On Matrices, Automata, and Double Counting

Matrix models are ubiquitous for constraint problems. Many such problems have a matrix of variables M, with the same constraint defined by a finite-state automaton A on each row of M and a global cardinality constraint gcc on each column of M. We give two methods for deriving, by double counting, necessary conditions on the cardinality variables of the gcc constraints from the automaton A. The first method yields linear necessary conditions and simple arithmetic constraints. The second method introduces the cardinality automaton, which abstracts the overall behaviour of all the row automata and can be encoded by a set of linear constraints. We evaluate the impact of our methods on a large set of nurse rostering problem instances

Domain k-Wise Consistency Made as Simple as Generalized Arc Consistency

Abstract. In Constraint Programming (CP), Generalized Arc Consistency (GAC) is the central property used for making inferences when solving Constraint Satisfaction Problems (CSPs). Developing simple and practical filtering algorithms based on consistencies stronger than GAC is a challenge for the CP community. In this paper, we propose to combine k-Wise Consistency (kWC) with GAC, where kWC states that every tuple in a constraint can be extended to every set of k − 1 additional constraints. Our contribution is as follows. First, we derive a domain-filtering consistency, called Domain k-Wise Consistency (DkWC), from the combination of kWC and GAC. Roughly speaking, this property corresponds to the pruning of values of GAC, when enforced on a CSP previously made kWC. Second, we propose a procedure to enforce DkWC, relying on an encoding of kWC to generate a modified CSP called k-interleaved CSP. Formally, we prove that enforcing GAC on the k-interleaved CSP corresponds to enforcing DkWC on the initial CSP. Consequently, we show that the strong DkWC can be enforced very easily in constraint solvers since the k-interleaved CSP is rather immediate to generate and only existing GAC propagators are required: in a nutshell, DkWC is made as simple and practical as GAC. Our experimental results show the benefits of our approach on a variety of benchmarks.

On the speed of constraint propagation and the time complexity of arc consistency testing

Establishing arc consistency on two relational structures is one of the most popular heuristics for the constraint satisfaction problem. We aim at determining the time complexity of arc consistency testing. The input structures $G$ and $H$ can be supposed to be connected colored graphs, as the general problem reduces to this particular case. We first observe the upper bound $O(e(G)v(H)+v(G)e(H))$, which implies the bound $O(e(G)e(H))$ in terms of the number of edges and the bound $O((v(G)+v(H))^3)$ in terms of the number of vertices. We then show that both bounds are tight up to a constant factor as long as an arc consistency algorithm is based on constraint propagation (like any algorithm currently known). Our argument for the lower bounds is based on examples of slow constraint propagation. We measure the speed of constraint propagation observed on a pair $G,H$ by the size of a proof, in a natural combinatorial proof system, that Spoiler wins the existential 2-pebble game on $G,H$. The proof size is bounded from below by the game length $D(G,H)$, and a crucial ingredient of our analysis is the existence of $G,H$ with $D(G,H)=\Omega(v(G)v(H))$. We find one such example among old benchmark instances for the arc consistency problem and also suggest a new, different construction.Comment: 19 pages, 5 figure

Bayesian Models for Multimodal Perception of 3D Structure and Motion

In this text we will formalise a novel solution, the Bayesian Volumetric Map (BVM), as a framework for a metric, short-term, egocentric spatial memory for multimodal perception of 3D structure and motion. This solution will enable the implementation of top-down mechanisms of attention guidance of perception towards areas of high entropy/uncertainty, so as to promote active exploration of the environment by the robotic perceptual system. In the process, we will to try address the inherent challenges of visual, auditory and vestibular sensor fusion through the BVM. In fact, it is our belief that perceptual systems are unable to yield truly useful descriptions of their environment without resorting to a temporal series of sensory fusion processed on a short-term memory such as the BVM

Constraint satisfaction parameterized by solution size

In the constraint satisfaction problem (CSP) corresponding to a constraint language (i.e., a set of relations) $\Gamma$, the goal is to find an assignment of values to variables so that a given set of constraints specified by relations from $\Gamma$ is satisfied. The complexity of this problem has received substantial amount of attention in the past decade. In this paper we study the fixed-parameter tractability of constraint satisfaction problems parameterized by the size of the solution in the following sense: one of the possible values, say 0, is "free," and the number of variables allowed to take other, "expensive," values is restricted. A size constraint requires that exactly $k$ variables take nonzero values. We also study a more refined version of this restriction: a global cardinality constraint prescribes how many variables have to be assigned each particular value. We study the parameterized complexity of these types of CSPs where the parameter is the required number $k$ of nonzero variables. As special cases, we can obtain natural and well-studied parameterized problems such as Independent Set, Vertex Cover, d-Hitting Set, Biclique, etc. In the case of constraint languages closed under substitution of constants, we give a complete characterization of the fixed-parameter tractable cases of CSPs with size constraints, and we show that all the remaining problems are W[1]-hard. For CSPs with cardinality constraints, we obtain a similar classification, but for some of the problems we are only able to show that they are Biclique-hard. The exact parameterized complexity of the Biclique problem is a notorious open problem, although it is believed to be W[1]-hard.Comment: To appear in SICOMP. Conference version in ICALP 201

The evolution of Internet addiction: A global perspective

Kimberly Young’s early work on Internet addiction (IA)has been pioneering and her early writings on the topic inspired many others to carry out research in the area. Young's (2015) recent paper on the 'evolution of Internet addiction' featured very little European research, and did not consider the main international evidence that has contributed to our current knowledge about the conceptualization, epidemiology, etiology, and course of Internet-related disorders. This short commentary paper elaborates on important literature omitted by Young that the present authors believe may be of use to researchers. We also address statements made in Young’s (2015) commentary that are incorrect (and therefore misleading) and not systematically substantiated by empirical evidence

Bayesian Action–Perception Computational Model: Interaction of Production and Recognition of Cursive Letters

In this paper, we study the collaboration of perception and action representations involved in cursive letter recognition and production. We propose a mathematical formulation for the whole perception–action loop, based on probabilistic modeling and Bayesian inference, which we call the Bayesian Action–Perception (BAP) model. Being a model of both perception and action processes, the purpose of this model is to study the interaction of these processes. More precisely, the model includes a feedback loop from motor production, which implements an internal simulation of movement. Motor knowledge can therefore be involved during perception tasks. In this paper, we formally define the BAP model and show how it solves the following six varied cognitive tasks using Bayesian inference: i) letter recognition (purely sensory), ii) writer recognition, iii) letter production (with different effectors), iv) copying of trajectories, v) copying of letters, and vi) letter recognition (with internal simulation of movements). We present computer simulations of each of these cognitive tasks, and discuss experimental predictions and theoretical developments

LUNEX5: A French FEL Test Facility Light Source Proposal

http://accelconf.web.cern.ch/AccelConf/IPAC2012/papers/tuppp005.pdfInternational audienceLUNEX5 is a new Free Electron Laser (FEL) source project aimed at delivering short and coherent X-ray pulses to probe ultrafast phenomena at the femto-second scale, to investigate extremely low density samples as well as to image individual nm scale objects

Autoantibodies to BRAF, a new family of autoantibodies associated with rheumatoid arthritis

International audienceBRAF (v raf murine sarcoma viral oncogene homologue B1) is a serine-threonine kinase involved in the mitogen-activated protein kinase (MAPK) signalling pathway, known to be implicated in the production of pro-inflammatory cytokines.We have observed that sera from rheumatoid arthritis (RA) patients recognize the BRAF's catalytic domain, which encompasses amino acids 416 to 766. Here, we identify peptide targets of anti-BRAF autoantibodies and test whether anti-BRAF autoantibodies may interfere with BRAF kinase activity.METHODS:Anti-BRAF autoantibodies were detected by ELISA (enzyme-linked immunosorbent assay) in the serum of RA patients and controls, using 40 overlapping 20mer peptides encompassing the catalytic domain of BRAF as immunosorbents. To test whether autoantibodies to BRAF influence BRAF kinase activity, we developed an in vitro phosphorylation assay of MEK1 (mitogen extracellular regulated kinase), a major BRAF substrate. MEK1 phosphorylation by BRAF was tested in the presence of purified anti-BRAF autoantibodies from RA patients or control antibody.RESULTS:We found that one BRAF peptide, P25 (656 to 675), is specifically recognized by autoantibodies from RA patients. Of interest, anti-P25 autoantibodies are detected in 21% of anti-CCP (cyclic citrullinated peptides) negative RA patients. Anti-BRAF autoantibodies activate the in vitro phosphorylation of MEK1 mediated by BRAF.CONCLUSIONS:Anti-BRAF autoantibodies from RA patients preferentially recognize one BRAF peptide: P25. Autoantibody responses to P25 are detected in 21% of anti-CCP negative RA patients. Most anti-BRAF autoantibodies activate BRAF kinase activity