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Automatic Performance Optimization of Stencil Codes
Get PDFA widely used class of codes are stencil codes. Their general structure is very simple: data points in a large grid are repeatedly recomputed from neighboring values. This predefined neighborhood is the so-called stencil. Despite their very simple structure, stencil codes are hard to optimize since only few computations are performed while a comparatively large number of values have to be accessed, i.e., stencil codes usually have a very low computational intensity. Moreover, the set of optimizations and their parameters also depend on the hardware on which the code is executed.
To cut a long story short, current production compilers are not able to fully optimize this class of codes and optimizing each application by hand is not practical. As a remedy, we propose a set of optimizations and describe how they can be applied automatically by a code generator for the domain of stencil codes. A combination of a space and time tiling is able to increase the data locality, which significantly reduces the memory-bandwidth requirements: a standard three-dimensional 7-point Jacobi stencil can be accelerated by a factor of 3. This optimization can target basically any stencil code, while others are more specialized. E.g., support for arbitrary linear data layout transformations is especially beneficial for colored kernels, such as a Red-Black Gauss-Seidel smoother. On the one hand, an optimized data layout for such kernels reduces the bandwidth requirements while, on the other hand, it simplifies an explicit vectorization.
Other noticeable optimizations described in detail are redundancy elimination techniques to eliminate common subexpressions both in a sequence of statements and across loop boundaries, arithmetic simplifications and normalizations, and the vectorization mentioned previously. In combination, these optimizations are able to increase the performance not only of the model problem given by Poisson’s equation, but also of real-world applications: an optical flow simulation and the simulation of a non-isothermal and non-Newtonian fluid flow
Neural Network Supervision: Notes on Loss Functions, Labels and Confidence Estimation
No full textWe consider a number of enhancements to the standard neural network training paradigm. First, we show that carefully designed parameter update rules may replace the need for a loss function and its gradient. We introduce a parameter update rule that generalises the standard cross-entropy gradient, and allows directly controlling the relative effect of easy and hard examples on the training process. We show that the proposed update rule cannot be derived by using a loss function and yields better classification accuracy compared to training with the standard cross-entropy loss.
In addition, we study the effect of the loss function choice on the learnt representations. We introduce the Single Logit Classification (SLC) task: classifying whether a given class is the correct class for a given example, in a computationally efficient manner, based on the appropriate class logit alone. A natural principle is proposed, the Principle of Logit Separation (PoLS), as a guideline for choosing and designing loss functions suitable for the SLC task. We mathematically analyse the alignment of eleven existing and novel loss functions with this principle. Experiment results show that using loss functions that are aligned with this principle results in a representation in the logits layer in which each logit is more informative of its class correctness, leading to a considerably better SLC accuracy.
Further, we attempt to alleviate the dependency of standard neural network models on large amounts of quality labels. The task of weakly supervised one-shot detection is considered, in which at training time the model is trained without any localisation labels, and at test time it needs to identify and localise instances of unseen classes. We propose the attention similarity networks (ASN) for this task. ASN use a Siamese neural network to compute a similarity score between an exemplar and different locations in a target example. Then, an attention mechanism performs localisation by learning to attend to the correct locations. The ASN model outperforms the relevant baselines for weakly supervised one-shot detection tasks in the audio and computer vision domains.
Finally, we consider the problem of quantifying prediction confidence in the regression setting. We propose two novel algorithms for emitting calibrated prediction intervals for neural network regressors, at any given confidence level. The two algorithms require binning of the output space and training the neural network regressor as a classifier. Then, the calibration algorithms choose the intervals in the output space, making sure they contain the amount of posterior probability mass that results in the desired confidence level
#relichat - informelles Lernen mit Twitter
No full textDer #relichat ist der wöchentlich stattfindende Twitter-Chat zur Religionspädagogik. In einer einstündigen, durch Fragen strukturierten Diskussion werden religionspädagogische Themen auf der Plattform Twitter unter Verwendung des Hashtags #relichat öffentlich diskutiert. Von 2017 bis zum Sommer 2020 fanden 89 #relichats statt, an denen sich etwa 220 Personen aus dem deutschsprachigen Raum aktiv beteiligt haben.
Die vorliegende Dissertation untersucht das Projekt #relichat hermeneutisch und evaluiert die Erfahrungen der Teilnehmer*innen am #relichat als informelles Fortbildungsformat.
Titel der Dissertation:
#relichat - informelles Lernen mit Twitter. Religionslehrer*innenfortbildung als sozial-konstruktivistische Vernetzung in Communities of Practice
Im hermeneutischen Teil der Arbeit werden die Aspekte der sozialen Medien, des informellen und konstruktivistischen Lernens, des vernetzten Lernens in Communities of Practice, des öffentlichen Lernens, aber auch des Selbstverständnisses der Kirche als Institution in der Öffentlichkeit erörtert.
Für die Evaluation wurde ein Mixed Methods-Design gewählt, welches das Projekt aus verschiedenen Positionen betrachtet und damit der Komplexität des Unterfangens gerecht wird. Neben der Betrachtung statistischer Daten (Zugriffszahlen, Beteiligung etc.), der Analyse der thematischen Entfaltung der Diskussionen nach dem Konzept der Themenkonstitution, der Analyse der Diskussionen nach der Methode der Textlinguistik, bildete die Evaluation qualitativer Interviews mit Beteiligten am #relichat das Zentrum der Forschung. Mit Hilfe der Methodologie der Grounded Theory wurde eine Theorie mit Verallgemeinerungsanspruch für das vernetzte Lernen mit sozialen Medien entwickelt, die über das konkrete Projekt #relichat hinaus reichen soll.
Als Ergebnis lässt sich zusammenfassen:
Lernen in und mit dem #relichat ist konstruktivistisches, informelles, selbstorganisiertes und selbstverantwortliches Lernen. Es kann als Fortbildung bezeichnet werden, insofern es eine Weiterentwicklung der eigenen religionspädagogischen Praxis bewirken kann. Eine besondere Rolle spielen die sozialen Beziehungen in der Community of Practice. Das Medium Twitter gibt die Rahmenbedingungen der Kommunikation vor.
Aufgrund der Erkenntnisse aus dem Forschungsprojekt #relichat ist davon auszugehen, dass es in Zukunft verstärkt Formen des informellen Lernens in Communities of Practice geben wird, die sich die Möglichkeiten sozialer Kommunikation in digitalen Medien zunutze machen werden. Das gilt für lebenslanges Lernen grundsätzlich, aber auch für Pädagog*innenfortbildung. Vernetzung wird als Ressource für Weiterbildung noch bedeutsamer werden.
Es werden in Zukunft Dialog und Kommunikation vermehrt in der Öffentlichkeit stattfinden, hierarchische Strukturen werden dadurch ihre gesellschaftliche Legitimation zunehmend verlieren
Innovate with Crowds. Co-Creation and Idea Evaluation in Internal and External Crowdsourcing.
No full textCrowdsourcing seems to be a promising approach for organizations to overcome challenges widely discussed in innovation and organizational research. However, the extent to which an organization can leverage the benefits from crowdsourcing is contingent on which type of crowd is addressed and how crowds are used. Based on unique data from crowdsourcing contests, the dissertation provides insights how to innovate with internal and external crowds in order to utilize their potential for co-creation and idea evaluation
Anhang I: Die Standards geordnet nach Lehrerbildungsphasen
Get PDFANHANG I: DIE STANDARDS GEORDNET NACH LEHRERBILDUNGSPHASEN
Pädagogisch-Didaktisches Praktikum
Studienbegleitendes Fachdidaktisches Praktikum
Referendariat
Professionelle Lehrkraf
Refugees Welcome? Eine Untersuchung zur Willkommenskultur und der Einstellungen der darin engagierten Flüchtlingshelfer
No full textEine Untersuchung zur Willkommenskultur und der Einstellungen der darin engagierten (bayerischen) Flüchtlingshelfer. Dissertation zur Erlangung des Doktorgrades im Fach Caritaswissenschaft und werteorientiertes Management am Lehrstuhl für Theologische Ethik der Universität Passau
Raumsemantische Grenzen. Ein interdisziplinäres künstlerisches Projekt für die Lehrer*innenbildung
No full textDieser Beitrag illustriert die Potenziale einer semiotischen Perspektive bei der Schaffung von Erfahrungsräumen für Information and Media Literacy. Am Beispiel eines Projektseminars zum Thema Grenzen, das im Sommersemester 2017 an der Universität Passau im Rahmen des vom BMBF geförderten Projekts SKILL stattfand, wird gleichzeitig interdisziplinäres Arbeiten durch den Verbund von Mediensemiotik und Kunstpädagogik veranschaulicht. Im Seminar entstanden bei freier Medienwahl künstlerische Arbeiten, die im Rahmenprogramm des Kongresses der deutschen Gesellschaft für Semiotik e.V. Grenzen: Kontakt, Kommunikation, Kontrast 2017 in Kooperation mit dem Kunstverein Passau e.V. ausgestellt wurden.
Das Projektseminar wird im Folgenden zunächst in den Rahmen der Relevanz von Information and Media Literacy für die Lehrer*innenbildung und der Entwicklung entsprechender Lehrformate an der Universität Passau eingebettet. Anschließend wird es mit Bezug auf seine semiotische Grundlage in didaktischen und methodischen Überlegungen umfassend dargestellt, um einen Eindruck davon zu vermitteln, welche Möglichkeiten die Verschränkung beider Disziplinen für das Erkunden medialer Konstruktivität bereithält
Complexity Analysis of Quantizations of Multidimensional Stochastic Differential Equations
Get PDFThe dissertation is located in the field of quantizations of certain stochastic processes, namely a solution X of a multidimensional stochastic differential equation (SDE). The quantization problem for X consists in approximating X by a a random element which takes only finitely many values. Our main interest lies in the investigation of the asymptotic behavior of the Nth minimal quantization error of X as N tends to infinity, which incorporates the determination of both the sharp rate of convergence and explicit asymptotic constants. Especially explicit asymptotic constants have been so far unknown in the context of multidimensional SDEs. Furthermore, as part of our analysis, we provide a method which yields a strongly asymptotically optimal sequence of N-quantization of X. In certain special cases our method is fully constructive and the algorithm is easy to implement
Computational Resource Abuse in Web Applications
Get PDFInternet browsers include Application Programming Interfaces (APIs) to support Web applications that require complex functionality, e.g., to let end users watch videos, make phone calls, and play video games. Meanwhile, many Web applications employ the browser APIs to rely on the user's hardware to execute intensive computation, access the Graphics Processing Unit (GPU), use persistent storage, and establish network connections.
However, providing access to the system's computational resources, i.e., processing, storage, and networking, through the browser creates an opportunity for attackers to abuse resources. Principally, the problem occurs when an attacker compromises a Web site and includes malicious code to abuse its visitor's computational resources. For example, an attacker can abuse the user's system networking capabilities to perform a Denial of Service (DoS) attack against third parties. What is more, computational resource abuse has not received widespread attention from the Web security community because most of the current specifications are focused on content and session properties such as isolation, confidentiality, and integrity.
Our primary goal is to study computational resource abuse and to advance the state of the art by providing a general attacker model, multiple case studies, a thorough analysis of available security mechanisms, and a new detection mechanism. To this end, we implemented and evaluated three scenarios where attackers use multiple browser APIs to abuse networking, local storage, and computation. Further, depending on the scenario, an attacker can use browsers to perform Denial of Service against third-party Web sites, create a network of browsers to store and distribute arbitrary data, or use browsers to establish anonymous connections similarly to The Onion Router (Tor). Our analysis also includes a real-life resource abuse case found in the wild, i.e., CryptoJacking, where thousands of Web sites forced their visitors to perform crypto-currency mining without their consent. In the general case, attacks presented in this thesis share the attacker model and two key characteristics: 1) the browser's end user remains oblivious to the attack, and 2) an attacker has to invest little resources in comparison to the resources he obtains.
In addition to the attack's analysis, we present how existing, and upcoming, security enforcement mechanisms from Web security can hinder an attacker and their drawbacks. Moreover, we propose a novel detection approach based on browser API usage patterns. Finally, we evaluate the accuracy of our detection model, after training it with the real-life crypto-mining scenario, through a large scale analysis of the most popular Web sites
Abstract Transducers for Software Analysis and Verification
No full textWhenever software faults can endanger human life, property, or the environment, the absence of faults must be ensured with utmost care and the best technologies available. Evidence is needed showing that all requirements are satisfied and that the risk of faults is reduced. One technique to conduct such a verification task—composed of the software to verify, the specification to check, and a model of the environment—is software model checking.
To conduct a verification task with a model checker, different models of the task are constructed. We distinguish between two types of task models: syntactic task models and semantic task models, which define the respective syntactic structure (control flow) and semantic structure (state transitions, invariants) of the verification task. When constructing such models, we can observe that similar structures and substructures reappear within and among different verification tasks. For example, the same assertions to check can appear in different functions, or the same predicate can be part of different invariants to describe sets of program states. Similarities that appear during the model construction process can be the result of solving similar reasoning problems, often solved using computationally expensive procedures (as typical for model checking), over and over again. Not reusing results of solving similar problems, not having a means for conducting repeated efforts automatically, or not trying to reduce the number of similar reasoning efforts, is a waste of precious resources.
To address these problems, we present a common conceptual and technical foundation for sharing syntactic and semantic task artifacts for reuse, within and among verification runs. Both the syntactic construction of a verification task and the construction of its semantic model—which describes all possible behaviors and states—are covered. We study how commonalities and regularities in the task models can be taken into account to facilitate the process of sharing task artifacts for reuse, and to make the overall verification process more efficient and effective. We introduce abstract transducers as the theoretical foundation of this thesis: a type of finite-state transducers with an inherent notion of abstraction for states, the input alphabet, and its output alphabet. Abstracting these transducers allows us to widen both the set of input words for that they produce output and the sets of output words. Abstract transducers are instantiated as task artifact transducers to map from program structures to task artifacts to share. We show that the notion of abstraction provides a means for increasing the scope for that task artifacts are shared for reuse. We present two instances of task artifact transducers: Yarn transducers and precision transducers. We use Yarn transducers for providing code to weave into the control-flow structure of a computer program, and present the Loom analysis as a means for orchestrating the weaving process. Precision transducers provide a means for sharing abstraction precisions for reuse, thus aid in defining the level of abstraction of a semantic task model. For both types of transducers, we provide empirical evidence on their practical applicability, for example, to verify Linux kernel modules, and show that they can help in increasing the verification performance