Heavy ion induced Single Event Phenomena (SEP) data for semiconductor devices from engineering testing

The accumulation of JPL data on Single Event Phenomena (SEP), from 1979 to August 1986, is presented in full report format. It is expected that every two years a supplement report will be issued for the follow-on period. This data for 135 devices expands on the abbreviated test data presented as part of Refs. (1) and (3) by including figures of Single Event Upset (SEU) cross sections as a function of beam Linear Energy Transfer (LET) when available. It also includes some of the data complied in the JPL computer in RADATA and the SPACERAD data bank. This volume encompasses bipolar and MOS (CMOS and MHNOS) device data as two broad categories for both upsets (bit-flips) and latchup. It also includes comments on less well known phenomena, such as transient upsets and permanent damage modes

A Survey of Constrained Combinatorial Testing

Combinatorial Testing (CT) is a potentially powerful testing technique, whereas its failure revealing ability might be dramatically reduced if it fails to handle constraints in an adequate and efficient manner. To ensure the wider applicability of CT in the presence of constrained problem domains, large and diverse efforts have been invested towards the techniques and applications of constrained combinatorial testing. In this paper, we provide a comprehensive survey of representations, influences, and techniques that pertain to constraints in CT, covering 129 papers published between 1987 and 2018. This survey not only categorises the various constraint handling techniques, but also reviews comparatively less well-studied, yet potentially important, constraint identification and maintenance techniques. Since real-world programs are usually constrained, this survey can be of interest to researchers and practitioners who are looking to use and study constrained combinatorial testing techniques

A facility to Search for Hidden Particles (SHiP) at the CERN SPS

A new general purpose fixed target facility is proposed at the CERN SPS accelerator which is aimed at exploring the domain of hidden particles and make measurements with tau neutrinos. Hidden particles are predicted by a large number of models beyond the Standard Model. The high intensity of the SPS 400~GeV beam allows probing a wide variety of models containing light long-lived exotic particles with masses below ${\cal O}$(10)~GeV/c$^2$, including very weakly interacting low-energy SUSY states. The experimental programme of the proposed facility is capable of being extended in the future, e.g. to include direct searches for Dark Matter and Lepton Flavour Violation.Comment: Technical Proposa

Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations

Nanophotonic structures have versatile applications including solar cells, anti-reflective coatings, electromagnetic interference shielding, optical filters, and light emitting diodes. To design and understand these nanophotonic structures, electrodynamic simulations are essential. These simulations enable us to model electromagnetic fields over time and calculate optical properties. In this work, we introduce frameworks and benchmarks to evaluate nanophotonic structures in the context of parametric structure design problems. The benchmarks are instrumental in assessing the performance of optimization algorithms and identifying an optimal structure based on target optical properties. Moreover, we explore the impact of varying grid sizes in electrodynamic simulations, shedding light on how evaluation fidelity can be strategically leveraged in enhancing structure designs.Comment: 31 pages, 31 figures, 4 tables. Accepted at the 37th Conference on Neural Information Processing Systems (NeurIPS 2023), Datasets and Benchmarks Trac

A Sparse Multi-Scale Algorithm for Dense Optimal Transport

Discrete optimal transport solvers do not scale well on dense large problems since they do not explicitly exploit the geometric structure of the cost function. In analogy to continuous optimal transport we provide a framework to verify global optimality of a discrete transport plan locally. This allows construction of an algorithm to solve large dense problems by considering a sequence of sparse problems instead. The algorithm lends itself to being combined with a hierarchical multi-scale scheme. Any existing discrete solver can be used as internal black-box.Several cost functions, including the noisy squared Euclidean distance, are explicitly detailed. We observe a significant reduction of run-time and memory requirements.Comment: Published "online first" in Journal of Mathematical Imaging and Vision, see DO

New approaches to protein docking

In the first part of this work, we propose new methods for protein docking. First, we present two approaches to protein docking with flexible side chains. The first approach is a fast greedy heuristic, while the second is a branch -&-cut algorithm that yields optimal solutions. For a test set of protease-inhibitor complexes, both approaches correctly predict the true complex structure. Another problem in protein docking is the prediction of the binding free energy, which is the the final step of many protein docking algorithms. Therefore, we propose a new approach that avoids the expensive and difficult calculation of the binding free energy and, instead, employs a scoring function that is based on the similarity of the proton nuclear magnetic resonance spectra of the tentative complexes with the experimental spectrum. Using this method, we could even predict the structure of a very difficult protein-peptide complex that could not be solved using any energy-based scoring functions. The second part of this work presents BALL (Biochemical ALgorithms Library), a framework for Rapid Application Development in the field of Molecular Modeling. BALL provides an extensive set of data structures as well as classes for Molecular Mechanics, advanced solvation methods, comparison and analysis of protein structures, file import/export, NMR shift prediction, and visualization. BALL has been carefully designed to be robust, easy to use, and open to extensions. Especially its extensibility, which results from an object-oriented and generic programming approach, distinguishes it from other software packages.Der erste Teil dieser Arbeit beschäftigt sich mit neuen Ansätzen zum Proteindocking. Zunächst stellen wir zwei Ansätze zum Proteindocking mit flexiblen Seitenketten vor. Der erste Ansatz beruht auf einer schnellen, gierigen Heuristik, während der zweite Ansatz auf branch-&-cut-Techniken beruht und das Problem optimal lösen kann. Beide Ansätze sind in der Lage die korrekte Komplexstruktur für einen Satz von Testbeispielen (bestehend aus Protease-Inhibitor-Komplexen) vorherzusagen. Ein weiteres, grösstenteils ungelöstes, Problem ist der letzte Schritt vieler Protein-Docking-Algorithmen, die Vorhersage der freien Bindungsenthalpie. Daher schlagen wir eine neue Methode vor, die die schwierige und aufwändige Berechnung der freien Bindungsenthalpie vermeidet. Statt dessen wird eine Bewertungsfunktion eingesetzt, die auf der Ähnlichkeit der Protonen-Kernresonanzspektren der potentiellen Komplexstrukturen mit dem experimentellen Spektrum beruht. Mit dieser Methode konnten wir sogar die korrekte Struktur eines Protein-Peptid-Komplexes vorhersagen, an dessen Vorhersage energiebasierte Bewertungsfunktionen scheitern. Der zweite Teil der Arbeit stellt BALL (Biochemical ALgorithms Library) vor, ein Rahmenwerk zur schnellen Anwendungsentwicklung im Bereich MolecularModeling. BALL stellt eine Vielzahl von Datenstrukturen und Algorithmen für die FelderMolekülmechanik,Vergleich und Analyse von Proteinstrukturen, Datei-Import und -Export, NMR-Shiftvorhersage und Visualisierung zur Verfügung. Beim Entwurf von BALL wurde auf Robustheit, einfache Benutzbarkeit und Erweiterbarkeit Wert gelegt. Von existierenden Software-Paketen hebt es sich vor allem durch seine Erweiterbarkeit ab, die auf der konsequenten Anwendung von objektorientierter und generischer Programmierung beruht