To systematically elucidate the central role of the cardiac autonomic nervous system(ANS)in maintaining cardiovascular homeostasis,analyze the pathological mechanisms underlying its dysregulation,integrate multidiscip...To systematically elucidate the central role of the cardiac autonomic nervous system(ANS)in maintaining cardiovascular homeostasis,analyze the pathological mechanisms underlying its dysregulation,integrate multidisciplinary research findings from the U.S.stimulating peripheral nerves to alleviate disease symptoms(SPARC)program,and evaluate the unique advantages and current limitations of acupuncture in modulating cardiac ANS function,ultimately proposing novel strategies for the prevention and treatment of cardiovascular diseases.A systematic literature review was conducted to synthesize current knowledge on the fundamental regulatory mechanisms of the cardiac ANS,the SPARC program's innovative contributions to neuroanatomy and neural pathway mapping,as well as clinical and experimental evidence supporting acupuncture's modulation of the ANS.The SPARC program has made significant progress in elucidating the anatomical organization and neural circuitry of the cardiac ANS through interdisciplinary collaboration,offering novel insights and methodological frameworks for studying cardiac autonomic regulation.Acupuncture,as a cornerstone of traditional medicine,has demonstrated both specificity and multi-target regulatory effects on the cardiac ANS through clinical and experimental studies.However,challenges remain,including an incomplete mechanistic understanding,technical limitations in research methodologies,and difficulties in translating findings into clinical practice.Future research on acupuncture should build upon its inherent strengths while advancing its integration with modern scientific and technological approaches.Strengthening interdisciplinary collaboration and leveraging artificial intelligence can open new frontiers in mechanistic exploration and technological innovation.These efforts will facilitate the internationalization of acupuncture research and contribute innovative perspectives and therapeutic strategies for cardiovascular disease prevention and treatment.展开更多
Understanding the properties of warm dense hydrogen is of key importance for the modeling of compact astrophysical objects and to understand and further optimize inertial confinement fusion applications.The workhorse ...Understanding the properties of warm dense hydrogen is of key importance for the modeling of compact astrophysical objects and to understand and further optimize inertial confinement fusion applications.The workhorse of warm dense matter theory is thermal density functional theory(DFT),which,however,suffers from two limitations:(i)its accuracy can depend on the utilized exchange-correlation functional,which has to be approximated,and(ii)it is generally limited to single-electron properties such as the density distribution.Here,we present a new ansatz combining time-dependent DFT results for the dynamic structure factor S_(ee)(q,ω)with static DFT results for the density response.This allows us to estimate the electron-electron static structure factor S_(ee)(q)of warm dense hydrogen with high accuracy over a broad range of densities and temperatures.In addition to its value for the study of warm dense matter,our work opens up new avenues for the future study of electronic correlations exclusively within the framework of DFT for a host of applications.展开更多
Large-scale proteomics studies can refine our understanding of health and disease and enable precision medicine.Here,we provide a detailed atlas of 2,920 plasma proteins linking to diseases(406 prevalent and 660 incid...Large-scale proteomics studies can refine our understanding of health and disease and enable precision medicine.Here,we provide a detailed atlas of 2,920 plasma proteins linking to diseases(406 prevalent and 660 incident)and 986 health-related traits in 53,026 individuals(median follow-up:14.8 years)from the UK Biobank,representing the most comprehensive proteome profiles to date.This atlas revealed 168,100 protein-disease associations and 554,488 protein-trait associations.展开更多
End-to-end Temporal Action Detection(TAD)has achieved remarkable progress in recent years,driven by innovations in model architectures and the emergence of Video Foundation Models(VFMs).However,existing TAD methods th...End-to-end Temporal Action Detection(TAD)has achieved remarkable progress in recent years,driven by innovations in model architectures and the emergence of Video Foundation Models(VFMs).However,existing TAD methods that perform full fine-tuning of pretrained video models often incur substantial computational costs,which become particularly pronounced when processing long video sequences.Moreover,the need for precise temporal boundary annotations makes data labeling extremely expensive.In low-resource settings where annotated samples are scarce,direct fine-tuning tends to cause overfitting.To address these challenges,we introduce Dynamic LowRank Adapter(DyLoRA),a lightweight fine-tuning framework tailored specifically for the TAD task.Built upon the Low-Rank Adaptation(LoRA)architecture,DyLoRA adapts only the key layers of the pretrained model via low-rank decomposition,reducing the number of trainable parameters to less than 5%of full fine-tuning methods.This significantly lowers memory consumption and mitigates overfitting in low-resource settings.Notably,DyLoRA enhances the temporal modeling capability of pretrained models by optimizing temporal dimension weights,thereby alleviating the representation misalignment of temporal features.Experimental results demonstrate that DyLoRA-TAD achieves impressive performance,with 73.9%mAP on THUMOS14,39.52%on ActivityNet-1.3,and 28.2%on Charades,substantially surpassing the best traditional feature-based methods.展开更多
In physics,our expectations for system behavior are often guided by intuitive arithmetic.For systems composed of identical units,we anticipate synergy of the contributions from these units,where 1+1=2.Conversely,for s...In physics,our expectations for system behavior are often guided by intuitive arithmetic.For systems composed of identical units,we anticipate synergy of the contributions from these units,where 1+1=2.Conversely,for systems built from opposing units,we expect cancellation of their contributions,where 1-1=0.This intuitive arithmetic has long underpinned our understanding of physical properties of materials,from electronic transport to optical responses.However,scientific breakthroughs often occur when nature reveals ways to circumvent these seemingly fundamental rules,opening new possibilities that challenge our deepest assumptions about material behavior.展开更多
Water electrolysis is pivotal for converting renewable energy into clean hydrogen fuel,addressing global energy demand sustainably.However,the development of highly efficient and cost-effective catalysts for the oxyge...Water electrolysis is pivotal for converting renewable energy into clean hydrogen fuel,addressing global energy demand sustainably.However,the development of highly efficient and cost-effective catalysts for the oxygen evolution reaction(OER)remains a significant challenge,particularly at the industrial scale.This report explores a newly discovered pathway,the oxide path mechanism(OPM) for OER-mechanism involving the oxide formation and evolution during the reaction,emphasizing its potential to overcome existing limitations.OPM enables direct O-O coupling without oxygen vacancies,offering superior stability.We detail both classical and innovative in-situ characterization techniques that are central to unraveling the OER mechanism.The advanced in-situ electrochemical techniques,such as inductively coupled plasma mass spectroscopy,X-ray photoelectron spectroscopy,and Mössbauer spectroscopy,coupled with in-situ structural analyses,provide crucial insights into the catalyst surface,the electrode-electrolyte interface and the kinetics of OER.This review provides a systematic analysis integrating classical electrochemical methods with advanced in-situ/operando techniques,specifically focusing on understanding OPM.While numerous studies have examined individual characterization methods,this study systematically integrates traditional electrochemical approaches with in-situ and operando techniques,offering critical insights into their complementary roles in elucidating reaction pathways.The integration of these methodologies provides unprecedented understanding of catalyst behavior under operational conditions,guiding the rational design of next-generation OER catalysts.Furthermore,we discuss essential standardized test toolkits and protocols,such as those for rotating disk electrode and membrane electrode assembly,which are vital for ensuring reproducibility and scalability in OER catalyst research.展开更多
In the field of intelligent surveillance,weakly supervised video anomaly detection(WSVAD)has garnered widespread attention as a key technology that identifies anomalous events using only video-level labels.Although mu...In the field of intelligent surveillance,weakly supervised video anomaly detection(WSVAD)has garnered widespread attention as a key technology that identifies anomalous events using only video-level labels.Although multiple instance learning(MIL)has dominated the WSVAD for a long time,its reliance solely on video-level labels without semantic grounding hinders a fine-grained understanding of visually similar yet semantically distinct events.In addition,insufficient temporal modeling obscures causal relationships between events,making anomaly decisions reactive rather than reasoning-based.To overcome the limitations above,this paper proposes an adaptive knowledgebased guidance method that integrates external structured knowledge.The approach combines hierarchical category information with learnable prompt vectors.It then constructs continuously updated contextual references within the feature space,enabling fine-grained meaning-based guidance over video content.Building on this,the work introduces an event relation analysis module.This module explicitly models temporal dependencies and causal correlations between video snippets.It constructs an evolving logic chain of anomalous events,revealing the process by which isolated anomalous snippets develop into a complete event.Experiments on multiple benchmark datasets show that the proposed method achieves highly competitive performance,achieving an AUC of 88.19%on UCF-Crime and an AP of 86.49%on XD-Violence.More importantly,the method provides temporal and causal explanations derived from event relationships alongside its detection results.This capability significantly advances WSVAD from a simple binary classification to a new level of interpretable behavior analysis.展开更多
Measuring cross sections of nuclear reactions,such as the so-called“Holy Grail”reaction,^(12)C(σ,γ)^(16)O,is essential for understanding stellar nucleosynthesis but presents significant challenges due to extremely...Measuring cross sections of nuclear reactions,such as the so-called“Holy Grail”reaction,^(12)C(σ,γ)^(16)O,is essential for understanding stellar nucleosynthesis but presents significant challenges due to extremely low cross sections.Key challenges include significant energy loss as ions penetrate the target material,limiting measurements to thin target layers.To overcome these obstacles,we propose a novel method,the in-target energy loss compensating(eLOC)method,specifically designed for gas targets,which utilizes a gas-filled magnetic field and accelerating electric fields to compensate for ion energy loss in the target.Simulations show that this approach significantly enhances the effective target thickness by over 140 times in the case of the“Holy Grail”reaction with an inverse-kinematics setup.This eLOC method may provide a powerful new tool for obtaining critical data in nuclear astrophysics,thereby advancing our understanding of stellar nucleosynthesis and the origins of elements in the universe,as well as benefiting other related fields such as isotope production.展开更多
Building reliable intent-based,task-oriented dialog systems typically requires substantial manual effort:designers must derive intents,entities,responses,and control logic from raw conversational data,then iterate unt...Building reliable intent-based,task-oriented dialog systems typically requires substantial manual effort:designers must derive intents,entities,responses,and control logic from raw conversational data,then iterate until the assistant behaves consistently.This paper investigates how far large language models(LLMs)can automate this development.In this paper,we use two reference corpora,Let’s Go(English,public transport)and MEDIA(French,hotel booking),to prompt four LLM families(GPT-4o,Claude,Gemini,Mistral Small)and generate the core specifications required by the rasa platform.These include intent sets with example utterances,entity definitions with slot mappings,response templates,and basic dialog flows.To structure this process,we introduce a model-and platform-agnostic pipelinewith two phases.The first normalizes and validates LLM-generated artifacts,enforcing crossfile consistency andmaking slot usage explicit.The second uses a lightweight dialog harness that runs scripted tests and incrementally patches failure points until conversations complete reliably.Across eight projects,all models required some targeted repairs before training.After applying our pipeline,all reached≥70%task completion(many above 84%),while NLU performance ranged from mid-0.6 to 1.0 macro-F1 depending on domain breadth.These results show that,with modest guidance,current LLMs can produce workable end-to-end dialog prototypes directly fromraw transcripts.Our main contributions are:(i)a reusable bootstrap method aligned with industry domain-specific languages(DSLs),(ii)a small set of high-impact corrective patterns,and(iii)a simple but effective harness for closed-loop refinement across conversational platforms.展开更多
Understanding the complex plasma dynamics in ultra-intense relativistic laser-solid interactions is of fundamental importance for applications of laser-plasma-based particle accelerators,the creation of high-energy-de...Understanding the complex plasma dynamics in ultra-intense relativistic laser-solid interactions is of fundamental importance for applications of laser-plasma-based particle accelerators,the creation of high-energy-density matter,understanding planetary science,and laser-driven fusion energy.However,experimental efforts in this regime have been limited by the lack of accessibility of over-critical densities and the poor spatiotemporal resolution of conventional diagnostics.Over the last decade,the advent of femtosecond brilliant hard X-ray free-electron lasers(XFELs)has opened new horizons to overcome these limitations.Here,for the first time,we present full-scale spatiotemporal measurements of solid-density plasma dynamics,including preplasma generation with tens of nanometer scale length driven by the leading edge of a relativistic laser pulse,ultrafast heating and ionization at the main pulse arrival,the laser-driven blast wave,and transient surface return current-induced compression dynamics up to hundreds of picoseconds after interaction.These observations are enabled by utilizing a novel combination of advanced X-ray diagnostics including small-angle X-ray scattering,resonant X-ray emission spectroscopy,and propagation-based X-ray phase-contrast imaging simultaneously at the European XFEL-HED beamline station.展开更多
Experiments with interacting high-velocity flows in a laser plasma can help answer fundamental questions in plasma physics and improve understanding of the mechanisms behind some astrophysical phenomena,such as the fo...Experiments with interacting high-velocity flows in a laser plasma can help answer fundamental questions in plasma physics and improve understanding of the mechanisms behind some astrophysical phenomena,such as the formation of collisionless shock waves,deceleration of accretion flows,and evolution of solar and stellar flares.This work presents the first direct experimental observations of stagnation and redirection of counterstreaming flows(jets)of laser plasma induced by intense laser pulses with intensity I~2×10^(18) W/cm^(2).Hybrid particlein-cell-fluid modeling,which takes into account the kinetic effects of ion motion and the evolution of the pressure tensor for electrons,demonstrates the compression of counterdirected toroidal self-generated magnetic fields embedded in counterstreaming plasma flows.The enhancement of the toroidal magnetic field in the interaction region results in plasma flow stagnation and redirection of the jets across the line of their initial propagation.展开更多
Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the...Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.展开更多
With the advent of the next-generation Air Traffic Control(ATC)system,there is growing interest in using Artificial Intelligence(AI)techniques to enhance Situation Awareness(SA)for ATC Controllers(ATCOs),i.e.,Intellig...With the advent of the next-generation Air Traffic Control(ATC)system,there is growing interest in using Artificial Intelligence(AI)techniques to enhance Situation Awareness(SA)for ATC Controllers(ATCOs),i.e.,Intelligent SA(ISA).However,the existing AI-based SA approaches often rely on unimodal data and lack a comprehensive description and benchmark of the ISA tasks utilizing multi-modal data for real-time ATC environments.To address this gap,by analyzing the situation awareness procedure of the ATCOs,the ISA task is refined to the processing of the two primary elements,i.e.,spoken instructions and flight trajectories.Subsequently,the ISA is further formulated into Controlling Intent Understanding(CIU)and Flight Trajectory Prediction(FTP)tasks.For the CIU task,an innovative automatic speech recognition and understanding framework is designed to extract the controlling intent from unstructured and continuous ATC communications.For the FTP task,the single-and multi-horizon FTP approaches are investigated to support the high-precision prediction of the situation evolution.A total of 32 unimodal/multi-modal advanced methods with extensive evaluation metrics are introduced to conduct the benchmarks on the real-world multi-modal ATC situation dataset.Experimental results demonstrate the effectiveness of AI-based techniques in enhancing ISA for the ATC environment.展开更多
Understanding the proton dynamic behavior in inorganic materials has long been a topic of intense fascination[1],especially in the field of electrochemical energy storage[2].One of the examples is the research of prot...Understanding the proton dynamic behavior in inorganic materials has long been a topic of intense fascination[1],especially in the field of electrochemical energy storage[2].One of the examples is the research of proton transport in transition metal oxides,which dates back to 1971[3]when RuO_(2) was discovered to be capable of storing protons via reversible redox reactions[4].In aqueous electrolytes,the thin film RuO_(2) electrode exhibits a surface pseudocapacitive behavior[5],which could be modified by the structural water in its hydrated form due to the facile Grotthuss hopping mode of protons along the established hydrogen bonds inside the bulk phase[6].Soon later,Goodenough et al.reported the capacitor-like behavior of amorphous MnO_(2)·xH_(2)O electrode in an aqueous KCl electrolyte[7],and further studies on the hydrated MnO_(2) electrodes prepared by sol-gel processes have soon discovered that the intercalation of protons from aqueous electrolytes plays an indispensable role in the charge storage mechanism[8].In recent years,the research interest on rechargeable aqueous batteries has fueled the renaissance of mechanistic study of proton transport in transition metal oxides[9],which can operate as cathodes or anodes via a topotactic insertion mechanism similar to that in Li-ion batteries[10].However,due to the challenges for experimental detection of local chemical environments of the inserted protons,a comprehensive understanding of proton dynamic behavior in these electrodes remains largely lacking.展开更多
In January 2025,the Beijing Chamber of Commerce(Singapore)was officially established.Coinciding with the 35th anniversary of China-Singapore diplomatic relations and the 60th anniversary of Singapore’s independence,i...In January 2025,the Beijing Chamber of Commerce(Singapore)was officially established.Coinciding with the 35th anniversary of China-Singapore diplomatic relations and the 60th anniversary of Singapore’s independence,its founding is part of a timely response to the growing needs of businesses and communities from both Beijing and Singapore to deepen mutual understanding and expand cooperation.As a Chinese person who has lived and worked in Singapore for many years,Feng Jianli,president of the Beijing Chamber of Commerce(Singapore),knows first-hand just how challenging it can be for new immigrants from Beijing to adapt to identity transitions and access local support.The chamber was founded not only to help them take root and thrive,but also to serve as a platform for deeper people-to-people exchange between Singapore and Beijing.展开更多
Fostering understanding between China and Africa through dialogue and exchanges Etienne Bankuwiha,a young sinologist from Burundi and a doctoral student at Nanjing University,received on the afternoon of 13 November t...Fostering understanding between China and Africa through dialogue and exchanges Etienne Bankuwiha,a young sinologist from Burundi and a doctoral student at Nanjing University,received on the afternoon of 13 November the message he had been eagerly awaiting-a reply from Chinese President Xi Jinping.展开更多
As an English traveler,diving into the heart of Chinese culture is like unlocking a world of rich experiences that offer a deeper understanding of the country and its people.Here is how,from my perspective,I have imme...As an English traveler,diving into the heart of Chinese culture is like unlocking a world of rich experiences that offer a deeper understanding of the country and its people.Here is how,from my perspective,I have immersed myself in various facets of Chinese culture.展开更多
1 When we immerse ourselves in the pages of a book,we are transported to worlds beyond our own,gaining insights into the human experience and expanding our understanding of ourselves and others.2 Reading can fill our ...1 When we immerse ourselves in the pages of a book,we are transported to worlds beyond our own,gaining insights into the human experience and expanding our understanding of ourselves and others.2 Reading can fill our lives and purify our souls.I enjoy reading and I love life.I enjoy reading Chinese classics such as the Four Great Classical Novels of Chinese literature.I explore the mythical and wonderful stories of demons and monsters in Journey to the West and experience the boldness of martial art heroes in Water Margin.展开更多
基金supported by the National Natural Science Foundation of China(82174513)Fundamental Research Funds for the Central Public Welfare Research Institutes(ZZ-JQ2023003).
文摘To systematically elucidate the central role of the cardiac autonomic nervous system(ANS)in maintaining cardiovascular homeostasis,analyze the pathological mechanisms underlying its dysregulation,integrate multidisciplinary research findings from the U.S.stimulating peripheral nerves to alleviate disease symptoms(SPARC)program,and evaluate the unique advantages and current limitations of acupuncture in modulating cardiac ANS function,ultimately proposing novel strategies for the prevention and treatment of cardiovascular diseases.A systematic literature review was conducted to synthesize current knowledge on the fundamental regulatory mechanisms of the cardiac ANS,the SPARC program's innovative contributions to neuroanatomy and neural pathway mapping,as well as clinical and experimental evidence supporting acupuncture's modulation of the ANS.The SPARC program has made significant progress in elucidating the anatomical organization and neural circuitry of the cardiac ANS through interdisciplinary collaboration,offering novel insights and methodological frameworks for studying cardiac autonomic regulation.Acupuncture,as a cornerstone of traditional medicine,has demonstrated both specificity and multi-target regulatory effects on the cardiac ANS through clinical and experimental studies.However,challenges remain,including an incomplete mechanistic understanding,technical limitations in research methodologies,and difficulties in translating findings into clinical practice.Future research on acupuncture should build upon its inherent strengths while advancing its integration with modern scientific and technological approaches.Strengthening interdisciplinary collaboration and leveraging artificial intelligence can open new frontiers in mechanistic exploration and technological innovation.These efforts will facilitate the internationalization of acupuncture research and contribute innovative perspectives and therapeutic strategies for cardiovascular disease prevention and treatment.
基金partially supported by the Center for Advanced Systems Understanding (CASUS), financed by Germany’s Federal Ministry of Education and Research and the Saxon State Government out of the State Budget approved by the Saxon State Parliamentthe European Union’s Just Transition Fund (JTF) within the project Röntgenlaser Optimierung der Laserfusion (ROLF), Contract No. 5086999001, co-financed by the Saxon State Government out of the State Budget approved by the Saxon State Parliament+3 种基金the European Research Council (ERC) under the European Union’s Horizon 2022 Research and Innovation Programme (Grant Agreement No. 101076233, “PREXTREME”)Computations were performed on a Bull Cluster at the Center for Information Services and High-Performance Computing (ZIH) at Technische Universität Dresden and at the Norddeutscher Verbund für Hoch- und Höchstleistungsrechnen (HLRN) under Grant No. mvp00024support by the National Natural Science Foundation of China under Grant No. 12274171support by the Advanced Materials–National Science and Technology Major Project (Grant No. 2024ZD0606900)
文摘Understanding the properties of warm dense hydrogen is of key importance for the modeling of compact astrophysical objects and to understand and further optimize inertial confinement fusion applications.The workhorse of warm dense matter theory is thermal density functional theory(DFT),which,however,suffers from two limitations:(i)its accuracy can depend on the utilized exchange-correlation functional,which has to be approximated,and(ii)it is generally limited to single-electron properties such as the density distribution.Here,we present a new ansatz combining time-dependent DFT results for the dynamic structure factor S_(ee)(q,ω)with static DFT results for the density response.This allows us to estimate the electron-electron static structure factor S_(ee)(q)of warm dense hydrogen with high accuracy over a broad range of densities and temperatures.In addition to its value for the study of warm dense matter,our work opens up new avenues for the future study of electronic correlations exclusively within the framework of DFT for a host of applications.
文摘Large-scale proteomics studies can refine our understanding of health and disease and enable precision medicine.Here,we provide a detailed atlas of 2,920 plasma proteins linking to diseases(406 prevalent and 660 incident)and 986 health-related traits in 53,026 individuals(median follow-up:14.8 years)from the UK Biobank,representing the most comprehensive proteome profiles to date.This atlas revealed 168,100 protein-disease associations and 554,488 protein-trait associations.
基金supported by the National Natural Science Foundation of China(Grant No.62266054)the Major Science and Technology Project of Yunnan Province(Grant No.202402AD080002)the Scientific Research Fund of the Yunnan Provincial Department of Education(Grant No.2025Y0302).
文摘End-to-end Temporal Action Detection(TAD)has achieved remarkable progress in recent years,driven by innovations in model architectures and the emergence of Video Foundation Models(VFMs).However,existing TAD methods that perform full fine-tuning of pretrained video models often incur substantial computational costs,which become particularly pronounced when processing long video sequences.Moreover,the need for precise temporal boundary annotations makes data labeling extremely expensive.In low-resource settings where annotated samples are scarce,direct fine-tuning tends to cause overfitting.To address these challenges,we introduce Dynamic LowRank Adapter(DyLoRA),a lightweight fine-tuning framework tailored specifically for the TAD task.Built upon the Low-Rank Adaptation(LoRA)architecture,DyLoRA adapts only the key layers of the pretrained model via low-rank decomposition,reducing the number of trainable parameters to less than 5%of full fine-tuning methods.This significantly lowers memory consumption and mitigates overfitting in low-resource settings.Notably,DyLoRA enhances the temporal modeling capability of pretrained models by optimizing temporal dimension weights,thereby alleviating the representation misalignment of temporal features.Experimental results demonstrate that DyLoRA-TAD achieves impressive performance,with 73.9%mAP on THUMOS14,39.52%on ActivityNet-1.3,and 28.2%on Charades,substantially surpassing the best traditional feature-based methods.
基金supported by the National Natural Science Foundation of China (Grant No.12374109)the National Key Research and Development Program of China (Grant No.2023YFA1406600)。
文摘In physics,our expectations for system behavior are often guided by intuitive arithmetic.For systems composed of identical units,we anticipate synergy of the contributions from these units,where 1+1=2.Conversely,for systems built from opposing units,we expect cancellation of their contributions,where 1-1=0.This intuitive arithmetic has long underpinned our understanding of physical properties of materials,from electronic transport to optical responses.However,scientific breakthroughs often occur when nature reveals ways to circumvent these seemingly fundamental rules,opening new possibilities that challenge our deepest assumptions about material behavior.
基金funded by the EU H2020 Marie Skłodowska-Curie Fellowship (1439425)the National Natural Science Foundation of China (No. 52171199 and 22479011)
文摘Water electrolysis is pivotal for converting renewable energy into clean hydrogen fuel,addressing global energy demand sustainably.However,the development of highly efficient and cost-effective catalysts for the oxygen evolution reaction(OER)remains a significant challenge,particularly at the industrial scale.This report explores a newly discovered pathway,the oxide path mechanism(OPM) for OER-mechanism involving the oxide formation and evolution during the reaction,emphasizing its potential to overcome existing limitations.OPM enables direct O-O coupling without oxygen vacancies,offering superior stability.We detail both classical and innovative in-situ characterization techniques that are central to unraveling the OER mechanism.The advanced in-situ electrochemical techniques,such as inductively coupled plasma mass spectroscopy,X-ray photoelectron spectroscopy,and Mössbauer spectroscopy,coupled with in-situ structural analyses,provide crucial insights into the catalyst surface,the electrode-electrolyte interface and the kinetics of OER.This review provides a systematic analysis integrating classical electrochemical methods with advanced in-situ/operando techniques,specifically focusing on understanding OPM.While numerous studies have examined individual characterization methods,this study systematically integrates traditional electrochemical approaches with in-situ and operando techniques,offering critical insights into their complementary roles in elucidating reaction pathways.The integration of these methodologies provides unprecedented understanding of catalyst behavior under operational conditions,guiding the rational design of next-generation OER catalysts.Furthermore,we discuss essential standardized test toolkits and protocols,such as those for rotating disk electrode and membrane electrode assembly,which are vital for ensuring reproducibility and scalability in OER catalyst research.
文摘In the field of intelligent surveillance,weakly supervised video anomaly detection(WSVAD)has garnered widespread attention as a key technology that identifies anomalous events using only video-level labels.Although multiple instance learning(MIL)has dominated the WSVAD for a long time,its reliance solely on video-level labels without semantic grounding hinders a fine-grained understanding of visually similar yet semantically distinct events.In addition,insufficient temporal modeling obscures causal relationships between events,making anomaly decisions reactive rather than reasoning-based.To overcome the limitations above,this paper proposes an adaptive knowledgebased guidance method that integrates external structured knowledge.The approach combines hierarchical category information with learnable prompt vectors.It then constructs continuously updated contextual references within the feature space,enabling fine-grained meaning-based guidance over video content.Building on this,the work introduces an event relation analysis module.This module explicitly models temporal dependencies and causal correlations between video snippets.It constructs an evolving logic chain of anomalous events,revealing the process by which isolated anomalous snippets develop into a complete event.Experiments on multiple benchmark datasets show that the proposed method achieves highly competitive performance,achieving an AUC of 88.19%on UCF-Crime and an AP of 86.49%on XD-Violence.More importantly,the method provides temporal and causal explanations derived from event relationships alongside its detection results.This capability significantly advances WSVAD from a simple binary classification to a new level of interpretable behavior analysis.
基金supported by the the National Key R&D Program of China (Grant Nos.2023YFA1606900 and 2022YFA1602301)the National Natural Science Foundation of China (Grant Nos.12235003,12435010,and 12147101)+3 种基金the Guangdong Major Project of Basic and Applied Basic Research (Grant No.2020B0301030008)the STCSM (Grant No.23590780100)the Natural Science Foundation of Shanghai (Grant No.23JC1400200)the China Postdoctoral Science Foundation (Grant No.2024M760483)。
文摘Measuring cross sections of nuclear reactions,such as the so-called“Holy Grail”reaction,^(12)C(σ,γ)^(16)O,is essential for understanding stellar nucleosynthesis but presents significant challenges due to extremely low cross sections.Key challenges include significant energy loss as ions penetrate the target material,limiting measurements to thin target layers.To overcome these obstacles,we propose a novel method,the in-target energy loss compensating(eLOC)method,specifically designed for gas targets,which utilizes a gas-filled magnetic field and accelerating electric fields to compensate for ion energy loss in the target.Simulations show that this approach significantly enhances the effective target thickness by over 140 times in the case of the“Holy Grail”reaction with an inverse-kinematics setup.This eLOC method may provide a powerful new tool for obtaining critical data in nuclear astrophysics,thereby advancing our understanding of stellar nucleosynthesis and the origins of elements in the universe,as well as benefiting other related fields such as isotope production.
基金This publication is part of the TrustBoost project,that has received funding from MICIU/AEI/10.13039/501100011033,from FEDER,UEIt is a coordinated project by a multidisciplinary team from the Universidad Politécnica de Madrid(UPM)and University of Granada(UGR),with two subprojects that address TrustBoost’s objectives:“Enhancing Trustworthiness in Conversational AI through Multimodal Affective Awareness”(Trust Boost-UPM,ref.PID2023-150584OB-C21)“Breaking the Duality of Conversational AI:Going beyond Guided Conversations While Ensuring Compliance with Domain Rules and Constraints”(Trust Boost-UGR,ref.PID2023-150584OB-C22).
文摘Building reliable intent-based,task-oriented dialog systems typically requires substantial manual effort:designers must derive intents,entities,responses,and control logic from raw conversational data,then iterate until the assistant behaves consistently.This paper investigates how far large language models(LLMs)can automate this development.In this paper,we use two reference corpora,Let’s Go(English,public transport)and MEDIA(French,hotel booking),to prompt four LLM families(GPT-4o,Claude,Gemini,Mistral Small)and generate the core specifications required by the rasa platform.These include intent sets with example utterances,entity definitions with slot mappings,response templates,and basic dialog flows.To structure this process,we introduce a model-and platform-agnostic pipelinewith two phases.The first normalizes and validates LLM-generated artifacts,enforcing crossfile consistency andmaking slot usage explicit.The second uses a lightweight dialog harness that runs scripted tests and incrementally patches failure points until conversations complete reliably.Across eight projects,all models required some targeted repairs before training.After applying our pipeline,all reached≥70%task completion(many above 84%),while NLU performance ranged from mid-0.6 to 1.0 macro-F1 depending on domain breadth.These results show that,with modest guidance,current LLMs can produce workable end-to-end dialog prototypes directly fromraw transcripts.Our main contributions are:(i)a reusable bootstrap method aligned with industry domain-specific languages(DSLs),(ii)a small set of high-impact corrective patterns,and(iii)a simple but effective harness for closed-loop refinement across conversational platforms.
基金funding from Grant No. HIDSS-0002 DASHH (Data Science in Hamburg-Helmholtz Graduate School for the Structure of Matter)partially supported by the Helmholtz Imaging platform through the project “Smart Phase.”
文摘Understanding the complex plasma dynamics in ultra-intense relativistic laser-solid interactions is of fundamental importance for applications of laser-plasma-based particle accelerators,the creation of high-energy-density matter,understanding planetary science,and laser-driven fusion energy.However,experimental efforts in this regime have been limited by the lack of accessibility of over-critical densities and the poor spatiotemporal resolution of conventional diagnostics.Over the last decade,the advent of femtosecond brilliant hard X-ray free-electron lasers(XFELs)has opened new horizons to overcome these limitations.Here,for the first time,we present full-scale spatiotemporal measurements of solid-density plasma dynamics,including preplasma generation with tens of nanometer scale length driven by the leading edge of a relativistic laser pulse,ultrafast heating and ionization at the main pulse arrival,the laser-driven blast wave,and transient surface return current-induced compression dynamics up to hundreds of picoseconds after interaction.These observations are enabled by utilizing a novel combination of advanced X-ray diagnostics including small-angle X-ray scattering,resonant X-ray emission spectroscopy,and propagation-based X-ray phase-contrast imaging simultaneously at the European XFEL-HED beamline station.
基金supported by Russian Science Foundation Grant No.24-62-00032.
文摘Experiments with interacting high-velocity flows in a laser plasma can help answer fundamental questions in plasma physics and improve understanding of the mechanisms behind some astrophysical phenomena,such as the formation of collisionless shock waves,deceleration of accretion flows,and evolution of solar and stellar flares.This work presents the first direct experimental observations of stagnation and redirection of counterstreaming flows(jets)of laser plasma induced by intense laser pulses with intensity I~2×10^(18) W/cm^(2).Hybrid particlein-cell-fluid modeling,which takes into account the kinetic effects of ion motion and the evolution of the pressure tensor for electrons,demonstrates the compression of counterdirected toroidal self-generated magnetic fields embedded in counterstreaming plasma flows.The enhancement of the toroidal magnetic field in the interaction region results in plasma flow stagnation and redirection of the jets across the line of their initial propagation.
基金supported in part by National Institute of Health(NIH),USA(Grant Nos.:R01GM126189,R01AI164266,and R35GM148196)the National Science Foundation,USA(Grant Nos.DMS2052983,DMS-1761320,and IIS-1900473)+3 种基金National Aero-nautics and Space Administration(NASA),USA(Grant No.:80NSSC21M0023)Michigan State University(MSU)Foundation,USA,Bristol-Myers Squibb(Grant No.:65109)USA,and Pfizer,USAsupported by the National Natural Science Foundation of China(Grant Nos.:11971367,12271416,and 11972266).
文摘Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.
基金supported by the National Natural Science Foundation of China(Nos.62371323,62401380,U2433217,U2333209,and U20A20161)Natural Science Foundation of Sichuan Province,China(Nos.2025ZNSFSC1476)+2 种基金Sichuan Science and Technology Program,China(Nos.2024YFG0010 and 2024ZDZX0046)the Institutional Research Fund from Sichuan University(Nos.2024SCUQJTX030)the Open Fund of Key Laboratory of Flight Techniques and Flight Safety,CAAC(Nos.GY2024-01A).
文摘With the advent of the next-generation Air Traffic Control(ATC)system,there is growing interest in using Artificial Intelligence(AI)techniques to enhance Situation Awareness(SA)for ATC Controllers(ATCOs),i.e.,Intelligent SA(ISA).However,the existing AI-based SA approaches often rely on unimodal data and lack a comprehensive description and benchmark of the ISA tasks utilizing multi-modal data for real-time ATC environments.To address this gap,by analyzing the situation awareness procedure of the ATCOs,the ISA task is refined to the processing of the two primary elements,i.e.,spoken instructions and flight trajectories.Subsequently,the ISA is further formulated into Controlling Intent Understanding(CIU)and Flight Trajectory Prediction(FTP)tasks.For the CIU task,an innovative automatic speech recognition and understanding framework is designed to extract the controlling intent from unstructured and continuous ATC communications.For the FTP task,the single-and multi-horizon FTP approaches are investigated to support the high-precision prediction of the situation evolution.A total of 32 unimodal/multi-modal advanced methods with extensive evaluation metrics are introduced to conduct the benchmarks on the real-world multi-modal ATC situation dataset.Experimental results demonstrate the effectiveness of AI-based techniques in enhancing ISA for the ATC environment.
基金financial support from the National Natural Science Foundation of China(22109003)the Basic and Applied Basic Research Foundation of Guangdong Province(2023A1515011391)+1 种基金Soft Science Research Project of Guangdong Province(No.2017B030301013)the Major Science and Technology Infrastructure Project of Material Genome Big-science Facilities Platform supported by Municipal Development and Reform Commission of Shenzhen.
文摘Understanding the proton dynamic behavior in inorganic materials has long been a topic of intense fascination[1],especially in the field of electrochemical energy storage[2].One of the examples is the research of proton transport in transition metal oxides,which dates back to 1971[3]when RuO_(2) was discovered to be capable of storing protons via reversible redox reactions[4].In aqueous electrolytes,the thin film RuO_(2) electrode exhibits a surface pseudocapacitive behavior[5],which could be modified by the structural water in its hydrated form due to the facile Grotthuss hopping mode of protons along the established hydrogen bonds inside the bulk phase[6].Soon later,Goodenough et al.reported the capacitor-like behavior of amorphous MnO_(2)·xH_(2)O electrode in an aqueous KCl electrolyte[7],and further studies on the hydrated MnO_(2) electrodes prepared by sol-gel processes have soon discovered that the intercalation of protons from aqueous electrolytes plays an indispensable role in the charge storage mechanism[8].In recent years,the research interest on rechargeable aqueous batteries has fueled the renaissance of mechanistic study of proton transport in transition metal oxides[9],which can operate as cathodes or anodes via a topotactic insertion mechanism similar to that in Li-ion batteries[10].However,due to the challenges for experimental detection of local chemical environments of the inserted protons,a comprehensive understanding of proton dynamic behavior in these electrodes remains largely lacking.
文摘In January 2025,the Beijing Chamber of Commerce(Singapore)was officially established.Coinciding with the 35th anniversary of China-Singapore diplomatic relations and the 60th anniversary of Singapore’s independence,its founding is part of a timely response to the growing needs of businesses and communities from both Beijing and Singapore to deepen mutual understanding and expand cooperation.As a Chinese person who has lived and worked in Singapore for many years,Feng Jianli,president of the Beijing Chamber of Commerce(Singapore),knows first-hand just how challenging it can be for new immigrants from Beijing to adapt to identity transitions and access local support.The chamber was founded not only to help them take root and thrive,but also to serve as a platform for deeper people-to-people exchange between Singapore and Beijing.
文摘Fostering understanding between China and Africa through dialogue and exchanges Etienne Bankuwiha,a young sinologist from Burundi and a doctoral student at Nanjing University,received on the afternoon of 13 November the message he had been eagerly awaiting-a reply from Chinese President Xi Jinping.
文摘As an English traveler,diving into the heart of Chinese culture is like unlocking a world of rich experiences that offer a deeper understanding of the country and its people.Here is how,from my perspective,I have immersed myself in various facets of Chinese culture.
文摘1 When we immerse ourselves in the pages of a book,we are transported to worlds beyond our own,gaining insights into the human experience and expanding our understanding of ourselves and others.2 Reading can fill our lives and purify our souls.I enjoy reading and I love life.I enjoy reading Chinese classics such as the Four Great Classical Novels of Chinese literature.I explore the mythical and wonderful stories of demons and monsters in Journey to the West and experience the boldness of martial art heroes in Water Margin.
