The Function of Speech in Husserl and Merleau-Ponty The submitted doctoral thesis is an attempt to describe the development of the intentional function of speech in Husserl and Merleau-Ponty. The intentional function is defined as the change of expressed meaning that is engendered by the expression itself. We trace Husserl's position from the Logical Investigations and the first book of his Ideas pertaining to a pure phenomenology and to a phenomenological philosophy, where he describes speech as the non- productive mirroring of other kinds of intentionality, to the late text The Origin of Geometry, where he discerns two functions of speech: it provides thought its ideality, which is different from the ideality of species; and it provides thought its objectivity, i.e. the form of object that lasts in history as identical. In The Phenomenology of Perception, Merleau-Ponty adopts Husserl's late position with several profound modifications. The starting-point ceases to be the linguistic sign, and speech becomes a kind of gesture. As a consequence, the difference between linguistic and non-linguistic ideality disappears. Furthermore, Merleau-Ponty holds that the expression accomplishes the meaning of what it expresses. In this way, speech becomes creative and ceases to be just an empty intention of...

[pt] Em uma série de aplicações, os pontos de dados podem ser representados como distribuições de probabilidade. Por exemplo, os documentos podem ser representados como modelos de tópicos, as imagens podem ser representadas como histogramas e também a música pode ser representada como uma distribuição de probabilidade. Neste trabalho, abordamos o problema do Vizinho Próximo Aproximado onde os pontos são distribuições de probabilidade e a função de distância é a divergência de Kullback-Leibler (KL). Mostramos como acelerar as estruturas de dados existentes, como a Bregman Ball Tree, em teoria, colocando a divergência KL como um produto interno. No lado prático, investigamos o uso de duas técnicas de indexação muito populares: Índice Invertido e Locality Sensitive Hashing. Os experimentos realizados em 6 conjuntos de dados do mundo real mostraram que o Índice Invertido é melhor do que LSH e Bregman Ball Tree, em termos de consultas por segundo e precisão. / [en] In a number of applications, data points can be represented as probability distributions. For instance, documents can be represented as topic models, images can be represented as histograms and also music can be represented as a probability distribution. In this work, we address the problem of the Approximate Nearest Neighbor where the points are probability distributions and the distance function is the Kullback-Leibler (KL) divergence. We show how to accelerate existing data structures such as the Bregman Ball Tree, by posing the KL divergence as an inner product embedding. On the practical side we investigated the use of two, very popular, indexing techniques: Inverted Index and Locality Sensitive Hashing. Experiments performed on 6 real world data-sets showed the Inverted Index performs better than LSH and Bregman Ball Tree, in terms of queries per second and precision.

GeV electron bunches accelerated on a centimeter scale device exemplify the extraordinary advances of laser-plasma acceleration. The combination of high charges from optimized injection schemes and intrinsic femtosecond short bunch duration yields kiloampere peak currents. Further enhancing the current while reducing the energy spread will pave the way for future application, e.g. the driver for compact secondary radiation sources such as high-field THz, high-brightness x-ray or gamma-ray sources. One essential key for beam transport to a specific application is an electron bunch with high quality beam parameters such as low energy spread as well as small divergence and spot size. The inherent micrometer size at the plasma exit is typically sufficient for an efficient coupling into a conventional beamline. However, energy spread and beam divergence require optimization before the beam can be transported efficiently. Induced by the high peak current, the beam loading regime can be used in order to achieve optimized beam parameters for beam transport. / In this thesis, the impact of beam loading on the transverse electron dynamic is systematically studied by investigating betatron radiation and electron beam divergence. For this reason, the bubble regime with self-truncated ionization injection (STII) is applied to set up a nanocoulomb-class laser wakefield accelerator. The accelerator is driven by 150TW laser pulses from the DRACO high power laser system. A supersonic gas jet provides a 3mm long acceleration medium with electron densities from 3 × 10^18 cm^−3 to 5 × 10^18 cm^−3. The STII scheme together with the employed setup yields highly reproducible injections with bunch charges of up to 0.5 nC. The recorded betatron radius at the accelerator exit is about one micron and reveals that the beam size stays at the same value. The optimal beam loading, which is observed at around 250 pC to 300 pC, leads to the minimum energy spread of ~40MeV and a 20% smaller divergence. It is demonstrated that an incomplete betatron phase mixing due to the small energy spread can explain the experimentally observed minimum beam divergence.

Title: Nonabsolutely convergent integrals Author: Krist'yna Kuncov'a Department: Department of Mathematical Analysis Supervisor: prof. RNDr. Jan Mal'y, DrSc., Department of Mathematical Analysis Abstract: In this thesis we develop the theory of nonabsolutely convergent Hen- stock-Kurzweil type packing integrals in different spaces. In the framework of metric spaces we define the packing integral and the uniformly controlled inte- gral of a function with respect to metric distributions. Applying the theory to the notion of currents we then prove a generalization of the Stokes theorem. In Rn we introduce the packing R and R∗ integrals, which are defined as charges - additive functionals on sets of bounded variation. We provide comparison with miscellaneous types of integrals such as R and R∗ integral in Rn or MCα integral in R. On the real line we then study a scale of integrals based on the so called p-oscillation. We show that our indefinite integrals are a.e. approximately differ- entiable and we give comparison with other nonabsolutely convergent integrals. Keywords: Nonabsolutely convergent integrals, BV sets, Henstock-Kurzweil in- tegral, Divergence theorem, Analysis in metric measure spaces 1

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<p>    </p> <p>Irregular control-flow structures like deeply nested conditional branches are common in real-world software applications. Improving the performance and efficiency of such programs is often challenging because it is difficult to analyze and optimize programs with irregular control flow. We observe that real-world programs contain similar or identical computations within different code paths of the conditional branches. Compilers can merge similar code to improve performance or code size. However, existing compiler optimizations like code hoisting/sinking, and tail merging do not fully exploit this opportunity. We propose a new technique called Control-Flow Melding (CFM) that can merge similar code sequences at the control-flow region level. We evaluate CFM in two applications. First, we show that CFM reduces the control divergence in GPU programs and improves the performance. Second, we apply CFM to CPU programs and show its effectiveness in reducing code size without sacrificing performance. In the next part of this dissertation, we investigate how CFM can be extended to improve dynamic test generation techniques like Dynamic Symbolic Execution (DSE). DSE suffers from path explosion problem when many conditional branches are present in the program. We propose a non-semantics-preserving branch elimination transformation called CFM-SE that reduces the number of symbolic branches in a program. We also provide a framework for detecting and reasoning about false positive bugs that might be added to the program by non-semantics-preserving transformations like CFM-SE. Furthermore, we evaluate CFM-SE on real-world applications and show its effectiveness in improving DSE performance and code coverage. </p>

In recent years, social media platforms such as Twitter and Facebook have emerged as effective tools for broadcasting messages worldwide during disaster events. With millions of messages posted through these services during such events, it has become imperative to identify valuable information that can help the emergency responders to develop effective relief efforts and aid victims. Many studies implied that the role of social media during disasters is invaluable and can be incorporated into emergency decision-making process. However, due to the "big data" nature of social media, it is very labor-intensive to employ human resources to sift through social media posts and categorize/classify them as useful information. Hence, there is a growing need for machine intelligence to automate the process of extracting useful information from the social media data during disaster events. This dissertation addresses the following questions: In a social media stream of messages, what is the useful information to be extracted that can help emergency response organizations to become more situationally aware during and following a disaster? What are the features (or patterns) that can contribute to automatically identifying messages that are useful during disasters? We explored a wide variety of features in conjunction with supervised learning algorithms to automatically identify messages that are useful during disaster events. The feature design includes sentiment features to extract the geo-mapped sentiment expressed in tweets, as well as tweet-content and user detail features to predict the likelihood of the information contained in a tweet to be quickly spread in the network. Further experimentation is carried out to see how these features help in identifying the informative tweets and filter out those tweets that are conversational in nature.

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