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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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All-in-one simulation-based inference 24

All-in-one simulation-based inference

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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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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...

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

IEEE Transactions on Green Communications and Networking

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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...

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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

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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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BaCaDI: Bayesian Causal Discovery with Unknown Interventions

arXiv

引用

arXiv 2022年

作者： Hägele, Alexander Rothfuss, Jonas Lorch, Lars Somnath, Vignesh Ram Schölkopf, Bernhard Krause, Andreas Department of Computer Science ETH Zürich Switzerland Max Planck Institute for Intelligent Systems Tübingen Germany IBM Research Zürich Switzerland

Inferring causal structures from experimentation is a central task in many domains. For example, in biology, recent advances allow us to obtain single-cell expression data under multiple interventions such as drugs or gene knockouts. However, the targets of the interventions are often uncertain or unknown and the number of observations limited. As a result, standard causal discovery methods can no longer be reliably used. To fill this gap, we propose a Bayesian framework (BaCaDI) for discovering and reasoning about the causal structure that underlies data generated under various unknown experimental or interventional conditions. BaCaDI is fully differentiable, which allows us to infer the complex joint posterior over the intervention targets and the causal structure via efficient gradient-based variational inference. In experiments on synthetic causal discovery tasks and simulated gene-expression data, BaCaDI outperforms related methods in identifying causal structures and intervention targets. © 2022, CC BY-NC-SA.

关键词： Gene expression

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6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications

引用

IEEE Wireless Communications Letters 2025年第6期14卷 1618-1622页

作者： Ren, Tianshi Zhang, Xianchao Zhu, Lipeng Ma, Wenyan Gao, Xiaozheng Zhang, Rui The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems China The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing314001 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of HongKong Guangdong Shenzhen518172 China

Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent algorithm to iteratively optimize the antenna position vector, array rotation vector, receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio. Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. © 2025 IEEE. All rights reserved.

关键词： Aircraft communication

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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 ...

引用

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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BRIDGING THE GAP TO REAL-WORLD OBJECT-CENTRIC LEARNING

arXiv

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arXiv 2022年

作者： Seitzer, Maximilian Horn, Max Zadaianchuk, Andrii Zietlow, Dominik Xiao, Tianjun Simon-Gabriel, Carl-Johann He, Tong Zhang, Zheng Schölkopf, Bernhard Brox, Thomas Locatello, Francesco Max-Planck Institute for Intelligent Systems Tübingen Germany Amazon Web Services United States Department of Computer Science ETH Zürich Switzerland

Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world. Allowing machine learning algorithms to derive this decomposition in an unsupervised way has become an important line of research. However, current methods are restricted to simulated data or require additional information in the form of motion or depth in order to successfully discover objects. In this work, we overcome this limitation by showing that reconstructing features from models trained in a self-supervised manner is a sufficient training signal for object-centric representations to arise in a fully unsupervised way. Our approach, DINOSAUR, significantly out-performs existing image-based object-centric learning models on simulated data and is the first unsupervised object-centric model that scales to real-world datasets such as COCO and PASCAL VOC. DINOSAUR is conceptually simple and shows competitive performance compared to more involved pipelines from the computer vision literature. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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