Exotic meson decay widths using lattice quantum chromodynamics

Most experiments in particle physics are scattering experiments, the analysis of which leads to masses, scattering phases, decay widths and other properties of one or multi-particle systems. Until the advent of Lattice Quantum Chromodynamics (LQCD) it was difficult to compare experimental results on low energy hadron-hadron scattering processes to the predictions of QCD, the current theory of strong interactions. The reason being, at low energies the QCD coupling constant becomes large and the perturbation expansion for scattering; amplitudes does not converge. To overcome this, one puts the theory onto a lattice, imposes a momentum cutoff, and computes the integral numerically. For particle masses, predictions of LQCD agree with experiment, but the area of decay widths is largely unexplored. ^ LQCD provides ab initio access to unusual hadrons like exotic mesons that are predicted to contain real gluonic structure. To study decays of these type resonances the energy spectra of a two-particle decay state in a finite volume of dimension L can be related to the associated scattering phase shift δ(k) at momentum k through exact formulae derived by Lüscher. Because the spectra can be computed using numerical Monte Carlo techniques, the scattering phases can thus be determined using Lüscher's formulae, and the corresponding decay widths can be found by fitting Breit-Wigner functions. ^ Results of such a decay width calculation for an exotic hybrid( h) meson (JPC = 1-+) are presented for the decay channel h → πa 1. This calculation employed Lüscher's formulae and an approximation of LQCD called the quenched approximation. Energy spectra for the h and πa1 systems were extracted using eigenvalues of a correlation matrix, and the corresponding scattering phase shifts were determined for a discrete set of πa1 momenta. Although the number of phase shift data points was sparse, fits to a Breit-Wigner model were made, resulting in a decay width of about 60 MeV. ^

Utilizando comitês de classificadores para predição de rendimento escolar

Educational Data Mining is an application domain in artificial intelligence area that has been extensively explored nowadays. Technological advances and in particular, the increasing use of virtual learning environments have allowed the generation of considerable amounts of data to be investigated. Among the activities to be treated in this context exists the prediction of school performance of the students, which can be accomplished through the use of machine learning techniques. Such techniques may be used for student’s classification in predefined labels. One of the strategies to apply these techniques consists in their combination to design multi-classifier systems, which efficiency can be proven by results achieved in other studies conducted in several areas, such as medicine, commerce and biometrics. The data used in the experiments were obtained from the interactions between students in one of the most used virtual learning environments called Moodle. In this context, this paper presents the results of several experiments that include the use of specific multi-classifier systems systems, called ensembles, aiming to reach better results in school performance prediction that is, searching for highest accuracy percentage in the student’s classification. Therefore, this paper presents a significant exploration of educational data and it shows analyzes of relevant results about these experiments.

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Using phase-space localized basis functions to obtain vibrational energies of molecules

For decades scientists have attempted to use ideas of classical mechanics to choose basis functions for calculating spectra. The hope is that a classically-motivated basis set will be small because it covers only the dynamically important part of phase space. One popular idea is to use phase space localized (PSL) basis functions. This thesis improves on previous efforts to use PSL functions and examines the usefulness of these improvements. Because the overlap matrix, in the matrix eigenvalue problem obtained by using PSL functions with the variational method, is not an identity, it is costly to use iterative methods to solve the matrix eigenvalue problem. We show that it is possible to circumvent the orthogonality (overlap) problem and use iterative eigensolvers. We also present an altered method of calculating the matrix elements that improves the performance of the PSL basis functions, and also a new method which more efficiently chooses which PSL functions to include. These improvements are applied to a variety of single well molecules. We conclude that for single minimum molecules, the PSL functions are inferior to other basis functions. However, the ideas developed here can be applied to other types of basis functions, and PSL functions may be useful for multi-well systems.

Human behaviour recognition in data-scarce domains

This paper presents the novel theory for performing multi-agent activity recognition without requiring large training corpora. The reduced need for data means that robust probabilistic recognition can be performed within domains where annotated datasets are traditionally unavailable. Complex human activities are composed from sequences of underlying primitive activities. We do not assume that the exact temporal ordering of primitives is necessary, so can represent complex activity using an unordered bag. Our three-tier architecture comprises low-level video tracking, event analysis and high-level inference. High-level inference is performed using a new, cascading extension of the Rao–Blackwellised Particle Filter. Simulated annealing is used to identify pairs of agents involved in multi-agent activity. We validate our framework using the benchmarked PETS 2006 video surveillance dataset and our own sequences, and achieve a mean recognition F-Score of 0.82. Our approach achieves a mean improvement of 17% over a Hidden Markov Model baseline.

A Collaborative Multiagent Framework based on Online Risk-Aware Planning and Decision-Making

Planning is an essential process in teams of multiple agents pursuing a common goal. When the effects of actions undertaken by agents are uncertain, evaluating the potential risk of such actions alongside their utility might lead to more rational decisions upon planning. This challenge has been recently tackled for single agent settings, yet domains with multiple agents that present diverse viewpoints towards risk still necessitate comprehensive decision making mechanisms that balance the utility and risk of actions. In this work, we propose a novel collaborative multi-agent planning framework that integrates (i) a team-level online planner under uncertainty that extends the classical UCT approximate algorithm, and (ii) a preference modeling and multicriteria group decision making approach that allows agents to find accepted and rational solutions for planning problems, predicated on the attitude each agent adopts towards risk. When utilised in risk-pervaded scenarios, the proposed framework can reduce the cost of reaching the common goal sought and increase effectiveness, before making collective decisions by appropriately balancing risk and utility of actions. 

Uma abordagem evolucionária para o planejamento online de rotas para múltiplos robôs móveis em ambiente multiagente

O problema de planejamento de rotas de robôs móveis consiste em determinar a melhor rota para um robô, em um ambiente estático e/ou dinâmico, que seja capaz de deslocá-lo de um ponto inicial até e um ponto final, também em conhecido como estado objetivo. O presente trabalho emprega o uso de uma abordagem baseada em Algoritmos Genéticos para o planejamento de rotas de múltiplos robôs em um ambiente complexo composto por obstáculos fixos e obstáculos moveis. Através da implementação do modelo no software do NetLogo, uma ferramenta utilizada em simulações de aplicações multiagentes, possibilitou-se a modelagem de robôs e obstáculos presentes no ambiente como agentes interativos, viabilizando assim o desenvolvimento de processos de detecção e desvio de obstáculos. A abordagem empregada busca pela melhor rota para robôs e apresenta um modelo composto pelos operadores básicos de reprodução e mutação, acrescido de um novo operador duplo de refinamento capaz de aperfeiçoar as melhores soluções encontradas através da eliminação de movimentos inúteis. Além disso, o calculo da rota de cada robô adota um método de geração de subtrechos, ou seja, não calcula apenas uma unica rota que conecta os pontos inicial e final do cenário, mas sim várias pequenas subrotas que conectadas formam um caminho único capaz de levar o robô ao estado objetivo. Neste trabalho foram desenvolvidos dois cenários, para avaliação da sua escalabilidade: o primeiro consiste em um cenário simples composto apenas por um robô, um obstáculo movel e alguns obstáculos fixos; já o segundo, apresenta um cenário mais robusto, mais amplo, composto por múltiplos robôs e diversos obstáculos fixos e moveis. Ao final, testes de desempenho comparativos foram efetuados entre a abordagem baseada em Algoritmos Genéticos e o Algoritmo A*. Como critério de comparação foi utilizado o tamanho das rotas obtidas nas vinte simulações executadas em cada abordagem. A analise dos resultados foi especificada através do Teste t de Student.

Fighting Evasive Malware with DVasion

Malware is a foundational component of cyber crime that enables an attacker to modify the normal operation of a computer or access sensitive, digital information. Despite the extensive research performed to identify such programs, existing schemes fail to detect evasive malware, an increasingly popular class of malware that can alter its behavior at run-time, making it difficult to detect using today’s state of the art malware analysis systems. In this thesis, we present DVasion, a comprehensive strategy that exposes such evasive behavior through a multi-execution technique. DVasion successfully detects behavior that would have been missed by traditional, single-execution approaches, while addressing the limitations of previously proposed multi-execution systems. We demonstrate the accuracy of our system through strong parallels with existing work on evasive malware, as well as uncover the hidden behavior within 167 of 1,000 samples.

The SOPHIE search for northern extrasolar planets: X. Detection and characterization of giant planets by the dozen

We present new radial velocity measurements of eight stars that were secured with the spectrograph SOPHIE at the 193 cm telescope of the Haute-Provence Observatory. The measurements allow detecting and characterizing new giant extrasolar planets. The host stars are dwarfs of spectral types between F5 and K0 and magnitudes of between 6.7 and 9.6; the planets have minimum masses Mp sin i of between 0.4 to 3.8 MJup and orbitalperiods of several days to several months. The data allow only single planets to be discovered around the first six stars (HD 143105, HIP 109600, HD 35759, HIP 109384, HD 220842, and HD 12484), but one of them shows the signature of an additional substellar companion in the system. The seventh star, HIP 65407, allows the discovery of two giant planets that orbit just outside the 12:5 resonance in weak mutual interaction. The last star, HD 141399, was already known to host a four-planet system; our additional data and analyses allow new constraints to be set on it. We present Keplerian orbits of all systems, together with dynamical analyses of the two multi-planet systems. HD 143105 is one of the brightest stars known to host a hot Jupiter, which could allow numerous follow-up studies to be conducted even though this is not a transiting system. The giant planets HIP 109600b, HIP 109384b, and HD 141399c are located in the habitable zone of their host star.

Optimizing last mile delivery using public transport with multiagent based control

The majority of research work carried out in the field of Operations-Research uses methods and algorithms to optimize the pick-up and delivery problem. Most studies aim to solve the vehicle routing problem, to accommodate optimum delivery orders, vehicles etc. This paper focuses on green logistics approach, where existing Public Transport infrastructure capability of a city is used for the delivery of small and medium sized packaged goods thus, helping improve the situation of urban congestion and greenhouse gas emissions reduction. It carried out a study to investigate the feasibility of the proposed multi-agent based simulation model, for efficiency of cost, time and energy consumption. Multimodal Dijkstra Shortest Path algorithm and Nested Monte Carlo Search have been employed for a two-phase algorithmic approach used for generation of time based cost matrix. The quality of the tour is dependent on the efficiency of the search algorithm implemented for plan generation and route planning. The results reveal a definite advantage of using Public Transportation over existing delivery approaches in terms of energy efficiency.

Optimal choice of electoral preference data

Electoral researchers are so much accustomed to analyzing the choice of the single most preferred party as the left-hand side variable of their models of electoral behavior that they often ignore revealed preference data. Drawing on random utility theory, their models predict electoral behavior at the extensive margin of choice. Since the seminal work of Luce and others on individual choice behavior, however, many social science disciplines (consumer research, labor market research, travel demand, etc.) have extended their inventory of observed preference data with, for instance, multiple paired comparisons, complete or incomplete rankings, and multiple ratings. Eliciting (voter) preferences using these procedures and applying appropriate choice models is known to considerably increase the efficiency of estimates of causal factors in models of (electoral) behavior. In this paper, we demonstrate the efficiency gain when adding additional preference information to first preferences, up to full ranking data. We do so for multi-party systems of different sizes. We use simulation studies as well as empirical data from the 1972 German election study. Comparing the practical considerations for using ranking and single preference data results in suggestions for choice of measurement instruments in different multi-candidate and multi-party settings.

Análise paramétrica da simulação composicional do processo de Drenagem Gravitacional Assistida por Gás (GAGD)

In Brazil and around the world, oil companies are looking for, and expected development of new technologies and processes that can increase the oil recovery factor in mature reservoirs, in a simple and inexpensive way. So, the latest research has developed a new process called Gas Assisted Gravity Drainage (GAGD) which was classified as a gas injection IOR. The process, which is undergoing pilot testing in the field, is being extensively studied through physical scale models and core-floods laboratory, due to high oil recoveries in relation to other gas injection IOR. This process consists of injecting gas at the top of a reservoir through horizontal or vertical injector wells and displacing the oil, taking advantage of natural gravity segregation of fluids, to a horizontal producer well placed at the bottom of the reservoir. To study this process it was modeled a homogeneous reservoir and a model of multi-component fluid with characteristics similar to light oil Brazilian fields through a compositional simulator, to optimize the operational parameters. The model of the process was simulated in GEM (CMG, 2009.10). The operational parameters studied were the gas injection rate, the type of gas injection, the location of the injector and production well. We also studied the presence of water drive in the process. The results showed that the maximum vertical spacing between the two wells, caused the maximum recovery of oil in GAGD. Also, it was found that the largest flow injection, it obtained the largest recovery factors. This parameter controls the speed of the front of the gas injected and determined if the gravitational force dominates or not the process in the recovery of oil. Natural gas had better performance than CO2 and that the presence of aquifer in the reservoir was less influential in the process. In economic analysis found that by injecting natural gas is obtained more economically beneficial than CO2

Two roads to planet detection

One of the most exciting discoveries in astrophysics of the last last decade is of the sheer diversity of planetary systems. These include "hot Jupiters", giant planets so close to their host stars that they orbit once every few days; "Super-Earths", planets with sizes intermediate to those of Earth and Neptune, of which no analogs exist in our own solar system; multi-planet systems with planets smaller than Mars to larger than Jupiter; planets orbiting binary stars; free-floating planets flying through the emptiness of space without any star; even planets orbiting pulsars. Despite these remarkable discoveries, the field is still young, and there are many areas about which precious little is known. In particular, we don't know the planets orbiting Sun-like stars nearest to our own solar system, and we know very little about the compositions of extrasolar planets. This thesis provides developments in those directions, through two instrumentation projects.

The first chapter of this thesis concerns detecting planets in the Solar neighborhood using precision stellar radial velocities, also known as the Doppler technique. We present an analysis determining the most efficient way to detect planets considering factors such as spectral type, wavelengths of observation, spectrograph resolution, observing time, and instrumental sensitivity. We show that G and K dwarfs observed at 400-600 nm are the best targets for surveys complete down to a given planet mass and out to a specified orbital period. Overall we find that M dwarfs observed at 700-800 nm are the best targets for habitable-zone planets, particularly when including the effects of systematic noise floors caused by instrumental imperfections. Somewhat surprisingly, we demonstrate that a modestly sized observatory, with a dedicated observing program, is up to the task of discovering such planets.

We present just such an observatory in the second chapter, called the "MINiature Exoplanet Radial Velocity Array," or MINERVA. We describe the design, which uses a novel multi-aperture approach to increase stability and performance through lower system etendue, as well as keeping costs and time to deployment down. We present calculations of the expected planet yield, and data showing the system performance from our testing and development of the system at Caltech's campus. We also present the motivation, design, and performance of a fiber coupling system for the array, critical for efficiently and reliably bringing light from the telescopes to the spectrograph. We finish by presenting the current status of MINERVA, operational at Mt. Hopkins observatory in Arizona.

The second part of this thesis concerns a very different method of planet detection, direct imaging, which involves discovery and characterization of planets by collecting and analyzing their light. Directly analyzing planetary light is the most promising way to study their atmospheres, formation histories, and compositions. Direct imaging is extremely challenging, as it requires a high performance adaptive optics system to unblur the point-spread function of the parent star through the atmosphere, a coronagraph to suppress stellar diffraction, and image post-processing to remove non-common path "speckle" aberrations that can overwhelm any planetary companions.

To this end, we present the "Stellar Double Coronagraph," or SDC, a flexible coronagraphic platform for use with the 200" Hale telescope. It has two focal and pupil planes, allowing for a number of different observing modes, including multiple vortex phase masks in series for improved contrast and inner working angle behind the obscured aperture of the telescope. We present the motivation, design, performance, and data reduction pipeline of the instrument. In the following chapter, we present some early science results, including the first image of a companion to the star delta Andromeda, which had been previously hypothesized but never seen.

A further chapter presents a wavefront control code developed for the instrument, using the technique of "speckle nulling," which can remove optical aberrations from the system using the deformable mirror of the adaptive optics system. This code allows for improved contrast and inner working angles, and was written in a modular style so as to be portable to other high contrast imaging platforms. We present its performance on optical, near-infrared, and thermal infrared instruments on the Palomar and Keck telescopes, showing how it can improve contrasts by a factor of a few in less than ten iterations.

One of the large challenges in direct imaging is sensing and correcting the electric field in the focal plane to remove scattered light that can be much brighter than any planets. In the last chapter, we present a new method of focal-plane wavefront sensing, combining a coronagraph with a simple phase-shifting interferometer. We present its design and implementation on the Stellar Double Coronagraph, demonstrating its ability to create regions of high contrast by measuring and correcting for optical aberrations in the focal plane. Finally, we derive how it is possible to use the same hardware to distinguish companions from speckle errors using the principles of optical coherence. We present results observing the brown dwarf HD 49197b, demonstrating the ability to detect it despite it being buried in the speckle noise floor. We believe this is the first detection of a substellar companion using the coherence properties of light.

A complexity-based diagnostic on the democratic unity roundtable mud: problems and alternatives (2008-2016)

Este trabajo exploratorio estudia al movimiento político Mesa de la Unidad Democrática (MUD), creada con el fin de oponerse la Gobierno socialista existente en venezuela. La crítica que este documento realiza, parte desde el punto de vista de la Ciencia de la Complejidad. Algunos conceptos clave de sistemas complejos han sido utilizados para explicar el funcionamiento y organización de la MUD, esto con el objetivo de generar un diagnóstico integral de los problemas que enfrenta, y evidenciar las nuevas percepciones sobre comportamientos perjudiciales que el partido tiene actualmente. Con el enfoque de la complejidad se pretende ayudar a comprender mejor el contexto que enmarca al partido y, para, finalmente aportar una serie de soluciones a los problemas de cohesión que presen