Tai Ji Quan: An overview of its history, health benefits, and cultural value

AbstractTai Ji Quan is considered to be a part of traditional Chinese Wushu (a martial art) and comprises various styles that have evolved historically from the Chen, Yang, Wǔ, Wú, and Sun families (schools). Recent simplification of the original classic styles has made Tai Ji Quan easier to adopt in practice. Thus, the traditional legacy of using Tai Ji Quan for self-defense, mindful nurturing of well-being, and fitness enhancement has been expanded to more contemporary applications that focus on promoting physical and mental health, enhancing general well-being, preventing chronic diseases, and being an effective clinical intervention for diverse medical conditions. As the impact of Tai Ji Quan on physical performance and health continues to grow, there is a need to better understand its historical impact and current status. This paper provides an overview of the evolution of Tai Ji Quan in China, its functional utility, and the scientific evidence of its health benefits, as well as how it has been a vehicle for enhancing cultural understanding and exchanging between East and West

On the Poisson Approximation to Photon Distribution for Faint Lasers

It is proved, that for a certain kind of input distribution, the strongly binomially attenuated photon number distribution can well be approximated by a Poisson distribution. This explains why we can adopt poissonian distribution as the photon number statistics for faint lasers. The error of such an approximation is quantitatively estimated. Numerical tests are carried out, which coincide with our theoretical estimations. This work lays a sound mathematical foundation for the well-known intuitive idea which has been widely used in quantum cryptography

Evaluating Large Language Models for Generalization and Robustness via Data Compression

Existing methods for evaluating large language models face challenges such as data contamination, sensitivity to prompts, and the high cost of benchmark creation. To address this, we propose a lossless data compression based evaluation approach that tests how models' predictive abilities generalize after their training cutoff. Specifically, we collect comprehensive test data spanning 83 months from 2017 to 2023 and split the data into training and testing periods according to models' training data cutoff. We measure: 1) the compression performance on the testing period as a measure of generalization on unseen data; and 2) the performance gap between the training and testing period as a measure of robustness. Our experiments test 14 representative large language models with various sizes on sources including Wikipedia, news articles, code, arXiv papers, and multi-modal data. We find that the compression rate of many models reduces significantly after their cutoff date, but models such as Mistral and Llama-2 demonstrate a good balance between performance and robustness. Results also suggest that models struggle to generalize on news and code data, but work especially well on arXiv papers. We also find the context size and tokenization implementation have a big impact of on the overall compression performance

Hybrid resource provisioning for cloud workflows with malleable and rigid tasks

[EN] In cloud computing, reserved and on-demand instances are generally provided by service providers. Hybridization of the two alternatives can considerably save costs when renting resources from the cloud. However, it is a big challenge to determine the appropriate amount of reserved and on-demand resources in terms of users' requirements. In this paper, the workflow scheduling problem with both reserved and on-demand instances is considered. The objective is to minimize the total rental cost under deadline constrains. The considered problem is mathematically modeled. A multiple sequence-based earliest finish time method is proposed to construct schedules for the workflows. Four different rules are used to generate initial task allocation sequences. Types and quantities of resources are determined by a free time block-based schedule construction mechanism. New sequences are generated by a variable neighborhood search method. Experimental and statistical analyses and results demonstrate that the proposed algorithm algorithm generates considerable cost savings when compared to the algorithms with only on-demand or reserved instances.l This work is supported by the National Key Research and Development Program of China (No. 2017YFB1400801), the National Natural Science Foundation of China (Nos. 61572127, 61872077, 61832004) and Collaborative Innovation Center of Wireless Communications Technology. Rub~en Ruiz is supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD-Optimization of scheduling problems in container yards" (No. DPI2015-65895-R) partly financed with FEDER funds.Chen, L.; Li, X.; Guo, Y.; Ruiz García, R. (2021). Hybrid resource provisioning for cloud workflows with malleable and rigid tasks. IEEE Transactions on Cloud Computing. 9(3):1089-1102. https://doi.org/10.1109/TCC.2019.2894836S108911029

Inhibition of Bacterial Ammonia Oxidation by Organohydrazines in Soil Microcosms

Hydroxylamine oxidation by hydroxylamine oxidoreductase (HAO) is a key step for energy-yielding in support of the growth of ammonia-oxidizing bacteria (AOB). Organohydrazines have been shown to inactivate HAO from Nitrosomonas europaea, and may serve as selective inhibitors to differentiate bacterial from archaeal ammonia oxidation due to the absence of bacterial HAO gene homolog in known ammonia-oxidizing archaea (AOA). In this study, the effects of three organohydrazines on activity, abundance, and composition of AOB and AOA were evaluated in soil microcosms. The results indicate that phenylhydrazine and methylhydrazine at the concentration of 100 μmol g−1 dry weight soil completely suppressed the activity of soil nitrification. Denaturing gradient gel electrophoresis fingerprinting and sequencing analysis of bacterial ammonia monooxygenase subunit A gene (amoA) clearly demonstrated that nitrification activity change is well paralleled with the growth of Nitrosomonas europaea-like AOB in soil microcosms. No significant correlation between AOA community structure and nitrification activity was observed among all treatments during the incubation period, although incomplete inhibition of nitrification activity occurred in 2-hydroxyethylhydrazine-amended soil microcosms. These findings show that the HAO-targeted organohydrazines can effectively inhibit bacterial nitrification in soil, and the mechanism of organohydrazine affecting AOA remains unclear

An Attention-based Graph Neural Network for Heterogeneous Structural Learning

In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations. Most of the existing methods conducted on HIN revise homogeneous graph embedding models via meta-paths to learn low-dimensional vector space of HIN. In this paper, we propose a novel Heterogeneous Graph Structural Attention Neural Network (HetSANN) to directly encode structural information of HIN without meta-path and achieve more informative representations. With this method, domain experts will not be needed to design meta-path schemes and the heterogeneous information can be processed automatically by our proposed model. Specifically, we implicitly represent heterogeneous information using the following two methods: 1) we model the transformation between heterogeneous vertices through a projection in low-dimensional entity spaces; 2) afterwards, we apply the graph neural network to aggregate multi-relational information of projected neighborhood by means of attention mechanism. We also present three extensions of HetSANN, i.e., voices-sharing product attention for the pairwise relationships in HIN, cycle-consistency loss to retain the transformation between heterogeneous entity spaces, and multi-task learning with full use of information. The experiments conducted on three public datasets demonstrate that our proposed models achieve significant and consistent improvements compared to state-of-the-art solutions