Amino acid chiral amplification using Monte Carlo dynamic

The present work focuses on the processes of chiral amplification that lead to the rapid growth of the enantiomeric excess in a solution, utilizing a lattice model and a suitable Glauber dynamics. The initial conditions stem from a racemic mixture or points near the racemic state. The aim is to understand the effect of some variables such as temperature, concentration and constants that define the interaction energies in the equilibrium concentration after the dynamic evolution of the system. Dynamic evolution involves a path towards phase equilibrium in a D-L-S system, where D and L represent opposite chiral molecules and S represents their poorly soluble solvent. Our results, pertaining to the phase equilibrium of the D-L-S system employing amino acids, faithfully reproduce several experimentally observed outcomes documented in the literature. Through simulations, we may understand how the system evolved over time, starting from a random configuration and moving toward an equilibrium state with the lowest possible potential energy. We were able to recreate phase diagrams that were strikingly close to those obtained experimentally by specifying an appropriate Glauber dynamics for the system. Finally, we will discuss some findings from the dynamics of the chiral amplification processes that were modeled.Comment: 21 pages, 12 figure

Bicriteria Rectilinear Shortest Paths among Rectilinear Obstacles in the Plane

Given a rectilinear domain P of h pairwise-disjoint rectilinear obstacles with a total of n vertices in the plane, we study the problem of computing bicriteria rectilinear shortest paths between two points s and t in P. Three types of bicriteria rectilinear paths are considered: minimum-link shortest paths, shortest minimum-link paths, and minimum-cost paths where the cost of a path is a non-decreasing function of both the number of edges and the length of the path. The one-point and two-point path queries are also considered. Algorithms for these problems have been given previously. Our contributions are threefold. First, we find a critical error in all previous algorithms. Second, we correct the error in a not-so-trivial way. Third, we further improve the algorithms so that they are even faster than the previous (incorrect) algorithms when h is relatively small. For example, for computing a minimum-link shortest s-t path, the previous algorithm runs in O(n log^{3/2} n) time while the time of our new algorithm is O(n + h log^{3/2} h)

Maximum Clique in Geometric Intersection Graphs

An intersection graph is a graph that represents some geometric objects as vertices, and joins edges between the nodes corresponding to the items that intersect. The maximum clique in a geometric intersec- tion graph is the largest mutually intersecting set of objects. In this thesis, the primary focus is to study the maximum clique in various geometric intersection graphs. We develop three results motivated by the maximum clique problem in the intersection graph of disks in the Euclidean plane. First, we improve the time complexity of calculating the maximum clique in unit disk graphs from O(n3 log n) to O(n2.5 log n). Second, we introduce a new technique called pair-oriented labelling. This method is used to show the NP- hardness of finding a maximum clique in various geometric intersection graphs, acting as a way to augment the commonly used co-2-subdivision approach. Finally, finding maximum clique in two classes of geometric intersection graphs are proven to be NP-hard. These are the intersection graph of disks and axis-aligned rectangles, and the outer triangle graph. The former is previously known to be NP-hard, and so this proof represents the use of pair-oriented labelling in a problem that was otherwise considered difficult to prove NP-hard using a co-2-subdivision approach. The outer triangle graph is a novel intersection graph, which therefore provides new NP-hardness results for finding a maximum clique in geometric intersection graphs

ICASE/LaRC Symposium on Visualizing Time-Varying Data

Time-varying datasets present difficult problems for both analysis and visualization. For example, the data may be terabytes in size, distributed across mass storage systems at several sites, with time scales ranging from femtoseconds to eons. In response to these challenges, ICASE and NASA Langley Research Center, in cooperation with ACM SIGGRAPH, organized the first symposium on visualizing time-varying data. The purpose was to bring the producers of time-varying data together with visualization specialists to assess open issues in the field, present new solutions, and encourage collaborative problem-solving. These proceedings contain the peer-reviewed papers which were presented at the symposium. They cover a broad range of topics, from methods for modeling and compressing data to systems for visualizing CFD simulations and World Wide Web traffic. Because the subject matter is inherently dynamic, a paper proceedings cannot adequately convey all aspects of the work. The accompanying video proceedings provide additional context for several of the papers

User hints for optimisation processes

Innovative improvements in the area of Human-Computer Interaction and User Interfaces have en-abled intuitive and effective applications for a variety of problems. On the other hand, there has also been the realization that several real-world optimization problems still cannot be totally auto-mated. Very often, user interaction is necessary for refining the optimization problem, managing the computational resources available, or validating or adjusting a computer-generated solution. This thesis investigates how humans can help optimization methods to solve such difficult prob-lems. It presents an interactive framework where users play a dynamic and important role by pro-viding hints. Hints are actions that help to insert domain knowledge, to escape from local minima, to reduce the space of solutions to be explored, or to avoid ambiguity when there is more than one optimal solution. Examples of user hints are adjustments of constraints and of an objective function, focusing automatic methods on a subproblem of higher importance, and manual changes of an ex-isting solution. User hints are given in an intuitive way through a graphical interface. Visualization tools are also included in order to inform about the state of the optimization process. We apply the User Hints framework to three combinatorial optimization problems: Graph Clus-tering, Graph Drawing and Map Labeling. Prototype systems are presented and evaluated for each problem. The results of the study indicate that optimization processes can benefit from human interaction. The main goal of this thesis is to list cases where human interaction is helpful, and provide an ar-chitecture for supporting interactive optimization. Our contributions include the general User Hints framework and particular implementations of it for each optimization problem. We also present a general process, with guidelines, for applying our framework to other optimization problems

Generation and Analysis of Content for Physics-Based Video Games

The development of artificial intelligence (AI) techniques that can assist with the creation and analysis of digital content is a broad and challenging task for researchers. This topic has been most prevalent in the field of game AI research, where games are used as a testbed for solving more complex real-world problems. One of the major issues with prior AI-assisted content creation methods for games has been a lack of direct comparability to real-world environments, particularly those with realistic physical properties to consider. Creating content for such environments typically requires physics-based reasoning, which imposes many additional complications and restrictions that must be considered. Addressing and developing methods that can deal with these physical constraints, even if they are only within simulated game environments, is an important and challenging task for AI techniques that intend to be used in real-world situations. The research presented in this thesis describes several approaches to creating and analysing levels for the physics-based puzzle game Angry Birds, which features a realistic 2D environment. This research was multidisciplinary in nature and covers a wide variety of different AI fields, leading to this thesis being presented as a compilation of published work. The central part of this thesis consists of procedurally generating levels for physics-based games similar to those in Angry Birds. This predominantly involves creating and placing stable structures made up of many smaller blocks, as well as other level elements. Multiple approaches are presented, including both fully autonomous and human-AI collaborative methodologies. In addition, several analyses of Angry Birds levels were carried out using current state-of-the-art agents. A hyper-agent was developed that uses machine learning to estimate the performance of each agent in a portfolio for an unknown level, allowing it to select the one most likely to succeed. Agent performance on levels that contain deceptive or creative properties was also investigated, allowing determination of the current strengths and weaknesses of different AI techniques. The observed variability in performance across levels for different AI techniques led to the development of an adaptive level generation system, allowing for the dynamic creation of increasingly challenging levels over time based on agent performance analysis. An additional study also investigated the theoretical complexity of Angry Birds levels from a computational perspective. While this research is predominately applied to video games with physics-based simulated environments, the challenges and problems solved by the proposed methods also have significant real-world potential and applications

Cuestiones notables en la teoría de localización

Tesis Univ. Complutense de Madrid. Dpto. de Estadística e I.O. Dir. por. Francisco José Cano Sevilla, leída en Madrid ca. 1989.Depto. de Estadística e Investigación OperativaFac. de Ciencias MatemáticasTRUEProQuestpu

Cuestiones notables en la teoría de localización

Tesis Univ. Complutense de Madrid. Dpto. de Estadística e I.O. Dir. por. Francisco José Cano Sevilla, leída en Madrid ca. 1989.Depto. de Estadística e Investigación OperativaFac. de Ciencias MatemáticasTRUEProQuestpu

Decomposing and packing polygons / Dania el-Khechen.

In this thesis, we study three different problems in the field of computational geometry: the partitioning of a simple polygon into two congruent components, the partitioning of squares and rectangles into equal area components while minimizing the perimeter of the cuts, and the packing of the maximum number of squares in an orthogonal polygon. To solve the first problem, we present three polynomial time algorithms which given a simple polygon P partitions it, if possible, into two congruent and possibly nonsimple components P 1 and P 2 : an O ( n 2 log n ) time algorithm for properly congruent components and an O ( n 3 ) time algorithm for mirror congruent components. In our analysis of the second problem, we experimentally find new bounds on the optimal partitions of squares and rectangles into equal area components. The visualization of the best determined solutions allows us to conjecture some characteristics of a class of optimal solutions. Finally, for the third problem, we present three linear time algorithms for packing the maximum number of unit squares in three subclasses of orthogonal polygons: the staircase polygons, the pyramids and Manhattan skyline polygons. We also study a special case of the problem where the given orthogonal polygon has vertices with integer coordinates and the squares to pack are (2 {604} 2) squares. We model the latter problem with a binary integer program and we develop a system that produces and visualizes optimal solutions. The observation of such solutions aided us in proving some characteristics of a class of optimal solutions