2,894 research outputs found
1. Helgoland Power and Energy Conference - 24. Dresdener Kreis 2023
Der Sammelband "1. Helgoland Power and Energy Conference" beinhaltet neben einem kurzen Bericht zum 24. Treffen des Dresdener Kreises 2023 wissenschaftliche BeitrĂ€ge von Doktoranden der beteiligten Hochschulinstitute zum Thema Elektroenergieversorgung. Der Dresdener Kreis setzt sich aus der Professur fĂŒr Elektroenergieversorgung der Technischen UniversitĂ€t Dresden, dem Fachgebiet Elektrische Anlagen und Netze der UniversitĂ€t Duisburg-Essen, dem Fachgebiet Elektrische Energieversorgung der Leibniz UniversitĂ€t Hannover und dem Lehrstuhl Elektrische Netze und Erneuerbare Energie der Otto-von-Guericke UniversitĂ€t Magdeburg zusammen und trifft sich einmal im Jahr zum fachlichen Austausch an einer der beteiligten UniversitĂ€ten
Classical and quantum algorithms for scaling problems
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
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
The Gallium Anomaly
In order to test the end-to-end operations of gallium solar neutrino
experiments, intense electron-capture sources were fabricated to measure the
responses of the radiochemical SAGE and GALLEX/GNO detectors to known fluxes of
low-energy neutrinos. Such tests were viewed at the time as a cross-check,
given the many tests of Ge recovery and counting that had been routinely
performed, with excellent results. However, the four Cr and Ar
source experiments yielded rates below expectations, a result commonly known as
the Ga anomaly. As the intensity of the electron-capture sources can be
measured to high precision, the neutrino lines they produce are fixed by known
atomic and nuclear rates, and the neutrino absorption cross section on
Ga is tightly constrained by the lifetime of Ge, no simple
explanation for the anomaly has been found. To check these calibration
experiments, a dedicated experiment BEST was performed, utilizing a neutrino
source of unprecedented intensity and a detector optimized to increase
statistics while providing some information on counting rate as a function of
distance from the source. The results BEST obtained are consistent with the
earlier solar neutrino calibration experiments, and when combined with those
measurements, yield a Ga anomaly with a significance of approximately
, under conservative assumptions. But BEST found no evidence of
distance dependence and thus no explicit indication of new physics. In this
review we describe the extensive campaigns carried out by SAGE, GALLEX/GNO, and
BEST to demonstrate the reliability and precision of their experimental
procedures, including Ge recovery, counting, and analysis. We also
describe efforts to define uncertainties in the neutrino capture cross section.
With the results from BEST, an anomaly remains.Comment: Invited submission to Progress in Particle and Nuclear Physic
Resolving particle acceleration and transport in the jets of the microquasar SS 433 with H.E.S.S. and HAWC
The microquasar SS 433 offers a unique laboratory to study the physics of mildly relativistic jets and the associated non-thermal processes. It hosts a compact binary system, from which a pair of counter-propagating jets is observed to emanate. The jets are resolved by observations out to distances of approximately 0.1 pc from the central source, but further out, they remain dark until they abruptly reappear at around 25 pc as bright X-ray sources. These outer jets were recently reported to be sources of TeV gamma-rays by the High Altitude Water Cherenkov (HAWC) observatory. This thesis presents a complete picture of the TeV emission from the jets of SS 433 including new data from the High Energy Stereoscopic System (H.E.S.S.) and the HAWC observatory.
To fully exploit the capabilities of the H.E.S.S. observations, a new approach to background rejection is presented. It is based on the detection of Cherenkov light from muons by large Imaging Atmospheric Cherenkov Telescopes (IACTs), such as the telescope located at the center of the H.E.S.S. array. The application of this technique leads to a factor four reduction in background above several tens of TeV in the
H.E.S.S. stereoscopic analysis.
This thesis presents the detection of the SS 433 outer jets for the first time with an IACT array using H.E.S.S.. The superior angular and energy resolution of H.E.S.S. compared to HAWC allow for a detailed study of the emission from the jets, including a measurement of the physical extension of the emission and of the spectra out to tens of TeV. These observations also reveal the presence of striking energy-
dependent morphology, ruling out a hadronic origin for the bulk of the gamma-ray emission. Photons above 10 TeV are observed only close to the base of the outer jets, implying efficient particle acceleration to very-high energies at that location. Evidence suggests that the acceleration is due to a shock, thus providing a clue to the long-standing question of the reappearance of the jets.
The observed energy-dependent morphology is modeled as a consequence of the particle cooling times and the advection flow of the jet, which constrains the jet dynamics and, in particular, results in an estimate of the velocity of the outer jets at their base. This solves several issues concerning the non-thermal processes occurring in the jets and their dynamics, but also opens up new questions that highlight our incomplete understanding of the SS 433 system.
A joint analysis of the H.E.S.S. and HAWC data would provide insights on the system across the entire range of TeV energies. To make this possible, a tool capable of reading and analyzing the data from both instruments is required. This thesis presents the extension and validation of an existing data format and analysis tool shared among IACTs to the data from particle detector arrays such as the HAWC observatory. This framework is then used to revisit the HAWC observations of the SS 433 region with the inclusion of additional data taken since the first detection was reported. The existence of this framework enables for the first time the joint analysis of the H.E.S.S. and HAWC data, the preliminary results of which are presente
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GRAPH REPRESENTATION LEARNING WITH BOX EMBEDDINGS
Graphs are ubiquitous data structures, present in many machine-learning tasks, such as link prediction of products and node classification of scientific papers. As gradient descent drives the training of most modern machine learning architectures, the ability to encode graph-structured data using a differentiable representation is essential to make use of this data. Most approaches encode graph structure in Euclidean space, however, it is non-trivial to model directed edges. The naive solution is to represent each node using a separate source and target vector, however, this can decouple the representation, making it harder for the model to capture information within longer paths in the graph.
In this dissertation, we propose to model graphs by representing each node as a \textit{box} (a Cartesian product of intervals) where directed edges are captured by the relative containment of one box in another. Theoretical proof shows that our proposed box embeddings have the expressiveness to represent any \emph{directed acyclic graph}. We also perform rigorous empirical evaluations of vector, hyperbolic, and region-based geometric representations on several families of synthetic and real-world directed graphs. Extensive experimental results suggest that the box containment can allow for transitive relationships to be modeled easily. We further propose t-Box, a variant of box embeddings that learns the temperature together during training. t-Box uses a learned smoothing parameter to achieve better representational capacity than vector models in low dimensions, while also avoiding performance saturation common to other geometric models in high dimensions.
Though promising, modeling directed graphs that both contain cycles and some element of transitivity, two properties common in real-world settings, is challenging. Box embeddings, which can be thought of as representing the graph as an intersection over some learned super-graphs, have a natural inductive bias toward modeling transitivity, but (as we prove) cannot model cycles. To address this issue, we propose binary code box embeddings, where a learned binary code selects a subset of graphs for intersection. We explore several variants, including global binary codes (amounting to a union over intersections) and per-vertex binary codes (allowing greater flexibility) as well as methods of regularization. Theoretical and empirical results show that the proposed models not only preserve a useful inductive bias of transitivity but also have sufficient representational capacity to model arbitrary graphs, including graphs with cycles.
Lastly, we discuss the use case where box embeddings are not free parameters but are produced by functions. In particular, we explore whether neural networks can map node features into the box space. This is critical in many real-world scenarios. On the one hand, graphs are sparse and the majority of vertices only have few connections or are completely isolated. On the other hand, there may exist rich node features such as attributes and descriptions, that could be useful for prediction tasks. The experimental analysis points out both the effectiveness and insufficiency of multi-layer perceptron-based encoders under different circumstances
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The Forward Physics Facility at the High-Luminosity LHC
High energy collisions at the High-Luminosity Large Hadron Collider (LHC) produce a large number of particles along the beam collision axis, outside of the acceptance of existing LHC experiments. The proposed Forward Physics Facility (FPF), to be located several hundred meters from the ATLAS interaction point and shielded by concrete and rock, will host a suite of experiments to probe standard model (SM) processes and search for physics beyond the standard model (BSM). In this report, we review the status of the civil engineering plans and the experiments to explore the diverse physics signals that can be uniquely probed in the forward region. FPF experiments will be sensitive to a broad range of BSM physics through searches for new particle scattering or decay signatures and deviations from SM expectations in high statistics analyses with TeV neutrinos in this low-background environment. High statistics neutrino detection will also provide valuable data for fundamental topics in perturbative and non-perturbative QCD and in weak interactions. Experiments at the FPF will enable synergies between forward particle production at the LHC and astroparticle physics to be exploited. We report here on these physics topics, on infrastructure, detector, and simulation studies, and on future directions to realize the FPFâs physics potential
The Distributed Complexity of Locally Checkable Labeling Problems Beyond Paths and Trees
We consider locally checkable labeling LCL problems in the LOCAL model of
distributed computing. Since 2016, there has been a substantial body of work
examining the possible complexities of LCL problems. For example, it has been
established that there are no LCL problems exhibiting deterministic
complexities falling between and . This line of
inquiry has yielded a wealth of algorithmic techniques and insights that are
useful for algorithm designers.
While the complexity landscape of LCL problems on general graphs, trees, and
paths is now well understood, graph classes beyond these three cases remain
largely unexplored. Indeed, recent research trends have shifted towards a
fine-grained study of special instances within the domains of paths and trees.
In this paper, we generalize the line of research on characterizing the
complexity landscape of LCL problems to a much broader range of graph classes.
We propose a conjecture that characterizes the complexity landscape of LCL
problems for an arbitrary class of graphs that is closed under minors, and we
prove a part of the conjecture.
Some highlights of our findings are as follows.
1. We establish a simple characterization of the minor-closed graph classes
sharing the same deterministic complexity landscape as paths, where ,
, and are the only possible complexity classes.
2. It is natural to conjecture that any minor-closed graph class shares the
same complexity landscape as trees if and only if the graph class has bounded
treewidth and unbounded pathwidth. We prove the "only if" part of the
conjecture.
3. In addition to the well-known complexity landscapes for paths, trees, and
general graphs, there are infinitely many different complexity landscapes among
minor-closed graph classes
Adaptive finite-time control of multi-agent systems with partial state constraints and input saturation via event-triggered strategy
This paper focuses on the finite-time control problem of multi-agent systems with input saturation, unknown nonlinear dynamics, external disturbances and partial state constraints via output feedback. Fuzzy logic system and fuzzy state observer are introduced to approximate the uncertain nonlinearities and estimate the unmeasurable states, respectively. The partial state constraints are dealt with by using the barrier Lyapunov function, so that all states of the system do not exceed the preset boundary values. In order to reduce the computational complexity of the virtual controller and save communication resources, a first-order filter and an event-triggered mechanism are introduced, respectively. It is proved that the Zeno behavior does not occur via the proposed event-triggered controller. By stability analysis, the finite-time convergence of tracking error to a small neighborhood of the origin is proven. The effectiveness of the theoretical results is verified by examples.http://wileyonlinelibrary.com/iet-cthhj2023Electrical, Electronic and Computer Engineerin
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