No description available.

This paper aims at implementing (or) extending the evaluation of Exhaustion Dominated Performance, a method used to compute the impact of the available memory and bandwidth over the execution time of a simulation software. This method has already been performed and tested using High Performance Linpack (a de facto for bench marking process) [1]. But in this paper, the experiment is repeated using the real world simulation software so as to prove that the method is applicable in practical. The thesis was conducted using the same experimental conditions and the results obtained proved that the method works find for real world applications also.

No description available.

Diss. Stockholm : Tekn. högsk., 2006.

Diss. Ronneby: Tekn. högsk., 2002.

Diss. (sammanfattning) Uppsala : Uppsala universitet, 2006. / Härtill 5 uppsatser.

Diss. (sammanfattning) Uppsala : Uppsala universitet, 2009. / Härtill 6 uppsatser.

Diss. Stockholm : Kungliga Tekniska högskolan, 2005.

Semantiska vektormodeller är en kraftfull teknik där ords mening kan representeras av vektorervilka består av siffror. Vektorerna tillåter geometriska operationer vilka fångar semantiskt viktigaförhållanden mellan orden de representerar. I denna studie implementeras och appliceras WEAT-metoden för att undersöka om statistiska förhållanden mellan ord som kan uppfattas somfördomsfulla existerar i en svensk semantisk vektormodell av en svensk nyhetstidning. Resultatetpekar på att ordförhållanden i vektormodellen har förmågan att återspegla flera av de sedantidigare IAT-dokumenterade fördomar som undersöktes. I studien implementeras och applicerasockså WEFAT-metoden för att undersöka vektormodellens förmåga att representera två faktiskastatistiska samband i verkligheten, vilket görs framgångsrikt i båda undersökningarna. Resultatenav studien som helhet ger stöd till metoderna som används och belyser samtidigt problematik medatt använda semantiska vektormodeller i språkteknologiska applikationer. / Word embeddings are a powerful technique where word meaning can be represented by vectors containing actual numbers. The vectors allow  geometric operations that capture semantically important relationships between the words. In this study WEAT is applied in order to examine whether statistical properties of words pertaining to bias can be found in a swedish word embedding trained on a corpus from a swedish newspaper. The results shows that the word embedding can represent several of the IAT documented biases that where tested. A second method, WEFAT, is applied to the word embedding in order to explore the embeddings ability to represent actual statistical properties, which is also done successfully. The results from this study lends support to the validity of both methods aswell as illuminating the issue of problematic relationships between words in word embeddings.

Natural language processing, a subfield of artificial intelligence and computer science, has recently been of great research interest due to the vast amount of information created on the internet in the modern era. One of the main natural language processing areas concerns sentiment analysis. This is a field that studies the polarity of human natural language and generally tries to categorize it as either positive, negative or neutral. In this thesis, sentiment analysis has been applied to research reports written by equity analysts. The objective has been to investigate if there exist a distinct distribution of the reports and if one is able to classify sentiment in these reports. The thesis consist of two parts; firstly investigating possibilities on how to divide the reports into different sentiment labelling regimes and secondly categorizing the sentiment using machine learning techniques. Logistic regression as well as several convolutional neural network structures has been used to classify the sentiment. Working with textual data requires the mapping of text to real valued values called features. Several feature extraction methods has been investigated including Bag of Words, term frequency-inverse document frequency and Word2vec. Out of the tested labelling regimes, classifying the documents using upgrades and downgrades of report recommendation shows the most promising potential. For this regime, the convolutional neural network architectures outperform logistic regression by a significant margin. Out of the networks tested, a double input channel utilizing two different Word2vec representations performs the best. The two different representations originate from different sources; one from the set of equity research reports and the other trained by the Google Brain team on an extensive Google news data set. This suggests that using one representation that represent topic specific words and one that is better at representing more common words enhances classification performance.