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Optimal Excitatory and Inhibitory Balance for High Learning Performance in Spiking Neural Networks with Long-Tailed Synaptic Weight Distributions

Optimal Excitatory and Inhibitory Balance for High Learning ...

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international joint conference on Neural Networks (IJCNN)

作者： Ibuki Matsumoto Sou Nobukawa Tomoki Kurikawa Nobuhiko Wagatsuma Yusuke Sakemi Takashi Kanamaru Nina Sviridova Kazuyuki Aihara Graduate School of Information and Computer Science Chiba Institute of Technology Chiba Japan Department of Complex and Intelligent Systems Future University Hakodate Hokkaido Japan Department of Information Science Toho University Chiba Japan Research Center for Mathematical Engineering Chiba Institute of Technology Chiba Japan School of Advanced Engineering Kogakuin University Tokyo Japan Department of Intelligent Systems Tokyo City University Tokyo Japan International Research Center for Neurointelligence The University of Tokyo Tokyo Japan

Excitatory/inhibitory (E/I) balance is significantly associated with cognitive function. Its imbalance impairs cognitive function, particularly in patients with psychiatric disorders. Recent physiological and modeling findings show that excitatory postsynaptic potentials (EPSPs) have a long-tailed distribution and contribute to the generation of spontaneous activity. Moreover, this spontaneous activity and its response to the external stimulus significantly alternate under the different E/I balance. However, the effects of the E/I balance under long-tailed EPSPs at the functional level remain unknown. Hence, to elucidate this relationship, we constructed a reservoir computing (RC) model generating the long-tailed distribution of EPSPs and investigated the effect of the E/I balance on the learning performance of RC in the memory capacity (MC) task, which measures how correctly delayed input signals can be reproduced. The results revealed that an appropriate E/I balance maximized the MC. This high MC was realized by recurrent spike propagation under long-tailed EPSPs. These findings contribute to the understanding of the effect of the E/I balance in physiologically relevant neural networks.

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Cricket Analytics: ODI Match Score Prediction and Resource Metric modeling using Machine Learning Model

Cricket Analytics: ODI Match Score Prediction and Resource M...

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Computing, Applications and systems (COMPAS), IEEE international conference on

作者： Md. Parvezur Rahman Mahin Effat Ara Raihan Ul Islam Dept. of Comp. Sci. & Eng. East West University Dhaka Bangladesh Dept. of Comp. Sci. & Eng. American International University of Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Sports analytics using machine learning is no longer a young area of computer science research that presents several difficulties. The purpose of this study is to develop a method for predicting game outcomes during an ODI cricket competition. Like all other papers, we adopted the factor-based prediction meaning the prediction will depend on various game factors like wickets left, overs left, current run, run rate, etc. Sometimes the models can not make good predictions based on these factors and we tried to improve that by adding weight to overs and wickets because these two vastly control the score of the game. We named this feature the resource factor. We used linear regression, polynomial regression, elastic net regression, Xgboost, and random forest and found that random forest works best for our dataset. For more interaction, we used a web application and we applied our best model to predict the score. For the test phase, the R 2 score for the random forest regressor is 0.9899 which is pretty good, and for the MAE and MSE scores, we have got 3.3569 and 37.3555.

关键词： measurement Analytical models Games Predictive models Prediction algorithms Polynomials Stakeholders Random forests Tuning Sports

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QUEST: Quality-informed Multi-agent Dispatching System for Optimal Mobile Crowdsensing

QUEST: Quality-informed Multi-agent Dispatching System for O...

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IEEE Annual joint conference: INFOCOM, IEEE computer and Communications Societies

作者： Zuxin Li Fanhang Man Xuecheng Chen Susu Xu Fan Dang Xiao-Ping Zhang Xinlei Chen Tsinghua Shenzhen International Graduate School Tsinghua University Department of Civil and System Engineering Johns Hopkins University Global Innovative Exchange Tsinghua University Pengcheng Lablatory RISC-V International Open Source Laboratory

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

We address the challenges in achieving optimal Quality of Information (QoI) for non-dedicated vehicular Mobile Crowdsensing (MCS) systems, by utilizing vehicles not originally designed for sensing purposes to provide real-time data while moving around the city. These challenges include the coupled sensing coverage and sensing reliability, as well as the uncertainty and time-varying vehicle status. To tackle these issues, we propose QUEST, a QUality-informed multi-agEnt diSpaTching system, that ensures high sensing coverage and sensing reliability in non-dedicated vehicular MCS. QUEST optimizes QoI by introducing a novel metric called ASQ (aggregated sensing quality), which considers both sensing coverage and sensing reliability jointly. Additionally, we design a mutual-aided truth discovery dispatching method to estimate sensing reliability and improve ASQ under uncertain vehicle statuses. Real-world data from our deployed MCS system in a metropolis is used for evaluation, demonstrating that QUEST achieves up to 26% higher ASQ improvement, leading to a reduction of reconstruction map errors by 32-65% for different reconstruction algorithms.

关键词： Uncertainty Crowdsensing Heuristic algorithms Computational modeling Urban areas Reliability engineering Dispatching

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Frequency-based Directional Sensing for Mobile Molecular Communication

Frequency-based Directional Sensing for Mobile Molecular Com...

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international Symposium on Soft Computing and Intelligent systems (SCIS)

作者： Motoharu Nishino Tadashi Nakano Department of Electrical and Information Engineering Osaka City University Japan Graduate School of Informatics Osaka Metropolitan University Japan

This paper proposes a mobile molecular communication system in which directional information is encoded on pulse frequency. In the proposed system, a sender bio-nanomachine generates pulses of signal molecules periodically, the pulses of signal molecules propagate in the environment, and the receiver bio-nanomachine learns the direction to the sender bio-nanomachine from the frequency of pulses that it observes. This paper presents its model and conducts computer simulations. Results show that receiver bio-nanomachines using frequency-based directional sensing are capable of moving closer to the sender bio-nanomachine successfully. Results also show that the performance of the frequency-based directional sensing scheme depends on the noise strength and that, as the noise strength increases, the mean time to reach the sender and its variance increase. The paper proposes a new frequency-based modulation scheme for mobile molecular communication and contributes to the field of molecular communication.

关键词： Time-frequency analysis Frequency modulation Biological system modeling computer simulation Computational modeling Receivers Repeaters

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Neuro-symbolic Generative AI Assistant for System Design

Neuro-symbolic Generative AI Assistant for System Design

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ACM and IEEE international conference on Formal Methods and Models for Co-Design (MEMOCODE)

作者： Susmit Jha Sumit Kumar Jha Alvaro Velasquez SRI International Florida International University University of Colorado Boulder

ISBN: (数字)9798350378023

ISBN: (纸本)9798350378030

The design of complex cyber-physical systems involves balancing multiple, often conflicting performance objectives. In practice, some design requirements remain implicit, embedded in the intuition and expertise of seasoned designers who have worked on similar systems for years. These designers rely on their experience to explore a limited set of promising design candidates, evaluating or simulating them with detailed but computationally slow scientific models. The typical goal is to produce a diverse array of high-performing configurations that offer flexibility in trade-offs and avoid premature commitment to a specific design. In this invited talk, we describe an AI assistant that leverages neuro-symbolic machine learning to automate parts of the system design process. Our approach extends oracle-guided inductive synthesis by integrating a hierarchy of oracles, ranging from slow, detailed scientific models to faster but lower-fidelity deep neural network surrogates and symbolic rules. This approach accelerates design iterations, especially during early design phases. We employ deep generative models in the form of fine-tuned large language models to learn the valid design space, followed by joint exploration and optimization across this learned manifold. This allows the generation of a diverse set of optimal designs based on specified performance objectives.

关键词： Manifolds Generative AI Large language models Computational modeling Machine learning Artificial neural networks Cyber-physical systems Distance measurement System analysis and design Optimization

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Spin Chemistry Simulation via Hybrid-Quantum Machine Learning 3

Spin Chemistry Simulation via Hybrid-Quantum Machine Learnin...

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3rd IEEE international conference on Quantum Computing and Engineering (QCE)

作者： Brokowski, Trevor J. Chowdhury, Farhan T. Smith, Luke D. Alvarez, Pedro Sandeep, Samarth Aiello, Clarice Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA 90095 USA Univ Exeter Dept Phys Exeter Devon England Univ Exeter Living Syst Inst Exeter Devon England Univ Estadual Campinas Inst Fis Campinas Brazil If & Only If Iff Technol Davis CA USA

ISBN: (数字)9781665491136

ISBN: (纸本)9781665491136

The Radical Pair Mechanism, a process by which magnetic fields modulate the kinetics of chemical reactions by influencing spin dynamics, is thought to underlie a variety of biological mechanisms and is inherently both quantum and noisy. Model simulations with limited electron-nuclear interactions have been conducted on classical devices resulting in experimentally verified final state results, however these simulations become too complex to simulate as the systems scale in size. This motivates us to propound the simulation of the relevant spin dynamics in a noisy quantum device to probe the currently unexplored open quantum systems aspects of the radical pair mechanism and to provide a novel theoretical tool for us to be able to tackle bottlenecks in simulating larger and more physically relevant biological spin systems. We employ a hybrid quantum machine learning approach where we evaluate a parametrized quantum circuit representing the evolution of the system on the quantum device and then update the parameters according to our objective cost function on a classical computer using a gradient optimizer and a noisy gradient free (SPSA) optimizer. We show that a 3 qubit circuit with a series of parametrized rotation gates representing the Hyperfine and Zeeman interactions can be optimized, resulting in a circuit that evolves a singlet state under different magnetic field angles. Moreover, we show that the noisy gradient free optimizer is better suited for optimizing on a noisy quantum device. However, the precision in the quantum circuit outputs is only sub-par at present, leaving room for further improvements.

关键词： Quantum system Biological system modeling Qubit Machine learning Logic gates Kinetic theory Noise measurement

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Dynamic Continual Learning: Harnessing Parameter Uncertainty for Improved Network Adaptation

Dynamic Continual Learning: Harnessing Parameter Uncertainty...

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international joint conference on Neural Networks (IJCNN)

作者： Christopher F. Angelini Nidhal C. Bouaynaya Dept. of Electrical and Computer Engineering Rowan University Glassboro New Jersey USA

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

When fine-tuning Deep Neural Networks (DNNs) to new data, DNNs are prone to overwriting network parameters required for task-specific functionality on previously learned tasks, resulting in a loss of performance on those tasks. We propose using parameter-based uncertainty to determine which parameters are relevant to a network’s learned function and regularize training to prevent change in these important parameters. We approach this regularization in two ways: (1), we constrain critical parameters from significant changes by associating more critical parameters with lower learning rates, thereby limiting alterations in those parameters; (2), important parameters are restricted from change by imposing a higher regularization weighting, causing parameters to revert to their states prior to the learning of subsequent tasks. We leverage a Bayesian Moment Propagation framework which learns network parameters concurrently with their associated uncertainties while allowing each parameter to contribute uncertainty to the network’s predictive distribution, avoiding the pitfalls of existing sampling-based methods. The proposed approach is evaluated for common sequential benchmark datasets and compared to existing published approaches from the Continual Learning community. Ultimately, we show improved Continual Learning performance for Average Test Accuracy and Backward Transfer metrics compared to sampling-based methods and other non-uncertainty-based approaches.

关键词： Continuing education Training Uncertain systems Uncertainty Limiting measurement uncertainty Benchmark testing

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Interval Agreement Weighted Average - Sensitivity to Data Set Features

Interval Agreement Weighted Average - Sensitivity to Data Se...

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IEEE international conference on Fuzzy systems (FUZZ-IEEE)

作者： Yu Zhao Christian Wagner Brendan Ryan Direnc Pekaslan Javier Navarro Lab for Uncertainty in Data and Decision Making (LUCID) School of Computer Science University of Nottingham United Kingdom Human Factors Research Group Faculty of Engineering University of Nottingham School of Engineering and Sciences United Kingdom School of Engineering and Sciences Technological Institute of Higher Studies of Monterrey Mexico

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

The growing use of intervals in fields like survey analysis necessitates effective aggregation methods that can summarize and represent such uncertain data representations. The Interval Agreement Approach (IAA) addresses this by aggregating interval responses into Fuzzy Sets (FSs), capturing both intra- and inter-participant agreement, while minimizing information loss. While offering a powerful modeling tool, the IAA does not natively offer a measure of central tendency, which is itself an interval of particular utility in real-world applications. In contrast, the Interval Weighted Average (IWA) has been used for directly measuring the central tendency of intervals. While straightforward and effective, it is not designed, nor able to, summarize interval data in terms of their agreement, as the IAA does. To bridge this gap, this paper introduces Interval Agreement Weighted Average (IAWA), which is specifically designed to reflect both the central tendency and agreement. This is achieved by first modeling interval agreement as FSs using the IAA, and then transforming these FSs into intervals using the IWA. We demonstrate the approach by conducting sensitivity analyses to explore the behavior of the proposed approach in detail. Our findings suggest that the IAWA is a highly effective measure of central tendency. Additionally, it also partially inherits IAA's ability to reflect the agreement of sets of intervals. We conclude by highlighting the potential and growth of the use of intervals in information elicitation, within, and beyond survey research, underpinning a new degree of understanding of both intra- and inter-source uncertainty.

关键词： Surveys Weight measurement Bridges Fuzzy sets Uncertainty Sensitivity analysis Fuzzy systems

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Diverse Instance Discovery: Vision-Transformer for Instance-Aware Multi-Label Image Recognition

Diverse Instance Discovery: Vision-Transformer for Instance-...

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2022 IEEE international conference on Multimedia and Expo, ICME 2022

作者： Hu, Yunqing Jin, Xuan Zhang, Yin Hong, Haiwen Zhang, Jingfeng Yan, Feihu He, Yuan Xue, Hui College of Computer Science and Technology Zhejiang University Hangzhou China Alibaba Group Hangzhou China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Previous works on multi-label image recognition (MLIR) usually use CNNs as a starting point for research. In this paper, we take pure Vision Transformer (ViT) as the research base and make full use of the advantages of Transformer with long-range dependency modeling to circumvent the disadvantages of CNNs limited to local receptive field. However, for multi-label images containing multiple objects from different categories, scales, and spatial relations, it is not optimal to use global information alone. Our goal is to leverage ViT's patch tokens and self-attention mechanism to mine rich instances in multi-label images, named diverse instance discovery (DiD). To this end, we propose a semantic category-aware module and a spatial relationship-aware module, respectively, and then combine the two by a re-constraint strategy to obtain instance-aware attention maps. Finally, we propose a weakly supervised object localization-based approach to extract multi-scale local features, to form a multi-view pipeline. Our method requires only weakly supervised information at the label level, no additional knowledge injection or other strongly supervised information is required. Experiments on three benchmark datasets show that our method significantly outperforms previous works and achieves state-of-the-art results under fair experimental comparisons. © 2022 IEEE.

关键词： Image recognition

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PV panel fault detection based on improved ResNet

PV panel fault detection based on improved ResNet

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Power Electronics, computer Applications (ICPECA), IEEE international conference on

作者： Ligang Wu Changxin Zhao Zushan Ding Xiao Zhang Yiding Wang Fei Sun Anming He Anhui Jiyuan Software Co. Ltd Hefei China State Grid Jiangsu Electric Power Co. Ltd Xuzhou China

At a time when photovoltaic power generation is becoming more and more mature, the maintenance of photovoltaic modules has also become a major problem. In order to avoid the environmental impact of photovoltaic hot spot damage photovoltaic panels, this leads to a decrease in power generation efficiency, as well as the current detection means small hot spots are often be missed, combined with the need for speed and accuracy in the UAV inspection and cleaning, for the traditional two-level detection method, a ResNet residual network-based full convolutional network model, and further use grayscale, filtering and edge gradient fitting and other measure to enhance the model evaluation conditions, achieving accurate hot spot location on PV *** experimental results show that the improved full convolutional network model is very useful for small photovoltaic panels. The experimental results show that the improved full convolutional network model is also effective in detecting small photovoltaic hot *** application of this model not only satisfies the requirements of PV inspection for real time, but also has a lower cost, stronger portability, can be applied to most scenarios and greatly reduces the requirements on the environment and the working state of PV modules.

关键词： Photovoltaic systems Adaptation models Power measurement Target recognition Computational modeling Inspection Real-time systems

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