Sub-action Prototype Learning for Point-level Weakly-supervised Temporal Action Localization

Point-level weakly-supervised temporal action localization (PWTAL) aims to localize actions with only a single timestamp annotation for each action instance. Existing methods tend to mine dense pseudo labels to alleviate the label sparsity, but overlook the potential sub-action temporal structures, resulting in inferior performance. To tackle this problem, we propose a novel sub-action prototype learning framework (SPL-Loc) which comprises Sub-action Prototype Clustering (SPC) and Ordered Prototype Alignment (OPA). SPC adaptively extracts representative sub-action prototypes which are capable to perceive the temporal scale and spatial content variation of action instances. OPA selects relevant prototypes to provide completeness clue for pseudo label generation by applying a temporal alignment loss. As a result, pseudo labels are derived from alignment results to improve action boundary prediction. Extensive experiments on three popular benchmarks demonstrate that the proposed SPL-Loc significantly outperforms existing SOTA PWTAL methods

Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data

Unsupervised anomaly detection methods are at the forefront of industrial anomaly detection efforts and have made notable progress. Previous work primarily used 2D information as input, but multi-modal industrial anomaly detection based on 3D point clouds and RGB images is just beginning to emerge. The regular approach involves utilizing large pre-trained models for feature representation and storing them in memory banks. However, the above methods require a longer inference time and higher memory usage, which cannot meet the real-time requirements of the industry. To overcome these issues, we propose a lightweight dual-branch reconstruction network(DBRN) based on RGB-D input, learning the decision boundary between normal and abnormal examples. The requirement for alignment between the two modalities is eliminated by using depth maps instead of point cloud input. Furthermore, we introduce an importance scoring module in the discriminative network to assist in fusing features from these two modalities, thereby obtaining a comprehensive discriminative result. DBRN achieves 92.8% AUROC with high inference efficiency on the MVTec 3D-AD dataset without large pre-trained models and memory banks.Comment: 8 pages, 5 figure

A Ratio Analysis of China Banks

Motivation. This study was motivated by the important role that Chinese banks play in the financial system of China. This role is higher than in many other developed and developing countries, because unlike in many other countries, in China banking system provides much more financing to the economy than its stock market. Also, Chinese banks recently became the top-ranking banking organizations globally in international bank rankings. In year 2007 no Chinese bank was in the list of top-10 banks by their pre-tax profit, but already in 2008 five banks from China stepped into the list and two of these banks led the ranking. Key research question. The key research question of this study is how financial performance, financial position, quality of loan portfolio of Chinese banks changed after the global financial crisis of 2007 - 2009? Another important question is to identify the significant and important determinants of the performance of Chinese banks. Methodology. Methodology of this study is based on the list of nine financial banking ratios that describe financial performance of the banks in a comprehensive manner. Included into the analysis are the ratios that describe profitability, liquidity, efficiency, financial leverage, quality of loan portfolio and its performance, and relative size of loan portfolio. Three types of empirical analysis are used in this study – dynamic analysis of the mean financial ratios, testing of the difference between the mean ratios for the crisis period versus the mean ratios in the after-crisis period; and regression analysis of the determinants of ROA and ROE of Chinese banks in the period during the global financial crisis and in the period after the crisis. Regression methodology uses panel data estimation methodology, as well as cross section estimation. The former is based on the averaged across time financial ratios for each bank and the two time period s are considered – 2007-2009 as the crisis period and 2010-2014 as the post-crisis period. Data. The data for this investigation was obtained from specialized well-recognized international financial database Bankscope that is maintained by Bureau van Dijk. Data was collected for 239 Chinese banks over the period from year 2007 to 2014 inclusively. The key results of the study show that the key changes that occurred to Chinese banks during the crisis were the twofold increase in the ratio of non-performing loans, decrease in the debt to total assets ratio, decrease in the NIM ratio, and also significant contraction in the ratio of net loans to total assets. Also, ROA is positively related to NIM, assets turnover, interest cover, and debt ratio

Improving information accessibility using online patient drug reviews

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 85-92).We address the problem of information accessibility for patients concerned about, pharmaceutical drug side effects and experiences. We create a new corpus of online patient-provided drug reviews and present our initial experiments on that corpus. We detect biases in term distributions that show a statistically significant association between a class of cholesterol-lowering drugs called statins, and a wide range of alarming disorders, including depression, memory loss, and heart failure. We also develop an initial language model for speech recognition in the medical domain, with transcribed data on sample patient comments collected with Amazon Mechanical Turk. Our findings show that patient-reported drug experiences have great potential to empower consumers to make more informed decisions about medical drugs, and our methods will be used to increase information accessibility for consumers.by Yueyang Alice Li.M.Eng

CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection

In the anomaly detection field, the scarcity of anomalous samples has directed the current research emphasis towards unsupervised anomaly detection. While these unsupervised anomaly detection methods offer convenience, they also overlook the crucial prior information embedded within anomalous samples. Moreover, among numerous deep learning methods, supervised methods generally exhibit superior performance compared to unsupervised methods. Considering the reasons mentioned above, we propose a self-supervised anomaly detection approach that combines contrastive learning with 2D-Flow to achieve more precise detection outcomes and expedited inference processes. On one hand, we introduce a novel approach to anomaly synthesis, yielding anomalous samples in accordance with authentic industrial scenarios, alongside their surrogate annotations. On the other hand, having obtained a substantial number of anomalous samples, we enhance the 2D-Flow framework by incorporating contrastive learning, leveraging diverse proxy tasks to fine-tune the network. Our approach enables the network to learn more precise mapping relationships from self-generated labels while retaining the lightweight characteristics of the 2D-Flow. Compared to mainstream unsupervised approaches, our self-supervised method demonstrates superior detection accuracy, fewer additional model parameters, and faster inference speed. Furthermore, the entire training and inference process is end-to-end. Our approach showcases new state-of-the-art results, achieving a performance of 99.6\% in image-level AUROC on the MVTecAD dataset and 96.8\% in image-level AUROC on the BTAD dataset.Comment: 6 pages,6 figure

Replay Attack Detection Based on Parity Space Method for Cyber-Physical Systems

The replay attack detection problem is studied from a new perspective based on parity space method in this paper. The proposed detection methods have the ability to distinguish system fault and replay attack, handle both input and output data replay, maintain certain control performance, and can be implemented conveniently and efficiently. First, the replay attack effect on the residual is derived and analyzed. The residual change induced by replay attack is characterized explicitly and the detection performance analysis based on two different test statistics are given. Second, based on the replay attack effect characterization, targeted passive and active design for detection performance enhancement are proposed. Regarding the passive design, four optimization schemes regarding different cost functions are proposed with optimal parity matrix solutions, and the unified solution to the passive optimization schemes is obtained; the active design is enabled by a marginally stable filter so as to enlarge the replay attack effect on the residual for detection. Simulations and comparison studies are given to show the effectiveness of the proposed methods