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15th International Conference on Intelligent Computing and Networking, IC-ICN 2024

作者： Kumar, Pramod Quraishi, Aadam Rusho, Maher Ali Raparthi, Mohan Degadwala, Sheshang Computer Science and Technology Ganga Institute of Technology and Management Haryana Jhajjar Kablana124104 India Intervention Treatment Institute HoustonTX United States Degree Program University of Colorado Boulder CO United States Software Engineer Alphabet Life Science DallasTX75063 United States Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9789819786305

Effectively harnessing feature correlations is crucial for optimal performance in video action recognition tasks, whether in spatial or temporal dimensions. Convolutional operations excel at capturing local features through correlations among nearby points, while self-attention mechanisms capture global information by enabling interactions among all feature points. However, the limitations of a single convolutional layer in comprehending holistic feature correlations and the tendency of self-attention layers to overlook local positional characteristics necessitate an innovative approach. We introduce the Spatial–Temporal Convolutional Attention Network, comprising the Spatial Convolutional Attention Network and the Temporal Convolutional Attention Network. This approach strategically combines the strengths of self-attention for global context and convolution for local features, mitigating the limitations of individual layers. Key innovations include [explicitly state innovations]. To reinforce the uniqueness of our approach, we provide specific comparisons with existing methods, highlighting its superiority. In rigorous experiments, our proposed network demonstrates enhanced recognition performance, quantified through metrics such as accuracy and F1 score improvements over baseline models. By effectively capturing feature correlations, our approach elevates the neural network's capacity for spatiotemporal modeling, marking a significant advancement in video action recognition. Elevating the neural network's capacity for spatiotemporal modeling. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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MarUCOD: Unknown but Concerned Object Detection in Maritime Environments 27th

MarUCOD: Unknown but Concerned Object Detection in Maritim...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yoon, Hajung Lee, Yoonji Lee, Hwijun Um, Daeho Choi, Hong Seok Choi, Jin Young Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of NexReal Inc. 178 Digital-ro Geumcheon-gu Seoul Korea Republic of

ISBN: (纸本)9783031784460

Detection of unknown but concerned objects such as diverse unknown suspicious objects in the sea area is a critical problem in military defense applications, but the problem is challenging because 1) a pre-trained detector cannot easily detect unknown but concerned objects, 2) it detects too many unconcerned objects such as usual objects in the coastal land area, and 3) we cannot easily establish a well-performing discriminator to divide the detected objects into unknown and known objects, as well as concerned and unconcerned objects because the unknown objects are not available, whereas the concerned and unconcerned objects are not clearly defined. To tackle this challenge, this paper proposes a real-time framework for unknown but concerned object detection in maritime environments by integrating object detection, segmentation, and out-of-distribution (OOD) detection techniques. In our framework, an object detector finds all object-like foregrounds by setting a low threshold and a segmentation deep-learning network filters out unconcerned foregrounds detected in the coastal land area. After that, to discriminate known or unknown objects among concerned objects detected in the sea area, a discriminator performs unsupervised OOD detection using bisecting K-means clustering. To boost the performance of the proposed framework, we apply a pre-processing scheme and a contrastive separation loss for segmentation. The proposed framework achieves a high detection rate of unknown but concerned objects with minimal detection of unconcerned objects (i.e., minimal false positives), surpassing baseline methods and demonstrating potential for enhanced maritime safety and security. The codes are open at https://***/AIX-Coast-Defense-PIL/MarUCOD. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： K-means clustering

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Data-Driven Incentive Mechanisms for Federated Learning in Vehicular Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Fathi, Faeze Montazeri, Mina N.araabi, Babak Du, Hongyang Niyato, Dusit Kebriaei, Hamed University of Tehran School of Ece College of Engineering Tehran Iran Urban Energy Systems Laboratory Dübendorf8600 Switzerland Nanyang Technological University School of Computer Science and Engineering The Energy Research Institute @ Ntu Interdisciplinary Graduate Program Singapore Nanyang Technological University School of Computer Science and Engineering Singapore School of Computer Science Tehran Iran

Centralized machine learning algorithms in vehicular networks face privacy and resource constraints. Federated Learning (FL) addresses these by enabling collaborative model training without sharing raw data. To incentivize vehicle participation despite resource constraints, we propose a contract between Roadside Units (RSUs) and vehicles, specifying required computation resources and rewards based on local data quality. The contract is designed to maximize the utility of the RSU, subject to constraints that ensure the participation of the vehicles in the task and truthful reporting of their data quality parameter. However, designing an optimal contract with classical methods is limited to specific utility functions. To extend the design methodology of optimal contracts to a broader range of utility functions, our study proposes a deep learning-based solution. Specifically, we present a novel application of Deep Neural Networks (DNNs), marking the first instance of applying DNNs in contract theory-based incentive mechanisms to achieve nearoptimal contracts. We design the DNN's structure to inherently satisfy specific constraints of the optimization problem, enhancing its efficiency and accuracy in determining the optimal contract. We utilize an augmented Lagrangian method to approximate the optimal contract in federated learning tasks. A case study on traffic sign recognition demonstrates the applicability and effectiveness of our method. Comparing our approach with the well-known generative diffusion model shows that in the FL scenarios, the DNN results are closer to the optimal contract compared to those of the generative diffusion model. © 1967-2012 IEEE.

关键词： Traffic signs

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Convergent effects of peptides on the initiation of feeding motor programs in the mollusk Aplysia

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Journal of Neurophysiology 2025年第5期133卷 1368-1379页

作者： Evans, Colin G. Barry, Michael A. Reaver, Carrie N. Patel, Paras R. Chestek, Cynthia A. Perkins, Matthew H. Jing, Jian Cropper, Elizabeth C. Department of Neuroscience Friedman Brain Institute Icahn School of Medicine at Mount Sinai New York NY United States Department of Biomedical Engineering Biointerfaces Institute University of Michigan Ann Arbor MI United States Department of Electrical Engineering and Computer Science Neurosciences Program Robotics Program University of Michigan Ann Arbor MI United States State Key Laboratory of Pharmaceutical Biotechnology Institute for Brain Sciences Chemistry and Biomedicine Innovation Center Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology Advanced Institute for Life Sciences School of Life Sciences Nanjing University Nanjing China

Neuropeptides configure the feeding network of Aplysia. For example, egestive activity is promoted by small cardioactive peptide (SCP), and ingestive activity is promoted by a combination of feeding circuit activating peptide (FCAP) and cerebral peptide 2 (CP-2). In addition, SCP and FCAP/CP-2 have a common network effect that does not contribute to motor program specification. They increase the excitability of an interneuron, B63. In this report, we further characterized the effects of peptides on B63. We performed voltage-clamp experiments and used a step protocol to look at steady-state currents. We found that SCP and FCAP/CP-2 both induced an inward current that was virtually absent in low-sodium saline. Previous work has established that B63 is unusual in the feeding circuit in that subthreshold depolarizations are autonomously generated that can trigger motor programs. Here, we show that this autonomous activity is more frequent in the presence of peptides. Previous studies have also shown that activity of the feeding central pattern generator (CPG) can be initiated by neurons that excite B63, e.g., by cerebral buccal interneuron 2 (CBI-2), a projection neuron that triggers biting-like motor programs. Here, we show that the latency of CBI-2-induced activity is decreased by stimulation of the esophageal nerve (EN) (which releases endogenous SCP). These results, taken together with previous results, indicate that peptides that act divergently to configure network activity additionally act convergently to promote motor program induction. We present data that suggest that this arrangement facilitates brief switches between ingestive and egestive motor activity. © 2025 The Authors.

关键词： central pattern generator invertebrate modulation motor program neuropeptide

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ReMP: Reusable Motion Prior for Multi-domain 3D Human Pose Estimation and Motion Inbetweening

ReMP: Reusable Motion Prior for Multi-domain 3D Human Pose E...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Hojun Jang Young Min Kim Dept. of Electrical and Computer Engineering Seoul National University Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

We present Reusable Motion prior (ReMP), an effective motion prior that can accurately track the temporal evolution of motion in various downstream tasks. Inspired by the success of foundation models, we argue that a robust spatio-temporal motion prior can encapsulate underlying 3D dynamics applicable to various sensor modalities. We learn the rich motion prior from a sequence of complete parametric models of posed human body shape. Our prior can easily estimate poses in missing frames or noisy measurements despite significant occlusion by employing a temporal attention mechanism. More interestingly, our prior can guide the system with incomplete and challenging input measurements to quickly extract critical information to estimate the sequence of poses, significantly improving the training efficiency for mesh sequence recovery. ReMP consistently outperforms the baseline method on diverse and practical 3D motion data, including depth point clouds, LiDAR scans, and IMU sensor data. Project page is available in https://***/ReMP.

关键词： Training Point cloud compression Three-dimensional displays Accuracy Tracking Shape Pose estimation Noise Parametric statistics Noise measurement

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Auto Digit Selection for Most Significant Digit First Multiplication

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IEEE Embedded Systems Letters 2025年

作者： Gorgin, Saeid Jaberipur, Ghassem Lee, Jeong-A Shin, Seokjoo Kim, Jungrae Computer Engineering Iranian Research Organization for Science and Technology Tehran Iran Semiconductor Systems Engineering Department College of Information and Communication Engineering SungKyunKwan University Korea Republic of Brain Pool Program for Chosun University Gwangju Korea Republic of Shahid Beheshti University Tehran Iran Chosun University Department of Computer Engineering Gwangju Korea Republic of

Performance in modern embedded systems, particularly those executing computation-intensive signal/image processing and machine learning algorithms, is critically dependent on the efficiency of multiplication operations. Serial multiplication offers key advantages in these resource-constrained environments, including low memory usage and minimal interconnect complexity. The most significant digit first (MSDF) serial multiplication scheme further enhances efficiency by enabling early termination and supporting variable precision. It is ideal for embedded applications where power and performance are critical. This paper proposes an auto-selection scheme for MSDF multiplication, where product digits are generated as a byproduct of residual maintenance, leading to speed, cost, and power efficiency improvements. We demonstrate the implementation of serial-serial and serial-parallel multipliers, where the multiplier operand in the latter is fully available throughout the computation. Simulation and synthesis results, compared to the most relevant previous works, show reductions in delay (16% and 20%), area consumption (11% and 23%), and power dissipation (7% and 20%), making our approach well-suited for embedded systems requiring efficient, low-power multiplication hardware. © 2009-2012 IEEE.

关键词： Left-to-Right Arithmetic Machine Learning Algorithms MSDF Multiplication Online Arithmetic

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PHORTEX: Physically-Informed Operational Robotic Trajectories for Scientific Expeditions

Field Robotics

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Field Robotics 2025年 4卷 138-181页

作者： Victoria L. Preston Genevieve E. Flaspohler John W. Fisher Anna P. M. Michel Nicholas Roy Applied Ocean Physics and Engineering Massachusetts Institute of Technology and Woods Hole Oceanographic Institution Joint Program Cambridge MA 02139 Electrical Engineering and Computer Science Massachusetts Institute of Technology and Woods Hole Oceanographic Institution Joint Program Cambridge MA 02139 Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA 02139 Applied Ocean Physics and Engineering Woods Hole Oceanographic Institution Woods Hole MA 02543

Mobile robots are increasingly used to collect valuable in situ samples during scientific expeditions. However, many phenomena of scientific interest—deep-sea hydrothermal plumes, algal blooms, warm-core eddies, and lava flows—are spatiotemporal distributions that evolve on spatial and temporal scales that complicate sample collection. Here, we consider the problem of charting the space-time dynamics of deep-sea hydrothermal plumes with the state-of-the-art autonomous underwater vehicle (AUV) Sentry. In the hydrothermal plume charting problem, the plume state is driven by complicated and unobserved dynamics in the deep sea. To effectively sample the moving plume, an autonomy system must infer plume dynamics from sparse, point observations, while respecting operational constraints of AUV Sentry that restrict the set of possible trajectories to nonadaptive, uniform-coverage patterns. We frame the plume charting problem as a sequential decision-making problem and formulate a mission planner PHORTEX (PHysically-informed Operational Robotic Trajectories for Expeditions) that strategically designs full mission trajectories for Sentry, where each mission plan is informed by the observations of the last. PHORTEX is composed of a trajectory optimizer, which maximizes expected samples collected within a moving plume, and PHUMES (PHysically-informed Uncertainty Models for Environment Spatiotemporality), a modeling framework that leverages an embedded simulator of idealized plume physics as an inductive bias to enable dynamics learning from extreme partial observations and a few Sentry deployments. In both simulation and in field trials at a hydrothermal site in the Gulf of California, we demonstrate that Sentry using PHORTEX learns to track a moving hydrothermal plume and gather samples that significantly improve upon baseline spatial and temporal diversity for use in downstream science tasks.

关键词： Trajectory Sensors Oceans Vehicle dynamics Robot sensing systems Adaptation models Spatiotemporal phenomena Decision making Marine robots Environmental monitoring Mobile robots

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YNote: A Novel Music Notation for Fine-Tuning LLMs in Music Generation

arXiv

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arXiv 2025年

作者： Lu, Shao-Chien Yeh, Chen-Chen Cho, Hui-Lin Hsu, Chun-Chieh Hsu, Tsai-Ling Wu, Cheng-Han Shih, Timothy K. Lin, Yu-Cheng Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan International Bachelor Program in Informatics Yuan Ze University Taoyuan Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan

The field of music generation using Large Language Models (LLMs) is evolving rapidly, yet existing music notation systems, such as MIDI, ABC Notation, and MusicXML, remain too complex for effective fine-tuning of LLMs. These formats are difficult for both machines and humans to interpret due to their variability and intricate structure. To address these challenges, we introduce YNote, a simplified music notation system that uses only four characters to represent a note and its pitch. YNote’s fixed format ensures consistency, making it easy to read and more suitable for fine-tuning LLMs. In our experiments, we fine-tuned GPT-2 (124M) on a YNote-encoded dataset and achieved BLEU and ROUGE scores of 0.883 and 0.766, respectively. With just two notes as prompts, the model was able to generate coherent and stylistically relevant music. We believe YNote offers a practical alternative to existing music notations for machine learning applications and has the potential to significantly enhance the quality of music generation using LLMs. Copyright © 2025, The Authors. All rights reserved.

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Light extraction improvement via ITO p-electrodes for InGaN red micro-LEDs emitting at 640 nm

Optics Continuum

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Optics Continuum 2025年第5期4卷 1040-1050页

作者： Cesur Altinkaya Mohammed A Najmi Daisuke Iida Kazuhiro Ohkawa Material Science and Engineering Program Physical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia cesur.altinkaya@kaust.edu.sa Electrical and Computer Engineering Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia daisuke.iida@kaust.edu.sa kazuhiro.ohkawa@kaust.edu.sa

We present light extraction efficiency (LEE) improvement for InGaN red micro-light emitting diodes (micro-LEDs) of various sizes operating at low current densities. We compared the characteristics of micro-LEDs with indium tin oxide (ITO) transparent p-electrodes with conventional opaque metal p-electrodes. 50 µm × 50 µm micro-LEDs with ITO p-electrodes achieved a peak on-wafer external quantum efficiency (EQE) of 2.54% with an emission wavelength of 640 nm at a current density as low as 0.4 A/cm 2 . This represents a 1.18-fold improvement in peak EQE compared to devices with metal p-electrodes. Light ray tracing simulation confirmed that the ITO p-electrodes exhibit 1.18 times higher light escape than metal-based micro-LEDs, validating the role of enhanced light extraction. These findings provide valuable insights for advancing high-definition display and VR applications.

关键词： Atomic layer deposition Indium tin oxide Light extraction Opacity Quantum efficiency Ray tracing

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Remote Sensing Scene Classification Based on Hybrid Convolutional Neural Network and Attention Module

Remote Sensing Scene Classification Based on Hybrid Convolut...

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Advancement in Data Science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Muhammad Yusuf Kardawi Alexander Agung Santoso Gunawan Profesional Engineer Program Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Remote sensing images present classification challenges due to the complexity of their structural and spatial patterns. This research explores a hybrid approach that combines convolutional neural network (CNN) and attention through feature fusion to improve scene classification accuracy in remote sensing images. The proposed architecture utilizes EfficientNet and VGGNet to extract depth features separately. The extracted features are then integrated with Dynamic Selfattention (DSA), which dynamically focuses the model on the most relevant information in the image. DSA allows the model to adaptively assign weights to different parts of the image, thus improving the discriminative ability of the model. Furthermore, a feature fusion technique is applied to combine information from different layers of the CNN and DSA modules. Experiments conducted on the UC Merced dataset showed accuracies of 0.9181 and 0.9167. These results show that the combination of CNN, DSA, and feature fusion is an effective and robust approach for remote sensing image classification.

关键词： Adaptation models Scene classification Accuracy Electronic learning Data science Feature extraction Convolutional neural networks Data mining Remote sensing Information systems

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