IS LATERAL ENTRY BENEFICIAL TO ELITE SWIMMERS? A PRELIMINARY STUDY

The purpose of this study was to improve performance of elite swimmers during the starting phase, by analysing whether the lateral entry technique is beneficial to athletes\u27 starting. Participants’ (N=12) motion data were obtained from a 3D Performance Analysis System (Kistler 9691A1) with three cameras. Extracted 14 parameters were analysed using paired samples t-test. Differences contributing to the lateral entry performance included decrease of both entry angle and maximum depth by 1° and 0.42 m respectively (p\u3c.01), and increase of push force by 0.08 of body weight (p\u3c.01). The preliminary results show that the lateral technique provides a 0.06 s reduction in time to reaching the first 5 m (p\u3c.05) compared to ordinary entry, suggesting performance improvements at start times

Counterfactual Generative Models for Time-Varying Treatments

Estimating the counterfactual outcome of treatment is essential for decision-making in public health and clinical science, among others. Often, treatments are administered in a sequential, time-varying manner, leading to an exponentially increased number of possible counterfactual outcomes. Furthermore, in modern applications, the outcomes are high-dimensional and conventional average treatment effect estimation fails to capture disparities in individuals. To tackle these challenges, we propose a novel conditional generative framework capable of producing counterfactual samples under time-varying treatment, without the need for explicit density estimation. Our method carefully addresses the distribution mismatch between the observed and counterfactual distributions via a loss function based on inverse probability weighting. We present a thorough evaluation of our method using both synthetic and real-world data. Our results demonstrate that our method is capable of generating high-quality counterfactual samples and outperforms the state-of-the-art baselines

Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification

Concept Bottleneck Models (CBM) are inherently interpretable models that factor model decisions into human-readable concepts. They allow people to easily understand why a model is failing, a critical feature for high-stakes applications. CBMs require manually specified concepts and often under-perform their black box counterparts, preventing their broad adoption. We address these shortcomings and are first to show how to construct high-performance CBMs without manual specification of similar accuracy to black box models. Our approach, Language Guided Bottlenecks (LaBo), leverages a language model, GPT-3, to define a large space of possible bottlenecks. Given a problem domain, LaBo uses GPT-3 to produce factual sentences about categories to form candidate concepts. LaBo efficiently searches possible bottlenecks through a novel submodular utility that promotes the selection of discriminative and diverse information. Ultimately, GPT-3's sentential concepts can be aligned to images using CLIP, to form a bottleneck layer. Experiments demonstrate that LaBo is a highly effective prior for concepts important to visual recognition. In the evaluation with 11 diverse datasets, LaBo bottlenecks excel at few-shot classification: they are 11.7% more accurate than black box linear probes at 1 shot and comparable with more data. Overall, LaBo demonstrates that inherently interpretable models can be widely applied at similar, or better, performance than black box approaches.Comment: 18 pages, 12 figures, 16 table

BAR: Blockwise Adaptive Recoding for Batched Network Coding

Multi-hop networks become popular network topologies in various emerging Internet of things applications. Batched network coding (BNC) is a solution to reliable communications in such networks with packet loss. By grouping packets into small batches and restricting recoding to the packets belonging to the same batch, BNC has a much smaller computational and storage requirements at the intermediate nodes compared with a direct application of random linear network coding. In this paper, we propose a practical recoding scheme called blockwise adaptive recoding (BAR) which learns the latest channel knowledge from short observations so that BAR can adapt to the fluctuation of channel conditions. We focus on investigating practical concerns such as the design of efficient BAR algorithms. We also design and investigate feedback schemes for BAR under imperfect feedback systems. Our numerical evaluations show that BAR has significant throughput gain for small batch size compared with the existing baseline recoding scheme. More importantly, this gain is insensitive to inaccurate channel knowledge. This encouraging result suggests that BAR is suitable to be realized in practice as the exact channel model and its parameters could be unknown and subject to change from time to time.Comment: submitted for journal publicatio

MPPT control technology based on the GWO-VINC algorithm

It is a challenging task to accurately track the global maximum power point (GMPP) in a changing environment in widely used photovoltaic (PV) systems. So far, a variety of maximum power point (MPP) tracking algorithms have been used in solar PV power systems. The classical algorithm is simple and fast to track the speed effectively in a constant environment, but it can get stuck at an extreme point in a variable environment. In this paper, the variable-step incremental conductance (VINC) method is combined with the gray wolf optimization (GWO) algorithm. Firstly, GWO conducts a global search. When the search reaches the area near GMPP, the next step of the search process is carried out based on the iteration number conditions of GWO. Enter the search process of VINC and determine whether the current search process is on the left or right side of the vertex based on the current search value. And adjust the duty cycle during the VINC search process using different variable step size methods based on the left and right sides, and finally accurately locate the GMPP value. To verify the robustness of the proposed algorithm, simulation, and experimental comparisons were conducted between the proposed method in the article and GWO and VINC. The tracking efficiency of static shadows, simulated dynamic shadows, and experimental static and dynamic shadows is 99.80%, 98.82%, 99.43%, and 98.51%, respectively. The tracking time of simulation and experiment is 46.49% and 89.34% faster than GWO and VINC technologies, respectively. The results show that compared with the GWO and VINC methods, the proposed method has improved tracking speed and efficiency. Moreover, compared with the method that combines the two intelligent algorithms, this method has fewer algorithm parameters, a simple calculation process, lower complexity, lower hardware requirements, and better actual implementation performance

He-Jie-Shen-Shi Decoction as an Adjuvant Therapy on Severe Coronavirus Disease 2019: A Retrospective Cohort and Potential Mechanistic Study

Combination therapy using Western and traditional Chinese medicines has shown notable effects on coronavirus disease 2019 (COVID-19). The He-Jie-Shen-Shi decoction (HJSS), composed of Bupleurum chinense DC., Scutellaria baicalensis Georgi, Pinellia ternata (Thunb.) Makino, Glycyrrhiza uralensis Fisch. ex DC., and nine other herbs, has been used to treat severe COVID-19 in clinical practice. The aim of this study was to compare the clinical efficacies of HJSS combination therapy and Western monotherapy against severe COVID-19 and to study the potential action mechanism of HJSS. From February 2020 to March 2020, 81 patients with severe COVID-19 in Wuhan Tongji Hospital were selected for retrospective cohort study. Network pharmacology was conducted to predict the possible mechanism of HJSS on COVID-19-related acute respiratory distress syndrome (ARDS). Targets of active components in HJSS were screened using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) and PharmMapper databases. The targets of COVID-19 and ARDS were obtained from GeneCards and Online Mendelian Inheritance in Man databases. The key targets of HJSS in COVID-19 and ARDS were obtained based on the protein–protein interaction network (PPI). Kyoto Encyclopedia of Genes and Genomes analysis (KEGG) was conducted to predict the pathways related to the targets of HJSS in COVID-19 and ARDS. A “herb-ingredient-target-pathway” network was established using Cytoscape 3.2.7. Results showed that the duration of the negative conversion time of nucleic acid was shorter in patients who received HJSS combination therapy. HJSS combination therapy also relieved fever in patients with severe COVID-19. Network pharmacology analysis identified interleukin (IL) 6, tumor necrosis factor (TNF), vascular endothelial growth factor A (VEGFA), catalase (CAT), mitogen-activated protein kinase (MAPK) 1, tumor protein p53 (TP53), CC-chemokine ligand (CCL2), MAPK3, prostaglandin-endoperoxide synthase 2 (PTGS2), and IL1B as the key targets of HJSS in COVID-19-related ARDS. KEGG analysis suggested that HJSS improved COVID-19-related ARDS by regulating hypoxia-inducible factor (HIF)-1, NOD-like receptor, TNF, T cell receptor, sphingolipid, PI3K-Akt, toll-like receptor, VEGF, FoxO, and MAPK signaling pathways. In conclusion, HJSS can be used as an adjuvant therapy on severe COVID-19. The therapeutic mechanisms may be involved in inhibiting viral replication, inflammatory response, and oxidative stress and alleviating lung injury. Further studies are required to confirm its clinical efficacies and action mechanisms