Disparities in maternal mental health outcomes persist despite the myriad of existing evidence based treatments and recent public health prevention policy efforts. Integrated health care delivery models such as Collab...Disparities in maternal mental health outcomes persist despite the myriad of existing evidence based treatments and recent public health prevention policy efforts. Integrated health care delivery models such as Collaborative Care and patient medical home models have the potential to reduce health disparities in clinic settings. These evidence-based approaches require multidisciplinary teams for successful implementation and to provide quality care to improve specified patient outcomes. However, strategies for successful collaboration and steps for critical reflection are often overlooked in clinical and health services research. Furthermore, a shared vision of social justice is essential in the process of building and sustaining patient-centered care models, but is often understated. The purpose of this paper is to describe the development and implementation of a social justice-informed hospital-based perinatal depression registry to address maternal health disparities. Our partnership is informed by community-based participatory research (CBPR) principles for carrying out health services research. We describe the steps for building a sustainable university-hospital collaboration between traditional and non-traditional researchers using principles from CBPR in a clinic setting.展开更多
Artificial intelligence(AI)in education is experiencing a transformative shift,fueled by foundation models with unprecedented capabilities.These advancements are reshaping educational paradigms and addressing challeng...Artificial intelligence(AI)in education is experiencing a transformative shift,fueled by foundation models with unprecedented capabilities.These advancements are reshaping educational paradigms and addressing challenges such as diverse student needs,resource gaps,and engagement.1 This paper examines three key trends:the shift from perception to cognition,the transition from generalized to personalized learning,and the rise of multimodal systems,as shown in Figure 1.Together,these trends open up new opportunities to tackle persistent challenges in the education sector.展开更多
Traditional quantitative investment research is encountering diminishing returns alongside rising labor and time costs.To overcome these challenges,we introduce the large investment model(LIM),a novel research paradig...Traditional quantitative investment research is encountering diminishing returns alongside rising labor and time costs.To overcome these challenges,we introduce the large investment model(LIM),a novel research paradigm designed to enhance both performance and efficiency at scale.LIM employs end-to-end learning and universal modeling to create an upstream foundation model,which is capable of autonomously learning comprehensive signal patterns from diverse financial data spanning multiple exchanges,instruments,and frequencies.These“global patterns”are subsequently transferred to downstream strategy modeling,optimizing performance for specific tasks.We detail the system architecture design of LIM,address the technical challenges inherent in this approach,and outline potential directions for future research.展开更多
Intelligent decision-making(IDM)is a cornerstone of artificial intelligence(AI)designed to automate or augment decision processes.Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make...Intelligent decision-making(IDM)is a cornerstone of artificial intelligence(AI)designed to automate or augment decision processes.Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps,such as AI agents and high-level reinforcement learning.Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision,language,and sensory data—into a cohesive decision-making process.Foundation models(FMs)have become pivotal in science and industry,transforming decision-making and research capabilities.Their large-scale,multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare,life sciences,and education.This survey examines IDM’s evolution,advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains,highlighting the challenges and opportunities in building efficient,adaptive,and ethical decision systems.展开更多
Quantitative investment(abbreviated as“quant”in this paper)is an interdisciplinary field combining financial engineering,computer science,mathematics,statistics,etc.Quant has become one of the mainstream investment ...Quantitative investment(abbreviated as“quant”in this paper)is an interdisciplinary field combining financial engineering,computer science,mathematics,statistics,etc.Quant has become one of the mainstream investment methodologies over the past decades,and has experienced three generations:quant 1.0,trading by mathematical modeling to discover mis-priced assets in markets;quant 2.0,shifting the quant research pipeline from small“strategy workshops”to large“alpha factories”;quant 3.0,applying deep learning techniques to discover complex nonlinear pricing rules.Despite its advantage in prediction,deep learning relies on extremely large data volume and labor-intensive tuning of“black-box”neural network models.To address these limitations,in this paper,we introduce quant 4.0 and provide an engineering perspective for next-generation quant.Quant 4.0 has three key differentiating components.First,automated artificial intelligence(AI)changes the quant pipeline from traditional hand-crafted modeling to state-of-the-art automated modeling and employs the philosophy of“algorithm produces algorithm,model builds model,and eventually AI creates AI.”Second,explainable AI develops new techniques to better understand and interpret investment decisions made by machine learning black boxes,and explains complicated and hidden risk exposures.Third,knowledge-driven AI supplements data-driven AI such as deep learning and incorporates prior knowledge into modeling to improve investment decisions,in particular for quantitative value investing.Putting all these together,we discuss how to build a system that practices the quant 4.0 concept.We also discuss the application of large language models in quantitative finance.Finally,we propose 10 challenging research problems for quant technology,and discuss potential solutions,research directions,and future trends.展开更多
文摘Disparities in maternal mental health outcomes persist despite the myriad of existing evidence based treatments and recent public health prevention policy efforts. Integrated health care delivery models such as Collaborative Care and patient medical home models have the potential to reduce health disparities in clinic settings. These evidence-based approaches require multidisciplinary teams for successful implementation and to provide quality care to improve specified patient outcomes. However, strategies for successful collaboration and steps for critical reflection are often overlooked in clinical and health services research. Furthermore, a shared vision of social justice is essential in the process of building and sustaining patient-centered care models, but is often understated. The purpose of this paper is to describe the development and implementation of a social justice-informed hospital-based perinatal depression registry to address maternal health disparities. Our partnership is informed by community-based participatory research (CBPR) principles for carrying out health services research. We describe the steps for building a sustainable university-hospital collaboration between traditional and non-traditional researchers using principles from CBPR in a clinic setting.
基金funded by the NSFC(no.62172393)the Major Public Welfare Project of Henan Province(no.201300311200).
文摘Artificial intelligence(AI)in education is experiencing a transformative shift,fueled by foundation models with unprecedented capabilities.These advancements are reshaping educational paradigms and addressing challenges such as diverse student needs,resource gaps,and engagement.1 This paper examines three key trends:the shift from perception to cognition,the transition from generalized to personalized learning,and the rise of multimodal systems,as shown in Figure 1.Together,these trends open up new opportunities to tackle persistent challenges in the education sector.
文摘Traditional quantitative investment research is encountering diminishing returns alongside rising labor and time costs.To overcome these challenges,we introduce the large investment model(LIM),a novel research paradigm designed to enhance both performance and efficiency at scale.LIM employs end-to-end learning and universal modeling to create an upstream foundation model,which is capable of autonomously learning comprehensive signal patterns from diverse financial data spanning multiple exchanges,instruments,and frequencies.These“global patterns”are subsequently transferred to downstream strategy modeling,optimizing performance for specific tasks.We detail the system architecture design of LIM,address the technical challenges inherent in this approach,and outline potential directions for future research.
基金supported by the National Natural Science Foundation of China under grant nos.62372470,72225011,62402414,U23B2059,62173034,32222070,62402017,72421002,62206303,62476264,62406312,62102266,52173241,and U23A20468the National Key Research and Development Program of China(2023YFD1900604)+8 种基金the Strategic Priority Research Program of the Chinese Academy of Science(XDB0680301)the Youth Innovation Promotion Association CAS(2023112)the National High Level Hospital Clinical Research funding(2022-PUMCH-A-014),the Beijing Natural Science Foundation(4244098)the Science and Technology Innovation Program of Hunan Province(2023RC3009)the Key Research and Development Program of Yunnan Province(202202AE090034)the MNR Key Laboratory for Geo-Environmental Monitoring of Greater Bay Area(GEMLab-2023001)the Science and Technology Innovation Key R&D Program of Chongqing(CSTB2024TIAD-STX0024)the China National Postdoctoral Program for Innovative Talents(BX20240385)the River Talent Recruitment Program of Guangdong Province(2019ZT08X603).
文摘Intelligent decision-making(IDM)is a cornerstone of artificial intelligence(AI)designed to automate or augment decision processes.Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps,such as AI agents and high-level reinforcement learning.Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision,language,and sensory data—into a cohesive decision-making process.Foundation models(FMs)have become pivotal in science and industry,transforming decision-making and research capabilities.Their large-scale,multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare,life sciences,and education.This survey examines IDM’s evolution,advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains,highlighting the challenges and opportunities in building efficient,adaptive,and ethical decision systems.
文摘Quantitative investment(abbreviated as“quant”in this paper)is an interdisciplinary field combining financial engineering,computer science,mathematics,statistics,etc.Quant has become one of the mainstream investment methodologies over the past decades,and has experienced three generations:quant 1.0,trading by mathematical modeling to discover mis-priced assets in markets;quant 2.0,shifting the quant research pipeline from small“strategy workshops”to large“alpha factories”;quant 3.0,applying deep learning techniques to discover complex nonlinear pricing rules.Despite its advantage in prediction,deep learning relies on extremely large data volume and labor-intensive tuning of“black-box”neural network models.To address these limitations,in this paper,we introduce quant 4.0 and provide an engineering perspective for next-generation quant.Quant 4.0 has three key differentiating components.First,automated artificial intelligence(AI)changes the quant pipeline from traditional hand-crafted modeling to state-of-the-art automated modeling and employs the philosophy of“algorithm produces algorithm,model builds model,and eventually AI creates AI.”Second,explainable AI develops new techniques to better understand and interpret investment decisions made by machine learning black boxes,and explains complicated and hidden risk exposures.Third,knowledge-driven AI supplements data-driven AI such as deep learning and incorporates prior knowledge into modeling to improve investment decisions,in particular for quantitative value investing.Putting all these together,we discuss how to build a system that practices the quant 4.0 concept.We also discuss the application of large language models in quantitative finance.Finally,we propose 10 challenging research problems for quant technology,and discuss potential solutions,research directions,and future trends.
