An Iterative Beam Search Algorithm for Degenerate Primer Selection

Single Nucleotide Polymorphism (SNP) Genotyping is an important molecular genetics process in the early stages of producing results that will be useful in the medical field. Due to inherent complexities in DNA manipulation and analysis, many different methods have been proposed for a standard assay. One of the proposed techniques for performing SNP Genotyping requires amplifying regions of DNA surrounding a large number of SNP loci. In order to automate a portion of this particular method, it is necessary to select a set of primers for the experiment. Selecting these primers can be formulated as the Multiple Degenerate Primer Design (MDPD) problem. In this thesis, we describe an iterative beam-search algorithm, Multiple, It-erative Primer Selector (MIPS), for MDPD. Theoretical and experimental analyses show that this algorithm performs well compared to the limits of degenerate primer design. Furthermore, MIPS outperforms an existing algorithm which was designed for a related degenerate primer selection problem. Further analysis shows that, due to the composition of the human genome, the results from MIPS may not be realized in practice. Consequently, we address the challenges involved in selecting a suitable set of degenerate primers and possible future improvements to the algorithm

Manifold Learning for Natural Image Sets, Doctoral Dissertation August 2006

The field of manifold learning provides powerful tools for parameterizing high-dimensional data points with a small number of parameters when this data lies on or near some manifold. Images can be thought of as points in some high-dimensional image space where each coordinate represents the intensity value of a single pixel. These manifold learning techniques have been successfully applied to simple image sets, such as handwriting data and a statue in a tightly controlled environment. However, they fail in the case of natural image sets, even those that only vary due to a single degree of freedom, such as a person walking or a heart beating. Parameterizing data sets such as these will allow for additional constraints on traditional computer vision problems such as segmentation and tracking. This dissertation explores the reasons why classical manifold learning algorithms fail on natural image sets and proposes new algorithms for parameterizing this type of data

Hotels-50K: A Global Hotel Recognition Dataset

Recognizing a hotel from an image of a hotel room is important for human trafficking investigations. Images directly link victims to places and can help verify where victims have been trafficked, and where their traffickers might move them or others in the future. Recognizing the hotel from images is challenging because of low image quality, uncommon camera perspectives, large occlusions (often the victim), and the similarity of objects (e.g., furniture, art, bedding) across different hotel rooms. To support efforts towards this hotel recognition task, we have curated a dataset of over 1 million annotated hotel room images from 50,000 hotels. These images include professionally captured photographs from travel websites and crowd-sourced images from a mobile application, which are more similar to the types of images analyzed in real-world investigations. We present a baseline approach based on a standard network architecture and a collection of data-augmentation approaches tuned to this problem domain

An Architecture to Support Learning-based Adaptation of Persistent Queries in Mobile Environments

Queries are frequently used by applications in dynamically formed mobile networks to discover and acquire information and services available in the surrounding environment. A number of inquiry strategies exist, each of which embodies an approach to disseminating a query and collecting results. The choice of inquiry strategy has different tradeoffs under different operating conditions. Therefore, it is beneficial to allow a query-based application to dynamically adapt its inquiry strategy to the changing environmental conditions. To promote development by non-expert domain programmers, we can automate the decision-making process associated with adapting the inquiry strategy. In this paper, we propose an architecture to support automated adaptative query processing for dynamic mobile environments. The decision-support module of our architecture relies on an instance-based learning approach to support context-aware adaptation of the inquiry strategy

Large-Scale Geo-Facial Image Analysis

While face analysis from images is a well-studied area, little work has explored the dependence of facial appearance on the geographic location from which the image was captured. To fill this gap, we constructed GeoFaces, a large dataset of geotagged face images, and used it to examine the geo-dependence of facial features and attributes, such as ethnicity, gender, or the presence of facial hair. Our analysis illuminates the relationship between raw facial appearance, facial attributes, and geographic location, both globally and in selected major urban areas. Some of our experiments, and the resulting visualizations, confirm prior expectations, such as the predominance of ethnically Asian faces in Asia, while others highlight novel information that can be obtained with this type of analysis, such as the major city with the highest percentage of people with a mustache