1,196 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Proceedings of SIRM 2023 - The 15th European Conference on Rotordynamics

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    It was our great honor and pleasure to host the SIRM Conference after 2003 and 2011 for the third time in Darmstadt. Rotordynamics covers a huge variety of different applications and challenges which are all in the scope of this conference. The conference was opened with a keynote lecture given by Rainer Nordmann, one of the three founders of SIRM “Schwingungen in rotierenden Maschinen”. In total 53 papers passed our strict review process and were presented. This impressively shows that rotordynamics is relevant as ever. These contributions cover a very wide spectrum of session topics: fluid bearings and seals; air foil bearings; magnetic bearings; rotor blade interaction; rotor fluid interactions; unbalance and balancing; vibrations in turbomachines; vibration control; instability; electrical machines; monitoring, identification and diagnosis; advanced numerical tools and nonlinearities as well as general rotordynamics. The international character of the conference has been significantly enhanced by the Scientific Board since the 14th SIRM resulting on one hand in an expanded Scientific Committee which meanwhile consists of 31 members from 13 different European countries and on the other hand in the new name “European Conference on Rotordynamics”. This new international profile has also been emphasized by participants of the 15th SIRM coming from 17 different countries out of three continents. We experienced a vital discussion and dialogue between industry and academia at the conference where roughly one third of the papers were presented by industry and two thirds by academia being an excellent basis to follow a bidirectional transfer what we call xchange at Technical University of Darmstadt. At this point we also want to give our special thanks to the eleven industry sponsors for their great support of the conference. On behalf of the Darmstadt Local Committee I welcome you to read the papers of the 15th SIRM giving you further insight into the topics and presentations

    Functional completeness of planar Rydberg blockade structures

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    The construction of Hilbert spaces that are characterized by local constraints as the low-energy sectors of microscopic models is an important step towards the realization of a wide range of quantum phases with long-range entanglement and emergent gauge fields. Here we show that planar structures of trapped atoms in the Rydberg blockade regime are functionally complete: Their ground state manifold can realize any Hilbert space that can be characterized by local constraints in the product basis. We introduce a versatile framework, together with a set of provably minimal logic primitives as building blocks, to implement these constraints. As examples, we present lattice realizations of the string-net Hilbert spaces that underlie the surface code and the Fibonacci anyon model. We discuss possible optimizations of planar Rydberg structures to increase their geometrical robustness.Comment: 33 pages, 14 figures, v2: fixed typos, added additional references and comment

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Dissecting regional heterogeneity and modeling transcriptional cascades in brain organoids

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    Over the past decade, there has been a rapid expansion in the development and utilization of brain organoid models, enabling three-dimensional in vivo-like views of fundamental neurodevelopmental features of corticogenesis in health and disease. Nonetheless, the methods used for generating cortical organoid fates exhibit widespread heterogeneity across different cell lines. Here, we show that a combination of dual SMAD and WNT inhibition (Triple-i protocol) establishes a robust cortical identity in brain organoids, while other widely used derivation protocols are inconsistent with respect to regional specification. In order to measure this heterogeneity, we employ single-cell RNA-sequencing (scRNA-Seq), enabling the sampling of the gene expression profiles of thousands of cells in an individual sample. However, in order to draw meaningful conclusions from scRNA-Seq data, technical artifacts must be identified and removed. In this thesis, we present a method to detect one such artifact, empty droplets that do not contain a cell and consist mainly of free-floating mRNA in the sample. Furthermore, from their expression profiles, cells can be ordered along a developmental trajectory which recapitulates the progression of cells as they differentiate. Based on this ordering, we model gene expression using a Bayesian inference approach in order to measure transcriptional dynamics within differentiating cells. This enables the ordering of genes along transcriptional cascades, statistical testing for differences in gene expression changes, and measuring potential regulatory gene interactions. We apply this approach to differentiating cortical neural stem cells into cortical neurons via an intermediate progenitor cell type in brain organoids to provide a detailed characterization of the endogenous molecular processes underlying neurogenesis.Im letzten Jahrzent hat die Entwicklung und Nutzung von Organoidmodellen des Gehirns stark zugenommen. Diese Modelle erlauben dreidimensionale, in-vivo ähnliche Einblicke in fundamentale Aspekte der neurologischen Entwicklung des Hirnkortex in Gesundheit und Krankheit. Jedoch weisen die Methoden, um die Entwicklung kortikaler Organoide zu verfolgen, starke Heterogenität zwischen verschiedenen Zelllinien auf. Hier weisen wir nach, dass eine Kombination dualer SMAD und WNT Hemmung (Triple-i Protokoll) eine konstante kortikale Zuordnung in Hirnorganoiden erzeugt, während andere, weit verbreitete und genutzte Protokolle in Bezug auf kortikale Spezifizierung keine konstanten Ergebnisse liefern. Um die Heterogenität zu messen, haben wir Einzelzell-RNA Sequenzierung (scRNA-Seq) benutzt, wodurch die Erfassung der Genexpression von Tausenden von Zellen in einer Probe möglich ist. Um jedoch sinnvolle Schlüsse aus diesen scRNA-Seq Daten zu ziehen, müssen technische Artifakte identifiziert und aus den Daten entfernt werden. In dieser Dissertation stellen wir eine Methode vor, um eines solcher Artifakte zu erkennen: leere Tröpfchen (ohne Zellen), die hauptsächlich aus freischwebender mRNA in der Probe bestehen. Weiterhin können Zellen anhand ihrer Genexpressionsprofile entlang einer Entwicklungsschiene angeordnet werden, die die Entwicklung der Zellen während ihrer Differenzierung rekapituliert. Auf der Grundlage dieser Entwicklungsreihenfolge modellieren wir die Genexpression mit einem Bayes’schen Inferenzansatz, um die Dynamik der Transkription in sich differenzierenden Zellen zu messen. Dies ermöglicht das Anordnen von Genen entlang einer Transkriptionskaskade, sowie statistische Untersuchungen in Hinblick auf Unterschiede in der Veränderung von Genexpression, und das Messen des Einflusses möglicher Regulationsgene. Wir wenden diese Methode an, um kortikale neuronale Stammzellen zu untersuchen, die sich über einen intermediären Vorläuferzelltyp in kortikale Neuronen in Hirnorganoiden differenzieren, und um eine detaillierte Charakterisierung der molekularen Prozesse zu liefern, die der Neurogenese zugrunde liegen

    A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

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    Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

    Analog Photonics Computing for Information Processing, Inference and Optimisation

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    This review presents an overview of the current state-of-the-art in photonics computing, which leverages photons, photons coupled with matter, and optics-related technologies for effective and efficient computational purposes. It covers the history and development of photonics computing and modern analogue computing platforms and architectures, focusing on optimization tasks and neural network implementations. The authors examine special-purpose optimizers, mathematical descriptions of photonics optimizers, and their various interconnections. Disparate applications are discussed, including direct encoding, logistics, finance, phase retrieval, machine learning, neural networks, probabilistic graphical models, and image processing, among many others. The main directions of technological advancement and associated challenges in photonics computing are explored, along with an assessment of its efficiency. Finally, the paper discusses prospects and the field of optical quantum computing, providing insights into the potential applications of this technology.Comment: Invited submission by Journal of Advanced Quantum Technologies; accepted version 5/06/202

    Revealing More Details: Image Super-Resolution for Real-World Applications

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    Measuring the impact of COVID-19 on hospital care pathways

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    Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted

    Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions

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    Contrastive learning is a powerful framework for learning self-supervised representations that generalize well to downstream supervised tasks. We show that multiple existing contrastive learning methods can be reinterpreted as learning kernel functions that approximate a fixed positive-pair kernel. We then prove that a simple representation obtained by combining this kernel with PCA provably minimizes the worst-case approximation error of linear predictors, under a straightforward assumption that positive pairs have similar labels. Our analysis is based on a decomposition of the target function in terms of the eigenfunctions of a positive-pair Markov chain, and a surprising equivalence between these eigenfunctions and the output of Kernel PCA. We give generalization bounds for downstream linear prediction using our Kernel PCA representation, and show empirically on a set of synthetic tasks that applying Kernel PCA to contrastive learning models can indeed approximately recover the Markov chain eigenfunctions, although the accuracy depends on the kernel parameterization as well as on the augmentation strength.Comment: Published at ICLR 202
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