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2023 IEEE Symposium Series on computational Intelligence, SSCI 2023

作者： Avramelou, Loukia Passalis, Nikolaos Tsoumakas, Grigorios Tefas, Anastasios Computational Intelligence and Deep Learning Group Aiia Lab Aristotle University of Thessaloniki Intelligent Systems Laboratory Department of Informatics Thessaloniki Greece

ISBN: (纸本)9781665430654

The financial market and public opinion are cor-related. This means that changes in the financial market can result in changes to public opinion and changes to public opinion can result in changes to the financial market. Accordingly, it is essential for understanding and interacting with the financial market to gather text content from online sources and process it. As a result of the rapid growth of social media and other online sources, we have seen an exponential rise in data, particularly textual data, in recent years. It can be difficult for a person to read, let alone process, the massive volumes of data generated every day. This indicates that we need automated methods for processing textual data and extracting useful information. Automated text summarization is a method of shortening huge amounts of text without losing essential information. Transformers, which can efficiently manage and analyze textual data, are state-of-the-art text summarization models. However, developing such an automated text summarization model specialized in a domain (e.g. finance) can be challenging since we lack necessary domain-specific summarization datasets. In this work, we propose a pipeline for fully automating the finetuning of a text summarization model in a specific domain, namely cryptocurrency domain, without the involvement of human annotators. To this end, we introduce a novel method for self-improvement of text summarization models which relies on a model assistant which encodes domain knowledge, enabling finetuning text summarization models in specific domains in which we lack specific-domain summarization datasets. The proposed method is evaluated on a cryptocurrency-related text summarization problem and three well-known Large Language Models (LLMs) used for text summarization. © 2023 IEEE.

关键词： Finetuning Large Language Models Natural Languange Processing Text Summarization

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Domain-Specific Large Language Model Finetuning using a Model Assistant for Financial Text Summarization

Domain-Specific Large Language Model Finetuning using a Mode...

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IEEE Symposium Series on computational Intelligence (SSCI)

作者： Loukia Avramelou Nikolaos Passalis Grigorios Tsoumakas Anastasios Tefas AIIA Lab Computational Intelligence and Deep Learning Group Department of Informatics Intelligent Systems Laboratory Aristotle University of Thessaloniki Thessaloniki Greece

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Reshaping the Cortical Connectivity Gradient by Long-Term Cognitive Training During Development

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Neuroscience Bulletin 2024年第1期40卷 50-64页

作者： Tianyong Xu Yunying Wu Yi Zhang Xi-Nian Zuo Feiyan Chen Changsong Zhou Bio-X Laboratory School of PhysicsZhejiang UniversityHangzhou 310027China Qiushi Academy for Advanced Studies Zhejiang UniversityHangzhou 310027China State Key Laboratory of Modern Optical Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China State Key Laboratory of Cognitive Neuroscience and Learning Bejing Normal UniversityBeijing 100875China Developmental Population Neuroscience Research Center IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijing 100875China Zhejiang Province Key Laboratory of Quantum Technology and Devices Zhejiang UniversityHangzhou 310027China Department of Physics Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems(Hong Kong)Institute of Computational and Theoretical StudiesHong Kong Baptist UniversityHong Kong 999077China

The organization of the brain follows a topologi-cal hierarchy that changes dynamically during ***,it remains unknown whether and how cognitive training administered over multiple years during develop-ment can modify this hierarchical *** measuring the brain and behavior of school children who had carried out abacus-based mental calculation(AMC)training for five years(starting from 7 years to 12 years old)in pre-training and post-training,we revealed the reshaping effect of long-term AMC intervention during development on the brain hierarchical *** observed the development-induced emergence of the default network,AMC training-promoted shifting,and regional changes in cortical ***,the training-induced gradient changes were located in visual and somatomotor areas in association with the visuospatial/motor-imagery *** found that gradient-based features can predict the math ability within *** findings provide novel insights into the dynamic nature of network recruitment impacted by long-term cognitive training during development.

关键词： Connectivity gradient Cognitive training Development Neural plasticity Abacus-based mental calculation

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Protein codes promote selective subcellular compartmentalization

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Science (New York, N.Y.) 2025年第6738期387卷 1095-1101页

作者： Kilgore, Henry R. Chinn, Itamar Mikhael, Peter G. Mitnikov, Ilan Van Dongen, Catherine Zylberberg, Guy Afeyan, Lena Banani, Salman F. Wilson-Hawken, Susana Lee, Tong Ihn Barzilay, Regina Young, Richard A. Whitehead Institute for Biomedical Research Cambridge MA United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology (MIT) Cambridge MA United States Abdul Latif Jameel Clinic for Machine Learning in Health MIT Cambridge MA United States Department of Biology MIT Cambridge MA United States Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA United States Computational and Systems Biology Program MIT Cambridge MA United States

Cells have evolved mechanisms to distribute ~10 billion protein molecules to subcellular compartments where diverse proteins involved in shared functions must assemble. In this study, we demonstrate that proteins with shared functions share amino acid sequence codes that guide them to compartment destinations. We developed a protein language model, ProtGPS, that predicts with high performance the compartment localization of human proteins excluded from the training set. ProtGPS successfully guided generation of novel protein sequences that selectively assemble in the nucleolus. ProtGPS identified pathological mutations that change this code and lead to altered subcellular localization of proteins. Our results indicate that protein sequences contain not only a folding code but also a previously unrecognized code governing their distribution to diverse subcellular compartments.

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Frequency-Resolved Connectome Hubs and Their Test-Retest Reliability in the Resting Human Brain

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Neuroscience Bulletin 2022年第5期38卷 519-532页

作者： Lei Wang Xiaodan Chen Yuehua Xu Miao Cao Xuhong Liao Yong He State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal UniversityBeijing 100875China Beijing Key Laboratory of Brain Imaging and Connectomics Beijing Normal UniversityBeijing 100875China IDG/McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China Institute of Science and Technology for Brain-Inspired Intelligence Fudan UniversityShanghai 200433China Key Laboratory of Computational Neuroscience and BrainInspired Intelligence(Fudan University) Ministry of EducationShanghai 200433China School of Systems Science Beijing Normal UniversityBeijing 100875China Chinese Institute for Brain Research Beijing 102206China

Functional hubs with disproportionately extensive connectivities play a crucial role in global information integration in human brain ***,most resting-state functional magnetic resonance imaging(R-fMRI)studies have identified functional hubs by examining spontaneous fluctuations of the blood oxygen level-dependent signal within a typical low-frequency band(e.g.,0.01–0.08 Hz or 0.01–0.1 Hz).Little is known about how the spatial distributions of functional hubs depend on frequency bands of ***,we used repeatedly measured R-fMRI data from 53 healthy young adults and a degree centrality analysis to identify voxelwise frequency-resolved functional hubs and further examined their test-retest reliability across two *** showed that a wide-range frequency band(0.01–0.24 Hz)accessible with a typical sampling rate(fsample=0.5 Hz)could be classified into three frequency bands with distinct patterns,namely,low-frequency(LF,0.01–0.06 Hz),middle-frequency(MF,0.06–0.16 Hz),and high-frequency(HF,0.16–0.24 Hz)*** functional hubs were mainly located in the medial and lateral frontal and parietal cortices in the LF band,and in the medial prefrontal cortex,superior temporal gyrus,parahippocampal gyrus,amygdala,and several cerebellar regions in the MF and HF *** hub regions exhibited fair to good test-retest reliability,regardless of the frequency *** presence of the three frequency bands was well replicated using an independent R-fMRI dataset from 45 healthy young *** findings demonstrate reliable frequency-resolved functional connectivity hubs in three categories,thus providing insights into the frequency-specific connectome organization in healthy and disordered brains.

关键词： Connectome Hubs Frequency Degree centrality Test-retest reliability

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A Structured Prediction Approach for Robot Imitation learning

arXiv

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arXiv 2023年

作者： Duan, Anqing Batzianoulis, Iason Camoriano, Raffaello Rosasco, Lorenzo Pucci, Daniele Billard, Aude Robotics and Machine Intelligence Laboratory The Hong Kong Polytechnic University Hong Kong Learning Algorithms and Systems Laboratory École Polytechnique Fédérale de Lausanne Lausanne Switzerland Visual And Multimodal Applied Learning Laboratory Politecnico di Torino Turin Italy DIBRIS Università degli Studi di Genova Genoa Italy Laboratory for Computational and Statistical Learning IIT@MIT Istituto Italiano di Tecnologia Massachusetts Institute of Technology CambridgeMA United States Center Università di Genova Genoa Italy Dynamic Interaction Control research line Italian Institute of Technology Genoa Italy

We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervised learning that enables learning models to operate on output spaces with complex structures. Through the lens of structured prediction, we show how robots can learn to imitate trajectories belonging to not only Euclidean spaces but also Riemannian manifolds. Exploiting ideas from information theory, we propose a class of loss functions based on the f-divergence to measure the information loss between the demonstrated and reproduced probabilistic trajectories. Different types of f-divergence will result in different policies, which we call imitation modes. Furthermore, our approach enables the incorporation of spatial and temporal trajectory modulation, which is necessary for robots to be adaptive to the change in working conditions. We benchmark our algorithm against state-of-the-art methods in terms of trajectory reproduction and adaptation. The quantitative evaluation shows that our approach outperforms other algorithms regarding both accuracy and efficiency. We also report real-world experimental results on learning manifold trajectories in a polishing task with a KUKA LWR robot arm, illustrating the effectiveness of our algorithmic framework. Copyright © 2023, The Authors. All rights reserved.

关键词： Trajectories

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3DReact: Geometric deep learning for chemical reactions

arXiv

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arXiv 2023年

作者： van Gerwen, Puck Briling, Ksenia R. Bunne, Charlotte Somnath, Vignesh Ram Laplaza, Ruben Krause, Andreas Corminboeuf, Clemence Laboratory for Computational Molecular Design Institute of Chemical Sciences and Engineering École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Learning & Adaptive Systems Group Department of Computer Science ETH Zurich Zurich8092 Switzerland

Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we introduce 3DReact, a geometric deep learning model to predict reaction properties from three-dimensional structures of reactants and products. We demonstrate that the invariant version of the model is sufficient for existing reaction datasets. We illustrate its competitive performance on the prediction of activation barriers on the GDB7-22-TS, Cyclo-23-TS and Proparg-21-TS datasets in different atom-mapping regimes. We show that, compared to existing models for reaction property prediction, 3DReact offers a flexible framework that exploits atom-mapping information, if available, as well as geometries of reactants and products (in an invariant or equivariant fashion). Accordingly, it performs systematically well across different datasets, atom-mapping regimes, as well as both interpolation and extrapolation tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Prediction models

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Acoustic Scene Classification Using Deep Residual Networks with Late Fusion of Separated High and Low Frequency Paths

Acoustic Scene Classification Using Deep Residual Networks w...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mark D. McDonnell Wei Gao Computational Learning Systems Laboratory School of Information Technology and Mathematical Sciences University of South Australia Mawson Lakes SA Australia

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

We investigate the problem of acoustic scene classification, using a deep residual network applied to log-mel spectrograms complemented by log-mel deltas and delta-deltas. We design the network to take into account that the temporal and frequency axes in spectrograms represent fundamentally different information. In particular, we use two pathways in the residual network: one for high frequencies and one for low frequencies, that were fused just two convolutional layers prior to the network output. We conduct experiments using two public 2019 DCASE datasets for acoustic scene classification; the first with binaural audio inputs recorded by a single device, and the second with single-channel audio inputs recorded through various devices. We show the performance of our models are significantly enhanced by the use of log-mel deltas, and that overall our approach is capable of training strong single models, without use of any supplementary data from outside the official challenge dataset, with excellent generalization to unknown devices. In particular, our approach achieved second place in 2019 DCASE Task 1b (0.4% behind the winning entry), and the best Task 1B evaluation results (by a large margin of over 5%) on test data from a device not used to record any training data.

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Modeling Histological Patterns for Differential Diagnosis of Atypical Breast Lesions 23rd

Modeling Histological Patterns for Differential Diagnosis of...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Parvatikar, Akash Choudhary, Om Ramanathan, Arvind Navolotskaia, Olga Carter, Gloria Tosun, Akif Burak Fine, Jeffrey L. Chennubhotla, S. Chakra Department of Computational and Systems Biology University of Pittsburgh Pittsburgh United States Data Science and Learning Argonne National Laboratory LemontIL United States Department of Pathology UPMC Magee-Womens Hospital Pittsburgh United States SpIntellx Inc. Pittsburgh United States

ISBN: (纸本)9783030597214

Our goal in this paper is to build parametric models for a dictionary of histological patterns that aid in the differential diagnosis of atypical breast lesions and evaluate the inferential power of these hand-crafted features. Diagnosis of high-risk atypical breast lesions is challenging and remains a critical component of breast cancer screening, presenting even for experienced pathologists a more difficult classification problem than the binary detection task of cancer vs not-cancer. Following guidelines in the WHO classification of the tumors of the breast (an essential reference for pathologists, clinicians and researchers) and in consultation with our team of breast sub-specialists (N = 3), we assembled a visual dictionary of sixteen histological patterns (e.g., cribriform, picket-fence), a subset that pathologists frequently use in making complex diagnostic decisions of atypical breast lesions. We invoke parametric models for each pattern using a mix of unary, binary and ternary features that account for morphological and architectural tissue properties. We use 1441 ductal regions of interest (ROIs) extracted automatically from 93 whole slide images (WSIs) with a computational pathology pipeline. We collected diagnostic labels for all of the ROIs: normal and columnar cell changes (CCC) as low-risk benign lesions (= 1124), and flat epithelium atypia (FEA) and atypical ductal hyperplasia (ADH) as high-risk benign lesions (= 317). We generate likelihood maps for each dictionary pattern across a given ROI and integrate this information to determine a diagnostic label of high- or low-risk. Our method has comparable classification accuracies to the pool of breast pathology sub-specialists. Our study enables a deeper understanding of the discordance among pathologists in diagnosing atypical breast lesions. © 2020, Springer Nature Switzerland AG.

关键词： Pathology

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Entorhinal-hippocampal interactions lead to globally coherent representations of space

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Current Research in Neurobiology 2022年第0期3卷 000-000页

作者： Zeng, Taiping Si, Bailu Li, Xiaoli International Research Center for Neurointelligence (WPI-IRCN) UTIAS The University of Tokyo Tokyo 113-0033 Japan Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Ministry of Education China School of Systems Science Beijing Normal University Beijing 100875 China Peng Cheng Laboratory Shenzhen 518055 China State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing 100875 China

The firing maps of grid cells in the entorhinal cortex are thought to provide an efficient metric system capable of supporting spatial inference in all environments. However, whether spatial representations of grid cells are determined by local environment cues or are organized into globally coherent patterns remains undetermined. We propose a navigation model containing a path integration system in the entorhinal cortex and a cognitive map system in the hippocampus. In the path integration system, grid cell network and head direction (HD) cell network integrate movement and visual information, and form attractor states to represent the positions and head directions of the animal. In the cognitive map system, a topological map is constructed capturing the attractor states of the path integration system as nodes and the transitions between attractor states as links. On loop closure, when the animal revisits a familiar place, the topological map is calibrated to minimize odometry errors. The change of the topological map is mapped back to the path integration system, to correct the states of the grid cells and the HD cells. The proposed model was tested on iRat, a rat-like miniature robot, in a realistic maze. Experimental results showed that, after familiarization of the environment, both grid cells and HD cells develop globally coherent firing maps by map calibration and activity correction. These results demonstrate that the hippocampus and the entorhinal cortex work together to form globally coherent metric representations of the environment. The underlying mechanisms of the hippocampal-entorhinal circuit in capturing the structure of the environment from sequences of experience are critical for understanding episodic memory. © 2022 The Authors

关键词： Global representations Grid cells Head direction cells Local environment anchors Simultaneous localization and mapping

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