Precious but convenient means of prevention and treatment: physiological molecular mechanisms of interaction between exercise and motor factors and Alzheimer’s disease

Disproportionate to the severity of Alzheimer’s disease (AD) and the huge number of patients, the exact treatment and prevention of AD is still being explored. With increasing ageing, the search for means to prevent and treat AD has become a high priority. In the search for AD, it has been suggested that exercise may be one of the more effective and less costly means of preventing and treating AD, and therefore a large part of current research is aimed at exploring the effectiveness of exercise in the prevention and treatment of AD. However, due to the complexity of the specific pathogenesis of AD, there are multiple hypotheses and potential mechanisms for exercise interventions in AD that need to be explored. This review therefore specifically summarises the hypotheses of the interaction between exercise and AD from a molecular perspective, based on the available evidence from animal models or human experiments, and explores them categorised according to the pathologies associated with AD: exercise can activate a number of signalling pathways inhibited by AD (e.g., Wnt and PI3K/Akt signalling pathways) and reactivate the effects of downstream factors regulated by these signalling pathways, thus acting to alleviate autophagic dysfunction, relieve neuroinflammation and mitigate Aβ deposition. In addition, this paper introduces a new approach to regulate the blood-brain barrier, i.e., to restore the stability of the blood-brain barrier, reduce abnormal phosphorylation of tau proteins and reduce neuronal apoptosis. In addition, this paper introduces a new concept.” Motor factors” or “Exerkines”, which act on AD through autocrine, paracrine or endocrine stimulation in response to movement. In this process, we believe there may be great potential for research in three areas: (1) the alleviation of AD through movement in the brain-gut axis (2) the prevention and treatment of AD by movement combined with polyphenols (3) the continued exploration of movement-mediated activation of the Wnt signalling pathway and AD

Self-driven Grounding: Large Language Model Agents with Automatical Language-aligned Skill Learning

Large language models (LLMs) show their powerful automatic reasoning and planning capability with a wealth of semantic knowledge about the human world. However, the grounding problem still hinders the applications of LLMs in the real-world environment. Existing studies try to fine-tune the LLM or utilize pre-defined behavior APIs to bridge the LLMs and the environment, which not only costs huge human efforts to customize for every single task but also weakens the generality strengths of LLMs. To autonomously ground the LLM onto the environment, we proposed the Self-Driven Grounding (SDG) framework to automatically and progressively ground the LLM with self-driven skill learning. SDG first employs the LLM to propose the hypothesis of sub-goals to achieve tasks and then verify the feasibility of the hypothesis via interacting with the underlying environment. Once verified, SDG can then learn generalized skills with the guidance of these successfully grounded subgoals. These skills can be further utilized to accomplish more complex tasks which fail to pass the verification phase. Verified in the famous instruction following task set-BabyAI, SDG achieves comparable performance in the most challenging tasks compared with imitation learning methods that cost millions of demonstrations, proving the effectiveness of learned skills and showing the feasibility and efficiency of our framework

UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy

In this work, we tackle the problem of learning universal robotic dexterous grasping from a point cloud observation under a table-top setting. The goal is to grasp and lift up objects in high-quality and diverse ways and generalize across hundreds of categories and even the unseen. Inspired by successful pipelines used in parallel gripper grasping, we split the task into two stages: 1) grasp proposal (pose) generation and 2) goal-conditioned grasp execution. For the first stage, we propose a novel probabilistic model of grasp pose conditioned on the point cloud observation that factorizes rotation from translation and articulation. Trained on our synthesized large-scale dexterous grasp dataset, this model enables us to sample diverse and high-quality dexterous grasp poses for the object point cloud.For the second stage, we propose to replace the motion planning used in parallel gripper grasping with a goal-conditioned grasp policy, due to the complexity involved in dexterous grasping execution. Note that it is very challenging to learn this highly generalizable grasp policy that only takes realistic inputs without oracle states. We thus propose several important innovations, including state canonicalization, object curriculum, and teacher-student distillation. Integrating the two stages, our final pipeline becomes the first to achieve universal generalization for dexterous grasping, demonstrating an average success rate of more than 60\% on thousands of object instances, which significantly outperforms all baselines, meanwhile showing only a minimal generalization gap.Comment: Accepted to CVPR 202

A Survey of Large Language Models

Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and grasping a language. As a major approach, language modeling has been widely studied for language understanding and generation in the past two decades, evolving from statistical language models to neural language models. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora, showing strong capabilities in solving various NLP tasks. Since researchers have found that model scaling can lead to performance improvement, they further study the scaling effect by increasing the model size to an even larger size. Interestingly, when the parameter scale exceeds a certain level, these enlarged language models not only achieve a significant performance improvement but also show some special abilities that are not present in small-scale language models. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size. Recently, the research on LLMs has been largely advanced by both academia and industry, and a remarkable progress is the launch of ChatGPT, which has attracted widespread attention from society. The technical evolution of LLMs has been making an important impact on the entire AI community, which would revolutionize the way how we develop and use AI algorithms. In this survey, we review the recent advances of LLMs by introducing the background, key findings, and mainstream techniques. In particular, we focus on four major aspects of LLMs, namely pre-training, adaptation tuning, utilization, and capacity evaluation. Besides, we also summarize the available resources for developing LLMs and discuss the remaining issues for future directions.Comment: ongoing work; 51 page

A comprehensive multimodal dataset for contactless lip reading and acoustic analysis

Small-scale motion detection using non-invasive remote sensing techniques has recently garnered significant interest in the field of speech recognition. Our dataset paper aims to facilitate the enhancement and restoration of speech information from diverse data sources for speakers. In this paper, we introduce a novel multimodal dataset based on Radio Frequency, visual, text, audio, laser and lip landmark information, also called RVTALL. Specifically, the dataset consists of 7.5 GHz Channel Impulse Response (CIR) data from ultra-wideband (UWB) radars, 77 GHz frequency modulated continuous wave (FMCW) data from millimeter wave (mmWave) radar, visual and audio information, lip landmarks and laser data, offering a unique multimodal approach to speech recognition research. Meanwhile, a depth camera is adopted to record the landmarks of the subject’s lip and voice. Approximately 400 minutes of annotated speech profiles are provided, which are collected from 20 participants speaking 5 vowels, 15 words, and 16 sentences. The dataset has been validated and has potential for the investigation of lip reading and multimodal speech recognition

Dynamic reversible evolution of solid electrolyte interface in nonflammable triethyl phosphate electrolyte enabling safe and stable potassium-ion batteries

Potassium-ion batteries (PIBs) are a favorable alternative to lithium-ion batteries (LIBs) for the large-scale electrochemical storage devices because of the high natural abundance of potassium resources. However, conventional PIB electrodes usually exhibit low actual capacities and poor cyclic stability due to the large radius of potassium ions (1.39 Å). In addition, the high reactivity of potassium metal raises serious safety concerns. These characteristics seriously inhibit the practical use of PIB electrodes. Here, zinc phosphide composites are rationally designed as PIB anodes for operation in a nonflammable triethyl phosphate (TEP) electrolyte to solve the above-mentioned issues. The optimized zinc phosphide composite with 20 wt% zinc phosphate presents a high specific capacity (571.1 mA h g−1 at 0.1 A g−1) and excellent cycling performance (484.9 mA h g−1 with the capacity retention of 94.5% after 1000 cycles at 0.5 A g−1) in the KFSI-TEP electrolyte. XPS depth profile analysis shows that the improved cycling stability of the composite is closely related to the reversible dynamic evolutions and conversions of the sulfur-containing species in the solid electrolyte interphase (SEI) during the charge/discharge process. This dynamic reversible SEI concept may provide a new strategy for the design of superior electrodes for PIBs

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Case Study Research in Tesla (China) Marketing Strategy Application During Covid-19

Background: In the past two years, the outbreak of the coronavirus has had a major impact on the world economy, and has a considerable negative impact on the performance and sales of the automobile manufacturing industry. Enterprises need to sum up their experience. Tesla's successful case can be used as a reference for analysis. . Purpose: In response to the substantial increase in sales performance of Tesla's Chinese market during the epidemic, relevant market strategy analysis was made, and the researchers tried to summarize relevant experience to provide reference for the automotive industry. Method: The researchers used a relatively flexible and exploratory qualitative approach, conducting semi-structured interviews with seven current Tesla employees and using secondary sources to aid in proving the veracity and viability of the information. Conclusion: The results show that most of the targeted strategies implemented by Tesla during the epidemic are effective, and the application of various strategies is related to changes in sales performance. The researchers collected raw data through interviews, analyzed why Tesla used these strategies, and evaluated the application effects of the main strategies. At the same time, the researchers also put forward our own views and opinions