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DATA AUGMENTATION FOR END-TO-END CODE-SWITCHING SPEECH RECOGNITION

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arXiv 2020年

作者： Du, Chenpeng Li, Hao Lu, Yizhou Wang, Lan Qian, Yanmin MoE Key Lab of Artificial Intelligence SpeechLab Department of Computer Science and Engineering AI Institute Shanghai Jiao Tong University Shanghai China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

Training a code-switching end-to-end automatic speech recognition (ASR) model normally requires a large amount of data, while code-switching data is often limited. In this paper, three novel approaches are proposed for code-switching data augmentation. Specifically, they are audio splicing with the existing code-switching data, and TTS with new code-switching texts generated by word translation or word insertion. Our experiments on 200 hours Mandarin-English code-switching dataset show that all the three proposed approaches yield significant improvements on code-switching ASR individually. Moreover, all the proposed approaches can be combined with recent popular SpecAugment, and an addition gain can be obtained. WER is significantly reduced by relative 24.0% compared to the system without any data augmentation, and still relative 13.0% gain compared to the system with only SpecAugment. Copyright © 2020, The Authors. All rights reserved.

关键词： Speech recognition

Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

Real-time Semantic Segmentation via Spatial-detail Guided Context Propagation

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arXiv 2020年

作者： Hao, Shijie Zhou, Yuan Guo, Yanrong Hong, Richang Cheng, Jun Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Chinese University of Hong Kong Hong Kong

Nowadays, vision-based computing tasks play an important role in various real-world applications. However, many vision computing tasks, e.g. semantic segmentation, are usually computationally expensive, posing a challenge to the computing systems that are resource-constrained but require fast response speed. Therefore, it is valuable to develop accurate and real-time vision processing models that only require limited computational resources. To this end, we propose the Spatial-detail Guided Context Propagation Network (SGCPNet) for achieving real-time semantic segmentation. In SGCPNet, we propose the strategy of spatial-detail guided context propagation. It uses the spatial details of shallow layers to guide the propagation of the low-resolution global contexts, in which the lost spatial information can be effectively reconstructed. In this way, the need for maintaining high-resolution features along the network is freed, therefore largely improving the model efficiency. On the other hand, due to the effective reconstruction of spatial details, the segmentation accuracy can be still preserved. In the experiments, we validate the effectiveness and efficiency of the proposed SGCPNet model. On the Citysacpes dataset, for example, our SGCPNet achieves 69.5% mIoU segmentation accuracy, while its speed reaches 178.5 FPS on 768×1536 images on a GeForce GTX 1080 Ti GPU card. In addition, SGCPNet is very lightweight and only contains 0.61 M parameters. The code will be released at https://***/zhouyuan888888/SGCPNet. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

Quantum Metamaterial for Broadband Detection of Single Microwave Photons

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Physical Review Applied 2021年第3期15卷 034074-034074页

作者： Arne L. Grimsmo Baptiste Royer John Mark Kreikebaum Yufeng Ye Kevin O’Brien Irfan Siddiqi Alexandre Blais Institut quantique and Départment de Physique Université de Sherbrooke Sherbrooke Québec J1K 2R1 Canada Centre for Engineered Quantum Systems School of Physics The University of Sydney Sydney Australia Department of Physics Yale University New Haven Connecticut 06520 USA Materials Science Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Quantum Nanoelectronics Laboratory Department of Physics University of California Berkeley California 94720 USA Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Computational Research Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Canadian Institute for Advanced Research Toronto Canada

Detecting traveling photons is an essential primitive for many quantum-information processing tasks. We introduce a single-photon detector design operating in the microwave domain, based on a weakly nonlinear metamaterial where the nonlinearity is provided by a large number of Josephson junctions. The combination of weak nonlinearity and large spatial extent circumvents well-known obstacles limiting approaches based on a localized Kerr medium. Using numerical many-body simulations we show that the single-photon detection fidelity increases with the length of the metamaterial to approach one at experimentally realistic lengths. A remarkable feature of the detector is that the metamaterial approach allows for a large detection bandwidth. The detector is nondestructive and the photon population wavepacket is minimally disturbed by the detection. This detector design offers promising possibilities for quantum information processing, quantum optics and metrology in the microwave frequency domain.

关键词： Josephson effect Quantum information architectures & platforms Quantum information with solid state qubits Quantum sensing Metamaterials Optical sources & detectors Superconducting devices Microwave techniques

IRIS:A method for predicting in vivo RNA secondary structures using PARIS data

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Quantitative Biology 2020年第4期8卷 369-381页

作者： Jianyu Zhou Pan Li Wanwen Zeng Wenxiu Ma Zhipeng Lu Rui Jiang Qiangfeng Cliff Zhang Tao Jiang Bioinformatics Division BNRISTTsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China MOE Key Laboratory of Bioinformatics Beijing Advanced Innovation Center for Structural BiologyCenter for Synthetic and Systems BiologyTsinghua-Peking Center for Life SciencesSchool of Life SciencesTsinghua UniversityBeijing 100084China College of Software Nankai UniversityTianjin 300071China Department of Statistics University of CaliforniaRiversideCA 92521USA Department of Pharmacology and Pharmaceutical Sciences University of Southern CaliforniaCA 90089USA Department of Automation Tsinghua UniversityBeijing 100084China Department of Computer Science and Engineering University of CaliforniaRiversideCA 92521USA

Background:RNA secondary structures play a pivotal role in posttranscriptional regulation and the functions of non-coding RNAs,yet in vivo RNA secondary structures remain ***(Psoralen Analysis of RNA Interactions and Structures)is a recently developed high-throughput sequencing-based approach that enables direct capture of RNA duplex structures in ***,the existence of incompatible,fuzzy pairing information obstructs the integration of PARIS data with the existing tools for reconstructing RNA secondary structure models at the single-base ***:We introduce IRIS,a method for predicting RNA secondary structure ensembles based on PARIS *** generates a large set of candidate RNA secondary structure models under the guidance of redistributed PARIS reads and then uses a Bayesian model to identify the optimal ensemble,according to both thermodynamic principles and PARIS ***:The predicted RNA structure ensembles by IRIS have been verified based on evolutionary conservation information and consistency with other experimental RNA structural *** is implemented in Python and freely available at http://***:IRIS capitalizes upon PARIS data to improve the prediction of in vivo RNA secondary structure *** expect that IRIS will enhance the application of the PARIS technology and shed more insight on in vivo RNA secondary structures.

关键词： RNA secondary structure PARIS data in vivo structure ensembles incompatible reads

Perspectives for self-driving labs in synthetic biology

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arXiv 2022年

作者： Martin, Hector Garcia Radivojevic, Tijana Zucker, Jeremy Bouchard, Kristofer Sustarich, Jess Peisert, Sean Arnold, Dan Hillson, Nathan Babnigg, Gyorgy Marti, Jose Manuel Mungall, Christopher J. Beckham, Gregg T. Waldburger, Lucas Carothers, James Sundaram, ShivShankar Agarwal, Deb Simmons, Blake A. Backman, Tyler Banerjee, Deepanwita Tanjore, Deepti Ramakrishnan, Lavanya Singh, Anup Lawrence Berkeley National Laboratory Biological Systems and Engineering Division BerkeleyCA United States Department of Energy Agile BioFoundry EmeryvilleCA United States Joint BioEnergy Institute EmeryvilleCA United States BCAM Basque Center for Applied Mathematics Bilbao Spain Lawrence Berkeley National Laboratory Scientific Data Division BerkeleyCA United States Helen Wills Neuroscience Institute Redwood Center for Theoretical Neuroscience BerkeleyCA United States Lawrence Berkeley National Laboratory Energy Storage and Distributed Resources Division BerkeleyCA United States University of California Davis Department of Computer Science DavisCA United States Global Security Computing Applications Division Lawrence Livermore National Laboratory LivermoreCA United States Engineering Directorate Lawrence Livermore National Laboratory LivermoreCA United States Center for Bioengineering Lawrence Livermore National Laboratory LivermoreCA United States Department of Chemical Engineering Molecular Engineering & Sciences Institute Center for Synthetic Biology University of Washington SeattleWA United States Biosciences Division Argonne National Laboratory ArgonneIL United States Advanced Biofuels and Bioproducts Process Development Unit Lawrence Berkeley National Laboratory BerkeleyCA United States Biomaterials and Biomanufacturing Division Sandia National Laboratories LivermoreCA United States Earth and Biological Sciences Division Pacific Northwest National Laboratories RichlandWA United States Resources and Enabling Sciences Center National Renewable Energy Laboratory GoldenCO80401 United States Department of Bioengineering University of California BerkeleyCA United States

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed towards solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology. © 2022, CC BY.

关键词： Synthetic biology

Comparison of the permittivity sensing capabilities of graphene-based nanohybrids and metal nanoparticle-based nanohybrids

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Comparison of the permittivity sensing capabilities of graph...

IEEE Conference on Nanotechnology

作者： Viraj Senevirathne Malin Premaratne Advanced Computing and Simulation Laboratory (AχL) Department of Electrical and Computer Systems Engineering Monash University Clayton VIC Australia

Nanohybrid comprising of a nanoresonator and a quantum emitter have shown enhanced optical properties compared to its constituents. Such nanohybrids are highly sensitive to their surrounding medium permittivity, making them suitable for sensing applications which relies on the background permittivity. This feature is a result of the change in resonance conditions in the nanoresonator, which is part of the nanohybrid. Here we investigate how different nanoresonators affect the sensing capabilities of the nanohybrid and use that information to make recommendations on the best nanores-onator for the nanohybrid depending on the application. Our analysis is based on cavity quantum electrodynamics formalism and thus rigorous and accurate. Our results indicate that the graphene resonator based nanohybrids can be used to detect broader permittivity range than MNP based nanohybrids.

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Herriott-cavity-assisted all-optical atomic vector magnetometer

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Physical Review A 2020年第5期101卷 053436-053436页

作者： B. Cai C.-P. Hao Z.-R. Qiu Q.-Q. Yu W. Xiao D. Sheng Hefei National Laboratory of Physical Sciences at the Microscale University of Science and Technology of China Hefei 230026 China Department of Precision Machinery and Precision Instrumentation Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes University of Science and Technology of China Hefei 230027 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science and Center for Quantum Information Technology Peking University Beijing 100871 China

We report an all-optical atomic vector magnetometer using dual Bell-Bloom optical pumping beams in a Rb vapor cell. This vector magnetometer consists of two orthogonal optical pumping beams, with amplitude modulations at Rb85 and Rb87 Larmor frequencies, respectively. We simultaneously detect atomic signals excited by these two pumping beams using a single probe beam in the third direction, and extract the field orientation information using the phase delays between the modulated atomic signals and the driving beams. By adding a Herriott cavity inside the vapor cell, we improve the magnetometer sensitivity. We study the performance of this vector magnetometer in a magnetic field ranging from 100 mG to 500 mG, and demonstrate a field angle sensitivity better than 10 μrad/Hz1/2 above 10 Hz.

关键词： Atomic & molecular processes in external fields Optical pumping Zeeman effect

Enhanced light-matter interactions in dielectric nanostructures via machine-learning approach

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advanced Photonics 2020年第2期2卷 57-67页

作者： Lei Xu Mohsen Rahmani Yixuan Ma Daria ASmirnova Khosro Zangeneh Kamali Fu Deng Yan Kei Chiang Lujun Huang Haoyang Zhang Stephen Gould Dragomir N.Neshev Andrey E.Miroshnichenko University of New South Wales School of Engineering and Information TechnologyCanberraAustralia Nottingham Trent University School of Science&TechnologyDepartment of EngineeringAdvanced Optics and Photonics LaboratoryNottinghamUnited Kingdom Australian National University Research School of PhysicsNonlinear Physics CentreCanberraAustralia Australian National University Research School of PhysicsARC Centre of Excellence for Transformative Meta-Optical Systems(TMOS)CanberraAustralia Queensland University of Technology School of Electrical Engineering and Computer ScienceBrisbaneQueenslandAustralia Australian National University College of Engineering and Computer ScienceCanberraAustralia

A key concept underlying the specific functionalities of metasurfaces is the use of constituent components to shape the wavefront of the light on *** are versatile,novel platforms for manipulating the scattering,color,phase,or intensity of ***,one of the typical approaches for designing a metasurface is to optimize one or two variables among a vast number of fixed parameters,such as various materials’properties and coupling effects,as well as the geometrical ***,this would require multidimensional space optimization through direct numerical ***,an alternative,popular approach allows for reducing the computational cost significantly based on a deep-learning-assisted *** utilize a deep-learning approach for obtaining high-quality factor(high-Q)resonances with desired characteristics,such as linewidth,amplitude,and spectral *** exploit such high-Q resonances for enhancedlight–matter interaction in nonlinearoptical metasurfaces and optomechanical vibrations,*** demonstrate that optimized metasurfaces achieve up to 400-fold enhancement of the third-harmonic generation;at the same time,they also contribute to 100-fold enhancement of the amplitude of optomechanical *** approach can be further used to realize structures with unconventional scattering responses.

关键词： machine learning dielectric nanostructures Fano resonance third-harmonic generation optoacoustics