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The Role of Permanent and Induced Electrostatic Dipole Moments for Schottky Barriersin Janus MXY/Graphene Heterostructures: A First Principles Study

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arXiv 2022年

作者： Chen, Yu-Qi Zhang, Huan-Huan Wen, Bo Li, Xi-Bo Chai, Yi-Feng Xu, Ying Wei, Xiao-Lin Yin, Wen-Jin Teobaldi, Gilberto School of Physics and Electronic Science Hunan University of Science and Technology Xiangtan411201 China Key Laboratory of Intelligent Sensors and Advanced Sensing Materials of Hunan Province Hunan University of Science and Technology Xiangtan411201 China School of Physics and Electronics Henan University Kaifeng475001 China Department of Physics Jinan University Guangzhou510632 China Department of Physics Laboratory for Quantum Engineering and Micro-Nano Energy Technology Xiangtan University Hunan Xiangtan411105 China Scientific Computing Department STFC UKRI Rutherford Appleton Laboratory Harwell Campus DidcotOX11 0QX United Kingdom School of Chemistry University of Southampton High Field Southampton SO17 1BJ United Kingdom

The Schottky barrier height (ESBH) is a crucialfactorin determining the transport properties of semiconductor materials as it directly regulatesthe carrier mobility in opto-electronics *** principle, van der Waals (vdW)Janusheterostructures offer an appealing avenue to controlling the ***, the underlying atomistic mechanismsare far from understood conclusively, which prompts for further research in the topic. To this end,here, we carryout an extensive first principles study of the electronic properties and ESHBof several vdW Janus MXY/Graphene(M=Mo, W;X, Y=S, Se, Te) *** results of the simulations show that by changing the composition and geometry of the heterostructure's interface, it is possible to controlitselectrical contact, thence electron transport properties, from Ohmicto Schottky with nearly one order of magnitude variationsin the *** analysis of the simulations enables rationalization of this highly attractive propertyon the basis of theinterplaybetween the permanent dipole moment of the Janus MXYsheet and the induced one due to interfacial charge redistribution at the MXY/Grinterface. Such an interplayis shown to be highly effectivein alteringthe electrostatic potential difference acrossthe vdW Janus heterostructure, determining its ESBH, thence Schottky (Ohmic) contact type. These computational findings contribute guidelines to control electrical contactsinJanus heterostructurestowardsrationaldesign of electrical contacts in nanoscale devices. © 2022, CC BY.

关键词： Van der Waals forces

Implementing Feedforward Neural Network Using DNA Strand Displacement Reactions

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Nano 2020年第1期16卷

作者： Siyan Zhu Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian 116622 P. R. China School of Computer Science and Technology Dalian University of Technology Dalian 116024 P. R. China

The ability of neural networks to process information intelligently has allowed them to be successfully applied in the fields of information processing, controls, engineering, medicine, and economics. The brain-like working mode of a neural network gives it incomparable advantages in solving complex nonlinear problems compared with other methods. In this paper, we propose a feedforward DNA neural network framework based on an enzyme-free, entropy-driven DNA reaction network that uses a modular design. A multiplication gate, an addition gate, a subtraction gate, and a threshold gate module based on the DNA strand displacement principle are cascaded into a single DNA neuron, and the neuron cascade is used to form a feedforward transfer neural network. We use this feedforward neural network to realize XOR logic operation and full adder logic operation, which proves that the molecular neural network system based on DNA strand displacement can carry out complex nonlinear operation and reflects the powerful potential of building these molecular neural networks.

关键词： Molecular programming DNA strand displacement neural network chemical reaction networks

Tomography is Necessary for Universal Entanglement Detection with Single-Copy Observables

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Physical Review Letters 2016年第23期116卷 230501-230501页

作者： Dawei Lu Tao Xin Nengkun Yu Zhengfeng Ji Jianxin Chen Guilu Long Jonathan Baugh Xinhua Peng Bei Zeng Raymond Laflamme Institute for Quantum Computing University of Waterloo Waterloo N2L 3G1 Ontario Canada State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney NSW 2007 Australia Department of Mathematics and Statistics University of Guelph Guelph Ontario N1G 2W1 Canada State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland 20742 USA Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Hefei Anhui 230036 China Canadian Institute for Advanced Research Toronto Ontario M5G 1Z8 Canada Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada

Entanglement, one of the central mysteries of quantum mechanics, plays an essential role in numerous tasks of quantum information science. A natural question of both theoretical and experimental importance is whether universal entanglement detection can be accomplished without full state tomography. In this Letter, we prove a no-go theorem that rules out this possibility for nonadaptive schemes that employ single-copy measurements only. We also examine a previously implemented experiment [H. Park et al., Phys. Rev. Lett. 105, 230404 (2010)], which claimed to detect entanglement of two-qubit states via adaptive single-copy measurements without full state tomography. In contrast, our simulation and experiment both support the opposite conclusion that the protocol, indeed, leads to full state tomography, which supplements our no-go theorem. These results reveal a fundamental limit of single-copy measurements in entanglement detection and provide a general framework of the detection of other interesting properties of quantum states, such as the positivity of partial transpose and the k-symmetric extendibility.

关键词： Entanglement detection Entanglement measures Quantum tomography Nuclear magnetic resonance

Constraining ultralight vector dark matter with the Parkes Pulsar Timing Array second data release

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Physical Review D 2022年第8期106卷 L081101-L081101页

作者： Yu-Mei Wu Zu-Cheng Chen Qing-Guo Huang Xingjiang Zhu N. D. Ramesh Bhat Yi Feng George Hobbs Richard N. Manchester Christopher J. Russell R. M. Shannon School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou 310024 China School of Physical Sciences University of Chinese Academy of Sciences No. 19A Yuquan Road Beijing 100049 China CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing 100190 China Department of Astronomy Beijing Normal University Beijing 100875 China Advanced Institute of Natural Sciences Beijing Normal University Zhuhai 519087 China International Centre for Radio Astronomy Research Curtin University Bentley Western Australia 6102 Australia Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou 311100 China Australia Telescope National Facility CSIRO Astronomy and Space Science P.O. Box 76 Epping New South Wales 1710 Australia CSIRO Scientific Computing Australian Technology Park Locked Bag 9013 Alexandria New South Wales 1435 Australia Centre for Astrophysics and Supercomputing Swinburne University of Technology Hawthorn Victoria 3122 Australia Australian Research Council Centre of Excellence in Gravitational Wave Discovery (OzGrav)

Composed of ultralight bosons, fuzzy dark matter provides an intriguing solution to challenges that the standard cold dark matter model encounters on subgalactic scales. The ultralight dark matter with mass m∼10−23 eV will induce a periodic oscillation in gravitational potentials with a frequency in the nanohertz band, leading to observable effects in the arrival times of radio pulses from pulsars. Unlike scalar dark matter, pulsar timing signals induced by the vector dark matter are dependent on the oscillation direction of the vector fields. In this work, we search for ultralight vector dark matter in the mass range of [2×10−24,2×10−22] eV through its gravitational effect in the Parkes Pulsar Timing Array (PPTA) second data release. Since no statistically significant detection is made, we place 95% upper limits on the local dark matter density as ρVF≲5 GeV/cm3 for m≲10−23 eV. As no preferred direction is found for the vector dark matter, these constraints are comparable to those given by the scalar dark matter search with an earlier 12-year dataset of PPTA.

关键词： Dark matter Gravitational waves

Corrections to “Cross-Modal Correlation Learning by Adaptive Hierarchical Semantic Aggregation” [Jun 16 1201-1216]

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IEEE Transactions on Multimedia 2016年第10期18卷 2127-2127页

作者： Yan Hua Shuhui Wang Siyuan Liu Anni Cai Qingming Huang School of Information Engineering Communication University of China Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Smeal College of Business Pennsylvania State University University Park PA USA Center of Cloud Computing Institute of Advanced Computing and Digital Engineering Shenzhen Institutes of Advanced Technology Shenzhen China Department of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Intellectual Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Presents corrections to the paper, "Cross-modal correlation learning by adaptive hierarchical semantic aggregation," (Hua, Y., et al) IEEE Trans. Multimedia, vol. 18, no. 6, pp. 1201-1216, Jun. 2016.

关键词： Semantics Learning (artificial intelligence) Ontologies Query processing

AutoSilicon: Scaling Up RTL Design Generation Capability of Large Language Models

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ACM Transactions on Design Automation of Electronic Systems 1000年

作者： Cangyuan Li Chujie Chen Yudong Pan Wenjun Xu Yiqi Liu Kaiyan Chang Yujie Wang Mengdi Wang Ying Wang Huawei Li Yinhe Han Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing China University of the Chinese Academy of Sciences Beijing China Hangzhou Institute for Advanced Study University of the Chinese Academy of Sciences Hangzhou China Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China SKLP Institute of Computing Technology Chinese Academy of Sciences Beijing China State Key Laboratory of Processor Institute of Computing Technology Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China Zhejiang Lab Hangzhou China

Hardware description language (HDL) code designing is a critical component of the chip design process, requiring substantial engineering and time resources. Recent advancements in large language models (LLMs), such as GPT series, have shown promise in automating HDL code generation. However, current LLM-based approaches face significant challenges in meeting real-world hardware design requirements, particularly in handling complex designs and ensuring code correctness. Our evaluations reveal that the functional correctness rate of LLM-generated HDL code significantly decreases as design complexity increases. In this paper, we propose the AutoSilicon framework, which aims to scale up the hardware design capability of LLMs. AutoSilicon incorporates an agent system, which 1) allows for the decomposition of large-scale, complex code design tasks into smaller, simpler tasks; 2) provides a compilation and simulation environment that enables LLMs to compile and test each piece of code it generates; and 3) introduces a series of optimization strategies. Experimental results demonstrate that AutoSilicon can scale hardware designs to projects with code equivalent to over 10,000 tokens. In terms of design quality, it further improves the syntax correctness rate and functional correctness rate compared with approaches that do not employ any extensions. For example, compared to directly generating HDL code using GPT-4-turbo, AutoSilicon enhances the syntax correctness rate by an average of 35.8% and improves functional correctness by an average of 35.6%.

关键词： Verilog Code Generation LLM Agent Verilog

Training-free image style alignment for self-adapting domain shift on handheld ultrasound devices

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arXiv 2024年

作者： Zeng, Hongye Zou, Ke Chen, Zhihao Gao, Yuchong Chen, Hongbo Zhang, Haibin Zhou, Kang Wang, Meng Goh, Rick Siow Mong Liu, Yong Jiang, Chang Zheng, Rui Fu, Huazhu School of Information Science and Technology ShanghaiTech University Shanghai China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China National Key Laboratory of Fundamental Science on Synthetic Vision College of Computer Science Sichuan University Chengdu China College of Intelligence and Computing Tianjin University Tianjin China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Beth Israel Deaconess Medical Center Harvard Medical School Boston United States Institute of High Performance Computing Agency for Science Technology and Research Singapore Singapore Department of Orthopedics Zhongshan Hospital Fudan University Shanghai China Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai China

Handheld ultrasound devices face usage limitations due to user inexperience and cannot benefit from supervised deep learning without extensive expert annotations. Moreover, the models trained on standard ultrasound device data are constrained by training data distribution and perform poorly when directly applied to handheld device data. In this study, we propose the Training-free Image Style Alignment (TISA) framework to align the style of handheld device data to those of standard devices. The proposed TISA can directly infer handheld device images without extra training and is suited for clinical applications. We show that TISA performs better and more stably in medical detection and segmentation tasks for handheld device data. We further validate TISA as the clinical model for automatic measurements of spinal curvature and carotid intima-media thickness. The automatic measurements agree well with manual measurements made by human experts and the measurement errors remain within clinically acceptable ranges. We demonstrate the potential for TISA to facilitate automatic diagnosis on handheld ultrasound devices and expedite their eventual widespread use. © 2024, CC BY-NC-SA.

关键词： Deep learning

Rethinking Exemplars for Continual Semantic Segmentation in Endoscopy Scenes: Entropy-based Mini-Batch Pseudo-Replay

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arXiv 2023年

作者： Wang, Guankun Bai, Long Wu, Yanan Chen, Tong Ren, Hongliang Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China School of Electrical and Information Engineering The University of Sydney Sydney Australia Department of Biomedical Engineering National University of Singapore Singapore Suzhou Research Institute National University of Singapore Suzhou China Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong Shenzhen Research Institute The Chinese University of Hong Kong Shenzhen China

Endoscopy is a widely used technique for the early detection of diseases or robotic-assisted minimally invasive surgery (RMIS). Numerous deep learning (DL)-based research works have been developed for automated diagnosis or processing of endoscopic view. However, existing DL models may suffer from catastrophic forgetting. When new target classes are introduced over time or cross institutions, the performance of old classes may suffer severe degradation. More seriously, data privacy and storage issues may lead to the unavailability of old data when updating the model. Therefore, it is necessary to develop a continual learning (CL) methodology to solve the problem of catastrophic forgetting in endoscopic image segmentation. To tackle this, we propose a Endoscopy Continual Semantic Segmentation (EndoCSS) framework that does not involve the storage and privacy issues of exemplar data. The framework includes a mini-batch pseudo-replay (MB-PR) mechanism and a self-adaptive noisy cross-entropy (SAN-CE) loss. The MB-PR strategy circumvents privacy and storage issues by generating pseudo-replay images through a generative model. Meanwhile, the MB-PR strategy can also correct the model deviation to the replay data and current training data, which is aroused by the significant difference in the amount of current and replay images. Therefore, the model can perform effective representation learning on both new and old tasks. SAN-CE loss can help model fitting by adjusting the model’s output logits, and also improve the robustness of training. Extensive continual semantic segmentation (CSS) experiments on public datasets demonstrate that our method can robustly and effectively address the catastrophic forgetting brought by class increment in endoscopy scenes. The results show that our framework holds excellent potential for real-world deployment in a streaming learning manner. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

Parameter-transferred wasserstein generative adversarial network (PT-WGAN) for low-dose PET image denoising

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arXiv 2019年

作者： Gong, Yu Shan, Hongming Teng, Yueyang Tu, Ning Li, Ming Liang, Guodong Wang, Ge Wang, Shanshan College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Key Laboratory of Intelligent Computing in Medical Images Ministry of Education Shenyang110169 China Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States PET-CT/MRI Center Molecular Imaging Center Wuhan University Renmin Hospital Wuhan430060 China Neusoft Medical Systems Co. Ltd Shenyang110167 China

Due to the widespread use of positron emission tomography (PET) in clinical practice, the potential risk of PET-associated radiation dose to patients needs to be minimized. However, with the reduction in the radiation dose, the resultant images may suffer from noise and artifacts that compromise diagnostic performance. In this paper, we propose a parameter-transferred Wasserstein generative adversarial network (PT-WGAN) for lowdose PET image denoising. The contributions of this paper are twofold: i) a PT-WGAN framework is designed to denoise lowdose PET images without compromising structural details, and ii) a task-specific initialization based on transfer learning is developed to train PT-WGAN using trainable parameters transferred from a pretrained model, which significantly improves the training efficiency of PT-WGAN. The experimental results on clinical data show that the proposed network can suppress image noise more effectively while preserving better image fidelity than recently published state-of-the-art methods. We make our code available at https://***/90n9-yu/PT-WGAN. Copyright © 2019, The Authors. All rights reserved.

关键词： Positron emission tomography