Value of Travel-Time Reliability: Commuters’ Route-Choice Behavior in the Twin Cities

Travel-time variability is a noteworthy factor in network performance. It measures the temporal uncertainty experienced by users in their movement between any two nodes in a network. The importance of the time variance depends on the penalties incurred by the users. In road networks, travelers consider the existence of this journey uncertainty in their selection of routes. This choice process takes into account travel-time variability and other characteristics of the travelers and the road network. In this complex behavioral response, a feasible decision is spawned based on not only the amalgamation of attributes, but also on the experience travelers incurred from previous situations. Over the past several years, the analysis of these behavioral responses (travelers’ route choices) to fluctuations in travel-time variability has become a central topic in transportation research. These have generally been based on theoretical approaches built upon Wardropian equilibrium, or empirical formulations using Random Utility Theory. This report focuses on the travel behavior of commuters using Interstate 394 (I-394) and the swapping (bridge) choice behavior of commuters crossing the Mississippi River in Minneapolis. The inferences of this report are based on collected Global Positioning System (GPS) tracking data and accompanying surveys. Furthermore, it also employs two distinct approaches (estimation of Value of Reliability [VOR] and econometric modeling with travelers’ intrapersonal data) in order to analyze the behavioral responses of two distinct sets of subjects in the Minneapolis-Saint Paul (Twin Cities) area

Variance components models in statistical genetics: extensions and applications

Variance components linkage analysis is a powerful method to detect quantitative trait loci (QTLs) for complex diseases. It has the advantages of easy applicability to large extended pedigrees and provides a good flexible framework to accommodate more complicated models like gene-gene, gene-environmental interactions. This dissertation consists of two major parts. In the first part, I propose two approaches for deriving relative-to-relative covariances that are indispensable for expanding the applications of standard variance components linkage approach to more complicated genetic models such as those involving genomic imprinting. In the first approach, I extend 'Li and Sacks' ITO method to model ordered genotypes and derive some generalized linear functions of the extended transition matrices. I demonstrate the wide applicability of this extension by applying it to calculate the covariance in unilineal and bilineal relatives under genomic imprinting. In the second approach, I derive a general formula for calculating the genetic covariance using ordered genotypes for any type of relative pair, which does not have the limitation of extended ITO method to biallelic loci and to unilineal and bilineal relatives. I also propose a recursive algorithm to calculate necessary coefficients in the formula, which opens up the possibility of calculating even inbred relative-to-relative covariance.In the second part of my dissertation, I discuss linkage evidence for susceptibility loci for adiposity-related phenotypes in the Samoan population, an extensive summary of our multicenter study "Genome-scan for Obesity Susceptibility Loci in Samoans". Obesity, BMI greater than or equal to 30 kg/m^2, in the U.S. has become a major and serious public health problem, affecting 33% of adults in 2002. Obesity increases risks for serious diet-related diseases, such as cardiovascular disease, type-2 diabetes, and certain forms of cancers. Obesity is a typical multi-factorial disease with overwhelming evidence of genetic effects, yet their roles in obesity are largely unknown. Our current research findings will help further understand the whole picture of the genetics of obesity, which may have great influence on early prevention and later interventions of human obesity, making it a fundamentally important contribution to public health

Inductive Bias and Modular Design for Sample-Efficient Neural Language Learning

Most of the world's languages suffer from the paucity of annotated data. This curbs the effectiveness of supervised learning, the most widespread approach to modelling language. Instead, an alternative paradigm could take inspiration from the propensity of children to acquire language from limited stimuli, in order to enable machines to learn any new language from a few examples. The abstract mechanisms underpinning this ability include 1) a set of in-born inductive biases and 2) the deep entrenchment of language in other perceptual and cognitive faculties, combined with the ability to transfer and recombine knowledge across these domains. The main contribution of my thesis is giving concrete form to both these intuitions. Firstly, I argue that endowing a neural network with the correct inductive biases is equivalent to constructing a prior distribution over its weights and its architecture (including connectivity patterns and non-linear activations). This prior is inferred by "reverse-engineering" a representative set of observed languages and harnessing typological features documented by linguists. Thus, I provide a unified framework for cross-lingual transfer and architecture search by recasting them as hierarchical Bayesian neural models. Secondly, the skills relevant to different language varieties and different tasks in natural language processing are deeply intertwined. Hence, the neural weights modelling the data for each of their combinations can be imagined as lying in a structured space. I introduce a Bayesian generative model of this space, which is factorised into latent variables representing each language and each task. By virtue of this modular design, predictions can generalise to unseen combinations by extrapolating from the data of observed combinations. The proposed models are empirically validated on a spectrum of language-related tasks (character-level language modelling, part-of-speech tagging, named entity recognition, and common-sense reasoning) and a typologically diverse sample of about a hundred languages. Compared to a series of competitive baselines, they achieve better performances in new languages in zero-shot and few-shot learning settings. In general, they hold promise to extend state-of-the-art language technology to under-resourced languages by means of sample efficiency and robustness to the cross-lingual variation.ERC (Consolidator Grant 648909) Lexical Google Research Faculty Award 201

GENOMICS OF THE GLOBALLY DISTRIBUTED ECHINOID GENUS Tripneustes

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018

A Quantitative Framework for Assessing Vulnerability and Redundancy of Freight Transportation Networks

Freight transportation networks are an important component of everyday life in modern society. Disruption to these networks can make peoples’ daily lives extremely difficult as well as seriously cripple economic productivity. This dissertation develops a quantitative framework for assessing vulnerability and redundancy of freight transportation networks. The framework consists of three major contributions: (1) a two- stage approach for estimating a statewide truck origin-destination (O-D) trip table, (2) a decision support tool for assessing vulnerability of freight transportation networks, and (3) a quantitative approach for measuring redundancy of freight transportation networks.The dissertation first proposes a two-stage approach to estimate a statewide truck O-D trip table. The proposed approach is supported by two sequential stages: the first stage estimates a commodity-based truck O-D trip table using the commodity flows derived from the Freight Analysis Framework (FAF) database, and the second stage uses the path flow estimator (PFE) concept to refine the truck trip table obtained from the first stage using the truck counts from the statewide truck count program. The model allows great flexibility of incorporating data at different spatial levels for estimating the truck O- D trip table. The results from the second stage provide us a better understanding of truck flows on the statewide truck routes and corridors, and allow us to better manage the anticipated impacts caused by network disruptions.A decision support tool is developed to facilitate the decision making system through the application of its database management capabilities, graphical user interface, GIS-based visualization, and transportation network vulnerability analysis. The vulnerability assessment focuses on evaluating the statewide truck-freight bottlenecks/chokepoints. This dissertation proposes two quantitative measures: O-D connectivity (or detour route) in terms of distance and freight flow pattern change in terms of vehicle miles traveled (VMT). The case study adopts a “what-if” analysis approach by generating the disruption scenarios of the structurally deficient bridges in Utah due to earthquakes. In addition, the potential impacts of disruptions to multiple bridges in both rural and urban areas are evaluated and compared to the single bridge failure scenarios.This dissertation also proposes an approach to measure the redundancy of freight transportation networks based on two main dimensions: route diversity and network spare capacity. The route diversity dimension is used to evaluate the existence of multiple efficient routes available for users or the degree of connections between a specific O-D pair. The network spare capacity dimension is used to quantify the network- wide spare capacity with an explicit consideration of congestion effect. These two dimensions can complement each other by providing a two-dimensional characterization of freight transportation network redundancy. Case studies of the Utah statewide transportation network and coal multimodal network are conducted to demonstrate the features of the vulnerability and redundancy measures and the applicability of the quantitative assessment methodology

Accessing spoken interaction through dialogue processing [online]

Zusammenfassung Unser Leben, unsere Leistungen und unsere Umgebung, alles wird derzeit durch Schriftsprache dokumentiert. Die rasante Fortentwicklung der technischen Möglichkeiten Audio, Bilder und Video aufzunehmen, abzuspeichern und wiederzugeben kann genutzt werden um die schriftliche Dokumentation von menschlicher Kommunikation, zum Beispiel Meetings, zu unterstützen, zu ergänzen oder gar zu ersetzen. Diese neuen Technologien können uns in die Lage versetzen Information aufzunehmen, die anderweitig verloren gehen, die Kosten der Dokumentation zu senken und hochwertige Dokumente mit audiovisuellem Material anzureichern. Die Indizierung solcher Aufnahmen stellt die Kerntechnologie dar um dieses Potential auszuschöpfen. Diese Arbeit stellt effektive Alternativen zu schlüsselwortbasierten Indizes vor, die Suchraumeinschränkungen bewirken und teilweise mit einfachen Mitteln zu berechnen sind. Die Indizierung von Sprachdokumenten kann auf verschiedenen Ebenen erfolgen: Ein Dokument gehört stilistisch einer bestimmten Datenbasis an, welche durch sehr einfache Merkmale bei hoher Genauigkeit automatisch bestimmt werden kann. Durch diese Art von Klassifikation kann eine Reduktion des Suchraumes um einen Faktor der Größenordnung 4­10 erfolgen. Die Anwendung von thematischen Merkmalen zur Textklassifikation bei einer Nachrichtendatenbank resultiert in einer Reduktion um einen Faktor 18. Da Sprachdokumente sehr lang sein können müssen sie in thematische Segmente unterteilt werden. Ein neuer probabilistischer Ansatz sowie neue Merkmale (Sprecherinitia­ tive und Stil) liefern vergleichbare oder bessere Resultate als traditionelle schlüsselwortbasierte Ansätze. Diese thematische Segmente können durch die vorherrschende Aktivität charakterisiert werden (erzählen, diskutieren, planen, ...), die durch ein neuronales Netz detektiert werden kann. Die Detektionsraten sind allerdings begrenzt da auch Menschen diese Aktivitäten nur ungenau bestimmen. Eine maximale Reduktion des Suchraumes um den Faktor 6 ist bei den verwendeten Daten theoretisch möglich. Eine thematische Klassifikation dieser Segmente wurde ebenfalls auf einer Datenbasis durchgeführt, die Detektionsraten für diesen Index sind jedoch gering. Auf der Ebene der einzelnen Äußerungen können Dialogakte wie Aussagen, Fragen, Rückmeldungen (aha, ach ja, echt?, ...) usw. mit einem diskriminativ trainierten Hidden Markov Model erkannt werden. Dieses Verfahren kann um die Erkennung von kurzen Folgen wie Frage/Antwort­Spielen erweitert werden (Dialogspiele). Dialogakte und ­spiele können eingesetzt werden um Klassifikatoren für globale Sprechstile zu bauen. Ebenso könnte ein Benutzer sich an eine bestimmte Dialogaktsequenz erinnern und versuchen, diese in einer grafischen Repräsentation wiederzufinden. In einer Studie mit sehr pessimistischen Annahmen konnten Benutzer eines aus vier ähnlichen und gleichwahrscheinlichen Gesprächen mit einer Genauigkeit von ~ 43% durch eine graphische Repräsentation von Aktivität bestimmt. Dialogakte könnte in diesem Szenario ebenso nützlich sein, die Benutzerstudie konnte aufgrund der geringen Datenmenge darüber keinen endgültigen Aufschluß geben. Die Studie konnte allerdings für detailierte Basismerkmale wie Formalität und Sprecheridentität keinen Effekt zeigen. Abstract Written language is one of our primary means for documenting our lives, achievements, and environment. Our capabilities to record, store and retrieve audio, still pictures, and video are undergoing a revolution and may support, supplement or even replace written documentation. This technology enables us to record information that would otherwise be lost, lower the cost of documentation and enhance high­quality documents with original audiovisual material. The indexing of the audio material is the key technology to realize those benefits. This work presents effective alternatives to keyword based indices which restrict the search space and may in part be calculated with very limited resources. Indexing speech documents can be done at a various levels: Stylistically a document belongs to a certain database which can be determined automatically with high accuracy using very simple features. The resulting factor in search space reduction is in the order of 4­10 while topic classification yielded a factor of 18 in a news domain. Since documents can be very long they need to be segmented into topical regions. A new probabilistic segmentation framework as well as new features (speaker initiative and style) prove to be very effective compared to traditional keyword based methods. At the topical segment level activities (storytelling, discussing, planning, ...) can be detected using a machine learning approach with limited accuracy; however even human annotators do not annotate them very reliably. A maximum search space reduction factor of 6 is theoretically possible on the databases used. A topical classification of these regions has been attempted on one database, the detection accuracy for that index, however, was very low. At the utterance level dialogue acts such as statements, questions, backchannels (aha, yeah, ...), etc. are being recognized using a novel discriminatively trained HMM procedure. The procedure can be extended to recognize short sequences such as question/answer pairs, so called dialogue games. Dialog acts and games are useful for building classifiers for speaking style. Similarily a user may remember a certain dialog act sequence and may search for it in a graphical representation. In a study with very pessimistic assumptions users are able to pick one out of four similar and equiprobable meetings correctly with an accuracy ~ 43% using graphical activity information. Dialogue acts may be useful in this situation as well but the sample size did not allow to draw final conclusions. However the user study fails to show any effect for detailed basic features such as formality or speaker identity