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

Vehicle Thermal Control with a Variable Area Inlet

This study developed a variable area inlet and controller that regulated the temperature of an electrical component with ram air. The intent of the variable area inlet was to reduce vehicle drag by eliminating inefficiencies associated with component cooling and fixed area inlets. These inefficiencies arise from vehicles moving at varying speeds through varying air temperatures. The hardware model consisted of an electrical component mounted inside a right-circular cylindrical duct. The variable area inlet, mounted in the front of the duct, consisted of a butterfly valve that was actuated by a stepper controller acted on the feedback signal of a thermocouple that was mounted on the electrical component. The system was successful in regulating the component temperature. A nonlinear simulation model was built and the thermal plant in the simulation was based on the electrical components empirically derived Nusselt number. Proportional, Proportional-Derivative (PD), and Proportional-Integral-Derivative (PID) controllers were built and tested. The PD and PID controllers did not appear to need any gain scheduling for the varying speed and temperature conditions. Lastly, a general design process was detailed. (AN

Race vs. threat: how teens perceive violence as a function of race

The relationship between race students’ perceptions of threat was examined using an examiner-made questionnaire with WV 6th, 9th, and 12th grade students. Eleven ambiguous scenarios and eight demographic questions were rated to measure the level of threat perceived by subjects. The results indicated that both Minority and Caucasian students found both black and white students the threatening

An SMP Soft Classification Algorithm for Remote Sensing

This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative guided spectral class rejection (CIGSCR) algorithm, a semiautomated classification algorithm for remote sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classification containing inherently more information than a comparable hard classification at an increased computational cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel algorithm development work here. Experimental results of applying parallel CIGSCR to an image with approximately 10^8 pixels and six bands demonstrate superlinear speedup. A soft two class classification is generated in just over four minutes using 32 processors

Adjusting process count on demand for petascale global optimization⋆

There are many challenges that need to be met before efficient and reliable computation at the petascale is possible. Many scientific and engineering codes running at the petascale are likely to be memory intensive, which makes thrashing a serious problem for many petascale applications. One way to overcome this challenge is to use a dynamic number of processes, so that the total amount of memory available for the computation can be increased on demand. This paper describes modifications made to the massively parallel global optimization code pVTdirect in order to allow for a dynamic number of processes. In particular, the modified version of the code monitors memory use and spawns new processes if the amount of available memory is determined to be insufficient. The primary design challenges are discussed, and performance results are presented and analyzed

Parallel Deterministic and Stochastic Global Minimization of Functions with Very Many Minima

The optimization of three problems with high dimensionality and many local minima are investigated under five different optimization algorithms: DIRECT, simulated annealing, Spall’s SPSA algorithm, the KNITRO package, and QNSTOP, a new algorithm developed at Indiana University

Effect of reheating on predictions following multiple-field inflation

We study the sensitivity of cosmological observables to the reheating phase following inflation driven by many scalar fields. We describe a method which allows semi-analytic treatment of the impact of perturbative reheating on cosmological perturbations using the sudden decay approximation. Focusing on $\mathcal{N}$-quadratic inflation, we show how the scalar spectral index and tensor-to-scalar ratio are affected by the rates at which the scalar fields decay into radiation. We find that for certain choices of decay rates, reheating following multiple-field inflation can have a significant impact on the prediction of cosmological observables.Comment: Published in PRD. 4 figures, 10 page

Increased resistance of plants to pathogens from multiple higher-order phylogenetic lineages

Transgenic plants, plant tissue, and propagation materials are disclosed that exhibit or convey increased resistance to pathogens of multiple higher-order phylogenetic lineages. The disclosed transgenic plants and plant tissues include plant cells containing a DNA construct encoding Gastrodia Anti-Fungal Protein (GAFP), also known as gastrodianin, an anti-fungal gene naturally occurring in a Chinese orchid, Gastrodia elata. Transgenic plants disclosed include herbaceous plants as well as woody plants, including fruit trees. Disclosed transgenic plants can also be beneficially utilized as rootstock, for instance rootstock for stone fruit crops such as peach, thereby conferring enhanced disease resistance to the rootstock without genetically altering the scion

WHEN A FAMILY MEMBER HAS A SCHIZOPHRENIC DISORDER: Practice Issues Across the Family Life Cycle

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72369/1/h0080366.pd