Tree-decomposition based heuristics for the two-dimensional bin packing problem with conflicts

This paper deals with the two-dimensional bin packing problem with conflicts (BPC-2D). Given a finite set of rectangular items, an unlimited number of rectangular bins and a conflict graph, the goal is to find a conflict-free packing of the items minimizing the number of bins used. In this paper, we propose a new framework based on a tree-decomposition for solving this problem. It proceeds by decomposing a BPC-2D instance into subproblems to be solved independently. Applying this decomposition method is not straightforward, since merging partial solutions is hard. Several heuristic strategies are proposed to make an effective use of the decomposition. Computational experiments show the practical effectiveness of our approach.     

Accelerated multiplicative updates and hierarchical ALS algorithms for nonnegative matrix factorization

Nonnegative matrix factorization (NMF) is a data analysis technique used in a great variety of applications such as text mining, image processing, hyperspectral data analysis, computational biology, and clustering. In this letter, we consider two well-known algorithms designed to solve NMF problems: the multiplicative updates of Lee and Seung and the hierarchical alternating least squares of Cichocki et al. We propose a simple way to significantly accelerate these schemes, based on a careful analysis of the computational cost needed at each iteration, while preserving their convergence properties. This acceleration technique can also be applied to other algorithms, which we illustrate on the projected gradient method of Lin. The efficiency of the accelerated algorithms is empirically demonstrated on image and text data sets and compares favorably with a state-of-the-art alternating nonnegative least squares algorithm.     

Estimating the Natural Illumination Conditions from a Single Outdoor Image

Given a single outdoor image, we present a method for estimating the likely illumination conditions of the scene. In particular, we compute the probability distribution over the sun position and visibility. The method relies on a combination of weak cues that can be extracted from different portions of the image: the sky, the vertical surfaces, the ground, and the convex objects in the image. While no single cue can reliably estimate illumination by itself, each one can reinforce the others to yield a more robust estimate. This is combined with a data-driven prior computed over a dataset of 6 million photos. We present quantitative results on a webcam dataset with annotated sun positions, as well as quantitative and qualitative results on consumer-grade photographs downloaded from Internet. Based on the estimated illumination, we show how to realistically insert synthetic 3-D objects into the scene, and how to transfer appearance across images while keeping the illumination consistent.     

A new robust algorithmic for multi-camera calibration with a 1D object under general motions without prior knowledge of any camera intrinsic parameter

In computer vision, camera calibration is a necessary process when the retrieval of information such as angles and distances is required. This paper addresses the multi-camera calibration problem with a single dimension calibration pattern under general motions. Currently, the known algorithms for solving this problem are based on the estimation of vanishing points. However, this estimate is very susceptible to noise, making the methods unsuitable for practical applications. Instead, this paper presents a new calibration algorithm, where the cameras are divided into binocular sets. The fundamental matrix of each binocular set is then estimated, allowing to perform a projective calibration of each camera. Then, the calibration is updated for the Euclidean space, ending the process. The calibration is possible without imposing any restrictions on the movement of the pattern and without any prior information about the cameras or motion. Experiments on synthetic and real images validate the new method and show that its accuracy makes it suitable also for practical applications.     

Desynchronization and inhibition of Kuramoto oscillators by scalar mean-field feedback

Motivated by neuroscience applications, and in particular by the deep brain stimulation treatment for Parkinson’s disease, we have recently derived a simplified model of an interconnected neuronal population under the effect of its mean-field proportional feedback. In this paper, we rely on that model to propose conditions under which proportional mean-field feedback achieves either oscillation inhibition or desynchronization. More precisely, we show that for small natural frequencies, this scalar control signal induces an inhibition of the collective oscillation. For the closed-loop system, this situation corresponds to a fixed point which is shown to be almost globally asymptotically stable in the fictitious case of zero natural frequencies and all-to-all coupling and feedback. In the case of an odd number of oscillators, this property is shown to be robust to small natural frequencies and heterogencities in both the coupling and feedback topology. On the contrary, for large natural frequencies, we show that scalar proportional mean-field feedback is able to induce desynchronization. After having recalled a formal definition for desynchronization, we show how it can be induced in a network of originally synchronized oscillators.     

Cognitive conflict in human-automation interactions: a psychophysiological study

The review of literature in sociology and distributed artificial intelligence reveals that the occurrence of conflict is a remarkable precursor to the disruption of multi-agent systems. The study of this concept could be applied to human factors concerns, as man-system conflict appears to provoke perseveration behavior and to degrade attentional abilities with a trend to excessive focus. Once entangled in such conflicts, the human operator will do anything to succeed in his current goal even if it jeopardizes the mission. In order to confirm these findings, an experimental setup, composed of a real unmanned ground vehicle, a ground station is developed. A scenario involving an authority conflict between the participants and the robot is proposed. Analysis of the effects of the conflict on the participants' cognition and arousal is assessed through heart-rate measurement (reflecting stress level) and eye-tracking techniques (index of attentional focus). Our results clearly show that the occurrence of the conflict leads to perseveration behavior and can induce higher heart rate as well as excessive attentional focus. These results are discussed in terms of task commitment issues and increased arousal. Moreover, our results suggest that individual differences may predict susceptibility to perseveration behavior.     

Minimum variance prediction and control for adaptive optics

Adaptive Optics (AO) systems enable to compensate the adverse effects of atmospheric turbulence on ground-based telescopes’ images in real time, using a deformable mirror (DM) inserted in the telescope’s optical path, and measurements provided by a wavefront sensor (WFS). This paper revisits minimum-variance (MV) control design for astronomical AO systems in a state-space framework. It presents a survey of the modeling and control issues arising in this multi-variable disturbance rejection problem. In a linear time-invariant framework, and under some mild assumptions, the optimal solution to MV control for AO systems is shown to be a discrete-time LQG controller. This result holds for a DM with instantaneous response, and for a fairly general class of DM’s dynamics. The state-space approach is extended to Wide-field Adaptive Optics (WfAO) configurations involving several DMs and/or WFSs. Integral-action control used in existing AO systems is compared with the LQG controller. Experimental WfAO results obtained on a laboratory test bench are presented, showing significant improvement in performance. Finally, open issues and perspectives of applicative and/or theoretical interests are discussed.     

Identification and Molecular Cloning of Putative Odorant-Binding Proteins from the American Palm Weevil, Rhynchophorus palmarum L.

We have identified and cloned the cDNAs encoding two odorant-binding proteins (OBPs) from the American palm weevil (APW) Rhynchophorus palmarum (Coleoptera, Curculionidae). Degenerate primers were designed from the N-terminal sequences and were used in polymerase chain reaction (PCR) in order to obtain full-length sequences in both males and females. In both sexes, two different cDNAs were obtained, encoding 123 and 115 amino acid-deduced sequences. Each sequence showed few amino acid differences between the sexes. The proteins were named RpalOBP2 and RpalOBP4 for male, RpalOBP2' and RpalOBP4' for female, with the types 2 and 4 presenting only 34% identities. These proteins shared high identity with previously described coleopteran OBPs. In native gels, RpalOBP2 clearly separated into two bands and RpalOBP4 into three bands, suggesting the presence of several conformational isomers. Thus, OBP diversity in this species may rely on both the presence of OBPs from different classes and the occurrence of isoforms for each OBP.     

Effective Tension Active Cross-Section of Reinforced Concrete Beams After Cracking

In the design of concrete structures, estimation of the deflection of the structural members under the service loads is often a critical factor. Strains and displacements are linked to the tension stiffening effect, which is not quantified in the standards. The CEB-FIP model code proposes an interesting simplified method for evaluating the concrete tension stiffening effect on reinforced concrete behavior. One of the parameters which enables the mechanical contribution of the concrete to be quantified is the effective tensile active section of the reinforced concrete beams. In this paper, a new method for calculating the effective tensile active section A _ct.ef is proposed. The calculation is based on an analytical model of stress distribution in the full depth of the concrete section. An experimental study on several reinforced concrete beams is reported. The results show that the new proposed model enhances the accuracy of the beam deflection predictions significantly.