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IEEE Symposium on Nuclear science (NSS/MIC)

作者： Wenbo Huang Greta S. P. Mok Department of Electrical and Computer Engineering Faculty of Science and Technology and the Center for Cognitive and Brain Sciences Institute of Collaborative Innovation University of Macau Taipa China

Parkinson’s disease (PD) can be early diagnosed using radionuclide imaging. Digital phantoms and Monte Carlo (MC) simulations serve as important research tools for PD imaging physics research. Here we designed and developed a new digital brain phantom population for different stages of PD. We selected 206 T1 MRI brain images and 160 DaTscan SPECT reconstructed images for normal subjects and patients with different PD stages from Parkinson’s Progression Markers Initiative (PPMI) datasets. We used the model-based segmentation tool FIRST to segment putamen and caudate region and used brain Extraction Tool (BET) to extract brain region from the MRI images, generating striatum and brain maps, respectively. Striatum maps were then rigidly registered to a referenced striatum model using Elastix, and the corresponding brain maps were transformed using the same parameters. Different activities were set on the maps based on the striatal binding ratio (SBR) in PPMI and the SPECT Visual interpretation assessment scheme. New phantoms were generated by randomly pairing the striatum and brain maps. Corresponding CT maps were generated by non-rigidly registered the XCAT-attenuation map to MRI images corresponding to the brain maps and transformed using the same parameters. We then used the Simind MC code to generate 120 realistic noisy projections with full physics modelling for each new brain phantom, based on a clinical SPECT scanner mounted with low-energy high-resolution parallel-hole collimators. OS-EM algorithm (8 iterations, 4 subsets) was used to reconstruct projections with and without attenuation correction (AC) respectively. SBR was evaluated on reconstructed images. Our results show that we could generate realistic PD SPECT data with the newly developed digital brain phantom population combined with a MC code, and AC is important for PD SPECT based on both visual assessment and SBR analysis. Our phantoms could be applied for further PD radionuclide imaging study.

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Neural foundations of mental simulation: future prediction of latent representations on dynamic scenes 23

Neural foundations of mental simulation: future prediction o...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Aran Nayebi Rishi Rajalingham Mehrdad Jazayeri Guangyu Robert Yang McGovern Institute for Brain Research MIT Cambridge MA Cambridge MA and Reality Labs Meta New York NY Cambridge MA and Department of Brain and Cognitive Sciences MIT Cambridge MA and Department of Electrical Engineering and Computer Science MIT

Humans and animals have a rich and flexible understanding of the physical world, which enables them to infer the underlying dynamical trajectories of objects and events, plausible future states, and use that to plan and anticipate the consequences of actions. However, the neural mechanisms underlying these computations are unclear. We combine a goal-driven modeling approach with dense neurophysiological data and high-throughput human behavioral readouts that contain thousands of comparisons to directly impinge on this question. Specifically, we construct and evaluate several classes of sensory-cognitive networks to predict the future state of rich, ethologically-relevant environments, ranging from self-supervised end-to-end models with pixel-wise or object-slot objectives, to models that future predict in the latent space of purely static image-pretrained or dynamic video-pretrained foundation models. We find that "scale is not all you need", and that many state-of-the-art machine learning models fail to perform well on our neural and behavioral benchmarks for future prediction. In fact, only one class of models matches these data well overall. We find that neural responses are currently best predicted by models trained to predict the future state of their environment in the latent space of pretrained foundation models optimized for dynamic scenes in a self-supervised manner. These models also approach the neurons' ability to predict the environmental state variables that are visually hidden from view, despite not being explicitly trained to do so. Finally, we find that not all foundation model latents are equal. Notably, models that future predict in the latent space of video foundation models that are optimized to support a diverse range of egocentric sensorimotor tasks, reasonably match both human behavioral error patterns and neural dynamics across all environmental scenarios that we were able to test. Overall, these findings suggest that the neural mechanisms a

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STREAMING INFERENCE FOR INFINITE NON-STATIONARY CLUSTERING

arXiv

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arXiv 2022年

作者： Schaeffer, Rylan Liu, Gabrielle Kaili-May Du, Yilun Linderman, Scott Fiete, Ila Rani Computer Science Stanford University United States Brain and Cognitive Sciences Massachusetts Institute of Technology United States Electrical Engineering & Computer Science Massachusetts Institute of Technology United States Statistics & Electrical Engineering Stanford University United States

Learning from a continuous stream of non-stationary data in an unsupervised manner is arguably one of the most common and most challenging settings facing intelligent agents. Here, we attack learning under all three conditions (unsupervised, streaming, non-stationary) in the context of clustering, also known as mixture modeling. We introduce a novel clustering algorithm that endows mixture models with the ability to create new clusters online, as demanded by the data, in a probabilistic, time-varying, and principled manner. To achieve this, we first define a novel stochastic process called the Dynamical Chinese Restaurant Process (Dynamical CRP), which is a non-exchangeable distribution over partitions of a set;next, we show that the Dynamical CRP provides a non-stationary prior over cluster assignments and yields an efficient streaming variational inference algorithm. We conclude with experiments showing that the Dynamical CRP can be applied on diverse synthetic and real data with Gaussian and non-Gaussian likelihoods. © 2022, CC BY.

关键词： Random processes

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Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning

arXiv

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arXiv 2022年

作者： Netanyahu, Aviv Shu, Tianmin Tenenbaum, Joshua Agrawal, Pulkit Dept. of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Dept. of Brain and Cognitive Science Massachusetts Institute of Technology CambridgeMA United States

In this work, we consider one-shot imitation learning for object rearrangement tasks, where an AI agent needs to watch a single expert demonstration and learn to perform the same task in different environments. To achieve a strong generalization, the AI agent must infer the spatial goal specification for the task. However, there can be multiple goal specifications that fit the given demonstration. To address this, we propose a reward learning approach, Graph-based Equivalence Mappings (GEM), that can discover spatial goal representations that are aligned with the intended goal specification, enabling successful generalization in unseen environments. Specifically, GEM represents a spatial goal specification by a reward function conditioned on i) a graph indicating important spatial relationships between objects and ii) state equivalence mappings for each edge in the graph indicating invariant properties of the corresponding relationship. GEM combines inverse reinforcement learning and active reward learning to efficiently improve the reward function by utilizing the graph structure and domain randomization enabled by the equivalence mappings. We conducted experiments with simulated oracles and with human subjects. The results show that GEM can drastically improve the generalizability of the learned goal representations over strong baselines.1 Copyright © 2022, The Authors. All rights reserved.

关键词： Mapping

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Leaf Disease Segmentation Using Uunet++ Architecture 3rd

Leaf Disease Segmentation Using Uunet++ Architecture

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3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Farooqui, Nafees Akhter Mishra, Amit Kumar Ray, Kanad Mallik, Saurav Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India Karnataka Bengaluru India Department of Physics Amity School of Applied Sciences Amity University Rajasthan Rajasthan Jaipur India Amity Cognitive Computing and Brain Informatics Centre Amity University Rajasthan Rajasthan303002 India Department of Environmental Health Harvard T H Chan School of Public Health BostonMA02115 United States Pharmacology & Toxicology University of Arizona TucsonAZ85721 United States

ISBN: (纸本)9789819739363

Segmentation of leaves is an initial step in image-based plant phenotyping, which has received more attention recently. Leaf segmentation shows features of the leaf and growth stages at the leaf level. Due to the significant overlapping between leaves and fluctuating environmental circumstances, such as intensity change and blur due to wind, segmentation of plant tissues like leaves is a most important issue in plant phenotyping. The leaf segmentation work has more complicated problems, such as leaf surface, genotypes, length, structure, and thickness variability. This study proposes an ultra-modern deep gaining knowledge of architectures: UUNet++, a convolutional neural network for advance segmentation. A vital assessment is done using computer Vision for Plant Phenotyping dataset (CVPPP). The plant phenotyping is performed on tobacco and Arabidopsis leaf of RGB images from plant village dataset. The proposed model needs less space as compared to the main UNet architecture. In contrast to typical deep learning image sets, this dataset contains a limited number of samples. Despite that, we achieve exceptional results in leaf segmentation, specifically in distinguishing the interior of leaves from the binary segmentation of the entire leaf. Furthermore, studies are necessary to accurately quantify the number of leaves. Without reducing performance measures, the disease classification inferences time is extremely efficient and accurate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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SAUCE: Synchronous and Asynchronous User-Customizable Environment for Multi-Agent LLM Interaction

arXiv

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arXiv 2024年

作者： Neuberger, Shlomo Eckhaus, Niv Berger, Uri Taubenfeld, Amir Stanovsky, Gabriel Goldstein, Ariel The Hebrew University Business School Jerusalem Israel School of Computer Science and Engineering The Hebrew University of Jerusalem School of Computing and Information Systems University of Melbourne Australia Department of Cognitive and Brain Sciences The Hebrew University of Jerusalem Israel Google Research United States

Many human interactions, such as political debates, are carried out in group settings, where there are arbitrarily many participants, each with different views and agendas. To explore such complex social settings, we present SAUCE: a customizable Python platform, allowing researchers to plug-and-play various LLMs participating in discussions on any topic chosen by the user. Our platform takes care of instantiating the models, scheduling their responses, managing the discussion history, and producing a comprehensive output log, all customizable through configuration files, requiring little to no coding skills. A novel feature of SAUCE is our asynchronous communication feature, where models decide when to speak in addition to what to say, thus modeling an important facet of human communication. We show SAUCE’s attractiveness in two initial experiments, and invite the community to use it in simulating various group simulations. © 2024, CC BY.

关键词： Chatbots

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Domain Generalization for Medical Image Analysis: A Review

arXiv

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arXiv 2023年

作者： Yoon, Jee Seok Oh, Kwanseok Shin, Yooseung Mazurowski, Maciej A. Suk, Heung-Il Department of Brain and Cognitive Engineering Korea University Seoul02841 Korea Republic of Department of Artificial Intelligence Korea University Seoul02841 Korea Republic of Departments of Radiology Biostatistics and Bioinformatics Electrical and Computer Engineering and Computer Science Duke University DurhamNC27705 United States Department of Artificial Intelligence The Department of Brain and Cognitive Engineering Korea University Seoul02841 Korea Republic of

Medical image analysis (MedIA) has become an essential tool in medicine and healthcare, aiding in disease diagnosis, prognosis, and treatment planning, and recent successes in deep learning (DL) have made significant contributions to its advances. However, deploying DL models for MedIA in real-world situations remains challenging due to their failure to generalize across the distributional gap between training and testing samples — a problem known as domain shift. Researchers have dedicated their efforts to developing various DL methods to adapt and perform robustly on unknown and out-of-distribution data distributions. This paper comprehensively reviews domain generalization studies specifically tailored for MedIA. We provide a holistic view of how domain generalization techniques interact within the broader MedIA system, going beyond methodologies to consider the operational implications on the entire MedIA workflow. Specifically, we categorize domain generalization methods into data-level, feature-level, model-level, and analysis-level methods. We show how those methods can be used in various stages of the MedIA workflow with DL equipped from data acquisition to model prediction and analysis. Furthermore, we critically analyze the strengths and weaknesses of various methods, unveiling future research opportunities. Copyright © 2023, The Authors. All rights reserved.

关键词： Reviews

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3D-to-2D-to-3D Conscious Learning

3D-to-2D-to-3D Conscious Learning

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Weng, Juyang Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States Cognitive Science Program Michigan State University East LansingMI48824 United States Neuroscience Program Michigan State University East LansingMI48824 United States Brain-Mind Institute OkemosMI48864 United States GENISAMA OkemosMI48864 United States

ISBN: (纸本)9781665441544

This is a theoretical paper on conscious learning for thoughts and creativity through general-purpose and autonomous imitation of demonstrations. This conscious learning is end-to-end (3D-to-2D-to-3D) and free from annotations of 2D images and 2D motor images (e.g., a bounding box to be attended to). The conscious learning algorithm directly takes that of the Developmental Networks that has been previously published extensively with rich experimental results. Apparently, humans and animals do this type of fully automated learning daily, but it is unclear a robot can do the same. Recently, [1], [2] presented a theory of conscious learning rooted in emergent universal Turing machines. It appeared to be the first algorithmic level theory of holistic consciousness, other than many papers in the literature about piecemeal consciousness. However, [1], [2] proved only conscious learning in motor-imposed training mode, namely 3D-to-2D taught by 2D motor impositions, free from 2D annotations. This paper fills the challenging gap in [1], [2] so the conscious learning is 3D-to-2D-to-3D (end-to-end) without motor-impositions or computing 'inverse kinematics'. This is a major departure from traditional AI-handcrafting symbolic labels that tend to be brittle (e.g., for driverless cars) and then 'spoon-feeding' pre-collected 'big data'. Autonomous imitations drastically reduce the teaching complexity compared to pre-collected 'big data', especially because no annotations of training data are needed. Furthermore, conscious learning allows creativity beyond what is taught. This work is directly related to consumer electronics because it requires large-scale on-the-fly brainoid chips in future wearable robots/devices for consumers. © 2022 IEEE.

关键词： Big data

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Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior

Automated Detection of Epileptic Spikes and Seizures Incorpo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Dong, Hanyang Sheng, Shurong Wang, Xiongfei Gao, Jia-Hong Sun, Yi Yang, Wanli Xiao, Kuntao Teng, Pengfei Luan, Guoming Lv, Zhao Anhui University School of Artificial Intelligence Hefei230601 China Hefei Comprehensive National Science Center Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei230088 China Sanbo Brain Hospital Capital Medical University Beijing Key Laboratory of Epilepsy Department of Neurosurgery Beijing China Peking University McGovern Institute for Brain Research Beijing China Peking University Center for Mri Research Academy for Advanced Interdisciplinary Studies Beijing China Peking University Beijing City Key Lab for Medical Physics and Engineering Institute of Heavy Ion Physics School of Physics Beijing China Anhui University Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei230601 China

ISBN: (纸本)9798350386226

A Magnetoencephalography (MEG) time-series recording consists of multi-channel signals collected by superconducting sensors, with each signal's intensity reflecting magnetic field changes over time at the sensor location. Automating epileptic MEG spike detection significantly reduces manual assessment time and effort, yielding substantial clinical benefits. Existing research addresses MEG spike detection by encoding neural network inputs with signals from all channel within a time segment, followed by classification. However, these methods overlook simultaneous spiking occurred from nearby sensors. We introduce a simple yet effective paradigm that first clusters MEG channels based on their sensor's spatial position. Next, a novel convolutional input module is designed to integrate the spatial clustering and temporal changes of the signals. This module is fed into a custom MEEG-ResNet3D developed by the authors, which learns to extract relevant features and classify the input as a spike clip or not. Our method achieves an F1 score of 94.73% on a large real-world MEG dataset Sanbo-CMR collected from two centers, outperforming state-of-the-art approaches by 1.85%. Moreover, it demonstrates efficacy and stability in the Electroencephalographic (EEG) seizure detection task, yielding an improved weighted F1 score of 1.4% compared to current state-of-the-art techniques evaluated on TUSZ, whch is the largest EEG seizure dataset. © 2024 IEEE.

关键词： Magnetoencephalography

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Can (A)I Change Your Mind?

arXiv

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arXiv 2025年

作者： Havin, Miriam Kleinman, Timna Wharton Koren, Moran Dover, Yaniv Goldstein, Ariel Department of Cognitive and Brain Sciences The Hebrew University of Jerusalem Israel The Federmann Center for the Study of Rationality The Hebrew University of Jerusalem Israel School of Computer Science and Engineering The Hebrew University of Jerusalem Israel Industrial Engineering and Management Department Ben Gurion Univeristy Israel The Hebrew University Business School Jerusalem Israel

The increasing integration of large language model (LLM) based conversational agents into everyday life raises critical cognitive and social questions about their potential to influence human opinions. Although previous studies have shown that LLM-based agents can generate persuasive content, these typically involve controlled, English-language settings. Addressing this, our preregistered study explored LLM’s persuasive capabilities in more ecological, unconstrained scenarios, examining both static (written paragraphs) and dynamic (conversations via Telegram) interaction types. Conducted entirely in Hebrew with 200 participants, the study assessed the persuasive effects of both LLM and human interlocutors on controversial civil policy topics. Results indicated that participants adopted LLM and human perspectives similarly, with significant opinion changes evident across all conditions, regardless of interlocutor type or interaction mode. Confidence levels increased significantly in most scenarios, except in static LLM interactions. These findings demonstrate LLM-based agents’ robust persuasive capabilities across diverse sources and settings, highlighting their potential impact on shaping public opinions. © 2025, CC BY.

关键词： Chatbots

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