179 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Identification of Novel Properties of Metabolic Systems Through Null-Space Analysis

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    Metabolic models provide a mathematical description of the complex network of biochemical reactions that sustain life. Among these, genome-scale models capture the entire metabolism of an organism, by encompassing all known biochemical reactions encoded by its genome. They are invaluable tools for exploring the metabolic potential of an organism, such as by predicting its response to different stimuli and identifying which reactions are essential for its survival. However, as the understanding of metabolism continues to grow, so too has the size and complexity of metabolic models, making the need for novel techniques that can simplify networks and extract specific features from them ever more important. This thesis addresses this challenge by leveraging the underlying structure of the network embodied by these models. Three different approaches are presented. Firstly, an algorithm that uses convex analysis techniques to decompose flux measurements into a set of fundamental flux pathways is developed and applied to a genome-scale model of Campylobacter jejuni in order to investigate its absolute requirement for environmental oxygen. This approach aims to overcome the computational limitations associated with the traditional technique of elementary mode analysis. Secondly, a method that can reduce the size of models by removing redundancies is introduced. This method identifies alternative pathways that lead from the same start to end product and is useful for identifying systematic errors that arise from model construction and for revealing information about the network’s flexibility. Finally, a novel technique for relating metabolites based on relationships between their concentration changes, or alternatively their chemical similarity, is developed based on the invariant properties of the left null-space of the stoichiometry matrix. Although various methods for relating the composition of metabolites exist, this technique has the advantage of not requiring any information apart from the model’s structure and allowed for the development of an algorithm that can simplify models and their analysis by extracting pathways containing metabolites that have similar composition. Furthermore, a method that uses the left null-space to facilitate the identification of un-balanced reactions in models is also presented

    Deep material networks for efficient scale-bridging in thermomechanical simulations of solids

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    We investigate deep material networks (DMN). We lay the mathematical foundation of DMNs and present a novel DMN formulation, which is characterized by a reduced number of degrees of freedom. We present a efficient solution technique for nonlinear DMNs to accelerate complex two-scale simulations with minimal computational effort. A new interpolation technique is presented enabling the consideration of fluctuating microstructure characteristics in macroscopic simulations

    Quantum Computing for Airline Planning and Operations

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    Classical algorithms and mathematical optimization techniques have beenused extensively by airlines to optimize their profit and ensure that regulationsare followed. In this thesis, we explore which role quantum algorithmscan have for airlines. Specifically, we have considered the two quantum optimizationalgorithms; the Quantum Approximate Optimization Algorithm(QAOA) and Quantum Annealing (QA). We present a heuristic that integratesthese quantum algorithms into the existing classical algorithm, whichis currently employed to solve airline planning problems in a state-of-the-artcommercial solver. We perform numerical simulations of QAOA circuits andfind that linear and quadratic algorithm depth in the input size can be requiredto obtain a one-shot success probability of 0.5. Unfortunately, we areunable to find performance guarantees. Finally, we perform experiments withD-wave’s newly released QA machine and find that it outperforms 2000Q formost instances

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Fully dynamic approximation schemes on planar and apex-minor-free graphs

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    The classic technique of Baker [J. ACM '94] is the most fundamental approach for designing approximation schemes on planar, or more generally topologically-constrained graphs, and it has been applied in a myriad of different variants and settings throughout the last 30 years. In this work we propose a dynamic variant of Baker's technique, where instead of finding an approximate solution in a given static graph, the task is to design a data structure for maintaining an approximate solution in a fully dynamic graph, that is, a graph that is changing over time by edge deletions and edge insertions. Specifically, we address the two most basic problems -- Maximum Weight Independent Set and Minimum Weight Dominating Set -- and we prove the following: for a fully dynamic nn-vertex planar graph GG, one can: * maintain a (1−ε)(1-\varepsilon)-approximation of the maximum weight of an independent set in GG with amortized update time f(ε)⋅no(1)f(\varepsilon)\cdot n^{o(1)}; and, * under the additional assumption that the maximum degree of the graph is bounded at all times by a constant, also maintain a (1+ε)(1+\varepsilon)-approximation of the minimum weight of a dominating set in GG with amortized update time f(ε)⋅no(1)f(\varepsilon)\cdot n^{o(1)}. In both cases, f(ε)f(\varepsilon) is doubly-exponential in poly(1/ε)\mathrm{poly}(1/\varepsilon) and the data structure can be initialized in time f(ε)⋅n1+o(1)f(\varepsilon)\cdot n^{1+o(1)}. All our results in fact hold in the larger generality of any graph class that excludes a fixed apex-graph as a minor.Comment: 37 pages, accepted to SODA '2

    The Catalog Problem:Deep Learning Methods for Transforming Sets into Sequences of Clusters

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    The titular Catalog Problem refers to predicting a varying number of ordered clusters from sets of any cardinality. This task arises in many diverse areas, ranging from medical triage, through multi-channel signal analysis for petroleum exploration to product catalog structure prediction. This thesis focuses on the latter, which exemplifies a number of challenges inherent to ordered clustering. These include learning variable cluster constraints, exhibiting relational reasoning and managing combinatorial complexity. All of which present unique challenges for neural networks, combining elements of set representation, neural clustering and permutation learning.In order to approach the Catalog Problem, a curated dataset of over ten thousand real-world product catalogs consisting of more than one million product offers is provided. Additionally, a library for generating simpler, synthetic catalog structures is presented. These and other datasets form the foundation of the included work, allowing for a quantitative comparison of the proposed methods’ ability to address the underlying challenge. In particular, synthetic datasets enable the assessment of the models’ capacity to learn higher order compositional and structural rules.Two novel neural methods are proposed to tackle the Catalog Problem, a set encoding module designed to enhance the network’s ability to condition the prediction on the entirety of the input set, and a larger architecture for inferring an input- dependent number of diverse, ordered partitional clusters with an added cardinality prediction module. Both result in an improved performance on the presented datasets, with the latter being the only neural method fulfilling all requirements inherent to addressing the Catalog Problem

    Operational Research: methods and applications

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    This is the final version. Available on open access from Taylor & Francis via the DOI in this recordThroughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    Operational research:methods and applications

    Get PDF
    Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order
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