Learning Action Maps of Large Environments via First-Person Vision

When people observe and interact with physical spaces, they are able to associate functionality to regions in the environment. Our goal is to automate dense functional understanding of large spaces by leveraging sparse activity demonstrations recorded from an ego-centric viewpoint. The method we describe enables functionality estimation in large scenes where people have behaved, as well as novel scenes where no behaviors are observed. Our method learns and predicts "Action Maps", which encode the ability for a user to perform activities at various locations. With the usage of an egocentric camera to observe human activities, our method scales with the size of the scene without the need for mounting multiple static surveillance cameras and is well-suited to the task of observing activities up-close. We demonstrate that by capturing appearance-based attributes of the environment and associating these attributes with activity demonstrations, our proposed mathematical framework allows for the prediction of Action Maps in new environments. Additionally, we offer a preliminary glance of the applicability of Action Maps by demonstrating a proof-of-concept application in which they are used in concert with activity detections to perform localization.Comment: To appear at CVPR 201

Assessment of treatment response in tuberculosis

Antibiotic treatment of tuberculosis has a duration of several months. There is significant variability of the host immune response and the pharmacokinetic-pharmacodynamic properties of Mycobacterium tuberculosis sub-populations at the site of disease. A limitation of sputum-based measures of treatment response may be sub-optimal detection and monitoring of Mycobacterium tuberculosis sub-populations. Potential biomarkers and surrogate endpoints should be benchmarked against hard clinical outcomes (failure/relapse/death) and may need tailoring to specific patient populations. Here, we assess the evidence supporting currently utilized and future potential host and pathogen-based models and biomarkers for monitoring treatment response in active and latent tuberculosis. Biomarkers for monitoring treatment response in extrapulmonary, pediatric and drug resistant tuberculosis are research priorities

UV friendly T-parity in the SU(6)/Sp(6) little Higgs model

Electroweak precision tests put stringent constraints on the parameter space of little Higgs models. Tree-level exchange of TeV scale particles in a generic little Higgs model produce higher dimensional operators that make contributions to electroweak observables that are typically too large. To avoid this problem a discrete symmetry dubbed T-parity can be introduced to forbid the dangerous couplings. However, it was realized that in simple group models such as the littlest Higgs model, the implementation of T-parity in a UV completion could present some challenges. The situation is analogous to the one in QCD where the pion can easily be defined as being odd under a new $Z_2$ symmetry in the chiral Lagrangian, but this $Z_2$ is not a symmetry of the quark Lagrangian. In this paper we examine the possibility of implementing a T-parity in the low energy $SU(6)/Sp(6)$ model that might be easier to realize in the UV. In our model, the T-parity acts on the low energy non-linear sigma model field in way which is different to what was originally proposed for the Littlest Higgs, and lead to a different low energy theory. In particular, the Higgs sector of this model is a inert two Higgs doublets model with an approximate custodial symmetry. We examine the contributions of the various sectors of the model to electroweak precision data, and to the dark matter abundance.Comment: 21 pages,4 figures. Clarifications added, typos corrected and references added. Published in JHE

Effects of the Interactions Between LPS and BIM on Workflow in Two Building Design Projects

Variability in design workflow causes delays and undermines the performance of building projects. As lean processes, the Last Planner System (LPS) and Building Information Modeling (BIM) can improve workflow in building projects through features that reduce waste. Since its introduction, BIM has had significant positive influence on workflow in building design projects, but these have been rarely considered in combination with LPS. This paper is part of a postgraduate research focusing on the implementation of LPS weekly work plans in two BIM-based building design projects to achieve better workflow. It reports on the interactions between lean principles of LPS and BIM functionalities in two building design projects that, from the perspective of an interaction matrix developed by Sacks et al. (2010a), promote workflow

Four lectures on probabilistic methods for data science

Methods of high-dimensional probability play a central role in applications for statistics, signal processing theoretical computer science and related fields. These lectures present a sample of particularly useful tools of high-dimensional probability, focusing on the classical and matrix Bernstein's inequality and the uniform matrix deviation inequality. We illustrate these tools with applications for dimension reduction, network analysis, covariance estimation, matrix completion and sparse signal recovery. The lectures are geared towards beginning graduate students who have taken a rigorous course in probability but may not have any experience in data science applications.Comment: Lectures given at 2016 PCMI Graduate Summer School in Mathematics of Data. Some typos, inaccuracies fixe

Restart-Based Fault-Tolerance: System Design and Schedulability Analysis

Embedded systems in safety-critical environments are continuously required to deliver more performance and functionality, while expected to provide verified safety guarantees. Nonetheless, platform-wide software verification (required for safety) is often expensive. Therefore, design methods that enable utilization of components such as real-time operating systems (RTOS), without requiring their correctness to guarantee safety, is necessary. In this paper, we propose a design approach to deploy safe-by-design embedded systems. To attain this goal, we rely on a small core of verified software to handle faults in applications and RTOS and recover from them while ensuring that timing constraints of safety-critical tasks are always satisfied. Faults are detected by monitoring the application timing and fault-recovery is achieved via full platform restart and software reload, enabled by the short restart time of embedded systems. Schedulability analysis is used to ensure that the timing constraints of critical plant control tasks are always satisfied in spite of faults and consequent restarts. We derive schedulability results for four restart-tolerant task models. We use a simulator to evaluate and compare the performance of the considered scheduling models