Efficient algorithms for finding critical subgraphs

AbstractThis paper presents algorithms to find vertex-critical and edge-critical subgraphs in a given graph G, and demonstrates how these critical subgraphs can be used to determine the chromatic number of G. Computational experiments are reported on random and DIMACS benchmark graphs to compare the proposed algorithms, as well as to find lower bounds on the chromatic number of these graphs. We improve the best known lower bound for some of these graphs, and we are even able to determine the chromatic number of some graphs for which only bounds were known

A tabu search heuristic for the Equitable Coloring Problem

The Equitable Coloring Problem is a variant of the Graph Coloring Problem where the sizes of two arbitrary color classes differ in at most one unit. This additional condition, called equity constraints, arises naturally in several applications. Due to the hardness of the problem, current exact algorithms can not solve large-sized instances. Such instances must be addressed only via heuristic methods. In this paper we present a tabu search heuristic for the Equitable Coloring Problem. This algorithm is an adaptation of the dynamic TabuCol version of Galinier and Hao. In order to satisfy equity constraints, new local search criteria are given. Computational experiments are carried out in order to find the best combination of parameters involved in the dynamic tenure of the heuristic. Finally, we show the good performance of our heuristic over known benchmark instances

Combining modelled snowpack stability with machine learning to predict avalanche activity

Predicting avalanche activity from meteorological and snow cover simulations is critical in mountainous areas to support operational forecasting. Several numerical and statistical methods have tried to address this issue. However, it remains unclear how combining snow physics, mechanical analysis of snow profiles and observed avalanche data improves avalanche activity prediction. This study combines extensive snow cover and snow stability simulations with observed avalanche occurrences within a random forest approach to predict avalanche situations at a spatial resolution corresponding to elevations and aspects of avalanche paths in a given mountain range. We develop a rigorous leave-one-out evaluation procedure including an independent evaluation set, confusion matrices and receiver operating characteristic curves. In a region of the French Alps (Haute-Maurienne) and over the period 1960–2018, we show the added value within the machine learning model of considering advanced snow cover modelling and mechanical stability indices instead of using only simple meteorological and bulk information. Specifically, using mechanically based stability indices and their time derivatives in addition to simple snow and meteorological variables increases the probability of avalanche situation detection from around 65 % to 76 %. However, due to the scarcity of avalanche events and the possible misclassification of non-avalanche situations in the training dataset, the predicted avalanche situations that are really observed remains low, around 3.3 %. These scores illustrate the difficulty of predicting avalanche occurrence with a high spatio-temporal resolution, even with the current data and modelling tools. Yet, our study opens perspectives to improve modelling tools supporting operational avalanche forecasting.</p

A Hybrid Artificial Bee Colony Algorithm for Graph 3-Coloring

The Artificial Bee Colony (ABC) is the name of an optimization algorithm that was inspired by the intelligent behavior of a honey bee swarm. It is widely recognized as a quick, reliable, and efficient methods for solving optimization problems. This paper proposes a hybrid ABC (HABC) algorithm for graph 3-coloring, which is a well-known discrete optimization problem. The results of HABC are compared with results of the well-known graph coloring algorithms of today, i.e. the Tabucol and Hybrid Evolutionary algorithm (HEA) and results of the traditional evolutionary algorithm with SAW method (EA-SAW). Extensive experimentations has shown that the HABC matched the competitive results of the best graph coloring algorithms, and did better than the traditional heuristics EA-SAW when solving equi-partite, flat, and random generated medium-sized graphs

A Recombination-Based Tabu Search Algorithm for the Winner Determination Problem

Abstract. We propose a dedicated tabu search algorithm (TSX_WDP) for the winner determination problem (WDP) in combinatorial auctions. TSX_WDP integrates two complementary neighborhoods designed re-spectively for intensification and diversification. To escape deep local optima, TSX_WDP employs a backbone-based recombination opera-tor to generate new starting points for tabu search and to displace the search into unexplored promising regions. The recombination operator operates on elite solutions previously found which are recorded in an global archive. The performance of our algorithm is assessed on a set of 500 well-known WDP benchmark instances. Comparisons with five state of the art algorithms demonstrate the effectiveness of our approach

Apolipoprotein O is mitochondrial and promotes lipotoxicity in heart

Diabetic cardiomyopathy is a secondary complication of diabetes with an unclear etiology. Based on a functional genomic evaluation of obesity-associated cardiac gene expression, we previously identified and cloned the gene encoding apolipoprotein O (APOO), which is overexpressed in hearts from diabetic patients. Here, we generated APOO-Tg mice, transgenic mouse lines that expresses physiological levels of human APOO in heart tissue. APOO-Tg mice fed a high-fat diet exhibited depressed ventricular function with reduced fractional shortening and ejection fraction, and myocardial sections from APOO-Tg mice revealed mitochondrial degenerative changes. In vivo fluorescent labeling and subcellular fractionation revealed that APOO localizes with mitochondria. Furthermore, APOO enhanced mitochondrial uncoupling and respiration, both of which were reduced by deletion of the N-terminus and by targeted knockdown of APOO. Consequently, fatty acid metabolism and ROS production were enhanced, leading to increased AMPK phosphorylation and Ppara and Pgc1a expression. Finally, we demonstrated that the APOO-induced cascade of events generates a mitochondrial metabolic sink whereby accumulation of lipotoxic byproducts leads to lipoapoptosis, loss of cardiac cells, and cardiomyopathy, mimicking the diabetic heart-associated metabolic phenotypes. Our data suggest that APOO represents a link between impaired mitochondrial function and cardiomyopathy onset, and targeting APOO-dependent metabolic remodeling has potential as a strategy to adjust heart metabolism and protect the myocardium from impaired contractility

Clustering Nominal and Numerical Data: A New Distance Concept for a Hybrid Genetic Algorithm

As intrinsic structures, like the number of clusters, is, for real data, a major issue of the clustering problem, we propose, in this paper, CHyGA (Clustering Hybrid Genetic Algorithm) an hybrid genetic algorithm for clustering. CHyGA treats the clustering problem as an optimization problem and searches for an optimal number of clusters characterized by an optimal distribution of instances into the clusters. CHyGA introduces a new representation of solutions and uses dedicated operators, such as one iteration of K-means as a mutation operator. In order to deal with nominal data, we propose a new definition of the cluster center concept and demonstrate its properties. Experimental results on classical benchmarks are given

Hypothalamic Reactive Oxygen Species Are Required for Insulin-Induced Food Intake Inhibition: An NADPH Oxidase–Dependent Mechanism

1939-327X (Electronic) Journal Article Research Support, Non-U.S. Gov'tOBJECTIVE: Insulin plays an important role in the hypothalamic control of energy balance, especially by reducing food intake. Emerging data point to a pivotal role of reactive oxygen species (ROS) in energy homeostasis regulation, but their involvement in the anorexigenic effect of insulin is unknown. Furthermore, ROS signal derived from NADPH oxidase activation is required for physiological insulin effects in peripheral cells. In this study, we investigated the involvement of hypothalamic ROS and NADPH oxidase in the feeding behavior regulation by insulin. RESEARCH DESIGN AND METHODS: We first measured hypothalamic ROS levels and food intake after acute intracerebroventricular injection of insulin. Second, effect of pretreatment with a ROS scavenger or an NADPH oxidase inhibitor was evaluated. Third, we examined the consequences of two nutritional conditions of central insulin unresponsiveness (fasting or short-term high-fat diet) on the ability of insulin to modify ROS level and food intake. RESULTS: In normal chow-fed mice, insulin inhibited food intake. At the same dose, insulin rapidly and transiently increased hypothalamic ROS levels by 36%. The pharmacological suppression of this insulin-stimulated ROS elevation, either by antioxidant or by an NADPH oxidase inhibitor, abolished the anorexigenic effect of insulin. Finally, in fasted and short-term high-fat diet-fed mice, insulin did not promote elevation of ROS level and food intake inhibition, likely because of an increase in hypothalamic diet-induced antioxidant defense systems. CONCLUSIONS: A hypothalamic ROS increase through NADPH oxidase is required for the anorexigenic effect of insulin