Exploring Object Relation in Mean Teacher for Cross-Domain Detection

Rendering synthetic data (e.g., 3D CAD-rendered images) to generate annotations for learning deep models in vision tasks has attracted increasing attention in recent years. However, simply applying the models learnt on synthetic images may lead to high generalization error on real images due to domain shift. To address this issue, recent progress in cross-domain recognition has featured the Mean Teacher, which directly simulates unsupervised domain adaptation as semi-supervised learning. The domain gap is thus naturally bridged with consistency regularization in a teacher-student scheme. In this work, we advance this Mean Teacher paradigm to be applicable for cross-domain detection. Specifically, we present Mean Teacher with Object Relations (MTOR) that novelly remolds Mean Teacher under the backbone of Faster R-CNN by integrating the object relations into the measure of consistency cost between teacher and student modules. Technically, MTOR firstly learns relational graphs that capture similarities between pairs of regions for teacher and student respectively. The whole architecture is then optimized with three consistency regularizations: 1) region-level consistency to align the region-level predictions between teacher and student, 2) inter-graph consistency for matching the graph structures between teacher and student, and 3) intra-graph consistency to enhance the similarity between regions of same class within the graph of student. Extensive experiments are conducted on the transfers across Cityscapes, Foggy Cityscapes, and SIM10k, and superior results are reported when comparing to state-of-the-art approaches. More remarkably, we obtain a new record of single model: 22.8% of mAP on Syn2Real detection dataset.Comment: CVPR 2019; The codes and model of our MTOR are publicly available at: https://github.com/caiqi/mean-teacher-cross-domain-detectio

Do we really need temporal convolutions in action segmentation?

Action classification has made great progress, but segmenting and recognizing actions from long untrimmed videos remains a challenging problem. Most state-of-the-art methods focus on designing temporal convolution-based models, but the inflexibility of temporal convolutions and the difficulties in modeling long-term temporal dependencies restrict the potential of these models. Transformer-based models with adaptable and sequence modeling capabilities have recently been used in various tasks. However, the lack of inductive bias and the inefficiency of handling long video sequences limit the application of Transformer in action segmentation. In this paper, we design a pure Transformer-based model without temporal convolutions by incorporating temporal sampling, called Temporal U-Transformer (TUT). The U-Transformer architecture reduces complexity while introducing an inductive bias that adjacent frames are more likely to belong to the same class, but the introduction of coarse resolutions results in the misclassification of boundaries. We observe that the similarity distribution between a boundary frame and its neighboring frames depends on whether the boundary frame is the start or end of an action segment. Therefore, we further propose a boundary-aware loss based on the distribution of similarity scores between frames from attention modules to enhance the ability to recognize boundaries. Extensive experiments show the effectiveness of our model

Research progress on the application of shoulder orthosis in rehabilitation of abnormal gait post-stroke hemiplegia

Post-stroke hemiplegia usually has an adverse impact on motor ability and stability. Patients often develop shoulder subluxation and abnormal gait due to muscle weakness， bilateral limb muscle tension imbalance， sensory abnormalities and poor joint and posture control， etc. Shoulder orthosis is often used to prevent or treat shoulder subluxation in the early stage of stroke hemiplegia， but it is still controversial. To explore the role of shoulder orthosis beyond the prevention and treatment of shoulder subluxation， and to provide theoretical basis for the selection and wearing of shoulder orthosis，the mechanism underlying the role of shoulder orthosis in improving abnormal gait post-stroke hemiplegia was elaborated， and the effects of different types of shoulder orthosis on the rehabilitation of abnormal gait post-stroke were compared

A critical review of cyber-physical security for building automation systems

Modern Building Automation Systems (BASs), as the brain that enables the smartness of a smart building, often require increased connectivity both among system components as well as with outside entities, such as optimized automation via outsourced cloud analytics and increased building-grid integrations. However, increased connectivity and accessibility come with increased cyber security threats. BASs were historically developed as closed environments with limited cyber-security considerations. As a result, BASs in many buildings are vulnerable to cyber-attacks that may cause adverse consequences, such as occupant discomfort, excessive energy usage, and unexpected equipment downtime. Therefore, there is a strong need to advance the state-of-the-art in cyber-physical security for BASs and provide practical solutions for attack mitigation in buildings. However, an inclusive and systematic review of BAS vulnerabilities, potential cyber-attacks with impact assessment, detection & defense approaches, and cyber-secure resilient control strategies is currently lacking in the literature. This review paper fills the gap by providing a comprehensive up-to-date review of cyber-physical security for BASs at three levels in commercial buildings: management level, automation level, and field level. The general BASs vulnerabilities and protocol-specific vulnerabilities for the four dominant BAS protocols are reviewed, followed by a discussion on four attack targets and seven potential attack scenarios. The impact of cyber-attacks on BASs is summarized as signal corruption, signal delaying, and signal blocking. The typical cyber-attack detection and defense approaches are identified at the three levels. Cyber-secure resilient control strategies for BASs under attack are categorized into passive and active resilient control schemes. Open challenges and future opportunities are finally discussed.Comment: 38 pages, 7 figures, 6 tables, submitted to Annual Reviews in Contro

Waterproofed Photomultiplier Tube Assemblies for the Daya Bay Reactor Neutrino Experiment

In the Daya Bay Reactor Neutrino Experiment 960 20-cm-diameter waterproof photomultiplier tubes are used to instrument three water pools as Cherenkov detectors for detecting cosmic-ray muons. Of these 960 photomultiplier tubes, 341 are recycled from the MACRO experiment. A systematic program was undertaken to refurbish them as waterproof assemblies. In the context of passing the water leakage check, a success rate better than 97% was achieved. Details of the design, fabrication, testing, operation, and performance of these waterproofed photomultiplier-tube assemblies are presented.Comment: 16 pages, 11 figures. Submitted to Nucl. Instr. Met

An absence of platelet activation following thalidomide treatment in vitro or in vivo

Increased risk of thromboembolism and platelet hyperreactivity has been reported in patients receiving thalidomide therapy. Whether thalidomide induces platelet activation directly or through other factors remains unclear. The aim of this study was to evaluate the effect of thalidomide on platelet activation under resting conditions in vitro and in vivo. Isolated human or mouse platelets were treated with different concentrations of thalidomide (10, 50 and 100 μg/ml) for 60 min at 37°C followed by analysis of platelet surface expression of platelet receptors GPIbα, GPVI, αIIbβ3 and P-selectin, and PAC-1 or fibrinogen binding, by flow cytometry and collagen- or ADP-induced platelet aggregation. In addition, thalidomide (200 mg/kg) was intraperitoneally injected into mice for analysis of the effect of thalidomide on platelet activation in vivo. No increased expression of P-selectin, PAC-1 or fibrinogen binding was observed in either human and mouse platelets after thalidomide treatment in vitro for 60 min at 37oC. Thalidomide treatment also did not affect expression of GPIbα, GPVI or αIIbβ3, nor did it affect collagen- or ADP-induced platelet aggregation at threshold concentrations. However, while mice injected with thalidomide displayed no increased surface expression of platelet P-selectin or αIIbβ3, there was a significantly shortened tail bleeding time, thrombin time, prothrombin time together with higher levels of Factor IX and fibrinogen. In conclusion, thalidomide at therapeutic doses does not directly induce platelet activation under resting conditions in vitro or in vivo, but results in increased procoagulant activity, which could explain the thalidomide-dependent prothrombotic tendency in patients.This research was supported by National Natural Science Foundation of China (grant no. 81400082 and 81370602), the Natural Science Foundation of Jiangsu Province (grant no. BK20140219), China Postdoctoral Science Foundation funded project (project no. 2015M570479), the funding for the Distinguished Professorship Program of Jiangsu Province, the Six Talent Peaks Project of Jiangsu Province (project no. WSN-133), the Shuangchuang Project of Jiangsu Province, the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, and the Science and Technology Foundation for the Selected Overseas Chinese Scholars, State Ministry of Human Resources and Social Security

Tv commercial classification by using multi-modal textual information

In this paper, we propose an approach for TV commercial video classification by the categories of advertised products or services (e.g. automobiles, healthcare products, etc). Since automatic speech recognition (ASR) and optical character recognition (OCR) can deliver meaningful textual information related to products or services, TV commercial video classification is formulated as the problem of text categorization. However, there exist two challenges. Firstly, the background music of TV commercials makes ASR techniques yield erroneous and deficient output transcripts. Secondly, even if ASR and OCR could work perfectly, the limited textual information from TV commercials do not suffice to train a generic and non-overfitting text categorizer. For the first issue, our approach resorts to the external resources to expand deficient ASR and OCR transcripts. The output transcripts of ASR and OCR are parsed to yield a few keywords, on which a Web searching is executed to retrieve relevant and semantically informative articles from World Wide Web (WWW). The retrieved articles are then utilized to construct textual feature vectors and perform text categorization on behalf of commercials. For the second issue, a topic-wise document corpus is constructed from the public corpora like Reuters-21578 or from the articles manually collected from WWW for the training of text categorizers. Experimental results have shown that the proposed approach alleviates the negative effects from weak ASR/OCR performance and yield a promising classification accuracy of 80.9%. 1