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NaFM: Pre-training a Foundation Model for Small-Molecule Natural Products

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arXiv 2025年

作者： Ding, Yuheng Wang, Yusong Qiang, Bo Yu, Jie Li, Qi Zhou, Yiran Liu, Zhenmin State Key Laboratory of Natural and Biomimetic Drugs School of Pharmaceutical Sciences Peking University Beijing100191 China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Paul G. Allen School of Computer Science & Engineering University of Washington SeattleWA98105 United States

Natural products, as metabolites from microorganisms, animals, or plants, exhibit diverse biological activities, making them crucial for drug discovery. Nowadays, existing deep learning methods for natural products research primarily rely on supervised learning approaches designed for specific downstream tasks. However, such one-model-for-a-task paradigm often lacks generalizability and leaves significant room for performance improvement. Additionally, existing molecular characterization methods are not well-suited for the unique tasks associated with natural products. To address these limitations, we have pre-trained a foundation model for natural products based on their unique properties. Our approach employs a novel pretraining strategy that is especially tailored to natural products. By incorporating contrastive learning and masked graph learning objectives, we emphasize evolutional information from molecular scaffolds while capturing side-chain information. Our framework achieves state-of-the-art (SOTA) results in various downstream tasks related to natural product mining and drug discovery. We first compare taxonomy classification with synthesized molecule-focused baselines to demonstrate that current models are inadequate for understanding natural synthesis. Furthermore, by diving into a fine-grained analysis at both the gene and microbial levels, NaFM demonstrates the ability to capture evolutionary information. Eventually, our method is experimented with virtual screening, illustrating informative natural product representations that can lead to more effective identification of potential drug candidates. © 2025, CC BY.

关键词： Metabolites

Long Live O-PCMs: Understanding Reliability Challenges of Optical Phase Change Materials

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Long Live O-PCMs: Understanding Reliability Challenges of Op...

Conference on Lasers and Electro-Optics (CLEO)

作者： Cosmin-Constantin Popescu Brian Mills Louis Martin Monier Luigi Ranno Yifei Zhang Qingyang Du Carlos Ríos Steven Vitale Christopher Roberts Paul Miller Vladimir Liberman Hyun Jung Kim Kiumars Aryana Myungkoo Kang Kathleen Richardson Dennis Callahan Sarah Geiger Tian Gu Juejun Hu Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge MA United States Charles Stark Draper Laboratory Cambridge MA United States Research Center for Intelligent Optoelectronic Computing Zhejiang Lab Hangzhou China Department of Materials Science and Engineering University of Maryland College Park MD United States Lincoln Laboratory Massachusetts Institute of Technology Lexington MA United States NASA Langley Research Center Hampton VA United States CREOL The College of Optics and Photonics University of Central Florida Orlando FL United States

The endurance of optical phase change material-based devices for integrated and free-space applications is explored and improvements are proposed based on limiting PCM dewetting, protective layer delamination, and ele... 详细信息

ISBN: (纸本)9781957171258

关键词： Analytical techniques Pulse generation Raman spectroscopy Refractive index Thin films Ultrafast lasers

TourLLM: Enhancing LLMs with Tourism Knowledge

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arXiv 2024年

作者： Wei, Qikai Yang, Mingzhi Wang, Jinqiang Mao, Wenwei Xu, Jiabo Ning, Huansheng School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Xinjiang Coal Resources Green Mining Ministry of Education Xinjiang Institute of Engineering Urumqi830023 China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083 China Guangxi Tourism Development One-Click Tour Digital Cultural Tourism Industry Co. Ltd. Guangxi530012 China

Recently, large language models (LLMs) have demonstrated their effectiveness in various natural language processing (NLP) tasks. However, the lack of tourism knowledge limits the performance of LLMs in tourist attraction presentations and travel planning. To address this challenge, we constructed a supervised fine-tuning dataset for the culture and tourism domain, named Cultour. This dataset consists of three parts: tourism knowledge base QA data, travelogues data, and tourism diversity QA data. Additionally, we propose TourLLM, a Qwen-based model supervised fine-tuned with Cultour, to improve the quality of the information provided about attractions and travel planning. To evaluate the performance of TourLLM, we employed both automatic and human evaluation, and we proposed a human evaluation criterion named CRA (Consistency, Readability, Availability). The experimental results demonstrate the effectiveness of the responses generated by the TourLLM. Our proposed Cultour is accessible at https://***/mrweiqk/Cultour. Copyright © 2024, The Authors. All rights reserved.

关键词： Natural language processing systems

ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals

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arXiv 2024年

作者： Chuang, Chun-Hsiang Chang, Kong-Yi Huang, Chih-Sheng Bessas, Anne-Mei The Research Center for Education and Mind Sciences The Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan The Product Development Division ELAN Microelectronics Corporation Hsinchu Taiwan The College of Artificial Intelligence National Yang Ming Chiao Tung University Hsinchu Taiwan The College of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan The Research Center for Education and Mind Sciences National Tsing Hua University Hsinchu Taiwan

Artifact removal in electroencephalography (EEG) is a longstanding challenge that significantly impacts neuroscientific analysis and brain-computer interface (BCI) performance. Tackling this problem demands advanced algorithms, extensive noisy-clean training data, and thorough evaluation strategies. This study presents the Artifact Removal Transformer (ART), an innovative EEG denoising model employing transformer architecture to adeptly capture the transient millisecond-scale dynamics characteristic of EEG signals. Our approach offers a holistic, end-to-end denoising solution for diverse artifact types in multichannel EEG data. We enhanced the generation of noisy-clean EEG data pairs using an independent component analysis, thus fortifying the training scenarios critical for effective supervised learning. We performed comprehensive validations using a wide range of open datasets from various BCI applications, employing metrics like mean squared error and signal-to-noise ratio, as well as sophisticated techniques such as source localization and EEG component classification. Our evaluations confirm that ART surpasses other deep-learning-based artifact removal methods, setting a new benchmark in EEG signal processing. This advancement not only boosts the accuracy and reliability of artifact removal but also promises to catalyze further innovations in the field, facilitating the study of brain dynamics in naturalistic environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

A Family of Subfield Hyperelliptic Curves for Use in Cryptography 22nd

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A Family of Subfield Hyperelliptic Curves for Use in Cryptog...

22nd International Conference on Information and Communications Security, ICICS 2020

作者： Ganguly, Anindya Das, Abhijit Chowdhury, Dipanwita Roy Mehta, Deval Crypto Research Lab Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Space Applications Center Ahmedabad Indian Space Research Organization Bengaluru India

ISBN: (纸本)9783030610777

This paper proposes a family of hyperelliptic curves of genus two for public-key cryptographic primitives. Being subfield curves, the members of this family are easy to generate. Although slightly slower than elliptic curves at the same security level, hyperelliptic curves of our family exhibit performance comparable to widely used hyperelliptic curves over prime fields. © 2020, Springer Nature Switzerland AG.

关键词： Geometry

Electrically reconfigurable phase-change transmissive metasurface

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arXiv 2023年

作者： Popescu, Cosmin Constantin Aryana, Kiumars Garud, Parth Dao, Khoi Phuong Vitale, Steven Liberman, Vladimir Bae, Hyung-Bin Lee, Tae-Woo Kang, Myungkoo Richardson, Kathleen A. Ríos Ocampo, Carlos A. Zhang, Yifei Gu, Tian Hu, Juejun Kim, Hyun Jung Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States NASA Langley Research Center HamptonVA23666 United States Lincoln Laboratory Massachusetts Institute of Technology LexingtonMA02421 United States KAIST Analysis Center Korea Advanced Institute of Science and Technology Yuseong-gu Daejeon34141 Korea Republic of CREOL The College of Optics & Photonics University of Central Florida OrlandoFL32816 United States University of Maryland Department of Materials Science & Engineering College ParkMD United States Materials Research Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States

Programmable and reconfigurable optics hold significant potential for transforming a broad spectrum of applications, spanning space explorations to biomedical imaging, gas sensing, and optical cloaking. The ability to adjust the optical properties of components like filters, lenses, and beam steering devices could result in dramatic reductions in size, weight, and power consumption in future optoelectronic devices. Among the potential candidates for reconfigurable optics, chalcogenide-based phase change materials (PCMs) offer great promise due to their non-volatile and analogue switching characteristics. Although PCM have found widespread use in electronic data storage, these memory devices are deeply sub-micron-sized. To incorporate phase change materials into free-space optical components, it is essential to scale them up to beyond several hundreds of microns while maintaining reliable switching characteristics. This study demonstrated a non-mechanical, non-volatile transmissive filter based on low-loss PCMs with a 200 µm×200 µm switching area. The device/metafilter can be consistently switched between low- and high-transmission states using electrical pulses with a switching contrast ratio of 5.5 dB. The device was reversibly switched for 1250 cycles before accelerated degradation took place. The work represents an important step toward realizing free-space reconfigurable optics based on PCMs. © 2023, CC BY.

关键词： Photonic devices

Single-Photon Emitters in Aluminum Nitride by Zr ion Implantation

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Single-Photon Emitters in Aluminum Nitride by Zr ion Implant...

Conference on Lasers and Electro-Optics (CLEO)

作者： Alexander Senichev Zachariah O. Martin Yongqiang Wang Han Htoon Alexei S. Lagutchev Alexandra Boltasseva Vladimir M. Shalaev School of Electrical and Computer Engineering Birck Nanotechnology Center and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette IN USA U.S. Department of Energy (DOE) National Quantum Information Science Research Center Quantum Science Center (QSC) Oak Ridge TN USA Materials Science and Technology Division Los Alamos National Laboratory Ion Beam Materials Laboratory Los Alamos NM USA Center for Integrated Nanotechnologies Materials Physics and Applications Division Los Alamos National Laboratory Los Alamos NM USA

We report on the generation of single-photon emitters in aluminum nitride films through Zr-ion implantation, which was predicted to form optically addressable spin defects. We studied implantation conditions, post-imp... 详细信息

ISBN: (纸本)9781957171258

关键词： Chemical vapor deposition Emission Fluorescence Ion beams Ions Photoluminescence

ERetinex: Event Camera Meets Retinex Theory for Low-Light Image Enhancement

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arXiv 2025年

作者： Guo, Xuejian Tian, Zhiqiang Wang, Yuehang Li, Siqi Jiang, Yu Du, Shaoyi Gao, Yue the School of Software Engineering Xi’an Jiaotong University Xi’an710049 China the College of Computer Science and Technology Jilin University Changchun130012 China the National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China BNRist THUIBCS KLISS BLBCI School of Software Tsinghua University Beijing100084 China

Low-light image enhancement aims to restore the under-exposure image captured in dark scenarios. Under such scenarios, traditional frame-based cameras may fail to capture the structure and color information due to the exposure time limitation. Event cameras are bio-inspired vision sensors that respond to pixel-wise brightness changes asynchronously. Event cameras’ high dynamic range is pivotal for visual perception in extreme low-light scenarios, surpassing traditional cameras and enabling applications in challenging dark environments. In this paper, inspired by the success of the retinex theory for traditional frame-based low-light image restoration, we introduce the first methods that combine the retinex theory with event cameras and propose a novel retinex-based low-light image restoration framework named ERetinex. Among our contributions, the first is developing a new approach that leverages the high temporal resolution data from event cameras with traditional image information to estimate scene illumination accurately. This method outperforms traditional image-only techniques, especially in low-light environments, by providing more precise lighting information. Additionally, we propose an effective fusion strategy that combines the high dynamic range data from event cameras with the color information of traditional images to enhance image quality. Through this fusion, we can generate clearer and more detail-rich images, maintaining the integrity of visual information even under extreme lighting conditions. The experimental results indicate that our proposed method outperforms state-of-the-art (SOTA) methods, achieving a gain of 1.0613 dB in PSNR while reducing FLOPS by 84.28%. The code is available at https://***/lodew920/ERetinex. © 2025, CC BY.

关键词： Cameras

Electro-optic tuning of a single-frequencyultranarrow linewidth microdisk laser

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Advanced Photonics 2022年第3期4卷 105-113页

作者： Jintian Lin Saeed Farajollahi Zhiwei Fang Ni Yao Renhong Gao Jianglin Guan Li Deng Tao Lu Min Wang Haisu Zhang Wei Fang Lingling Qiao Ya Cheng Chinese Academy of Sciences(CAS) Shanghai Institute of Optics and Fine Mechanics(SIOM)State Key Laboratory of High Field Laser Physics and CAS Center for Excellence in Ultra-Intense Laser ScienceShanghaiChina University of Chinese Academy of Sciences Center of Materials Science and Optoelectronics EngineeringBeijingChina University of Victoria Department of Electrical and Computer EngineeringVictoriaBritish ColumbiaCanada East China Normal University School of Physics and Electronic ScienceXXL—The Extreme Optoelectromechanics LaboratoryShanghaiChina Research Center for Intelligent Sensing Zhejiang LabHangzhouChina Zhejiang University College of Optical Science and EngineeringThe Interdisciplinary Center for Quantum InformationState Key Laboratory of Modern Optical InstrumentationHangzhouChina East China Normal University State Key Laboratory of Precision SpectroscopyShanghaiChina Jiaxing Institute of Zhejiang University Intelligent Optics&Photonics Research CenterJiaxingChina Shanxi University Collaborative Innovation Center of Extreme OpticsTaiyuanChina Shandong Normal University Collaborative Innovation Center of Light Manipulations and ApplicationsJinanChina Shanghai Research Center for Quantum Sciences Shang

Single-frequency ultranarrow linewidth on-chip microlasers with a fast wavelength tunability play a game-changing role in a broad spectrum of applications ranging from coherent communication,light detection and ranging,to metrology and *** and fabrication of such light sources remain a challenge due to the difficulties in making a laser cavity that has an ultrahigh optical quality(Q)factor and supports only a single lasing frequency ***,we demonstrate a unique single-frequency ultranarrow linewidth lasing mechanism on an erbium ion-doped lithium niobate(LN)microdisk through simultaneous excitation of high-Q polygon modes at both pump and laser *** the polygon modes are sparse within the optical gain bandwidth compared with the whispering gallery mode counterpart,while their Q factors(above 10 million)are even higher due to the significantly reduced scattering on their propagation paths,single-frequency lasing with a linewidth as narrow as 322 Hz is *** measured linewidth is three orders of magnitude narrower than the previous record in on-chip LN ***,enabled by the strong linear electro-optic effect of LN,real-time electro-optical tuning of the microlaser with a high tuning efficiency of∼50 pm∕100 V is demonstrated.

关键词： lasers lithium niobate microcavities integrated opti