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Peng Zhang

Publications and source records attributed to Peng Zhang.

At least 469 records · Page 26Linked to original sources
arXiv2023

A spectral-timing study of the inner flow geometry in MAXI J1535--571 with $Insight$-HXMT and NICER

We have performed a spectral-timing analysis on the black hole X-ray binary MAXI J1535--571 during its 2017 outburst, with the aim of exploring the evolution of the inner accretion flow geometry. X-ray reverberation lags are observed in the hard-intermediate state (HIMS) and soft-intermediate state (SIMS) of the outburst. During the HIMS, the characteristic frequency of the reverberation lags $ν_0$ (the frequency at which the soft lag turns to zero in the lag-frequency spectra) increases when the spectrum softens. This reflects a reduction of the spatial distance between the corona and accretion disc, when assuming the measured time lags are associated with the light travel time. We also find a strong correlation between $ν_0$ and type-C Quasi Periodic Oscillation (QPO) centroid frequency $ν_{QPO}$, which can be well explained by the Lense-Thirring (L-T) precession model under a truncated disk geometry. Despite the degeneracy in the spectral modellings, our results suggest that the accretion disc is largely truncated in the low hard state (LHS), and moves inward as the spectrum softens. Combine the spectral modelling results with the $ν_0$ - $ν_{QPO}$ evolution, we are inclined to believe that this source probably have a truncated disk geometry in the hard state.

astro-ph.HE↗
arXiv2023

Exploring the Potential of Integrated Optical Sensing and Communication (IOSAC) Systems with Si Waveguides for Future Networks

Advanced silicon photonic technologies enable integrated optical sensing and communication (IOSAC) in real time for the emerging application requirements of simultaneous sensing and communication for next-generation networks. Here, we propose and demonstrate the IOSAC system on the silicon nitride (SiN) photonics platform. The IOSAC devices based on microring resonators are capable of monitoring the variation of analytes, transmitting the information to the terminal along with the modulated optical signal in real-time, and replacing bulk optics in high-precision and high-speed applications. By directly integrating SiN ring resonators with optical communication networks, simultaneous sensing and optical communication are demonstrated by an optical signal transmission experimental system using especially filtering amplified spontaneous emission spectra. The refractive index (RI) sensing ring with a sensitivity of 172 nm/RIU, a figure of merit (FOM) of 1220, and a detection limit (DL) of 8.2*10-6 RIU is demonstrated. Simultaneously, the 1.25 Gbps optical on-off-keying (OOK) signal is transmitted at the concentration of different NaCl solutions, which indicates the bit-error-ratio (BER) decreases with the increase in concentration. The novel IOSAC technology shows the potential to realize high-performance simultaneous biosensing and communication in real time and further accelerate the development of IoT and 6G networks.

eess.SP↗
arXiv2023

A first-principles investigation of the origin of superconductivity in TlBi$_2$

The intermetallic compound TlBi$_2$ crystallizes in the MgB$_2$ structure and becomes superconducting below 6.2 K. Considering that both Tl and Bi have heavy atomic masses, it is puzzling why TlBi$_2$ is a conventional phonon-mediated superconductor. We have performed comprehensive first-principles calculations of the electronic structures, the phonon dispersions and the electron-phonon couplings for TlBi$_2$. The $6p$ orbitals of bismuth dominate over the states near the Fermi level, forming strong intra-layer $p_{x/y}$ and inter-layer $p_z$ $σ$ bonds which is known to have strong electron-phonon coupling. In addition, the large spin-orbit coupling interaction in TlBi$_2$ increases its electron-phonon coupling constant significantly. As a result, TlBi$_2$, with a logarithmic phonon frequency average one tenth that of MgB$_2$, is a phonon-mediated superconductor.

cond-mat.supr-con↗
arXiv2023

MachMap: End-to-End Vectorized Solution for Compact HD-Map Construction

This report introduces the 1st place winning solution for the Autonomous Driving Challenge 2023 - Online HD-map Construction. By delving into the vectorization pipeline, we elaborate an effective architecture, termed as MachMap, which formulates the task of HD-map construction as the point detection paradigm in the bird-eye-view space with an end-to-end manner. Firstly, we introduce a novel map-compaction scheme into our framework, leading to reducing the number of vectorized points by 93% without any expression performance degradation. Build upon the above process, we then follow the general query-based paradigm and propose a strong baseline with integrating a powerful CNN-based backbone like InternImage, a temporal-based instance decoder and a well-designed point-mask coupling head. Additionally, an extra optional ensemble stage is utilized to refine model predictions for better performance. Our MachMap-tiny with IN-1K initialization achieves a mAP of 79.1 on the Argoverse2 benchmark and the further improved MachMap-huge reaches the best mAP of 83.5, outperforming all the other online HD-map construction approaches on the final leaderboard with a distinct performance margin (> 9.8 mAP at least).

cs.CV↗
arXiv2023

Deep learning radiomics for assessment of gastroesophageal varices in people with compensated advanced chronic liver disease

Objective: Bleeding from gastroesophageal varices (GEV) is a medical emergency associated with high mortality. We aim to construct an artificial intelligence-based model of two-dimensional shear wave elastography (2D-SWE) of the liver and spleen to precisely assess the risk of GEV and high-risk gastroesophageal varices (HRV). Design: A prospective multicenter study was conducted in patients with compensated advanced chronic liver disease. 305 patients were enrolled from 12 hospitals, and finally 265 patients were included, with 1136 liver stiffness measurement (LSM) images and 1042 spleen stiffness measurement (SSM) images generated by 2D-SWE. We leveraged deep learning methods to uncover associations between image features and patient risk, and thus conducted models to predict GEV and HRV. Results: A multi-modality Deep Learning Risk Prediction model (DLRP) was constructed to assess GEV and HRV, based on LSM and SSM images, and clinical information. Validation analysis revealed that the AUCs of DLRP were 0.91 for GEV (95% CI 0.90 to 0.93, p < 0.05) and 0.88 for HRV (95% CI 0.86 to 0.89, p < 0.01), which were significantly and robustly better than canonical risk indicators, including the value of LSM and SSM. Moreover, DLPR was better than the model using individual parameters, including LSM and SSM images. In HRV prediction, the 2D-SWE images of SSM outperform LSM (p < 0.01). Conclusion: DLRP shows excellent performance in predicting GEV and HRV over canonical risk indicators LSM and SSM. Additionally, the 2D-SWE images of SSM provided more information for better accuracy in predicting HRV than the LSM.

q-bio.TO↗
arXiv2023

WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences

We present WebGLM, a web-enhanced question-answering system based on the General Language Model (GLM). Its goal is to augment a pre-trained large language model (LLM) with web search and retrieval capabilities while being efficient for real-world deployments. To achieve this, we develop WebGLM with strategies for the LLM-augmented retriever, bootstrapped generator, and human preference-aware scorer. Specifically, we identify and address the limitations of WebGPT (OpenAI), through which WebGLM is enabled with accuracy, efficiency, and cost-effectiveness advantages. In addition, we propose systematic criteria for evaluating web-enhanced QA systems. We conduct multi-dimensional human evaluation and quantitative ablation studies, which suggest the outperformance of the proposed WebGLM designs over existing systems. WebGLM with the 10-billion-parameter GLM (10B) is shown to perform better than the similar-sized WebGPT (13B) and even comparably to WebGPT (175B) in human evaluation. The code, demo, and data are at \url{https://github.com/THUDM/WebGLM}.

cs.CL↗
arXiv2023

Leading components and pressure-induced color changes in N-doped lutetium hydride

Recent experimental study by Dias {\it et al.} claims to have discovered room-temperature superconductivity in lutetium-nitrogen-hydrogen system at 1 GPa [Nature 615, 244 (2023)], which sheds light on the long-held dream of ambient superconductivity. However, all follow-up experiments found no evidence of superconductivity. The compositions and the crystal structures of the lutetium-nitrogen-hydrogen system remain unknown. By employing the density functional theory based structure prediction algorithm, we suggest that in lutetium-nitrogen-hydrogen the major component is LuH$_2$ (Fm$\bar{3}$m), together with minor LuN (Fm$\bar{3}$m). The blue LuH$_2$ at ambient pressure will turn into purple and red color at higher pressures, possibly accompanied by the formation of vacancies at hydrogen-sites. In LuH$_2$ and LuN, the density of states at the Fermi level is dominated by the Lu-5d orbitals, while those from hydrogen and nitrogen are very small, leading to the absence of superconductivity in these two compounds. Nitrogen-doping to LuH$_2$ fails to enhance the superconductivity as well. In this work, we identify the leading components in N-doped lutetium hydride, explain its intriguing color changes under pressure, and elucidate why superconductivity is absent in the follow-up experiments.

cond-mat.supr-con↗
arXiv2023

Are Intermediate Layers and Labels Really Necessary? A General Language Model Distillation Method

The large scale of pre-trained language models poses a challenge for their deployment on various devices, with a growing emphasis on methods to compress these models, particularly knowledge distillation. However, current knowledge distillation methods rely on the model's intermediate layer features and the golden labels (also called hard labels), which usually require aligned model architecture and enough labeled data respectively. Moreover, the parameters of vocabulary are usually neglected in existing methods. To address these problems, we propose a general language model distillation (GLMD) method that performs two-stage word prediction distillation and vocabulary compression, which is simple and surprisingly shows extremely strong performance. Specifically, GLMD supports more general application scenarios by eliminating the constraints of dimension and structure between models and the need for labeled datasets through the absence of intermediate layers and golden labels. Meanwhile, based on the long-tailed distribution of word frequencies in the data, GLMD designs a strategy of vocabulary compression through decreasing vocabulary size instead of dimensionality. Experimental results show that our method outperforms 25 state-of-the-art methods on the SuperGLUE benchmark, achieving an average score that surpasses the best method by 3%.

cs.CL↗
arXiv2023

GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model

Currently, the reduction in the parameter scale of large-scale pre-trained language models (PLMs) through knowledge distillation has greatly facilitated their widespread deployment on various devices. However, the deployment of knowledge distillation systems faces great challenges in real-world industrial-strength applications, which require the use of complex distillation methods on even larger-scale PLMs (over 10B), limited by memory on GPUs and the switching of methods. To overcome these challenges, we propose GKD, a general knowledge distillation framework that supports distillation on larger-scale PLMs using various distillation methods. With GKD, developers can build larger distillation models on memory-limited GPUs and easily switch and combine different distillation methods within a single framework. Experimental results show that GKD can support the distillation of at least 100B-scale PLMs and 25 mainstream methods on 8 NVIDIA A100 (40GB) GPUs.

cs.CL↗
arXiv2023

Deep Reinforcement Learning in Finite-Horizon to Explore the Most Probable Transition Pathway

In many scientific and engineering problems, noise and nonlinearity are unavoidable, which could induce interesting mathematical problem such as transition phenomena. This paper focuses on efficiently discovering the most probable transition pathway for stochastic dynamical systems employing reinforcement learning. With the Onsager-Machlup action functional theory to quantify rare events in stochastic dynamical systems, finding the most probable pathway is equivalent to solving a variational problem on the action functional. When the action function cannot be explicitly expressed by paths near the reference orbit, the variational problem needs to be converted into an optimal control problem. First, by integrating terminal prediction into the reinforcement learning framework, we develop a Terminal Prediction Deep Deterministic Policy Gradient (TP-DDPG) algorithm to deal with the finite-horizon optimal control issue in a forward way. Next, we present the convergence analysis of our algorithm for the value function in terms of the neural network's approximation error and estimation error. Finally, we conduct various experiments in different dimensions for the transition problems in applications to illustrate the effectiveness of our algorithm.

math.OC↗
arXiv2023

AD-NEGF: An End-to-End Differentiable Quantum Transport Simulator for Sensitivity Analysis and Inverse Problems

Since proposed in the 70s, the Non-Equilibrium Green Function (NEGF) method has been recognized as a standard approach to quantum transport simulations. Although it achieves superiority in simulation accuracy, the tremendous computational cost makes it unbearable for high-throughput simulation tasks such as sensitivity analysis, inverse design, etc. In this work, we propose AD-NEGF, to our best knowledge the first end-to-end differentiable NEGF model for quantum transport simulations. We implement the entire numerical process in PyTorch, and design customized backward pass with implicit layer techniques, which provides gradient information at an affordable cost while guaranteeing the correctness of the forward simulation. The proposed model is validated with applications in calculating differential physical quantities, empirical parameter fitting, and doping optimization, which demonstrates its capacity to accelerate the material design process by conducting gradient-based parameter optimization.

cond-mat.mes-hall↗
arXiv2023

Proxy Re-encryption based Fair Trade Protocol for Digital Goods Transactions via Smart Contracts

With the massive amount of digital data generated everyday, transactions of digital goods become a trend. One of the essential requirements for such transactions is fairness, which is defined as that both of the seller and the buyer get what they want, or neither. Current fair trade protocols generally involve a trusted third-party (TTP), which achieves fairness by heavily relying on the TTP's behaviors and the two parties' trust in the TTP. With the emergence of Blockchain, its decentralization and transparency make it a very good candidate to replace the TTP. In this work, we attempt to design a secure and fair protocol for digital goods transactions through smart contracts on Blockchain. To ensure security of the digital goods, we propose an advanced passive proxy re-encryption (PRE) scheme, which enables smart contracts to transfer the decryption right to a buyer after receiving his/her payment. Furthermore, based on smart contracts and the proposed passive PRE scheme, a fair trade protocol for digital goods transactions is proposed, whose fairness is guaranteed by the arbitration protocol. The proposed protocol supports Ciphertext publicity and repeatable sale, while involving less number of interactions. Comprehensive experiment results validate the feasibility and effectiveness of the proposed protocol.

cs.CR↗
arXiv2023

Ground calibration of Gamma-Ray Detectors of GECAM-C

As a new member of GECAM mission, GECAM-C (also named High Energy Burst Searcher, HEBS) was launched onboard the SATech-01 satellite on July 27th, 2022, which is capable to monitor gamma-ray transients from $\sim$ 6 keV to 6 MeV. As the main detector, there are 12 gamma-ray detectors (GRDs) equipped for GECAM-C. In order to verify the GECAM-C GRD detector performance and to validate the Monte Carlo simulations of detector response, comprehensive on-ground calibration experiments have been performed using X-ray beam and radioactive sources, including Energy-Channel relation, energy resolution, detection efficiency, SiPM voltage-gain relation and the non-uniformity of positional response. In this paper, the detailed calibration campaigns and data analysis results for GECAM-C GRDs are presented, demonstrating the excellent performance of GECAM-C GRD detectors.

astro-ph.IM↗
arXiv2023

Discovery of two rotational modulation periods from a young hierarchical triple system

GW~Ori is a young hierarchical triple system located in $λ$ Orionis, consisting of a binary (GW~Ori\,A and B), a tertiary star (GW~Ori\,C) and a rare circumtriple disk. Due to the limited data with poor accuracy, several short-period signals were detected in this system, but the values from different studies are not fully consistent. As one of the most successful transiting surveys, the Transiting Exoplanet Survey Satellite (TESS) provides an unprecedented opportunity to make a comprehensive periodic analysis of GW~Ori. In this work we discover two significant modulation signals by analyzing the light curves of GW~Ori's four observations from TESS, i.e., 3.02 $\pm$ 0.15\,d and 1.92 $\pm$ 0.06\,d, which are very likely to be the rotational periods caused by starspot modulation on the primary and secondary components, respectively. We calculate the inclinations of GW~Ori\,A and B according to the two rotational periods. The results suggest that the rotational plane of GW~Ori\,A and B and the orbital plane of the binary are almost coplanar. We also discuss the aperiodic features in the light curves; these may be related to unstable accretion. The light curves of GW~Ori also include a third (possible) modulation signal with a period of 2.51$\pm$0.09\,d, but the third is neither quite stable nor statistically significant.

astro-ph.SR↗
arXiv2023

Achieving Maximum Efficiency in Schnorr-based Multi-signature and Applications in Blockchain

Multi-signature aggregates signatures from multiple users on the same message into a joint signature, which is widely applied in blockchain to reduce the percentage of signatures in blocks and improve the throughput of transactions. The $k$-sum attacks are one of the major challenges to design secure multi-signature schemes. In this work, we address $k$-sum attacks from a novel angle by defining a Public Third Party (PTP), which is an automatic process that can be verifiable by the public and restricts the signing phase from continuing until receiving commitments from all signers. Further, a two-round multi-signature scheme MEMS with PTP is proposed, which is secure based on discrete logarithm assumption in the random oracle model. As each signer communicates directly with the PTP instead of other co-signers, the total amount of communications is significantly reduced. In addition, as PTP participates in the computation of the aggregation and signing algorithms, the computation cost left for each signer and verifier remains the same as the basis Schnorr signature. To the best of our knowledge, this is the maximum efficiency that a Schnorr-based multi-signature scheme can achieve. Further, MEMS is applied in blockchain platform, e.g., Fabric, to improve the transaction efficiency.

cs.CR↗
arXiv2023

Gradient Impedance Matching Layers Enable Broadband Water-Air Sound Transmission

Efficient sound transmission across the water-air interface has always been expected in the field of ocean exploration. However, the existing researches are mainly concentrated on the narrow-band transmission based on resonance, which greatly limits the transmission capacity and efficiency. Here, we combined the air-based and water-based metafluids to realize an exponential gradient impedance matching layer for broadband water-air sound transmission. By cooperatively adjusting the sound velocity and thickness in the matching layers, we modulated the required acoustic parameters of each layer into a reasonable range, which can be conveniently achieved by the proposed metafluids. A matching layer sample was constructed and validated in a water tank. Experimental results show that the proposed matching layer can achieve an average sound energy transmission enhancement above 16.7dB from 880Hz to 1760Hz across the water-air interface. Moreover, we use the proposed matching layer to demonstrate a multicolor picture transmission from air to water, which shows extremely high communication capacity and accuracy. Our work is promising for more applications based on water-air transmission and opens a new avenue to the design and implementation of the extreme impedance matching case.

physics.app-ph↗
arXiv2023

Binary stochasticity enabled highly efficient neuromorphic deep learning achieves better-than-software accuracy

Deep learning needs high-precision handling of forwarding signals, backpropagating errors, and updating weights. This is inherently required by the learning algorithm since the gradient descent learning rule relies on the chain product of partial derivatives. However, it is challenging to implement deep learning in hardware systems that use noisy analog memristors as artificial synapses, as well as not being biologically plausible. Memristor-based implementations generally result in an excessive cost of neuronal circuits and stringent demands for idealized synaptic devices. Here, we demonstrate that the requirement for high precision is not necessary and that more efficient deep learning can be achieved when this requirement is lifted. We propose a binary stochastic learning algorithm that modifies all elementary neural network operations, by introducing (i) stochastic binarization of both the forwarding signals and the activation function derivatives, (ii) signed binarization of the backpropagating errors, and (iii) step-wised weight updates. Through an extensive hybrid approach of software simulation and hardware experiments, we find that binary stochastic deep learning systems can provide better performance than the software-based benchmarks using the high-precision learning algorithm. Also, the binary stochastic algorithm strongly simplifies the neural network operations in hardware, resulting in an improvement of the energy efficiency for the multiply-and-accumulate operations by more than three orders of magnitudes.

cs.NE↗
arXiv2023

Quantum Simulation of Polarized Light-induced Electron Transfer with A Trapped-ion Qutrit System

Electron transfer within and between molecules is crucial in chemistry, biochemistry, and energy science. This study describes a quantum simulation method that explores the influence of light polarization on the electron transfer between two molecules. By implementing precise and coherent control among the quantum states of trapped atomic ions, we can induce quantum dynamics that mimic the electron transfer dynamics in molecules. We use $3$-level systems (qutrits), rather than traditional two-level systems (qubits) to enhance the simulation efficiency and realize high-fidelity simulations of electron transfer dynamics. We treat the quantum interference between the electron coupling pathways from a donor with two degenerate excited states to an acceptor and analyze the transfer efficiency. We also examine the potential error sources that enter the quantum simulations. The trapped ion systems have favorable scalings with system size compared to those of classical computers, promising access to electron-transfer simulations of increasing richness.

quant-ph↗