Deep Perceptual Mapping for Thermal to Visible Face Recognition

Cross modal face matching between the thermal and visible spectrum is a much de- sired capability for night-time surveillance and security applications. Due to a very large modality gap, thermal-to-visible face recognition is one of the most challenging face matching problem. In this paper, we present an approach to bridge this modality gap by a significant margin. Our approach captures the highly non-linear relationship be- tween the two modalities by using a deep neural network. Our model attempts to learn a non-linear mapping from visible to thermal spectrum while preserving the identity in- formation. We show substantive performance improvement on a difficult thermal-visible face dataset. The presented approach improves the state-of-the-art by more than 10% in terms of Rank-1 identification and bridge the drop in performance due to the modality gap by more than 40%.Comment: BMVC 2015 (oral

MUSCLE: A Simulation Toolkit Modeling Low Energy Muon Beam Transport in Crystals

The project involves the development of MUSCLE (MUonS Cascade at Low Energy), a collection of programs written in C++ and Mathematica to numerically simulate the passage of low energy muon beams through crystals. Monte Carlo methods employing binary collision approximation calculations and appropriate molecular dynamics algorithms are implemented to construct the trajectories and determine the spatial distribution of stopped muons in single crystals. Channeling of muon particles along certain crystal planes are also found. Binary collision approximation and molecular dynamics algorithms are compared and the possible effect of channeling is discussed

An Ontological Foundation for Agile Modeling with UML

Some proponents of agile systems development have advocated for agile conceptual modeling for requirements analysis. Agile modeling focuses on creating simple models that focus on key requirements. The Unified Modeling Language (UML) is used in agile modeling, but using UML in an agile fashion requires that modelers be selective in choosing constructs consistent with agile principles such as maintaining simplicity and minimal modeling. This research aims to provide a theoretical foundation for choosing UML constructs for agile modeling. We perform an ontology-based analysis of UML modeling constructs to prioritize them for use in agile modeling. We propose that UML constructs that correspond to more primitive ontological concepts are more useful for creating agile models than constructs that represent derived concepts. We have identified a core group of UML constructs that correspond to primitive ontological concepts and argue that agile modelers will find these constructs more useful in modeling problem domains

A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking

Person re identification is a challenging retrieval task that requires matching a person's acquired image across non overlapping camera views. In this paper we propose an effective approach that incorporates both the fine and coarse pose information of the person to learn a discriminative embedding. In contrast to the recent direction of explicitly modeling body parts or correcting for misalignment based on these, we show that a rather straightforward inclusion of acquired camera view and/or the detected joint locations into a convolutional neural network helps to learn a very effective representation. To increase retrieval performance, re-ranking techniques based on computed distances have recently gained much attention. We propose a new unsupervised and automatic re-ranking framework that achieves state-of-the-art re-ranking performance. We show that in contrast to the current state-of-the-art re-ranking methods our approach does not require to compute new rank lists for each image pair (e.g., based on reciprocal neighbors) and performs well by using simple direct rank list based comparison or even by just using the already computed euclidean distances between the images. We show that both our learned representation and our re-ranking method achieve state-of-the-art performance on a number of challenging surveillance image and video datasets. The code is available online at: https://github.com/pse-ecn/pose-sensitive-embeddingComment: CVPR 2018: v2 (fixes, added new results on PRW dataset

Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model

Pedestrian attribute inference is a demanding problem in visual surveillance that can facilitate person retrieval, search and indexing. To exploit semantic relations between attributes, recent research treats it as a multi-label image classification task. The visual cues hinting at attributes can be strongly localized and inference of person attributes such as hair, backpack, shorts, etc., are highly dependent on the acquired view of the pedestrian. In this paper we assert this dependence in an end-to-end learning framework and show that a view-sensitive attribute inference is able to learn better attribute predictions. Our proposed model jointly predicts the coarse pose (view) of the pedestrian and learns specialized view-specific multi-label attribute predictions. We show in an extensive evaluation on three challenging datasets (PETA, RAP and WIDER) that our proposed end-to-end view-aware attribute prediction model provides competitive performance and improves on the published state-of-the-art on these datasets.Comment: accepted BMVC 201

Master of Science

thesisIt is common to extract isosurfaces from simulation eld data to visualize and gain understanding of the underlying physical phenomenon being simulated. As the input parameters of the simulation change, the resulting isosurface varies, and there has been increased interest in quantifying and visualization of these variations as part of the larger interest in uncertainty quantification. In this thesis, we propose an analysis and visualization pipeline for examining the intrinsic variation in isosurfaces caused by simulation parameter perturbation. Drawing inspiration from the shape modeling community, we incorporate the use of heat-kernel signatures (HKS) with a simple nite-difference approach for quantifying the degree to which a region (or even a point) on an isosurface has undergone intrinsic change. Coupled with a clustering technique and the use of color maps, our pipeline allows the user to select the level of fidelity with which they wish to evaluate and visualize the amount of intrinsic change. The pipeline is described with a simple example to walk the reader through the different steps, and experimental validation of parameter choices in the pipeline is provided to justify our design. Then we present canonical and simulation examples to demonstrate the pipeline's use in different applications

Resveratrol Induced Apoptosis in a Human Adenosquamous Carcinoma Cell Line (CAL-27 Cells)

Radiotherapy and surgery are the two principal modalities in the treatment of head and neck cancers, and both therapies can result in severe adverse effects and ultimately lower the quality of life. It is of paramount importance to develop reagents that target the cancer cell specifically without affecting the normal non-cancer cells. Using the tongue cancer cell line Cal 27 as a model system, we dissected the molecular mechanism of the resveratrol-induced cancer cell apoptosis. After demonstrating that resveratrol induces the cancer cell apoptosis in a dose- and time-dependent manner, a systemic apoptosis protein array was conducted to identify the resveratrol-induced proteins pertinent to the apoptotic pathways. Ten of the 43 proteins included in the array were up- or down-regulated by resveratrol by about 50 percent. Finally, the activation of caspase-3 and the cleavage of PARP in resveratrol-induced apoptotic cells were confirmed by western blot. We postulate resveratrol induces apoptosis in Cal-27 cells which will render the cells from being able to repair double-stranded-break in the DNA as both P53 and P21 will be up regulated and thus leading to senesce of the cell replication, suggesting that resveratrol could potentially serve as a chemo-preventive reagent