symbolic Models
No full textInternational audienceIn this paper, we develop a compositional approach to abstraction and safety synthesis for a general class of discrete time nonlinear systems. Our approach makes it possible to define a symbolic abstraction by composing a set of symbolic subsystems that are overlapping in the sense that they can share some common state variables. We develop compositional safety synthesis techniques using such overlapping symbolic subsystems. Comparisons, in terms of conservativeness and of computational complexity, between abstractions and controllers obtained from different system decompositions are provided. Numerical experiments show that the proposed approach for symbolic control synthesis enables a significant complexity reduction with respect to the centralized approach, while reducing the conservatism with respect to compositional approaches using non-overlapping subsystems
A quantum-based semiotic model for textual semantics
No full textInternational audienceThe paper aims to provide a semiotic interpretation of the role played by entanglement in quantum-based models aimed to information retrieval and suggests possible improvements. Actual models are capable of retrieving documents relevant to a query composed of a keyword and its acceptation expressed by a given context. The paper also considers some analogies between this technique and quantum-based approaches in other disciplines to discuss the consequence of this quantum turn, as epistemology and philosophy of language are concerned
Capturing the relevant problems leading to pain and usage driven innovations: the DSM Value Bucket algorithm
No full textInternational audienceThe Dependency Structure Modelling Value Bucket (DSM-VB) tool is integrated into Radical Innovation Design (RID) methodology in order to explore the front end of innovation in need seeker mode. The determination of innovation opportunities, here called value buckets, has been automated by matrix representations of dependencies between problems or pain points, usage situations and existing solutions. Three matrices are built along the problem setting stage of a RID process. The first matrix expresses which problems occur during usage scenarios, the second how far existing solutions cover problems, and the third to what degree existing solutions are useful in usage situations. Combining these three matrices results in a matrix of value buckets, which represents the combinations of important problems which occur during characteristic usage situations and for which few existing solutions are useful or efficient. This outcome allows focused creativity workshops to be run, resulting in usage innovations with a high likelihood of market success