Cutting Planes Width and the Complexity of Graph Isomorphism Refutations

The width complexity measure plays a central role in Resolution and other propositional proof systems like Polynomial Calculus (under the name of degree). The study of width lower bounds is the most extended method for proving size lower bounds, and it is known that for these systems, proofs with small width also imply the existence of proofs with small size. Not much has been studied, however, about the width parameter in the Cutting Planes (CP) proof system, a measure that was introduced by Dantchev and Martin in 2011 under the name of CP cutwidth. In this paper, we study the width complexity of CP refutations of graph isomorphism formulas. For a pair of non-isomorphic graphs G and H, we show a direct connection between the Weisfeiler-Leman differentiation number WL(G, H) of the graphs and the width of a CP refutation for the corresponding isomorphism formula Iso(G, H). In particular, we show that if WL(G, H) ? k, then there is a CP refutation of Iso(G, H) with width k, and if WL(G, H) > k, then there are no CP refutations of Iso(G, H) with width k-2. Similar results are known for other proof systems, like Resolution, Sherali-Adams, or Polynomial Calculus. We also obtain polynomial-size CP refutations from our width bound for isomorphism formulas for graphs with constant WL-dimension

Design and implementation of a system for integrating material and process selection in automated manufacturing

Today's manufacturing environment is characterized by competition and continuous change in product and process requirements. The concept of "design for manufacturability" integrates product specifications with manufacturing capabilities by considering the design and manufacturing phases as an integrated system, evaluating the combined system during the design phase of a product , and adjusting the design for maximum efficiency and production economics. This research focuses on one aspect of design for manufacturability, that of process technology evaluation for a specified product design. The objective of the proposed system developed in this study is to evaluate technology alternatives for manufacturing a specified part design and to identify the best combination of product-process characteristics that would minimize production costs within the constraints set by the product's functional requirements and available processing technology. The research objectives are accomplished by developing a simulation based analysis system. The user inputs product specifications through structural screens. The system maintains data bases of work and tool materials, and machining operations. Based on user input, the system then extracts appropriate information from these data bases, and analyzes of the production system in terms of production economics, and other operational measures such as throughput times and work-in-process inventories. Sensitivity analysis may then be performed to explore tradeoffs in design and production parameters. The system is completely integrated, and a user with no prior experience of either simulation or data base technology can use the system effectively

Abrasive machining with MQSL

Grinding and polishing of engineered components are critical aspects of the precision manufacturing of high performance, quality assured products. Elevated process temperatures, however, are a common and for the most part undesirable feature of the grinding process. High process temperatures increase the likelihood of microstructural change within the immediate subsurface layer and are detrimental to the strength and performance of the manufactured products. Increasing processing costs and tighter environmental legislation are encouraging industry to seek innovative fluid application techniques as significant savings in production can be achieved. In this context, and with sponsorship from three industrial partners, namely; Fives Cinetic, Fuchs Lubricants plc and Southside Thermal Sciences Ltd, and also from the Engineering and Physical Science Research Council (EPSRC), this research aimed to develop an understanding of Minimum Quantity Solid Lubrication (MQSL) as a method for abrasive machining, with particular reference to the control of surface temperatures. Improving the lubricity of Minimum Quantity Lubrication (MQL) fluids reduces the frictional source of process heat and controls the finish surface temperature. The application of effective solid lubricants is known as Minimum Quantity Solid Lubrication (MQSL). Molybdenum Disulphide (MoS2), Calcium Fluoride (CaF2), and hexagonal Boron Nitride (hBN) were compared against a semi-synthetic water soluble machining fluid (Fuchs EcoCool). A series of Taguchi factorial experimental trials assessed their performances through ANOVA (ANalysis Of VAriance) statistical method. The hBN produced the lowest grinding temperatures of the solid lubricants tested, although they still remained higher than those achieved using the EcoCool control. The reduction of the machining fluid enabled a Charged Coupled Device (CCD) sensor to be fitted into the grinding machine. The recorded movement in the emitted spectrum from the grinding chips was compared to experimental and modelled process temperatures. This showed that the wavelengths of the chip light correlated to the temperature of the finish grinding surface. This greatly contributed to determining the feasibility of constructing a non-destructive, non-invasive, thermally-adaptive control system for controlling grinding surface temperatures.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Algorithms for Exact Structure Discovery in Bayesian Networks

Bayesian networks are compact, flexible, and interpretable representations of a joint distribution. When the network structure is unknown but there are observational data at hand, one can try to learn the network structure. This is called structure discovery. This thesis contributes to two areas of structure discovery in Bayesian networks: space--time tradeoffs and learning ancestor relations. The fastest exact algorithms for structure discovery in Bayesian networks are based on dynamic programming and use excessive amounts of space. Motivated by the space usage, several schemes for trading space against time are presented. These schemes are presented in a general setting for a class of computational problems called permutation problems; structure discovery in Bayesian networks is seen as a challenging variant of the permutation problems. The main contribution in the area of the space--time tradeoffs is the partial order approach, in which the standard dynamic programming algorithm is extended to run over partial orders. In particular, a certain family of partial orders called parallel bucket orders is considered. A partial order scheme that provably yields an optimal space--time tradeoff within parallel bucket orders is presented. Also practical issues concerning parallel bucket orders are discussed. Learning ancestor relations, that is, directed paths between nodes, is motivated by the need for robust summaries of the network structures when there are unobserved nodes at work. Ancestor relations are nonmodular features and hence learning them is more difficult than modular features. A dynamic programming algorithm is presented for computing posterior probabilities of ancestor relations exactly. Empirical tests suggest that ancestor relations can be learned from observational data almost as accurately as arcs even in the presence of unobserved nodes.Algoritmeja Bayes-verkkojen rakenteen tarkkaan oppimiseen Bayes-verkot ovat todennäköisyysmalleja, joiden avulla voidaan kuvata muuttujien välisiä suhteita. Bayes-verkko koostuu kahdesta osasta: rakenteesta ja kuhunkin muuttujaan liittyvästä ehdollisesta todennäköisyysjakaumasta. Rakenteen puolestaan muodostaa muuttujien välisiä riippuvuuksia kuvaava suunnattu syklitön verkko. Kun tarkasteltavaa ilmiötä hyvin kuvaavaa Bayes-verkkoa ei tunneta ennalta, mutta ilmiöön liittyvistä muuttujista on kerätty havaintoaineistoa, voidaan sopivia algoritmeja käyttäen yrittää löytää verkkorakenne, joka sovittuu aineistoon mahdollisimman hyvin. Nopeimmat tarkat rakenteenoppimisalgoritmit perustuvat niin kutsuttuun dynaamiseen ohjelmointiin, eli ne pitävät välituloksia muistissa ja näin välttävät suorittamasta samoja laskuja useaan kertaan. Vaikka tällaiset menetelmät ovat suhteellisen nopeita, niiden haittapuolena on suuri muistinkäyttö, joka estää suurten verkkojen rakenteen oppimisen. Väitöskirjan alkuosa käsittelee rakenteenoppimisalgoritmeja, jotka tasapainottelevat ajan- ja muistinkäytön välillä. Kirjassa esitellään menetelmiä, joilla verkon rakenne voidaan oppia tehokkaasti käyttäen hyväksi kaikki käytössä oleva tila. Uusi menetelmä mahdollistaa entistä suurempien verkkojen rakenteen oppimisen. Edellä mainittu menetelmä yleistetään ratkaisemaan Bayes-verkkojen rakenteenoppimisen lisäksi myös niin kutsuttuja permutaatio-ongelmia, joista tunnetuin lienee kauppamatkustajan ongelma. Väitöskirjan loppuosa käsittelee muuttujien välisien esi-isäsuhteiden oppimista. Kyseiset suhteet ovat kiinnostavia, sillä ne antavat lisätietoa muuttujien sekä suorista että epäsuorista syy-seuraussuhteista. Väitöskirjassa esitetään algoritmi esi-isäsuhteiden todennäköisyyksien laskemiseen. Algoritmin toimintaa tutkitaan käytännössä ja todetaan, että esi-isäsuhteita pystytään oppimaan melko hyvin jopa silloin, kun useat havaitsemattomat muuttujat vaikuttavat aineiston muuttujiin

Revisitando o Método de Ranking de Pontos Extremos para o Problema da Mochila Linear.

O problema da mochila linear visa encontrar um subconjunto de itens que otimize uma função objetivo sem exceder uma capacidade de mochila dada. É um dos problemas mais estudados em otimização combinatória e que nas últimas décadas vem sendo muito utilizado nas áreas de produção e administração. Na literatura existem vários métodos para resolver efetivamente esse problema. Porém, o método de ranking de pontos extremos busca a solução do problema analisando os vértices adjacentes ao vértice que resolve o problema relaxado para encontrar soluções alternativas. Quando foi apresentado em 1973, mostrou resultados interessantes, mas não tem sido mais utilizado pelos pesquisadores há aproximadamente 40 anos. Nessa dissertação será retomado o conceito de ranking de pontos extremos com o objetivo de determinar se foi acertada a decisão dos pesquisadores de não utilizar mais esse método. Para tal propósito o desempenho do ranking de pontos extremos foi comparado com o desempenho de dois métodos branch-and-bound. Um utiliza o método simplex para resolver os problemas, branch-and-bound-simplex(BBS), enquanto o segundo utiliza o método proposto por Danztig para achar a solução do problema da mochila contínuo, branch-and-bound-Dantzig(BBD). Os resultados obtidos mostraram que o método BBD é o melhor dos três tanto em eficácia como em rapidez, já o ranking de pontos extremos se apresentou competitivo ao BBD em problemas com até 500 variáveis, piorando rapidamente à medida que o tamanho dos problemas aumentava. Os métodos BBD e de ranking de pontos extremos obtiveram sempre as respostas ótimas. O BBS, dependendo das características de alguns problemas, não atingiu o ótimo, sendo por esse fato considerado como o pior de todos. O que faz concluir que sim, é justificado ter deixado de usar o método de ranking de pontos extremos para resolver o problema da mochila linear já que existem outros métodos com desempenho melhor

Component-based synthesis of motion planning algorithms

Combinatory Logic Synthesis generates data or runnable programs according to formal type specifications. Synthesis results are composed based on a user-specified repository of components, which brings several advantages for representing spaces of high variability. This work suggests strategies to manage the resulting variations by proposing a domain-specific brute-force search and a machine learning-based optimization procedure. The brute-force search involves the iterative generation and evaluation of machining strategies. In contrast, machine learning optimization uses statistical models to enable the exploration of the design space. The approaches involve synthesizing programs and meta-programs that manipulate, run, and evaluate programs. The methodologies are applied to the domain of motion planning algorithms, and they include the configuration of programs belonging to different algorithmic families. The study of the domain led to the identification of variability points and possible variations. Proof-of-concept repositories represent these variability points and incorporate them into their semantic structure. The selected algorithmic families involve specific computation steps or data structures, and corresponding software components represent possible variations. Experimental results demonstrate that CLS enables synthesis-driven domain-specific optimization procedures to solve complex problems by exploring spaces of high variability.Combinatory Logic Synthesis (CLS) generiert Daten oder lauffähige Programme anhand von formalen Typspezifikationen. Die Ergebnisse der Synthese werden auf Basis eines benutzerdefinierten Repositories von Komponenten zusammengestellt, was diverse Vorteile für die Beschreibung von Räumen mit hoher Variabilität mit sich bringt. Diese Arbeit stellt Strategien für den Umgang mit den resultierenden Variationen vor, indem eine domänen-spezifische Brute-Force Suche und ein maschinelles Lernverfahren für die Untersuchung eines Optimierungsproblems aufgezeigt werden. Die Brute-Force Suche besteht aus der iterativen Generierung und Evaluation von Frässtrategien. Im Gegensatz dazu nutzt der Optimierungsansatz statistische Modelle zur Erkundung des Entwurfsraums. Beide Ansätze synthetisieren Programme und Metaprogramme, welche Programme bearbeiten, ausführen und evaluieren. Diese Methoden werden auf die Domäne der Bewegungsplanungsalgorithmen angewendet und sie beinhalten die Konfiguration von Programmen, welche zu unterschiedlichen algorithmischen Familien gehören. Die Untersuchung der Domäne führte zur Identifizierung der Variabilitätspunkte und der möglichen Variationen. Entsprechende Proof of Concept Implementierungen in Form von Repositories repräsentieren jene Variabilitätspunkte und beziehen diese in ihre semantische Struktur ein. Die gewählten algorithmischen Familien sehen bestimmte Berechnungsschritte oder Datenstrukturen vor, und entsprechende Software Komponenten stellen mögliche Variationen dar. Versuchsergebnisse belegen, dass CLS synthese-getriebene domänenspezifische Optimierungsverfahren ermöglicht, welche komplexe Probleme durch die Exploration von Räumen hoher Variabilität lösen

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum

Characterization and Modelling of Composites

Composites have increasingly been used in various structural components in the aerospace, marine, automotive, and wind energy sectors. The material characterization of composites is a vital part of the product development and production process. Physical, mechanical, and chemical characterization helps developers to further their understanding of products and materials, thus ensuring quality control. Achieving an in-depth understanding and consequent improvement of the general performance of these materials, however, still requires complex material modeling and simulation tools, which are often multiscale and encompass multiphysics. This Special Issue aims to solicit papers concerning promising, recent developments in composite modeling, simulation, and characterization, in both design and manufacturing areas, including experimental as well as industrial-scale case studies. All submitted manuscripts will undergo a rigorous review process and will only be considered for publication if they meet journal standards. Selected top articles may have their processing charges waived at the recommendation of reviewers and the Guest Editor