Trespassing the Boundaries: Labeling Temporal Bounds for Object Interactions in Egocentric Video

Manual annotations of temporal bounds for object interactions (i.e. start and end times) are typical training input to recognition, localization and detection algorithms. For three publicly available egocentric datasets, we uncover inconsistencies in ground truth temporal bounds within and across annotators and datasets. We systematically assess the robustness of state-of-the-art approaches to changes in labeled temporal bounds, for object interaction recognition. As boundaries are trespassed, a drop of up to 10% is observed for both Improved Dense Trajectories and Two-Stream Convolutional Neural Network. We demonstrate that such disagreement stems from a limited understanding of the distinct phases of an action, and propose annotating based on the Rubicon Boundaries, inspired by a similarly named cognitive model, for consistent temporal bounds of object interactions. Evaluated on a public dataset, we report a 4% increase in overall accuracy, and an increase in accuracy for 55% of classes when Rubicon Boundaries are used for temporal annotations.Comment: ICCV 201

Investigating Uranium incorporation in modern carbonates by sequential extraction: Applied to the Permian - Triassic boundary in Lung Cam, Vietnam

The Uranium (U) isotopic system can be used to model the extent of global-scale ocean anoxia by utilizing the 238U/235U ratios as a paleo-redox indicator (δ238U). While recent studies have shown promise with the use of this novel proxy, variability is seen in modern carbonate sediment samples suggesting that more work is needed in order to understand elemental U uptake during early marine diagenesis. This thesis utilizes a sequential extraction methodology in order to understand the distribution of authigenic U within carbonate sediments. This thesis consists of four parts, (1) an evaluation and modification of a sequential extraction methodology for U uptake in modern carbonate sediments, (2) application of the modified sequential extraction method to the study U distribution within chemical fractions within Bahamian bulk sediments, (3) the application of the modified methodology to study the U distribution across the Permian–Triassic boundary from the Lung Cam section in Northern Vietnam, and (4) the implications of authigenic U toward the δ238U paleo-redox marker. Results show that a sequential extraction can be successful within carbonate sediments. The results of this sequential extraction shows that the majority of authigenic U is found within the exchangeable and carbonate fraction. This thesis hypothesizes that this U component is a non-crystalline U(IV) species. Furthermore, this authigenic U component was also found within the Permian–Triassic section located in Lung Cam, Vietnam, thus illustrating preservation of heavy authigenic U within the rock record

Estimating Effects and Making Predictions from Genome-Wide Marker Data

In genome-wide association studies (GWAS), hundreds of thousands of genetic markers (SNPs) are tested for association with a trait or phenotype. Reported effects tend to be larger in magnitude than the true effects of these markers, the so-called ``winner's curse.'' We argue that the classical definition of unbiasedness is not useful in this context and propose to use a different definition of unbiasedness that is a property of the estimator we advocate. We suggest an integrated approach to the estimation of the SNP effects and to the prediction of trait values, treating SNP effects as random instead of fixed effects. Statistical methods traditionally used in the prediction of trait values in the genetics of livestock, which predates the availability of SNP data, can be applied to analysis of GWAS, giving better estimates of the SNP effects and predictions of phenotypic and genetic values in individuals.Comment: Published in at http://dx.doi.org/10.1214/09-STS306 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Data Understanding Applied to Optimization

The goal of this research is to explore and develop software for supporting visualization and data analysis of search and optimization. Optimization is an ever-present problem in science. The theory of NP-completeness implies that the problems can only be resolved by increasingly smarter problem specific knowledge, possibly for use in some general purpose algorithms. Visualization and data analysis offers an opportunity to accelerate our understanding of key computational bottlenecks in optimization and to automatically tune aspects of the computation for specific problems. We will prototype systems to demonstrate how data understanding can be successfully applied to problems characteristic of NASA's key science optimization tasks, such as central tasks for parallel processing, spacecraft scheduling, and data transmission from a remote satellite

Learning Temporal Sentence Grounding From Narrated EgoVideos

The onset of long-form egocentric datasets such as Ego4D and EPIC-Kitchens presents a new challenge for the task of Temporal Sentence Grounding (TSG). Compared to traditional benchmarks on which this task is evaluated, these datasets offer finer-grained sentences to ground in notably longer videos. In this paper, we develop an approach for learning to ground sentences in these datasets using only narrations and their corresponding rough narration timestamps. We propose to artificially merge clips to train for temporal grounding in a contrastive manner using text-conditioning attention. This Clip Merging (CliMer) approach is shown to be effective when compared with a high performing TSG method -- e.g. mean R@1 improves from 3.9 to 5.7 on Ego4D and from 10.7 to 13.0 on EPIC-Kitchens. Code and data splits available from: https://github.com/keflanagan/CliMerComment: Accepted in BMVC 202

Multi-locus models of genetic risk of disease

Background: Evidence for genetic contribution to complex diseases is described by recurrence risks to relatives of diseased individuals. Genome-wide association studies allow a description of the genetics of the same diseases in terms of risk loci, their effects and allele frequencies. To reconcile the two descriptions requires a model of how risks from individual loci combine to determine an individual's overall risk