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TechRxiv 2022年

作者： Alexandropoulos, George C. Jamali, Vahid Schober, Robert Poor, H. Vincent Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Department of Electrical and Computer Engineering Princeton University United States Germany

In this paper, we present a low overhead beam management approach for near-field millimeter-wave multi-antenna communication systems enabled by Reconfigurable Intelligent Surfaces (RISs). We devise a novel variable-width hierarchical phase-shift codebook suitable for both the near- and far-field of the RIS, and present a fast alignment algorithm for the RIS phase shifts and the transceiver beamformers. Indicative performance evaluation results are shown, verifying the effectiveness of the proposed approach in comparison with various benchmark schemes. © 2022, CC BY.

关键词： Millimeter waves

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Indoor Channel Characterization With Extremely Large Reconfigurable Intelligent Surfaces at 300 GHz

Indoor Channel Characterization With Extremely Large Reconfi...

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European Conference on Antennas and Propagation, EuCAP

作者： Fabio Cardoso Sérgio Matos Luís M. Pessoa George C. Alexandropoulos Department of Information Science and Technology University Institute of Lisbon Portugal Instituto de Telecomunicações Portugal INESC TEC Portugal Faculty of Engineering University of Porto Portugal Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Department of Electrical and Computer Engineering University of Illinois Chicago IL USA

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

The technology of Reconfigurable Intelligent Surfaces (RISs) is lately being considered as a boosting component for various indoor wireless applications, enabling wave propagation control and coverage extension. However, the incorporation of extremely large RISs, as recently being considered for ultrahigh capacity industrial environments at subTHz frequencies, imposes certain challenges for indoor channel characterization. In particular, such RISs contribute additional multipath components and their large sizes with respect to the signal wavelength lead to near-field propagation. To this end, ray tracing approaches become quite cumbersome and need to be rerun for different RIS unit cell designs. In this paper, we present a novel approach for the incorporation of RISs in indoor multipath environments towards their efficient channel characterization. An 100 × 100 RIS design with 2-bit resolution unit cells realizing a fixed anomalous reflection at 300 GHz is presented, whose radar cross section patterns are obtained via full-wave simulations. It is showcased that the RIS behavior can be conveniently approximated by a three-ray model, which can be efficiently incorporated within available ray tracing tools, and that the far-field approximation is valid for even very small distances from the RIS.

关键词： Wireless communication Indoor communication Radar cross-sections Europe Reconfigurable intelligent surfaces Ray tracing Boosting Antennas and propagation Terahertz communications

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Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models

arXiv

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arXiv 2025年

作者： Lai, Yuxiang Zhong, Jike Li, Ming Zhao, Shitian Yang, Xiaofeng Li, Yuheng Psounis, Konstantinos Department of Computer Science and Informatics Emory University AtlantaGA30322 United States Department of Computer Science Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Department of Computer Science University of Tokyo Tokyo113-8654 Japan Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA30332 United States Department of Radiation Oncology Winship Cancer Institute Emory University AtlantaGA30322 United States

Vision-language models (VLMs) have achieved impressive progress in natural image reasoning, yet their potential in medical imaging remains underexplored. Medical vision-language tasks demand precise understanding and clinically coherent answers, which are difficult to achieve due to the complexity of medical data and the scarcity of high-quality expert annotations. These challenges limit the effectiveness of conventional supervised fine-tuning (SFT) and Chain-of-Thought (CoT) strategies that work well in general domains. To address these challenges, we propose Med-R1, a reinforcement learning (RL)-enhanced vision-language model designed to improve generalization and reliability in medical reasoning. Built on the DeepSeek strategy, Med-R1 adopts Group Relative Policy Optimization (GRPO) to encourage reward-guided learning beyond static annotations. We comprehensively evaluate Med-R1 across eight distinct medical imaging modalities. Med-R1 achieves a 29.94% improvement in average accuracy over its base model Qwen2-VL-2B, and even outperforms Qwen2-VL-72B—a model with 36× more parameters. To assess cross-task generalization, we further evaluate Med-R1 on five question types. Med-R1 outperforms Qwen2-VL-2B by 32.06% in question-type generalization, also surpassing Qwen2-VL-72B. We further explore the thinking process in Med-R1, a crucial component for the success of Deepseek-R1. Our results show that omitting intermediate rationales (No-Thinking-Med-R1) not only improves in-domain and cross-domain generalization with less training, but also challenges the assumption that more reasoning always helps. These findings suggest that in medical VQA, it is not reasoning itself, but its quality and domain alignment, that determine effectiveness. Together, these results highlight that RL improves medical reasoning and generalization, enabling efficient and reliable VLMs for real-world deployment. © 2025, CC BY.

关键词： Reinforcement learning

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Sharding:A Scalability Solutions for Blockchain Networks

Sharding:A Scalability Solutions for Blockchain Networks

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Dattatray Raghunath Kale Amolkumar N Jadhav Kruishna Prasad K Somanath Janardan Salunkhe Sumit Hirve Chandrashekhar Goswami Department of Computer Science & Engg MIT School of Computing MIT Art Design and Technology University Pune India Department of Computer Science & Engg Annasaheb Dange College of Engineering and Technology Ashta India Department of Computer Science & Engg Shrinivas University Mangalore India Department of Computer Science & Engg D Y Patil Agriculture and Technical University India Faculty of Computing and Informatics Sir Padampat Singhania University Udaipur Rajasthan

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Blockchain technology's decentralized and immutable data storage has changed a number of sectors. But typical blockchain networks scalability issues prevent them from being widely used for large-scale applications. By dividing the blockchain network into smaller, more controllable sections known as shards, sharding has become a viable remedy for scalability issues. This paper offers a thorough introduction to sharding as a blockchain network scalability solution. We explore the basic ideas of sharding as well as its advantages, drawbacks, and several sharding strategies. We also analyze experimental results and real-world implementations to assess how well sharding contributes to increased blockchain scalability. Lastly, we talk about possible developments in sharding approaches and future research paths to further improve the scalability of blockchain networks.

关键词： Industries Sharding Scalability Memory Throughput Blockchains Security

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Assessment of Driver’s Stress using Multimodal Biosignals and Regularized Deep Kernel Learning

Assessment of Driver’s Stress using Multimodal Biosignals a...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Vishal Singh Roha Nagarajan Ganapathy Nicolai Spicher Sriparna Saha Thomas M. Deserno Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School Brunswick Germany Department of Computer Science Engineering Indian Institute of Technology Patna India Department of Biomedical Engineering Indian Institute of Technology Hyderabad India Institute for Medical Informatics University Medical Center Göttingen Göttingen Germany

In this work, we classify the stress state of car drivers using multimodal physiological signals and regularized deep kernel learning. Using a driving simulator in a controlled environment, we acquire electrocardiography (ECG), electrodermal activity (EDA), photoplethysmography (PPG), and respiration rate (RESP) from N = 10 healthy drivers in experiments of 25min duration with different stress states (5min resting, 10min driving, 10min driving + answering cognitive questions). We manually remove unusable segments and approximately 4h of data remain. Multimodal time and frequency features are extracted and employed to regularized deep kernel machine learning based on a fusion framework. Task-specific representations of different physiological signals are combined using intermediate fusion. Subsequently, the fused multimodal features are fed a support vector machine (SVM) and a random forest (RF) for stress classification. The experimental results show that the proposed approach can discriminate between stress states. The combination of PPG and ECG using RF as classifier yields the highest F1-score of 0.97 in the test set. PPG only and RF yield a maximum F1-score of 0.90. Furthermore, subject-specific cross-validation improves performance. ECG and PPG signals are reliable in classifying the stress state of a car driver. In summary, the proposed framework could be extended to real-time stress state assessment in driving conditions.

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Integration of Large Language Models into Higher Education: A Perspective from Learners

Integration of Large Language Models into Higher Education: ...

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International Symposium on computers in Education (SIIE)

作者： Katerina Zdravkova Fisnik Dalipi Fredrik Ahlgren Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje N. Macedonia Department of Informatics Faculty of Technology Linnaeus University Kalmar Sweden Department of Computer Science and Media Technology Faculty of Technology Linnaeus University Kalmar Sweden

Large language models (LLMs) are being criticized for copyright infringement, inadvertent bias in training data, a danger to human innovation, the possibility of distributing incorrect or misleading information, and prejudice. Due to their popularity among students, the introduction of many comparable apps, and the inability to resist unfair and fraudulent student usage, their educational use needs to be adapted and harmonized. The incorporation of LLMs should be defined not only by pedagogues and educational institutions, but also by students who will actively utilize them to learn and prepare assignments. In order to find out what students from two universities think and suggest about LLMs use in education, they were asked to give their contribution by answering the survey that was conducted at the beginning of the spring semester of academic 2022/23. Their feedback was quantitatively and qualitatively analyzed, showing in a better light what students think about LLMs and how and why they would use them. Based on the analysis, the authors propose an original strategy for integrating LLMs into education. The proposed approach is also adapted for those students who are not interested in using LLMs and for those who prefer the hybrid mode by combining their own research with LLMs generated recommendations. The authors expect that by implementing the proposed strategy, schools will benefit from a better education in which research, creativity, academic honesty, recognition of false information, and the ability to improve knowledge will prevail.

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Hybrid Energy System Analysis for a Swimming Pool Complex using HOMER Pro

Hybrid Energy System Analysis for a Swimming Pool Complex us...

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International Universities Power engineering Conference (UPEC)

作者： Timea Farkas Paula Unguresan Mihaela Cretu Denisa Stet Levente Czumbil Andrei Ceclan Claudia Muresan Alexis Polycarpou Dan D. Micu Energy Transition Research Center Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus

ISBN: (数字)9781665455053

ISBN: (纸本)9781665455060

The paper investigates hybrid energy systems that could be applied in case of a swimming pool complex in order to reduce exploitation costs and increase renewable energy usage. The presented approach starts with the assessment of the chosen building at the level of energy consumption, and utility costs, along with two proposed system configurations: one proposed by the RE-COGNITION innovation project, and one proposed by the authors based on the market solutions. The mentioned cases represent two hybrid energy systems using conventional sources, renewable energy, and highly efficient cogeneration units. The two proposed system configurations are simulated using the HOMER Pro program which allows a wide analysis along with techno-economic indicators of the different scenarios. Results and comparisons are presented to highlight the feasibility of the two proposed cases.

关键词： Renewable energy sources Analytical models Technological innovation Costs Technical management Sensitivity analysis Wind speed

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Smart Traffic Light Using Raspberry Pi and Digital Image Processing

Smart Traffic Light Using Raspberry Pi and Digital Image Pro...

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International Conference on Wireless and Telematics (ICWT)

作者： Helfy Susilawati Shelmy Luthfi Pathur Rahman Ade Rukmana Iik Muhamad Malik Matin Sarbini Nanang Ismail Electrical Engineering Departement Universitas Garut Garut Indonesia Computer and Informatics Engineering Jakarta State Polytechnic Jakarta Indonesia Psychology Department UIN Sunan Gunung DJati Bandung Bandung Indonesia Departement of Electrical Engineering UIN Sunan Gunung DJati Bandung Bandung Indonesia

Congestion is one of the problems in traffic, especially at intersections with traffic lights. In this study, a traffic light management system was developed by utilizing image processing that can adjust the duration in the queue depending on road conditions. The system is developed using Raspberry Pi 4 as the controller. The system will capture the image of the traffic conditions via cameras and then perform the image processing using OpenCV and Tensorflow to calculate the number of vehicles. The number of vehicles will be sent to the webserver database to be processed as the input for traffic system settings. The testing shows that the system can set the timing of the traffic lights. When there are 3 vehicles in the queue with traffic light is red then the system set the timing for the green light to 10 seconds. Additional of 1 vehicle in the queue will increase the duration of the green light by 4 seconds.

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Grammar Error Correction in Indonesian Sports News: Comparing the Performance of Pre-Trained T5 and BART Models

Grammar Error Correction in Indonesian Sports News: Comparin...

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informatics International Conference (BTS-I2C), Beyond Technology Summit on

作者： Muh Adnan Esa Prakasa Yuyun Najirah Umar Nasrullah Adi Sadli A. Edeth Fuari Anatasya Hazriani Mashur Razak dept. of informatics engineering Handayani University of Makassar Makassar Indonesian National Research and Innovation Agency research center for data and information science Bandung Indonesian dept. of computer system Handayani University Makassar Makassar Indonesian

ISBN: (数字)9798331508579

ISBN: (纸本)9798331508586

This research compares the performance of two pre-trained models based on the transformer, namely text-to-text transfer transformer (T5) and bidirectional and auto-regressive transformer (BART), through a fine-tuned for correcting grammar in Indonesian sports news texts. We used vennify/t5-base-grammar-correction and onionLad/grammar-correction-bart-base, was trained using a dataset of 10,075 sentences from Indonesian language sports news sites. We tested the model performance using bilingual evaluation understudy (BLEU), google's language evaluation understanding (GLEU), training loss, and validation loss. The results show that BART was more efficient in learning data patterns and has better generalization ability, supported by BLEU and GLEU scores of 0.8560 and 0.9655. Meanwhile, T5 maintains simpler sentence structures, supported by BLEU and GLEU scores of 0.8120 and 0.9564. BART excels at handling more complex contexts, making it more effective at accurately correcting errors in dynamic sports news. Both models have their respective advantages, with BART being better at correcting detailed grammatical errors, while T5 offers consistent correction of more simple sentence structures.

关键词： Training Measurement Analytical models Accuracy Computational modeling computer architecture Transformers Grammar Sports Context modeling

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An Iterated Local Search Algorithm for Commuting Bus Routing Problem with Latest Arrival Time Constraint

An Iterated Local Search Algorithm for Commuting Bus Routing...

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2022 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2022

作者： Hashimoto, H. Hu, Y. Sugiura, T. Takada, Y. Yagiura, M. Tokyo University of Marine Science and Technology Dept. Logistics and Information Engineering Tokyo Japan Tokyo University of Science Dept. Applied Mathematics Tokyo Japan Nagoya University Graduate School of Information Science Dept. Computer Science and Mathematical Informatics Nagoya Japan

ISBN: (数字)9781665486873

ISBN: (纸本)9781665486873

Given a set of bus stops and a set of employees, the commuting bus routing problem with latest arrival time constraint (CBRP-LATC) aims to determine routes of buses to carry every employee from the company to one of the bus stops at which he/she wants to get off. Each employee walks to his/her destination after getting off the bus and has a desired time when he/she wants to arrive there, which is called the latest arrival time. The objective is to find a set of routes with the minimum total traveling cost that covers all employees' destinations under bus capacity and latest arrival time constraints. We propose an iterated local search algorithm in which we use two kinds of neighborhood operations called set-del/1-ins and VRP-OPT∗. The set-del/1ins operation consists of three neighborhood operations, setdel, 1-ins and set-del-ins to remove or assign employees from or to a bus route. The VRP-OPT∗ is a local search algorithm to improve the distances of routes based on four neighborhood operations, 2-opt, 2-opt∗, relocate and cross-exchange. Computational results show the effectiveness of the two neighborhood operations. © 2022 IEEE.

关键词： Personnel

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