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Processes and measurements: a framework for understanding neural oscillations in field potentials

Trends in Cognitive sciences 2025年第5期29卷 448-466页

作者： van Bree, Sander Levenstein, Daniel Krause, Matthew R. Voytek, Bradley Gao, Richard Department of Medicine Justus Liebig University Giessen Germany Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Centre for Cognitive Neuroimaging School of Psychology and Neuroscience University of Glasgow Glasgow United Kingdom MILA – Quebec AI Institute MontrealQC Canada Montreal Neurological Institute and Hospital McGill University MontrealQC Canada Department of Cognitive Science Halıcıŏglu Data Science Institute Kavli Institute for Brain & Mind University of California San Diego La JollaCA United States Machine Learning in Science Excellence Cluster Machine Learning and Tübingen AI Center University of Tübingen Tübingen Germany

Various neuroscientific theories maintain that brain oscillations are important for neuronal computation, but opposing views claim that these macroscale dynamics are ‘exhaust fumes’ of more relevant processes. Here, we approach the question of whether oscillations are functional or epiphenomenal by distinguishing between measurements and processes, and by reviewing whether causal or inferentially useful links exist between field potentials, electric fields, and neurobiological events. We introduce a vocabulary for the role of brain signals and their underlying processes, demarcating oscillations as a distinct entity where both processes and measurements can exhibit periodicity. Leveraging this distinction, we suggest that electric fields, oscillating or not, are causally and computationally relevant, and that field potential signals can carry information even without causality. © 2024 The Authors

关键词： Neurons

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Enhancing Prognostication in Colorectal Cancer with Integrated machine learning for Improved Survival Prediction

Enhancing Prognostication in Colorectal Cancer with Integrat...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Velmurugan, R. Suresh, K. Thilagaraj, T. Sharma, Meenakshi Saranya, D. Bangalore India Department of Computer Science Bangalore India Moodlakatte Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Kundapura India Global Group of Institutes Punjab Amritsar India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India

ISBN: (纸本)9798331509828

machine learning methods are recently used to predict patient survival in colorectal cancer using such models as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), VGG16, and Support Vector machines (SVM). Taking advantage of a combination of CT, MRI scan images, and clinical records with drug recommendations, the study also checks to see how these models compare for distinguishing between patients in terms of their illness course-whether they are going to get better or worse over time. The results reveal VGG16 has better accuracy than CNN, RNN and SVM;as the highest-performing model tested, it also demonstrates superior precision, recall and F1-score. The research findings also validate these proposed models as they compare favorably with existing literature. This presents a promising proposition: a new, revolutionary approach to using artificial intelligence to boost prognostic accuracy. © 2025 IEEE.

关键词： Convolutional neural networks

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Dynamic Trust Management in IoT Networks using Reputation-Based Systems

Dynamic Trust Management in IoT Networks using Reputation-Ba...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Naidu, D J Samatha Jain, Upma Shruthi, S. Rao, K. B. V. Brahma Iqbal, Syed Mohammad Uzair Natrayan, L. Annamacharya Pg College of Computer Studies Department of Master of Computer Applications India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Bms Institute of Technology and Management Department of Artificial Intelligence and Machine Learning Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering India Symbiosis Law School Department of Law Pune India Simats Saveetha School of Engineering Department of Mechanical Engineering India

ISBN: (纸本)9798350375442

The Internet of Things (IoT) enables intelligent data exchange across many kinds of interconnected devices in a diverse and unpredictable physical setting. The Internet of Things (IoT) network used in this study is a cluster-based sensor network that includes a base station and an application server that offers various services in a cloud setting. In this setting, assaults may occur in both device-to-device and user-to-device communication. For pervasive devices with limited resources, the use of conventional encryption techniques to address this problem is not practical. In addition, the majority of current trust-based security solutions either address the user level or the devices level. Therefore, this study aims to tackle environment-specific security concerns at the device and user levels in light of the environment's inherent dynamic and dispersed nature. This research explored the usage of a Trust Based Security Framework (TBSF) and Reputation Based Systems in the Internet of Things (IoT) to accurately describe the actions of devices and users. Fuzzy logic was used to calculate both the device trust and the user trust. A trust-based access control system, secure cluster head selection, and safe routing were all built using these trust values. At first, the security of device-to-device communication was addressed by the design and effective implementation of the Trust Aware Device Access Control System (TADACS). © 2024 IEEE.

关键词： Access control

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Exploratory Data Analysis and Classification of Employee Retention based on Logistic Regression Model 9

Exploratory Data Analysis and Classification of Employee Ret...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Bongale, Anupkumar M Dharrao, Deepak Urolagin, Siddhaling University Symbiosis Institute of Technology Department of Artificial Intelligence and Machine Learning Pune India University Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India Dubai International Academic City Birla Institute of Technology & Science Department of Computer Science and Engineering Dubai United Arab Emirates

ISBN: (纸本)9798350397376

Nowadays, a huge number of companies are using employee data records to understand the employee churn rate in the company. Companies use predictive analytics to predict the chances of an employee leaving the company. Organizations are effectively using Human Resource (HR) predictive analytics to increase revenue and turnover. This paper is about building a predictive model on the employee dataset to predict if an employee is likely to leave or continue service in the employed company. The steps carried out for visualization and Exploratory Data Analysis (EDA) has been highlighted in this paper by bringing out valuable insights from the employee dataset. Observations were made to the features that determine the chances of an employee leaving the company. For the prediction and classification of employee retention, a Logistic Regression (LR) model is applied to the dataset. The article focuses on the analysis of data and understanding the working of the LR model for predicting categorical features. The performance of the applied model was evaluated using a confusion matrix. The accuracy of the model was calculated from values in the confusion matrix. © 2023 IEEE.

关键词： Predictive analytics

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Understanding finetuning for factual knowledge extraction 24

Understanding finetuning for factual knowledge extraction

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Proceedings of the 41st International Conference on machine learning

作者： Gaurav Ghosal Tatsunori Hashimoto Aditi Raghunathan Department of Machine Learning Carnegie Mellon University Pittsburgh Department of Computer Science Stanford University Stanford

In this work, we study the impact of QA finetuning data on downstream factuality. We show that fine-tuning on lesser-known facts that are poorly stored during pretraining yields significantly worse factuality than fine-tuning on wellknown facts, even when all facts are seen during pretraining. We prove this phenomenon theoretically, showing that training on lesser-known facts can lead the model to ignore subject entity names and instead output a generic plausible response even when the relevant factual knowledge is encoded in the model. On three question answering benchmarks (PopQA, Entity Questions, and MMLU) and two language models (Llama-2-7B and Mistral-7B), we find that (i) finetuning on a completely factual but lesser-known subset of the data deteriorates downstream factuality (5-10%) and (ii) finetuning on a subset of better-known examples matches or outperforms finetuning on the entire dataset. Ultimately, our results shed light on the interaction between pretrained knowledge and finetuning data and demonstrate the importance of taking into account how facts are stored in the pretrained model when fine-tuning for knowledgeintensive tasks.

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WHOLE-SONG HIERARCHICAL GENERATION OF SYMBOLIC MUSIC USING CASCADED DIFFUSION MODELS

arXiv

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arXiv 2024年

作者： Wang, Ziyu Min, Lejun Xia, Gus Computer Science Department NYU Shanghai China Machine Learning Department Mbzuai United Arab Emirates

Recent deep music generation studies have put much emphasis on long-term generation with structures. However, we are yet to see high-quality, well-structured whole-song generation. In this paper, we make the first attempt to model a full music piece under the realization of compositional hierarchy. With a focus on symbolic representations of pop songs, we define a hierarchical language, in which each level of hierarchy focuses on the semantics and context dependency at a certain music scope. The high-level languages reveal whole-song form, phrase, and cadence, whereas the low-level languages focus on notes, chords, and their local patterns. A cascaded diffusion model is trained to model the hierarchical language, where each level is conditioned on its upper levels. Experiments and analysis show that our model is capable of generating full-piece music with recognizable global verse-chorus structure and cadences, and the music quality is higher than the baselines. Additionally, we show that the proposed model is controllable in a flexible way. By sampling from the interpretable hierarchical languages or adjusting pre-trained external representations, users can control the music flow via various features such as phrase harmonic structures, rhythmic patterns, and accompaniment *** Codes 68Txx Copyright © 2024, The Authors. All rights reserved.

关键词： Textures

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Both gain-and loss-of-function variants of KCNA1 are associated with paroxysmal kinesigenic dyskinesia

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Journal of Genetics and Genomics 2024年第8期51卷 801-810页

作者： Wan-Bing Sun Jing-Xin Fu Yu-Lan Chen Hong-Fu Li Zhi-Ying Wu Dian-Fu Chen Department of Medical Genetics and Center for Rare Diseases and Department of Neurologyand Zhejiang Key Laboratory of Rare Diseases for Precision Medicine and Clinical Translation in Second Affiliated HospitalZhejiang University School of MedicineHangzhouZhejiang 310009China Nanhu Brain-computer Interface Institute HangzhouZhejiang 314050China MOE Frontier Science Center for Brain Science and Brain-machine Integration School of Brain Science and Brain MedicineZhejiang UniversityHangzhouZhejiang 310012China

KCNA1 is the coding gene for Kv1.1 voltage-gated potassium-channelα*** variants of KCNA1 have been reported to manifest as paroxysmal kinesigenic dyskinesia(PKD),but the correlation between them remains unclear due to the phenotypic complexity of KCNA1 variants as well as the rarity of PKD *** the whole exome sequencing followed by Sanger sequencing,we screen for potential pathogenic KCNA1 variants in patients clinically diagnosed with paroxysmal movement disorders and identify three previously unreported missense variants of KCNA1 in three unrelated Chinese *** proband of one family(c.496G>A,p.A166T)manifests as episodic ataxia type 1,and the other two(c.877G>A,p.V293I and c.1112C>A,p.T371A)manifest as *** pathogenicity of these variants is confirmed by functional studies,suggesting that p.A166T and p.T371A cause a loss-of-function of the channel,while p.V293I leads to a gain-of-function with the property of voltage-dependent gating and activation kinetic *** reviewing the locations of PKD-manifested KCNA1 variants in Kv1.1 protein,we find that these variants tend to cluster around the pore domain,which is similar to ***,our study strengthens the correlation between KCNA1 variants and PKD and provides more information on genotype–phenotype correlations of KCNA1 channelopathy.

关键词： Paroxysmal kinesigenic dyskinesia KCNA1 Loss-of-function Gain-of-function Channelopathy Episodicataxiatype1

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Impacts of Fake Reviews on Dietary Supplements and Healthcare Products in Social Media 15

Impacts of Fake Reviews on Dietary Supplements and Healthcar...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kannal, Nithin M Asmathunnisa, N. Kallimani, Jagadish S M S Ramaiah Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bangalore India M S Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India Visvesvaraya Technological University Karnataka Belagavi India

ISBN: (纸本)9798350370249

In today's digital landscape, the prevalence of misinformation is a pressing concern, particularly on social media platforms where approximately $42.8 \%$ of shared content is identified as inaccurate and fake, as highlighted by a 2019 study from Loughborough University's Online Civic Culture Centre. This phenomenon extends beyond mere inconvenience, with false information on political issues and product reviews posing significant threats to societal trust, business integrity, and political discourse. Furthermore, 'The State of Fake Reviews - Statistics and Trends' by InvespCRO (2023) underscores a concerning trend: the steady rise in fake reviews from 2019 to 2023, indicative of a persistent challenge in maintaining online authenticity. This article delves into the intriguing world of online reviews, focusing specifically on the impact of falsified reviews within the realm of supplements and health products. With the power to sway consumer opinions and shape company reputations, false reviews wield considerable influence, often distorting market perceptions and consumer choices. By immersing ourselves in a hypothetical scenario where a product garners an alluring yet deceitful review, we unravel the ripple effects across various sectors, illuminating the far-reaching consequences of deceptive practices. © 2024 IEEE.

关键词： Dietary Supplements Fake Reviews Global Forecast on Online Products Healthcare Products Social Media

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Email Spam Detection using Graph based Convolutional Neural Network with MALO 2

Email Spam Detection using Graph based Convolutional Neural ...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Murthy, G. N. Keshava Nayana, C.P. Harshitha, P. Sandhya, M. Siddaganga Institute Of Technology Department Of Electronics And Instrumentation Engineering Karnataka Tumakuru India East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India Nitte Meenakshi Institute Of Technology Artificial Intelligence And Machine Learning Bengaluru India

ISBN: (纸本)9798350381665

Every day, millions of individuals and organizations rely on email as a fundamental tool for communication, business transactions, and information exchange. However, the pervasive issue of spam email has escalated in recent years, paralleling the exponential growth in email usage. This surge in spam poses a significant challenge for both individuals and businesses, hindering their ability to effectively manage and filter incoming emails. To confront the complexities inherent in spam email classification, such as the inability of conventional models to adequately capture pertinent information due to document length constraints and the lack of subject matter expertise leading to suboptimal classification outcomes, a novel approach is proposed. This study advocates for the integration of a knowledge graph and a graph convolutional neural network (GCNN) as the foundation for a robust classification model. Utilizing Bidirectional Encoder Representations from Transformers (BERT), the text data is encoded, ensuring a comprehensive representation of semantic meaning. The graph network is then initialized with vectors containing dense semantic information for each word. By leveraging a trained entity-relationship extraction model, the interdependencies between entities and other syntactic information are extracted and utilized as edges within the GCNN. Moreover, the model's weights are optimized using the principle of Maximum A Posteriori (MAP) in Latent Optimization (MALO). The incorporation of graph structures enables the model to discern intricate semantic relationships between words, enhancing its ability to comprehend the underlying context of the text. This innovative technique transcends traditional email spam detection methods by amalgamating knowledge-driven characteristics with data-driven features, thereby advancing the efficacy of classification endeavors. © 2024 IEEE.

关键词： Classification (of information)

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Overcoming Common Flaws in the Evaluation of Selective Classification Systems 38

Overcoming Common Flaws in the Evaluation of Selective Class...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Traub, Jeremias Bungert, Till J. Lüth, Carsten T. Baumgartner, Michael Maier-Hein, Klaus H. Maier-Hein, Lena Jäger, Paul F. Heidelberg Interactive Machine Learning Group Germany Helmholtz Imaging DKFZ Heidelberg Germany Germany Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg69120 Germany Faculty of Mathematics and Computer Science University of Heidelberg Germany Heidelberg Germany

Selective Classification, wherein models can reject low-confidence predictions, promises reliable translation of machine-learning based classification systems to real-world scenarios such as clinical diagnostics. While current evaluation of these systems typically assumes fixed working points based on pre-defined rejection thresholds, methodological progress requires benchmarking the general performance of systems akin to the AUROC in standard classification. In this work, we define 5 requirements for multi-threshold metrics in selective classification regarding task alignment, interpretability, and flexibility, and show how current approaches fail to meet them. We propose the Area under the Generalized Risk Coverage curve (AUGRC), which meets all requirements and can be directly interpreted as the average risk of undetected failures. We empirically demonstrate the relevance of AUGRC on a comprehensive benchmark spanning 6 data sets and 13 confidence scoring functions. We find that the proposed metric substantially changes metric rankings on 5 out of the 6 data sets. © 2024 Neural information processing systems foundation. All rights reserved.

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