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

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 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

Deep Neural Networks for Inverse Problems with Pseudodifferential Operators: An Application to Limited-Angle Tomography

We propose a novel convolutional neural network (CNN), called \Psi DONet, designed for learning pseudodifferential operators (\Psi DOs) in the context of linear inverse problems. Our starting point is the iterative soft thresholding algorithm (ISTA), a well-known algorithm to solve sparsity-promoting minimization problems. We show that, under rather general assumptions on the forward operator, the unfolded iterations of ISTA can be interpreted as the successive layers of a CNN, which in turn provides fairly general network architectures that, for a specific choice of the parameters involved, allow us to reproduce ISTA, or a perturbation of ISTA for which we can bound the coefficients of the filters. Our case study is the limited-angle X-ray transform and its application to limited-angle computed tomography (LA-CT). In particular, we prove that, in the case of LA-CT, the operations of upscaling, downscaling, and convolution, which characterize our \Psi DONet and most deep learning schemes, can be exactly determined by combining the convolutional nature of the limited-angle Xray transform and basic properties defining an orthogonal wavelet system. We test two different implementations of \Psi DONet on simulated data from limited-angle geometry, generated from the ellipse data set. Both implementations provide equally good and noteworthy preliminary results, showing the potential of the approach we propose and paving the way to applying the same idea to other convolutional operators which are \Psi DOs or Fourier integral operators

Biological validation of coenzyme Q redox state by HPLC-EC measurement: relationship between coenzyme Q redox state and coenzyme Q content in rat tissues

AbstractThe properties of coenzymes Q (CoQ9 and CoQ10) are closely linked to their redox state (CoQox/total CoQ)×100. In this work, CoQ redox state was biologically validated by high performance liquid chromatography-electrochemical measurement after modulation of mitochondrial electron flow of cultured cells by molecules increasing (rotenone, carbonyl cyanide chlorophenylhydrazone) or decreasing (antimycin) CoQ oxidation. The tissue specificity of CoQ redox state and content were investigated in control and hypoxic rats. In control rats, there was a strong negative linear regression between tissular CoQ redox state and CoQ content. Hypoxia increased CoQ9 redox state and decreased CoQ9 content in a negative linear relationship in the different tissues, except the heart and lung. This result demonstrates that, under conditions of mitochondrial impairment, CoQ redox control is tissue-specific

Optimality Clue for Graph Coloring Problem

In this paper, we present a new approach which qualifies or not a solution found by a heuristic as a potential optimal solution. Our approach is based on the following observation: for a minimization problem, the number of admissible solutions decreases with the value of the objective function. For the Graph Coloring Problem (GCP), we confirm this observation and present a new way to prove optimality. This proof is based on the counting of the number of different k-colorings and the number of independent sets of a given graph G. Exact solutions counting problems are difficult problems (\#P-complete). However, we show that, using only randomized heuristics, it is possible to define an estimation of the upper bound of the number of k-colorings. This estimate has been calibrated on a large benchmark of graph instances for which the exact number of optimal k-colorings is known. Our approach, called optimality clue, build a sample of k-colorings of a given graph by running many times one randomized heuristic on the same graph instance. We use the evolutionary algorithm HEAD [Moalic et Gondran, 2018], which is one of the most efficient heuristic for GCP. Optimality clue matches with the standard definition of optimality on a wide number of instances of DIMACS and RBCII benchmarks where the optimality is known. Then, we show the clue of optimality for another set of graph instances. Optimality Metaheuristics Near-optimal

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

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

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