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Search asymmetry was used to test two theories of sustained attention lapses currently debated in the literature: the boredom-mindlessness theory and the resource depletion-mental fatigue theory. Participants performed feature present and a feature absent target detection tasks using either a sustained attention to response task (high Go low No-Go) or a traditionally formatted task (high No-Go low Go) response format. In addition to performance, functional near infrared spectroscopy was employed to measure lateral cerebral oxygenation levels and self-reports of tense arousal, energetic arousal, task related and unrelated thoughts occurring during the tasks were utilised. Detections were lower and reaction times longer in the feature absent search than the feature present search regardless of response format. Detections were lower, but reaction times shorter in the sustained attention to response task than the traditionally formatted task regardless of feature search. Greater right than left frontal hemisphere activation occurred in the sustained attention to response task than the traditionally formatted task. In addition, the sustained attention to response task was more fatiguing based on self-reports than the traditionally formatted task, but there were no differences in Task-Unrelated Thoughts across task conditions. Overall, the results of this study support a resource theory explanation of sustained attention lapses, not a mindlessness-boredom theory explanation. Moreover, the results suggest the sustained attention to response task places high response inhibition, not sustained attention, demands on participants.

The main goal of this research is to produce a novel and efficient searching application by means of best match and proximity searching with particular application to very large numeric and textual data stores. In today's world a huge amount of information is produced. Almost every part of our society is touched by systems that collect, store and analyse data. As an example I mention the case of scientific instrumentation: new sensors capture massive amounts of information (e.g. new telescopes acquiring data from different regions of the spectrum). Description of biological and chemical interactions also produce complex and large amounts of data. It is in this context that a big challenge for current analysis algorithms is presented. Many of the traditional methods for data analysis do not scale well in massive data sets nor in very high dimensional spaces. In this work I introduce a novel (ultrametric) distance called Baire based on the longest common prefix and show how it can be used to produce clusters through grouping data in 'bins' taking linear or O(n) computational time. Furthermore, it follows that this distance can be strictly fitted to a hierarchy tree. This is a property that proves very useful for classifying, storing, accessing and retrieving information. I go further to apply this methodology on data from different scientific areas such as astronomy and chemistry to create groups or clusters. Additionally I apply this method to document sets for clustering and retrieval. In particular, I look into the new area of enterprise search to propose a new method to support scalable search and clustering.

Prolonged experience of high and long-term unemployment has led many governments to a renewed interest in active labour market policies. In particular, targeted wage subsidies have been seen as a means of both directly getting longterm unemployed people into work, and improving their future prospects of finding and keeping jobs. We examine three issues. Firstly, we look at the macroeconomic theory of targeted wage subsidies, and, to a lesser extent, job search assistance, within efficiency wage, union bargaining and search theoretic frameworks. Subsidies directly increase labour demand, but we also find that their effectiveness is enhanced by general equilibrium effects from targeting: wage pressure is reduced; and the average quality of the unemployed pool rises as long-term unemployed workers are removed from it, increasing the incentives for other firms to open vacancies. Secondly we address the optimal degree of policy targeting, using an extension of the Mortensen-Pissarides job creation and destruction model. We argue that there are real gains to targeting the long-term unemployed, but also diminishing returns. Hence, as the level of policy expenditure rises, the extent of targeting should fall. Simulating the model for the UK, we find that policy could have a significant impact on equilibrium unemployment, with more modest welfare gains. Finally, we look at longer-term employability effects by evaluating the Australian Special Youth Employment Training Program (SYETP). Controlling for selection bias using a bivariate probit, we find that participation increased the chances of having a job by 26% between 8 and 13 months after subsidy expiry, and 20% a year later. Much of this gain arose from retention of initially subsidised jobs, but even excluding this, participants were significantly more likely to be employed in subsequent years than if they had not gone on the programme.

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Search Frictions, Unemployment, and Housing in Cities: Theory and Policies We propose an urban search-matching model with land development. We characterize the steady-state equilibrium and then discuss the issue of efficiency. We find that the transportation and housing policies are more efficient if the unemployment rate is low, while the entry-cost policy is more efficient if the unemployment rate is high. Land Development, Search Frictions, and City Structure This paper analyzes the interactions between labor and housing (and land) markets in a city. Unemployment, the spatial structure of a city, land development, housing demand, prices of housing and land are all endogenously determined. Then, we characterize two different spatial configurations. To better understand how two equilibria are affected by land and labor market parameters, we implement a comparative steady state analysis. We further explored the effects of policies. Search for Jobs or Crimes? This paper develops a competitive search model where unemployed workers allocate their time between the search for legal jobs and opportunities for committing crimes. We analyze the effects of labor market policies and crime policies. We show that the market equilibrium is socially inefficient when there is crime. We also find that workers' individual choice of years of education is less than the socially efficient one. Rural-Urban Migration in Developing Countries: Labor Market Institutions and Policies The paper studies rural-urban migration under different labor market institutions in developing countries. Specifically, we consider two types of labor market institutions where workers in urban firms are unionized or not. We find that unionization of workers raises unemployment, urban wages, and rural employment, reduces rural wages and urban employment and increases inequality between the rural and the urban sector. We also compare two institutions under different policies.

Code search is an integral part of software development and program comprehension. The difficulty of code search lies in the inability to guess the exact words used in the code. Therefore, it is crucial for keyword-based code search to expand queries with semantically related words, e.g., synonyms and abbreviations, to increase the search effectiveness. However, it is limited to rely on resources such as English dictionaries and WordNet to obtain semantically related words in software, because many words that are semantically related in software are not semantically related in English. On the other hand, many words that are semantically related in English are not semantically related in software. This thesis proposes a simple and general technique to automatically infer semantically re- lated words (referred to as rPairs) in software by leveraging the context of words in comments and code. In addition, we propose a ranking algorithm on the rPair results and study cross-project rPairs on two sets of software with similar functionality, i.e., media browsers and operating sys- tems. We achieve a reasonable accuracy in nine large and popular code bases written in C and Java. Our further evaluation against the state of art shows that our technique can achieve a higher precision and recall. In addition, the proposed ranking algorithm improves the rPair extraction accuracy by bringing correct rPairs to the top of the list. Our cross-project study successfully discovers overlapping rPairs among projects of similar functionality and finds that cross-project rPairs are more likely to be correct than project-specific rPairs. Since the cross-project rPairs are highly likely to be general for software of the same type, the discovered overlapping rPairs can benefit other projects of the same type that have not been anaylyzed.

The entertainment industry is an impactful part of the U.S. economy. My thesis explores the way Americans consume popular music and how the U.S. economic environment affects the permeability of the music industry to new artists. I use discrete-choice probit models to examine the top 10 weekly singles from the Billboard Hot 100 between 2006 and 2013. I analyze the economic factors and artist characteristics that affect an unestablished artist’s entry into the top 10 of the chart and achievement of the number one chart spot. I also use a Cox proportional hazard model to examine the effects of economic factors and artist characteristics on the number of weeks an artist’s single stays in the top 10 of the Hot 100 chart. I find that having a previous single in the top 100 decreases the predicted probability of a new artist’s song being in the top 10, and having previous singles in the top 10 or top 100 decreases number of weeks an artist’s subsequent single spends in the top 10 of the chart. Additionally, level of GDP per capita increases the number of weeks an artist’s single stays in the top 10 of the chart. Economic well-being perpetuates stability in the consumption of music, and modern culture consumers demonstrate a preference for newness in their endorsement of unestablished artists. As demonstrated by the use of singles between 2006 and 2013, new technologies decrease the costs of engaging with new artists for consumers and allow an artist to achieve success regardless of the artist’s previous success.

Today, the advent of networking technologies and computer hardware have enabled more and more inexpensive PCs, various mobile devices, smart phones, PDAs, sensors and cameras to be linked to the Internet with better connectivity. In recent years, we have witnessed the emergence of several instances of distributed applications, providing infrastructures for social interactions over large-scale wide-area networks and facilitating the ways users share and publish data. User generated data today range from simple text files to (semi-) structured documents and multimedia content. With the emergence of Semantic Web, the number of features (associated with a content) that are used in order to index those large amounts of heterogenous pieces of data is growing dramatically. The feature sets associated with each content type can grow continuously as we discover new ways of describing a content in formulated terms. As the number of dimensions in the feature data grow (as high as 100 to 1000), it becomes harder and harder to search for information in a dataset due to the curse of dimensionality and it is not appropriate to use naive search methods, as their performance degrade to linear search. As an alternative, we can distribute the content and the query processing load to a set of peers in a distributed Peer-to-Peer (P2P) network and incorporate high-dimensional distributed search techniques to attack the problem. Currently, a large percentage of Internet traffic consists of video and music files shared and exchanged over P2P networks. In most present services, searching for music is performed through keyword search and naive string-matching algorithms using collaborative filtering techniques which mostly use tag based approaches. In music information retrieval (MIR) systems, the main goal is to make recommendations similar to the music that the user listens to. In these systems, techniques based on acoustic feature extraction can be employed to achieve content-based music similarity search (i.e., searching through music based on what can be heard from the music track). Using these techniques we can devise an automated measure of similarity that can replace the need for human experts (or users) who assign descriptive genre tags and meta-data to each recording and solve the famous cold-start problem associated with the collaborative filtering techniques. In this work we explore the advantages of distributed structures by efficiently distributing the content features and query processing load on the peers in a P2P network. Using a family of Locality Sensitive Hash (LSH) functions based on p-stable distributions we propose an efficient, scalable and load-balanced system, capable of performing K-Nearest-Neighbor (KNN) and Range queries. We also propose a new load-balanced indexing algorithm and evaluate it using our Java based simulator. Our results show that this P2P design ensures load-balancing and guarantees logarithmic number of hops for query processing. Our system is extensible to be used with all types of multi-dimensional feature data and it can also be employed as the main indexing scheme of a multipurpose recommendation system. / Graduate