Multi-stage Factorized Spatio-Temporal Representation for RGB-D Action and Gesture Recognition

RGB-D action and gesture recognition remain an interesting topic in human-centered scene understanding, primarily due to the multiple granularities and large variation in human motion. Although many RGB-D based action and gesture recognition approaches have demonstrated remarkable results by utilizing highly integrated spatio-temporal representations across multiple modalities (i.e., RGB and depth data), they still encounter several challenges. Firstly, vanilla 3D convolution makes it hard to capture fine-grained motion differences between local clips under different modalities. Secondly, the intricate nature of highly integrated spatio-temporal modeling can lead to optimization difficulties. Thirdly, duplicate and unnecessary information can add complexity and complicate entangled spatio-temporal modeling. To address the above issues, we propose an innovative heuristic architecture called Multi-stage Factorized Spatio-Temporal (MFST) for RGB-D action and gesture recognition. The proposed MFST model comprises a 3D Central Difference Convolution Stem (CDC-Stem) module and multiple factorized spatio-temporal stages. The CDC-Stem enriches fine-grained temporal perception, and the multiple hierarchical spatio-temporal stages construct dimension-independent higher-order semantic primitives. Specifically, the CDC-Stem module captures bottom-level spatio-temporal features and passes them successively to the following spatio-temporal factored stages to capture the hierarchical spatial and temporal features through the Multi- Scale Convolution and Transformer (MSC-Trans) hybrid block and Weight-shared Multi-Scale Transformer (WMS-Trans) block. The seamless integration of these innovative designs results in a robust spatio-temporal representation that outperforms state-of-the-art approaches on RGB-D action and gesture recognition datasets.Comment: ACM MM'2

mTOR Signaling in X/A‐Like Cells Contributes to Lipid Homeostasis in Mice

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147831/1/hep30229-sup-0001-FigS1-S8.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147831/2/hep30229_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147831/3/hep30229.pd

Video Infringement Detection via Feature Disentanglement and Mutual Information Maximization

The self-media era provides us tremendous high quality videos. Unfortunately, frequent video copyright infringements are now seriously damaging the interests and enthusiasm of video creators. Identifying infringing videos is therefore a compelling task. Current state-of-the-art methods tend to simply feed high-dimensional mixed video features into deep neural networks and count on the networks to extract useful representations. Despite its simplicity, this paradigm heavily relies on the original entangled features and lacks constraints guaranteeing that useful task-relevant semantics are extracted from the features. In this paper, we seek to tackle the above challenges from two aspects: (1) We propose to disentangle an original high-dimensional feature into multiple sub-features, explicitly disentangling the feature into exclusive lower-dimensional components. We expect the sub-features to encode non-overlapping semantics of the original feature and remove redundant information. (2) On top of the disentangled sub-features, we further learn an auxiliary feature to enhance the sub-features. We theoretically analyzed the mutual information between the label and the disentangled features, arriving at a loss that maximizes the extraction of task-relevant information from the original feature. Extensive experiments on two large-scale benchmark datasets (i.e., SVD and VCSL) demonstrate that our method achieves 90.1% TOP-100 mAP on the large-scale SVD dataset and also sets the new state-of-the-art on the VCSL benchmark dataset. Our code and model have been released at https://github.com/yyyooooo/DMI/, hoping to contribute to the community.Comment: This paper is accepted by ACM MM 202

Effect of HIV-infection and menopause status on raltegravir pharmacokinetics in the blood and genital tract

This study describes first dose and steady state pharmacokinetics of raltegravir (RAL) in cervicovaginal fluid (CVF) and blood plasma (BP)

Chemokine (C-C Motif) Ligand 2 (CCL2) in Sera of Patients with Type 1 Diabetes and Diabetic Complications

Chemokine (C-C motif) ligand 2 (CCL2), commonly known as monocyte chemoattractant protein-1 (MCP-1), has been implicated in the pathogenesis of many diseases characterized by monocytic infiltration. However, limited data have been reported on MCP-1 in type 1 diabetes (T1D) and the findings are inconclusive and inconsistent.In this study, MCP-1 was measured in the sera from 2,472 T1D patients and 2,654 healthy controls using a Luminex assay. The rs1024611 SNP in the promoter region of MCP-1 was genotyped for a subset of subjects (1764 T1D patients and 1323 controls) using the TaqMan-assay.Subject age, sex or genotypes of MCP-1 rs1024611SNP did not have a major impact on serum MCP-1 levels in either healthy controls or patients. While hemoglobin A1c levels did not have a major influence on serum MCP-1 levels, the mean serum MCP-1 levels are significantly higher in patients with multiple complications (mean = 242 ng/ml) compared to patients without any complications (mean = 201 ng/ml) (p = 3.5×10(-6)). Furthermore, mean serum MCP-1 is higher in controls (mean = 261 ng/ml) than T1D patients (mean = 208 ng/ml) (p<10(-23)). More importantly, the frequency of subjects with extremely high levels (>99(th) percentile of patients or 955 ng/ml) of serum MCP-1 is significantly lower in the T1D group compared to the control group (odds ratio = 0.11, p<10(-33)).MCP-1 may have a dual role in T1D and its complications. While very high levels of serum MCP-1 may be protective against the development of T1D, complications are associated with higher serum MCP-1 levels within the T1D group

Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review

The literature on portfolio selection and risk measurement has considerably advanced in recent years. The aim of the present paper is to trace the development of the literature and identify areas that require further research. This paper provides a literature review of the characteristics of financial data, commonly used models of portfolio selection, and portfolio risk measurement. In the summary of the characteristics of financial data, we summarize the literature on fat tail and dependence characteristic of financial data. In the portfolio selection model part, we cover three models: mean-variance model, global minimum variance (GMV) model and factor model. In the portfolio risk measurement part, we first classify risk measurement methods into two categories: moment-based risk measurement and moment-based and quantile-based risk measurement. Moment-based risk measurement includes time-varying covariance matrix and shrinkage estimation, while moment-based and quantile-based risk measurement includes semi-variance, VaR and CVaR