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Jun Chen,1 Shaohui Jia,2 Chenggen Guo,3 Zhiwei Fan,1 Weiyi Yan,1 Kunwei Dong4 1School of Graduate, Wuhan Sport University, Wuhan, 430079, People’s Republic of China; 2School of Sports Medicine, Wuhan Sport University, Wuhan, 430079, People’s Republic of China; 3School of Sports Training, Wuhan Sport University, Wuhan, 430079, People’s Republic of China; 4School of Arts, Wuhan Sport University, Wuhan, 430079, People’s Republic of ChinaCorrespondence: Kunwei Dong, School of Arts, Wuhan Sport University, Wuhan, 430079, People’s Republic of China, Email 383239567@qq.comAbstract: With the increasingly severe situation of obesity and population aging, there is growing concern about sarcopenia obesity (SO). SO refers to the coexistence of obesity and sarcopenia, which imposes a heavier burden on individuals and society compared to obesity or sarcopenia alone. Therefore, comprehending the pathogenesis of SO and implementing effective clinical interventions are vital for its prevention and treatment. This review uses a comprehensive literature search and analysis of PubMed, Web of Science, and CNKI databases, with search terms including “Sarcopenic obesity”, “exercise”, “cytokines”, “inflammation”, “mitochondrial quality control”, and “microRNA”, covering relevant studies published up to July 2024. The results indicate that the pathogenesis of SO is complex, involving mechanisms like age-related changes in body composition, hormonal alterations, inflammation, mitochondrial dysfunction, and genetic and epigenetic factors. Regarding exercise interventions for SO, aerobic exercise can reduce fat mass, resistance exercise can increase skeletal muscle mass and strength, and combined exercise can achieve both, making it the optimal intervention for SO. The potential mechanisms by which exercise may prevent and treat SO include regulating cytokine secretion, inhibiting inflammatory pathways, improving mitochondrial quality, and mediating microRNA e

We study the certifiable robustness of ML classifiers on dirty datasets that could contain missing values. A test point is certifiably robust for an ML classifier if the classifier returns the same prediction for that test point, regardless of which cleaned version (among exponentially many) of the dirty dataset the classifier is trained on. In this paper, we show theoretically that for Naive Bayes Classifiers (NBC) over dirty datasets with missing values: (i) there exists an efficient polynomial time algorithm to decide whether multiple input test points are all certifiably robust over a dirty dataset; and (ii) the data poisoning attack, which aims to make all input test points certifiably non-robust by inserting missing cells to the clean dataset, is in polynomial time for single test points but NP-complete for multiple test points. Extensive experiments demonstrate that our algorithms are efficient and outperform existing baselines., Comment: 22 pages, 10 figures

Most data analytical pipelines often encounter the problem of querying inconsistent data that violate pre-determined integrity constraints. Data cleaning is an extensively studied paradigm that singles out a consistent repair of the inconsistent data. Consistent query answering (CQA) is an alternative approach to data cleaning that asks for all tuples guaranteed to be returned by a given query on all (in most cases, exponentially many) repairs of the inconsistent data. This paper identifies a class of acyclic select-project-join (SPJ) queries for which CQA can be solved via SQL rewriting with a linear time guarantee. Our rewriting method can be viewed as a generalization of Yannakakis's algorithm for acyclic joins to the inconsistent setting. We present LinCQA, a system that can output rewritings in both SQL and non-recursive Datalog rules for every query in this class. We show that LinCQA often outperforms the existing CQA systems on both synthetic and real-world workloads, and in some cases, by orders of magnitude.

We report new methods to control the soliton generation in ring microresonators via the soliton blockade and symmetry breaking in the bidirectionally pumped and coupled ring systems.

There is a large body of recent work applying machine learning (ML) techniques to query optimization and query performance prediction in relational database management systems (RDBMSs). However, these works typically ignore the effect of \textit{intra-parallelism} -- a key component used to boost the performance of OLAP queries in practice -- on query performance prediction. In this paper, we take a first step towards filling this gap by studying the problem of \textit{tuning the degree of parallelism (DOP) via ML techniques} in Microsoft SQL Server, a popular commercial RDBMS that allows an individual query to execute using multiple cores. In our study, we cast the problem of DOP tuning as a {\em regression} task, and examine how several popular ML models can help with query performance prediction in a multi-core setting. We explore the design space and perform an extensive experimental study comparing different models against a list of performance metrics, testing how well they generalize in different settings: $(i)$ to queries from the same template, $(ii)$ to queries from a new template, $(iii)$ to instances of different scale, and $(iv)$ to different instances and queries. Our experimental results show that a simple featurization of the input query plan that ignores cost model estimations can accurately predict query performance, capture the speedup trend with respect to the available parallelism, as well as help with automatically choosing an optimal per-query DOP.

Recursive query processing has experienced a recent resurgence, as a result of its use in many modern application domains, including data integration, graph analytics, security, program analysis, networking and decision making. Due to the large volumes of data being processed, several research efforts, across multiple communities, have explored how to scale up recursive queries, typically expressed in Datalog. Our experience with these tools indicated that their performance does not translate across domains (e.g., a tool design for large-scale graph analytics does not exhibit the same performance on program-analysis tasks, and vice versa). As a result, we designed and implemented a general-purpose Datalog engine, called RecStep, on top of a parallel single-node relational system. In this paper, we outline the different techniques we use in RecStep, and the contribution of each technique to overall performance. We also present results from a detailed set of experiments comparing RecStep with a number of other Datalog systems using both graph analytics and program-analysis tasks, summarizing pros and cons of existing techniques based on the analysis of our observations. We show that RecStep generally outperforms the state-of-the-art parallel Datalog engines on complex and large-scale Datalog program evaluation, by a 4-6X margin. An additional insight from our work is that we show that it is possible to build a high-performance Datalog system on top of a relational engine, an idea that has been dismissed in past work in this area.

We develop new models and algorithms for learning the temporal dynamics of the topic polytopes and related geometric objects that arise in topic model based inference. Our model is nonparametric Bayesian and the corresponding inference algorithm is able to discover new topics as the time progresses. By exploiting the connection between the modeling of topic polytope evolution, Beta-Bernoulli process and the Hungarian matching algorithm, our method is shown to be several orders of magnitude faster than existing topic modeling approaches, as demonstrated by experiments working with several million documents in under two dozens of minutes., Comment: NeurIPS 2019