Interactive Spatiotemporal Token Attention Network for Skeleton-based General Interactive Action Recognition

Recognizing interactive action plays an important role in human-robot interaction and collaboration. Previous methods use late fusion and co-attention mechanism to capture interactive relations, which have limited learning capability or inefficiency to adapt to more interacting entities. With assumption that priors of each entity are already known, they also lack evaluations on a more general setting addressing the diversity of subjects. To address these problems, we propose an Interactive Spatiotemporal Token Attention Network (ISTA-Net), which simultaneously model spatial, temporal, and interactive relations. Specifically, our network contains a tokenizer to partition Interactive Spatiotemporal Tokens (ISTs), which is a unified way to represent motions of multiple diverse entities. By extending the entity dimension, ISTs provide better interactive representations. To jointly learn along three dimensions in ISTs, multi-head self-attention blocks integrated with 3D convolutions are designed to capture inter-token correlations. When modeling correlations, a strict entity ordering is usually irrelevant for recognizing interactive actions. To this end, Entity Rearrangement is proposed to eliminate the orderliness in ISTs for interchangeable entities. Extensive experiments on four datasets verify the effectiveness of ISTA-Net by outperforming state-of-the-art methods. Our code is publicly available at https://github.com/Necolizer/ISTA-NetComment: IROS 2023 Camera-ready version. Project website: https://necolizer.github.io/ISTA-Net

A compact representation of human actions by sliding coordinate coding

Human action recognition remains challenging in realistic videos, where scale and viewpoint changes make the problem complicated. Many complex models have been developed to overcome these difficulties, while we explore using low-level features and typical classifiers to achieve the state-of-the-art performance. The baseline model of feature encoding for action recognition is bag-of-words model, which has shown high efficiency but ignores the arrangement of local features. Refined methods compensate for this problem by using a large number of co-occurrence descriptors or a concatenation of the local distributions in designed segments. In contrast, this article proposes to encode the relative position of visual words using a simple but very compact method called sliding coordinates coding (SCC). The SCC vector of each kind of word is only an eight-dimensional vector which is more compact than many of the spatial or spatial–temporal pooling methods in the literature. Our key observation is that the relative position is robust to the variations of video scale and view angle. Additionally, we design a temporal cutting scheme to define the margin of coding within video clips, since visual words far away from each other have little relationship. In experiments, four action data sets, including KTH, Rochester Activities, IXMAS, and UCF YouTube, are used for performance evaluation. Results show that our method achieves comparable or better performance than the state of the art, while using more compact and less complex models.Published versio

A Synthetic Plasmid Toolkit for Shewanella oneidensis MR-1

Shewanella oneidensis MR-1 is a platform microorganism for understanding extracellular electron transfer (EET) with a fully sequenced and annotated genome. In comparison to other model microorganisms such as Escherichia coli, the available plasmid parts (such as promoters and replicons) are not sufficient to conveniently and quickly fine-tune the expression of multiple genes in S. oneidensis MR-1. Here, we constructed and characterized a plasmid toolkit that contains a set of expression vectors with a combination of promoters, replicons, antibiotic resistance genes, and an RK2 origin of transfer (oriT) cassette, in which each element can be easily changed by fixed restriction enzyme sites. The expression cassette is also compatible with BioBrick synthetic biology standards. Using green fluorescent protein (GFP) as a reporter, we tested and quantified the strength of promoters. The copy number of different replicons was also measured by real-time quantitative PCR. We further transformed two compatible plasmids with different antibiotic resistance genes into the recombinant S. oneidensis MR-1, enabling control over the expression of two different fluorescent proteins. This plasmid toolkit was further used for overexpression of the MtrCAB porin-c-type cytochrome complex in the S. oneidensis ΔmtrA strain. Tungsten trioxide (WO3) reduction and microbial fuel cell (MFC) assays revealed that the EET efficiency was improved most significantly when MtrCAB was expressed at a moderate level, thus demonstrating the utility of the plasmid toolkit in the EET regulation in S. oneidensis. The plasmid toolkit developed in this study is useful for rapid and convenient fine-tuning of gene expression and enhances the ability to genetically manipulate S. oneidensis MR-1

Modeling and Simulation of Thermomechanical Elasto-viscoplastic Material and Ductile Fracture with the Material Point Method

This dissertation focuses on applications and extensions of the Material Point Method(MPM) in simulating ductile fracture and thermomechanical material behavior for baking and cooking. We conclude the two major contributions as follows:First, we present novel techniques for simulating and visualizing ductile fracture with MPM. We utilize traditional particle-based MPM [SZS95] as well as the Lagrangian energy formulation of [JSS15] that formulates the deformation gradient and potential energy through a tetrahedron mesh. We model failure and fracture via elasto-plasticity with damage. Material shows elastic behavior until the deformation exceeds a Rankine or von Mises yield criterion, at which point irreversible damage starts to occur. We introduce a softening model that shrinks the yield surface until a damage threshold is reached. Once damaged the material Lam� coefficients are modified to represent failure. We design visualization techniques for rendering the boundary of the material and its intersections with evolving crack surfaces. Our approach uses a simple and efficient element splitting strategy for tetrahedron meshes to represent crack surfaces. For traditional particle-based MPM we use an initial Delaunay tetrahedralization to connect randomly initialized MPM particles and form a reference mesh. Our visualization technique is a postprocess and can be run separately after the MPM simulation for efficiency. We demonstrate the strength of our method with a number of challenging simulations of ductile failure with considerable and persistent self contact.Our second contribution is a Material Point Method for visual simulation of baking breads, cookies, pancakes and similar materials. We develop a novel thermomechanical model using mixture theory to resolve interactions between individual water, gas and solid species. Heat transfer with thermal expansion is used to model thermal variations in material properties. Water based mass transfer is resolved through the porous mixture. Gas represents carbon dioxide produced by leavening agents in the baking process, and the solid component is modeled as a visco-elastoplastic material to represent its varied and complex rheological properties. Water content in the mixture reduces during the baking process according to Fick’s Law which contributes to the drying and cracking of crust at the material boundary. Carbon dioxide gas produced by leavening agents during baking creates internal pressure that causes rising. The visco-elastoplastic model for the dough is temperature dependent and is used to model melting and solidification. We discretize the governing equations using a novel Material Point Method designed to track the solid phase of the mixture

The Impact of Internet Use on the Happiness of Chinese Civil Servants: A Mediation Analysis Based on Self-Rated Health

With the rapid socioeconomic development of China, studies related to Internet use and civil servants’ happiness have become a research hotspot in Chinese academia. This study empirically analysed the impact of Internet use on the happiness of Chinese civil servants using a sample of 3793 civil servants in Hunan Province, China. It showed that Internet use significantly enhanced the subjective well-being of Chinese civil servants. Furthermore, heterogeneity analysis revealed significant heterogeneity in the effect of the Internet on civil servants’ happiness, which varied across civil service groups with different education and gender. Moreover, the effect of Internet use on the happiness of the male and better educated civil servant groups was more pronounced than in the female and less educated civil servant groups. Additionally, mediation analysis revealed that Internet use and the happiness of civil servants were not linear, with health having a significant mediating effect. This indicates that Internet use helps civil servants maintain good health, and thereby enhances the happiness of civil servants. In addition, we also use a propensity score matching model (PSM) to address the endogeneity problem due to sample selectivity bias. The results show that the estimates are more robust after eliminating sample selectivity bias. The effect of Internet use on civil servants’ subjective well-being would be underestimated if the sample selectivity bias is not removed

Differences between complex epithelial neoplasms of the ovary and high-grade serous ovarian cancer: a retrospective observational cohort study

Abstract Background Complex epithelial neoplasms of the ovary (CENO), an uncommon pathological histotype in ovarian cancer, comprises adenosquamous carcinoma and adenocarcinoma with metaplasia. Owing to the rarity of relevant reports, there are currently no statistics on outcomes based on large samples. Meanwhile high-grade serous ovarian cancer (HGSOC) is the most common histotype in ovarian cancer which has a recognized first-line treatment regimen and poor prognosis. Thus, we aimed to determine the characteristics, prognosis, and independent predictors of survival for CENO, compare them with those of HGSOC and construct prognostic predictive models and nomograms. Methods We used the Surveillance, Epidemiology, and End Results (SEER) database to determine patients diagnosed with CENO or HGSOC from 2000 to 2017. Clinical, demographic, and treatment characteristics were compared between these groups. Propensity score matching, Cox risk regression analysis, Kaplan–Meier survival curves, and the Least Absolute Shrinkage and Selection Operator regression analysis were employed for analyzing the data. Results Here, 31,567 patients with HGSOC and 216 patients with CENO between 2000 and 2017 in the SEER database were enrolled. Age < 57 years, unmarried, and early-stage diseases were more common in patients with CENO than in those with HGSOC. Women with CENO were less likely to receive adjuvant chemotherapy (65.7% vs. 79.4%) but more likely to receive radiotherapy (6.0% vs. 0.8%; both p < 0.001) than those with HGSOC. Year of diagnosis, surgery status, number of primary tumors, grade, and FIGO stage were independent prognostic factors for overall and cancer-specific survival in CENO. Overall survival rates were significantly lower for CENO than for more malignant HGSOC. Conclusions In summary, CENO was rare in ovarian cancer, while the year of diagnosis, surgery status, number of primary tumors, grade, and FIGO stage were independent prognostic factors. Compared with other common malignant ovarian tumors, CENO had a poor prognosis. Prognostic predictive models and nomograms had been determined to predict the individual survival rates of patients with CENO. These methods could improve evaluations of survival and therapeutic decisions for patients