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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Peng, Rita Huan-Ting Parmar, Nishaal Williamson, Jordan N. Sirpal, Parikshat Mulyana, Beni E. Rajabtabar Darvish, Mahmood Sung, Joohwan Sidorov, Evgeny V. Yang, Yuan Department of Bioengineering Grainger College of Engineering University of Illinois Urbana-Champaign UrbanaIL United States Clinical Imaging Research Center Stephenson Family Clinical Research Institute Carle Foundation Hospital UrbanaIL United States Department of Electrical and Computer Engineering University of Oklahoma TulsaOK74135 United States Department of Neurology University of Oklahoma Health Sciences Center Oklahoma CityOK73104 United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign UrbanaIL United States Department of Physical Therapy and Human Movement Sciences Northwestern University ChicagoIL United States

ISBN: (纸本)9798350371499

Sensory feedback is of vital importance in motor control, yet is rarely studied in diseases which frequently result in motor deficiency, such as hemiparetic stroke. This study employs the laterality index (LI) of somatosensory evoked potentials (SEPs) to investigate whether sensory feedback is altered in hemiparetic stroke during movements of the paretic arm, with a hemispheric shift from the lesioned hemisphere toward contralesional hemisphere. Through experimental design involving the isometric lifting of the paretic arms during tactile finger stimulation and the analysis of LI in SEPs P50 and N100, we found: 1) increased contralesional sensory activity in stroke participants when they are receiving sensory input in their paretic hand for both P50 and N100 and 2) the contralesional N100 activity is enhanced when stroke participants are performing an isometric arm lifting task. These results indicate a timedependent shifting of sensory feedback from the sensorimotor areas in the lesioned side to the contralesional side of the stroke brain. © 2024 IEEE.

关键词： Sensory feedback

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Parts2Words: Learning Joint Embedding of Point Clouds and Texts by Bidirectional Matching Between Parts and Words

Parts2Words: Learning Joint Embedding of Point Clouds and Te...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Chuan Tang Xi Yang Bojian Wu Zhizhong Han Yi Chang School of Artificial Intelligence Jilin University China MoE Engineering Research Center of Knowledge-Driven Human-Machine Intelligence China Zhejiang University The Department of Computer Science Wayne State University USA

Shape-Text matching is an important task of high-level shape understanding. Current methods mainly represent a 3D shape as multiple 2D rendered views, which obviously can not be understood well due to the structural ambiguity caused by self-occlusion in the limited number of views. To resolve this issue, we directly represent 3D shapes as point clouds, and propose to learn joint embedding of point clouds and texts by bidirectional matching between parts from shapes and words from texts. Specifically, we first segment the point clouds into parts, and then leverage optimal transport method to match parts and words in an optimized feature space, where each part is represented by aggregating features of all points within it and each word is abstracted by its contextual information. We optimize the feature space in order to enlarge the similarities between the paired training samples, while simultaneously maximizing the margin between the unpaired ones. Experiments demonstrate that our method achieves a significant improvement in accuracy over the SOTAs on multi-modal retrieval tasks under the Text2Shape dataset. Codes are available at here.

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Discovering Chunks in Neural Embeddings for Interpretability

arXiv

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arXiv 2025年

作者： Wu, Shuchen Alaniz, Stephan Schulz, Eric Akata, Zeynep Explainable Machine Learning Lab Helmholtz Munich Germany Max Planck Institute for Biological Cybernetics Germany Munich Center for Machine Learning Germany Department of Computer Science Technical University of Munich Germany Institute for Human-Centered AI Helmholtz Munich Germany

Understanding neural networks is challenging due to their high-dimensional, interacting components. Inspired by human cognition, which processes complex sensory data by chunking it into recurring entities, we propose leveraging this principle to interpret artificial neural population activities. Biological and artificial intelligence share the challenge of learning from structured, naturalistic data, and we hypothesize that the cognitive mechanism of chunking can provide insights into artificial systems. We first demonstrate this concept in recurrent neural networks (RNNs) trained on artificial sequences with imposed regularities, observing that their hidden states reflect these patterns, which can be extracted as a dictionary of chunks that influence network responses. Extending this to large language models (LLMs) like LLaMA, we identify similar recurring embedding states corresponding to concepts in the input, with perturbations to these states activating or inhibiting the associated concepts. By exploring methods to extract dictionaries of identifiable chunks across neural embeddings of varying complexity, our findings introduce a new framework for interpreting neural networks, framing their population activity as structured reflections of the data they process. © 2025, CC BY.

关键词： Network embeddings

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We Are Sending You Back... to the Optimum! Fuzzy Time Travel Particle Swarm Optimization 28th

We Are Sending You Back... to the Optimum! Fuzzy Time Trav...

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Papetti, Daniele M. Tangherloni, Andrea Coelho, Vasco Besozzi, Daniela Cazzaniga, Paolo Nobile, Marco S. Department of Informatics Systems and Communication University of Milano-Bicocca Milan Italy Department of Computer Science Bocconi University Milan Italy Department of Human and Social Sciences University of Bergamo Bergamo Italy Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Venice Italy Research Center Milan Italy

ISBN: (纸本)9783031900648

Particle Swarm Optimization (PSO) is a swarm intelligence meta-heuristics whose performance highly depends on the selection of its hyper-parameters, which control the particles’ exploration and exploitation capabilities during the search process. Since the tuning of the hyper-parameters is problem-dependent, settings-free methods are preferable. Fuzzy Self-Tuning PSO (FST-PSO) is a settings-free variant of PSO that exploits a Fuzzy Rule-Based System to determine the best hyper-parameter values for each particle dynamically. Despite this advantage, the optimization process might get stuck in local optima. Here, we propose a what-if strategy to generate and explore alternate swarm histories: what happens if we could go back in time and change something in the past so that one particle "was" different at the beginning of the optimization? Specifically, whenever the global best particle does not improve for a number of iterations, it is terminated and re-initialized, meanwhile sending the rest of the swarm back to the initial configuration. This approach, called "time travel" FST-PSO (FTT-PSO), works under the assumption that the convergence of any particle towards an optimum is strongly related to the influence of the global best particle. We compare the performance of FTT-PSO against FST-PSO on the benchmark suites used in IEEE CEC and GECCO competitions. Our results show that time traveling allows for outperforming both the standard and the multistart versions of FST-PSO. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Particle swarm optimization (PSO)

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Asynchronous stochastic gradient descent with decoupled backpropagation and layer-wise updates

arXiv

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arXiv 2024年

作者： Fokam, Cabrel Teguemne Nazeer, Khaleelulla Khan König, Lukas Kappel, David Subramoney, Anand Center for Cognitive Interaction Technology Universität Bielefeld Germany Institut für Neuroinformatik Ruhr Universität Bochum Germany Chair of Highly-Parallel VLSI Systems and Neuro-Microelectronics Technische Universität Dresden Germany Department of Computer Science Royal Holloway University of London United Kingdom

The increasing size of deep learning models has made distributed training across multiple devices essential. However, current methods such as distributed data-parallel training suffer from large communication and synchronization overheads when training across devices, leading to longer training times as a result of suboptimal hardware utilization. Asynchronous stochastic gradient descent (ASGD) methods can improve training speed, but are sensitive to delays due to both communication and differences throughput. Moreover, the backpropagation algorithm used within ASGD workers is bottlenecked by the interlocking between its forward and backward passes. Current methods also do not take advantage of the large differences in the computation required for the forward and backward passes. Therefore, we propose an extension to ASGD called Partial Decoupled ASGD (PD-ASGD) that addresses these issues. PD-ASGD uses separate threads for the forward and backward passes, decoupling the updates and allowing for a higher ratio of forward to backward threads than the usual 1:1 ratio, leading to higher throughput. PD-ASGD also performs layer-wise (partial) model updates concurrently across multiple threads. This reduces parameter staleness and consequently improves robustness to delays. Our approach yields close to state-of-the-art results while running up to 5.95× faster than synchronous data parallelism in the presence of delays, and up to 2.14× times faster than comparable ASGD algorithms by achieving higher model flops utilization. We mathematically describe the gradient bias introduced by our method, establish an upper bound, and prove *** Codes G.1.6 Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Automatic detection and classification algorithm of thyroid nodules in CT images based on YOLOv5s

Automatic detection and classification algorithm of thyroid ...

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International Conference on computer and Communications (ICCC)

作者： Ruifeng Guo Lei Xu Jiahui Guo Hongliang Wang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Department of Endocrinology and Metabolism The First Hospital of China Medical University Shenyang China

Thyroid nodular lesions are one of the most common diseases in the population. As the number of patients increases, it is clinically important to provide physicians with an accurate early aid to determine the benignity and malignancy of thyroid nodules in order to avoid unnecessary surgery and reduce patient burden. In this paper, we propose an improved Yolov5s-based target detection algorithm for benign and malignant thyroid nodules for thyroid nodule classification and identification. Replacing the backbone network of Yolov5s with ResNet18 to reduce the complexity of the model and the number of parameters; introducing the CBAM attention mechanism into the Yolov5s algorithm to improve the ability to extract features; Modify the spatial pyramid pooling module SPP to SimSPPF to improve the training speed of the algorithm; replace GIoU Loss with CIoU-Focal Loss as the loss function of the algorithm to improve the localization accuracy while increasing the regression rate of the bounding box. The experimental results show that the improved Yolov5s achieved an average accuracy of 92.9%, which is 2.9 percentage points higher than the average accuracy of the original algorithm, and can effectively improve the accuracy and efficiency of nodule detection.

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Advanced Web Traffic Modelling and Forecasting with a Hybrid Predictive Approach

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Journal of Web Engineering 2025年第3期24卷 409-456页

作者： Ujjwal Thakur Sunil K. Singh Sudhakar Kumar Harmanjot Singh Varsha Arya Brij B. Gupta Razaz Waheeb Attar Ahmed Alhomoud Kwok Tai Chui Department of CSE Chandigarh College of Engineering & Technology Chandigarh India Hong Kong Metropolitan University (HKMU) Hong Kong SAR China Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Computer Science and Information Engineering Asia University Taichung Taiwan China Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India University of Economics and Human Science Warsaw Poland Management Department College of Business Administration Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science College of Science Northern Border University Arar Saudi Arabia

Web traffic analysis is crucial for optimising user experience and engagement. This research explores a hybrid approach combining traditional statistical methods, like the autoregressive integrated moving average (ARIMA) model, with advanced techniques such as long short-term memory (LSTM) neural networks and the Prophet model. ARIMA effectively captures linear trends, seasonal effects, and cyclic behaviours, while LSTM handles complex non-linear patterns, and Prophet addresses seasonal variations and missing data. The hybrid model demonstrated 93% accuracy in predicting web traffic, highlighting the benefits of integrating these methodologies. This approach enables businesses to better manage resources, boost user engagement, and improve revenue. Future research will focus on refining hybrid models by incorporating new data features and ensemble methods to further enhance prediction accuracy, ultimately advancing the understanding of web traffic trends and user behaviour.

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Consistency Approximation: Incremental Feature Selection Based on Fuzzy Rough Set Theory

SSRN

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SSRN 2023年

作者： Zhao, Jie Wu, Daiyang Wu, JiaXin Ye, Wenhao Huang, Faliang Wang, Jiahai See-To, Wing-Kuen Eric Department of Data Science and Business Intelligence Guangdong University of Technology Guangzhou China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong

Fuzzy Rough Set Theory (FRST)-based feature selection has been widely used as a preprocessing step to handle dynamic and large datasets. However, large-scale or high-dimensional datasets remain intractable for FRST-based feature selection approaches due to high space complexity and unsatisfactory classification performance. To overcome these challenges, we propose a Consistency Approximation (CA)-based framework for incremental feature selection. By exploring CA, we introduce a novel significance measure and a tri-accelerator. The CA-based significance measure provides a mechanism for each sample in the universe to keep members with different class labels within its fuzzy neighbourhood as far as possible, while keeping members with the same label as close as possible. Furthermore, our tri-accelerator reduces the search space and decreases the computational space with a theoretical lower bound. The experimental results demonstrate the superiority of our proposed algorithm compared to state-of-the-art methods on efficiency and classification accuracy, especially for large-scale and high-dimensional datasets. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Incremental Feature Selection for Dynamic Incomplete Data Using Sub-Tolerance Relation

SSRN

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SSRN 2023年

作者： Zhao, Jie Ling, Yun Huang, Faliang Wang, Jiahai See-To, Eric Wing kuen Department of Data Science and Business Intelligence Guangdong University of Technology Guangzhou China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong

TRS theory is widely used for feature selection with incomplete data. However, the traditional positive region-based measure ignores uncertainty, resulting in redundant features and poor classification accuracy. In addition, the computation of the positive region incurs significant time and space costs. To address these limitations, we extend the tolerance class to a Subrelation Tolerance Class (STC) by decomposing the tolerance relation into two subrelations. STC transforms feature selection into a two-stage certainty measurement by partitioning the universe into certainty and uncertainty regions. By progressively filtering out certain regions, the computational space is reduced. Furthermore, we propose a new significance measure that incorporates both certain and uncertain information. This measure effectively reduces redundant features, resulting in improved computational efficiency and classification accuracy. Using STC and our measure, we develop an incremental feature selection algorithm for dealing with incomplete streaming data, which is validated through extensive experiments and comparisons. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Do Text-to-Text Multi-Task Learners Suffer from Task Conflict?

arXiv

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arXiv 2022年

作者： Mueller, David Andrews, Nicholas Dredze, Mark Department of Computer Science Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States

Traditional multi-task learning architectures train a single model across multiple tasks through a shared encoder followed by task-specific decoders. Learning these models often requires specialized training algorithms that address task-conflict in the shared parameter updates, which otherwise can lead to negative transfer. A new type of multi-task learning within NLP homogenizes multi-task architectures as a shared encoder and language model decoder, which does surprisingly well across a range of diverse tasks (Raffel et al., 2020). Does this new architecture suffer from task-conflicts that require specialized training algorithms? We study how certain factors in the shift towards text-to-text models affects multi-task conflict and negative transfer, finding that both directional conflict and transfer are surprisingly constant across architectures. © 2022, CC BY.

关键词： Signal encoding

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