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Das größte Problem bei der Erstellung von MR-Anwendungen besteht darin, dass sie meistens durch Programmierung erstellt werden. Daher muss ein Autor spezielles Fachwissen über MR-Technologie und zumindest allgemeine Programmierkenntnisse mitbringen, um eine MR-Anwendung erstellen zu können. Dieser Erstellungsprozess soll mit Hilfe von MR-Autorensystemen, die derzeit auf dem Markt existieren und in der Forschung entwickelt werden, vereinfacht werden. Dies war ein Grund, warum diese Arbeit sich zum Ziel erklärte, zu überprüfen, inwieweit die Erstellung von MRAnwendungen durch Einsatz von MR-Autorensystemen vereinfacht wird. Ein weiteres Hauptziel war die Erstellung einer repräsentativen MR-Anwendung, die in dieser Arbeit als MR-Referenzanwendung bezeichnet wird. Sie sollte vor allem bei weiteren Entwicklungen als Vorlage dienen können und auf Basis von standardisierten Vorgehensmodellen, wie das Wasserfallmodell, erstellt werden. Ganz wichtig war es noch im Rahmen dieser Arbeit zu bestätigen, dass standardisierte Vorgehensmodelle auf MR-Anwendungen übertragbar sind. Um diese Ziele zu erreichen, sind in dieser Arbeit viele Schritte befolgt worden, die jeweils als Teilziele betrachtet werden können. Die MR-Referenzanwendung , die im Rahmen dieser Arbeit erstellt wurde, sollte mit Hilfe eines MR-Autorensystems umgesetzt werden. Um das richtige MRAutorensystem dafür auszusuchen, wurden im Rahmen einer Analyse fakultative und obligatorische Anforderungen an MR-Autorensysteme definiert, worin auch Funktionen identifiziert wurden, die ein solches System bereitstellen sollte. Das Anbieten einer Vorschau ist ein Beispiel für diese Funktionen, die bei der Erstellung von MR-Anwendungen eine essentielle Rolle spielen können. Die obligatorischen Anforderungen sind welche, die jedes Softwaresystem erfüllen soll, während die fakultativen das Ziel der Verbesserung von Autorensystemen verfolgen. Mit Hilfe der Analyse wurde ein Vergleich zwischen bekannten MR-Autorensystemen gezogen, dessen Ergebnis AMIRE als ein für die Ziele dieser Arbeit geeignetes MR-Autorensystem identifizierte. Für die MR-Referenzanwendung , die ähnliche Funktionen aufweisen sollte wie andere typische MR-Anwendungen wurden Funktionen, Anwendungsfälle und Design der Oberfläche spezifiziert. Diese Spezifikation wurde unabhängig von dem ausgesuchten Autorensystem durchgeführt, um darin analog zur Software-Technik das Augenmerk auf fachliche und nicht auf technische Aspekte zu legen. Um ans Ziel zu gelangen, wurde die MR-Referenzanwendung durch AMIRE realisiert, jedoch musste zuvor ihre Spezifikation auf dieses MR-Autorensystem überführt werden. Bei der Überführung wurde die Realisierung aus technischer Sicht betrachtet, das heißt es wurden verschiedene Vorbereitungen, wie die Auswahl der benötigten Komponenten, die Planung der Anwendungslogik und die Aufteilung der Anwendung in verschiedenen Zuständen, durchgeführt. Nach der gelungenen Realisierung und beispielhaften Dokumentation der MRReferenzanwendung konnte die Arbeit bewertet werden, worin die erzielten Resultate den Zielen der Arbeit gegenübergestellt wurden. Die Ergebnisse bestätigen, dass mit AMIRE die Entwicklung einer MR-Anwendung ohne Spezialwissen möglich ist und dass diese Arbeit alle ihrer Ziele innerhalb des festgelegten Zeitrahmens erreicht hat.
Context unification is a variant of second order unification. It can also be seen as a generalization of string unification to tree unification. Currently it is not known whether context unification is decidable. A specialization of context unification is stratified context unification, which is decidable. However, the previous algorithm has a very bad worst case complexity. Recently it turned out that stratified context unification is equivalent to satisfiability of one-step rewrite constraints. This paper contains an optimized algorithm for strati ed context unification exploiting sharing and power expressions. We prove that the complexity is determined mainly by the maximal depth of SO-cycles. Two observations are used: i. For every ambiguous SO-cycle, there is a context variable that can be instantiated with a ground context of main depth O(c*d), where c is the number of context variables and d is the depth of the SO-cycle. ii. the exponent of periodicity is O(2 pi ), which means it has an O(n)sized representation. From a practical point of view, these observations allow us to conclude that the unification algorithm is well-behaved, if the maximal depth of SO-cycles does not grow too large.
We analyse a continued fraction algorithm (abbreviated CFA) for arbitrary dimension n showing that it produces simultaneous diophantine approximations which are up to the factor 2^((n+2)/4) best possible. Given a real vector x=(x_1,...,x_{n-1},1) in R^n this CFA generates a sequence of vectors (p_1^(k),...,p_{n-1}^(k),q^(k)) in Z^n, k=1,2,... with increasing integers |q^{(k)}| satisfying for i=1,...,n-1 | x_i - p_i^(k)/q^(k) | <= 2^((n+2)/4) sqrt(1+x_i^2) |q^(k)|^(1+1/(n-1)) By a theorem of Dirichlet this bound is best possible in that the exponent 1+1/(n-1) can in general not be increased.
High-energy physics experiments aim to deepen our understanding of the fundamental structure of matter and the governing forces. One of the most challenging aspects of the design of new experiments is data management and event selection. The search for increasingly rare and intricate physics events asks for high-statistics measurements and sophisticated event analysis. With progressively complex event signatures, traditional hardware-based trigger systems reach the limits of realizable latency and complexity. The Compressed Baryonic Matter experiment (CBM) employs a novel approach for data readout and event selection to address these challenges. Self-triggered, free-streaming detectors push all data to a central compute cluster, called First-level Event Selector (FLES), for software-based event analysis and selection. While this concept solves many issues present in classical architectures, it also sets new challenges for the design of the detector readout systems and online event selection.
This thesis presents an efficient solution to the data management challenges presented by self-triggered, free-streaming particle detectors. The FLES must receive asynchronously streamed data from a heterogeneous detector setup at rates of up to 1 TB/s. The real-time processing environment implies that all components have to deliver high performance and reliability to record as much valuable data as possible. The thesis introduces a time-based data model to partition the input streams into containers of fixed length in experiment time for efficient data management. These containers provide all necessary metadata to enable generic, detector-subsystem-agnostic data distribution across the entire cluster. An analysis shows that the introduced data overhead is well below 1 % for a wide range of system parameters.
Furthermore, a concept and the implementation of a detector data input interface for the CBM FLES, optimized for resource-efficient data transport, are presented. The central element of the architecture is an FPGA-based PCIe extension card for the FLES entry nodes. The hardware designs developed in the thesis enable interfacing with a diverse set of detector systems. A custom, high-throughput DMA design structures data in a way that enables low-overhead access and efficient software processing. The ability to share the host DMA buffers with other devices, such as an InfiniBand HCA, allows for true zero-copy data distribution between the cluster nodes. The discussed FLES input interface is fully implemented and has already proven its reliability in production operation in various physics experiments.
We empirically investigate algorithms for solving Connected Components in the external memory model. In particular, we study whether the randomized O(Sort(E)) algorithm by Karger, Klein, and Tarjan can be implemented to compete with practically promising and simpler algorithms having only slightly worse theoretical cost, namely Borůvka’s algorithm and the algorithm by Sibeyn and collaborators. For all algorithms, we develop and test a number of tuning options. Our experiments are executed on a large set of different graph classes including random graphs, grids, geometric graphs, and hyperbolic graphs. Among our findings are: The Sibeyn algorithm is a very strong contender due to its simplicity and due to an added degree of freedom in its internal workings when used in the Connected Components setting. With the right tunings, the Karger-Klein-Tarjan algorithm can be implemented to be competitive in many cases. Higher graph density seems to benefit Karger-Klein-Tarjan relative to Sibeyn. Borůvka’s algorithm is not competitive with the two others.
Channel routing is an NP-complete problem. Therefore, it is likely that there is no efficient algorithm solving this problem exactly.In this paper, we show that channel routing is a fixed-parameter tractable problem and that we can find a solution in linear time for a fixed channel width.We implemented our approach for the restricted layer model. The algorithm finds an optimal route for channels with up to 13 tracks within minutes or up to 11 tracks within seconds.Such narrow channels occur for example as a leaf problem of hierarchical routers or within standard cell generators.
Driven by rapid technological advancements, the amount of data that is created, captured, communicated, and stored worldwide has grown exponentially over the past decades. Along with this development it has become critical for many disciplines of science and business to being able to gather and analyze large amounts of data. The sheer volume of the data often exceeds the capabilities of classical storage systems, with the result that current large-scale storage systems are highly distributed and are comprised of a high number of individual storage components. As with any other electronic device, the reliability of storage hardware is governed by certain probability distributions, which in turn are influenced by the physical processes utilized to store the information. The traditional way to deal with the inherent unreliability of combined storage systems is to replicate the data several times. Another popular approach to achieve failure tolerance is to calculate the block-wise parity in one or more dimensions. With better understanding of the different failure modes of storage components, it has become evident that sophisticated high-level error detection and correction techniques are indispensable for the ever-growing distributed systems. The utilization of powerful cyclic error-correcting codes, however, comes with a high computational penalty, since the required operations over finite fields do not map very well onto current commodity processors. This thesis introduces a versatile coding scheme with fully adjustable fault-tolerance that is tailored specifically to modern processor architectures. To reduce stress on the memory subsystem the conventional table-based algorithm for multiplication over finite fields has been replaced with a polynomial version. This arithmetically intense algorithm is better suited to the wide SIMD units of the currently available general purpose processors, but also displays significant benefits when used with modern many-core accelerator devices (for instance the popular general purpose graphics processing units). A CPU implementation using SSE and a GPU version using CUDA are presented. The performance of the multiplication depends on the distribution of the polynomial coefficients in the finite field elements. This property has been used to create suitable matrices that generate a linear systematic erasure-correcting code which shows a significantly increased multiplication performance for the relevant matrix elements. Several approaches to obtain the optimized generator matrices are elaborated and their implications are discussed. A Monte-Carlo-based construction method allows it to influence the specific shape of the generator matrices and thus to adapt them to special storage and archiving workloads. Extensive benchmarks on CPU and GPU demonstrate the superior performance and the future application scenarios of this novel erasure-resilient coding scheme.
A novel method for identifying the nature of QCD transitions in heavy-ion collision experiments is introduced. PointNet based Deep Learning (DL) models are developed to classify the equation of state (EoS) that drives the hydrodynamic evolution of the system created in Au-Au collisions at 10 AGeV. The DL models were trained and evaluated in different hypothetical experimental situations. A decreased performance is observed when more realistic experimental effects (acceptance cuts and decreased resolutions) are taken into account. It is shown that the performance can be improved by combining multiple events to make predictions. The PointNet based models trained on the reconstructed tracks of charged particles from the CBM detector simulation discriminate a crossover transition from a first order phase transition with an accuracy of up to 99.8%. The models were subjected to several tests to evaluate the dependence of its performance on the centrality of the collisions and physical parameters of fluid dynamic simulations. The models are shown to work in a broad range of centralities (b=0–7 fm). However, the performance is found to improve for central collisions (b=0–3 fm). There is a drop in the performance when the model parameters lead to reduced duration of the fluid dynamic evolution or when less fraction of the medium undergoes the transition. These effects are due to the limitations of the underlying physics and the DL models are shown to be superior in its discrimination performance in comparison to conventional mean observables.
We present an implementation of an interpreter LRPi for the call-by-need calculus LRP, based on a variant of Sestoft's abstract machine Mark 1, extended with an eager garbage collector. It is used as a tool for exact space usage analyses as a support for our investigations into space improvements of call-by-need calculi.
We consider unification of terms under the equational theory of two-sided distributivity D with the axioms x*(y+z) = x*y + x*z and (x+y)*z = x*z + y*z. The main result of this paper is that Dunification is decidable by giving a non-deterministic transformation algorithm. The generated unification are: an AC1-problem with linear constant restrictions and a second-order unification problem that can be transformed into a word-unification problem that can be decided using Makanin's algorithm. This solves an open problem in the field of unification. Furthermore it is shown that the word-problem can be decided in polynomial time, hence D-matching is NP-complete.
We show how Sestoft’s abstract machine for lazy evaluation of purely functional programs can be extended to evaluate expressions of the calculus CHF – a process calculus that models Concurrent Haskell extended by imperative and implicit futures. The abstract machine is modularly constructed by first adding monadic IO-actions to the machine and then in a second step we add concurrency. Our main result is that the abstract machine coincides with the original operational semantics of CHF, w.r.t. may- and should-convergence.
Ambiguity and communication
(2009)
The ambiguity of a nondeterministic finite automaton (NFA) N for input size n is the maximal number of accepting computations of N for an input of size n. For all k, r 2 N we construct languages Lr,k which can be recognized by NFA's with size k poly(r) and ambiguity O(nk), but Lr,k has only NFA's with exponential size, if ambiguity o(nk) is required. In particular, a hierarchy for polynomial ambiguity is obtained, solving a long standing open problem (Ravikumar and Ibarra, 1989, Leung, 1998).
Gegenstand dieser Arbeit war die Analyse der Komplexität von Kosten- und Erlösrechnungssystemen und ihrer Auswirkung auf die Auswahl geeigneter Instrumente für die EDV-gestützte Realisierung dieser Systeme, wobei insbesondere auf die bisherigen Ansätze der Datenbank- und Wissensuntersrutzung der Kosten- und Erlösrechnung eingegangen werden sollte. Das zweite Kapitel befaßt sich mit einer Analyse der Komplexität der in Deutschland am weitesten verbreiteten Kosten- und Erlösrechnungssysteme. Die Untersuchung der grundlegenden Gestaltungsmerkmale von Kosten- und Erlösrechnungssystemen auf ihre Komplexitätsrelevanz zeigte, daß einige Merkmale die Komplexität sehr stark beeinflussen, andere dagegen kaum, darunter auch in der betriebswirtschaftlichen Diskussion so wesentliche wie der verwendete Kostenbegriff. Den größten Einfluß auf die Komplexität von Kosten- und Erlösrechnungssystemen besitzen die Kosten- und Erlösstrukturierung sowie die Verarbeitungsarten, -methoden und -inhalte. Ein Vergleich der Grenzplankostenrechnung nach Kn.GER und FLAUT, stellvertretend Im überwiegend zweckmonistische Kostenrechnungssysteme, und der Einzelkostenrechnung nach RIEBEL als zweckpluralistischem Kosten- und Erlösrechnungssystem bezüglich der komplexitätsrelevanten Merkmale ergab eindeutige Unterschiede zwischen diesen Systemen. Während die Grenzplankostenrechnung polynomiale Platz- und Funktionskomplexitäten niedriger Grade (überwiegend quadratisch und nur im Rahmen der innerbetrieblichen Leistungsverrechnung kubisch) aufweist, treten in der Einzelkostenrechnung an mehreren entscheidenden Stellen exponentielle Komplexitäten auf. Die Analyse der Komplexität dieser beiden Kosten- und Erlösrechnungssystemen zeigt einen eindeutigen Zusammenhang zwischen vielseitiger Auswertbarkeit und der Komplexität eines Systems auf, der bei einer Beurteilung von Kosten- und Erlösrechnungssystemen berücksichtigt werden muß. Für die Gestaltung von Kosten- und Erlösrechnungssystemen bedeutet dies eine grundsätzliche Wahlmöglichkeit zwischen Systemen begrenzter Auswertbarkeit und niedriger Komplexität sowie Systemen mit größerer Auswertungsvielfalt, aber deutlich höherer Komplexität. Die Komplexität von Kosten- und Erlösrechnungssystemen ist jedoch nicht als eine Folge der Auswahl eines Rechnungssystems zu betrachten, sondern resultiert letztlich aus der Komplexität einer Unternehmung und ihrer Umwelt, die unterschiedlich detailliert abgebildet werden können. Da diese Komplexitäten in Zukunft eher noch zunehmen werden, ist grundSätzlich mit einem Trend zu universelleren und komplexeren Systemen zu rechnen. Die Erweiterung der Grenzplankostenrechnung hin zu größerer Komplexität sowie die Entwicklung neuerer Ansätze wie der Prozeßkostenrechnung bestätigen beide diesen Trend. Für die weitere Untersuchung wird vorausgesetzt, daß die Grenzplankostenrechnung und die Einzelkostenrechnung die entgegengesetzten Enden eines Komplexitätsspektrums von Kosten- und Erlösrechnungssystemen bilden und daher auch das Spektrum der Anforderungen an die Instrumente zu ihrer EDV-Implementierung begrenzen. Unter einer Anzahl von neueren Entwicklungen in der EDV wurden daher zwei Konzepte ausgewählt, die zur Behandlung verschiedener Aspekte der Komplexität geeignet sind: Datenbanksysteme zur Behandlung der Platzkomplexität und Wissenssysteme zur Behandlung der Funktionskomplexität. Im folgenden werden die Erfahrungen, die bei der Realisierung von Datenbank- und Wissenssystemen für die Kosten- und Erlösrechnung gemacht wurden, unter dem Gesichtspunkt der Komplexität von Kosten- und Erlösrechnungssystemen bewertet. Bei der Betrachtung von Datenbanksystemen ist zu berücksichtigen, daß sich im Laufe der Zeit zwei unterschiedliche Anwendungstypen herauskristallisiert haben: konventionelle Datenbankanwendungen, die den herkömmlichen Paradigmen von Datenbanksystemen entsprechen, und neuere Datenbankanwendungen, die z.T. wesentlich höhere Anforderungen stellen und so die Entwicklung neuer Datenbanksysteme erforderlich machten. Beide Systeme der Kosten- und Erlösrechnung eignen sich grundSätzlich als Datenbankanwendungen, d.h. sie rechtfertigen den Einsatz von Datenbanksystemen zur Verwaltung ihrer Datenmengen. Während die Grenzplankostenrechnung aber den konventionellen Datenbankanwendungen zuzurechnen ist, weist die Einzelkostenrechnung bereits wesentliche Merkmale neuerer Datenbankanwendungen auf. Im Gegensatz zu Datenbanksystemen sind die Anforderungen an Wissenssysteme und ihre Eigenschaften sehr unpräzise, z.T. sogar widersprüchlich formuliert. Auf der Basis der gängigen Eigenschaftskataloge erscheint die Kosten- und Erlösrechnung nicht als typische Wissenssystemanwendung. Trotzdem wurden bereits mehrere Wissenssysteme für Kosten- und Erlösrechnungsprobleme (Abweichungsanalyse, Betriebsergebnisanalyse, Bestimmung von Preisuntergrenzen, konstruktionsbegleitende Kalkulation und Teilprobleme der Prozeßkostenrechnung) realisiert, von denen jedes einige der Eignungskriterien für Wissenssystemanwendungen erfüllt. Die behandelten Beispiele für Wissenssysteme im Rahmen der Kosten- und Erlösrechnung basieren überwiegend auf der Grenzplankostenrechnung. Es ist daher anzunehmen, daß die Einzelkostenrechnung auf Grund ihrer höheren Komplexität weitere Anwendungsprobleme für Wissenssysteme enthält. Insgesamt sind jedoch die Unterschiede zwischen der Grenzplankostenrechnung und der Einzelkostenrechnung im Hinblick auf den Einsatz von Wissenssystemen wesentlich weniger ausgeprägt als dies für den Einsatz von Datenbanksystemen der Fall war. Nachdem beide Systeme der Kosten- und Erlösrechnung sowohl als Datenbankanwendungen geeignet sind als auch Anwendungsprobleme für Wissenssysteme aufweisen, ist auch die Verbindung von Wissenssystemen und Datenbanksystemen in Betracht zu ziehen. Daher wurde im Anschluß die jeweiligen Vor- und Nachteile von Datenbank- und Wissenssysteme gegenübergestellt. Die Vorteile von Datenbanksystemen liegen auf den maschinennäheren Ebenen, auf denen die Vorkehrungen für Datenschutz, Datensicherung, reibungslosen Mehrbenutzerbetrieb sowie die effiziente Ausführung der Operationen geschaffen werden. Die Vorteile von Wissenssystemen liegen in der größeren Mächtigkeit der Problemlösungskomponente, der Wissenserweiterungskomponente und der Erklärungskomponente. Ein neueres Beispiel für eine Zusammenarbeit von Datenbank- und Wissenssystemen ist die Auswertung eines speziell für derartige Zwecke angelegten Data Warehouse durch das Data Mining sowie andere Analysesysteme. Ein Data Warehouse stimmt in wesentlichen Merkmalen mit der Grundrechnung der Einzelkostenrechnung überein und zeigt, daß eine Grundrechnung auf der Basis heutiger EDV -Systeme realisierbar ist. Zur Auswertung einer Datenbank dieser Größe sind spezielle Analysesysteme notwendig. Für standardisierte Auswertungen eines Data Warehouse wurden OLAP-Systeme entwickelt, deren Operationen Verallgemeinerungen mehrdimensionaler Deckungsbeitragsrechnungen sind. Bei nicht standardisierbaren Auswertungen empfiehlt sich dagegen der Einsatz von Wissenssystemen, für den das Data Mining ein Beispiel liefert. Diese Kombination von Datenbanksystem, konventionellen und Kl-Auswertungen erscheint für eine Verwendung in der Kosten- und Erlösrechnung bestens geeignet. Das vierte Kapitel befaßt sich mit Ansätzen zur Strukturierung von Daten- und Wissensbasen, die bei Datenbanksystemen als Datenmodelle, bei Wissenssystemen als Wissensrepräsentationstechniken bezeichnet werden. Dabei wurde der Unterteilung des dritten Kapitels gefolgt und zwischen konventionellen und neueren Datenmodellen sowie Wissensrepräsentationstechniken unterschieden. Die Betrachtung des Relationenmodells als Vertreters der konventionellen Datenmodelle ergab, daß es für die Grenzplankostenrechnung völlig ausreicht. Die Erfahrungen mit der Realisierung einer Grundrechnung auf der Basis des Relationenmodells haben dagegen gezeigt, daß seine syntaktischen und semantischen Mängel zu weitgehenden Vereinfachungen beim Schemaentwurf zwingen, die wiederum die Operationen der Auswertungsrechnungen unnötig komplizieren. Aus der Vielzahl semantischer und objektorientierter Datenmodelle, die für neuere Datenbankanwendungen entwickelt wurden, hat sich trotz Unterschieden in Details eine Anzahl von Konzepten herauskristallisiert, die den meisten dieser DatenmodelIe gemeinsam sind. Mit Hilfe dieser Konzepte sind die Probleme, die bei der Verwendung des Relationenmodelis auftraten, vermeidbar. Im Grunde sind daher fast alle semantischen und objektorientierten Entwurfsmodelle zur ModelIierung einer Grundrechnung geeignet. Wichtig ist jedoch,daß die Grundrechnung auch mit einem Datenbanksystem realisiert wird, dem eines dieser Datenmodelle zugrunde liegt, da bei einer Transformation auf ein relationales Datenmodell wesentliche Entwurfsüberlegungen - und damit der größte Teil des Vorteils,den semantische und objektorientierte Entwurfsmodelle bieten -, verloren gehen. Zur Realisierung einer Grundrechnung erscheinen objektrelationale Datenbanksysteme am besten geeignet, da sie einerseits objektorientierte Konzepte mit mächtigen und komfortablen Anfragesprachen verbinden und andererseits aufwärtskompatibel zu den weitverbreiteten relationalen Datenbanksystemen sind. Da sich die objektorientierten Datenmodelle als für die Modellierung einer Grundrechnung geeignet erwiesen haben, wurden unter dem Gesichtspunkt der Verbindung von Datenbank- und Wissenssystemen nur objektorientierte Wissensrepräsentationstechniken in Betracht gezogen. Zwischen semantischen und objektorientierten Datenmodellen einerseits und objektorientierten Wissensrepräsentationstechniken, vor allem semantischen Netzen und Frames, andererseits bestehen weitgehende Übereinstimmungen. Daher können z.B. framebasierte Wissenssysteme direkt auf objektorientierten Datenbanksystemen realisiert werden. Inzwischen werden aber auch objektorientierte Programmiersprachen wie C++ oder Smalltalk zur Implementierung von Wissenssystemen verwendet, von denen die objektorientierte Sprache C++ am geeignetsten erscheint, da die meisten objektorientierten und objektrelationalen Datenbanksysteme eine C++-Schnittstelle aufweisen. Abschließend ist daher festzustellen, daß das Paradigma der Objektorientierung, das in Entwurfssprachen, Datenmodellen, Wissensrepräsentationstechniken und Programmiersprachen wesentliche Einflüsse ausgeübt hat, für die Realisierung der datenbankgestützten Grundrechnung eines zweckpluralistischen Kosten- und Erlösrechnungssystems wie der Einzelkostenrechnung sowie darauf aufbauender Auswertungsrechnungen, die z.T. als Wissenssysteme realisiert werden, wesentliche Vorteile besitzt. Über die adäquatere ModelIierung der Strukturen hinaus entsteht durch den Einsatz objektorientierter Techniken zum Entwurf und zur Implementierung aller System teile ein möglichst homogenes System, das nicht zusätzlich zu der inhärenten Komplexität noch weitere Probleme durch ungeeignete Darstellungskonzepte oder schlechte Abstimmung schafft.
Motivated by tools for automaed deduction on functional programming languages and programs, we propose a formalism to symbolically represent $\alpha$-renamings for meta-expressions. The formalism is an extension of usual higher-order meta-syntax which allows to $\alpha$-rename all valid ground instances of a meta-expression to fulfill the distinct variable convention. The renaming mechanism may be helpful for several reasoning tasks in deduction systems. We present our approach for a meta-language which uses higher-order abstract syntax and a meta-notation for recursive let-bindings, contexts, and environments. It is used in the LRSX Tool -- a tool to reason on the correctness of program transformations in higher-order program calculi with respect to their operational semantics. Besides introducing a formalism to represent symbolic $\alpha$-renamings, we present and analyze algorithms for simplification of $\alpha$-renamings, matching, rewriting, and checking $\alpha$-equivalence of symbolically $\alpha$-renamed meta-expressions.
Magnetoencephalography (MEG) measures neural activity non-invasively and at an excellent temporal resolution. Since its invention (Cohen, 1968, 1972), MEG has proven a most valuable tool in neurocognitive (Salmelin et al., 1994) and clinical research (Stufflebeam et al., 2009; Van ’t Ent et al., 2003). MEG is able to measure rapid changes in electrophysiological neural signals related to sensory and cognitive processes. The magnetic fields measured outside the head by MEG directly reflect the cortical currents generated by the synchronised activity of thousands of neuronal sources. This distinguishes MEG from functional magnetic resonance imaging (fMRI), where measurements are only indirectly related to electrophysiological activity through neurovascular coupling...
Seminar: 10501 - Advances and Applications of Automata on Words and Trees. The aim of the seminar was to discuss and systematize the recent fast progress in automata theory and to identify important directions for future research. For this, the seminar brought together more than 40 researchers from automata theory and related fields of applications. We had 19 talks of 30 minutes and 5 one-hour lectures leaving ample room for discussions. In the following we describe the topics in more detail.
From 12.12.2010 to 17.12.2010, the Dagstuhl Seminar 10501 "Advances and Applications of Automata on Words and Trees" was held in Schloss Dagstuhl - Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
The hepatitis C virus (HCV) RNA replication cycle is a dynamic intracellular process occurring in three-dimensional space (3D), which is difficult both to capture experimentally and to visualize conceptually. HCV-generated replication factories are housed within virus-induced intracellular structures termed membranous webs (MW), which are derived from the Endoplasmatic Reticulum (ER). Recently, we published 3D spatiotemporal resolved diffusion–reaction models of the HCV RNA replication cycle by means of surface partial differential equation (sPDE) descriptions. We distinguished between the basic components of the HCV RNA replication cycle, namely HCV RNA, non-structural viral proteins (NSPs), and a host factor. In particular, we evaluated the sPDE models upon realistic reconstructed intracellular compartments (ER/MW). In this paper, we propose a significant extension of the model based upon two additional parameters: different aggregate states of HCV RNA and NSPs, and population dynamics inspired diffusion and reaction coefficients instead of multilinear ones. The combination of both aspects enables realistic modeling of viral replication at all scales. Specifically, we describe a replication complex state consisting of HCV RNA together with a defined amount of NSPs. As a result of the combination of spatial resolution and different aggregate states, the new model mimics a cis requirement for HCV RNA replication. We used heuristic parameters for our simulations, which were run only on a subsection of the ER. Nevertheless, this was sufficient to allow the fitting of core aspects of virus reproduction, at least qualitatively. Our findings should help stimulate new model approaches and experimental directions for virology.
We investigate methods and tools for analyzing translations between programming languages with respect to observational semantics. The behavior of programs is observed in terms of may- and mustconvergence in arbitrary contexts, and adequacy of translations, i.e., the reflection of program equivalence, is taken to be the fundamental correctness condition. For compositional translations we propose a notion of convergence equivalence as a means for proving adequacy. This technique avoids explicit reasoning about contexts, and is able to deal with the subtle role of typing in implementations of language extensions.
We investigate methods and tools for analyzing translations between programming languages with respect to observational semantics. The behavior of programs is observed in terms of may- and mustconvergence in arbitrary contexts, and adequacy of translations, i.e., the reflection of program equivalence, is taken to be the fundamental correctness condition. For compositional translations we propose a notion of convergence equivalence as a means for proving adequacy. This technique avoids explicit reasoning about contexts, and is able to deal with the subtle role of typing in implementations of language extensions.
We investigate methods and tools for analysing translations between programming languages with respect to observational semantics. The behaviour of programs is observed in terms of may- and mustconvergence in arbitrary contexts, and adequacy of translations, i.e., the reflection of program equivalence, is taken to be the fundamental correctness condition. For compositional translations we propose a notion of convergence equivalence as a means for proving adequacy. This technique avoids explicit reasoning about contexts, and is able to deal with the subtle role of typing in implementations of language extensions.
We investigate methods and tools for analysing translations between programming languages with respect to observational semantics. The behaviour of programs is observed in terms of may- and mustconvergence in arbitrary contexts, and adequacy of translations, i.e., the reflection of program equivalence, is taken to be the fundamental correctness condition. For compositional translations we propose a notion of convergence equivalence as a means for proving adequacy. This technique avoids explicit reasoning about contexts, and is able to deal with the subtle role of typing in implementations of language extensions.
We investigate methods and tools for analysing translations between programming languages with respect to observational semantics. The behaviour of programs is observed in terms of may- and must-convergence in arbitrary contexts, and adequacy of translations, i.e., the reflection of program equivalence, is taken to be the fundamental correctness condition. For compositional translations we propose a notion of convergence equivalence as a means for proving adequacy. This technique avoids explicit reasoning about contexts, and is able to deal with the subtle role of typing in implementations of language extension.
In der vorliegenden Arbeit wurde ein klinisches Alarmsystem für septische Schock-Patienten aufgebaut. Zweckmäßigerweise wurden hierfür metrische körpereigene Variablen verwendet, da Analysen belegt haben, dass die metrischen Daten besser zur Alarmgenerierung geeignet sind als die symbolischen Daten. Für das Training des adaptiven Neuro-Fuzzy-Systems wurden die Daten der letzten Tage des Intensivaufenthalts verwendet, da in diesem Zeitraum, im Gegensatz zu den ersten Tagen, eine gute Klassifikationsperformanz erreicht wurde. Die daraus resultierenden Alarmhistorien liefern zuverlässige Hinweise für den Intensivmediziner auf besonders kritische Patienten. Durch diese Arbeit wird es möglich werden, den medizinischen SOFA-Score, der aus 10 Variablen zusammengesetzt ist, durch die einfachere Kombination "Systolischer Blutdruck / Diastolischer Blutdruck / Thrombozyten" zu ersetzen mit einer mindestens genauso guten Performanz. Durch die Hinzunahme weiterer Variablen ist es möglich, die Performanz des SOFA-Scores zu überbieten, wobei der SOFA-Score bereits die beste Klassifikationsperformanz unter den getesteten Scores erreichte. Die erzeugten Regeln konnten die Klassifikationsentscheidung sinnvoll untermauern. Im Gegensatz zur automatischen Regelgenerierung war es Ärzten nicht möglich ahnlich sinnvolle formale Regeln zu formulieren.
This paper describes the problems and an adaptive solution for process control in rubber industry. We show that the human and economical benefits of an adaptive solution for the approximation of process parameters are very attractive. The modeling of the industrial problem is done by the means of artificial neural networks. For the example of the extrusion of a rubber profile in tire production our method shows good results even using only a few training samples.
In bioinformatics, biochemical pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically learning the parameters is necessary. In this paper, for the small, important example of inflammation modeling a network is constructed and different learning algorithms are proposed. It turned out that due to the nonlinear dynamics evolutionary approaches are necessary to fit the parameters for sparse, given data. Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence - ICTAI 2003
In bioinformatics, biochemical pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically learning the parameters is necessary. In this paper, for the small, important example of inflammation modeling a network is constructed and different learning algorithms are proposed. It turned out that due to the nonlinear dynamics evolutionary approaches are necessary to fit the parameters for sparse, given data. Keywords: model parameter adaption, septic shock. coupled differential equations, genetic algorithm.
The Internet as the biggest human library ever assembled keeps on growing. Although all kinds of information carriers (e.g. audio/video/hybrid file formats) are available, text based documents dominate. It is estimated that about 80% of all information worldwide stored electronically exists in (or can be converted into) text form. More and more, all kinds of documents are generated by means of a text processing system and are therefore available electronically. Nowadays, many printed journals are also published online and may even discontinue to appear in print form tomorrow. This development has many convincing advantages: the documents are both available faster (cf. prepress services) and cheaper, they can be searched more easily, the physical storage only needs a fraction of the space previously necessary and the medium will not age. For most people, fast and easy access is the most interesting feature of the new age; computer-aided search for specific documents or Web pages becomes the basic tool for information-oriented work. But this tool has problems. The current keyword based search machines available on the Internet are not really appropriate for such a task; either there are (way) too many documents matching the specified keywords are presented or none at all. The problem lies in the fact that it is often very difficult to choose appropriate terms describing the desired topic in the first place. This contribution discusses the current state-of-the-art techniques in content-based searching (along with common visualization/browsing approaches) and proposes a particular adaptive solution for intuitive Internet document navigation, which not only enables the user to provide full texts instead of manually selected keywords (if available), but also allows him/her to explore the whole database.
Efficient algorithms for object recognition are crucial for the newly robotics and computer vision applications that demand real-time and on-line methods. Some examples are autonomous systems, navigating robots, autonomous driving. In this work, we focus on efficient semantic segmentation, which is the problem of labeling each pixel of an image with a semantic class.
Our aim is to speed-up all of the parts of the semantic segmentation pipeline. We also aim at delivering a labeling solution on a time budget, that can be decided on-the-fly. For this purpose, we analyze all the components of the semantic segmentation pipeline, and identify the computational bottleneck of each of them. The different components of the pipeline are over-segmenting the image with local regions, extracting features and classify the local regions, and the final inference of the image labeling with semantic classes. We focus on each of these steps.
First, we introduce a new superpixel algorithm to over-segment the image. Our superpixel method runs in real-time and can deliver a solution at any time budget. Then, for feature extraction, we focus on the framework that computes descriptors and encodes them, followed by a pooling step. We see that the encoding step is the bottleneck, for computational efficiency and performance. We present a novel assignment-based encoding formulation, that allows for the design of a new, very efficient, encoding. Finally, the image labeling output is obtained modeling the dependencies with a Conditional Random Field (CRF). In semantic image segmentation, the computational cost of instantiating the potentials is much higher than MAP inference. We introduce Active MAP inference to on-the-fly select a subset of potentials to be instantiated in the energy function, leaving the rest as unknown, and to estimate the MAP labeling from such incomplete energy function.
We perform experiments on all proposed methods for the different parts of the semantic segmentation pipeline. We show that our superpixel extraction achieves higher accuracy than state-of-the-art on standard superpixel benchmark, while it runs in real-time. We test our feature encoding on standard image classification and segmentation benchmarks, and we show that our method achieves competitive results with the state-of-the-art, and requires less time and memory. Finally, results for semantic segmentation benchmark show that Active MAP inference achieves similar levels of accuracy but with major efficiency gains.
Active efficient coding explains the development of binocular vision and its failure in amblyopia
(2020)
The development of vision during the first months of life is an active process that comprises the learning of appropriate neural representations and the learning of accurate eye movements. While it has long been suspected that the two learning processes are coupled, there is still no widely accepted theoretical framework describing this joint development. Here, we propose a computational model of the development of active binocular vision to fill this gap. The model is based on a formulation of the active efficient coding theory, which proposes that eye movements as well as stimulus encoding are jointly adapted to maximize the overall coding efficiency. Under healthy conditions, the model self-calibrates to perform accurate vergence and accommodation eye movements. It exploits disparity cues to deduce the direction of defocus, which leads to coordinated vergence and accommodation responses. In a simulated anisometropic case, where the refraction power of the two eyes differs, an amblyopia-like state develops in which the foveal region of one eye is suppressed due to inputs from the other eye. After correcting for refractive errors, the model can only reach healthy performance levels if receptive fields are still plastic, in line with findings on a critical period for binocular vision development. Overall, our model offers a unifying conceptual framework for understanding the development of binocular vision.
The interaction between Λ baryons and kaons/antikaons is a crucial ingredient for the strangeness S=0 and S=−2 sector of the meson--baryon interaction at low energies. In particular, the ΛK¯¯¯¯ might help in understanding the origin of states such as the Ξ(1620), whose nature and properties are still under debate. Experimental data on Λ−K and Λ−K¯¯¯¯ systems are scarce, leading to large uncertainties and tension between the available theoretical predictions constrained by such data. In this Letter we present the measurements of Λ−K+⊕Λ¯¯¯¯−K− and Λ−K−⊕Λ¯¯¯¯−K+ correlations obtained in the high-multiplicity triggered data sample in pp collisions at s√=13 TeV recorded by ALICE at the LHC. The correlation function for both pairs is modeled using the Lednicky−Lyuboshits analytical formula and the corresponding scattering parameters are extracted. The Λ−K−⊕Λ¯¯¯¯−K+ correlations show the presence of several structures at relative momenta k∗ above 200 MeV/c, compatible with the Ω baryon, the Ξ(1690), and Ξ(1820) resonances decaying into Λ−K− pairs. The low k∗ region in the Λ−K−⊕Λ¯¯¯¯−K+ also exhibits the presence of the Ξ(1620) state, expected to strongly couple to the measured pair. The presented data allow to access the ΛK+ and ΛK− strong interaction with an unprecedented precision and deliver the first experimental observation of the Ξ(1620) decaying into ΛK−.
The interaction between Λ baryons and kaons/antikaons is a crucial ingredient for the strangeness S=0 and S=−2 sector of the meson−baryon interaction at low energies. In particular, the ΛK¯¯¯¯ might help in understanding the origin of states such as the Ξ(1620), whose nature and properties are still under debate. Experimental data on Λ−K and Λ−K¯¯¯¯ systems are scarce, leading to large uncertainties and tension between the available theoretical predictions constrained by such data. In this Letter we present the measurements of Λ−K+⊕Λ¯¯¯¯−K− and Λ−K−⊕Λ¯¯¯¯−K+ correlations obtained in the high-multiplicity triggered data sample in pp collisions at s√=13 TeV recorded by ALICE at the LHC. The correlation function for both pairs is modeled using the Lednicky−Lyuboshits analytical formula and the corresponding scattering parameters are extracted. The Λ−K−⊕Λ¯¯¯¯−K+ correlations show the presence of several structures at relative momenta k∗ above 200 MeV/c, compatible with the Ω baryon, the Ξ(1690), and Ξ(1820) resonances decaying into Λ−K− pairs. The low k∗ region in the Λ−K−⊕Λ¯¯¯¯−K+ also exhibits the presence of the Ξ(1620) state, expected to strongly couple to the measured pair. The presented data allow to access the ΛK+ and ΛK− strong interaction with an unprecedented precision and deliver the first experimental observation of the Ξ(1620) decaying into ΛK−.
The interaction between Λ baryons and kaons/antikaons is a crucial ingredient for the strangeness S=0 and S=−2 sector of the meson–baryon interaction at low energies. In particular, the ΛK‾ might help in understanding the origin of states such as the Ξ(1620), whose nature and properties are still under debate. Experimental data on Λ–K and Λ–K‾ systems are scarce, leading to large uncertainties and tension between the available theoretical predictions constrained by such data. In this Letter we present the measurements of Λ–K⊕+Λ‾–K− and Λ–K⊕−Λ‾–K+ correlations obtained in the high-multiplicity triggered data sample in pp collisions at s=13 TeV recorded by ALICE at the LHC. The correlation function for both pairs is modeled using the Lednický–Lyuboshits analytical formula and the corresponding scattering parameters are extracted. The Λ–K⊕−Λ‾–K+ correlations show the presence of several structures at relative momenta k⁎ above 200 MeV/c, compatible with the Ω baryon, the Ξ(1690), and Ξ(1820) resonances decaying into Λ–K− pairs. The low k⁎ region in the Λ–K⊕−Λ‾–K+ also exhibits the presence of the Ξ(1620) state, expected to strongly couple to the measured pair. The presented data allow to access the ΛK+ and ΛK− strong interaction with an unprecedented precision and deliver the first experimental observation of the Ξ(1620) decaying into ΛK−.
The interaction between Λ baryons and kaons/antikaons is a crucial ingredient for the strangeness S=0 and S=−2 sector of the meson--baryon interaction at low energies. In particular, the ΛK¯¯¯¯ might help in understanding the origin of states such as the Ξ(1620), whose nature and properties are still under debate. Experimental data on Λ−K and Λ−K¯¯¯¯ systems are scarce, leading to large uncertainties and tension between the available theoretical predictions constrained by such data. In this Letter we present the measurements of Λ−K+⊕Λ¯¯¯¯−K− and Λ−K−⊕Λ¯¯¯¯−K+ correlations obtained in the high-multiplicity triggered data sample in pp collisions at s√=13 TeV recorded by ALICE at the LHC. The correlation function for both pairs is modeled using the Lednicky−Lyuboshits analytical formula and the corresponding scattering parameters are extracted. The Λ−K−⊕Λ¯¯¯¯−K+ correlations show the presence of several structures at relative momenta k∗ above 200 MeV/c, compatible with the Ω baryon, the Ξ(1690), and Ξ(1820) resonances decaying into Λ−K− pairs. The low k∗ region in the Λ−K−⊕Λ¯¯¯¯−K+ also exhibits the presence of the Ξ(1620) state, expected to strongly couple to the measured pair. The presented data allow to access the ΛK+ and ΛK− strong interaction with an unprecedented precision and deliver the first experimental observation of the Ξ(1620) decaying into ΛK−.
Acceleration of Biomedical Image Processing and Reconstruction with FPGAs
Increasing chip sizes and better programming tools have made it possible to increase the boundaries of application acceleration with reconfigurable computer chips. In this thesis the potential of acceleration with Field Programmable Gate Arrays (FPGAs) is examined for applications that perform biomedical image processing and reconstruction. The dataflow paradigm was used to port the analysis of image data for localization microscopy and for 3D electron tomography from an imperative description towards the FPGA for the first time.
After the primitives of image processing on FPGAs are presented, a general workflow is given for analyzing imperative source code and converting it to a hardware pipeline where every node processes image data in parallel. The theoretical foundation is then used to accelerate both example applications. For localization microscopy, an acceleration of 185 compared to an Intel i5 450 CPU was achieved, and electron tomography could be sped up by a factor of 5 over an Nvidia Tesla C1060 graphics card while maintaining full accuracy in both cases.
We present the FPGA implementation of an algorithm [4] that computes implications between signal values in a boolean network. The research was performed as a masterrsquos thesis [5] at the University of Frankfurt. The recursive algorithm is rather complex for a hardware realization and therefore the FPGA implementation is an interesting example for the potential of reconfigurable computing beyond systolic algorithms. A circuit generator was written that transforms a boolean network into a network of small processing elements and a global control logic which together implement the algorithm. The resulting circuit performs the computation two orders of magnitudes faster than a software implementation run by a conventional workstation.
The early prediction of mortality is one of the unresolved tasks in intensive care medicine. This contribution models medical symptoms as observations cased by transitions between hidden markov states. Learning the underlying state transition probabilities results in a prediction probability success of about 91%. The results are discussed and put in relation to the model used. Finally, the rationales for using the model are reflected: Are there states in the septic shock data?
Current deep learning methods are regarded as favorable if they empirically perform well on dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual learning, where consecutively arriving data is investigated. The core challenge is framed as protecting previously acquired representations from being catastrophically forgotten. However, comparison of individual methods is nevertheless performed in isolation from the real world by monitoring accumulated benchmark test set performance. The closed world assumption remains predominant, i.e. models are evaluated on data that is guaranteed to originate from the same distribution as used for training. This poses a massive challenge as neural networks are well known to provide overconfident false predictions on unknown and corrupted instances. In this work we critically survey the literature and argue that notable lessons from open set recognition, identifying unknown examples outside of the observed set, and the adjacent field of active learning, querying data to maximize the expected performance gain, are frequently overlooked in the deep learning era. Hence, we propose a consolidated view to bridge continual learning, active learning and open set recognition in deep neural networks. Finally, the established synergies are supported empirically, showing joint improvement in alleviating catastrophic forgetting, querying data, selecting task orders, while exhibiting robust open world application.
Natural Language Processing (NLP) for big data requires an efficient and sophisticated infrastructure to complete tasks both fast and correctly. Providing an intuitive and lightweight interaction with a framework that abstracts and simplifies complex tasks assists in reaching this goal. This bachelor thesis extends the NLP framework Docker Unified UIMA Interface (DUUI) by an API and a web-based graphical user interface to control and manage pipelines for automated analysis of large quantities of natural language. The extension aims to reduce the entry barrier into the field as well as to accelerate the creation and management of pipelines according to UIMA standards. Pipelines can be executed in the browser or using the web API directly and then monitored on a document level. The evaluation in usability and user experience indicates that the implementation benefits the framework by making its usage more user friendly, lightweight, and intuitive while also making the management of pipelines more efficient.
One of the most interesting domains of feedforward networks is the processing of sensor signals. There do exist some networks which extract most of the information by implementing the maximum entropy principle for Gaussian sources. This is done by transforming input patterns to the base of eigenvectors of the input autocorrelation matrix with the biggest eigenvalues. The basic building block of these networks is the linear neuron, learning with the Oja learning rule. Nevertheless, some researchers in pattern recognition theory claim that for pattern recognition and classification clustering transformations are needed which reduce the intra-class entropy. This leads to stable, reliable features and is implemented for Gaussian sources by a linear transformation using the eigenvectors with the smallest eigenvalues. In another paper (Brause 1992) it is shown that the basic building block for such a transformation can be implemented by a linear neuron using an Anti-Hebb rule and restricted weights. This paper shows the analog VLSI design for such a building block, using standard modules of multiplication and addition. The most tedious problem in this VLSI-application is the design of an analog vector normalization circuitry. It can be shown that the standard approaches of weight summation will not give the convergence to the eigenvectors for a proper feature transformation. To avoid this problem, our design differs significantly from the standard approaches by computing the real Euclidean norm. Keywords: minimum entropy, principal component analysis, VLSI, neural networks, surface approximation, cluster transformation, weight normalization circuit.
The well-known proof of termination of reduction in simply typed calculi is adapted to a monomorphically typed lambda-calculus with case and constructors and recursive data types. The proof differs at several places from the standard proof. Perhaps it is useful and can be extended also to more complex calculi.
We introduce a new method for representing and solving a general class of non-preemptive resource-constrained project scheduling problems. The new approach is to represent scheduling problems as de- scriptions (activity terms) in a language called RSV, which allows nested expressions using pll, seq, and xor. The activity-terms of RSV are similar to concepts in a description logic. The language RSV generalizes previous approaches to scheduling with variants insofar as it permits xor's not only of atomic activities but also of arbitrary activity terms. A specific semantics that assigns their set of active schedules to activity terms shows correctness of a calculus normalizing activity terms RSV similar to propositional DNF-computation. Based on RSV, this paper describes a diagram-based algorithm for the RSV-problem which uses a scan-line principle. The scan-line principle is used for determining and resolving the occurring resource conflicts and leads to a nonredundant generation of all active schedules and thus to a computation of the optimal schedule.
Non-coding variations located within regulatory elements may alter gene expression by modifying Transcription Factor (TF) binding sites and thereby lead to functional consequences like various traits or diseases. To understand these molecular mechanisms, different TF models are being used to assess the effect of DNA sequence variations, such as Single Nucleotide Polymorphisms (SNPs). However, few statistical approaches exist to compute statistical significance of results but they often are slow for large sets of SNPs, such as data obtained from a genome-wide association study (GWAS) or allele-specific analysis of chromatin data.
Results We investigate the distribution of maximal differential TF binding scores for general computational models that assess TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on in vitro and in vivo data sets showed that our new approach improves on an existing method in terms of performance and speed. In applications on large sets of eQTL and GWAS SNPs we could illustrate the usefulness of the novel statistic to highlight cell type specific regulators and TF target genes.
Conclusions Our approach allows the evaluation of DNA changes that induce differential TF binding in a fast and accurate manner, permitting computations on large mutation data sets. An implementation of the novel approach is freely available at https://github.com/SchulzLab/SNEEP.
We study the following problem: given x element Rn either find a short integer relation m element Zn, so that =0 holds for the inner product <.,.>, or prove that no short integer relation exists for x. Hastad, Just Lagarias and Schnorr (1989) give a polynomial time algorithm for the problem. We present a stable variation of the HJLS--algorithm that preserves lower bounds on lambda(x) for infinitesimal changes of x. Given x \in {\RR}^n and \alpha \in \NN this algorithm finds a nearby point x' and a short integer relation m for x'. The nearby point x' is 'good' in the sense that no very short relation exists for points \bar{x} within half the x'--distance from x. On the other hand if x'=x then m is, up to a factor 2^{n/2}, a shortest integer relation for \mbox{x.} Our algorithm uses, for arbitrary real input x, at most \mbox{O(n^4(n+\log \alpha))} many arithmetical operations on real numbers. If x is rational the algorithm operates on integers having at most \mbox{O(n^5+n^3 (\log \alpha)^2 + \log (\|q x\|^2))} many bits where q is the common denominator for x.
After a short introduction into traditional image transform coding, multirate systems and multiscale signal coding the paper focuses on the subject of image encoding by a neural network. Taking also noise into account a network model is proposed which not only learns the optimal localized basis functions for the transform but also learns to implement a whitening filter by multi-resolution encoding. A simulation showing the multi-resolution capabilitys concludes the contribution.
Poster presentation: Introduction Dopaminergic neurons in the midbrain show a variety of firing patterns, ranging from very regular firing pacemaker cells to bursty and irregular neurons. The effects of different experimental conditions (like pharmacological treatment or genetical manipulations) on these neuronal discharge patterns may be subtle. Applying a stochastic model is a quantitative approach to reveal these changes. ...
A partial rehabilitation of side-effecting I/O : non-determinism in non-strict functional languages
(1996)
We investigate the extension of non-strict functional languages like Haskell or Clean by a non-deterministic interaction with the external world. Using call-by-need and a natural semantics which describes the reduction of graphs, this can be done such that the Church-Rosser Theorems 1 and 2 hold. Our operational semantics is a base to recognise which particular equivalencies are preserved by program transformations. The amount of sequentialisation may be smaller than that enforced by other approaches and the programming style is closer to the common one of side-effecting programming. However, not all program transformations used by an optimising compiler for Haskell remain correct in all contexts. Our result can be interpreted as a possibility to extend current I/O-mechanism by non-deterministic deterministic memoryless function calls. For example, this permits a call to a random number generator. Adding memoryless function calls to monadic I/O is possible and has a potential to extend the Haskell I/O-system.