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12th International Conference on Learning Representations, ICLR 2024

作者： Tang, Yehui Xiong, Hao Yang, Nianzu Xiao, Tailong Yan, Junchi Department of Computer Science and Engineering Shanghai Jiao Tong University China Institute of Quantum Sensing and Information Processing Shanghai Jiao Tong University China Hefei National Laboratory Hefei China

Estimating the properties of quantum systems such as quantum phase has been critical in addressing the essential quantum many-body problems in physics and chemistry. Deep learning models have been recently introduced to property estimation, surpassing conventional statistical approaches. However, these methods are tailored to the specific task and quantum data at hand. It remains an open and attractive question for devising a more universal task-agnostic pretraining model for quantum property estimation. In this paper, we propose LLM4QPE, a large language model style quantum task-agnostic pretraining and finetuning paradigm that 1) performs unsupervised pretraining on diverse quantum systems with different physical conditions;2) uses the pretrained model for supervised finetuning and delivers high performance with limited training data, on downstream tasks. It mitigates the cost for quantum data collection and speeds up convergence. Extensive experiments show the promising efficacy of LLM4QPE in various tasks including classifying quantum phases of matter on Rydberg atom model and predicting two-body correlation function on anisotropic Heisenberg model. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Deep learning

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Model-Driven Deep Joint Source-Channel Coding over Time-Varying Channels 26

Model-Driven Deep Joint Source-Channel Coding over Time-Vary...

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26th International ITG Workshop on Smart Antennas, WSA 2023 and 13th Conference on Systems, Communications, and Coding, SCC 2023

作者： Karamanli, Can Tung, Tze-Yang Gündüz, Deniz Information Processing and Communications Laboratory Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom

ISBN: (纸本)9783800760510

Machine learning techniques have recently offered powerful tools for designing joint source-channel coding (JSCC) schemes with impressive performance results. Recent works have shown that deep learning (DL) based JSCC schemes (Deep- JSCC) for wireless image transmission can outperform stateof- the-art separate source and channel coding designs. However, existing works on DL based JSCC schemes either require many models trained for specific channel conditions, increasing the storage requirements, or need to introduce additional parameters in the model to adapt to new channel conditions, increasing the complexity of the architecture. In this paper, we propose DeepJSCC-MMSE, an end-to-end optimized JSCC scheme for wireless image transmission over wireless fading channels, in which a single DeepJSCC encoder/decoder pair is trained in concatenation with a model-driven channel equalization module at the receiver, which can adapt to any channel condition without additional model parameters. We show that DeepJSC-MMSE can outperform parameterized equalization techniques, without requiring the encoder and decoder to be retrained for new channel conditions, paving the way for generalized DL based JSCC schemes. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Channel coding

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Synchronous Bi-directional Pedestrian Trajectory Prediction with Error Compensation 16th

Synchronous Bi-directional Pedestrian Trajectory Prediction ...

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16th Asian Conference on computer Vision, ACCV 2022

作者： Xie, Ce Li, Yuanman Liang, Rongqin Dong, Li Li, Xia College of Electronics and Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Department of Computer Science Ningbo University Ningbo China

ISBN: (纸本)9783031263507

Pedestrian trajectory prediction as an essential part of reasoning human motion behaviors, has been deployed in a number of vision applications, such as autonomous navigation and video surveillance. Most existing methods adopt autoregressive frameworks to forecast the future trajectory, where the trajectory is iteratively generated based on the previous outputs. Such a process will suffer from large accumulated errors over the long-term forecast horizon. To address this issue, in this paper, we propose a Synchronous Bi-Directional framework (SBD) with error compensation for pedestrian trajectory prediction, which can greatly alleviate the error accumulation during prediction. Specifically, we first develop a bi-directional trajectory prediction mechanism, and force the predicting procedures for two opposite directions to be synchronous through a shared motion characteristic. Different from previous works, the mutual constraints inherent to our framework from the synchronous opposite-predictions can significantly prevent the error accumulation. In order to reduce the possible prediction error in each timestep, we further devise an error compensation network to model and compensate for the positional deviation between the ground-truth and the predicted trajectory, thus improving the prediction accuracy of our scheme. Experiments conducted on the Stanford Drone dataset and the ETH-UCY dataset show that our method achieves much better results than existing algorithms. Particularly, by resorting to our alleviation methodology for the error accumulation, our scheme exhibits superior performance in the long-term pedestrian trajectory prediction. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Error compensation

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CSCL-DTI: Predicting drug-target interaction through cross-view and self-supervised contrastive learning

CSCL-DTI: Predicting drug-target interaction through cross-v...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Xuan Zhang, Xi Yu, Zu-Guo Long, Yahui Zeng, Xiangxiang Yu, Philip S. Xiangtan University College of Computer Science China Xiangtan University Key Laboratory of Intelligent Computing and Information Processing Ministry of Education China Singapore Hunan University College of Information Science and Engineering China University of Illinois Chicago Department of Computer Science United States

ISBN: (纸本)9798350386226

Accurately predicting drug-target interactions (DTI) is a critical step in drug discovery. Existing methods of DTI prediction primarily employ Simplified Molecular-Input Line-Entry System (SMILES) sequences or molecular graphs to learn drug representations. However, the features learned by such single-view approach is prone to incomplete. While some multiview methods that consider the views of both SMILES sequences and molecular graphs have been developed, these methods often fall in short in capturing potential interactions between views. In this work, we propose a novel dual contrastive learning framework CSCL-DTI for DTI prediction. First, we design a contrastive-enhanced cross-view representation learning (CVRL) to learn representations for drugs. In this module, Transformer-based and graph convolutional network (GCN)-based encoders are separately adopted to learn view-specific representations, followed by contrastive learning to enrich the representations by accounting for the potential interplay between local chemical context and topological structure. Second, we combine Transformer with self-supervised contrastive learning (SSCL) to learn representations for targets by modelling protein amino acids sequences. The scheme allows to effectively preserve the intrinsic characteristics of the sequences. Finally, we introduce a bilinear attention network to obtain an integrated representation by adaptively incorporating drug and target representations. Benchmarking experiments on two datasets demonstrated that CSCL-DTI1 outperforms six state-of-the-art methods. © 2024 IEEE.

关键词： Contrastive Learning

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Fuzzy Intelligence Learning Based on Bounded Rationality in IoMT Systems: A Case Study in Parkinson's Disease

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IEEE Transactions on Computational Social Systems 2023年第4期10卷 1607-1621页

作者： Zhang, Chao Ding, Juanjuan Zhan, Jianming Sangaiah, Arun Kumar Li, Deyu Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University School of Computer and Information Technology Taiyuan030006 China Hubei Minzu University School of Mathematics and Statistics Enshi445000 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As a cause of interfering with routine activities, freezing of gait (FOG) is a severe syndrome of Parkinson's disease (PD) and usually performs as an abrupt and momentary inability to effective stepping forward. Advanced wearable acceleration sensors based on socially implemented Internet of medical things (IoMT) devices can remotely provide a platform for recognizing FOG. However, due to the diverse data acquisition modes that appear in classic IoMT devices, the obtained data may contain imprecise, hesitant, and incomplete ones. Meanwhile, the bounded rationality owned by neurologists usually has a big impact on using wearable acceleration sensors to predict illnesses. Therefore, the objective of this article lies in exploring a fuzzy intelligence learning approach based on bounded rationality in IoMT systems and providing a valid scheme for biomedical data analysis. Specifically, a brand-new three-way group decision-making approach by means of TODIM (an acronym in Portuguese for interactive multicriteria decision-making) with incomplete dual hesitant fuzzy (DHF) information and its applications in detecting FOG in PD using IoMT devices are systematically explored. First, taking advantage of DHF sets (DHFSs) when depicting realistic group decision information, the concept of multigranulation (MG) incomplete DHF information systems is built. Second, adjustable MG DHF probabilistic rough sets (PRSs) are further put forward via DHF similarity relations. Third, a three-way group decision-making approach is constructed by virtue of adjustable MG DHF PRSs and TODIM. Finally, the validity, effectiveness, and practicality of the constructed three-way group decision-making approach are investigated by a University of California, Irvine (UCI) dataset with several experimental analyses in the background of FOG detection in PD using IoMT devices. The experimental result indicates that the developed fuzzy intelligence learning approach achieves reasonable diagnostic conclusions

关键词： Diagnosis

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Covert Communications in Active-IOS Aided Uplink NOMA Systems With Full-Duplex Receiver

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8419-8424页

作者： Kang, Xueyu Qi, Nan Lv, Lu Boulogeorgos, Alexandros-Apostolos A. Tsiftsis, Theodoros A. Liu, Hongwu Shandong Jiaotong University School of Information Science and Electrical Engineering Jinan250357 China Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing210016 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Xidian University School of Telecommunications Engineering Xi'an710071 China University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China

In this paper, an active intelligent omni-surface (A-IOS) is deployed to aid uplink transmissions in a non-orthogonal multiple access (NOMA) system. In order to shelter the covert signal embedded in the superposition transmissions, a multi-antenna full-duplex (FD) receiver is utilized at the base-station to recover signal in addition to jamming the warden. With the aim of maximizing the covert rate, the FD transmit and receive beamforming, A-IOS refraction and reflection beamforming, NOMA transmit power, and FD jamming power are jointly optimized. To tackle the non-convex covert rate maximization problem subject to the highly coupled system parameters, an alternating optimization algorithm is designed to iteratively solve the decoupled sub-problems of optimizing the system parameters. The optimal solutions for the sub-problems of the NOMA transmit power and FD jamming power optimizations are derived in closed-form. To tackle the rank-one constrained non-convex fractional programming of the A-IOS beamforming and FD beamforming, a penalized Dinkelbach transformation approach is proposed to resort to the optimal solutions via semidefinite programming. Numerical results clarify that the deployment of the A-IOS significantly improves the covert rate compared with the passive-IOS aided uplink NOMA system. It is also found that the proposed scheme provides better covert communication performance with the optimized NOMA transmit power and FD jamming power compared with the benchmark schemes. © 1967-2012 IEEE.

关键词： Jamming

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24.9-GHz-bandwidth VCSEL enables 170-Gbit/s OFDM, GFDM, and DMT transmissions

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Photonics Research 2024年第2期12卷 377-390页

作者： Wu, Wei-Li Cheng, Chih-Hsien Matsumoto, Atsushi Akahane, Kouichi Yang, Yun-Cheng Su, Borching Wu, Chao-Hsin Feng, Milton Lin, Gong-Ru Graduate Institute of Photonics and Optoelectronics Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan Optical Access Technology Laboratory Photonic ICT Research Center Network Research Institute National Institute of Information and Communications Technology Koganei184-8795 Japan Graduate Institute of Electrical Engineering Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan Graduate Institute of Communication Engineering Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan Department of Electrical and Computer Engineering Microelectronics and Nanotechnology Laboratory University of Illinois at Urbana–Champaign ChampaignIL61801 United States NTU-Tektronix Joint Research Center National Taiwan University Tektronix Inc. Taipei10617 Taiwan

The direct modulation and fiber transmission performances of a bi-layer-oxide-confined 850-nm vertical cavity surface emitting laser (VCSEL) containing an emission aperture size of 3.5 μm with versatile novel complex data formats are compared. With delivering quadrature amplitude modulation-orthogonal frequency-division multiplexing (QAM-OFDM), generalized frequency-division multiplexing (GFDM), and discrete multi-tone (DMT) data streams, the highest data rate allowable for encoding the 3.5-μm-aperture VCSEL is demonstrated under back-to-back (BtB) and 100-m OM5-multi-mode fiber (OM5-MMF) transmissions. The newly designed 3.5-μm-aperture VCSEL contains InGaAs/AlGaAs multiple quantum wells and bi-layer-oxide-confined aperture to enhance its differential gain, reduce its thermal resistance, and improve its parasitic resistance for providing broader encoding bandwidth. Optimizing the VCSEL bias at 9 mA (11Ith) makes the lasing with three to four transverse modes suffer from a low modal dispersion effect, which also provides its 3-dB bandwidth as wide as 24.9 GHz and suppresses its RIN level to −138.0 dBc∕Hz for broadband OFDM/GFDM/DMT data encoding. By using the pre-distortion technique on the OFDM to compensate the SNR degradation, the OFDM modified with a pre-leveling slope of 0.5 dB/GHz can provide 160 Gbit/s for BtB and 140 Gbit/s for 100-m OM5-MMF transmissions. By dividing the N OFDM subcarriers into K equally-sized GFDM subcarriers and M equally-sized GFDM subsymbols, the QAM-GFDM with K = 2 can effectively enhance the out-of-band suppression to 35.9 dB and reduce the peak-to-average-power ratio to 9.7 dB, which can support 168 Gbit/s under BtB and 144 Gbit/s in 100-m OM5-MMF transmissions. To achieve the maximal spectral-usage efficiency, the DMT with the adaptive bit-loading algorithm is employed to improve the data rate up to 172 Gbit/s for BtB and 146 Gbit/s for 100-m OM5-MMF conditions. These advanced pre-leveled 16-QAM OFDM, GFDM, and DMT formats carried b

关键词： Surface emitting lasers

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electrically tunable planar liquid-crystal singlets for simultaneous spectrometry and imaging

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Light(Science & Applications) 2024年第11期13卷 2589-2598页

作者： Zhou Zhou Yiheng Zhang Yingxin Xie Tian Huang Zile Li Peng Chen Yan-qing Lu Shaohua Yu Shuang Zhang Guoxing Zheng Electronic Information School and School of MicroelectronicsWuhan UniversityWuhan 430072China NUS Graduate School National University of SingaporeSingapore 119077Singapore Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and Manipulationand College of Engineering and Applied SciencesNanjing UniversityNanjing 210093China Peng Cheng Laboratory Shenzhen 518055China New Cornerstone Science Laboratory Department of PhysicsUniversity of Hong KongHong KongChina Department of Electrical and Electronic Engineering University of Hong KongHong KongChina Wuhan Institute of Quantum Technology Wuhan 430206China

Conventional hyperspectral cameras cascade lenses and spectrometers to acquire the spectral datacube,which forms the fundamental framework for hyperspectral ***,this cascading framework involves tradeoffs among spectral and imaging performances when the system is driven toward ***,we propose a spectral singlet lens that unifies optical imaging and computational spectrometry functions,enabling the creation of minimalist,miniaturized and high-performance hyperspectral *** a paradigm,we capitalize on planar liquid crystal optics to implement the proposed framework,with each liquid-crystal unit cell acting as both phase modulator and electrically tunable spectral *** with various targets show that the resulting millimeter-scale hyperspectral camera exhibits both high spectral fidelity(>95%)and high spatial resolutions(~1.7 times the diffraction limit).The proposed“two-in-one”framework can resolve the conflicts between spectral and imaging resolutions,which paves a practical pathway for advancing hyperspectral imaging systems toward miniaturization and portable applications.

关键词： crystal planar singlet

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Non-Hermitian polarization quantized by time-reversal symmetry

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Science China(Physics,Mechanics & Astronomy) 2024年第12期67卷 141-147页

作者： Jing Cheng Jinbing Hu Zhigao Hu Lin Chen Minghui Lu Songlin Zhuang Technical Center for Multifunctional Magneto-Optical Spectroscopy(Shanghai) Engineering Research Center of Nanophotonics&Advanced Instrument(Ministry of Education)Department of Physics and Electronic ScienceEast of China Normal UniversityShanghai200241China College of Optical-Electrical Information and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan430074China National Laboratory of Solid State Microstructures Science and Engineering Nanjing UniversityNanjing210093China

It is well known that in one-dimensional(1D) crystalline insulators,the electric polarization is a manifestation of Berry phase,which can not be quantized by time-reversal symmetry(TRS) as in Hermitian physics TRS does not induce any topological phase in one *** this paper we report that even though associated with complex eigenenergies a 1D non-Hermitian insulator obeying only TRS is capable of presenting quantized bulk *** underlying physical reason is unveiled:TRS guarantees the complex energies to come in pair(E,E*),and the corresponding decaying and amplifying wave functions also come in pair and have the same variation rate,hence,giving rise to a stable wannier *** electron transport is performed by means of charge pumping process,which verifies the physical mechanism *** last,we discuss the possible experimental implementation of the proposed model by means of twisted-π gauge flux.

关键词： non-Hermitian polarization time-reversal symmetry complex hopping

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Stable Implementation Of Voice Activity Detector using Zero-Phase Zero Frequency Resonator On FPGA 19

Stable Implementation Of Voice Activity Detector using Zero-...

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19th Annual IEEE International Conference and Expo on Real Time Communications at IIT, RTC 2023

作者： Jabbar, Syed Abdul Barche, Purva Gurugubelli, Krishna Azeemuddin, Syed Vuppala, Anil Kumar Ltrc IIIT-H Speech Processing Laboratory Hyderabad India IIIT-Hyderabad The University of Mississippi Cvest Department of Electrical and Computer Engineering MS United States

ISBN: (纸本)9798350327373

Voice activity detection (VAD), is a signal processing technique used to determine whether a given speech signal contains voiced or unvoiced segments. VAD is used in various applications such as Speech Coding, Voice Controlled Systems, speech feature extraction, etc. For example, in Adaptive multi-rate (AMR) speech coding, VAD is used as an efficient way of coding different speech frames at different bit rates. In this paper, we implemented the application of a Zero-Phase Zero Frequency Resonator (ZP-ZFR) as VAD on hardware. ZP-ZFR is an Infinite Impulse Response (IIR) filter that offers the advantage of requiring a lower filter order, making it suitable for hardware implementation. The proposed system is implemented on the TIMIT database using the Nexys Video Artix-7 FPGA board. The hardware design is carried out using Vivado 2021.1, a popular tool for FPGA development. The Hardware Description Language (HDL) used for implementation is Verilog. The proposed system achieves good performance with low complexity. Therefore this work is implemented on hardware, which can be used in various applications. © 2023 IEEE.

关键词： MATLAB

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