How to apply tree decomposition ideas in large networks?

Graph decompositions are the natural generalisation of tree decompositions where the decomposition tree is replaced by a genuine graph. Recently they found theoretical applications in the theory of sparsity, topological graph theory, structural graph theory and geometric group theory. We demonstrate applicability of graph decompositions on large networks by implementing an efficient algorithm and testing it on road networks

Struktur und Funktion von Sexualpheromonen der Diatomee Seminavis robusta

Aufgrund des Aufbaus der Zellwand verringert sich die Zellgröße von Diatomeen mit jeder Zellteilung. Die Initialgröße kann meist nur durch sexuelle Reproduktion wiederhergestellt werden, welche durch Pheromone vermittelt wird. Ziel dieser Arbeit war die Erforschung des ersten Diatomeenpheromons, wobei als Modellorganismus Seminavis robusta genutzt wurde. Durch einen Bioassay zum Nachweis der Pheromonwirkung konnte herausgefunden werden, dass das Pheromonsystem aus drei Pheromonen besteht, wovon zwei die sexuellen Vorgänge induzieren und synchronisieren und das dritte als Lockstoff dient. Die Untersuchung des Lockstoffs erfolgte über metabolomische und massenspektrometrische Methoden, wobei „L-Diprolin“, das zyklische Dipeptid aus der Aminosäure L-Prolin, als potenzieller Lockstoff identifiziert wurde. L-Diprolin wurde synthetisiert und mittels verschiedener Bioassays als erstes Diatomeenpheromon bestätigt. Dabei zeigte sich, dass das synthetische Enantiomer D-Diprolin eine vergleichbare Aktivität hat. Eine Racemisierung von L-Diprolin während der Pheromonperzeption könnte die biologische Aktivität von D-Diprolin erklären. Diese Untersuchungen erforderten die Bestimmung der Enantiomerenzusammensetzung des Lockstoffs während des Paarungsvorganges. Dabei konnte die Trennung der synthetischen Diprolinenantiomere mittels überkritischer Flüssigchromatographie verwirklicht werden. Die Ergebnisse implizieren jedoch keine Racemisierung von L-Diprolin während der Pheromon-Rezeptor-Interaktion. Durch eine Kombination von metabolomischer Analyse und Bioassay-geleiteter Fraktionierung konnte die Identität eines Sex-induzierenden Pheromons ermittelt werden. Dabei wurde eine sulfatierte und polyhydroxylierte Substanz mit einer Masse von 843.209 Da als Pheromon identifiziert. Es konnte gezeigt werden, dass dieses Pheromon für die Induktion der Diprolinbildung und für die Arretierung des Zellzyklus im Paarungspartner verantwortlich ist

Dileptons from hot heavy static photons

We compute the production rate of lepton pair by static photons at finite temperature at two-loop order. We treat the infrared region of the gluon phase space carefully by using a hard thermal loop gluon propagator. The result is free of infrared and collinear divergences and exhibits an enhancement which produces a result of order $\sim e^2 g^3$ instead of $\sim e^2 g^4$ as would be expected from ordinary perturbation theory.Comment: 14 pages, 2 figure

David Hume on Banking and Hoarding

David Hume opposes banks and favors hoarding. The only bank he reluctantly approves of is a public, 100% reserve bank. Other banks increase money supply and prices, hindering exports and economic growth. For Hume, a 100% reserve public bank would lead to ‘‘the destruction of paper-credit’’ ([1752] 1985, p. 285), fostering economic growth instead by preventing inflation. Additionally, a 100% reserve bank hoards a large quantity of gold and silver, which is available in case of national emergency

NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking

The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics

NeuroBench:Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking

The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics

Mathematical concepts for the micromechanical modelling of dislocation dynamics with a phase-field approach

International audienceThis contribution reviews mathematical concepts of micro-mechanical modeling in the phase-field approach applied to dislocation dynamics. The intention is twofold: On the one hand, modelling of dislocation dynamics is a very recent field of development in phase-field theory, in comparison to the simulation of diffusional phase transformation and related micro-structure evolution problems in materials science. The reason is that modelling dislocation dynamics poses several challenges for phase-field concepts which go beyond purely diffusional problems in materials science as, e.g., dendritic solidification, as we point out in Sect.3. On the other hand, the modelling of dislocations has triggered further wide-ranging developments of phase-field based models for deformation problems. This is an important development, since a comprehensive model for deformation problems should include displacive as well as diffusional degrees of freedom from the atomic scale to the microscale. This is something phase-field theory is capable of, as dicussed in this review article. We aim to give an overview on relevant mathematical concepts, and to stimulate further steps in this direction

NeuroBench:A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of nearly 100 co-authors across over 50 institutions in industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we present initial performance baselines across various model architectures on the algorithm track and outline the system track benchmark tasks and guidelines. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community