The label-feedback hypothesis (Lupyan, 2012) states that language can modulate visual processing. In particular, hearing or reading aloud target names (labels) speeds up performance in visual search tasks by facilitating target detection. The current study conceptually expands previous investigations by comparing the effect of a multimodal label presentation (i.e., an audio+visual, AV, priming label), with that of a multimodal (i.e. noise+visual, NV, label) and two unimodal (i.e. audio, A, label or visual, V, label) control conditions in a visual search task. Furthermore, the name of a category (i.e. a label at the superordinate level) was used as a cue, instead of the more commonly used target name (a basic level label), with targets belonging to one of three categories: proper weapons, improper weapons, garments. According to legislation, objects are typically classified as weapons if they are offensive per se (referred to here as proper) and if they are adapted for use as weapons or carried with the intent of causing injury (referred to here as improper), with specific regulations on their usage and possession in public spaces. However, little evidence exists on the validity of this distinction in psychology, despite a widespread recognition of the importance of psychological states and perceptions in risk assessment. These categories were found to vary for their structure, improper weapons being an ad hoc category unlike proper weapons and garments, and for the perceived dangerousness and familiarity of their exemplars, which increase from garments to proper weapons. Results of the two experiments on the label-feedback effect show an overall transient facilitation of visual search performance in the AV condition compared to control conditions. However, for improper weapons, no significant difference was found between the AV and the NV label condition, suggesting that the specificity of the superordinate label effect can be modulated by the structural properties of a category. The findings are consistent with the idea that the AV condition prompted an "up regulation" of the label, a requirement for enhancing the label’s beneficial effects, with the exception of improper weapons. They also highlight the status of the category of improper weapons and offer additional proof that sets it apart from proper weapons.

Der Weg von der Entwicklung bis zur Zulassung neuer Virostatika ist bis heute mit hohen Kosten und einem großen Zeitaufwand verbunden. Sollten jedoch bereits zugelassene antivirale Medikamente eine Wirkung auf andere virale Infektionen zeigen, könnte dieser Prozess stark verkürzt werden. Daher war es Ziel dieser Arbeit, den Effekt von zugelassenen Medikamenten, gegen HSV-1, mCMV, hCMV, RSV, Parainfluenzavirus-3, DENV-2, CHIKV, Poliovirus, Masernvirus und HIV-1 zu evaluieren. Getestet wurden die Polymeraseinhibitoren ACV, GCV, CDV, sowie das neuere Medikament T-705 und die reversen Transkriptase-Inhibitoren TDF, 3TC, AZT und ABC. Außerdem die Proteaseinhibitoren SMV, GRV, DCV, LDV, ELB, VEL, SOF und DSV. TDF senkte in einer Konzentration von 10 µM die Infektiosität von HSV-1 und mCMV bis zu 1 Größenordnung. Auch ABC senkte die Infektiosität von HSV-1 und mCMV in einer Konzentration von 30 µM um 0,4 bzw. 0,6 Größenordnungen. AZT und ELB senkten die Infektiosität bei Infektionen mit HSV-1 in einer Konzentration von 30 µM um 0,4 Größenordnungen. VEL senkte die Infektiosität von mCMV bis zu einer Konzentration von 2 µM um 0,7 Größenordnungen. Durch die Substanzen ELB und LDV konnte die Replikation von DENV-2 bei einer Konzentration von 10 µM um 0,6 bzw. 0,8 Größenordnungen gesenkt werden. Die Substanzen zeigten jedoch keinen Effekt auf Infektionen mit CHIKV und Poliovirus, sodass für beide Substanzen ein virusspezifischer Effekt anzunehmen ist. Es wurde keine Wirkung der Substanzen gegen Infektionen mit Masernvirus, RSV oder Parainfluenzavirus-3 in den Versuchen beobachtet. Es wurde gezeigt, dass die verwendeten Methoden eine schnelle und effektive Möglichkeit darstellen, neue direkt-antivirale Medikamente zu etablieren. Zudem stellen die gefundenen Wirkstoffe eine gute Grundlage als Leitsubstanzen zur Entwicklung neuer Wirkstoffe dar. Weitere Versuche mit Kombinationen der wirksamen Substanzen sollten zur weiteren Therapiefindung durchgeführt werden. Damit hat die vorgelegte Arbeit eine hohe Relevanz für die weitere Forschung. / To this day, the process from development to approval of new antivirals is associated with high costs and a great deal of time. This process could be greatly shortened, if an effect of approved antiviral drugs on other viral infections could be shown. Therefore, the aim of this study was to evaluate the effect of approved drugs against infections with HSV-1, mCMV, hCMV, RSV, parainfluenza virus-3, DENV-2, CHIKV, poliovirus, measles virus and HIV-1. The polymerase inhibitors ACV, GCV, CDV, as well as the newer drug T-705 and the reverse transcriptase inhibitors TDF, 3TC, AZT and ABC were tested. In addition, the protease inhibitors SMV, GRV, DCV, LDV, ELB, VEL, SOF and DSV were included. TDF reduced the infectivity of HSV-1 and mCMV by up to 1 order of magnitude at a concentration of 10 μM. ABC also reduced the infectivity of HSV-1 and mCMV by 0.4 and 0.6 orders of magnitude, respectively, at a concentration of 30 μM. AZT and ELB reduced infectivity by 0.4 orders of magnitude in HSV-1 infections at a concentration of 30 μM. VEL reduced the infectivity of mCMV by 0.7 orders of magnitude up to a concentration of 2 μM. The substances ELB and LDV reduced the replication of DENV-2 by 0.6 and 0.8 orders of magnitude, respectively, at a concentration of 10 μM. However, the substances had no effect on CHIKV and poliovirus infections, and it was assumed that both substances had a virus-specific effect. No effect of the substances against infections with measles virus, RSV or parainfluenza virus-3 was observed in the trials. The methods used proved to be a fast and effective way to establish new direct antiviral drugs. In addition, the used drugs provide a good basis as lead substances for the development of new antiviral drugs. Further experiments with combinations of the active substances should be carried out in order to find further antiviral therapies. Thus, the submitted work has a high relevance for further research.

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Att människor vill ha god självkännedom är vi medvetna om sedan ett par tusen år tillbaka. Därför är vi inte förvånande av att försäljningen av smarta enheter som loggar aktivitets- data ökar årligen. Hur väl gemene man förstår sin insamlade data kan dock variera. Datan presenteras oftast för användaren genom grafer och stapeldiagram eller på en tidslinje där användaren kan följa sin dag, men det är inte alla användare som greppar helheten och kan identifiera sina livsmönster. I den här studien vill vi ta reda på om man kan med hjälp av självannotering öka en individs förståelse av sin data genom att självannotera. I relaterad forskning hittar vi tre applikationer Labels, ZhiWo och WalkNRide. Vi har utgått från relaterad forskning och utvecklat en prototyp-applikation enligt design research metoden som vi kallar Anotify. Anotify möjliggör för användare att kunna självannotera sina fysis- ka aktiviteter. Prototypen är kopplad till Sony Lifelogs API som tillgodoser Anotify med fysiska aktiviteter som användaren utfört. För att utvärdera ifall våra testdeltagare får en ökad förståelse genom självannotering utför vi en komparativ studie som pågår under tre veckor där testdeltagarna använder Lifelog och Anotify var för sig samt parallellt. Studi- en innefattar fyra testdeltagare som delas upp i två grupper om två personer. Resultatet utvinner vi genom semistrukturerade intervjuer med testdeltagarna efter testperioden är slut. Slutligen validerar vi resultatet genom att jämföra våra resultat med vad tidigare forskning har kommit fram till. / Since a couple of thousand years back people have desired self-awareness. Therefore, we are not surprised that sales of smart devices that log activity data increase annually. How well the average person understand his collected data may nevertheless vary. The data is usually presented by graphs and bar charts or on a timeline where the user can follow his day, but not all users can grasp the whole and identify their living patterns. In our study, we want to find out if one can, by means of self annotation increase an individual’s understanding of his data by self annotation. In related research we find three applications Labels, ZhiWo and WalkNRide who studies this topic. We have based our research on these applications and developed a prototype application (Anotify) according to the design research method. Anotify enables users to self- annotate their physical activities. The prototype is linked to the Sony Lifelog API, which provides Anotify with physical activities that the user performs. To evaluate if our test participants get an increased understanding of self annotation, we perform a comparative study that lasts three weeks in which the test participants use Lifelog and Anotify individually as well as in parallel. The study comprises four test participants divided into two groups of two people. The results are obtained through semistructured interviews with the test participants after the end of the test period. Finally, we validate the result by comparing our results with previous research.

In this thesis we have present effective approaches for two classic Nature Language Processing tasks: Multi-label Text Classification(MLTC) and Sentiment Analysis(SA) based on two datasets. For MLTC, a robust deep learning approach based on convolution neural network(CNN) has been introduced. We have done this on almost one million records with a related label list consists of 20 labels. We have divided our data set into three parts, training set, validation set and test set. Our CNN based model achieved great result measured in F1 score. For SA, data set was more informative and well-structured compared with MLTC. A traditional word embedding method, Word2Vec was used for generating word vector of each text records. Following that, we employed several classic deep learning models such as Bi-LSTM, RCNN, Attention mechanism and CNN to extract sentiment features. In the next step, a classification frame was designed to graded. At last, the start-of-art language model, BERT which use transfer learning method was employed. In conclusion, we compared performance of RNN-based model, CNN-based model and pre-trained language model on classification task and discuss their applicability. / Thesis / Master of Science in Electrical and Computer Engineering (MSECE) / This theis purposed two deep learning solution to both multi-label classification problem and sentiment analysis problem.

DNA chip technology has drawn tremendous attention since it emerged in the mid 90 s as a method that expedites gene sequencing by over 100-fold. DNA chip, also called DNA microarray, is a combinatorial technology in which different single-stranded DNA (ssDNA) molecules of known sequences are immobilized at specific spots. The immobilized ssDNA strands are called probes. In application, the chip is exposed to a solution containing ssDNA of unknown sequence, called targets, which are labeled with fluorescent dyes. Due to specific molecular recognition among the base pairs in the DNA, the binding or hybridization occurs only when the probe and target sequences are complementary. The nucleotide sequence of the target is determined by imaging the fluorescence from the spots. The uncertainty of background in signal detection and statistical error in data analysis, primarily due to the error in the DNA amplification process and statistical distribution of the tags in the target DNA, have become the fundamental barriers in bringing the technology into application for clinical diagnostics. Furthermore, the dye and tagging process are expensive, making the cost of DNA chips inhibitive for clinical testing. These limitations and challenges make it difficult to implement DNA chip methods as a diagnostic tool in a pathology laboratory. The objective of this dissertation research is to provide an alternative approach that will address the above challenges.. In this research, a label-free assay is designed and studied. Polystyrene (PS), a commonly used polymeric material, serves as the fluorescence agent. Probe ssDNA is covalently immobilized on polystyrene thin film that is supported by a reflecting substrate. When this chip is exposed to excitation light, fluorescence light intensity from PS is detected as the signal. Since the optical constants and conformations of ssDNA and dsDNA (double stranded DNA) are different, the measured fluorescence from PS changes for the same intensity of excitation light.. The fluorescence contrast is used to quantify the amount of probe-target hybridization. A mathematical model that considers multiple reflections and scattering is developed to explain the mechanism of the fluorescence contrast which depends on the thickness of the PS film. Scattering is the dominant factor that contributes to the contrast. The potential of this assay to detect single nucleotide polymorphism is also tested. / Ph. D.

TRANSIMS (Transportation Analysis Simulation System) is part of a multi-track Travel Model Improvement Program sponsored by the U. S. Department of Transportation (DOT), and the Environmental Protection Agency (EPA). The main objective of this thesis is to enhance and implement a principal module in TRANSIMS, called the Route Planner Module. The purpose of the Route Planner Module is to find time-dependent label-constrained shortest paths for transportation activities performed by travelers in the system. There are several variations of shortest path problems and algorithms that vary by application, contexts, complexity, required data, and computer implementation techniques. In general, these variants require some combination of the following inputs: a network consisting of nodes and links, and a travel time function on each link, which could be a time-independent or a time-dependent function, where the time-dependent functions account for time-of-day delays resulting from actual travel conditions such as peak-hour congestion. The problem then seeks a shortest path between one or more origin-destination pairs. A new variant, introduced in the context of TRANSIMS and which is the focus of the present study, also specifies labels for each arc denoting particular modes of travel, along with strings of admissible labels that delineate the permissible travel mode sequences that could be adopted by the user in traveling from the origin to the destination of the trip. The technique adopted by TRANSIMS to identify a suitable travel route for any user is a variant of Dijkstra's procedure for finding shortest paths, which is suitably modified to accommodate time-dependent travel times and label sequence constraints. The underlying problem is referred to as a Time-Dependent Label-Constrained Shortest Path Problem. The main objective of this research is to improve upon this procedure and study its implementation in order to develop a more effective scheme for determining time-dependent label-constrained shortest paths as a practical routing tool in multimodal transportation networks. Specifically, we enhance the following features of this procedure: (a) We recommend a method to work implicitly with a certain composition graph G* that combines the transportation network with the admissible label-sequence graph. This graph G* captures all possible paths for a given single trip starting from the origin node and ending at the destination node, while conforming with the admissible mode string. (b) We use more modern partitioned shortest path algorithmic schemes to implement the time-dependent label-constrained procedure. (c) We introduce the notion of curtailing search based on various indicators of progress and projected travel times to complete the trip. Finally, computer programs in C++ are written to implement the proposed overall algorithm, and are applied to solve some real multimodal transportation network problems. The indicators used to evaluate the performance of the algorithm include (i) time taken for computation on the real network, (ii) quality of solution obtained, (iii) ease of implementation, and (iv) extensibility of the algorithm for solving other variants of the shortest path problem. The results exhibit that the proposed algorithm, even without the approximate curtailing of the search process, exhibits good performance in finding optimal routes for real multimodal transportation networks. Although the various heuristic curtailments result in only approximate solutions, typically, they run much faster than the exact algorithm for the intuitive reason that the shortest path tree developed grows more pointedly in the direction of the destination. Among the different strategies implemented, our results suggest that the scheme based on the geometric structure of the underlying network, using either a constant predictive term, or multiplying this term with a suitable exponential decay function, yields an attractive candidate for heuristically curtailing the search. / Master of Science

The economics of information is a relatively new and important field of economics. This dissertation analyzes the role of information in three case studies within three different branches of economics: health economics, environmental economics, and finance and banking. First I analyze parental nutritional label usage and its effect of children's dietary outcomes (i.e. Health Eating Index and Body Mass Index). I show that parental usage of nutritional labels is associated with a better quality of their children's diet as well as an overall improvement in their health as measured by their Body Mass Index. Secondly, I study the behavioral effect of length of residency on water demand in the arid cities of Reno and Sparks in Nevada. In this case, I observe that social interaction among households affects their water usage. In particular, newcomers' watering behaviors are influenced by the prevailing social norms among neighbors that have lived in the arid area for a longer period of time. Finally, I compare the performance of local versus larger national and regional lending institutions in the years leading to the 2007 mortgage crisis. I find that local or community lenders have a significantly lower foreclosure rate during these years. Local lenders presumably base their origination decisions on an interpersonal relationship with their customers. This provides them with information that is not contained within the standard risk metrics generally used in loan applications. I discuss the policy implications of these results for each case study. / Ph. D.