Dance Your Latents: Consistent Dance Generation through Spatial-temporal Subspace Attention Guided by Motion Flow

The advancement of generative AI has extended to the realm of Human Dance Generation, demonstrating superior generative capacities. However, current methods still exhibit deficiencies in achieving spatiotemporal consistency, resulting in artifacts like ghosting, flickering, and incoherent motions. In this paper, we present Dance-Your-Latents, a framework that makes latents dance coherently following motion flow to generate consistent dance videos. Firstly, considering that each constituent element moves within a confined space, we introduce spatial-temporal subspace-attention blocks that decompose the global space into a combination of regular subspaces and efficiently model the spatiotemporal consistency within these subspaces. This module enables each patch pay attention to adjacent areas, mitigating the excessive dispersion of long-range attention. Furthermore, observing that body part's movement is guided by pose control, we design motion flow guided subspace align & restore. This method enables the attention to be computed on the irregular subspace along the motion flow. Experimental results in TikTok dataset demonstrate that our approach significantly enhances spatiotemporal consistency of the generated videos.Comment: 10 pages, 5 figure

A non-oscillatory multi-moment finite volume scheme with boundary gradient switching

In this work we propose a new formulation for high-order multi-moment constrained finite volume (MCV) method. In the one-dimensional building-block scheme, three local degrees of freedom (DOFs) are equidistantly defined within a grid cell. Two candidate polynomials for spatial reconstruction of third-order are built by adopting one additional constraint condition from the adjacent cells, i.e. the DOF at middle point of left or right neighbour. A boundary gradient switching (BGS) algorithm based on the variation-minimization principle is devised to determine the spatial reconstruction from the two candidates, so as to remove the spurious oscillations around the discontinuities. The resulted non-oscillatory MCV3-BGS scheme is of fourth-order accuracy and completely free of case-dependent ad hoc parameters. The widely used benchmark tests of one- and two-dimensional scalar and Euler hyperbolic conservation laws are solved to verify the performance of the proposed scheme in this paper. The MCV3-BGS scheme is very promising for the practical applications due to its accuracy, non-oscillatory feature and algorithmic simplicity

Mediator role of presence of meaning and self-esteem in the relationship of social support and death anxiety

IntroductionDeath anxiety has increased following the COVID-19 pandemic. Although terror management theory has suggested social support, presence of meaning and self-esteem functioned as death anxiety buffers, few existing works have explored the mechanism of how social support, presence of meaning, and self-esteem buffer death anxiety. To identify these mechanisms is the aim of this study.MethodsOur cross-sectional study was conducted with 1167 people in China from 19 May 2020 to 1 June 2020 during the COVID-19 outbreak. The average age of participants was 26 years. Data were by questionnaire, including demographic information, the Templer's Death anxiety scale, the multidimensional scale of perceived social support, the presence of meaning scale, and the Rosenberg self-esteem scale.ResultsResults using structural equation modeling showed presence of meaning and self-esteem fully mediated the relationship between social support and death anxiety, respectively and sequentially. The proposed model showed good fit of indices: χ2 = 243.384, df = 58, p < 0.001; CFI = 0.968, TLI = 0.954, RMSEA = 0.052, SRMR = 0.044.DiscussionThis study demonstrates significant mediator roles of presence of meaning and self-esteem in the relationship of social support and death anxiety. Multi-component interventions are needed to manage death anxiety by targeting increasing social support, presence of meaning and self-esteem and increasing presence of meaning and self-esteem when social support is diminished in the pandemic

Characterization of an aspartate aminotransferase encoded by YPO0623 with frequent nonsense mutations in Yersinia pestis

Yersinia pestis, the causative agent of plague, is a genetically monomorphic bacterial pathogen that evolved from Yersinia pseudotuberculosis approximately 7,400 years ago. We observed unusually frequent mutations in Y. pestis YPO0623, mostly resulting in protein translation termination, which implies a strong natural selection. These mutations were found in all phylogenetic lineages of Y. pestis, and there was no apparent pattern in the spatial distribution of the mutant strains. Based on these findings, we aimed to investigate the biological function of YPO0623 and the reasons for its frequent mutation in Y. pestis. Our in vitro and in vivo assays revealed that the deletion of YPO0623 enhanced the growth of Y. pestis in nutrient-rich environments and led to increased tolerance to heat and cold shocks. With RNA-seq analysis, we also discovered that the deletion of YPO0623 resulted in the upregulation of genes associated with the type VI secretion system (T6SS) at 26°C, which probably plays a crucial role in the response of Y. pestis to environment fluctuations. Furthermore, bioinformatic analysis showed that YPO0623 has high homology with a PLP-dependent aspartate aminotransferase in Salmonella enterica, and the enzyme activity assays confirmed its aspartate aminotransferase activity. However, the enzyme activity of YPO0623 was significantly lower than that in other bacteria. These observations provide some insights into the underlying reasons for the high-frequency nonsense mutations in YPO0623, and further investigations are needed to determine the exact mechanism

Validation and reliability test of Chinese language patient-reported impact of symptoms in schizophrenia scale

BackgroundPatient-reported outcomes, or subjective evaluations directly reflecting the patient’s views, feelings, and judgments, are now being used to evaluate the outcomes of care and treatment of people with schizophrenia. In this study, we used an updated tool, the patient-reported impact of symptoms in schizophrenia scale (PRISS), translated into Chinese languages to assess the subjective experiences of schizophrenia patients.ObjectiveThis study aimed to test the psychometrics of the Chinese languages PRISS (CL-PRISS).MethodThis study used the Chinese version of PRISS (CL-PRISS), acquired from the harmonized English-language version. A total of 280 patients enrolled in this study were asked to complete the CL-PRISS, the positive and negative syndrome scale (PANSS), and the World Health Organization Disability Assessment Schedule (WHO-DAS). Construct and concurrent validity was tested using the confirmatory factor analysis (CFA) and Spearman correlation coefficient, respectively. The reliability of CL-PRISS was tested using Cronbach’s α coefficient and the internal correlation coefficient.ResultsConfirmatory factor analysis (CFA) analysis demonstrated three major factors in CL_PRISS: the first factor is productive experiences, the second factor is affective-negative, and the third factor experiences. The factor loadings between items and factors ranged from 0.436 to 0.899 (RMSEA = 0.029, TLI = 0.940, CFI = 0.921). The correlation coefficient between the CL_PRISS and PANSS was 0.845, and between the CL-PRISS and WHO-DAS was 0.886. The ICC of the total CL_PRISS was 0.913, and Cronbach’s α was 0.903.ConclusionThe Chinese version of the PRISS (CL_PRISS) can be effectively used for assessing the subjective experience of Chinese patients with schizophrenia