Body schema is indispensable for sensorimotor control and learning, but it remains unclear whether it is associated with cognitive functions. Data from patients with spatial neglect support this view; yet observations in healthy participants are inconsistent. Here I conducted two sets of experiments examining influences of trunk position: the first probed attention and spatial working memory using a change detection task and a two-back task; the second used different versions of the Posner paradigm to examine whether head-on-trunk position governs disengagement of attention. In none of the experiments did I observe that trunk turns altered performance in the left versus right visual field in an ipsiversive fashion as reported in neglect. Nevertheless, I found that trunk-right position improved performance at eccentric locations of the visual field. The data are inconsistent with previous findings of head-on-trunk effects in normal participants. Further studies are required to clarify these discrepancies.

Nevirapine (NVP) can cause serious skin rashes and hepatotoxicity. It also causes an immune-mediated skin rash in rats but not hepatotoxicity. There is strong evidence that the rash is due to 12-hydroxynevirapine (12-OH-NVP), which is further metabolized to a reactive benzylic sulfate in the skin. This could both act as a hapten and induce a danger signal. In contrast, most of the covalent binding in the liver appears to involve oxidation of the methyl group leading to a reactive quinone methide. In this study we examined the effects of NVP and 12-OH-NVP on gene expression in the liver and skin. Both NVP and 12-OH-NVP induced changes in the liver, but the list of genes was different, presumably reflecting different bioactivation pathways. In contrast, many more genes were up-regulated in the skin by 12-OH-NVP than by NVP, which is consistent with the hypothesis that the 12-hydroxylation pathway is involved in causing the rash. Some genes up-regulated by 12-OH-NVP were Trim63, S100a7a, and IL22ra2, etc. Up-regulation of genes such as S100a7a, which is considered a danger signal, supports the danger hypothesis. Up-regulation of genes such as the ubiquitin ligase and Trim63 are consistent with protein-adduct formation. Up-regulation of IL-22ra2 gene suggests an immune response. These results provide important clues to how NVP causes induction of an immune response, in some cases leading to an idiosyncratic drug reaction.

Access technologies often deal with the classification of several physiological and biomechanical signals. In most previous studies involving access technologies, a single classifier has been trained. Despite reported success of these single classifiers, classification accuracies are often below clinically viable levels. One approach to improve upon the performance of these classifiers is to utilize the state of- the-art multiple classifier systems (MCS). Because MCS invoke more than one classifier, more information can be exploited from the signals, potentially leading to higher classification performance than that achievable with single classifiers. Moreover, by decreasing the feature space dimensionality of each classifier, the speed of the system can be increased. MCSs may combine classifiers on three levels: abstract, rank, or measurement level. Among them, abstract-level MCSs have been the most widely applied in the literature given the flexibility of the abstract level output, i.e., class labels may be derived from any type of classifier and outputs from multiple classifiers, each designed within a different context, can be easily combined. In this thesis, we develop two new abstract-level MCSs based on "reputation" values of individual classifiers: the static reputation-based algorithm (SRB) and the dynamic reputation-based algorithm (DRB). In SRB, each individual classifier is applied to a “validation set”, which is disjoint from training and test sets, to estimate its reputation value. Then, each individual classifier is assigned a weight proportional to its reputation value. Finally, the total decision of the classification system is computed using Bayes rule. We have applied this method to the problem of dysphagia detection in adults with neurogenic swallowing difficulties. The aim was to discriminate between safe and unsafe swallows. The weighted classification accuracy exceeded 85% and, because of its high sensitivity, the SRB approach was deemed suitable for screening purposes. In the next step of this dissertation, I analyzed the SRB algorithm mathematically and examined its asymptotic behavior. Specifically, I contrasted the SRB performance against that of majority voting, the benchmark abstract-level MCS, in the presence of different types of noise. In the second phase of this thesis, I exploited the idea of the Dirichlet reputation system to develop a new MCS method, the dynamic reputation-based algorithm, which is suitable for the classification of non-stationary signals. In this method, the reputation of each classifier is updated dynamically whenever a new sample is classified. At any point in time, a classifier’s reputation reflects the classifier’s performance on both the validation and the test sets. Therefore, the effect of random high-performance of weak classifiers is appropriately moderated and likewise, the effect of a poorly performing individual classifier is mitigated as its reputation value, and hence overall influence on the final decision is diminished. We applied DRB to the challenging problem of discerning physiological responses from nonverbal youth with severe disabilities. The promising experimental results encourage further development of reputation-based multi-classifier systems in the domain of access technology research.

Gegenstand dieser Arbeit ist die Klassifikation von funktionellen Elektromyographie-Signalen des Nervus facialis, die bei Parotidektomien und sanierenden Ohr-Operationen aufgezeichnet wurden. Hierfür wurde eine detaillierte Analyse der intraoperativ auftretenden Aktionen Stimulation, Koagulation, Einsatz der Fräse und Spülung an Hand von geeigneten Signalparametern (Amplitude, Dauer, Fläche/Symmetrie, Leistung und Frequenz) durchgeführt. Darüber hinaus erfolgte eine Gegenüberstellung der EMG-Daten der zwei durchgeführten operativen Eingriffe und ein Vergleich der zwei untersuchten Erfolgsorgane des Nervus facialis (Mm. orbiculares). Dabei zeigten sich in allen Parametern relevante Unterschiede zwischen den verschiedenen Kategorien. Auf Grund dieser Ergebnisse lässt sich schlussfolgern, dass es möglich ist, ein Klassifikationsschema für die intraoperativen EMG Signale des Nervus facialis zu entwickeln. Dieses ist unabhängig von der Art des durchgeführten Eingriffs und unabhängig vom beobachteten Fazialisast. Als weiterführendes Ziel soll diese Klassifikation der Kontrolle kraftgetriebener Instrumente nach dem Prinzip „Navigated Control“ dienen.

Identifying the emergence and development of new technologies has become an essential ability for firms competing in dynamic environments. Nonetheless, current technology intelligence practices are unstructured and vaguely defined. Moreover, the existing literature in future technology studies lacks strong, systematic explanations of what technologies are, where technologies come from, and how new technologies emerge and evolve. The present study builds on Structuration Theory, and proposes the structurational model of emerging technologies (SMET). The SMET suggests not only an ongoing view of technologies as social objects, but also a process for thinking through scientifically the complex, multidimensional and emergent dynamic of social and technological change. The SMET proposes that the emergence and development of a new technology can be tracked by examining systematically and collectively the extent of development of its technology-related social structure – its degree of structuration. The degree of structuration of a technology is an ongoing process instantiated in social practices, and can be observed through visible patterns or specific social outcomes of systemic activity organized in three analytical dimensions: structures of meaning, power, and legitimacy. The SMET assumes that the conceptual initiation of a new technology triggers new patterns of social activity or a signal of technological change; thus, the variation in the slope or trajectory of the degree of structuration of a technology may indicate an early signal of technological change. The SMET sets a foundation for identifying early signals of technological change when it is used on a systematic basis. Empirically, the study conducted an exploratory case study in the Internet industry. The study employed a sequential transformative mixed method procedure, and relied on 77 Internet experts to create retrospectively a systematic and collective interpretation of the Internet industry in the last ten (10) years. The test of hypotheses was based on only seven (7) Internet technologies due to time and instrumental constraints. The results confirm the fundamental relationships among constructs in the model, and support, thus, the SMET. The degree of structuration of a technology is revealed as a process independent of individuals’ participation in the enactment of a technology. Technological outcomes are explained by the extent of development of structures of meaning, power, and legitimacy (i.e., the degree of structuration of a technology). Moreover, influential technological outcomes shape individuals’ perspectives over time – i.e., the structurational effect. Hence, the study not only provides evidence that supports this novel theoretical framework, but also illustrates methodologically how to identify the emergence and development of new technologies. Likewise, the study discusses the implications of these results for technology management practices (e.g., product and technology development, innovation policies, and technology transfer activities). Lastly, the study recognizes limitations and suggests further research avenues.

As the CMOS technology continues to scale into the deep sub-micron regime, the demand for higher frequencies and higher levels of integration poses a significant challenge for the clock generation and distribution design of microprocessors. Hence, skew optimization schemes are necessary to limit clock inaccuracies to a small fraction of the clock period. In this thesis, a crude deskew buffer (CDB) is designed to facilitate an adaptive deskewing scheme that reduces the clock skew in an ASIC clock network under manufacturing process, supply voltage, and temperature (PVT)variations. The crude deskew buffer adopts a DLL structure and functions on a 1GHz nominal clock frequency with an operating frequency range of 800MHz to 1.2GHz. An approximate 91.6ps phase resolution is achieved for all simulation conditions including various process corners and temperature variation. When the crude deskew buffer is applied to seven ASIC clock networks with each under various PVT variations, a maximum of 67.1% reduction in absolute maximum clock skew has been achieved. Furthermore, the maximum phase difference between all the clock signals in the seven networks have been reduced from 957.1ps to 311.9ps, a reduction of 67.4%. Overall, the CDB serves two important purposes in the proposed deskewing methodology: reducing the absolute maximum clock skew and synchronizes all the clock signals to a certain limit for the fine deskewing scheme. By generating various clock phases, the CDB can also be potentially useful in high speed debugging and testing where the clock duty cycle can be adjusted accordingly. Various positive and negative duty cycle values can be generated based on the phase resolution and the number of clock phases being “hot swapped”. For a 500ps duty cycle, the following values can be achieved for both the positive and negative duty cycle: 224ps, 316ps, 408ps, 592ps, 684ps, and 776ps.

Epilepsy is a serious neurological disorder characterized by recurrent unprovoked seizures due to abnormal or excessive neuronal activity in the brain. An estimated 50 million people around the world suffer from this condition, and it is classified as the second most serious neurological disease known to humanity, after stroke. With early and accurate detection of seizures, doctors can gain valuable time to administer medications and other such anti-seizure countermeasures to help reduce the damaging effects of this crippling disorder. The time-varying dynamics and high inter-individual variability make early prediction of a seizure state a challenging task. Many studies have shown that EEG signals do have valuable information that, if correctly analyzed, could help in the prediction of seizures in epileptic patients before their occurrence. Several mathematical transforms have been analyzed for its correlation with seizure onset prediction and a series of experiments were done to certify their strengths. New algorithms are presented to help clarify, monitor, and cross-validate the classification of EEG signals to predict the ictal (i.e. seizure) states, specifically the preictal, interictal, and postictal states in the brain. These new methods show promising results in detecting the presence of a preictal phase prior to the ictal state.

The aim of this paper is to test and further improve fundamental analysis models developed by Piotroski (2000) and Rados and Lovric (2009). The improvement seeks to reverse the information in the previous models by taking relative importance and strength of both positive and negative fundamental signals into consideration. The theoretical framework used includes the efficient market hypothesis, fundamental analysis and investing in high book-to-market companies. The Piotroski model, two Rados’s and Lovric’s models and two variations of our model were tested on a portfolio consisting of high book-to-market companies from the Stockholm Stock Exchange during the period 1999-2008. The results show that our EDA Model was the most successful at identifying short selling candidates, as EDA Low portfolio rendered market adjusted returns of -19% on average. Moreover, our EDC model was the best performing at identifying buy-and-hold candidates, with an average annual market adjusted return of 31,5%. The success of our models implies that the market is not using the information captured by them fully and in a timely manner.

As the CMOS technology continues to scale into the deep sub-micron regime, the demand for higher frequencies and higher levels of integration poses a significant challenge for the clock generation and distribution design of microprocessors. Hence, skew optimization schemes are necessary to limit clock inaccuracies to a small fraction of the clock period. In this thesis, a crude deskew buffer (CDB) is designed to facilitate an adaptive deskewing scheme that reduces the clock skew in an ASIC clock network under manufacturing process, supply voltage, and temperature (PVT)variations. The crude deskew buffer adopts a DLL structure and functions on a 1GHz nominal clock frequency with an operating frequency range of 800MHz to 1.2GHz. An approximate 91.6ps phase resolution is achieved for all simulation conditions including various process corners and temperature variation. When the crude deskew buffer is applied to seven ASIC clock networks with each under various PVT variations, a maximum of 67.1% reduction in absolute maximum clock skew has been achieved. Furthermore, the maximum phase difference between all the clock signals in the seven networks have been reduced from 957.1ps to 311.9ps, a reduction of 67.4%. Overall, the CDB serves two important purposes in the proposed deskewing methodology: reducing the absolute maximum clock skew and synchronizes all the clock signals to a certain limit for the fine deskewing scheme. By generating various clock phases, the CDB can also be potentially useful in high speed debugging and testing where the clock duty cycle can be adjusted accordingly. Various positive and negative duty cycle values can be generated based on the phase resolution and the number of clock phases being “hot swapped”. For a 500ps duty cycle, the following values can be achieved for both the positive and negative duty cycle: 224ps, 316ps, 408ps, 592ps, 684ps, and 776ps.

Epilepsy is a serious neurological disorder characterized by recurrent unprovoked seizures due to abnormal or excessive neuronal activity in the brain. An estimated 50 million people around the world suffer from this condition, and it is classified as the second most serious neurological disease known to humanity, after stroke. With early and accurate detection of seizures, doctors can gain valuable time to administer medications and other such anti-seizure countermeasures to help reduce the damaging effects of this crippling disorder. The time-varying dynamics and high inter-individual variability make early prediction of a seizure state a challenging task. Many studies have shown that EEG signals do have valuable information that, if correctly analyzed, could help in the prediction of seizures in epileptic patients before their occurrence. Several mathematical transforms have been analyzed for its correlation with seizure onset prediction and a series of experiments were done to certify their strengths. New algorithms are presented to help clarify, monitor, and cross-validate the classification of EEG signals to predict the ictal (i.e. seizure) states, specifically the preictal, interictal, and postictal states in the brain. These new methods show promising results in detecting the presence of a preictal phase prior to the ictal state.