Deep Attributes Driven Multi-Camera Person Re-identification

The visual appearance of a person is easily affected by many factors like pose variations, viewpoint changes and camera parameter differences. This makes person Re-Identification (ReID) among multiple cameras a very challenging task. This work is motivated to learn mid-level human attributes which are robust to such visual appearance variations. And we propose a semi-supervised attribute learning framework which progressively boosts the accuracy of attributes only using a limited number of labeled data. Specifically, this framework involves a three-stage training. A deep Convolutional Neural Network (dCNN) is first trained on an independent dataset labeled with attributes. Then it is fine-tuned on another dataset only labeled with person IDs using our defined triplet loss. Finally, the updated dCNN predicts attribute labels for the target dataset, which is combined with the independent dataset for the final round of fine-tuning. The predicted attributes, namely \emph{deep attributes} exhibit superior generalization ability across different datasets. By directly using the deep attributes with simple Cosine distance, we have obtained surprisingly good accuracy on four person ReID datasets. Experiments also show that a simple metric learning modular further boosts our method, making it significantly outperform many recent works.Comment: Person Re-identification; 17 pages; 5 figures; In IEEE ECCV 201

Real-world Human Re-identification: Attributes and Beyond.

PhDSurveillance systems capable of performing a diverse range of tasks that support human intelligence and analytical efforts are becoming widespread and crucial due to increasing threats upon national infrastructure and evolving business and governmental analytical requirements. Surveillance data can be critical for crime-prevention, forensic analysis, and counter-terrorism activities in both civilian and governmental agencies alike. However, visual surveillance data must currently be parsed by trained human operators and therefore any utility is offset by the inherent training and staffing costs as a result. The automated analysis of surveillance video is therefore of great scientific interest. One of the open problems within this area is that of reliably matching humans between disjoint surveillance camera views, termed re-identification. Automated re-identification facilitates human operational efficiency in the grouping of disparate and fragmented people observations through space and time into individual personal identities, a pre-requisite for higher-level surveillance tasks. However, due to the complex nature of realworld scenes and the highly variable nature of human appearance, reliably re-identifying people is non-trivial. Most re-identification approaches developed so far rely on low-level visual feature matching approaches that aim to match human detections against a known gallery of potential matches. However, for many applications an initial detection of a human may be unavailable or a low-level feature representation may not be sufficiently invariant to photometric or geometric variability inherent between camera views. This thesis begins by proposing a “mid-level” human-semantic representation that exploits expert human knowledge of surveillance task execution to the task of re-identifying people in order to compute an attribute-based description of a human. It further shows how this attribute-based description is synergistic with low-level data-derived features to enhance re-identification accuracy and subsequently gain further performance benefits by employing a discriminatively learned distance metric. Finally, a novel “zero-shot” scenario is proposed in which a visual probe is unavailable but re-identification is still possible via a manually provided semantic attribute description. The approach is extensively evaluated using several public benchmark datasets. One challenge in constructing an attribute-based and human-semantic representation is the requirement for extensive annotation. Mitigating this annotation cost in order to present a realistic and scalable re-identification system, is motivation for the second technical area of this thesis, where transfer-learning and data-mining are investigatedin two different approaches. Discriminative methods trade annotation cost for enhanced performance. Because discriminative person re-identification models operate between two camera views, annotation cost therefore scales quadratically on the number of cameras in the entire network. For practical re-identification, this 4 is an unreasonable expectation and prohibitively expensive. By leveraging flexible multi-source transfer of re-identification models, part of this cost may be alleviated. Specifically, it is possible to leverage prior re-identification models learned for a set of source-view pairs (domains), and flexibly combine those to obtain good re-identification performance for a given target-view pair with greatly reduced annotation requirements. The volume of exhaustive annotation effort required for attribute-driven re-identification scales linearly on the number of cameras and attributes. Real-world operation of an attributeenabled, distributed camera network would also require prohibitive quantities of annotation effort by human experts. This effort is completely avoided by taking a data-driven approach to attribute computation, by learning an effective associated representation by crawling large volumes of Internet data. By training on a larger and more diverse array of examples, this representation is more view-invariant and generalisable than attributes trained on conventional scales. These automatically discovered attributes are shown to provide a valuable representation that significantly improves re-identification performance. Moreover, a method to map them onto existing expert-annotated-ontologies is contributed. In the final contribution of this thesis, the underlying assumptions about visual surveillance equipment and re-identification are challenged and the thesis motivates a novel research area using dynamic, mobile platforms. Such platforms violate the common assumption shared by most previous research, namely that surveillance devices are always stationary, relative to the observed scene. The most important new challenge discovered in this exciting area is that the unconstrained video is too challenging for traditional approaches to applying discriminative methods that rely on the explicit modelling of appearance translations when modelling view-pairs, or even a single view. A new dataset was collected by a remote-operated vehicle using control software developed to simulate a fully-autonomous re-identification unmanned aerial vehicle programmed to fly in proximity with humans until images of sufficient quality for re-identification are obtained. Variations of the standard re-identification model are investigated in an enhanced re-identification paradigm, and new challenges with this distinct form of re-identification are elucidated. Finally, conventional wisdom regarding re-identification in light of these observations is re-examined

The Hurricane Exposure, Adversity, and Recovery Tool (HEART): Developing and Validating a Risk Screening Instrument for Youth Exposed to Hurricane Harvey

Given the increasing regularity with which severe (named) hurricanes arise, there is a need for valid, practically useful measures that facilitate child-centered post-hurricane situation analysis and needs assessment. Measures that accurately assess the most potent hurricane-related risk factors are essential to identifying youth at risk for developing posttraumatic stress reactions and providing them with effective post-disaster support. With feedback from community stakeholders (e.g., school personnel, physicians and hospital staff, community clinicians), we developed the Hurricane Exposure, Adversity, and Recovery Tool (HEART), a 29-item self-report measure of hurricane risk factors. Test development procedures included: (1) Reviewing the literature regarding hurricane exposure-related risk factors in youth; (2) Generating a developmentally-informed test item pool; (3) Conducting interviews with clinicians, as well as youth impacted by Hurricane Harvey, to evaluate the comprehensibility and acceptability of candidate items; and (4) evaluating endorsement rates for hurricane exposure-related risk factors among (N = 107) youth in an outpatient clinic specializing in the treatment of childhood trauma and loss. Disaster-related exposure, pre-existing indicators of risk, and ongoing post-disaster adversities were correlated with posttraumatic stress and depressive symptoms. These results provide support for an integrative approach to post-hurricane screening for both hurricane-specific (e.g., witnessing injuries) and non-specific (e.g., prior trauma) factors

Dietary and Pharmacologic Manipulations of Host Lipids and Their Interaction with the Gut Microbiome in Non-Human Primates

The gut microbiome influences nutrient processing as well as host physiology. Plasma lipid levels have been associated with the microbiome, although the underlying mechanisms are largely unknown, and the effects of dietary lipids on the gut microbiome in humans are not well-studied. We used a compilation of four studies utilizing non-human primates (Chlorocebus aethiops and Macaca fascicularis) with treatments that manipulated plasma lipid levels using dietary and pharmacological techniques, and characterized the microbiome using 16S rDNA. High-fat diets significantly reduced alpha diversity (Shannon) and the Firmicutes/Bacteroidetes ratio compared to chow diets, even when the diets had different compositions and were applied in different orders. When analyzed for differential abundance using DESeq2, Bulleidia, Clostridium, Ruminococcus, Eubacterium, Coprocacillus, Lachnospira, Blautia, Coprococcus, and Oscillospira were greater in both chow diets while Succinivibrio, Collinsella, Streptococcus, and Lactococcus were greater in both high-fat diets (oleic blend or lard fat source). Dietary cholesterol levels did not affect the microbiome and neither did alterations of plasma lipid levels through treatments of miR-33 antisense oligonucleotide (anti-miR-33), Niemann–Pick C1-Like 1 (NPC1L1) antisense oligonucleotide (ASO), and inducible degrader of LDLR (IDOL) ASO. However, a liver X receptor (LXR) agonist shifted the microbiome and decreased bile acid levels. Fifteen genera increased with the LXR agonist, while seven genera decreased. Pseudomonas increased on the LXR agonist and was negatively correlated to deoxycholic acid, cholic acid, and total bile acids while Ruminococcus was positively correlated with taurolithocholic acid and taurodeoxycholic acid. Seven of the nine bile acids identified in the feces significantly decreased due to the LXR agonist, and total bile acids (nmol/g) was reduced by 62%. These results indicate that plasma lipid levels have, at most, a modest effect on the microbiome, whereas bile acids, derived in part from plasma lipids, are likely responsible for the indirect relationship between lipid levels and the microbiome

Metabolic adaptation to weight loss: implications for the athlete

Abstract Optimized body composition provides a competitive advantage in a variety of sports. Weight reduction is common among athletes aiming to improve their strength-to-mass ratio, locomotive efficiency, or aesthetic appearance. Energy restriction is accompanied by changes in circulating hormones, mitochondrial efficiency, and energy expenditure that serve to minimize the energy deficit, attenuate weight loss, and promote weight regain. The current article reviews the metabolic adaptations observed with weight reduction and provides recommendations for successful weight reduction and long term reduced-weight maintenance in athletes

Grazing Away the Amazon

https://digitalcommons.wpi.edu/gps-posters/1764/thumbnail.jp

Status of ISS Water Management and Recovery

Water management on ISS is responsible for the provision of water to the crew for drinking water, food preparation, and hygiene, to the Oxygen Generation System (OGS) for oxygen production via electrolysis, to the Waste & Hygiene Compartment (WHC) for flush water, and for experiments on ISS. This paper summarizes water management activities on the ISS US Segment as of May 2018 and provides a status of the performance and issues related to the operation of the Water Processor Assembly (WPA) and Urine Processor Assembly (UPA)

Status of ISS Water Management and Recovery

Water management on ISS is responsible for the provision of water to the crew for drinking water, food preparation, and hygiene, to the Oxygen Generation System (OGS) for oxygen production via electrolysis, to the Waste & Hygiene Compartment (WHC) for flush water, and for experiments on ISS. This paper summarizes water management activities on the ISS US Segment and provides a status of the performance and issues related to the operation of the Water Processor Assembly (WPA) and Urine Processor Assembly (UPA). This paper summarizes the on-orbit status as of June 2017 and describes the technical challenges encountered and lessons learned over the past year

beta-Hydroxy-beta-methylbutyrate free acid reduces markers of exercise-induced muscle damage and improves recovery in resistance-trained men

The purpose of the present study was to determine the effects of short-term supplementation with the free acid form of beta-hydroxy-beta-methylbutyrate (HMB-FA) on indices of muscle damage, protein breakdown, recovery and hormone status following a high-volume resistance training session in trained athletes. A total of twenty resistance-trained males were recruited to participate in a high-volume resistance training session centred on full squats, bench presses and dead lifts. Subjects were randomly assigned to receive either 3 g/d of HMB-FA or a placebo. Immediately before the exercise session and 48 h post-exercise, serum creatine kinase (CK), urinary 3-methylhistadine (3-MH), testosterone, cortisol and perceived recovery status (PRS) scale measurements were taken. The results showed that CK increased to a greater extent in the placebo (329%) than in the HMB-FA group (104%) (P=0.004, d=1.6). There was also a significant change for PRS, which decreased to a greater extent in the placebo (9.1 (SEM 0.4) to 4.6 (SEM 0.5)) than in the HMB-FA group (9.1 (SEM 0.3) to 6.3 (SEM 0.3)) (P=0.005, d = -0.48). Muscle protein breakdown, measured by 3-MH analysis, numerically decreased with HMB-FA supplementation and approached significance (P=0.08, d = 0.12). There were no acute changes in plasma total or free testosterone, cortisol or C-reactive protein. In conclusion, these results suggest that an HMB-FA supplement given to trained athletes before exercise can blunt increases in muscle damage and prevent declines in perceived readiness to train following a high-volume, muscle-damaging resistance-training session