8,147 research outputs found
Proceedings Of The East Asia Joint Symposium On Fields And Strings 2021
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
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
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
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
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
.
Topologically stable stationary singular flows, which we call Kelvinons, are
introduced. They have a conserved velocity circulation around
the loop and another one for an infinitesimal closed loop
encircling , 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 playing the role of
Planck's constant.
Kelvinons are fixed points of the loop equation at WKB limit . The anomalous Hamiltonian for the Kelvinons contains a large parameter
. 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
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
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
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
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
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
- …