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arXiv 2025年

作者： Ma, Youmi Mizuki, Sakae Fujii, Kazuki Nakamura, Taishi Ohi, Masanari Shimada, Hinari Shiotani, Taihei Saito, Koshiro Maeda, Koki Hattori, Kakeru Okamoto, Takumi Ishida, Shigeki Yokota, Rio Takamura, Hiroya Okazaki, Naoaki Department of Computer Science School of Computing Institute of Science Tokyo Japan National Institute of Advanced Industrial Science and Technology Japan National Institute of Informatics Japan

Instruction tuning is crucial for enabling Large Language Models (LLMs) to solve real-world tasks. Prior work has shown the effectiveness of instruction-tuning data synthesized solely from LLMs, raising a fundamental question: Do we still need human-originated signals for instruction tuning? This work answers the question affirmatively: we build state-of-the-art instruction-tuning datasets sourced from human-written instructions, by simply pairing them with LLM-generated responses. LLMs fine-tuned on our datasets consistently outperform those fine-tuned on existing ones. Our data construction approach can be easily adapted to other languages;we build datasets for Japanese and confirm that LLMs tuned with our data reach state-of-the-art performance. Analyses suggest that instruction-tuning in a new language allows LLMs to follow instructions, while the tuned models exhibit a notable lack of culture-specific knowledge in that language. The datasets and fine-tuned models will be publicly available1. Our datasets, synthesized with open-weight LLMs, are openly distributed under permissive licenses, allowing for diverse use cases. © 2025, CC BY.

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IoT-Enabled Smart Irrigation System with ABiLSTM for Optimized Water Management and Crop Yields

IoT-Enabled Smart Irrigation System with ABiLSTM for Optimiz...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Abhay Chaturvedi Praveena Rao Venkata Maruti Prasad S Malini T S. Prabagar K. Bharathi Department of Electronics and Communication Engineering GLA University Mathurai India Department of Civil Engineering Institute of Aeronautical Engineering Hyderabad India Department of BS & H Aditya Institute of Technology & Management Tekkali India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering School of Advanced Computing Alliance University Bangalore India Department of Agricultural Engineering Erode Sengunthar Engineering College Perundurai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

An efficient management of water resources in agriculture is now crucial in light of the growing global population and increased demand for water. Innovative solutions are needed to improve water efficiency since conventional irrigation methods are not sustainable anymore. Through the use of precision agriculture and the Internet of Things (IoT), smart irrigation has revolutionised water management, leading to increased crop yields. In order to improve the efficiency of decision-making, this research presents an Internet of Things (IoT) smart irrigation system that uses SA-BiLSTM (Self-Attention Bidirectional Long Short-Term Memory). Using Min-Max and Z-score normalisation approaches for feature scaling, the methodology includes data preparation, feature selection, and model training. Methods based on ranking, such as the Chi-Square Test, Pearson Correlation Coefficient (PCC), and Mutual Information (MI), were used to prioritise feature selection. Classification and prediction testing showed that the proposed SA-BiLSTM model outperformed state-of-the-art models like HGAT and Linear SVM, with an astounding 95.63% accuracy. Improved resource allocation and irrigation effectiveness, leading to increased agricultural output, are highlighted by the results, which highlight the effectiveness of advanced machine learning approaches in precision agriculture.

关键词： Support vector machines Precision agriculture Irrigation Accuracy Predictive models Feature extraction Internet of Things Water resources Long short term memory Mutual information

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IoT-Enabled Smart Irrigation System with ABiLSTM for Optimized Water Management and Crop Yields 3

IoT-Enabled Smart Irrigation System with ABiLSTM for Optimiz...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Chaturvedi, Abhay Rao, Praveena Venkata Maruti Prasad, S. Malini, T. Prabagar, S. Bharathi, K. Gla University Department of Electronics and Communication Engineering Mathura India Institute of Aeronautical Engineering Department of Civil Engineering Hyderabad India Aditya Institute of Technology & Management Department of Bs & H Tekkali India Sri Krishna College of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India School of Advanced Computing Alliance University Department of Computer Science and Engineering Bangalore India Erode Sengunthar Engineering College Department of Agricultural Engineering Perundurai India

ISBN: (纸本)9798331508456

关键词： Resource allocation

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A Robust Design of Real-Time Resilient Smile Recognition System using Hybrid Deep Learning Principles

A Robust Design of Real-Time Resilient Smile Recognition Sys...

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International Conference on Communications and Signal Processing

作者： Aswathy K Cherian S. Devipriya Bhushan P. Saoji B. Mallikeswari R. Thiagarajan R. Krishnamoorthy Department Of Computing Technologies SRM Institute Of Science And Technology Kattankulathur Chennai Department of CSE Jerusalem College of Engineering Chennai India Instrumentation Engineering Bharati Vidyapeeth college of Engineering Navimumbai Dept of Computer JusticeBasheer Ahmed Sayeed college for Women Chennai India Department of IT Prathyusha Engineering College Chennai Tamilnadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

We often convey our happiness via smiling or making a happy face. Simple, latent feelings like contentment and joy are brought to the surface in this way. In terms of social communication, this is the most difficult and potent task there is. One function of digital cameras is smile recognition, which prevents taking pictures of people unless they are smiling. There are two distinct stages to smile detection. After identifying a face in a video or picture, it watches for a grin. To be more precise, we may state that a motion detector takes a video or image and breaks it down into frames. It then examines the frameworks, such as the balanced level and flash, for the face area. The camera can tell whether someone is laughing or smiling because it looks for certain facial features—a jutted jaw, narrowed eyes, and an elevated cheekbone among others. The components of computer architecture and software advancements are advancing at a rapid pace, which is leading to a progressive increase in customer needs for electric products. A growing number of people are interested in human computer interaction systems that differ from the conventional user interface, which includes the keyboard and mouse. This article depicts the realtime implementation of deep learning algorithms in computer settings with limited resources. In this study, we provide HLSR, a new kind of deep learning model that combines the XGBoost and Convolutional Neural Network (CNN) algorithms for smile recognition. To get a clear picture of how well the suggested technique works, we cross-validate the HLSR model with the current CNN model.

关键词： Deep learning Face recognition Computational modeling Neural networks Signal processing algorithms Signal processing Software

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Entirety of Quantum Uncertainty and Its Experimental Verification

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Chinese Physics Letters 2021年第7期38卷 11-16页

作者： Jie Xie Li Zhou Aonan Zhang Huichao Xu Man-Hong Yung Ping Xu Nengkun Yu Lijian Zhang National Laboratory of Solid State Microstructures College of Engineering and Applied Sciences and School of PhysicsNanjing UniversityNanjing 210093China Collaborative Innovation Center of Advanced Microstructures Nanjing UniversityNanjing 210093China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China Centre for Quantum Software and Information School of SoftwareFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNSWAustralia

As a foundation of quantum physics,uncertainty relations describe ultimate limit for the measurement uncertainty of incompatible ***,uncertainty relations are formulated by mathematical bounds for a specific *** we present a method for geometrically characterizing uncertainty relations as an entire area of variances of the observables,ranging over all possible input *** find that for the pair of position and momentum operators,Heisenberg's uncertainty principle points exactly to the attainable area of the variances of position and ***,for finite-dimensional systems,we prove that the corresponding area is necessarily semialgebraic;in other words,this set can be represented via finite polynomial equations and inequalities,or any finite union of such *** particular,we give the analytical characterization of the areas of variances of(a)a pair of one-qubit observables and(b)a pair of projective observables for arbitrary dimension,and give the first experimental observation of such areas in a photonic system.

关键词： observable union operators

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Jointly Optimizing Terahertz based Sensing and Communications in Vehicular Networks: A Dynamic Graph Neural Network Approach

arXiv

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arXiv 2024年

作者： Li, Xuefei Chen, Mingzhe Hu, Ye Zhang, Zhilong Liu, Danpu Mao, Shiwen The Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States The Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States

In this paper, the problem of vehicle service mode selection (sensing, communication, or both) and vehicle connections within terahertz (THz) enabled joint sensing and communications over vehicular networks is studied. The considered network consists of several service provider vehicles (SPVs) that can provide: 1) only sensing service, 2) only communication service, and 3) both services, sensing service request vehicles, and communication service request vehicles. Based on the vehicle network topology and their service accessibility, SPVs strategically select service request vehicles to provide sensing, communication, or both services. This problem is formulated as an optimization problem, aiming to maximize the number of successfully served vehicles by jointly determining the service mode of each SPV and its associated vehicles. To solve this problem, we propose a dynamic graph neural network (GNN) model that selects appropriate graph information aggregation functions according to the vehicle network topology, thus extracting more vehicle network information compared to traditional static GNNs that use fixed aggregation functions for different vehicle network topologies. Using the extracted vehicle network information, the service mode of each SPV and its served service request vehicles will be determined. Simulation results show that the proposed dynamic GNN based method can improve the number of successfully served vehicles by up to 17% and 28% compared to a GNN based algorithm with a fixed neural network model and a conventional optimization algorithm without using GNNs. Copyright © 2024, The Authors. All rights reserved.

关键词： Vehicles

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从四方到立方:高性能太阳能电池的钙钛矿相结构演变

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science Bulletin 2023年第2期68卷 141-145,M0003页

作者：魏旗梁昊 Yuki Haruta Makhsud Saidaminov 米启兮 Michael Saliba 崔光磊宁志军 School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China High Performance Computing Department National Supercomputing Center in ShenzhenShenzhen 200441China Department of Chemistry and Department of Electrical&Computer Engineering and Centre for Advanced Materials and Related Technologies(CAMTEC) University of VictoriaVictoria V8P 5C2Canada Institute for Photovoltaics University of StuttgartStuttgart 70569Germany Qingdao Industrial Energy Storage Research Institute Qingdao Institute of Bioenergy and Bioprocess TechnologyChinese Academy of SciencesQingdao 266101China

Crystal structure, which is generally determined by the composition, ionic radius and valence state of constituent elements, plays a significant role in the material’s fundamental properties such as optical, electronic, and mechanical characters. The manipulation of the crystal structure through compositional engineering, and understanding the relationship between crystal structure and properties are essential for the development of materials.

关键词：太阳能电池结构演变钙钛矿相

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Machine Learning-Assisted Protein Structure Prediction: An AI Approach for Biochemical Insights

Machine Learning-Assisted Protein Structure Prediction: An A...

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International Conference on science Technology Engineering and Management (ICONSTEM)

作者： S. Radha Mahendran Akriti Dogra Dinesh Mendhe S. B G Tilak Babu Shriniket Dixit Surya Pratap Singh Department of Bioinformatics Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Department of Oral & Maxillofacial Surgery Manav Rachna Dental College Faridabad Haryana Office of Research Computing Rutgers State University of New Jersey New Brunswick New Jersey Department of ECE Aditya Engineering College Surampalem School of Computer Science Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Multi-Disciplinary Research Unit UPUMS Etawah UP

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Recently, machine learning has changed the field of bioinformatics by making it possible to identify and understand protein structures in ways that were not possible before. This paper gives a thorough examination of a unique way for guessing protein structure. It uses complex machine learning algorithms to give you deep biological information. We use cutting-edge deep learning models along with traditional computer methods to improve the accuracy and usefulness of predicting protein structures. The suggested AI-assisted method uses a variety of protein structures and attention processes to find complex spatial connections and long-range interactions within protein sequences. It does this by using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and CNNs. Using machine learning to predict protein structures can be very useful, and this work shows how it can effectively connect computational biology and experimental research. Our method not only makes predictive modeling better, but it also gives researchers a useful way to learn more about the complex links between protein structure and function as the field grows. The study's results could speed up the process of finding new drugs and help us learn more about how different diseases work at the molecular level.

关键词： Proteins Drugs Deep learning Recurrent neural networks Machine learning algorithms Computational modeling Predictive models

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Machine Learning for Drug Discovery: Predicting Drug-Protein Binding Affinities using Graph Convolutional Networks

Machine Learning for Drug Discovery: Predicting Drug-Protein...

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Recent Trends in computer science and Technology (ICRTCST), International Conference on

作者： S. RadhaMahendran Akriti Dogra Dinesh Mendhe S. B G Tilak Babu Shriniket Dixit Surya Pratap Singh Department of Bioinformatics School of Life Sciences Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Department of Oral & Maxillofacial Surgery Manav Rachna Dental College Faridabad Haryana Computer and Information Research Scientist Office of Research Computing Rutgers State University of New Jersey New Brunswick New Jersey Department of ECE Aditya Engineering College Surampalem School of Computer Science Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Multi-Disciplinary Research Unit UPUMS Etawah UP

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

Determining the potential interactions between ligands and proteins is a crucial first step in developing new drugs. Graph BAR, our novel prediction model, makes accurate guesses about the strength of ligand-protein bindings by means of a graph convolutional neural network. When compared to graph convolutional neural networks, standard convolutional neural network models are more resource and time intensive to manage. The interaction between proteins and ligands is shown via a network consisting of many adjacency meshes. The atomic distances dictate the alterations to these matrices. Additionally, the atomic properties can be shown in a feature matrix. Using the PDBbind datasets, we verified the functionality of graph convolution and evaluated Graph BAR's ability to forecast the binding strengths of proteins and ligands. In order to maximize The model's performance was improved by utilizing ways for adding additional input, which took advantage of the processing capability of graph convolutional neural networks. Graph BAR's predictions appear to have potential for use in drug creation due to its great accuracy and quickness. The results of our research suggest that Graph Convolutional Networks have the potential to expedite the discovery of new medications by properly predicting the interactions between pharmaceuticals and proteins.

关键词： Proteins Drugs computer science Accuracy Convolution Machine learning Predictive models

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High resolution synthetic residential energy use profiles for the United States

arXiv

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arXiv 2022年

作者： Thorve, Swapna Baek, Young Yun Swarup, Samarth Mortveit, Henning Marathe, Achla Vullikanti, Anil Marathe, Madhav Network Systems Science and Advanced Computing Biocomplexity Institute and Initiative University of Virginia United States Engineering Systems and Environment University of Virginia United States Department of Computer Science University of Virginia United States

Efficient energy consumption is crucial for achieving sustainable energy goals in the era of climate change and grid modernization. Thus, it is vital to understand how energy is consumed at finer resolutions such as household in order to plan demand-response events or analyze impacts of weather, electricity prices, electric vehicles, solar, and occupancy schedules on energy consumption. However, availability and access to detailed energy-use data, which would enable detailed studies, has been rare. In this paper, we release a unique, large-scale, digital-twin of residential energy-use dataset for the residential sector across the contiguous United States covering millions of households. The data comprise of hourly energy use profiles for synthetic households, disaggregated into Thermostatically Controlled Loads (TCL) and appliance use. The underlying framework is constructed using a bottom-up approach. Diverse open-source surveys and first principles models are used for end-use modeling. Extensive validation of the synthetic dataset has been conducted through comparisons with reported energy-use data. We present a detailed, open, high resolution, residential energy-use dataset for the United States. © 2022, CC BY.

关键词： Energy utilization

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