Inferring genome-scale rearrangement phylogeny and ancestral gene order: a Drosophila case study

A simple, fast, and biologically-inspired computational approach to infer genome-scale rearrangement phylogeny and ancestral gene order has been developed and applied to eight Drosophila genomes, providing insights into evolutionary chromosomal dynamics

Book of Abstracts of the Sixth SIAM Workshop on Combinatorial Scientific Computing

Book of Abstracts of CSC14 edited by Bora UçarInternational audienceThe Sixth SIAM Workshop on Combinatorial Scientific Computing, CSC14, was organized at the Ecole Normale Supérieure de Lyon, France on 21st to 23rd July, 2014. This two and a half day event marked the sixth in a series that started ten years ago in San Francisco, USA. The CSC14 Workshop's focus was on combinatorial mathematics and algorithms in high performance computing, broadly interpreted. The workshop featured three invited talks, 27 contributed talks and eight poster presentations. All three invited talks were focused on two interesting fields of research specifically: randomized algorithms for numerical linear algebra and network analysis. The contributed talks and the posters targeted modeling, analysis, bisection, clustering, and partitioning of graphs, applied in the context of networks, sparse matrix factorizations, iterative solvers, fast multi-pole methods, automatic differentiation, high-performance computing, and linear programming. The workshop was held at the premises of the LIP laboratory of ENS Lyon and was generously supported by the LABEX MILYON (ANR-10-LABX-0070, Université de Lyon, within the program ''Investissements d'Avenir'' ANR-11-IDEX-0007 operated by the French National Research Agency), and by SIAM

The Optimum Communication Spanning Tree Problem : properties, models and algorithms

For a given cost matrix and a given communication requirement matrix, the OCSTP is defined as finding a spanning tree that minimizes the operational cost of the network. OCST can be used to design of more efficient communication and transportation networks, but appear also, as a subproblem, in hub location and sequence alignment problems. This thesis studies several mixed integer linear optimization formulations of the OCSTP and proposes a new one. Then, an efficient Branch & Cut algorithm derived from the Benders decomposition of one of such formulations is used to successfully solve medium-sized instances of the OCSTP. Additionally, two new combinatorial lower bounds, two new heuristic algorithms and a new family of spanning tree neighborhoods based on the Dandelion Code are presented and tested.Postprint (published version

Application of genetic algorithms to group technology.

Lee Wai Hung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 108-115).Chapter 1 --- Introduction --- p.8Chapter 1.1 --- Introduction to Group Technology --- p.8Chapter 1.2 --- Cell design --- p.9Chapter 1.3 --- Objectives of the research --- p.11Chapter 1.4 --- Organization of thesis --- p.11Chapter 2 --- Literature review --- p.13Chapter 2.1 --- Introduction --- p.13Chapter 2.2 --- Standard models --- p.14Chapter 2.2.1 --- Array-based methods --- p.16Chapter 2.2.2 --- Cluster identification --- p.16Chapter 2.2.3 --- Graph-based methods --- p.17Chapter 2.2.4 --- Integer programming --- p.17Chapter 2.2.5 --- Seed-based --- p.18Chapter 2.2.6 --- Similarity coefficient --- p.18Chapter 2.2.7 --- Artificial intelligence methods --- p.19Chapter 2.3 --- Generalized models --- p.19Chapter 2.3.1 --- Machine assignment models --- p.20Chapter 2.3.2 --- Part family models --- p.20Chapter 2.3.3 --- Cell formation models --- p.21Chapter 3 --- Genetic cell formation algorithm --- p.22Chapter 3.1 --- Introduction --- p.22Chapter 3.2 --- TSP formulation for a permutation of machines --- p.23Chapter 3.3 --- Genetic algorithms --- p.26Chapter 3.3.1 --- Representation and basic crossover operators --- p.27Chapter 3.3.2 --- Fitness function --- p.28Chapter 3.3.3 --- Initialization --- p.29Chapter 3.3.4 --- Parent selection strategies --- p.30Chapter 3.3.5 --- Crossover --- p.31Chapter 3.3.6 --- Mutation --- p.37Chapter 3.3.7 --- Replacement --- p.38Chapter 3.3.8 --- Termination --- p.38Chapter 3.4 --- Formation of machine cells and part families --- p.39Chapter 3.4.1 --- Objective functions --- p.39Chapter 3.4.2 --- Machine assignment --- p.42Chapter 3.4.3 --- Part assignment --- p.43Chapter 3.5 --- Implementation --- p.43Chapter 3.6 --- An illustrative example --- p.45Chapter 3.7 --- Comparative Study --- p.49Chapter 3.8 --- Conclusions --- p.50Chapter 4 --- A multi-chromosome GA for minimizing total intercell and intracell moves --- p.55Chapter 4.1 --- Introduction --- p.55Chapter 4.2 --- The model --- p.57Chapter 4.3 --- Solution techniques to the workload model --- p.61Chapter 4.3.1 --- Logendran's original approach --- p.62Chapter 4.3.2 --- Standard representation - the GA approach --- p.63Chapter 4.3.3 --- Multi-chromosome representation --- p.65Chapter 4.4 --- Comparative Study --- p.70Chapter 4.4.1 --- Problem 1 --- p.70Chapter 4.4.2 --- Problem 2 --- p.71Chapter 4.4.3 --- Problem 3 --- p.75Chapter 4.4.4 --- Problem 4 --- p.76Chapter 4.5 --- Bi-criteria Model --- p.79Chapter 4.5.1 --- Experimental results --- p.85Chapter 4.6 --- Conclusions --- p.85Chapter 5 --- Integrated design of cellular manufacturing systems in the presence of alternative process plans --- p.88Chapter 5.1 --- Introduction --- p.88Chapter 5.1.1 --- Literature review --- p.90Chapter 5.1.2 --- Motivation --- p.92Chapter 5.2 --- Mathematical models --- p.93Chapter 5.2.1 --- Notation --- p.93Chapter 5.2.2 --- Objective functions --- p.95Chapter 5.3 --- Our solution --- p.96Chapter 5.4 --- Illustrative example and analysis of results --- p.98Chapter 5.4.1 --- Solution for objective function 1 --- p.101Chapter 5.4.2 --- Solution for objective function 2 --- p.102Chapter 5.5 --- Conclusions --- p.103Chapter 6 --- Conclusions --- p.104Chapter 6.1 --- Summary of achievements --- p.104Chapter 6.2 --- Future works --- p.10

Semi-supervised tensor-based graph embedding learning and its application to visual discriminant tracking

An appearance model adaptable to changes in object appearance is critical in visual object tracking. In this paper, we treat an image patch as a 2-order tensor which preserves the original image structure. We design two graphs for characterizing the intrinsic local geometrical structure of the tensor samples of the object and the background. Graph embedding is used to reduce the dimensions of the tensors while preserving the structure of the graphs. Then, a discriminant embedding space is constructed. We prove two propositions for finding the transformation matrices which are used to map the original tensor samples to the tensor-based graph embedding space. In order to encode more discriminant information in the embedding space, we propose a transfer-learningbased semi-supervised strategy to iteratively adjust the embedding space into which discriminative information obtained from earlier times is transferred. We apply the proposed semi-supervised tensor-based graph embedding learning algorithm to visual tracking. The new tracking algorithm captures an object’s appearance characteristics during tracking and uses a particle filter to estimate the optimal object state. Experimental results on the CVPR 2013 benchmark dataset demonstrate the effectiveness of the proposed tracking algorithm

Statistical Physics of Design

Modern life increasingly relies on complex products that perform a variety of functions. The key difficulty of creating such products lies not in the manufacturing process, but in the design process. However, design problems are typically driven by multiple contradictory objectives and different stakeholders, have no obvious stopping criteria, and frequently prevent construction of prototypes or experiments. Such ill-defined, or "wicked" problems cannot be "solved" in the traditional sense with optimization methods. Instead, modern design techniques are focused on generating knowledge about the alternative solutions in the design space. In order to facilitate such knowledge generation, in this dissertation I develop the "Systems Physics" framework that treats the emergent structures within the design space as physical objects that interact via quantifiable forces. Mathematically, Systems Physics is based on maximal entropy statistical mechanics, which allows both drawing conceptual analogies between design problems and collective phenomena and performing numerical calculations to gain quantitative understanding. Systems Physics operates via a Model-Compute-Learn loop, with each step refining our thinking of design problems. I demonstrate the capabilities of Systems Physics in two very distinct case studies: Naval Engineering and self-assembly. For the Naval Engineering case, I focus on an established problem of arranging shipboard systems within the available hull space. I demonstrate the essential trade-off between minimizing the routing cost and maximizing the design flexibility, which can lead to abrupt phase transitions. I show how the design space can break into several locally optimal architecture classes that have very different robustness to external couplings. I illustrate how the topology of the shipboard functional network enters a tight interplay with the spatial constraints on placement. For the self-assembly problem, I show that the topology of self-assembled structures can be reliably encoded in the properties of the building blocks so that the structure and the blocks can be jointly designed. The work presented here provides both conceptual and quantitative advancements. In order to properly port the language and the formalism of statistical mechanics to the design domain, I critically re-examine such foundational ideas as system-bath coupling, coarse graining, particle distinguishability, and direct and emergent interactions. I show that the design space can be packed into a special information structure, a tensor network, which allows seamless transition from graphical visualization to sophisticated numerical calculations. This dissertation provides the first quantitative treatment of the design problem that is not reduced to the narrow goals of mathematical optimization. Using statistical mechanics perspective allows me to move beyond the dichotomy of "forward" and "inverse" design and frame design as a knowledge generation process instead. Such framing opens the way to further studies of the design space structures and the time- and path-dependent phenomena in design. The present work also benefits from, and contributes to the philosophical interpretations of statistical mechanics developed by the soft matter community in the past 20 years. The discussion goes far beyond physics and engages with literature from materials science, naval engineering, optimization problems, design theory, network theory, and economic complexity.PHDPhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163133/1/aklishin_1.pd

The state of the art in empirical user evaluation of graph visualizations

While graph drawing focuses more on the aesthetic representation of node-link diagrams, graph visualization takes into account other visual metaphors making them useful for graph exploration tasks in information visualization and visual analytics. Although there are aesthetic graph drawing criteria that describe how a graph should be presented to make it faster and more reliably explorable, many controlled and uncontrolled empirical user studies flourished over the past years. The goal of them is to uncover how well the human user performs graph-specific tasks, in many cases compared to previously designed graph visualizations. Due to the fact that many parameters in a graph dataset as well as the visual representation of them might be varied and many user studies have been conducted in this space, a state-of-the-art survey is needed to understand evaluation results and findings to inform the future design, research, and application of graph visualizations. In this paper, we classify the present literature on the topmost level into graph interpretation, graph memorability, and graph creation where the users with their tasks stand in focus of the evaluation not the computational aspects. As another outcome of this work, we identify the white spots in this field and sketch ideas for future research directions

Algorithms for the restricted linear coloring arrangement problem

The aim of this project is to develop efficient algorithms for solving or approximating the Minimum Restricted Linear Coloring Arrangement Problem. It is the first approach to its algorithms, and we will face the problem from different perspectives: constraint programming, backtracking, greedy, and genetic algorithms. As a second goal we are interested in providing theoretical results for particular graphs