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Modern experiments in heavy ion collisions operate with huge data rates that can not be fully stored on the currently available storage devices. Therefore the data flow should be reduced by selecting those collisions that potentially carry the information of the physics interest. The future CBM experiment will have no simple criteria for selecting such collisions and requires the full online reconstruction of the collision topology including reconstruction of short-lived particles.
In this work the KF Particle Finder package for online reconstruction and selection of short-lived particles is proposed and developed. It reconstructs more than 70 decays, covering signals from all the physics cases of the CBM experiment: strange particles, strange resonances, hypernuclei, low mass vector mesons, charmonium, and open-charm particles.
The package is based on the Kalman filter method providing a full set of the particle parameters together with their errors including position, momentum, mass, energy, lifetime, etc. It shows a high quality of the reconstructed particles, high efficiencies, and high signal to background ratios.
The KF Particle Finder is extremely fast for achieving the reconstruction speed of 1.5 ms per minimum-bias AuAu collision at 25 AGeV beam energy on single CPU core. It is fully vectorized and parallelized and shows a strong linear scalability on the many-core architectures of up to 80 cores. It also scales within the First Level Event Selection package on the many-core clusters up to 3200 cores.
The developed KF Particle Finder package is a universal platform for short- lived particle reconstruction, physics analysis and online selection.
Jeden Tag werden 2,5 Trillionen Bytes an Daten generiert. Diese enorme Menge an Daten wird beispielsweise durch digitale Bilder, Videos, Beiträge in den sozialen Medien, intelligente Sensoren, Einzelhandels- und Finanztransaktionen und GPS-Signale von Handys erzeugt. Das ist Big Data. Es besteht kein Zweifel daran, dass Big Data und das, was wir damit tun, das Potential hat, ein signifikanter Treiber für Innovationen und Wertschöpfung zu werden.
Network graphs have become a popular tool to represent complex systems composed of many interacting subunits; especially in neuroscience, network graphs are increasingly used to represent and analyze functional interactions between multiple neural sources. Interactions are often reconstructed using pairwise bivariate analyses, overlooking the multivariate nature of interactions: it is neglected that investigating the effect of one source on a target necessitates to take all other sources as potential nuisance variables into account; also combinations of sources may act jointly on a given target. Bivariate analyses produce networks that may contain spurious interactions, which reduce the interpretability of the network and its graph metrics. A truly multivariate reconstruction, however, is computationally intractable because of the combinatorial explosion in the number of potential interactions. Thus, we have to resort to approximative methods to handle the intractability of multivariate interaction reconstruction, and thereby enable the use of networks in neuroscience. Here, we suggest such an approximative approach in the form of an algorithm that extends fast bivariate interaction reconstruction by identifying potentially spurious interactions post-hoc: the algorithm uses interaction delays reconstructed for directed bivariate interactions to tag potentially spurious edges on the basis of their timing signatures in the context of the surrounding network. Such tagged interactions may then be pruned, which produces a statistically conservative network approximation that is guaranteed to contain non-spurious interactions only. We describe the algorithm and present a reference implementation in MATLAB to test the algorithm’s performance on simulated networks as well as networks derived from magnetoencephalographic data. We discuss the algorithm in relation to other approximative multivariate methods and highlight suitable application scenarios. Our approach is a tractable and data-efficient way of reconstructing approximative networks of multivariate interactions. It is preferable if available data are limited or if fully multivariate approaches are computationally infeasible.
Inspiration for artificial biologically inspired computing is often drawn from neural systems. This article shows how to analyze neural systems using information theory with the aim of obtaining constraints that help to identify the algorithms run by neural systems and the information they represent. Algorithms and representations identified this way may then guide the design of biologically inspired computing systems. The material covered includes the necessary introduction to information theory and to the estimation of information-theoretic quantities from neural recordings. We then show how to analyze the information encoded in a system about its environment, and also discuss recent methodological developments on the question of how much information each agent carries about the environment either uniquely or redundantly or synergistically together with others. Last, we introduce the framework of local information dynamics, where information processing is partitioned into component processes of information storage, transfer, and modification – locally in space and time. We close by discussing example applications of these measures to neural data and other complex systems.
Angaben aus der Verlagsmeldung:
Ob beim Aufrufen einer Webseite, beim Versenden einer E-Mail oder beim Hochfrequenzhandel an der Börse: Auf ihrem Weg durch die Weiten digitaler Netze durchqueren Bits zahlreiche Knoten, an denen eine Reihe von Mikroentscheidungen getroffen werden. Diese Entscheidungen betreffen den besten Pfad zum Ziel, die Verarbeitungsgeschwindigkeit oder die Priorität zwischen den ankommenden Paketen.
In ihrer vielschichtigen Gestalt bilden solche Mikroentscheidungen eine bislang nur marginal beachtete Dimension von Kontrolle und Überwachung im 21. Jahrhundert. Sie sind sowohl die kleinste Einheit als auch die technische Voraussetzung einer gegenwärtigen Politik digitaler Netzwerke – und des Widerstands gegen sie. Die aktuellen Debatten um Netzneutralität und Edward Snowdens Enthüllung der NSA-Überwachung bilden dabei lediglich die Spitze des Eisbergs. Auf dem Spiel steht nicht weniger als die Zukunft des Internets, wie wir es kennen.
Be it in the case of opening a website, sending an email, or high-frequency trading, bits and bytes of information have to cross numerous nodes at which micro-decisions are made. These decisions concern the most efficient path through the network, the processing speed, or the priority of incoming data packets.
Despite their multifaceted nature, micro-decisions are a dimension of control and surveillance in the twenty-first century that has received little critical attention. They represent the smallest unit and the technical precondition of a contemporary network politics – and of our potential opposition to it. The current debates regarding net neutrality and Edward Snowden’s revelation of NSA surveillance are only the tip of the iceberg. What is at stake is nothing less than the future of the Internet as we know it.
This paper shows equivalence of several versions of applicative similarity and contextual approximation, and hence also of applicative bisimilarity and contextual equivalence, in LR, the deterministic call-by-need lambda calculus with letrec extended by data constructors, case-expressions and Haskell's seq-operator. LR models an untyped version of the core language of Haskell. The use of bisimilarities simplifies equivalence proofs in calculi and opens a way for more convenient correctness proofs for program transformations. The proof is by a fully abstract and surjective transfer into a call-by-name calculus, which is an extension of Abramsky's lazy lambda calculus. In the latter calculus equivalence of our similarities and contextual approximation can be shown by Howe's method. Similarity is transferred back to LR on the basis of an inductively defined similarity. The translation from the call-by-need letrec calculus into the extended call-by-name lambda calculus is the composition of two translations. The first translation replaces the call-by-need strategy by a call-by-name strategy and its correctness is shown by exploiting infinite trees which emerge by unfolding the letrec expressions. The second translation encodes letrec-expressions by using multi-fixpoint combinators and its correctness is shown syntactically by comparing reductions of both calculi. A further result of this paper is an isomorphism between the mentioned calculi, which is also an identity on letrec-free expressions.
The calculus LRP is a polymorphically typed call-by-need lambda calculus extended by data constructors, case-expressions, seq-expressions and type abstraction and type application. This report is devoted to the extension LRPw of LRP by scoped sharing decorations. The extension cannot be properly encoded into LRP if improvements are defined w.r.t. the number of lbeta, case, and seq-reductions, which makes it necessary to reconsider the claims and proofs of properties. We show correctness of improvement properties of reduction and transformation rules and also of computation rules for decorations in the extended calculus LRPw. We conjecture that conservativity of the embedding of LRP in LRPw holds.
This report documents the extension LRPw of LRP by sharing decorations. We show correctness of improvement properties of reduction and transformation rules and also of computation rules for decorations in the extended calculus LRPw. We conjecture that conservativity of the embedding of LRP in LRPw holds.
An improvement is a correct program transformation that optimizes the program, where the criterion is that the number of computation steps until a value is obtained is decreased. This paper investigates improvements in both { an untyped and a polymorphically typed { call-by-need lambda-calculus with letrec, case, constructors and seq. Besides showing that several local optimizations are improvements, the main result of the paper is a proof that common subexpression elimination is correct and an improvement, which proves a conjecture and thus closes a gap in Moran and Sands' improvement theory. We also prove that several different length measures used for improvement in Moran and Sands' call-by-need calculus and our calculus are equivalent.