1,278 research outputs found
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Image-Based Modeling of Bridges and Its Applications to Evaluating Resiliency of Transportation Networks
Modern urban areas are heavily dependent on transportation networks to sustain their economic life. Hence, when vital components of a regional network are disrupted, economic losses are inevitable. As evidenced by 1989, Loma Prieta and 1994, Northridge earthquakes, the seismic damages experienced by bridges alone result in extensive traffic delays and rerouting, not only hindering emergency response but also causing indirect economic losses that far surpass the direct cost of damage to infrastructure. Nevertheless, in many areas of the U.S., transportation networks lack the resilience required to sustain the potential demands of natural hazards. Traditional hazard assessment methods, in theory, provide the tools required for predicting the vulnerabilities associated with natural hazards. Nonetheless, due to their abstractions of the complex infrastructure and the coupled regional behavior, they often fall short of that expectation. This study proposes a semi-automated image-based model generation framework for producing structure-specific models and fragility functions of bridges. The framework effectively fuses geometric and semantic information extracted from Google Street View images with centerline curve geometry, surface topology, and various relevant metadata to construct extremely accurate geometric representations of bridges. Then, using class statistics available in the literature for bridge structural properties, the framework generates structural models. Both the performance of the geometry extraction procedure and the structural modeling method proposed here are validated by comparison against the structural model of a real-life bridge developed based on as-built drawings.In principle, these models can be utilized to assess physical damage for any type of hazard, but in this study, the focus is limited to seismic applications. Thus to relate the damage resulting from seismic demands from ground shaking, bridge-specific fragility functions are developed for 100 bridge structures in the immediate surroundings of Ports of Los Angeles and Long Beach. Using these fragility curves, the physical damage resulting from a magnitude 7.3 scenario earthquake on Palos Verdes fault is predicted. Subsequently, the effects of the bridge infrastructure damage to the transportation patterns in the Los Angeles metropolitan area are investigated in terms of various resilience metrics
Statistical Methods to Enhance Clinical Prediction with High-Dimensional Data and Ordinal Response
Der technologische Fortschritt ermöglicht es heute, die moleculare
Konfiguration einzelner Zellen oder ganzer Gewebeproben zu
untersuchen. Solche in groĂźen Mengen produzierten
hochdimensionalen Omics-Daten aus der Molekularbiologie lassen sich
zu immer niedrigeren Kosten erzeugen und werden so immer
häufiger auch in klinischen Fragestellungen eingesetzt.
Personalisierte Diagnose oder auch die Vorhersage eines
Behandlungserfolges auf der Basis solcher Hochdurchsatzdaten stellen
eine moderne Anwendung von Techniken aus dem maschinellen Lernen dar.
In der Praxis werden klinische Parameter, wie etwa der
Gesundheitszustand oder die Nebenwirkungen einer Therapie, häufig auf
einer ordinalen Skala erhoben (beispielsweise gut, normal,
schlecht).
Es ist verbreitet, Klassifikationsproblme mit ordinal skaliertem
Endpunkt wie generelle Mehrklassenproblme zu behandeln und somit die
Information, die in der Ordnung zwischen den Klassen enthalten ist, zu
ignorieren. Allerdings kann das Vernachlässigen dieser Information zu
einer verminderten KlassifikationsgĂĽte fĂĽhren oder sogar eine
ungĂĽnstige ungeordnete Klassifikation erzeugen.
Klassische Ansätze, einen ordinal skalierten Endpunkt direkt zu
modellieren, wie beispielsweise mit einem kumulativen Linkmodell,
lassen sich typischerweise nicht auf hochdimensionale Daten anwenden.
Wir präsentieren in dieser Arbeit hierarchical twoing (hi2) als
einen Algorithmus fĂĽr die Klassifikation hochdimensionler Daten in
ordinal Skalierte Kategorien. hi2 nutzt die Mächtigkeit der
sehr gut verstandenen binären Klassifikation, um auch in ordinale
Kategorien zu klassifizieren. Eine Opensource-Implementierung von
hi2 ist online verfĂĽgbar.
In einer Vergleichsstudie zur Klassifikation von echten wie von
simulierten Daten mit ordinalem Endpunkt produzieren etablierte
Methoden, die speziell fĂĽr geordnete Kategorien entworfen wurden,
nicht generell bessere Ergebnisse als state-of-the-art
nicht-ordinale Klassifikatoren. Die Fähigkeit eines Algorithmus, mit
hochdimensionalen Daten umzugehen, dominiert die
Klassifikationsleisting. Wir zeigen, dass unser Algorithmus hi2
konsistent gute Ergebnisse erzielt und in vielen Fällen besser
abschneidet als die anderen Methoden
A Graph Theoretic Clustering Algorithm based on the Regularity Lemma and Strategies to Exploit Clustering for Prediction
The fact that clustering is perhaps the most used technique for exploratory data analysis is only a semaphore that underlines its fundamental importance. The general problem statement that broadly describes clustering as the identification and classification of patterns into coherent groups also implicitly indicates it\u27s utility in other tasks such as supervised learning. In the past decade and a half there have been two developments that have altered the landscape of research in clustering: One is improved results by the increased use of graph theoretic techniques such as spectral clustering and the other is the study of clustering with respect to its relevance in semi-supervised learning i.e. using unlabeled data for improving prediction accuracies. In this work an attempt is made to make contributions to both these aspects. Thus our contributions are two-fold: First, we identify some general issues with the spectral clustering framework and while working towards a solution, we introduce a new algorithm which we call Regularity Clustering which makes an attempt to harness the power of the Szemeredi Regularity Lemma, a remarkable result from extremal graph theory for the task of clustering. Secondly, we investigate some practical and useful strategies for using clustering unlabeled data in boosting prediction accuracy. For all of these contributions we evaluate our methods against existing ones and also apply these ideas in a number of settings
Soundtrack recommendation for images
The drastic increase in production of multimedia content has emphasized the research concerning its organization and retrieval. In this thesis, we address the problem of music retrieval when a set of images is given as input query, i.e., the problem of soundtrack recommendation for images. The task at hand is to recommend appropriate music to be played during the presentation of a given set of query images. To tackle this problem, we formulate a hypothesis that the knowledge appropriate for the task is contained in publicly available contemporary movies. Our approach, Picasso, employs similarity search techniques inside the image and music domains, harvesting movies to form a link between the domains. To achieve a fair and unbiased comparison between different soundtrack recommendation approaches, we proposed an evaluation benchmark. The evaluation results are reported for Picasso and the baseline approach, using the proposed benchmark. We further address two efficiency aspects that arise from the Picasso approach. First, we investigate the problem of processing top-K queries with set-defined selections and propose an index structure that aims at minimizing the query answering latency. Second, we address the problem of similarity search in high-dimensional spaces and propose two enhancements to the Locality Sensitive Hashing (LSH) scheme. We also investigate the prospects of a distributed similarity search algorithm based on LSH using the MapReduce framework. Finally, we give an overview of the PicasSound|a smartphone application based on the Picasso approach.Der drastische Anstieg von verfügbaren Multimedia-Inhalten hat die Bedeutung der Forschung über deren Organisation sowie Suche innerhalb der Daten hervorgehoben. In dieser Doktorarbeit betrachten wir das Problem der Suche nach geeigneten Musikstücken als Hintergrundmusik für Diashows. Wir formulieren die Hypothese, dass die für das Problem erforderlichen Kenntnisse in öffentlich zugänglichen, zeitgenössischen Filmen enthalten sind. Unser Ansatz, Picasso, verwendet Techniken aus dem Bereich der Ähnlichkeitssuche innerhalb von Bild- und Musik-Domains, um basierend auf Filmszenen eine Verbindung zwischen beliebigen Bildern und Musikstücken zu lernen. Um einen fairen und unvoreingenommenen Vergleich zwischen verschiedenen Ansätzen zur Musikempfehlung zu erreichen, schlagen wir einen Bewertungs-Benchmark vor. Die Ergebnisse der Auswertung werden, anhand des vorgeschlagenen Benchmarks, für Picasso und einen weiteren, auf Emotionen basierenden Ansatz, vorgestellt. Zusätzlich behandeln wir zwei Effizienzaspekte, die sich aus dem Picasso Ansatz ergeben. (i) Wir untersuchen das Problem der Ausführung von top-K Anfragen, bei denen die Ergebnismenge ad-hoc auf eine kleine Teilmenge des gesamten Indexes eingeschränkt wird. (ii) Wir behandeln das Problem der Ähnlichkeitssuche in hochdimensionalen Räumen und schlagen zwei Erweiterungen des Lokalitätssensitiven Hashing (LSH) Schemas vor. Zusätzlich untersuchen wir die Erfolgsaussichten eines verteilten Algorithmus für die Ähnlichkeitssuche, der auf LSH unter Verwendung des MapReduce Frameworks basiert. Neben den vorgenannten wissenschaftlichen Ergebnissen beschreiben wir ferner das Design und die Implementierung von PicassSound, einer auf Picasso basierenden Smartphone-Anwendung
Methodology and Algorithms for Pedestrian Network Construction
With the advanced capabilities of mobile devices and the success of car navigation systems, interest in pedestrian navigation systems is on the rise. A critical component of any navigation system is a map database which represents a network (e.g., road networks in car navigation systems) and supports key functionality such as map display, geocoding, and routing. Road networks, mainly due to the popularity of car navigation systems, are well defined and publicly available. However, in pedestrian navigation systems, as well as other applications including urban planning and physical activities studies, road networks do not adequately represent the paths that pedestrians usually travel. Currently, there are no techniques to automatically construct pedestrian networks, impeding research and development of applications requiring pedestrian data. This coupled with the increased demand for pedestrian networks is the prime motivation for this dissertation which is focused on development of a methodology and algorithms that can construct pedestrian networks automatically.
A methodology, which involves three independent approaches, network buffering (using existing road networks), collaborative mapping (using GPS traces collected by volunteers), and image processing (using high-resolution satellite and laser imageries) was developed. Experiments were conducted to evaluate the pedestrian networks constructed by these approaches with a pedestrian network baseline as a ground truth. The results of the experiments indicate that these three approaches, while differing in complexity and outcome, are viable for automatically constructing pedestrian networks
Optimisation approaches for data mining in biological systems
The advances in data acquisition technologies have generated massive amounts of data that present considerable challenge for analysis. How to efficiently and automatically mine through the data and extract the maximum value by identifying the hidden patterns is an active research area, called data mining. This thesis tackles several problems in data mining, including data classification, regression analysis and community detection in complex networks, with considerable applications in various biological systems. First, the problem of data classification is investigated. An existing classifier has been adopted from literature and two novel solution procedures have been proposed, which are shown to improve the predictive accuracy of the original method and significantly reduce the computational time. Disease classification using high throughput genomic data is also addressed. To tackle the problem of analysing large number of genes against small number of samples, a new approach of incorporating extra biological knowledge and constructing higher level composite features for classification has been proposed. A novel model has been introduced to optimise the construction of composite features. Subsequently, regression analysis is considered where two piece-wise linear regression methods have been presented. The first method partitions one feature into multiple complementary intervals and ts each with a distinct linear function. The other method is a more generalised variant of the previous one and performs recursive binary partitioning that permits partitioning of multiple features. Lastly, community detection in complex networks is investigated where a new optimisation framework is introduced to identify the modular structure hidden in directed networks via optimisation of modularity. A non-linear model is firstly proposed before its linearised variant is presented. The optimisation framework consists of two major steps, including solving the non-linear model to identify a coarse initial partition and a second step of solving repeatedly the linearised models to re fine the network partition
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MONITORING MAMMALS AT MULTIPLE SCALES: CASE STUDIES FROM CARNIVORE COMMUNITIES
Carnivores are distributed widely and threatened by habitat loss, poaching, climate change, and disease. They are considered integral to ecosystem function through their direct and indirect interactions with species at different trophic levels. Given the importance of carnivores, it is of high conservation priority to understand the processes driving carnivore assemblages in different systems. It is thus essential to determine the abiotic and biotic drivers of carnivore community composition at different spatial scales and address the following questions: (i) What factors influence carnivore community composition and diversity? (ii) How do the factors influencing carnivore communities vary across spatial and temporal scales? (iii) At local scales, what are the roles of within- and between- guild interactions in structuring carnivore communities? I use carnivore distribution data from protected areas across the global tropics to understand how multiple biodiversity metrics track one another at the regional scale. At the landscape scale, I apply carnivore community data from India to understand how spatiotemporal interactions at the local scale influence carnivore distribution patterns at larger scales. Lastly, at the local scale, I implement a field study of carnivore community occupancy at Kasanka National Park in Zambia using camera traps to understand spatiotemporal patterns of distribution. I employ these studies to delineate the importance of scale in developing a theoretical and applied understanding of community ecology, monitoring mammals at the community level, and conserving carnivore communities. This research shows that carnivore conservation and biodiversity monitoring at the community level is both context-specific and scale dependent
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