2,452 research outputs found

    Classical and quantum algorithms for scaling problems

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    This thesis is concerned with scaling problems, which have a plethora of connections to different areas of mathematics, physics and computer science. Although many structural aspects of these problems are understood by now, we only know how to solve them efficiently in special cases.We give new algorithms for non-commutative scaling problems with complexity guarantees that match the prior state of the art. To this end, we extend the well-known (self-concordance based) interior-point method (IPM) framework to Riemannian manifolds, motivated by its success in the commutative setting. Moreover, the IPM framework does not obviously suffer from the same obstructions to efficiency as previous methods. It also yields the first high-precision algorithms for other natural geometric problems in non-positive curvature.For the (commutative) problems of matrix scaling and balancing, we show that quantum algorithms can outperform the (already very efficient) state-of-the-art classical algorithms. Their time complexity can be sublinear in the input size; in certain parameter regimes they are also optimal, whereas in others we show no quantum speedup over the classical methods is possible. Along the way, we provide improvements over the long-standing state of the art for searching for all marked elements in a list, and computing the sum of a list of numbers.We identify a new application in the context of tensor networks for quantum many-body physics. We define a computable canonical form for uniform projected entangled pair states (as the solution to a scaling problem), circumventing previously known undecidability results. We also show, by characterizing the invariant polynomials, that the canonical form is determined by evaluating the tensor network contractions on networks of bounded size

    On the path integration system of insects: there and back again

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    Navigation is an essential capability of animate organisms and robots. Among animate organisms of particular interest are insects because they are capable of a variety of navigation competencies solving challenging problems with limited resources, thereby providing inspiration for robot navigation. Ants, bees and other insects are able to return to their nest using a navigation strategy known as path integration. During path integration, the animal maintains a running estimate of the distance and direction to its nest as it travels. This estimate, known as the `home vector', enables the animal to return to its nest. Path integration was the technique used by sea navigators to cross the open seas in the past. To perform path integration, both sailors and insects need access to two pieces of information, their direction and their speed of motion over time. Neurons encoding the heading and speed have been found to converge on a highly conserved region of the insect brain, the central complex. It is, therefore, believed that the central complex is key to the computations pertaining to path integration. However, several questions remain about the exact structure of the neuronal circuit that tracks the animal's heading, how it differs between insect species, and how the speed and direction are integrated into a home vector and maintained in memory. In this thesis, I have combined behavioural, anatomical, and physiological data with computational modelling and agent simulations to tackle these questions. Analysis of the internal compass circuit of two insect species with highly divergent ecologies, the fruit fly Drosophila melanogaster and the desert locust Schistocerca gregaria, revealed that despite 400 million years of evolutionary divergence, both species share a fundamentally common internal compass circuit that keeps track of the animal's heading. However, subtle differences in the neuronal morphologies result in distinct circuit dynamics adapted to the ecology of each species, thereby providing insights into how neural circuits evolved to accommodate species-specific behaviours. The fast-moving insects need to update their home vector memory continuously as they move, yet they can remember it for several hours. This conjunction of fast updating and long persistence of the home vector does not directly map to current short, mid, and long-term memory accounts. An extensive literature review revealed a lack of available memory models that could support the home vector memory requirements. A comparison of existing behavioural data with the homing behaviour of simulated robot agents illustrated that the prevalent hypothesis, which posits that the neural substrate of the path integration memory is a bump attractor network, is contradicted by behavioural evidence. An investigation of the type of memory utilised during path integration revealed that cold-induced anaesthesia disrupts the ability of ants to return to their nest, but it does not eliminate their ability to move in the correct homing direction. Using computational modelling and simulated agents, I argue that the best explanation for this phenomenon is not two separate memories differently affected by temperature but a shared memory that encodes both the direction and distance. The results presented in this thesis shed some more light on the labyrinth that researchers of animal navigation have been exploring in their attempts to unravel a few more rounds of Ariadne's thread back to its origin. The findings provide valuable insights into the path integration system of insects and inspiration for future memory research, advancing path integration techniques in robotics, and developing novel neuromorphic solutions to computational problems

    Posthuman Creative Styling can a creative writer’s style of writing be described as procedural?

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    This thesis is about creative styling — the styling a creative writer might use to make their writing unique. It addresses the question as to whether such styling can be described as procedural. Creative styling is part of the technique a creative writer uses when writing. It is how they make the text more ‘lively’ by use of tips and tricks they have either learned or discovered. In essence these are rules, ones the writer accrues over time by their practice. The thesis argues that the use and invention of these rules can be set as procedures. and so describe creative styling as procedural. The thesis follows from questioning why it is that machines or algorithms have, so far, been incapable of producing creative writing which has value. Machine-written novels do not abound on the bookshelves and writing styled by computers is, on the whole, dull in comparison to human-crafted literature. It came about by thinking how it would be possible to reach a point where writing by people and procedural writing are considered to have equal value. For this reason the thesis is set in a posthuman context, where the differences between machines and people are erased. The thesis uses practice to inform an original conceptual space model, based on quality dimensions and dynamic-inter operation of spaces. This model gives an example of the procedures which a posthuman creative writer uses when engaged in creative styling. It suggests an original formulation for the conceptual blending of conceptual spaces, based on the casting of qualities from one space to another. In support of and informing its arguments are ninety-nine examples of creative writing practice which show the procedures by which style has been applied, created and assessed. It provides a route forward for further joint research into both computational and human-coded creative writing

    High-Speed Scanning Tunneling Microscopy on Thin Oxide Film Systems

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    DĂŒnne Silizium- und Germaniumdioxidfilme auf Ru(0001)-Kristallen werden hinsichtlich dynamischer Prozesse untersucht. Zwischen Oxidfilm und Substrat befinden sich Sauerstoffatome, die eine ent-scheidende Rolle in diesen Systemen spielen. ZunĂ€chst werden diese Sauerstofflagen auf Ru(0001) mittels Hochgeschwindigkeits-Rastertunnelmikroskopie (STM) analysiert. Daraufhin wird die GeO2-Monolage auf Ru(0001) bei hohen Bildraten mit einer selbstentwickelten halbautomatischen Netz-werkdetektion untersucht. Schließlich wird die SiO2-Bilage auf Ru(0001) mit konventionellen sowie mit schnellen STM-Messungen bei Raumtemperatur und bei 600 K abgebildet. Um schnelle Messungen bei hohen Temperaturen zu realisieren, wird ein Hochgeschwindigkeits-STM konstruiert, welches bei unterschiedlichen Temperaturen betrieben werden kann. Unkon-ventionelle Spiralgeometrien ermöglichen verzerrungsfreie Bilder in weniger als 10 ms aufzunehmen. Die adsorbierten Sauerstofflagen werden erstmals bei hohen Bildraten untersucht. Die experimen-tellen Ergebnisse werden durch extern durchgefĂŒhrte Dichtefunktionaltheorie-Berechnungen ergĂ€nzt. In den auf Ru(0001) bei Raumtemperatur stabilen Sauerstofflagen O(2×2), O(2×1) und 3O(2×2) werden dynamische Prozesse beobachtet. Die Besetzung des Zwischenzustandes entlang des Diffusionspfades und schnelle "Umklapp"-Prozesse eindimensionaler Linien werden auf atomarer Ebene aufgelöst. Komplexe DomĂ€nengrenzen in der GeO2-Monolage auf Ru(0001) werden mit Hochgeschwindigkeits-STM abgebildet. Die Messungen an der SiO2-Bilage auf Ru(0001) zeigen dynamische Änderungen des Abbildungskontrasts, die mit den mobilen Sauertsoffatomen an der GrenzflĂ€che zusammenhĂ€ngen können. Messungen bei hohen Temperaturen zeigen dynamische KontrastĂ€nderungen von mesoskopischen Strukturen. Diese Messungen stellen die ersten schnellen Hochtemperatur-STM-Aufnahmen des Siliziumdioxidfilms dar und bilden die Grundlage fĂŒr kĂŒnftige Studien zu dynamischen VerĂ€nderungen in dĂŒnnen Oxidschichtsystemen.Dynamics related to thin silicon- and germanium dioxide films that are grown on Ru(0001) crystals are investigated. Between the film and the metal support oxygen species are present that play a crucial role for these film systems. First, these oxygen adlayers on Ru(0001) are analyzed by high-speed scan-ning tunneling microscopy (STM) with the focus on dynamic processes. In a next step, the monolayer of germanium dioxide (germania) supported on Ru(0001) is studied at elevated frame rates and with a self-designed semi-automated network detection. Finally, the bilayer of silicon dioxide (silica) on Ru(0001) is studied by conventional and by high-speed STM both at room temperature and at 600 K. To realize fast STM measurements at elevated temperatures, a high-speed STM is designed that can operate at variable temperatures. Images are acquired in less than 10 ms with unconventional spiral scan patterns. The dynamics in oxygen adlayers are investigated for the first time at elevated frame rates. Experimental results are supported by density functional theory (DFT) calculations performed externally. Dynamic events are observed in the oxygen adlayers that are stable on Ru(0001) at room temperature, namely O(2×2), O(2×1), and 3O(2×2). The occupation of an intermediate state along the oxygen diffusion pathway and fast "flipping" events of atomic one-dimensional stripe patterns are observed. On the germania monolayer on Ru(0001), complex domain boundary structures are resolved with high-speed STM. In high-speed scans on the silica bilayer on Ru(0001), dynamic changes of the imaging contrast are observed that may relate to the mobile species in the oxygen interfacial layer. Measurements at elevated temperature reveal dynamic contrast changes of mesoscopic features. These measurements constitute the first high-speed STM scans on the silica film at elevated temperatures and form the basis for future studies with the focus on dynamic processes in thin oxide film systems

    Economic power dispatch solutions incorporating stochastic wind power generators by moth flow optimizer

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    Optimization encourages the economical and efficient operation of the electrical system. Most power system problems are nonlinear and nonconvex, and they frequently ask for the optimization of two or more diametrically opposed objectives. The numerical optimization revolution led to the introduction of numerous evolutionary algorithms (EAs). Most of these methods sidestep the problems of early convergence by searching the universe for the ideal. Because the field of EA is evolving, it may be necessary to reevaluate the usage of new algorithms to solve optimization problems involving power systems. The introduction of renewable energy sources into the smart grid of the present enables the emergence of novel optimization problems with an abundance of new variables. This study's primary purpose is to apply state-of-the-art variations of the differential evolution (DE) algorithm for single-objective optimization and selected evolutionary algorithms for multi-objective optimization issues in power systems. In this investigation, we employ the recently created metaheuristic algorithm known as the moth flow optimizer (MFO), which allows us to answer all five of the optimal power flow (OPF) difficulty objective functions: (1) reducing the cost of power generation (including stochastic solar and thermal power generation), (2) diminished power, (3) voltage variation, (4) emissions, and (5) reducing both the cost of power generating and emissions. Compared to the lowest figures provided by comparable approaches, MFO's cost of power production for IEEE-30 and IEEE-57 bus architectures is 888.7248perhourand 888.7248 per hour and 31121.85 per hour, respectively. This results in hourly cost savings between 1.23 % and 1.92%. According to the facts, MFO is superior to the other algorithms and might be utilized to address the OPF problem

    Mean-shift exploration in shape assembly of robot swarms

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    The fascinating collective behaviors of biological systems have inspired extensive studies on shape assembly of robot swarms. Here, we propose a strategy for shape assembly of robot swarms based on the idea of mean-shift exploration: when a robot is surrounded by neighboring robots and unoccupied locations, it would actively give up its current location by exploring the highest density of nearby unoccupied locations in the desired shape. This idea is realized by adapting the mean-shift algorithm, which is an optimization technique widely used in machine learning for locating the maxima of a density function. The proposed strategy empowers robot swarms to assemble highly complex shapes with strong adaptability, as verified by experiments with swarms of 50 ground robots. The comparison between the proposed strategy and the state-of-the-art demonstrates its high efficiency especially for large-scale swarms. The proposed strategy can also be adapted to generate interesting behaviors including shape regeneration, cooperative cargo transportation, and complex environment exploration

    Efficient Deep Learning for Real-time Classification of Astronomical Transients

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    A new golden age in astronomy is upon us, dominated by data. Large astronomical surveys are broadcasting unprecedented rates of information, demanding machine learning as a critical component in modern scientific pipelines to handle the deluge of data. The upcoming Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory will raise the big-data bar for time- domain astronomy, with an expected 10 million alerts per-night, and generating many petabytes of data over the lifetime of the survey. Fast and efficient classification algorithms that can operate in real-time, yet robustly and accurately, are needed for time-critical events where additional resources can be sought for follow-up analyses. In order to handle such data, state-of-the-art deep learning architectures coupled with tools that leverage modern hardware accelerators are essential. The work contained in this thesis seeks to address the big-data challenges of LSST by proposing novel efficient deep learning architectures for multivariate time-series classification that can provide state-of-the-art classification of astronomical transients at a fraction of the computational costs of other deep learning approaches. This thesis introduces the depthwise-separable convolution and the notion of convolutional embeddings to the task of time-series classification for gains in classification performance that are achieved with far fewer model parameters than similar methods. It also introduces the attention mechanism to time-series classification that improves performance even further still, with significant improvement in computational efficiency, as well as further reduction in model size. Finally, this thesis pioneers the use of modern model compression techniques to the field of photometric classification for efficient deep learning deployment. These insights informed the final architecture which was deployed in a live production machine learning system, demonstrating the capability to operate efficiently and robustly in real-time, at LSST scale and beyond, ready for the new era of data intensive astronomy

    Under construction: infrastructure and modern fiction

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    In this dissertation, I argue that infrastructural development, with its technological promises but widening geographic disparities and social and environmental consequences, informs both the narrative content and aesthetic forms of modernist and contemporary Anglophone fiction. Despite its prevalent material forms—roads, rails, pipes, and wires—infrastructure poses particular formal and narrative problems, often receding into the background as mere setting. To address how literary fiction theorizes the experience of infrastructure requires reading “infrastructurally”: that is, paying attention to the seemingly mundane interactions between characters and their built environments. The writers central to this project—James Joyce, William Faulkner, Karen Tei Yamashita, and Mohsin Hamid—take up the representational challenges posed by infrastructure by bringing transit networks, sanitation systems, and electrical grids and the histories of their development and use into the foreground. These writers call attention to the political dimensions of built environments, revealing the ways infrastructures produce, reinforce, and perpetuate racial and socioeconomic fault lines. They also attempt to formalize the material relations of power inscribed by and within infrastructure; the novel itself becomes an imaginary counterpart to the technologies of infrastructure, a form that shapes and constrains what types of social action and affiliation are possible

    20th SC@RUG 2023 proceedings 2022-2023

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