8,147 research outputs found

    Proceedings Of The East Asia Joint Symposium On Fields And Strings 2021

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    This volume contains the proceedings of the East Asia Joint Symposium on Fields and Strings 2021, held at the Media Center of Osaka City University on November 22-27, 2021. About 160 physicists from all over East Asia attended physically or joined online this symposium and more than 50 researchers presented their results in the invited lectures, the short talks or the poster session. Quantum field theory and string theory in the context of several exciting developments were discussed, which include frontiers of supersymmetric gauge theory, anomalies and higher form symmetries, and several issues on quantum gravity and black holes

    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    PAC-Bayesian Treatment Allocation Under Budget Constraints

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    This paper considers the estimation of treatment assignment rules when the policy maker faces a general budget or resource constraint. Utilizing the PAC-Bayesian framework, we propose new treatment assignment rules that allow for flexible notions of treatment outcome, treatment cost, and a budget constraint. For example, the constraint setting allows for cost-savings, when the costs of non-treatment exceed those of treatment for a subpopulation, to be factored into the budget. It also accommodates simpler settings, such as quantity constraints, and doesn't require outcome responses and costs to have the same unit of measurement. Importantly, the approach accounts for settings where budget or resource limitations may preclude treating all that can benefit, where costs may vary with individual characteristics, and where there may be uncertainty regarding the cost of treatment rules of interest. Despite the nomenclature, our theoretical analysis examines frequentist properties of the proposed rules. For stochastic rules that typically approach budget-penalized empirical welfare maximizing policies in larger samples, we derive non-asymptotic generalization bounds for the target population costs and sharp oracle-type inequalities that compare the rules' welfare regret to that of optimal policies in relevant budget categories. A closely related, non-stochastic, model aggregation treatment assignment rule is shown to inherit desirable attributes.Comment: 70 pages, 7 figure

    Model-agnostic Measure of Generalization Difficulty

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    The measure of a machine learning algorithm is the difficulty of the tasks it can perform, and sufficiently difficult tasks are critical drivers of strong machine learning models. However, quantifying the generalization difficulty of machine learning benchmarks has remained challenging. We propose what is to our knowledge the first model-agnostic measure of the inherent generalization difficulty of tasks. Our inductive bias complexity measure quantifies the total information required to generalize well on a task minus the information provided by the data. It does so by measuring the fractional volume occupied by hypotheses that generalize on a task given that they fit the training data. It scales exponentially with the intrinsic dimensionality of the space over which the model must generalize but only polynomially in resolution per dimension, showing that tasks which require generalizing over many dimensions are drastically more difficult than tasks involving more detail in fewer dimensions. Our measure can be applied to compute and compare supervised learning, reinforcement learning and meta-learning generalization difficulties against each other. We show that applied empirically, it formally quantifies intuitively expected trends, e.g. that in terms of required inductive bias, MNIST < CIFAR10 < Imagenet and fully observable Markov decision processes (MDPs) < partially observable MDPs. Further, we show that classification of complex images << few-shot meta-learning with simple images. Our measure provides a quantitative metric to guide the construction of more complex tasks requiring greater inductive bias, and thereby encourages the development of more sophisticated architectures and learning algorithms with more powerful generalization capabilities.Comment: Accepted at ICML 2023, 28 pages, 6 figure

    Statistical Equilibrium of Circulating Fluids

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    We are investigating the inviscid limit of the Navier-Stokes equation, and we find previously unknown anomalous terms in Hamiltonian, Dissipation, and Helicity, which survive this limit and define the turbulent statistics. We find various topologically nontrivial configurations of the confined Clebsch field responsible for vortex sheets and lines. In particular, a stable vortex sheet family is discovered, but its anomalous dissipation vanishes as ν\sqrt{\nu}. Topologically stable stationary singular flows, which we call Kelvinons, are introduced. They have a conserved velocity circulation Γα\Gamma_\alpha around the loop CC and another one Γβ\Gamma_\beta for an infinitesimal closed loop C~\tilde C encircling CC, leading to a finite helicity. The anomalous dissipation has a finite limit, which we computed analytically. The Kelvinon is responsible for asymptotic PDF tails of velocity circulation, \textbf{perfectly matching numerical simulations}. The loop equation for circulation PDF as functional of the loop shape is derived and studied. This equation is \textbf{exactly} equivalent to the Schr\"odinger equation in loop space, with viscosity ν\nu playing the role of Planck's constant. Kelvinons are fixed points of the loop equation at WKB limit ν0\nu \rightarrow 0. The anomalous Hamiltonian for the Kelvinons contains a large parameter logΓβν\log \frac{|\Gamma_\beta|}{\nu}. The leading powers of this parameter can be summed up, leading to familiar asymptotic freedom, like in QCD. In particular, the so-called multifractal scaling laws are, as in QCD, modified by the powers of the logarithm.Comment: 246 pages, 96 figures, and six appendixes. Submitted to Physics Reports. Revised the energy balance analysis and discovered asymptotic freedom leading to powers of logarithm of scale modifying K41 scaling law

    Dielectric-barrier discharge plasma actuators for turbulent friction-drag manipulation via spanwise oscillations

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    Ein Plasmaaktuator wird über instationäre Betriebsmodi angesteuert, um wandnahe Fluidoszillationen zu erzeugen. Das Ziel ist es, spannweitig oszillierende Wände zugunsten einer Verringerung des turbulenten Reibungswiderstands nachzuahmen. Da der Aktuator keine beweglichen Teile besitzt, könnte er sich als nicht-mechanischer Ersatz der oszillierenden Wand eignen. Die Kombination von Betriebsmodus und zugrundeliegender Elektrodenanordnung ist eine Neuerung, welche die spannweitige Homogenität der Strömung solcher virtuellen Wandoszillationen verbessert. Die mechanische Charakterisierung wird mittels eines planaren Feldmessverfahrens durchgeführt, um sowohl die induzierten Strömungstopologien als auch die Effekte von Volumenkraft und „virtueller Wandgeschwindigkeit“, d.h. Reaktion des Fluids, aufzuzeigen. Daraus wird zur Bewertung und Optimierung der Leistungsfähigkeit des Aktuators ein universelles Diagramm hinsichtlich aktuatorspezifischer Parameter abgeleitet. Da die berechnete Volumenkraft die Art der Kraftausübung gut widerspiegelt, kann diese modellhaft zu verbesserten numerischen Simulationen der Aktuatorik dienen. Ferner wird eine neue Vorgehensweise für die Bestimmung der elektrischen Leistung von Aktuatoren mit mehreren Hochspannungselektroden bereitgestellt, welche die potenzielle Abschätzung des Nettogewinns in aktiven Kontrollszenarien ermöglicht. Zuletzt wird die unmittelbare Auswirkung der oszillatorischen Kraftausübung auf den Reibungswiderstand in der Querebene einer voll entwickelten turbulenten Kanalströmung mittels einer stereoskopischen Feldmesstechnik untersucht. Im Wesentlichen verbleibt die Strömung im sich entwickelnden Stadium und erfährt auf dem Aktuator eine Erhöhung des Reibungswiderstands, während sich dieser stromab des Aktuators verringert

    Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement

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    Spaces with locally varying scale of measurement, like multidimensional structures with differently scaled dimensions, are pretty common in statistics and machine learning. Nevertheless, it is still understood as an open question how to exploit the entire information encoded in them properly. We address this problem by considering an order based on (sets of) expectations of random variables mapping into such non-standard spaces. This order contains stochastic dominance and expectation order as extreme cases when no, or respectively perfect, cardinal structure is given. We derive a (regularized) statistical test for our proposed generalized stochastic dominance (GSD) order, operationalize it by linear optimization, and robustify it by imprecise probability models. Our findings are illustrated with data from multidimensional poverty measurement, finance, and medicine.Comment: Accepted for the 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023

    Learning disentangled speech representations

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    A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody. The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions. In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks. This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically

    Special Topics in Information Technology

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    This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2020-21 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists

    Differential Models, Numerical Simulations and Applications

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    This Special Issue includes 12 high-quality articles containing original research findings in the fields of differential and integro-differential models, numerical methods and efficient algorithms for parameter estimation in inverse problems, with applications to biology, biomedicine, land degradation, traffic flows problems, and manufacturing systems
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