281 research outputs found
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
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