CSD: Discriminance with Conic Section for Improving Reverse k Nearest Neighbors Queries

The reverse $k$ nearest neighbor (R$k$NN) query finds all points that have the query point as one of their $k$ nearest neighbors ($k$NN), where the $k$NN query finds the $k$ closest points to its query point. Based on the characteristics of conic section, we propose a discriminance, named CSD (Conic Section Discriminance), to determine points whether belong to the R$k$NN set without issuing any queries with non-constant computational complexity. By using CSD, we also implement an efficient R$k$NN algorithm CSD-R$k$NN with a computational complexity at $O(k^{1.5}\cdot log\,k)$. The comparative experiments are conducted between CSD-R$k$NN and other two state-of-the-art RkNN algorithms, SLICE and VR-R$k$NN. The experimental results indicate that the efficiency of CSD-R$k$NN is significantly higher than its competitors

Association Between VDR FokI Polymorphism and Intervertebral Disk Degeneration

AbstractIntervertebral disk degeneration (IDD) is strongly associated with genetic predisposition and environmental susceptibility. Several studies been conducted to investigate the potential association between IDD and FokI polymorphism located in the gene encoding the vitamin D receptor (VDR), and inconsistent conclusions had been reached among different ethnic populations. In order to assess the association between the FokI polymorphism and the risk of IDD, we performed a comprehensive and systematic meta-analysis. Candidate articles were retrieved from PubMed, EMBASE, China National Knowledge Infrastructure (CNKI), and China Biology Medical (CBM) with strict inclusion criteria in January 2015. Among the 54 articles that were retrieved, only eight studies met the inclusion criteria. The pooled data analysis based on allele contrast, homozygote, heterozygote, dominant, and recessive models revealed no significant correlation between the FokI polymorphism and the risk of IDD. However, when stratified by ethnicity, significant associations were detected for Hispanics based on allele contrast (OR=1.395, 95% CI=1.059–1.836, P=0.018), homozygote (OR=1.849, 95% CI=1.001–3.416, P=0.049), heterozygote (OR=1.254, 95% CI=1.049–1.498, P=0.013), and dominant (OR=1.742, 95% CI=1.174–2.583, P=0.006) models, and for Asians using the dominant model (OR=1.293, 95% CI=1.025–1.632, P=0.030), whereas there is no significant association detected for Caucasians. In conclusion, FokI polymorphism is not generally associated with IDD, but there is increased risk for IDD in Hispanics and Asians carrying FokI allele T

Transformed Low-Rank Parameterization Can Help Robust Generalization for Tensor Neural Networks

Achieving efficient and robust multi-channel data learning is a challenging task in data science. By exploiting low-rankness in the transformed domain, i.e., transformed low-rankness, tensor Singular Value Decomposition (t-SVD) has achieved extensive success in multi-channel data representation and has recently been extended to function representation such as Neural Networks with t-product layers (t-NNs). However, it still remains unclear how t-SVD theoretically affects the learning behavior of t-NNs. This paper is the first to answer this question by deriving the upper bounds of the generalization error of both standard and adversarially trained t-NNs. It reveals that the t-NNs compressed by exact transformed low-rank parameterization can achieve a sharper adversarial generalization bound. In practice, although t-NNs rarely have exactly transformed low-rank weights, our analysis further shows that by adversarial training with gradient flow (GF), the over-parameterized t-NNs with ReLU activations are trained with implicit regularization towards transformed low-rank parameterization under certain conditions. We also establish adversarial generalization bounds for t-NNs with approximately transformed low-rank weights. Our analysis indicates that the transformed low-rank parameterization can promisingly enhance robust generalization for t-NNs.Comment: 46 pages, accepted to NeurIPS 2023. We have corrected several typos in the first version (arXiv:2303.00196

OmniMotionGPT: Animal Motion Generation with Limited Data

Our paper aims to generate diverse and realistic animal motion sequences from textual descriptions, without a large-scale animal text-motion dataset. While the task of text-driven human motion synthesis is already extensively studied and benchmarked, it remains challenging to transfer this success to other skeleton structures with limited data. In this work, we design a model architecture that imitates Generative Pretraining Transformer (GPT), utilizing prior knowledge learned from human data to the animal domain. We jointly train motion autoencoders for both animal and human motions and at the same time optimize through the similarity scores among human motion encoding, animal motion encoding, and text CLIP embedding. Presenting the first solution to this problem, we are able to generate animal motions with high diversity and fidelity, quantitatively and qualitatively outperforming the results of training human motion generation baselines on animal data. Additionally, we introduce AnimalML3D, the first text-animal motion dataset with 1240 animation sequences spanning 36 different animal identities. We hope this dataset would mediate the data scarcity problem in text-driven animal motion generation, providing a new playground for the research community.Comment: The project page is at https://zshyang.github.io/omgpt-website

Learning from Tensors: Tensor Learning for Tensorial Data Analysis

Data with rich spatial information are commonly acquired in the real-world. These data are often represented by multi-way arrays, i.e., tensors. For those also with temporal information, they can be sketched as tensorial time series. Tensorial data, including tensorial time series are closely related to “big data”, because they are often with the “4V” features where data are in the large volumes and variety, require the high velocity to process them and can be with veracity caused by outliers, noises, missing values, etc., in practice. Existing methods either flatten tensors into vectors or impose strong assumptions. The former can cause the extreme large number of parameters and fail to process large volume data with the high velocity, whereas the latter cannot effectively deal with the challenge from the veracity and variety. This thesis consists of three topics which can form a pipeline to analyse tensorial data, including tensorial time series, with efficacy and other desired characteristics, to address the “4V” features. Firstly, for data preprocessing, we proposed a dimensionality reduction model Tensor-Train Parameterisation for Ultra Dimensionality Reduction (TTPUDR) specifically for ultra-dimensional data which are converted from tensors. Also, they have dimensions larger than the number of samples, which violates the assumption of many past methods. TTPUDR efficiently and effectively captures complicated spatial information in these data, avoids the curse-of-dimensionality problem and copes with extreme outliers. In the second and the third topics, we proposed a series of tensor neural differential equations to exploit complicated nonlinear spatial and temporal information for tensorial time series prediction, including irregular ones with unequally-spaced time steps which violate the equidistance assumption on time steps of many existing methods. For models proposed in all three topics, their efficacy is proved with theoretical guarantees. In numerical experiments, all proposed models outperform the existing models and demonstrate their efficiency and effectiveness on complicated spatial information and/or temporal information analysis in tensorial data, including tensorial time series

Current Research of Trichinellosis in China

Trichinellosis, caused by Trichinella, is an emerging or re-emerging zoonotic parasitic disease, which is distributed worldwide with major socio-economic importance in some developing countries. In particular, it has been calculated that more than 40 million people are at risk of Trichinella infection in China. This review summarizes the current information on the epidemiology, laboratory diagnosis and vaccines of trichinellosis in China. Moreover, study of the treatment potential of using Trichinella for immune-related diseases and cancer, as well as the transcription and post-transcription modification of Trichinella were also collected, providing viewpoints for future investigations. Current advances in research will help us to develop new strategies for the prevention and control of trichinellosis and may potentially yield biological agents for treating other diseases