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AstroLLaMA-Chat: Scaling AstroLLaMA with Conversational and Diverse Datasets

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arXiv 2024年

作者： Perkowski, Ernest Pan, Rui Nguyen, Tuan Dung Ting, Yuan-Sen Kruk, Sandor Zhang, Tong O’Neill, Charlie Jablonska, Maja Sun, Zechang Smith, Michael J. Liu, Huiling Schawinski, Kevin Iyer, Kartheik Ciucă, Ioana Camino Bajo del Castillo s/n Villanueva de la Cañada Madrid28692 Spain Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA19104 United States Research School of Astronomy & Astrophysics Australian National University Cotter Rd. WestonACT2611 Australia School of Computing Australian National University ActonACT2601 Australia Department of Astronomy The Ohio State University ColumbusOH43210 United States The Ohio State University ColumbusOH43210 United States Department of Computer Science University of Illinois Urbana-Champaign United States Mathematical Science Institute Australian National University ActonACT2601 Australia Department of Astronomy MongManWai Building Tsinghua University Beijing100084 China Aspia Space Tremough Innovation Centre PenrynTR10 9TA United Kingdom Department of Modern Physics University of Science and Technology of China Anhui Hefei230026 China Modulos Technoparkstrasse 1 Zurich8005 Switzerland Columbia Astrophysics Laboratory Columbia University New YorkNY10027 United States

We explore the potential of enhancing LLM performance in astronomy-focused question-answering through targeted, continual pre-training. By employing a compact 7B-parameter LLaMA-2 model and focusing exclusively on a curated set of astronomy corpora—comprising abstracts, introductions, and conclusions—we achieve notable improvements in specialized topic comprehension. While general LLMs like GPT-4 excel in broader question-answering scenarios due to superior reasoning capabilities, our findings suggest that continual pre-training with limited resources can still enhance model performance on specialized topics. Additionally, we present an extension of AstroLLaMA: the fine-tuning of the 7B LLaMA model on a domain-specific conversational dataset, culminating in the release of the chat-enabled AstroLLaMA for community use. Comprehensive quantitative benchmarking is currently in progress and will be detailed in an upcoming full paper. The model, AstroLLaMA-Chat, is now available at https://***/universeTBD, providing the first open-source conversational AI tool tailored for the astronomy community. Copyright © 2024, The Authors. All rights reserved.

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Comparative study of automatic urban building extraction methods from remote sensing data 1st

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Comparative study of automatic urban building extraction met...

1st International Conference on Artificial Intelligence and Cognitive Computing, AICC 2018

作者： Pendyala, V. S. S. N. Gopala Krishna Kalluri, Hemantha Kumar Venkataraman, V. Raghu Rao, C.V. National Remote Sensing Centre HyderabadTelangana India Department of Computer Science and Engineering Vignan’s Foundation for Science Technology and Research VadlamudiAndhra Pradesh India

ISBN: (纸本)9789811315794

Building foot prints and building count information in urban areas are very much essential for planning and monitoring developmental activities, efficient natural resource utilization, and provision of civic facilities by governments. Remote sensing data such as satellite/aerial imagery in association with digital elevation model is widely used for automatic extraction of building information. Many researchers have developed different methods for maximizing the detection percentage with minimum errors. A comparative study of different methods available in the literature is presented in this paper by analyzing the primary data sets, derived data sets, and their usage in the automated and semiautomated extraction methods. It is found that the success of the method for automatic building detection in urban areas primarily depends on using combination of high-resolution image data with digital elevation model. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Buildings

Machine learning-based reconfigurable intelligent surface-aided MIMO systems

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arXiv 2021年

作者： Nguyen, Nhan Thanh Nguyen, Ly V. Huynh-The, Thien Nguyen, Duy H.N. Swindlehurst, A. Lee Juntti, Markku Centre for Wireless Communications University of Oulu P.O.Box 4500 FI-90014 Finland Computational Science Research Center San Diego State University CA United States ICT Convergence Research Center Kumoh National Institute of Technology Gyeongsangbuk-do39177 Korea Republic of Department of Electrical and Computer Engineering San Diego State University CA United States Department of Electrical Engineering and Computer Science University of California IrvineCA United States

Reconfigurable intelligent surface (RIS) technology has recently emerged as a spectral- and cost-efficient approach for wireless communications systems. However, existing hand-engineered schemes for passive beamforming design and optimization of RIS, such as the alternating optimization (AO) approaches, require a high computational complexity, especially for multiple-input-multiple-output (MIMO) systems. To overcome this challenge, we propose a low-complexity unsupervised learning scheme, referred to as learning-phase-shift neural network (LPSNet), to efficiently find the solution to the spectral efficiency maximization problem in RIS-aided MIMO systems. In particular, the proposed LPSNet has an optimized input structure and requires a small number of layers and nodes to produce efficient phase shifts for the RIS. Simulation results for a 16 × 2 MIMO system assisted by an RIS with 40 elements show that the LPSNet achieves 97.25% of the SE provided by the AO counterpart with more than a 95% reduction in complexity. © 2021, CC BY.

关键词： MIMO systems

On demand single photon generation and coherent control of excitons from resonantly driven nanowire quantum dots

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arXiv 2024年

作者： Gao, Jun Krishna, Govind Yeung, Edith Yu, Lingxi Gangopadhyay, Sayan Chan, Kai-Sum Huang, Chiao-Tzu Descamps, Thomas Reimer, Michael E. Poole, Philip J. Dalacu, Dan Zwiller, Val Elshaari, Ali W. KTH Royal Institute of Technology Department of Applied Physics Albanova University Centre Roslagstullbacken 21 Stockholm106 91 Sweden National Research Council of Canada OttawaONK1A 0R6 Canada University of Ottawa OttawaONK1N 6N5 Canada Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada Quantum Bridge Technologies Inc. 108 College Street TorontoM5G 0C6 Canada Department of Electrophysics National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute for Quantum Computing Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Coherent control of single photon sources is a key requirement for the advancement of photonic quantum technologies. Among them, nanowire-based quantum dot sources are popular due to their potential for on-chip hybrid integration. Here we demonstrate on-demand single-photon generation (g(2)(0)(X∗) = 0.078 and g(2)(0)(X) = 0.03) from resonantly excited InAsP/InP nanowire quantum dots and observe Rabi oscillations in the dot emission, indicating successful coherent manipulation of the excitonic states in the nanowire. We also measure a low emission time jitter for resonant excitation as compared to above-band excitation. This work addresses the long-standing challenge of resonantly exciting nanowire-quantum dots. It paves the way for hybrid quantum photonic integration, enabling spin-photon entanglement and matter memories on-chip. © 2024, CC BY.

关键词： Semiconductor quantum dots

Erratum: “Reducing solid conduction in electrothermally driven MEMS Pirani gauge using integrated polymeric thin film” [Appl. Phys. Lett. 120(8), 084101 (2022)]

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Applied Physics Letters 2023年第14期123卷

作者： Manu Garg Dhairya S. Arya Sushil Kumar Yi Chiu Pushpapraj Singh Centre for Applied Research in Electronics (CARE) Indian Institute of Technology Delhi New Delhi 110016 India International College of Semiconductor Technology (ICST) National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan

This article1 was originally published with an error in the affiliation information for the first and the fourth author. The corrected affiliations should appea

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An Emerging Clone (ST2096) of Klebsiella pneumoniae Clonal Complex 14 With Enhanced Virulence Causes an Outbreak in Saudi Arabia

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Journal of Infection and Public Health 2020年第2期13卷 363-364页

作者： S. Hala C. Paul Antony M. Alshehri A. Alsaedi O.A. Al Thaqafi G. Al-Ahmadi M. Kaaki M. Alazmi T.B. Alhaj-Hussein M. Yasen M.H. Zowawi A. Al-Amri A. Pain King Abdullah University Of Science And Technology Ministry of National Guard Health Affairs King Abdullah International Medical Research Centre College of Computer Science and Engineering University of Hail WHO Collaborating Centre for Infection Prevention and Control and GCC Center for Infection Control The University of Queensland UQ Centre for Clinical Research

Democratizing Uncertainty Quantification

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arXiv 2024年

作者： Seelinger, Linus Reinarz, Anne Lykkegaard, Mikkel B. Akers, Robert Alghamdi, Amal M.A. Aristoff, David Bangerth, Wolfgang Bénézech, Jean Diez, Matteo Frey, Kurt Jakeman, John D. Jørgensen, Jakob S. Kim, Ki-Tae Kent, Benjamin M. Martinelli, Massimiliano Parno, Matthew Pellegrini, Riccardo Petra, Noemi Riis, Nicolai A.B. Rosenfeld, Katherine Serani, Andrea Tamellini, Lorenzo Villa, Umberto Dodwell, Tim J. Scheichl, Robert Scientific Computing Center Karlsruhe Institute of Technology Karlsruhe Germany Department of Computer Science Durham University Durham United Kingdom digiLab Exeter United Kingdom UK Atomic Energy Authority Oxford United Kingdom Lyngby Denmark Department of Mathematics Colorado State University Fort CollinsCO United States Department of Mathematics Department of Geosciences Colorado State University Fort CollinsCO United States Centre for Integrated Materials Processes and Structures Department of Mechanical Engineering University of Bath Bath United Kingdom National Research Council Institute of Marine Engineering Rome Italy Institute for Disease Modeling Global Health Division Bill & Melinda Gates Foundation United States Optimization and Uncertainty Quantification Sandia National Laboratories AlbuquerqueNM United States University of California Merced United States National Research Council-Institute for Applied Mathematics and Information Technologies "E. Magenes" Pavia Italy Solea Energy ThetfordVT United States Copenhagen Imaging ApS Herlev Denmark The University of Texas Austin United States Heidelberg University Germany

Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In this paper, we introduce UM-Bridge (the UQ and Modeling Bridge), a high-level abstraction and software protocol that facilitates universal interoperability of UQ software with simulation codes. It breaks down the technical complexity of advanced UQ applications and enables separation of concerns between experts. UM-Bridge democratizes UQ by allowing effective interdisciplinary collaboration, accelerating the development of advanced UQ methods, and making it easy to perform UQ analyses from prototype to High Performance Computing (HPC) scale. In addition, we present a library of ready-to-run UQ benchmark problems, all easily accessible through UM-Bridge. These benchmarks support UQ methodology research, enabling reproducible performance comparisons. We demonstrate UM-Bridge with several scientific applications, harnessing HPC resources even using UQ codes not designed with HPC support. © 2024, CC BY-NC-ND.

关键词： Application programs

Enhancing sustainability in manufacturing: A case study on reducing changeover time through single minute exchange of dies and risk analysis

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AIP Conference Proceedings 2024年第1期3050卷

作者： Harnam Singh Farwaha Prem Singh Mukesh Kumar Nishant Ranjan Harpreet Kaur Department of Mechanical & Production Engineering Guru Nanak Dev Engineering College Ludhiana INDIA Industrial & Production Eng. Dr. B.R. Ambedkar National Institute of Technology Jalandhar INDIA Department of Mechanical Engineering & University Centre for Research & Development Chandigarh University Mohali INDIA Department of Computer Science Engineering Guru Nanak Dev Polytechnic College Ludhiana INDIA

The method that is most frequently utilised to speed up changeover is known as Single Minute Exchange of Dies (SMED). SMED helps shorten lead times and boost system flexibility so that the unit may react rapidly to changes in market trends or customer requests. To achieve positive outcomes, SMED must be implemented effectively. Instead of shifting internal operations to external activities, more emphasis needs to be placed on streamlining and simplifying existing activities. In the food processing business, where cleaning various parts was the primary task during the changeover, this study concentrated on the application of SMED. Before the deployment of SMED, operator-specific time studies were carried out at two separate stations (Station A & Station B), and observed actions were critically examined while using the ECRS tool. Failure mode effect analysis (FMEA), which prioritises potential risks that could lengthen changeover times, was incorporated into the procedure. Based on risk analysis, twelve root causes were prioritised, and an action plan was created while considering risk analysis trends. To benefit, rapid fixes and other changes were made when necessary. At stations A and B, the results of a post-SMED time analysis showed a reduction in changeover time of 35.54% and 49.94%, respectively. The production unit’s overall equipment effectiveness (OEE) improved to 0.89.

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Findings of the sentiment analysis of Dravidian languages in code-mixed text

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arXiv 2021年

作者： Chakravarthi, Bharathi Raja Priyadharshini, Ruba Thavareesan, Sajeetha Chinnappa, Dhivya Thenmozhi, Durairaj Sherly, Elizabeth McCrae, John P. Hande, Adeep Ponnusamy, Rahul Banerjee, Shubhanker Vasantharajan, Charangan Insight SFI Research Centre for Data Analytics Data Science Institute National University of Ireland Galway ULTRA Arts and Science College Tamil Nadu Madurai India Eastern University Sri Lanka Thomson Reuters United States Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu India Indian Institute of Information Technology and Management Kerala India Indian Institute of Information Technology Tiruchirappalli ADAPT Centre Data Science Institute National University Of Ireland Galway Department of Computer Science and Engineering University of Moratuwa Sri Lanka

We present the results of the Dravidian-CodeMix shared task1 held at FIRE 2021, a track on sentiment analysis for Dravidian Languages in Code-Mixed Text. We describe the task, its organization, and the submitted systems. This shared task is the continuation of last year’s Dravidian-CodeMix shared task2 held at FIRE 2020. This year’s tasks included code-mixing at the intra-token and inter-token levels. Additionally, apart from Tamil and Malayalam, Kannada was also introduced. We received 22 systems for Tamil-English, 15 systems for Malayalam-English, and 15 for Kannada-English. The top system for Tamil-English, Malayalam-English and Kannada-English scored weighted average F1-score of 0.711, 0.804, and 0.630, respectively. In summary, the quality and quantity of the submission show that there is great interest in Dravidian languages in code-mixed setting and state of the art in this domain still needs more improvement. © 2021, CC BY.

关键词： Sentiment analysis