arXiv subjects

Peng Zhang

Publications and source records attributed to Peng Zhang.

At least 487 records · Page 27Linked to original sources

arXiv2023

Two Rapid Power Iterative DOA Estimators for UAV Emitter Using Massive/Ultra-massive Receive Array

To provide rapid direction finding (DF) for unmanned aerial vehicle (UAV) emitter in future wireless networks, a low-complexity direction of arrival (DOA) estimation architecture for massive multiple input multiple output (MIMO) receiver arrays is constructed. In this paper, we propose two strategies to address the extremely high complexity caused by eigenvalue decomposition of the received signal covariance matrix. Firstly, a rapid power-iterative rotational invariance (RPI-RI) method is proposed, which adopts the signal subspace generated by power iteration to gets the final direction estimation through rotational invariance between subarrays. RPI-RI makes a significant complexity reduction at the cost of a substantial performance loss. In order to further reduce the complexity and provide a good directional measurement result, a rapid power-iterative Polynomial rooting (RPI-PR) method is proposed, which utilizes the noise subspace combined with polynomial solution method to get the optimal direction estimation. In addition, the influence of initial vector selection on convergence in the power iteration is analyzed, especially when the initial vector is orthogonal to the incident wave. Simulation results show that the two proposed methods outperform the conventional DOA estimation methods in terms of computational complexity. In particular, the RPIPR method achieves more than two orders of magnitude lower complexity than conventional methods and achieves performance close to CRLB. Moreover, it is verified that the initial vector and the relative error have a significant impact on the performance of the computational complexity.

cs.IT↗

arXiv2023

Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation

In dynamic interaction graphs, user-item interactions usually follow heterogeneous patterns, represented by different structural information, such as user-item co-occurrence, sequential information of user interactions and the transition probabilities of item pairs. However, the existing methods cannot simultaneously leverage all three structural information, resulting in suboptimal performance. To this end, we propose TriSIM4Rec, a triple structural information modeling method for accurate, explainable and interactive recommendation on dynamic interaction graphs. Specifically, TriSIM4Rec consists of 1) a dynamic ideal low-pass graph filter to dynamically mine co-occurrence information in user-item interactions, which is implemented by incremental singular value decomposition (SVD); 2) a parameter-free attention module to capture sequential information of user interactions effectively and efficiently; and 3) an item transition matrix to store the transition probabilities of item pairs. Then, we fuse the predictions from the triple structural information sources to obtain the final recommendation results. By analyzing the relationship between the SVD-based and the recently emerging graph signal processing (GSP)-based collaborative filtering methods, we find that the essence of SVD is an ideal low-pass graph filter, so that the interest vector space in TriSIM4Rec can be extended to achieve explainable and interactive recommendation, making it possible for users to actively break through the information cocoons. Experiments on six public datasets demonstrated the effectiveness of TriSIM4Rec in accuracy, explainability and interactivity.

cs.IR↗

arXiv2023

CogDL: A Comprehensive Library for Graph Deep Learning

Graph neural networks (GNNs) have attracted tremendous attention from the graph learning community in recent years. It has been widely adopted in various real-world applications from diverse domains, such as social networks and biological graphs. The research and applications of graph deep learning present new challenges, including the sparse nature of graph data, complicated training of GNNs, and non-standard evaluation of graph tasks. To tackle the issues, we present CogDL, a comprehensive library for graph deep learning that allows researchers and practitioners to conduct experiments, compare methods, and build applications with ease and efficiency. In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries. By utilizing this unified trainer, CogDL can optimize the GNN training loop with several training techniques, such as mixed precision training. Moreover, we develop efficient sparse operators for CogDL, enabling it to become the most competitive graph library for efficiency. Another important CogDL feature is its focus on ease of use with the aim of facilitating open and reproducible research of graph learning. We leverage CogDL to report and maintain benchmark results on fundamental graph tasks, which can be reproduced and directly used by the community.

cs.SI↗

arXiv2023

Implementing Microwave Impedance Microscopy in a Dilution Refrigerator

We report the implementation of a dilution-refrigerator-based scanning microwave impedance microscope (MIM) with a base temperature of ~ 100 mK. The vibration noise of our apparatus with tuning-fork feedback control is as low as 1 nm. Using this setup, we have demonstrated the imaging of quantum anomalous Hall states in magnetically (Cr and V) doped (Bi, Sb)2Te3 thin films grown on mica substrates. Both the conductive edge modes and topological phase transitions near coercive fields of Cr-doped and V-doped layers are visualized in the field-dependent results. Our work establishes the experimental platform for investigating nanoscale quantum phenomena under ultralow temperatures.

cond-mat.mes-hall↗

arXiv2023

Acoustic Metafluid for Independent manipulation of the Mass Density and Bulk Modulus

Tuning the mass density and bulk modulus independently is the key to manipulate the propagation of sound wave. Acoustic metamaterials provide a feasible method to realize various acoustic parameters. However, the relevant studies are mainly concentrated in air, and the huge impedance difference makes it difficult to directly extend these airborne structures to underwater application. Here, we propose a metafluid to realize independent manipulation of the mass density and bulk modulus underwater. The metafluid is composed of hollow regular polygons immersed in the water. By adjusting the side number of the hollow regular polygons and choosing proper materials, the effective mass density and bulk modulus of the metafluid could be modulated independently. Based on the flexible adjustment method, metafluids with same impedance but different sound velocities are designed and used to realize an underwater impedance-matched gradient index lens. In addition, by combining the proposed metafluid with other artificial structures, acoustic parameters with great anisotropy can be achieved, which is exemplified by the design and demonstration of an impedance-matched underwater acoustic carpet cloak. This work can expand the practicability of underwater metamaterials and pave the way for future potential engineering applications in the practical underwater devices.

physics.app-ph↗

arXiv2023

Dynamical Behaviors of Small-scale Buoyant Diffusion Flame Oscillators in Externally Swirling Flows

Small-scale flickering buoyant diffusion flames in externally swirling flows were computationally investigated with a particular interest in identifying and characterizing various distinct dynamical behaviors of the flame oscillators under different swirling flow conditions. By varying the external swirl, six distinct flame dynamical modes, such as the flickering flame, the oscillating flame, the steady flame, the lifted flame, the spiral flame, and the flame with a vortex bubble, were computationally identified in both physical and phase spaces and analyzed from the perspective of vortex dynamics. Specifically, the frequency of buoyancy-induced flame flicker nonlinearly increases with the swirling intensity in the weak swirl regime. Further increasing the swirling intensity causes the vortex shedding to occur either around the flame tip or downstream of the flame, and the flame stops flickering but oscillates with small amplitude or stays in a steady state. A sufficiently high swirling intensity locally extinguishes the flame at its base, leading to a lifted flame. In addition, the spiral mode and the vortex-bubble mode were found for the flame at large swirl angles. Through establishing the phase portrait for featuring the flow in flames, the dynamical behaviors are presented and compared in phase space.

physics.flu-dyn↗

arXiv2023

Noise analysis of the atomic superheterodyne receiver based on flat-top laser beams

Since its theoretical sensitivity is limited by quantum noise, radio wave sensing based on Rydberg atoms has the potential to replace its traditional counterparts with higher sensitivity and has developed rapidly in recent years. However, as the most sensitive atomic radio wave sensor, the atomic superheterodyne receiver lacks a detailed noise analysis to pave its way to achieve theoretical sensitivity. In this work, we quantitatively study the noise power spectrum of the atomic receiver versus the number of atoms, where the number of atoms is precisely controlled by changing the diameters of flat-top excitation laser beams. The results show that under the experimental conditions that the diameters of excitation beams are less than or equal to 2 mm and the read-out frequency is larger than 70 kHz, the sensitivity of the atomic receiver is limited only by the quantum noise and, in the other conditions, limited by classical noises. However, the experimental quantum-projection-noise-limited sensitivity this atomic receiver reaches is far from the theoretical sensitivity. This is because all atoms involved in light-atom interaction will contribute to noise, but only a fraction of them participating in the radio wave transition can provide valuable signals. At the same time, the calculation of the theoretical sensitivity considers both the noise and signal are contributed by the same amount of atoms. This work is essential in making the sensitivity of the atomic receiver reach its ultimate limit and is significant in quantum precision measurement.

quant-ph↗

arXiv2023

CASP-Net: Rethinking Video Saliency Prediction from an Audio-VisualConsistency Perceptual Perspective

Incorporating the audio stream enables Video Saliency Prediction (VSP) to imitate the selective attention mechanism of human brain. By focusing on the benefits of joint auditory and visual information, most VSP methods are capable of exploiting semantic correlation between vision and audio modalities but ignoring the negative effects due to the temporal inconsistency of audio-visual intrinsics. Inspired by the biological inconsistency-correction within multi-sensory information, in this study, a consistency-aware audio-visual saliency prediction network (CASP-Net) is proposed, which takes a comprehensive consideration of the audio-visual semantic interaction and consistent perception. In addition a two-stream encoder for elegant association between video frames and corresponding sound source, a novel consistency-aware predictive coding is also designed to improve the consistency within audio and visual representations iteratively. To further aggregate the multi-scale audio-visual information, a saliency decoder is introduced for the final saliency map generation. Substantial experiments demonstrate that the proposed CASP-Net outperforms the other state-of-the-art methods on six challenging audio-visual eye-tracking datasets. For a demo of our system please see our project webpage.

cs.CV↗

arXiv2023

Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization

With the fast development of quantitative portfolio optimization in financial engineering, lots of AI-based algorithmic trading strategies have demonstrated promising results, among which reinforcement learning begins to manifest competitive advantages. However, the environment from real financial markets is complex and hard to be fully simulated, considering the observation of abrupt transitions, unpredictable hidden causal factors, heavy tail properties and so on. Thus, in this paper, first, we adopt a heavy-tailed preserving normalizing flows to simulate high-dimensional joint probability of the complex trading environment and develop a model-based reinforcement learning framework to better understand the intrinsic mechanisms of quantitative online trading. Second, we experiment with various stocks from three different financial markets (Dow, NASDAQ and S&P) and show that among these three financial markets, Dow gets the best performance based on various evaluation metrics under our back-testing system. Especially, our proposed method is able to mitigate the impact of unpredictable financial market crises during the COVID-19 pandemic period, resulting in a lower maximum drawdown. Third, we also explore the explanation of our RL algorithm. (1), we utilize the pattern causality method to study the interactive relation among different stocks in the environment. (2), We analyze the dynamic loss and actor loss to ensure the convergence of our strategies. (3), by visualizing high dimensional state transition data comparisons from real and virtual buffers with t-SNE, we uncover some effective patterns of better portfolio optimization strategies. (4), we also utilize eigenvalue analysis to study the convergence properties of the environmen's model.

q-fin.MF↗

arXiv2023

The performance of SiPM-based gamma-ray detector (GRD) of GECAM-C

As a new member of GECAM mission, the GECAM-C (also called High Energy Burst Searcher, HEBS) is a gamma-ray all-sky monitor onboard SATech-01 satellite, which was launched on July 27th, 2022 to detect gamma-ray transients from 6 keV to 6 MeV, such as Gamma-Ray Bursts (GRBs), high energy counterpart of Gravitational Waves (GWs) and Fast Radio Bursts (FRBs), and Soft Gamma-ray Repeaters (SGRs). Together with GECAM-A and GECAM-B launched in December 2020, GECAM-C will greatly improve the monitoring coverage, localization, as well as temporal and spectral measurements of gamma-ray transients. GECAM-C employs 12 SiPM-based Gamma-Ray Detectors (GRDs) to detect gamma-ray transients . In this paper, we firstly give a brief description of the design of GECAM-C GRDs, and then focus on the on-ground tests and in-flight performance of GRDs. We also did the comparison study of the SiPM in-flight performance between GECAM-C and GECAM-B. The results show GECAM-C GRD works as expected and is ready to make scientific observations.

astro-ph.IM↗

arXiv2023

Cross calibration of gamma-ray detectors (GRD) of GECAM-C

The gamma-ray detectors (GRDs) of GECAM-C onborad SATech-01 satellite is designed to monitor gamma-ray transients all over the sky from 6 keV to 6 MeV. The energy response matrix is the key to do spectral measurements of bursts, which is usually generated from GEANT4 simulation and partially verified by the ground calibration. In this work, energy response matrix of GECAM-C GRD is cross-calibrated with Fermi/GBM and Swift/BAT using a sample of Gamma-Ray Bursts (GRBs) and Soft Gamma-Ray Repeaters (SGRs). The calibration results show there is a good agreement between GECAM-C and other reasonably well calibrated instrument (i.e. Fermi/GBM and Swift/BAT). We also find that different GRD detectors of GECAM-C also show consistency with each other. All these results indicate that GECAM-C GRD can provide reliable spectral measurements.

astro-ph.IM↗

arXiv2023

Beamforming Design for RIS-Aided AF Relay Networks

Since reconfigurable intelligent surface (RIS) is considered to be a passive reflector for rate performance enhancement, a RIS-aided amplify-and-forward (AF) relay network is presented. By jointly optimizing the beamforming matrix at AF relay and the phase shifts matrices at RIS, two schemes are put forward to address a maximizing signal-to-noise ratio (SNR) problem. Firstly, aiming at achieving a high rate, a high-performance alternating optimization (AO) method based on Charnes-Cooper transformation and semidefinite programming (CCT-SDP) is proposed, where the optimization problem is decomposed to three subproblems solved by CCT-SDP and rank-one solutions can be recovered by Gaussian randomization. While the optimization variables in CCT-SDP method are matrices, which leads to extremely high complexity. In order to reduce the complexity, a low-complexity AO scheme based on Dinkelbachs transformation and successive convex approximation (DT-SCA) is put forward, where matrices variables are transformed to vector variables and three decoupled subproblems are solved by DT-SCA. Simulation results verify that compared to two benchmarks (i.e. a RIS-assisted AF relay network with random phase and a AF relay network without RIS), the proposed CCT-SDP and DT-SCA schemes can harvest better rate performance. Furthermore, it is revealed that the rate of the low-complexity DT-SCA method is close to that of CCT-SDP method.

cs.IT↗

arXiv2023

A Localization Method of High Energy Transients for All-Sky Gamma-Ray Monitor

Fast and reliable localization of high-energy transients is crucial for characterizing the burst properties and guiding the follow-up observations. Localization based on the relative counts of different detectors has been widely used for all-sky gamma-ray monitors. There are two major methods for this counts distribution localization: $χ^{2}$ minimization method and the Bayesian method. Here we propose a modified Bayesian method that could take advantage of both the accuracy of the Bayesian method and the simplicity of the $χ^{2}$ method. With comprehensive simulations, we find that our Bayesian method with Poisson likelihood is generally more applicable for various bursts than $χ^{2}$ method, especially for weak bursts. We further proposed a location-spectrum iteration approach based on the Bayesian inference, which could alleviate the problems caused by the spectral difference between the burst and location templates. Our method is very suitable for scenarios with limited computation resources or time-sensitive applications, such as in-flight localization software, and low-latency localization for rapid follow-up observations.

astro-ph.HE↗

arXiv2023

Proximity-induced quasi-one-dimensional superconducting quantum anomalous Hall state: a promising scalable top-down approach towards localized Majorana modes

In this work, ~100 nm wide quantum anomalous Hall insulator (QAHI) nanoribbons are etched from a two-dimensional QAHI film. One part of the nanoribbon is covered with superconducting Nb, while the other part is connected to an Au lead via two-dimensional QAHI regions. Andreev reflection spectroscopy measurements were performed, and multiple in-gap conductance peaks were observed in three different devices. In the presence of an increasing magnetic field perpendicular to the QAHI film, the multiple in-gap peak structure evolves into a single zero-bias conductance peak (ZBCP). Theoretical simulations suggest that the measurements are consistent with the scenario that the increasing magnetic field drives the nanoribbons from a multi-channel occupied regime to a single channel occupied regime, and that the ZBCP may be induced by zero energy Majorana modes as previously predicted [24]. Although further experiments are needed to clarify the nature of the ZBCP, we provide initial evidence that quasi-1D QAHI nanoribbon/superconductor heterostructures are new and promising platforms for realizing zero-energy Majorana modes.

cond-mat.supr-con↗

arXiv2023

A network-based biomarkers discovery of Cold/Hot ZHENG chronic gastritis and Cold/Hot herbs of formulae

Objective: To discover biomarkers and uncover the mechanism of Cold/Hot ZHENG (syndrome in traditional Chinese medicine) chronic gastritis (CG) and Cold/Hot herbs in traditional Chinese medicine (TCM) formulae on systematic biology. Background: CG is a common inflammatory disease and the diagnosis of CG in TCM can be classified into Cold ZHENG (Asthenic Cold) and Hot ZHENG (Excess Hot). However, the molecular features of Cold/Hot ZHENG in CG and the mechanism of Cold/Hot herbs in formulae for CG remained unclear. Methods: Based on data of 35 patients of Cold/Hot ZHENG CG and 3 scRNA-seq CG samples, we conduct analysis with transcriptomics datasets and algorithms, to discover biomarkers for Cold/Hot ZHENG CG. And we collected 25 formulae (with traditional effects related to Cold/Hot ZHENG) for CG and corresponding 89 Cold/Hot herbs (including Warm/Cool herbs) to discover features and construct target networks of Cold/Hot herbs on the basis of network target and enrichment analysis. Results: Biomarkers of Cold/Hot ZHENG CG represented by CCL2 and LEP suggested that Hot ZHENG CG might be characterized by over-inflammation and exuberant metabolism, and Cold ZHENG CG showed a trend of suppression in immune regulation and energy metabolism. And biomarkers of Cold/Hot ZHENG showed also significant changes in the progression of gastric cancer. And biomarkers and pathways of Hot herbs intend to regulate immune responses and energy metabolism, while those of Cold herbs were likely to participate in anti-inflammation effect. Conclusion: In this study, we found that the biomarkers and mechanism of Cold/Hot ZHENG CG and those of Cold/Hot herbs were closely related to the regulation of immune and metabolisms. These findings may reflect the mechanism, build bridges between multiple views of Cold/Hot ZHENG and Cold/Hot herbs, and provide a research paradigm for further achieving precision TCM.

q-bio.OT↗

arXiv2023

GIPA: A General Information Propagation Algorithm for Graph Learning

Graph neural networks (GNNs) have been widely used in graph-structured data computation, showing promising performance in various applications such as node classification, link prediction, and network recommendation. Existing works mainly focus on node-wise correlation when doing weighted aggregation of neighboring nodes based on attention, such as dot product by the dense vectors of two nodes. This may cause conflicting noise in nodes to be propagated when doing information propagation. To solve this problem, we propose a General Information Propagation Algorithm (GIPA in short), which exploits more fine-grained information fusion including bit-wise and feature-wise correlations based on edge features in their propagation. Specifically, the bit-wise correlation calculates the element-wise attention weight through a multi-layer perceptron (MLP) based on the dense representations of two nodes and their edge; The feature-wise correlation is based on the one-hot representations of node attribute features for feature selection. We evaluate the performance of GIPA on the Open Graph Benchmark proteins (OGBN-proteins for short) dataset and the Alipay dataset of Alibaba. Experimental results reveal that GIPA outperforms the state-of-the-art models in terms of prediction accuracy, e.g., GIPA achieves an average ROC-AUC of $0.8901\pm 0.0011$, which is better than that of all the existing methods listed in the OGBN-proteins leaderboard.

cs.LG↗

arXiv2023

Personalized Graph Signal Processing for Collaborative Filtering

The collaborative filtering (CF) problem with only user-item interaction information can be solved by graph signal processing (GSP), which uses low-pass filters to smooth the observed interaction signals on the similarity graph to obtain the prediction signals. However, the interaction signal may not be sufficient to accurately characterize user interests and the low-pass filters may ignore the useful information contained in the high-frequency component of the observed signals, resulting in suboptimal accuracy. To this end, we propose a personalized graph signal processing (PGSP) method for collaborative filtering. Firstly, we design the personalized graph signal containing richer user information and construct an augmented similarity graph containing more graph topology information, to more effectively characterize user interests. Secondly, we devise a mixed-frequency graph filter to introduce useful information in the high-frequency components of the observed signals by combining an ideal low-pass filter that smooths signals globally and a linear low-pass filter that smooths signals locally. Finally, we combine the personalized graph signal, the augmented similarity graph and the mixed-frequency graph filter by proposing a pipeline consisting of three key steps: pre-processing, graph convolution and post-processing. Extensive experiments show that PGSP can achieve superior accuracy compared with state-of-the-art CF methods and, as a nonparametric method, PGSP has very high training efficiency.

cs.IR↗

arXiv2023

ReGANIE: Rectifying GAN Inversion Errors for Accurate Real Image Editing

The StyleGAN family succeed in high-fidelity image generation and allow for flexible and plausible editing of generated images by manipulating the semantic-rich latent style space.However, projecting a real image into its latent space encounters an inherent trade-off between inversion quality and editability. Existing encoder-based or optimization-based StyleGAN inversion methods attempt to mitigate the trade-off but see limited performance. To fundamentally resolve this problem, we propose a novel two-phase framework by designating two separate networks to tackle editing and reconstruction respectively, instead of balancing the two. Specifically, in Phase I, a W-space-oriented StyleGAN inversion network is trained and used to perform image inversion and editing, which assures the editability but sacrifices reconstruction quality. In Phase II, a carefully designed rectifying network is utilized to rectify the inversion errors and perform ideal reconstruction. Experimental results show that our approach yields near-perfect reconstructions without sacrificing the editability, thus allowing accurate manipulation of real images. Further, we evaluate the performance of our rectifying network, and see great generalizability towards unseen manipulation types and out-of-domain images.

cs.CV↗