A deterministic approximation algorithm for computing the permanent of a 0, 1 matrix

We consider the problem of computing the permanent of a n by n matrix. For a class of matrices corresponding to constant degree expanders we construct a deterministic polynomial time approximation algorithm to within a multiplicative factor ( 1 + ∈)[superscript η] for arbitrary∈ > 0. This is an improvement over the best known approximation factor e[superscript η] obtained in Linial, Samorodnitsky and Wigderson (2000), though the latter result was established for arbitrary non-negative matrices. Our results use a recently developed deterministic approximation algorithm for counting partial matchings of a graph (Bayati, Gamarnik, Katz, Nair and Tetali (2007)) and Jerrum–Vazirani method (Jerrum and Vazirani (1996)) of approximating permanent by near perfect matchings

Algorithmic issues in queueing systems and combinatorial counting problems

Includes bibliographical references (leaves 111-118).Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2008.(cont.) However, these randomized algorithms can never provide proven upper or lower bounds on the number of objects they are counting, but can only give probabilistic estimates. We propose a set of deterministic algorithms for counting such objects for three classes of counting problems. They are interesting both because they give an alternative approach to solving these problems, and because unlike MCMC algorithms, they provide provable bounds on the number of objects. The algorithms we propose are for special cases of counting the number of matchings, colorings, or perfect matchings (permanent), of a graph.Multiclass queueing networks are used to model manufacturing, computer, supply chain, and other systems. Questions of performance and stability arise in these systems. There is a body of research on determining stability of a given queueing system, which contains algorithms for determining stability of queueing networks in some special cases, such as the case where there are only two stations. Yet previous attempts to find a general characterization of stability of queueing networks have not been successful.In the first part of the thesis, we contribute to the understanding of why such a general characterization could not be found. We prove that even under a relatively simple class of static buffer priority scheduling policies, stability of deterministic multiclass queueing network is, in general, an undecidable problem. Thus, there does not exist an algorithm for determining stability of queueing networks, even under those relatively simple assumptions. This explains why such an algorithm, despite significant efforts, has not been found to date. In the second part of the thesis, we address the problem of finding algorithms for approximately solving combinatorial graph counting problems. Counting problems are a wide and well studied class of algorithmic problems, that deal with counting certain objects, such as the number of independent sets, or matchings, or colorings, in a graph. The problems we address are known to be #P-hard, which implies that, unless P = #P, they can not be solved exactly in polynomial time. It is known that randomized approximation algorithms based on Monte Carlo Markov Chains (MCMC) solve these problems approximately, in polynomial time.by Dmitriy A. Katz-Rogozhnikov.Ph.D

Coupling Pre-Reforming and Partial Oxidation for LPG Conversion to Syngas

Coupling of the pre-reforming and partial oxidation was considered for the conversion of liquefied petroleum gas to syngas for the feeding applications of solid oxide fuel cells. Compared with conventional two step steam reforming, it allows the amount of water required for the process, and therefore the energy needed for water evaporation, to be lowered; substitution of high-potential heat by lower ones; and substitution of expensive tubular steam reforming reactors by adiabatic ones. The supposed process is more productive due to the high reaction rate of partial oxidation. The obtained syngas contains only ca. 10 vol.% H2O and ca. 50 vol.% of H2 + CO, which is attractive for the feeding application of solid oxide fuel cells. Compared with direct partial oxidation of liquefied petroleum gas, the suggested scheme is more energy efficient and overcomes problems with coke formation and catalyst overheating. The proof-of-concept experiments were carried out. The granular Ni-Cr2O3-Al2O3 catalyst was shown to be effective for propane pre-reforming at 350–400 °C, H2O:C molar ratio of 1.0, and flow rate of 12,000 h−1. The composite Rh/Ce0.75Zr0.25O2-δ–ƞ-Al2O3/FeCrAl catalyst was shown to be active and stable under conditions of partial oxidation of methane-rich syngas after pre-reforming and provided a syngas (H2 + CO) productivity of 28 m3·Lcat−1·h−1 (standard temperature and pressure)

A Natural Language Processing System for Extracting Evidence of Drug Repurposing from Scientific Publications

More than 200 generic drugs approved by the U.S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer. Due to their long history of safe patient use, low cost, and widespread availability, repurposing of these drugs represents a major opportunity to rapidly improve outcomes for cancer patients and reduce healthcare costs. In many cases, there is already evidence of efficacy for cancer, but trying to manually extract such evidence from the scientific literature is intractable. In this emerging applications paper, we introduce a system to automate non-cancer generic drug evidence extraction from PubMed abstracts. Our primary contribution is to define the natural language processing pipeline required to obtain such evidence, comprising the following modules: querying, filtering, cancer type entity extraction, therapeutic association classification, and study type classification. Using the subject matter expertise on our team, we create our own datasets for these specialized domain-specific tasks. We obtain promising performance in each of the modules by utilizing modern language processing techniques and plan to treat them as baseline approaches for future improvement of individual components

Effect of Ce/Zr Composition on Structure and Properties of Ce1−xZrxO2 Oxides and Related Ni/Ce1−xZrxO2 Catalysts for CO2 Methanation

Ce1−xZrxO2 oxides (x = 0.1, 0.25, 0.5) prepared via the Pechini route were investigated using XRD analysis, N2 physisorption, TEM, and TPR in combination with density functional theory calculations. The Ni/Ce1−xZrxO2 catalysts were characterized via XRD analysis, SEM-EDX, TEM-EDX, and CO chemisorption and tested in carbon dioxide methanation. The obtained Ce1−xZrxO2 materials were single-phase solid solutions. The increase in Zr content intensified crystal structure strains and favored the reducibility of the Ce1−xZrxO2 oxides but strongly affected their microstructure. The catalytic activity of the Ni/Ce1−xZrxO2 catalysts was found to depend on the composition of the Ce1−xZrxO2 supports. The detected negative effect of Zr content on the catalytic activity was attributed to the decrease in the dispersion of the Ni0 nanoparticles and the length of metal–support contacts due to the worsening microstructure of Ce1−xZrxO2 oxides. The improvement of the redox properties of the Ce1−xZrxO2 oxide supports through cation modification can be negated by changes in their microstructure and textural characteristics