Exact Algorithms for <Emphasis Type="Italic">L</Emphasis>(2,1)-Labeling of Graphs

The notion of distance constrained graph labelings, motivated by the Frequency Assignment Problem, reads as follows: A mapping from the vertex set of a graph G=(V,E) into an interval of integers {0,…,k} is an L(2,1)-labeling of G of span k if any two adjacent vertices are mapped onto integers that are at least 2 apart, and every two vertices with a common neighbor are mapped onto distinct integers. It is known that for any fixed k≥4, deciding the existence of such a labeling is an NP-complete problem. We present exact exponential time algorithms that are faster than the naive O ^*((k+1) ⁿ ) algorithm that would try all possible mappings. The improvement is best seen in the first NP-complete case of k=4, where the running time of our algorithm is O(1.3006 ⁿ ). Furthermore we show that dynamic programming can be used to establish an O(3.8730 ⁿ ) algorithm to compute an optimal L(2,1)-labeling.     

Towards a continuous microfluidic rheometer

In a previous paper we presented a way to measure the rheological properties of complex fluids on a microfluidic chip (Guillot et al., Langmuir 22:6438, 2006). The principle of our method is to use parallel flows between two immiscible fluids as a pressure sensor. In fact, in a such flow, both fluids flow side by side and the size occupied by each fluid stream depends only on both flow rates and on both viscosities. We use this property to measure the viscosity of one fluid knowing the viscosity of the other one, both flow rates and the relative size of both streams in a cross-section. We showed that using a less viscous fluid as a reference fluid allows to define a mean shear rate with a low standard deviation in the other fluid. This method allows us to measure the flow curve of a fluid with less than 250 μL of fluid. In this paper we implement this principle in a fully automated set up which controls the flow rate, analyzes the picture and calculates the mean shear rate and the viscosity of the studied fluid. We present results obtained for Newtonian fluids and complex fluids using this set up and we compare our data with cone and plate rheometer measurements. By adding a mixing stage in the fluidic network we show how this set up can be used to characterize in a continuous way the evolution of the rheological properties as a function of the formulation composition. We illustrate this by measuring the rheological curve of four formulations of polyethylene oxide solution with only 1.3 mL of concentrated polyethylene oxide solution. This method could be very useful in screening processes where the viscosity range and the behavior of the fluid to an applied stress must be evaluated.     

Valence-Driven Connectivity Encoding for 3D Meshes

In this paper, we propose a valence-driven, single-resolution encoding technique for lossless compression of triangle mesh connectivity. Building upon a valence-based approach pioneered by Touma and Gotsman²² , we design a new valence-driven conquest for arbitrary meshes that always guarantees smaller compression rates than the original method. Furthermore, we provide a novel theoretical entropy study of our technique, hinting the optimality of the valence-driven approach. Finally, we demonstrate the practical efficiency of this approach (in agreement with the theoretical prediction) on a series of test meshes, resulting in the lowest compression ratios published so far, for both irregular and regular meshes, small or large.     

Relation between Breast Cancer and High Glycemic Index or Glycemic Load: A Meta-analysis of Prospective Cohort Studies

Breast cancer is the commonest form of cancer in women worldwide. It has been suggested that chronic hyperinsulinemia associated with insulin resistance plays a role in breast cancer etiology. To test the hyperinsulinemia hypothesis, a dietary pattern associated with a high glycemic index and glycemic load, both proxies for chronic hyperinsulinemia, should be associated with an increased risk of breast cancer. A meta-analysis restricted to prospective cohort studies was undertaken using a random effects model with tests for statistical significance, publication bias and heterogeneity. The metric for analysis was the risk of breast cancer in the highest relative to the lowest glycemic index and glycemic load dietary pattern. A dietary pattern with a high glycemic index was associated with a summary relative risk (SRR) of 1.05 (95% CI: 1.00, 1.11), and a high glycemic load with a SRR of 1.06 (95% CI: 1.00, 1.13). Adjustments for body mass index [BMI], physical activity and other lifestyle factors did not influence the SRR, nor did menopausal status and estrogen receptor status of the tumor. In conclusion, the current evidence supports a modest association between a dietary pattern with high glycemic index or glycemic load and the risk of breast cancer.     

Accurate junction detection and characterization in line-drawing images

In this paper, we present a new approach for junction detection and characterization in line-drawing images. We formulate this problem as searching for optimal meeting points of median lines. In this context, the main contribution of the proposed approach is three-fold. First, a new algorithm for the determination of the support region is presented using the linear least squares technique, making it robust to digitization effects. Second, an efficient algorithm is proposed to detect and conceptually remove all distorted zones, retaining reliable line segments only. These line segments are then locally characterized to form a local structure representation of each crossing zone. Finally, a novel optimization algorithm is presented to reconstruct the junctions. Junction characterization is then simply derived. The proposed approach is very highly robust to common geometry transformations and can resist a satisfactory level of noise/degradation. Furthermore, it works very efficiently in terms of time complexity and requires no prior knowledge of the document content. Extensive evaluations have been performed to validate the proposed approach using other baseline methods. An application of symbol spotting is also provided, demonstrating quite good results.     

Comparison between five stochastic global search algorithms for optimizing thermoelectric generator designs

In this study, the best settings of five heuristics are determined for solving a mixed-integer non-linear multi-objective optimization problem. The algorithms treated in the article are: ant colony optimization, genetic algorithm, particle swarm optimization, differential evolution, and teaching-learning basic algorithm. The optimization problem consists in optimizing the design of a thermoelectric device, based on a model available in literature. Results showed that the inner settings can have different effects on the algorithm performance criteria depending on the algorithm. A formulation based on the weighted sum method is introduced for solving the multiobjective optimization problem with optimal settings. It was found that the five heuristic algorithms have comparable performances. Differential evolution generated the highest number of non-dominated solutions in comparison with the other algorithms.     

Using immersive virtual reality and anatomically correct computer-generated characters in the forensic assessment of deviant sexual preferences

Penile plethysmography (PPG) is the gold standard for the assessment of sexual interests, especially among sex offenders of children. Nonetheless, this method faces some ethical limitations inherent to the nature of its stimuli and could benefit from the improvement of its ecological validity. The use of computer-generated characters (CGC) in virtual immersion for PPG assessment might help address these issues. A new application developed to design made-to-measure anatomically correct virtual characters compatible with the Tanner developmental stages is presented. The main purpose of this study was to determine how the virtual reality (VR) modality compares to the standard auditory modality on their capacity to generate sexual arousal profiles and deviance differentials indicative of sexual interests. The erectile responses of 22 sex offenders of children and 42 non-deviant adult males were recorded. While both stimulus modalities generated significantly different genital arousal profiles for sex offenders of children and non-deviant males, deviance differentials calculated from the VR modality allowed for significantly higher classification accuracy. Performing receiver operating characteristic analyses further assessed discriminant potential. Auditory modality yielded an area under the curve (AUC) of 0.79 (SE = 0.059) while CGC in VR yielded an AUC of 0.90 (SE = 0.052). Overall, results suggest that the VR modality allows significantly better group classification accuracy and discriminant validity than audio stimuli, which provide empirical support for the use of this new method for PPG assessment. Additionally, the potential use of VR in interventions pertaining to self-regulation of sexual offending is addressed in conclusion.     

Analysis of muscle length effect on an S type motor-unit fractional multi-model

Muscle models are useful in bio-inspired robotic, because they allow to reproduce accurately natural motion. When they are used for robotic issue, they need to be compact and embeddable. The non-integer model order has the advantage to ensure a parametrical parsimony that permits to implant it easily on an embedded system. Thus, a fractional multi-model of muscle was identified and presented in later paper. This model is able to predict the response of a motor unit to an electrical stimulation, considering isometric contractions (that is to say, muscle length is constant). There are three different physiological types of motor unit (FR, FF, and S). The aim of this work is to study muscle length impact on the multi-model and the limitations of the linear multi-model. Previous paper was published using FR type motor unit. In this paper, the results of the study using S type motor unit are presented.     

3D line-support grid flattening for more accurate geostatistical reservoir population with petrophysical properties

Reservoir models are essential if we need to clearly understand the fossil resources and, hence, to make better use of them. Feeding these models with physical properties on the basis of wells data is a key step in their construction. Line-support (LS) grid is the most popular grid in reservoir engineering, it is massively used for reservoir simulations. In the current methods used to populate with properties the LS grid of a reservoir unit, a Cartesian grid of equivalent size (in each direction), obtained by averaging the edge lengths, is first of all completed. The properties calculated in this way are then transferred as they are into the initial LS grid, because there is cell-for-cell correspondence. This leads to distortion of the Cartesian grid, making it fit the shape of the LS grid. This has the effect of altering calculations of correlation distances between well markers in geostatistical population simulations. Consequently, this primarily induces distortions on the simulated bodies. To resolve this problem, in this paper, we propose innovative methods for a “smooth” conversion from the LS grid of the structural space to the Cartesian grid of the geostatistical population space. The basic principle is to calculate the correlation distances between wells on the basis of “quasi-isometric” flattening of the stratigraphic unit LS grid in the population space. This same flattening technique is then used for inverse transfer of the properties from the population space to the structural space.