Over the last two decades,the dogma that cell fate is immutable has been increasingly challenged,with important implications for regenerative medicine.The brea kth rough discovery that induced pluripotent stem cells c...Over the last two decades,the dogma that cell fate is immutable has been increasingly challenged,with important implications for regenerative medicine.The brea kth rough discovery that induced pluripotent stem cells could be generated from adult mouse fibroblasts is powerful proof that cell fate can be changed.An exciting extension of the discovery of cell fate impermanence is the direct cellular reprogram ming hypothesis-that terminally differentiated cells can be reprogrammed into other adult cell fates without first passing through a stem cell state.展开更多
The brain's extracellular matrix(ECM),which is comprised of protein and glycosaminoglycan(GAG)scaffolds,constitutes 20%-40% of the human brain and is considered one of the largest influencers on brain cell functio...The brain's extracellular matrix(ECM),which is comprised of protein and glycosaminoglycan(GAG)scaffolds,constitutes 20%-40% of the human brain and is considered one of the largest influencers on brain cell functioning(Soles et al.,2023).Synthesized by neural and glial cells,the brain's ECM regulates a myriad of homeostatic cellular processes,including neuronal plasticity and firing(Miyata et al.,2012),cation buffering(Moraws ki et al.,2015),and glia-neuron interactions(Anderson et al.,2016).Considering the diversity of functions,dynamic remodeling of the brain's ECM indicates that this understudied medium is an active participant in both normal physiology and neurological diseases.展开更多
Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequent...Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.展开更多
This study proposes a novel approach to optimizing individual work schedules for book digitization using mixed-integer programming (MIP). By leveraging the power of MIP solvers, we aimed to minimize the overall digiti...This study proposes a novel approach to optimizing individual work schedules for book digitization using mixed-integer programming (MIP). By leveraging the power of MIP solvers, we aimed to minimize the overall digitization time while considering various constraints and process dependencies. The book digitization process involves three key steps: cutting, scanning, and binding. Each step has specific requirements and limitations such as the number of pages that can be processed simultaneously and potential bottlenecks. To address these complexities, we formulate the problem as a one-machine job shop scheduling problem with additional constraints to capture the unique characteristics of book digitization. We conducted a series of experiments to evaluate the performance of our proposed approach. By comparing the optimized schedules with the baseline approach, we demonstrated significant reductions in the overall processing time. In addition, we analyzed the impact of different weighting schemes on the optimization results, highlighting the importance of identifying and prioritizing critical processes. Our findings suggest that MIP-based optimization can be a valuable tool for improving the efficiency of individual work schedules, even in seemingly simple tasks, such as book digitization. By carefully considering specific constraints and objectives, we can save time and leverage resources by carefully considering specific constraints and objectives.展开更多
With the rapid development of artificial intelligence technology,AIGC(Artificial Intelligence-Generated Content)has triggered profound changes in the field of high-level language programming courses.This paper deeply ...With the rapid development of artificial intelligence technology,AIGC(Artificial Intelligence-Generated Content)has triggered profound changes in the field of high-level language programming courses.This paper deeply explored the application principles,advantages,and limitations of AIGC in intelligent code generation,analyzed the new mode of human-computer collaboration in high-level language programming courses driven by AIGC,discussed the impact of human-computer collaboration on programming efficiency and code quality through practical case studies,and looks forward to future development trends.This research aims to provide theoretical and practical guidance for high-level language programming courses and promote innovative development of high-level language programming courses under the human-computer collaboration paradigm.展开更多
Computing-in-memory(CIM)has been a promising candidate for artificial-intelligent applications thanks to the absence of data transfer between computation and storage blocks.Resistive random access memory(RRAM)based CI...Computing-in-memory(CIM)has been a promising candidate for artificial-intelligent applications thanks to the absence of data transfer between computation and storage blocks.Resistive random access memory(RRAM)based CIM has the advantage of high computing density,non-volatility as well as high energy efficiency.However,previous CIM research has predominantly focused on realizing high energy efficiency and high area efficiency for inference,while little attention has been devoted to addressing the challenges of on-chip programming speed,power consumption,and accuracy.In this paper,a fabri-cated 28 nm 576K RRAM-based CIM macro featuring optimized on-chip programming schemes is proposed to address the issues mentioned above.Different strategies of mapping weights to RRAM arrays are compared,and a novel direct-current ADC design is designed for both programming and inference stages.Utilizing the optimized hybrid programming scheme,4.67×programming speed,0.15×power saving and 4.31×compact weight distribution are realized.Besides,this macro achieves a normalized area efficiency of 2.82 TOPS/mm2 and a normalized energy efficiency of 35.6 TOPS/W.展开更多
With the widespread application of large language models(LLMs)in natural language processing and code generation,traditional High-Level Language Programming courses are facing unprecedented challenges and opportunitie...With the widespread application of large language models(LLMs)in natural language processing and code generation,traditional High-Level Language Programming courses are facing unprecedented challenges and opportunities.As a core programming language for computer science majors,C language remains irreplaceable due to its foundational nature and engineering adaptability.This paper,based on the rapid development of large model technologies,proposes a systematic reform design for C language teaching,focusing on teaching objectives,content structure,teaching methods,and evaluation systems.The article suggests a teaching framework centered on“human-computer collaborative programming,”integrating prompt training,AI-assisted debugging,and code generation analysis,aiming to enhance students’problem modeling ability,programming expression skills,and AI collaboration literacy.展开更多
Every year, around the world, between 250,000 and 500,000 people suffer a spinal cord injury(SCI). SCI is a devastating medical condition that arises from trauma or disease-induced damage to the spinal cord, disruptin...Every year, around the world, between 250,000 and 500,000 people suffer a spinal cord injury(SCI). SCI is a devastating medical condition that arises from trauma or disease-induced damage to the spinal cord, disrupting the neural connections that allow communication between the brain and the rest of the body, which results in varying degrees of motor and sensory impairment. Disconnection in the spinal tracts is an irreversible condition owing to the poor capacity for spontaneous axonal regeneration in the affected neurons.展开更多
More than seventy years before airplanes were invented,a twelve⁃year⁃old girl named Ada Lovelace dreamed of flying.She studied birds and experimented with materials to make wings,even writing a guide called Flyology.B...More than seventy years before airplanes were invented,a twelve⁃year⁃old girl named Ada Lovelace dreamed of flying.She studied birds and experimented with materials to make wings,even writing a guide called Flyology.But her curiosity didnt stop there.展开更多
With the rapid development of modern science and technology,the era of artificial intelligence has quietly come.Against the background of the new era,students’learning needs,learning resource acquisition methods,teac...With the rapid development of modern science and technology,the era of artificial intelligence has quietly come.Against the background of the new era,students’learning needs,learning resource acquisition methods,teachers’teaching concepts,teaching tools,and so on have changed significantly.How to carry out teaching reform based on this change has become one of the important issues facing educators,and the same is true for the teaching of computer programming courses.This paper focuses on the teaching reform of AI-enabled computer programming courses,analyzes its basic problems,and puts forward corresponding reform countermeasures to provide a useful reference for front-line teachers.展开更多
Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem....Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.展开更多
Soft robots, inspired by the flexibility and versatility of biological organisms, have potential in a variety of applications. Recent advancements in magneto-soft robots have demonstrated their abilities to achieve pr...Soft robots, inspired by the flexibility and versatility of biological organisms, have potential in a variety of applications. Recent advancements in magneto-soft robots have demonstrated their abilities to achieve precise remote control through magnetic fields, enabling multi-modal locomotion and complex manipulation tasks. Nonetheless, two main hurdles must be overcome to advance the field: developing a multi-component substrate with embedded magnetic particles to ensure the requisite flexibility and responsiveness, and devising a cost-effective,straightforward method to program three-dimensional distributed magnetic domains without complex processing and expensive machinery. Here, we introduce a cost-effective and simple heat-assisted in-situ integrated molding fabrication method for creating magnetically driven soft robots with three-dimensional programmable magnetic domains. By synthesizing a composite material with neodymium-iron-boron(NdFeB) particles embedded in a polydimethylsiloxane(PDMS) and Ecoflex matrix(PDMS:Ecoflex = 1:2 mass ratio, 50% magnetic particle concentration), we achieved an optimized balance of flexibility, strength, and magnetic responsiveness. The proposed heat-assisted in-situ magnetic domains programming technique,performed at an experimentally optimized temperature of 120℃, resulted in a 2 times magnetization strength(9.5 mT) compared to that at 20℃(4.8 m T), reaching a saturation level comparable to a commercial magnetizer. We demonstrated the versatility of our approach through the fabrication of six kinds of robots, including two kinds of two-dimensional patterned soft robots(2D-PSR), a circular six-pole domain distribution magnetic robot(2D-CSPDMR), a quadrupedal walking magnetic soft robot(QWMSR), an object manipulation robot(OMR), and a hollow thin-walled spherical magneto-soft robot(HTWSMSR). The proposed method provides a practical solution to create highly responsive and adaptable magneto-soft robots.展开更多
As one of the core courses for computer-related majors,the Python programming course has become increasingly important in the era of artificial intelligence.It aims to help students develop good computer thinking and ...As one of the core courses for computer-related majors,the Python programming course has become increasingly important in the era of artificial intelligence.It aims to help students develop good computer thinking and improve their abilities in programming and data analysis.The application of artificial intelligence technology in the teaching of Python programming courses is of great significance for optimizing the allocation of teaching resources,enriching students’learning experience,and significantly improving teaching quality.Based on this,this paper first briefly expounds on the importance of applying artificial intelligence technology in the teaching of Python programming courses.On this basis,it focuses on exploring effective strategies for the teaching reform of Python programming courses based on artificial intelligence technology,hoping to provide new ideas for the teaching of Python programming courses and contribute to cultivating more Python programming talents with artificial intelligence literacy.展开更多
With the continuous advancement of the New Engineering Education initiative,universities are raising the standards for cultivating engineering talents.C Programming Language,as a core course for computer science and r...With the continuous advancement of the New Engineering Education initiative,universities are raising the standards for cultivating engineering talents.C Programming Language,as a core course for computer science and related majors,plays a fundamental role in developing logical thinking,programming skills,and engineering practice.However,problems such as outdated content,weak practical connections,and single assessment methods still exist in current teaching,which affects both learning outcomes and students’skill development.Based on the outcome-based education(OBE)approach and supported by AI-assisted teaching tools,this paper proposes a reform plan focusing on teaching content,instructional methods,and evaluation systems.The goal is to enhance students’overall abilities and practical innovation skills,and to align the course more closely with modern industry needs.展开更多
Prenatal caffeine exposure(PCE)leads to intrauterine growth retardation and altered glucose homeostasis after birth,but the underlying mechanism remains unclear.This study aims to investigate the alteration of pancrea...Prenatal caffeine exposure(PCE)leads to intrauterine growth retardation and altered glucose homeostasis after birth,but the underlying mechanism remains unclear.This study aims to investigate the alteration of pancreatic development and insulin biosynthesis in the PCE female offspring and explore the intrauterine programming mechanism.Pregnant rats were orally treated with 120 mg/(kg·day)of caffeine from gestational day(GD)9 to 20.Results showed that fetal pancreaticβ-cells in the PCE group exhibited reduced mass and impaired insulin synthesis function,as evidenced by decreased expression of developmental and functional genes and reduced pancreatic insulin content.At postnatal week(PW)12,the PCE offspring exhibited glucose intolerance,diminishedβ-cell mass,and lower blood insulin levels.However,by PW28,glucose tolerance showed some improvement.Both in vivo and in vitro findings collectively indicated that excessive serum corticosterone(CORT)levels of the PCE fetuses may act through the activation of the pancreatic glucocorticoid receptor(GR)and recruitment of histone deacetylase 9(HDAC9),leading to H3K9 deacetylation in promoter and downregulation of insulin-like growth factor 1(IGF1),thereby inhibiting pancreatic islet morphogenesis and insulin synthesis in fetal rats.Furthermore,the PCE offspring after birth exhibited decreased blood CORT levels,increased H3K9 acetylation in promoter and upregulated gene expression of the pancreatic IGF1 promoter region,accompanied by elevated insulin biosynthesis.However,when exposed to chronic stress,the above changes were totally reversed.Conclusively,“glucocorticoid-insulin like growth factor 1(GC-IGF1)axis”programming may be involved in pancreaticβ-cell dysplasia and dysfunction in the PCE female offspring.展开更多
Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programm...Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world problems.This paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human intervention.Partial modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the algorithm.By designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update formula.The Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the CSO.To validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark suite.Furthermore,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking process.The experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.展开更多
Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to en...Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.展开更多
The electricity-hydrogen integrated energy system(EH-IES)enables synergistic operation of electricity,heat,and hydrogen subsystems,supporting renewable energy integration and efficient multi-energy utilization in futu...The electricity-hydrogen integrated energy system(EH-IES)enables synergistic operation of electricity,heat,and hydrogen subsystems,supporting renewable energy integration and efficient multi-energy utilization in future low carbon societies.However,uncertainties from renewable energy and load variability threaten system safety and economy.Conventional chance-constrained programming(CCP)ensures reliable operation by limiting risk.However,increasing source-load uncertainties that can render CCP models infeasible and exacerbate operational risks.To address this,this paper proposes a risk-adjustable chance-constrained goal programming(RACCGP)model,integrating CCP and goal programming to balance risk and cost based on system risk assessment.An intelligent nonlinear goal programming method based on the state transition algorithm(STA)is developed,along with an improved discretized step transformation,to handle model nonlinearity and enhance computational efficiency.Experimental results show that the proposed model reduces costs while controlling risk compared to traditional CCP,and the solution method outperforms average sample sampling in efficiency and solution quality.展开更多
Learning-based methods have become mainstream for solving residential energy scheduling problems. In order to improve the learning efficiency of existing methods and increase the utilization of renewable energy, we pr...Learning-based methods have become mainstream for solving residential energy scheduling problems. In order to improve the learning efficiency of existing methods and increase the utilization of renewable energy, we propose the Dyna actiondependent heuristic dynamic programming(Dyna-ADHDP)method, which incorporates the ideas of learning and planning from the Dyna framework in action-dependent heuristic dynamic programming. This method defines a continuous action space for precise control of an energy storage system and allows online optimization of algorithm performance during the real-time operation of the residential energy model. Meanwhile, the target network is introduced during the training process to make the training smoother and more efficient. We conducted experimental comparisons with the benchmark method using simulated and real data to verify its applicability and performance. The results confirm the method's excellent performance and generalization capabilities, as well as its excellence in increasing renewable energy utilization and extending equipment life.展开更多
In this paper,a distributed adaptive dynamic programming(ADP)framework based on value iteration is proposed for multi-player differential games.In the game setting,players have no access to the information of others&#...In this paper,a distributed adaptive dynamic programming(ADP)framework based on value iteration is proposed for multi-player differential games.In the game setting,players have no access to the information of others'system parameters or control laws.Each player adopts an on-policy value iteration algorithm as the basic learning framework.To deal with the incomplete information structure,players collect a period of system trajectory data to compensate for the lack of information.The policy updating step is implemented by a nonlinear optimization problem aiming to search for the proximal admissible policy.Theoretical analysis shows that by adopting proximal policy searching rules,the approximated policies can converge to a neighborhood of equilibrium policies.The efficacy of our method is illustrated by three examples,which also demonstrate that the proposed method can accelerate the learning process compared with the centralized learning framework.展开更多
基金supported by Canada First Research Excellence Fund,Medicine by Design(to CMM)。
文摘Over the last two decades,the dogma that cell fate is immutable has been increasingly challenged,with important implications for regenerative medicine.The brea kth rough discovery that induced pluripotent stem cells could be generated from adult mouse fibroblasts is powerful proof that cell fate can be changed.An exciting extension of the discovery of cell fate impermanence is the direct cellular reprogram ming hypothesis-that terminally differentiated cells can be reprogrammed into other adult cell fates without first passing through a stem cell state.
基金supported by National Institute on Aging(NIH-NIA)R21 AG074152(to KMA)National Institute of Allergy and Infectious Diseases(NIAID)grant DP2 AI171150(to KMA)Department of Defense(DoD)grant AZ210089(to KMA)。
文摘The brain's extracellular matrix(ECM),which is comprised of protein and glycosaminoglycan(GAG)scaffolds,constitutes 20%-40% of the human brain and is considered one of the largest influencers on brain cell functioning(Soles et al.,2023).Synthesized by neural and glial cells,the brain's ECM regulates a myriad of homeostatic cellular processes,including neuronal plasticity and firing(Miyata et al.,2012),cation buffering(Moraws ki et al.,2015),and glia-neuron interactions(Anderson et al.,2016).Considering the diversity of functions,dynamic remodeling of the brain's ECM indicates that this understudied medium is an active participant in both normal physiology and neurological diseases.
基金supported by the National Natural Science Foundation of China(No.62203256)。
文摘Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.
文摘This study proposes a novel approach to optimizing individual work schedules for book digitization using mixed-integer programming (MIP). By leveraging the power of MIP solvers, we aimed to minimize the overall digitization time while considering various constraints and process dependencies. The book digitization process involves three key steps: cutting, scanning, and binding. Each step has specific requirements and limitations such as the number of pages that can be processed simultaneously and potential bottlenecks. To address these complexities, we formulate the problem as a one-machine job shop scheduling problem with additional constraints to capture the unique characteristics of book digitization. We conducted a series of experiments to evaluate the performance of our proposed approach. By comparing the optimized schedules with the baseline approach, we demonstrated significant reductions in the overall processing time. In addition, we analyzed the impact of different weighting schemes on the optimization results, highlighting the importance of identifying and prioritizing critical processes. Our findings suggest that MIP-based optimization can be a valuable tool for improving the efficiency of individual work schedules, even in seemingly simple tasks, such as book digitization. By carefully considering specific constraints and objectives, we can save time and leverage resources by carefully considering specific constraints and objectives.
基金Education and Teaching Research Project of Beijing University of Technology(ER2024KCB08)。
文摘With the rapid development of artificial intelligence technology,AIGC(Artificial Intelligence-Generated Content)has triggered profound changes in the field of high-level language programming courses.This paper deeply explored the application principles,advantages,and limitations of AIGC in intelligent code generation,analyzed the new mode of human-computer collaboration in high-level language programming courses driven by AIGC,discussed the impact of human-computer collaboration on programming efficiency and code quality through practical case studies,and looks forward to future development trends.This research aims to provide theoretical and practical guidance for high-level language programming courses and promote innovative development of high-level language programming courses under the human-computer collaboration paradigm.
基金supported in part by the National Natural Science Foundation of China (62422405, 62025111,62495100, 92464302)the STI 2030-Major Projects(2021ZD0201200)+1 种基金the Shanghai Municipal Science and Technology Major Projectthe Beijing Advanced Innovation Center for Integrated Circuits
文摘Computing-in-memory(CIM)has been a promising candidate for artificial-intelligent applications thanks to the absence of data transfer between computation and storage blocks.Resistive random access memory(RRAM)based CIM has the advantage of high computing density,non-volatility as well as high energy efficiency.However,previous CIM research has predominantly focused on realizing high energy efficiency and high area efficiency for inference,while little attention has been devoted to addressing the challenges of on-chip programming speed,power consumption,and accuracy.In this paper,a fabri-cated 28 nm 576K RRAM-based CIM macro featuring optimized on-chip programming schemes is proposed to address the issues mentioned above.Different strategies of mapping weights to RRAM arrays are compared,and a novel direct-current ADC design is designed for both programming and inference stages.Utilizing the optimized hybrid programming scheme,4.67×programming speed,0.15×power saving and 4.31×compact weight distribution are realized.Besides,this macro achieves a normalized area efficiency of 2.82 TOPS/mm2 and a normalized energy efficiency of 35.6 TOPS/W.
基金Education and Teaching Research Project of Beijing University of Technology(ER2024KCB08)。
文摘With the widespread application of large language models(LLMs)in natural language processing and code generation,traditional High-Level Language Programming courses are facing unprecedented challenges and opportunities.As a core programming language for computer science majors,C language remains irreplaceable due to its foundational nature and engineering adaptability.This paper,based on the rapid development of large model technologies,proposes a systematic reform design for C language teaching,focusing on teaching objectives,content structure,teaching methods,and evaluation systems.The article suggests a teaching framework centered on“human-computer collaborative programming,”integrating prompt training,AI-assisted debugging,and code generation analysis,aiming to enhance students’problem modeling ability,programming expression skills,and AI collaboration literacy.
基金financially supported by Ministerio de Ciencia e Innovación projects SAF2017-82736-C2-1-R to MTMFin Universidad Autónoma de Madrid and by Fundación Universidad Francisco de Vitoria to JS+2 种基金a predoctoral scholarship from Fundación Universidad Francisco de Vitoriafinancial support from a 6-month contract from Universidad Autónoma de Madrida 3-month contract from the School of Medicine of Universidad Francisco de Vitoria。
文摘Every year, around the world, between 250,000 and 500,000 people suffer a spinal cord injury(SCI). SCI is a devastating medical condition that arises from trauma or disease-induced damage to the spinal cord, disrupting the neural connections that allow communication between the brain and the rest of the body, which results in varying degrees of motor and sensory impairment. Disconnection in the spinal tracts is an irreversible condition owing to the poor capacity for spontaneous axonal regeneration in the affected neurons.
文摘More than seventy years before airplanes were invented,a twelve⁃year⁃old girl named Ada Lovelace dreamed of flying.She studied birds and experimented with materials to make wings,even writing a guide called Flyology.But her curiosity didnt stop there.
文摘With the rapid development of modern science and technology,the era of artificial intelligence has quietly come.Against the background of the new era,students’learning needs,learning resource acquisition methods,teachers’teaching concepts,teaching tools,and so on have changed significantly.How to carry out teaching reform based on this change has become one of the important issues facing educators,and the same is true for the teaching of computer programming courses.This paper focuses on the teaching reform of AI-enabled computer programming courses,analyzes its basic problems,and puts forward corresponding reform countermeasures to provide a useful reference for front-line teachers.
基金supported by the Start-up Fund from Hainan University(No.KYQD(ZR)-20077)。
文摘Three-dimensional(3D)single molecule localization microscopy(SMLM)plays an important role in biomedical applications,but its data processing is very complicated.Deep learning is a potential tool to solve this problem.As the state of art 3D super-resolution localization algorithm based on deep learning,FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing,even though it has greatly improved the data processing throughput.In this paper,a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM.This new algorithm uses the feature compression method to reduce the parameters of the model,and combines it with pipeline programming to accelerate the inference process of the deep learning model.The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy,which can realize real-time processing of 256×256 pixels size images.The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering,and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.
基金supported by National Natural Science Foundation of China(Grant Nos.62473277,62473275,62133004,52105072,and 62073230)Jiangsu Provincial Outstanding Youth Program(Grant No.BK20230072)+5 种基金National Key R&D Program of China(Grant Nos.2022YFC3802302 and 2023YFB4705600)Suzhou Industrial Foresight and Key Core Technology Project(Grant No.SYC2022044)Zhejiang Provincial Natural Science Foundation of China(Grant No.LZ24E050004)Shenzhen Polytechnic High-level Talent Start-up Project(Grant No.6023330006K)Shenzhen Science and Technology Program(Grant No.JCYJ20210324132810026)a Grant from Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems,Grants from Jiangsu QingLan Project and Jiangsu 333 high-level talents.
文摘Soft robots, inspired by the flexibility and versatility of biological organisms, have potential in a variety of applications. Recent advancements in magneto-soft robots have demonstrated their abilities to achieve precise remote control through magnetic fields, enabling multi-modal locomotion and complex manipulation tasks. Nonetheless, two main hurdles must be overcome to advance the field: developing a multi-component substrate with embedded magnetic particles to ensure the requisite flexibility and responsiveness, and devising a cost-effective,straightforward method to program three-dimensional distributed magnetic domains without complex processing and expensive machinery. Here, we introduce a cost-effective and simple heat-assisted in-situ integrated molding fabrication method for creating magnetically driven soft robots with three-dimensional programmable magnetic domains. By synthesizing a composite material with neodymium-iron-boron(NdFeB) particles embedded in a polydimethylsiloxane(PDMS) and Ecoflex matrix(PDMS:Ecoflex = 1:2 mass ratio, 50% magnetic particle concentration), we achieved an optimized balance of flexibility, strength, and magnetic responsiveness. The proposed heat-assisted in-situ magnetic domains programming technique,performed at an experimentally optimized temperature of 120℃, resulted in a 2 times magnetization strength(9.5 mT) compared to that at 20℃(4.8 m T), reaching a saturation level comparable to a commercial magnetizer. We demonstrated the versatility of our approach through the fabrication of six kinds of robots, including two kinds of two-dimensional patterned soft robots(2D-PSR), a circular six-pole domain distribution magnetic robot(2D-CSPDMR), a quadrupedal walking magnetic soft robot(QWMSR), an object manipulation robot(OMR), and a hollow thin-walled spherical magneto-soft robot(HTWSMSR). The proposed method provides a practical solution to create highly responsive and adaptable magneto-soft robots.
文摘As one of the core courses for computer-related majors,the Python programming course has become increasingly important in the era of artificial intelligence.It aims to help students develop good computer thinking and improve their abilities in programming and data analysis.The application of artificial intelligence technology in the teaching of Python programming courses is of great significance for optimizing the allocation of teaching resources,enriching students’learning experience,and significantly improving teaching quality.Based on this,this paper first briefly expounds on the importance of applying artificial intelligence technology in the teaching of Python programming courses.On this basis,it focuses on exploring effective strategies for the teaching reform of Python programming courses based on artificial intelligence technology,hoping to provide new ideas for the teaching of Python programming courses and contribute to cultivating more Python programming talents with artificial intelligence literacy.
基金funded by Xinjiang Natural Science Foundation of China(2023D01C52)University Key Project(2023YSZD004).
文摘With the continuous advancement of the New Engineering Education initiative,universities are raising the standards for cultivating engineering talents.C Programming Language,as a core course for computer science and related majors,plays a fundamental role in developing logical thinking,programming skills,and engineering practice.However,problems such as outdated content,weak practical connections,and single assessment methods still exist in current teaching,which affects both learning outcomes and students’skill development.Based on the outcome-based education(OBE)approach and supported by AI-assisted teaching tools,this paper proposes a reform plan focusing on teaching content,instructional methods,and evaluation systems.The goal is to enhance students’overall abilities and practical innovation skills,and to align the course more closely with modern industry needs.
基金supported by grants from the National Key Research and Development Program of China(2020YFA0803900)the National Natural Science Foundation of China(U23A20407,82414020,81703631)the Hubei Provincial Natural Science Foundation of China(2024AFB742)。
文摘Prenatal caffeine exposure(PCE)leads to intrauterine growth retardation and altered glucose homeostasis after birth,but the underlying mechanism remains unclear.This study aims to investigate the alteration of pancreatic development and insulin biosynthesis in the PCE female offspring and explore the intrauterine programming mechanism.Pregnant rats were orally treated with 120 mg/(kg·day)of caffeine from gestational day(GD)9 to 20.Results showed that fetal pancreaticβ-cells in the PCE group exhibited reduced mass and impaired insulin synthesis function,as evidenced by decreased expression of developmental and functional genes and reduced pancreatic insulin content.At postnatal week(PW)12,the PCE offspring exhibited glucose intolerance,diminishedβ-cell mass,and lower blood insulin levels.However,by PW28,glucose tolerance showed some improvement.Both in vivo and in vitro findings collectively indicated that excessive serum corticosterone(CORT)levels of the PCE fetuses may act through the activation of the pancreatic glucocorticoid receptor(GR)and recruitment of histone deacetylase 9(HDAC9),leading to H3K9 deacetylation in promoter and downregulation of insulin-like growth factor 1(IGF1),thereby inhibiting pancreatic islet morphogenesis and insulin synthesis in fetal rats.Furthermore,the PCE offspring after birth exhibited decreased blood CORT levels,increased H3K9 acetylation in promoter and upregulated gene expression of the pancreatic IGF1 promoter region,accompanied by elevated insulin biosynthesis.However,when exposed to chronic stress,the above changes were totally reversed.Conclusively,“glucocorticoid-insulin like growth factor 1(GC-IGF1)axis”programming may be involved in pancreaticβ-cell dysplasia and dysfunction in the PCE female offspring.
文摘Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world problems.This paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human intervention.Partial modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the algorithm.By designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update formula.The Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the CSO.To validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark suite.Furthermore,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking process.The experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.
基金supported by the National Natural Science Foundation of China(No.92371206)the Postgraduate Scientific Research Innovation Project of Hunan Province,China(No.CX2023063).
文摘Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.
基金Project(2022YFC2904502)supported by the National Key Research and Development Program of ChinaProject(62273357)supported by the National Natural Science Foundation of China。
文摘The electricity-hydrogen integrated energy system(EH-IES)enables synergistic operation of electricity,heat,and hydrogen subsystems,supporting renewable energy integration and efficient multi-energy utilization in future low carbon societies.However,uncertainties from renewable energy and load variability threaten system safety and economy.Conventional chance-constrained programming(CCP)ensures reliable operation by limiting risk.However,increasing source-load uncertainties that can render CCP models infeasible and exacerbate operational risks.To address this,this paper proposes a risk-adjustable chance-constrained goal programming(RACCGP)model,integrating CCP and goal programming to balance risk and cost based on system risk assessment.An intelligent nonlinear goal programming method based on the state transition algorithm(STA)is developed,along with an improved discretized step transformation,to handle model nonlinearity and enhance computational efficiency.Experimental results show that the proposed model reduces costs while controlling risk compared to traditional CCP,and the solution method outperforms average sample sampling in efficiency and solution quality.
基金supported in part by the National Key Research and Development Program of China(2024YFB4709100,2021YFE0206100)the National Natural Science Foundation of China(62073321)+1 种基金the National Defense Basic Scientific Research Program(JCKY2019203C029)the Science and Technology Development Fund,Macao SAR,China(0015/2020/AMJ)
文摘Learning-based methods have become mainstream for solving residential energy scheduling problems. In order to improve the learning efficiency of existing methods and increase the utilization of renewable energy, we propose the Dyna actiondependent heuristic dynamic programming(Dyna-ADHDP)method, which incorporates the ideas of learning and planning from the Dyna framework in action-dependent heuristic dynamic programming. This method defines a continuous action space for precise control of an energy storage system and allows online optimization of algorithm performance during the real-time operation of the residential energy model. Meanwhile, the target network is introduced during the training process to make the training smoother and more efficient. We conducted experimental comparisons with the benchmark method using simulated and real data to verify its applicability and performance. The results confirm the method's excellent performance and generalization capabilities, as well as its excellence in increasing renewable energy utilization and extending equipment life.
基金supported by the Aeronautical Science Foundation of China(20220001057001)an Open Project of the National Key Laboratory of Air-based Information Perception and Fusion(202437)
文摘In this paper,a distributed adaptive dynamic programming(ADP)framework based on value iteration is proposed for multi-player differential games.In the game setting,players have no access to the information of others'system parameters or control laws.Each player adopts an on-policy value iteration algorithm as the basic learning framework.To deal with the incomplete information structure,players collect a period of system trajectory data to compensate for the lack of information.The policy updating step is implemented by a nonlinear optimization problem aiming to search for the proximal admissible policy.Theoretical analysis shows that by adopting proximal policy searching rules,the approximated policies can converge to a neighborhood of equilibrium policies.The efficacy of our method is illustrated by three examples,which also demonstrate that the proposed method can accelerate the learning process compared with the centralized learning framework.
