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17th international symposium on Functional and Logic programming (FLOPS)

作者： Suwa, Takashi Igarashi, Atsushi Kyoto Univ Grad Sch Informat Kyoto Japan Natl Inst Informat Tokyo Japan

ISBN: (纸本)9789819722990;9789819723003

We propose MetaFM, a novel ML-style module system that enables users to decompose multi-stage programs (i.e., programs written in a typed multi-stage programming language) into loosely coupled components in a manner natural with respect to type abstraction. The distinctive aspect of MetaFM is that it allows values at different stages to be bound in a single structure (i.e., struct ... end). This feature is crucial, for example, for defining a function and a macro that use one abstract type in common, without revealing the implementation detail of that type. MetaFM also accommodates functors, higher-kinded types, the with type-construct, etc. with staging features. For defining semantics and proving type safety, we employ an elaboration technique, i.e., type-directed translation to a target language, inspired by the formalization of F-ing Modules. Specifically, we give a set of elaboration rules for converting MetaFM programs into System F omega(<>), a multi-stage extension of System F omega, and prove that the elaboration preserves typing. Additionally, our language supports cross-stage persistence (CSP), a feature for code reuse spanning more than one stage, without breaking type safety.

关键词： Staging Code generation Macros Module systems

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12th international symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2024

12th International Symposium on Leveraging Applications of F...

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12th international symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2024

ISBN: (纸本)9783031737084

The proceedings contain 25 papers. The special focus in this conference is on Leveraging Applications of Formal Methods, Verification and Validation. The topics include: Klaim in the Making;formal Approaches for Modeling and Analysis of Business Process Collaborations;an Abstract Account of Up-to Techniques for Inductive Behavioural Relations;language Equivalence from Nondeterministic to Weighted Automata—and Back;a Process Algebraic View of In/Out Prisoners;towards a Formal Testing Theory for Quantum Processes;testing Quantum Processes;the ProbInG Project: Advancing Automatic Analysis of Probabilistic Loops;towards a Probabilistic programming Approach to Analyse Collective Adaptive systems;Can AI Help with the Formalization of Railway Cybersecurity Requirements?;white-Box Validation of Collective Adaptive systems by Statistical Model Checking and Process Mining;analysing Collective Adaptive systems by Proving Theorems;engineering Ethical-Aware Collective Adaptive systems;emerging Synchrony in Applauding Audiences: Formal Analysis and Specification;flocks of Birds: A Quantitative Evaluation;strategies in Spatio-Temporal Logics for Multi-agent systems;function-as-a-Service Allocation Policies Made Formal;asynchronous Multiparty Sessions with Internal Delegation: Dedicated to Rocco De Nicola on the Occasion of his 70th Birthday;code-Centric Code Generation;attack Tree Generation via Process Mining;local Spaces in Soft Concurrent Constraint programming Oriented to Security;Riding the Data Storms: Specifying and Analysing IoT Security Requirements with SURFING;preventing Out-of-Gas Exceptions by Typing.

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12th international symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2024

12th International Symposium on Leveraging Applications of F...

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12th international symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2024

ISBN: (纸本)9783031753794

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12th international symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2024

12th International Symposium on Leveraging Applications of F...

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12th international symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2024

ISBN: (纸本)9783031751066

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Design Automation of CMOS Op-Amps Using Statistical Geometric programming

Design Automation of CMOS Op-Amps Using Statistical Geometri...

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IEEE international symposium on Circuits and systems (ISCAS)

作者： Chowdhury, Sangjukta R. Bhardwaj, Sumit Kitchen, Jennifer Arizona State Univ Dept Elect Comp & Energy Engn Tempe AZ 85287 USA

ISBN: (纸本)9781665484855

This work proposes a novel design automation (DA) technique that uses a multifaceted approach combining Multivariate Regression with Geometric programming (GP) to design analog circuits. Previous DA methods employing GP have typically used analytical derivations of the various design equations representing an analog circuit. The proposed DA method eliminates the need for analytical derivations by using simulation data and multivariate regression to generate statistical models combined with GP to solve these statistical expressions with respect to optimum circuit design parameters. This presented statistical GP method has been applied to successfully design a five-transistor two-stage operational amplifier and a folded cascode amplifier in a TSMC 65nm CMOS technology. The presented statistical GP DA results are comparable to the design results obtained from both analytical GP and manual design by an experienced analog design engineer.

关键词： CMOS Op-Amps Design Automation Geometric programming Linear Regression Statistical GP

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From Womb to Well-being: The Intricate Link between Psychoneuroendocrinology, Metabolism, and Fetal programming for Optimal Neurodevelopment and Health

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PSYCHONEUROENDOCRINOLOGY 2024年 160卷 106743-106743页

作者： Reid, Brie Brown Univ Providence RI 02912 USA

Neuropsychiatric and physical illnesses are frequently co-morbid. Many of these mental and physical health outcomes can be traced back to the fetal period, a critical period of development that lays the groundwork for brain and organ system architecture. Fetal programming is a key concept within the broader developmental origins of health and disease framework, which recognizes the importance of the prenatal and early postnatal environment to health throughout life. The fetus adapts to environmental exposures, and altered biological systems can have long- term effects on child and adult health (Barker, 2007). Individual variations in exposure to psychological stress and dysregulated metabolism in the prenatal period may impact fetal development, with long-term effects on the child's neurocognitive and physical health (Bale, 2010; Godfrey & Barker, 2001; Massin, 2001; Tamashiro & Moran, 2010). The fetus is sensitive to important in-utero exposures, such as maternal nutritional, psychological, and endocrine signals (Cottrell & Seckl, 2009). These signals are believed to convey information about the surrounding environment to the fetus in order to prepare it for life after birth and to allow for changes in the programming of growing systems (Howland, 2017). The fetal and early neonatal period is critical for understanding the application of psychoneuroendocrinology from basic science to better health. Research examining the fetal origins of health and disease has long studied the individual impacts of psychoneuroendocrinology and metabolism early in life. A growing body of research seeks to integrate the study of psychoneuroendocrinology, stress, and metabolism together into our fundamental understanding of early growth and the developing brain. The studies presented in this multidisciplinary symposium span novel biomarker discovery, prenatal maternal metabolic functioning and infant neuroimaging, and a clinical trial of a maternal mental health intervention and offspr

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Robust Scheduling With Temporal Decomposition of Integrated Electrical-Heating System Based on Dynamic programming Formulation

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2023年第4期59卷 5087-5100页

作者： Xiong, Houbo Chen, Zhe Zhang, Xiaoyan Wang, Chutong Shi, Yunhui Guo, Chuangxin Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China State Grid Zhejiang Elect Power Co Elect Power Res Inst Hangzhou 310027 Peoples R China Zhejiang Elect Power Dispatching Ctr Hangzhou 310027 Peoples R China

To realize the potential of the integrated electrical-heating systems (IEHS) for coping with the uncertain renewable energy, a reserve scheduling model based on the dynamic programming transformed multi-stage adaptive robust optimization (DMRO) is proposed in this article. The DMRO model enforces the non-anticipativity of scheduling process via temporal decomposed framework, and enhances the formulation flexibility for time-coupled equipment, such as energy storage systems (ESS). The whole scheduling model is split into two problems under the decomposition constructure, which consists of a reserve interval generation (RG) problem for making the unit commitment and systems' reserve plan, and a multi-stage economic operation (EO) problem aiming with simulating the worst-case scheduling process. To obtain the global optimal, a nested Benders decomposition algorithm is employed to solve the DMRO model. Case studies on the 6-Bus-5-node IEHS and the 118-Bus-24-node IEHS demonstrate the effectiveness of the proposed model and the solution methodology.

关键词： Energy storage systems integrated electrical-heating system multi-stage adaptive robust optimization renewable energy reserve scheduling temporal decomposition

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Efficient Resource-Aware Neural Architecture Search with a Neuro-Symbolic Approach 16

Efficient Resource-Aware Neural Architecture Search with a N...

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16th IEEE international symposium on Embedded Multicore/Many-core systems-on-Chip (MCSoC)

作者： Bellodi, Elena Bertozzi, Davide Bizzarri, Alice Favalli, Michele Fraccaroli, Michele Zese, Riccardo Univ Ferrara Dept Engn Ferrara Italy Univ Manchester Comp Sci Dept Manchester Lancs England Unitec SpA Lugo Italy Univ Ferrara DOCPAS Ferrara Italy

ISBN: (纸本)9798350393613

Hardware-aware Neural Architectural Search (NAS) is gaining momentum to enable the deployment of deep learning on edge devices with limited computing capabilities. Incorporating device-related objectives such as affordable floating point operations, latency, power, memory usage, etc. into the optimization process makes searching for the most efficient neural architecture more complicated, since both model accuracy and hardware cost should guide the search. The main concern with most state-of-the-art hardware-aware NAS strategies is that they propose for evaluation also trivially infeasible network models for the capabilities of the hardware platform at hand. Moreover, previously generated models are frequently not exploited to intelligently generate new ones, leading to prohibitive computational costs for practical relevance. This paper aims to boost the computational efficiency of hardware-aware NAS by means of a neuro-symbolic framework revolving around a Probabilistic Inductive Logic programming module to define and exploit a set of symbolic rules. This component learns and refines the probabilities associated with the rules, allowing the framework to adapt and improve over time, thus quickly narrowing down the search space toward the most promising neural architectures.

关键词： architecture search neural network hardware accelerators neural networks probabilistic logic programming

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Integrating interactive performance analysis in Jupyter Notebooks for parallel programming education

Integrating interactive performance analysis in Jupyter Note...

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1st international Conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Oden, Lena Noelp, Klaus Brauner, Philipp Univ Hagen Comp Engn Hagen Germany Rhein Westfal TH Aachen Human Comp Interact Ctr Aachen Germany

ISBN: (纸本)9798350364613;9798350364606

Understanding the performance behavior of parallel applications is important in many ways, but doing so is not easy. Most open source analysis tools are written for the command line. We are building on these proven tools to provide an interactive performance analysis experience within Jupyter Notebooks when developing parallel code with MPI, OpenMP, or both. Our solution makes it possible to measure the execution time, perform profiling and tracing, and visualize the results within the notebooks. For ease of use, it provides both a graphical JupyterLab extension and a C++ API. The JupyterLab extension shows a dialog where the user can select the type of analysis and its parameters. Internally, this tool uses Score -P, Scalasca, and Cube to generate profiling and tracing data. This tight integration gives students easy access to profiling tools and helps them better understand concepts such as benchmarking, scalability and performance bottlenecks. In addition to the technical development, the article presents hands-on exercises from our well-established parallel programming course. We conclude with a qualitative and quantitative evaluation with 19 students, which shows a positive effect of the tools on the students' perceived competence.

关键词： Jupyter parallel programming performance analysis interactive programming high perfonnance computing

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A Resource Allocation Method in Multi-access Edge Computing Environment Based on Deep Reinforcement Learning 17

A Resource Allocation Method in Multi-access Edge Computing ...

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17th IEEE international symposium on Embedded Multicore/Many-core systems-on-Chip, MCSoC 2024

作者： Li, Huilin Nakamura, Hiroshi Takase, Hideki The University of Tokyo Tokyo Japan

ISBN: (纸本)9798331530471

In order to help user terminal devices (UTDs) efficiently handle computation-intensive and delay-sensitive tasks, the use of multi-access edge computing (MEC) has been proposed. However, owing to the performance limitations of UTDs and limited network resources, optimizing the energy consumption of UTDs under the constraint of a certain delay condition remains a key area of research. Therefore, this paper presents a resource allocation optimization strategy for MEC environments. First, we formulated the optimization problem as a constrained nonlinear programming problem, with the aim of minimizing the total time delay and energy consumption of UTDs under a specific delay constraint. Second, we proposed a reinforcement learning algorithm that uses an actor network to predict the next action based on the current state and a critic network to evaluate the quality of the prediction. A stable convergence solution for the original optimization problem was obtained within a limited number of training episodes. Finally, the performance of the proposed algorithm was compared with that of other classical methods, which demonstrated that the proposed algorithm outperforms other methods. © 2024 IEEE.

关键词： constrained nonlinear programming deep reinforcement learning multi-access edge computing resource allocation

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