Object-based, distributed online real-time communication system modeling and development.

With the advent of the Internet and World Wide Web, online communication becomes increasingly popular. As broadband network technologies are becoming widely accessible, the media rich, highly interactive online applications for virtual enterprise, distance education etc. will be a reality. How to take full advantages of technologies available and effectively integrate them into a cohesive distributed distance education system that addresses adequately the scalability and the openness still pose a challenging question. To address this need, we have developed an approach based on the principles of component-based development, utilizing standard component infrastructure and its services. We apply this approach to the online real-time communication system with capability of rich real-time-interactions, which addresses key requirements for distance learning. We provide a rigorous modeling and specification of the system, which describes the attributes that the distributed system must exhibit and prescribes the behavior of the system. This modeling shall allow software engineers to examine the behavior of systems under the development. Based on this specification, we have developed a prototype of the distributed system to validate the effectiveness of our approach. The system is implemented with Java (JDK1.2) and the middleware is CORBA. The system is capable of handling multi-party real-time communication for a range of media types in a uniform manner. This system encompasses management facility such as authentication and allows dynamic services generation. Running tests of this system in both Intranet and Internet environment have shown satisfactory results of this approach. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2000 .S52. Source: Masters Abstracts International, Volume: 39-02, page: 0532. Adviser: Indra A. Tjandra. Thesis (M.Sc.)--University of Windsor (Canada), 2000

BASAR:Black-box Attack on Skeletal Action Recognition

Skeletal motion plays a vital role in human activity recognition as either an independent data source or a complement. The robustness of skeleton-based activity recognizers has been questioned recently, which shows that they are vulnerable to adversarial attacks when the full-knowledge of the recognizer is accessible to the attacker. However, this white-box requirement is overly restrictive in most scenarios and the attack is not truly threatening. In this paper, we show that such threats do exist under black-box settings too. To this end, we propose the first black-box adversarial attack method BASAR. Through BASAR, we show that adversarial attack is not only truly a threat but also can be extremely deceitful, because on-manifold adversarial samples are rather common in skeletal motions, in contrast to the common belief that adversarial samples only exist off-manifold. Through exhaustive evaluation and comparison, we show that BASAR can deliver successful attacks across models, data, and attack modes. Through harsh perceptual studies, we show that it achieves effective yet imperceptible attacks. By analyzing the attack on different activity recognizers, BASAR helps identify the potential causes of their vulnerability and provides insights on what classifiers are likely to be more robust against attack. Code is available at https://github.com/realcrane/BASAR-Black-box-Attack-on-Skeletal-Action-Recognition.Comment: Accepted in CVPR 202

MageAdd: Real-Time Interaction Simulation for Scene Synthesis

While recent researches on computational 3D scene synthesis have achieved impressive results, automatically synthesized scenes do not guarantee satisfaction of end users. On the other hand, manual scene modelling can always ensure high quality, but requires a cumbersome trial-and-error process. In this paper, we bridge the above gap by presenting a data-driven 3D scene synthesis framework that can intelligently infer objects to the scene by incorporating and simulating user preferences with minimum input. While the cursor is moved and clicked in the scene, our framework automatically selects and transforms suitable objects into scenes in real time. This is based on priors learnt from the dataset for placing different types of objects, and updated according to the current scene context. Through extensive experiments we demonstrate that our framework outperforms the state-of-the-art on result aesthetics, and enables effective and efficient user interactions

Fast 3D Indoor Scene Synthesis by Learning Spatial Relation Priors of Objects

We present a framework for fast synthesizing indoor scenes, given a room geometry and a list of objects with learnt priors.Unlike existing data-driven solutions, which often learn priors by co-occurrence analysis and statistical model fitting, our methodmeasures the strengths of spatial relations by tests for complete spatial randomness (CSR), and learns discrete priors based onsamples with the ability to accurately represent exact layout patterns. With the learnt priors, our method achieves both acceleration andplausibility by partitioning the input objects into disjoint groups, followed by layout optimization using position-based dynamics (PBD)based on the Hausdorff metric. Experiments show that our framework is capable of measuring more reasonable relations amongobjects and simultaneously generating varied arrangements in seconds compared with the state-of-the-art works.</p

EyelashNet: A Dataset and A Baseline Method for Eyelash Matting

Eyelashes play a crucial part in the human facial structure and largely affect the facial attractiveness in modern cosmetic design. However, the appearance and structure of eyelashes can easily induce severe artifacts in high-fidelity multi-view 3D face reconstruction. Unfortunately it is highly challenging to remove eyelashes from portrait images using both traditional and learning-based matting methods due to the delicate nature of eyelashes and the lack of eyelash matting dataset. To this end, we present EyelashNet, the first eyelash matting dataset which contains 5,400 high-quality eyelash matting data captured from real world and 5,272 virtual eyelash matting data created by rendering avatars. Our work consists of a capture stage and an inference stage to automatically capture and annotate eyelashes instead of tedious manual efforts. The capture is based on a specifically-designed fluorescent labeling system. By coloring the eyelashes with a safe and invisible fluorescent substance, our system takes paired photos with colored and normal eyelashes by turning the equipped ultraviolet (UVA) flash on and off. We further correct the alignment between each pair of photos and use a novel alpha matte inference network to extract the eyelash alpha matte. As there is no prior eyelash dataset, we propose a progressive training strategy that progressively fuses captured eyelash data with virtual eyelash data to learn the latent semantics of real eyelashes. As a result, our method can accurately extract eyelash alpha mattes from fuzzy and self-shadow regions such as pupils, which is almost impossible by manual annotations. To validate the advantage of EyelashNet, we present a baseline method based on deep learning that achieves state-of-the-art eyelash matting performance with RGB portrait images as input. We also demonstrate that our work can largely benefit important real applications including high-fidelity personalized avatar and cosmetic design

Effects of Melanocortin 3 and 4 Receptor Deficiency on Energy Homeostasis in Rats

Melanocortin-3 and 4 receptors (MC3R and MC4R) can regulate energy homeostasis, but their respective roles especially the functions of MC3R need more exploration. Here Mc3r and Mc4r single and double knockout (DKO) rats were generated using CRISPR-Cas9 system. Metabolic phenotypes were examined and data were compared systematically. Mc3r KO rats displayed hypophagia and decreased body weight, while Mc4r KO and DKO exhibited hyperphagia and increased body weight. All three mutants showed increased white adipose tissue mass and adipocyte size. Interestingly, although Mc3r KO did not show a significant elevation in lipids as seen in Mc4r KO, DKO displayed even higher lipid levels than Mc4r KO. DKO also showed more severe glucose intolerance and hyperglycaemia than Mc4r KO. These data demonstrated MC3R deficiency caused a reduction of food intake and body weight, whereas at the same time exhibited additive effects on top of MC4R deficiency on lipid and glucose metabolism. This is the first phenotypic analysis and systematic comparison of Mc3r KO, Mc4r KO and DKO rats on a homogenous genetic background. These mutant rats will be important in defining the complicated signalling pathways of MC3R and MC4R. Both Mc4r KO and DKO are good models for obesity and diabetes research

Electroacupuncture Regulates Pain Transition Through Inhibiting PKCε and TRPV1 Expression in Dorsal Root Ganglion

Many cases of acute pain can be resolved with few side effects. However, some cases of acute pain may persist beyond the time required for tissue injury recovery and transit to chronic pain, which is hard to treat. The mechanisms underlying pain transition are not entirely understood, and treatment strategies are lacking. In this study, the hyperalgesic priming model was established on rats to study pain transition by injection of carrageenan (Car) and prostaglandin E2 (PGE2). The expression levels of protein kinase C epsilon (PKCε) and transient receptor potential vanilloid 1 (TRPV1) in the L4-L6 dorsal root ganglion (DRG) were investigated. Electroacupuncture (EA) is a form of acupuncture in which a small electric current is passed between a pair of acupuncture needles. EA was administrated, and its effect on hyperalgesia and PKCε and TRPV1 expression was investigated. The PKCε-TRPV1 signaling pathway in DRG was implicated in the pain transition. EA increased the pain threshold of model animals and regulated the high expression of PKCε and TRPV1. Moreover, EA also regulated hyperalgesia and high TRPV1 expression induced by selective PKCε activation. We also found that EA partly increased chronic pain threshold, even though it was only administered between the Car and PGE2 injections. These findings suggested that EA could prevent the transition from acute to chronic pain by inhibiting the PKCε and TRPV1 expression in the peripheral nervous system