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1974 Annual ACM Conference/Annual Meeting, ACM 1974

作者： Thomas, Joseph W. Organick, Elliott I. Computer Science Division University of Utah Salt Lake CityUT United States Division of Applied Mathematics Brown University ProvidenceRI02912 United States

ISBN: (纸本)9781450378505

Operational models for the semantics of programming languages often employ data structure diagrams for representing computation states (snapshots). Provided that these data structures are simple enough, automation of their display is feasible, supplying "visible semantics" for the languages modelled. One automation technique is described for displaying ALGOL program snapshots, based on the Contour Model, and the structure of the implemented display processor is discussed. Extensions and generalizations of this processor for use with a wide variety of programming languages are suggested. 1974 ACM. All rights reserved.

关键词： Semantics

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Neuromorphic architecture optimization for task-specific dynamic learning 19

Neuromorphic architecture optimization for task-specific dyn...

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2019 International Conference on Neuromorphic Systems, ICONS 2019

作者： Madireddy, Sandeep Yanguas-Gil, Angel Balaprakash, Prasanna Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Applied Materials Division Argonne National Laboratory LemontIL United States

ISBN: (纸本)9781450376808

The ability to learn and adapt in real time is a central feature of biological systems. Neuromorphic architectures demonstrating such versatility can greatly enhance our ability to efficiently process information at the edge. A key challenge, however, is to understand which learning rules are best suited for specific tasks and how the relevant hyperparameters can be fine-tuned. In this work, we introduce a conceptual framework in which the learning process is integrated into the network itself. This allows us to cast meta-learning as a mathematical optimization problem. We employ DeepHyper, a scalable, asynchronous model-based search, to simultaneously optimize the choice of meta-learning rules and their hyperparameters. We demonstrate our approach with two different datasets, MNIST and FashionMNIST, using a network architecture inspired by the learning center of the insect brain. Our results show that optimal learning rules can be dataset-dependent even within similar tasks. This dependency demonstrates the importance of introducing versatility and flexibility in the learning algorithms. It also illuminates experimental findings in insect neuroscience that have shown a heterogeneity of learning rules within the insect mushroom body. © 2019 Association for Computing Machinery.

关键词： Optimization

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Large language models for diabetes training:a prospective study

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science Bulletin 2025年第6期70卷 934-942页

作者： Haoxuan Li Zehua Jiang Zhouyu Guan Yuqian Bao Yuexing Liu Tingting Hu Jiajia Li Ruhan Liu Liang Wu Di Cheng Hongwei Ji Yong Wang Ya-Xing Wang Carol Y.Cheung Yingfeng Zheng Jihong Wang Zhen Li Weibing Wu Cynthia Ciwei Lim Yong Mong Bee Hong Chang Tan Elif I.Ekinci David C.Klonoff Justin B.Echouffo-Tcheugui Nestoras Mathioudakis Leonor Corsino Rafael Simó Charumathi Sabanayagam Gavin Siew Wei Tan Ching-Yu Cheng Tien Yin Wong Huating Li Chun Cai Lijuan Mao Lee-Ling Lim Yih-Chung Tham Bin Sheng Weiping Jia Shanghai University of Sport Shanghai 200438China School of Clinical Medicine Beijing Tsinghua Changgung HospitalTsinghua MedicineTsinghua UniversityBeijing 100084China Beijing Visual Science and Translational Eye Research Institute(BERI) Tsinghua MedicineTsinghua UniversityBeijing 100084China Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MoE Key Lab of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China CEMS NCMISHCMSMADISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematics University of Chinese Academy of SciencesChinese Academy of SciencesBeijing 100049China Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital Medical UniversityBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China k.Department of Ophthalmology and Visual Sciences The Chinese University of Hong KongHong Kong 999077China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular DiseasesGuangzhou 510060China Department of Renal Medicine Singapore General HospitalSingHealth-Duke Academic Medical CentreSingapore169

Diabetes poses a considerable global health challenge,with varying levels of diabetes knowledge among healthcare professionals,highlighting the importance of diabetes *** Language Models(LLMs)provide new insights into diabetes training,but their performance in diabetes-related queries remains uncertain,especially outside the English language like *** first evaluated the performance of ten LLMs:ChatGPT-3.5,ChatGPT-4.0,Google Bard,LlaMA-7B,LlaMA2-7B,Baidu ERNIE Bot,Ali Tongyi Qianwen,MedGPT,HuatuoGPT,and Chinese LlaMA2-7B on diabetes-related queries,based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC)and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United ***,we assessed the training of primary care physicians(PCPs)without and with the assistance of ChatGPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical *** found that ChatGPT-4.0 outperformed other LLMs in the English examination,achieving a passing accuracy of 62.50%,which was significantly higher than that of Google Bard,LlaMA-7B,and *** the NCE-CPFC examination,ChatGPT-4.0,Ali Tongyi Qianwen,Baidu ERNIE Bot,Google Bard,MedGPT,and ChatGPT-3.5 successfully passed,whereas LlaMA2-7B,HuatuoGPT,Chinese LLaMA2-7B,and LlaMA-7B ***-4.0(84.82%)surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%).In summary,LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language,and hold great potential to assist future diabetes training for physicians globally.

关键词： Diabetes Diabetestraining Largelanguagemodels Primarydiabetescare Prospectivestudy

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A granular model for crowd motion and pedestrian flow

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Journal of the London Mathematical Society 2025年第5期111卷

作者： Noureddine Igbida José Miguel Urbano Institut de recherche XLIM UMR-CNRS 7252 Faculté des Sciences et Techniques Université de Limoges Limoges France Applied Mathematics and Computational Sciences (AMCS) Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia CMUC Department of Mathematics University of Coimbra Coimbra Portugal

We study a granular model for congested crowd motion and pedestrian flow. Our approach is based on an approximation through a Hele-Shaw type equation involving a degenerate operator of p $p $ -Laplacian type and a linear drift, for which we prove existence and uniqueness using nonlinear semigroup methods and the doubling variables technique. Our main result shows that, as p → ∞ $p \rightarrow \infty$ , the weak solutions of the p $p$ -problem converge to a solution of the congested crowd motion problem interpreted in a variational sense.

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Graph sparsification by effective resistances

Graph sparsification by effective resistances

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作者： Spielman, Daniel A. Srivastava, Nikhil Program in Applied Mathematics Department of Computer Science Yale University New Haven CT 06520 United States Department of Computer Science Yale University New Haven CT 06520 United States

We present a nearly linear time algorithm that produces high-quality spectral sparsifiers of weighted graphs. Given as input a weighted graph G = (V, E, w) and a parameter ∈ > 0, we produce a weighted subgraph H = (V, E˜, w˜) of G such that |E˜| = O(n logn/∈2) and all x ∈ RV satisfy (1 - ∈) Σuv∈E (x(u) - x(v))2wuv ≤ Σuv∈E˜ (x(u) - x(v))2w˜uv ≤ (1 + ∈) Σuv∈E (x(u) - x(v))2wuv· This improves upon the spectral sparsifiers constructed by Spielman and Teng, which had O(nlogcn) edges for some large constant c, and upon the cut sparsifiers of Benczúr and Karger, which only satisfied these inequalities for x ∈ {0, 1}V. A key ingredient in our algorithm is a subroutine of independent interest: a nearly linear time algorithm that builds a data structure from which we can query the approximate effective resistance between any two vertices in a graph in O(logn) time. © 2011 Society for Industrial and applied mathematics.

关键词： Clustering algorithms

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Optimized Runge-Kutta Methods with Automatic Step Size Control for Compressible Computational Fluid Dynamics

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Communications on applied mathematics and Computation 2022年第4期4卷 1191-1228页

作者： Hendrik Ranocha Lisandro Dalcin Matteo Parsani David I.Ketcheson Applied Mathematics Münster University of MünsterMunsterGermany Computer Electrical and Mathematical Science and Engineering Division(CEMSE) Extreme Computing Research Center(ECRC)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Computer Electrical and Mathematical Science and Engineering Division(CEMSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We develop error-control based time integration algorithms for compressible fluid dynam-ics(CFD)applications and show that they are efficient and robust in both the accuracy-limited and stability-limited *** on discontinuous spectral element semidis-cretizations,we design new controllers for existing methods and for some new embedded Runge-Kutta *** demonstrate the importance of choosing adequate controller parameters and provide a means to obtain these in *** compare a wide range of error-control-based methods,along with the common approach in which step size con-trol is based on the Courant-Friedrichs-Lewy(CFL)*** optimized methods give improved performance and naturally adopt a step size close to the maximum stable CFL number at loose tolerances,while additionally providing control of the temporal error at tighter *** numerical examples include challenging industrial CFD applications.

关键词： Explicit Runge-Kutta methods Step size control Compressible Euler equations Compressible Navier-Stokes equations hp-adaptive spatial discretizations

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Learning representation and control in continuous Markov decision processes

Learning representation and control in continuous Markov dec...

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21st National Conference on Artificial Intelligence and the 18th Innovative Applications of Artificial Intelligence Conference, AAAI-06/IAAI-06

作者： Mahadevan, Sridhar Maggioni, Mauro Ferguson, Kimberly Osentoski, Sarah Department of Computer Science University of Massachusetts 140 Governor's Drive Amherst MA 01003 Program in Applied Mathematics Department of Mathematics Yale University New Haven CT 06510

ISBN: (纸本)1577352815

This paper presents a novel framework for simultaneously learning representation and control in continuous Markov decision processes. Our approach builds on the framework of proto-value functions, in which the underlying representation or basis functions are automatically derived from a spectral analysis of the state space manifold. The proto-value functions correspond to the eigenfunctions of the graph Laplacian. We describe an approach to extend the eigenfunctions to novel states using the Nyström extension. A least-squares policy iteration method is used to learn the control policy, where the underlying subspace for approximating the value function is spanned by the learned proto-value functions. A detailed set of experiments is presented using classic benchmark tasks, including the inverted pendulum and the mountain car, showing the sensitivity in performance to various parameters, and including comparisons with a parametric radial basis function method. Copyright © 2006, American Association for Artificial Intelligence (***). All rights reserved.

关键词： Knowledge representation

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Stability, multiplicity and global continuation of symmetric periodic solutions of a nonlinear Volterra integral equation

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Japan Journal of applied mathematics 1985年第2期2卷 433-469页

作者： Chow, S.-N. Diekmann, O. Mallet-Paret, J. Michigan State University East Lansing 48824 Michigan United States Centre for Mathematics and Computer Science Amsterdam Netherlands Division of Applied Mathematics Brown University Providence 02912 Rhode Island United States

Results on existence, multiplicity, stability, global continuation and limiting behaviour when ε↓0 of periodic solutions of {Mathematical expression} are derived for the case of a nonlinear function f having certain monotonicity and symmetry properties. The proofs are based on the following two observations: (i) the right-hand side of (E) defines an operator which maps a cone of two-periodic functions with symmetry and positivity properties into itself;and (ii) slowly oscillating solutions of the linear variational equation correspond to dominant Floquet multipliers. © 1985 JJAM Publishing Committee.

关键词： Floquet multipliers global Hopf bifurcation invariant cone periodic solutions singular perturbation slowly oscillating solutions stability Volterra convolution integral equation

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BUILDING MACHINE LEARNING LIMITED AREA MODELS: KILOMETER-SCALE WEATHER FORECASTING IN REALISTIC SETTINGS

arXiv

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arXiv 2025年

作者： Adamov, Simon Oskarsson, Joel Denby, Leif Landelius, Tomas Hintz, Kasper Christiansen, Simon Schicker, Irene Osuna, Carlos Lindsten, Fredrik Fuhrer, Oliver Schemm, Sebastian Federal Office for Meteorology and Climatology MeteoSwiss Institute of Atmospheric and Climate Science Department of Environmental Science ETH Zurich Switzerland Division of Statistics and Machine Learning Department of Computer and Information Science Linköping University Sweden Danish Meteorological Institute Swedish Meteorological and Hydrological Institute Sweden GeoSphere Austria Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom

Machine learning is revolutionizing global weather forecasting, with models that efficiently produce highly accurate forecasts. Apart from global forecasting there is also a large value in high-resolution regional weather forecasts, focusing on accurate simulations of the atmosphere for a limited area. Initial attempts have been made to use machine learning for such limited area scenarios, but these experiments do not consider realistic forecasting settings and do not investigate the many design choices involved. We present a framework for building kilometer-scale machine learning limited area models with boundary conditions imposed through a flexible boundary forcing method. This enables boundary conditions defined either from reanalysis or operational forecast data. Our approach employs specialized graph constructions with rectangular and triangular meshes, along with multi-step rollout training strategies to improve temporal consistency. We perform systematic evaluation of different design choices, including the boundary width, graph construction and boundary forcing integration. Models are evaluated across both a Danish and a Swiss domain, two regions that exhibit different orographical characteristics. Verification is performed against both gridded analysis data and in-situ observations, including a case study for the storm Ciara in February 2020. Both models achieve skillful predictions across a wide range of variables, with our Swiss model outperforming the numerical weather prediction baseline for key surface variables. With their substantially lower computational cost, our findings demonstrate great potential for machine learning limited area models in the future of regional weather forecasting. Copyright © 2025, The Authors. All rights reserved.

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Value function approximation with diffusion wavelets and Laplacian eigenfunctions

Value function approximation with diffusion wavelets and Lap...

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2005 Annual Conference on Neural Information Processing Systems, NIPS 2005

作者： Mahadevan, Sridhar Maggioni, Mauro Department of Computer Science University of Massachusetts Amherst MA 01003 United States Program in Applied Mathematics Department of Mathematics Yale University New Haven CT 06511 United States

ISBN: (纸本)9780262232531

We investigate the problem of automatically constructing efficient representations or basis functions for approximating value functions based on analyzing the structure and topology of the state space. In particular, two novel approaches to value function approximation are explored based on automatically constructing basis functions on state spaces that can be represented as graphs or manifolds: one approach uses the eigenfunctions of the Laplacian, in effect performing a global Fourier analysis on the graph;the second approach is based on diffusion wavelets, which generalize classical wavelets to graphs using multiscale dilations induced by powers of a diffusion operator or random walk on the graph. Together, these approaches form the foundation of a new generation of methods for solving large Markov decision processes, in which the underlying representation and policies are simultaneously learned.

关键词： Diffusion

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