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A Study in Activation: Towards a Common Lexicon and Functional Taxonomy in Cognitive Architectures 18

A Study in Activation: Towards a Common Lexicon and Function...

18th International Conference on Cognitive Modelling, ICCM 2020 - Co-located with the 53rd Annual Meeting of the Society for Mathematical Psychology

作者： Kugele, Sean Franklin, Stan Department of Computer Science Institute for Intelligent Systems University of Memphis MemphisTN38152 United States

ISBN: (纸本)9780998508245

Activation has become a pervasive concept in many scientific disciplines, including cognitive and neural modeling, and AI. Unfortunately, its applications and functions are so broad and varied that it is difficult for practitioners to discuss the topic in precise and meaningful ways. This is particularly apparent in cognitive architectures, where a wider breadth of activation’s utilities and forms have been explored. To help combat these terminological difficulties, and hopefully facilitate productive discourse and the development of future applications, we introduce (1) a lexicon of activation-related concepts, and (2) a functional taxonomy that enumerates many activation-related "design patterns" that have appeared in cognitive architectures. We demonstrate our taxonomy by applying it to the LIDA cognitive architecture, which includes one of the most varied and comprehensive adoptions of activation-related functionality. © 2020 Proceedings of ICCM 2020 - 18th International Conference on Cognitive Modelling. All Rights Reserved.

关键词： activation cognitive architectures LIDA

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Designing Consensus-Based Distributed Filtering over Directed Graphs

arXiv

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arXiv 2024年

作者： Lyu, Xiaoxu Wen, Guanghui Lv, Yuezu Duan, Zhisheng Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Systems Science School of Mathematics Southeast University Nanjing211189 China MIIT Key Laboratory of Complex-field Intelligent Sensing Beijing Institute of Technology Beijing100081 China State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China

This paper proposes a novel consensus-on-only-measurement distributed filter over directed graphs under the collectively observability condition. First, the distributed filter structure is designed with an augmented leader-following measurement fusion strategy. Subsequently, two parameter design methods are presented, and the consensus gain parameter is devised utilizing local information exclusively rather than global information. Additionally, the lower bound of the fusion step is derived to guarantee a uniformly upper bound of the estimation error covariance. Moreover, the lower bounds of the convergence rates of the steady-state performance gap between the proposed algorithm and the centralized filter are provided with the fusion step approaching infinity. The analysis demonstrates that the convergence rate is, at a minimum, as rapid as exponential convergence under the spectral norm condition of the communication graph. The transient performance is also analyzed with the fusion step tending to infinity. The inherent trade-off between the communication cost and the filtering performance is revealed from the analysis of the steady-state performance and the transient performance. Finally, the theoretical results are substantiated through the validation of two simulation examples. Copyright © 2024, The Authors. All rights reserved.

关键词： Wiener filtering

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Accurate Prior-centric Monocular Positioning with Offline LiDAR Fusion

Accurate Prior-centric Monocular Positioning with Offline Li...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jinhao He Huaiyang Huang Shuyang Zhang Jianhao Jiao Chengju Liu Ming Liu Thrust of Robotics and Autonomous Systems The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Baidu Autonomous Driving Technology Department (ADT) Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Computer Science University College London London UK School of Electronics and Information Engineering Tongji University Shanghai China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Unmanned vehicles usually rely on Global Positioning System (GPS) and Light Detection and Ranging (LiDAR) sensors to achieve high-precision localization results for navigation purpose. However, this combination with their associated costs and infrastructure demands, poses challenges for widespread adoption in mass-market applications. In this paper, we aim to use only a monocular camera to achieve comparable onboard localization performance by tracking deep-learning visual features on a LiDAR-enhanced visual prior map. Experiments show that the proposed algorithm can provide centimeter-level global positioning results with scale, which is effortlessly integrated and favorable for low-cost robot system deployment in real-world applications.

关键词： Location awareness Visualization Laser radar Robot vision systems Cameras Sensor systems Robustness

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Fully Decentralized Design of Initialization-free Distributed Network Size Estimation

arXiv

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arXiv 2024年

作者： Lee, Donggil Kim, Taekyoo Lee, Seungjoon Shim, Hyungbo Center for Intelligent and Interactive Robotics Korea Institute of Science and Technology Seoul02792 Korea Republic of The Train Control and Communication Team Korea Railroad Research Institute Uiwang16105 Korea Republic of NAVER Labs Seongnam13161 Korea Republic of ASRI Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

In this paper, we propose a distributed scheme for estimating the network size, which refers to the total number of agents in a network. By leveraging a synchronization technique for multi-agent systems, we devise an agent dynamics that ensures convergence to an equilibrium point located near the network size regardless of its initial condition. Our approach is based on an assumption that each agent has a unique identifier, and an estimation algorithm for obtaining the largest identifier value. By adopting this approach, we successfully implement the agent dynamics in a fully decentralized manner, ensuring accurate network size estimation even when some agents join or leave the network. © 2024, CC BY-NC-ND.

关键词： Multi agent systems

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"Conscious" Multi-Modal Perceptual Learning for Grounded Simulation-Based Cognition 42

"Conscious" Multi-Modal Perceptual Learning for Grounded Sim...

引用

42nd Annual Meeting of the Cognitive science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020

作者： Kugele, Sean Franklin, Stan Department of Computer Science Institute for Intelligent Systems University of Memphis MemphisTN38152 United States

Barsalou (1999) presented a simulation-based theory of grounded cognition called Perceptual Symbol systems. According to this theory, a fully functional conceptual system can be implemented using only modal representations (aka perceptual symbols) and simulations. While the theory has gained considerable neuroscientific and experimental support, there is an urgent need for computational accounts that flesh out the theory. The current paper explores one approach for implementing these computational foundations. We present an implementation of perceptual symbols, simulators, simulation-based perception, and "conscious" multi-modal perceptual learning based on generative neural networks, called β-variational autoencoders, combined with LIDA, a biologically-inspired cognitive architecture. We show that our implementation satisfies many of the properties attributed to perceptual symbol systems, and provides a solid foundation for future computational work in perception, categorization, and simulation-based cognition. © 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Biomimetics

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An avoiding overlap method between acoustic shadow and organ for automated ultrasound diagnosis and treatment 11

An avoiding overlap method between acoustic shadow and organ...

引用

11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Matsuyama, Momoko Koizumi, Norihiro Nishiyama, Yu Tsumura, Ryosuke Tsukihara, Hiroyuki Numata, Kazushi The University of Electro-Communications Department of Mechanical and Intelligent Systems Engineering Chofu Japan The University of Electro-Communications Department of Computer and Network Engineering Chofu Japan Health and Medical Research Institute National Institute of Advanced Industrial Science and Technology Line 4 Tsukuba Japan The University of Tokyo Department of Cardiothoracic Surgery Bunnkyo-Ku Japan Yokohama City University Medical Center Gastroenterological Center Yokohama Japan

ISBN: (纸本)9781665492324

Ultrasound diagnosis and treatment have attracted attention because they are noninvasive and allow real-time observation of lesions. However, it is difficult to accurately estimate the location of the treatment area because of information loss due to acoustic shadows caused by the reflection of sound waves from hard tissues such as bone. In this study, we propose a probe manipulation method that uses deep learning to avoid overlap between organs and shadows for automated diagnosis. © 2022 IEEE.

关键词： Ultrasonics

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All-in-one simulation-based inference 24

All-in-one simulation-based inference

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Manuel Gloeckler Michael Deistler Christian Weilbach Frank Wood Jakob H. Macke Machine Learning in Science University of Tübingen and Tübingen AI Center Tübingen Germany Department of Computer Science University of British Columbia Vancouver Canada Machine Learning in Science University of Tübingen and Tübingen AI Center Tübingen Germany and Max Planck Institute for Intelligent Systems Department Empirical Inference Tübingen Germany

Amortized Bayesian inference trains neural networks to solve stochastic inference problems using model simulations, thereby making it possible to rapidly perform Bayesian inference for any newly observed data. However, current simulation-based amortized inference methods are simulation-hungry and inflexible: They require the specification of a fixed parametric prior, simulator, and inference tasks ahead of time. Here, we present a new amortized inference method-- the Simformer--which overcomes these limitations. By training a probabilistic diffusion model with transformer architectures, the Simformer outperforms current state-of-the-art amortized inference approaches on benchmark tasks and is substantially more flexible: It can be applied to models with function-valued parameters, it can handle inference scenarios with missing or unstructured data, and it can sample arbitrary conditionals of the joint distribution of parameters and data, including both posterior and likelihood. We showcase the performance and flexibility of the Simformer on simulators from ecology, epidemiology, and neuroscience, and demonstrate that it opens up new possibilities and application domains for amortized Bayesian inference on simulation-based models.

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Energy Efficiency Optimization for Full-Duplex D2D Communications Underlaying Distributed Antenna systems

IEEE Transactions on Green Communications and Networking

引用

IEEE Transactions on Green Communications and Networking 2024年

作者： Liu, Zhan Liao, Zhiyuan Li, Chunquan Zhang, Zhijun Yu, Junzhi Liu, P.X. Hunan University of Humanities Science and Technology School of Information Loudi417000 China Nanchang University School of Information Engineering Nanchang330031 China Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang330031 China South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

In this paper, we investigate the total system energy efficiency (EE) of full-duplex (FD) device-to-device (D2D) communications underlaying distributed antenna systems (DAS), where remote access units (RAUs), D2D users (DUs), and cellular users (CUs) are all capable of FD operation. Specifically, we jointly optimize subcarrier assignment and power allocation under the quality of service (QoS) requirements of CUs and DUs and the maximum power constraints of RAUs, CUs, and DUs. In addition, we propose a novel spectrum sharing strategy that allows each subcarrier to be assigned to multiple CUs and/or multiple D2D pairs (DPs) for flexibility. To solve the formulated non-convex optimization problem, we first employ fractional programming to transform the objective function in the optimization problem from the fractional form into the equivalent subtractive form. Then, an efficient iterative resource allocation algorithm is proposed, which needs to solve an inner problem in each iteration. After relaxing the variables and introducing penalty factors, the non-convex inner problem is transformed into a convex problem through the successive convex approximation (SCA) method and solved by iterative algorithm. Simulation results demonstrate that the proposed algorithm can considerably improve the system EE compared to other benchmark schemes. Furthermore, the proposed spectrum sharing strategy is superior to the existing sharing strategies. © 2017 IEEE.

关键词： Convex optimization

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COMPUTATIONAL FLUID-STRUCTURE INTERACTION FRAMEWORK FOR SIMULATING CHARACTERISTIC DEFORMATIONS IN INSECT FLAPPING WINGS 9

COMPUTATIONAL FLUID-STRUCTURE INTERACTION FRAMEWORK FOR SIMU...

引用

9th International Conference on Computational Methods for Coupled Problems in science and Engineering, COUPLED PROBLEMS 2021

作者： Onishi, Minato Ishihara, Daisuke Department of Computer Science and System Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka Fukuoka820-8502 Japan Department of Intelligent and Control Systems Kyushu Institute of Technology Kyushu Institute of Technology 680-4 Kawazu Iizuka Fukuoka820-8502 Japan

In this study, a computational fluid-structure interaction (FSI) framework for characteristic deformations in insect's wings is proposed. The proposed framework consists of a pixel wing model using a structured shell finite element mesh, a projection method for the monolithic FSI monolithic equations using an algebraic splitting, and the FSI dynamic similarity law to measure dynamic similarity between model's and actual insect's flights. It is shown that the proposed framework can directly simulate passive feathering and cambering in insect's wings caused by the FSI, whose magnitudes are very close to those of actual insects. © 2021 9th International Conference on Computational Methods for Coupled Problems in science and Engineering, COUPLED PROBLEMS 2021. All rights reserved.

关键词： Fluid structure interaction

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Uncovering Neural Scaling Laws in Molecular Representation Learning

arXiv

引用

arXiv 2023年

作者： Chen, Dingshuo Zhu, Yanqiao Zhang, Jieyu Du, Yuanqi Li, Zhixun Liu, Qiang Wu, Shu Wang, Liang Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences China Department of Computer Science University of California Los Angeles United States The Paul G. Allen School of Computer Science and Engineering University of Washington United States Department of Computer Science Cornell University United States Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

Molecular Representation Learning (MRL) has emerged as a powerful tool for drug and materials discovery in a variety of tasks such as virtual screening and inverse design. While there has been a surge of interest in advancing model-centric techniques, the influence of both data quantity and quality on molecular representations is not yet clearly understood within this field. In this paper, we delve into the neural scaling behaviors of MRL from a data-centric viewpoint, examining four key dimensions: (1) data modalities, (2) dataset splitting, (3) the role of pre-training, and (4) model capacity. Our empirical studies confirm a consistent power-law relationship between data volume and MRL performance across these dimensions. Additionally, through detailed analysis, we identify potential avenues for improving learning efficiency. To challenge these scaling laws, we adapt seven popular data pruning strategies to molecular data and benchmark their performance. Our findings underline the importance of data-centric MRL and highlight possible directions for future research. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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