arXiv subjects

Yi Zhang

Publications and source records attributed to Yi Zhang.

At least 865 records · Page 48Linked to original sources

arXiv2022

Discovery of a pair density wave state in a monolayer high-Tc iron-based superconductor

The pair density wave (PDW) is an extraordinary superconducting state where Cooper pairs carry nonzero momentum. It can emerge when the full condensation of zero momentum Cooper pairs is frustrated. Evidence for the existence of intrinsic PDW order in high-temperature (high-Tc) cuprate superconductors and kagome superconductors has emerged recently. However, the PDW order in iron-based high-Tc superconductors has not been observed experimentally. Here, using scanning tunneling microscopy/spectroscopy, we report the discovery of the PDW state in monolayer iron-based high-Tc Fe(Te,Se) films grown on SrTiO3(001) substrates. The PDW state with a period of λ~3.6a_Fe (a_Fe is the distance between neighboring Fe atoms) is observed at the domain walls by the spatial electronic modulations of the local density of states, superconducting gap, and the π-phase shift boundaries of the PDW around the dislocations of the intertwined charge density wave order. The discovery of the PDW state in the monolayer Fe(Te,Se) film provides a low-dimensional platform to study the interplay between the correlated electronic states and unconventional Cooper pairing in high-Tc superconductors.

cond-mat.supr-con↗

arXiv2022

Ground-state properties via machine learning quantum constraints

Ground-state properties are central to our understanding of quantum many-body systems. At first glance, it seems natural and essential to obtain the ground state before analyzing its properties; however, its exponentially large Hilbert space has made such studies costly, if not prohibitive, on sufficiently large system sizes. Here, we propose an alternative strategy based upon the expectation values of an ensemble of operators and the elusive yet vital quantum constraints between them, where the search for ground-state properties simply equates to classical constrained minimization. These quantum constraints are generally obtainable via sampling and then machine learning on a large number of systematically consistent quantum many-body states. We showcase our perspective on 1D fermion chains and spin chains for applicability, effectiveness, caveats, and unique advantages, especially for strongly correlated systems, thermodynamic-limit systems, property designs, etc.

cond-mat.str-el↗

arXiv2022

Density-wave tendency from a topological nodal-line perspective

The understanding of density waves is a vital component of our insight into electronic quantum matters. Here, we propose an additional mosaic to the existing mechanisms such as Fermi-surface nesting, electron-phonon coupling, and exciton condensation. In particular, we find that certain 2D spin density-wave systems are equivalent to 3D Dirac nodal-line systems in the presence of a magnetic field, whose electronic structure takes the form of Dirac-fermion Landau levels and allows a straightforward analysis of its optimal filling. The subsequent minimum-energy wave vector varies over a continuous range and shows no direct connection to the original Fermi surfaces in 2D. Also, we carry out numerical calculations where the results on model examples support our theory. Our study points out that we have yet to attain a complete understanding of the emergent density wave formalism.

cond-mat.str-el↗

arXiv2022

Tunable Polarization-Multiplexed Achromatic Dielectric Metalens

Tunable metasurfaces provide a compact and efficient strategy for optical components that require active wavefront shaping. Varifocal metalens is one of the most important applications. However, the existing tunable metalens rarely serves broadband wavelengths restricting their applications in broadband imaging and color display due to chromatic aberration. Herein, we demonstrate an electrically tunable polarization-multiplexed varifocal achromatic dielectric metalens integrated with twisted nematic liquid crystals (TNLCs) in the visible region. The phase profiles at different wavelengths under two orthogonal polarization channels are customized by the particle swarm optimization algorithm and optimally matched with the metaunits database to achieve polarization-multiplexed dispersion manipulation including achromatic performance. By combining the broadband linear polarization conversion ability of TNLC, the tunability of varifocal achromatic metalens is realized by applying different voltages. Further, the electrically tunable customized dispersion-manipulated metalens and switchable color metaholograms are demonstrated. The proposed devices will accelerate the application of metasurfaces in broadband zoom imaging, AR/VR displays, and spectral detection.

physics.optics↗

arXiv2022

HiGNN: Hierarchical Informative Graph Neural Networks for Molecular Property Prediction Equipped with Feature-Wise Attention

Elucidating and accurately predicting the druggability and bioactivities of molecules plays a pivotal role in drug design and discovery and remains an open challenge. Recently, graph neural networks (GNN) have made remarkable advancements in graph-based molecular property prediction. However, current graph-based deep learning methods neglect the hierarchical information of molecules and the relationships between feature channels. In this study, we propose a well-designed hierarchical informative graph neural networks framework (termed HiGNN) for predicting molecular property by utilizing a co-representation learning of molecular graphs and chemically synthesizable BRICS fragments. Furthermore, a plug-and-play feature-wise attention block is first designed in HiGNN architecture to adaptively recalibrate atomic features after the message passing phase. Extensive experiments demonstrate that HiGNN achieves state-of-the-art predictive performance on many challenging drug discovery-associated benchmark datasets. In addition, we devise a molecule-fragment similarity mechanism to comprehensively investigate the interpretability of HiGNN model at the subgraph level, indicating that HiGNN as a powerful deep learning tool can help chemists and pharmacists identify the key components of molecules for designing better molecules with desired properties or functions. The source code is publicly available at https://github.com/idruglab/hignn.

cs.LG↗

arXiv2022

SemanticAxis: Exploring Multi-attribute Data by Semantics Construction and Ranking Analysis

Mining the distribution of features and sorting items by combined attributes are two common tasks in exploring and understanding multi-attribute (or multivariate) data. Up to now, few have pointed out the possibility of merging these two tasks into a united exploration context and the potential benefits of doing so. In this paper, we present SemanticAxis, a technique that achieves this goal by enabling analysts to build a semantic vector in two-dimensional space interactively. Essentially, the semantic vector is a linear combination of the original attributes. It can be used to represent and explain abstract concepts implied in local (outliers, clusters) or global (general pattern) features of reduced space, as well as serving as a ranking metric for its defined concepts. In order to validate the significance of combining the above two tasks in multi-attribute data analysis, we design and implement a visual analysis system, in which several interactive components cooperate with SemanticAxis seamlessly and expand its capacity to handle complex scenarios. We prove the effectiveness of our system and the SemanticAxis technique via two practical cases.

cs.DB↗

arXiv2022

Research on Multi-Objective Planning of Electric Vehicle Charging Stations Considering the Condition of Urban Traffic Network

As an important supporting facility for electric vehicles, the reasonable planning and layout of charging stations are of great significance to the development of electric vehicles. However, the planning and layout of charging stations is affected by various complex factors such as policy economy, charging demand, user charging comfort, and road traffic conditions. How to weigh various factors to construct a reasonable model of charging station location and capacity has become a major difficulty in the field of electric vehicle charging facility planning. Firstly, this paper constructs the location and capacity optimization model of the charging station with the goal of maximizing the revenue of operators and minimizing the user's charging additional cost. At the same time, the road time-consuming index is introduced to quantify the impact of road congestion on the user's charging additional cost, so as to effectively improve the user's satisfaction during charging. Then, aiming at the charging station planning model, a non-dominated sorting genetic algorithm with an elite strategy (NSGA-II) based on chaos initialization and arithmetic crossover operator is proposed. Finally, taking the Haidian District of Beijing as the simulation object, the results show that compared with the situation of urban traffic networks not considered, the model proposed in this paper significantly reduces the cost of lost time of users by 11.4% and the total additional cost of users' charging by 7.6%. It not only ensures the economy of the system, but also effectively improves the charging satisfaction of users, which further verifies the feasibility and effectiveness of the model, and can provide a reference for the planning and layout of charging stations in the future.

eess.SY↗

arXiv2022

Impact of Loss Model Selection on Power Semiconductor Lifetime Prediction in Electric Vehicles

Power loss estimation is an indispensable procedure to conduct lifetime prediction for power semiconductor device. The previous studies successfully perform steady-state power loss estimation for different applications, but which may be limited for the electric vehicles (EVs) with high dynamics. Based on two EV standard driving cycle profiles, this paper gives a comparative study of power loss estimation models with two different time resolutions, i.e., the output period average and the switching period average. The correspondingly estimated power losses, thermal profiles, and lifetime clearly pointed out that the widely applied power loss model with the output period average is limited for EV applications, in particular for the highly dynamic driving cycle. The difference in the predicted lifetime can be up to 300 times due to the unreasonable choice the loss model, which calls for the industry attention on the differences of the EVs and the importance of loss model selection in lifetime prediction.

eess.SY↗

arXiv2022

The eROSITA Final Equatorial Depth Survey (eFEDS): X-ray emission around star-forming and quiescent galaxies at $0.05<z<0.3$

We aim at characterizing the hot phase of the Circum-Galactic Medium in a large sample of galaxies. We stack X-ray events from the SRG/eROSITA eFEDS survey around central galaxies in the GAMA 9hr field to construct radially projected soft X-ray luminosity profiles as a function of their stellar mass and specific star formation rate. We consider samples of quiescent (star-forming) galaxies in the stellar mass range $2\times 10^{10}$ -- $10^{12}$ M$_\odot$ ($3\times 10^9$ -- $6\times10^{11}$ M$_\odot$). For quiescent galaxies, the X-ray profiles are clearly extended throughout the available mass range; however, the measured profile is likely biased high due to projection effects, as these galaxies tend to live in dense and hot environments. For the most massive star forming samples ($\geq10^{11}$ M$_\odot$), there is a hint of detection of extended emission. For star-forming galaxies with $< 10^{11}$ M$_\odot$ the X-ray stacked profiles are compatible with unresolved sources and consistent with the expected emission from faint Active Galactic Nuclei and X-ray binaries. We measure for the first time the mean relation between average X-ray luminosity and stellar mass separately for quiescent and star-forming galaxies. High-mass ($\geq 10^{11}$ M$_\odot$) star-forming or quiescent galaxies follow the expected scaling of virialized hot haloes, while lower mass star-forming galaxies show a less prominent luminosity and a weaker dependence on stellar mass, consistent with empirical models of the weak AGN population. When comparing our results with state-of-the art numerical simulations, we find an overall consistency on large ($>80$ kpc) scales at masses $\geq 10^{11}$ M$_\odot$, but disagreement on the small scales, where brighter than observed compact cores are predicted. Simulations also do not predict the clear differentiation that we observe between quiescent and star-forming galaxies.

astro-ph.GA↗

arXiv2022

Ultra-wideband Antireflection Assisted by Continuously Varying Temporal Medium

We demonstrate that reflectionless propagation of electromagnetic waves between two different materials can be achieved by designing an intermediate temporal medium, which can work in an ultra-wide frequency band. Such a temporal medium is designed with consideration of a multi-stage variation of the material' s permittivity in the time domain. The multi-stage temporal permittivity is formed by a cascaded quarter-wave temporal coating, which is an extension of the antireflection temporal coating by Pacheco-Peña et al [[1] Optica 7, 323 (2020)]. The strategy to render ultra-wideband antireflection temporal medium is discussed analytically and verified numerically. In-depth analysis shows that the multi-stage design of the temporal media implies a continuously temporal variation of the material' s constitutive parameters, thus an ultra-wideband antireflection temporal medium is reasonably obtained. As an illustrative example for application, the proposed temporal medium is adopted to realize impedance matching between a dielectric slab and free space, which validates our new findings.

physics.optics↗

arXiv2022

A non-invasive fault location method for modular multilevel converters under light load conditions

This paper proposes a non-invasive fault location method for modular multilevel converters (MMC) considering light load conditions. The prior-art fault location methods of the MMC are often developed and verified under full load conditions. However, it is revealed that the faulty arm current will be suppressed to be unipolar when the open-circuit fault happens on the submodule switch under light load. This leads to the capacitor voltage of the healthy and faulty submodules rising or falling with the same variations, increasing the difficulty of fault location. The proposed approach of injecting the second-order circulating current will rebuild the bipolar arm current of the MMC and enlarge the capacitor voltage deviations between the healthy and faulty SMs. As a result, the fault location time is significantly shortened. The simulations are carried out to validate the effectiveness of the proposed approach, showing that the fault location time is reduced to 1/6 compared with the condition without second-order circulating current injection.

eess.SY↗

arXiv2022

Robust Speaker Extraction Network Based on Iterative Refined Adaptation

Speaker extraction aims to extract target speech signal from a multi-talker environment with interference speakers and surrounding noise, given the target speaker's reference information. Most speaker extraction systems achieve satisfactory performance on the premise that the test speakers have been encountered during training time. Such systems suffer from performance degradation given unseen target speakers and/or mismatched reference voiceprint information. In this paper we propose a novel strategy named Iterative Refined Adaptation (IRA) to improve the robustness and generalization capability of speaker extraction systems in the aforementioned scenarios. Given an initial speaker embedding encoded by an auxiliary network, the extraction network can obtain a latent representation of the target speaker, which is fed back to the auxiliary network to get a refined embedding to provide more accurate guidance for the extraction network. Experiments on WSJ0-2mix-extr and WHAM! dataset confirm the superior performance of the proposed method over the network without IRA in terms of SI-SDR and PESQ improvement.

eess.AS↗

arXiv2022

Deep Multi-task Network for Delay Estimation and Echo Cancellation

Echo path delay (or ref-delay) estimation is a big challenge in acoustic echo cancellation. Different devices may introduce various ref-delay in practice. Ref-delay inconsistency slows down the convergence of adaptive filters, and also degrades the performance of deep learning models due to 'unseen' ref-delays in the training set. In this paper, a multi-task network is proposed to address both ref-delay estimation and echo cancellation tasks. The proposed architecture consists of two convolutional recurrent networks (CRNNs) to estimate the echo and enhanced signals separately, as well as a fully-connected (FC) network to estimate the echo path delay. Echo signal is first predicted, and then is combined with reference signal together for delay estimation. At the end, delay compensated reference and microphone signals are used to predict the enhanced target signal. Experimental results suggest that the proposed method makes reliable delay estimation and outperforms the existing state-of-the-art solutions in inconsistent echo path delay scenarios, in terms of echo return loss enhancement (ERLE) and perceptual evaluation of speech quality (PESQ). Furthermore, a data augmentation method is studied to evaluate the model performance on different portion of synthetical data with artificially introduced ref-delay.

eess.AS↗

arXiv2022

Learning Degradation Representations for Image Deblurring

In various learning-based image restoration tasks, such as image denoising and image super-resolution, the degradation representations were widely used to model the degradation process and handle complicated degradation patterns. However, they are less explored in learning-based image deblurring as blur kernel estimation cannot perform well in real-world challenging cases. We argue that it is particularly necessary for image deblurring to model degradation representations since blurry patterns typically show much larger variations than noisy patterns or high-frequency textures.In this paper, we propose a framework to learn spatially adaptive degradation representations of blurry images. A novel joint image reblurring and deblurring learning process is presented to improve the expressiveness of degradation representations. To make learned degradation representations effective in reblurring and deblurring, we propose a Multi-Scale Degradation Injection Network (MSDI-Net) to integrate them into the neural networks. With the integration, MSDI-Net can handle various and complicated blurry patterns adaptively. Experiments on the GoPro and RealBlur datasets demonstrate that our proposed deblurring framework with the learned degradation representations outperforms state-of-the-art methods with appealing improvements. The code is released at https://github.com/dasongli1/Learning_degradation.

cs.CV↗

arXiv2022

Gravitational anomalies of fermionic higher-spin fields

Using the Atiyah-Singer index theorem, we formally compute gravitational anomalies for fermionic higher-spin fields in two, six and ten dimensions, as well as the U(1) mixed gauge-gravitational anomaly in four dimensions. In all cases, anomaly cancellations are found for an infinite tower of fields with alternating chiralities.

hep-th↗

arXiv2022

Optimal Dynamic Orchestration in NDN-based Computing Networks

Named Data Networking (NDN) offers promising advantages in deploying next-generation service applications over distributed computing networks. We consider the problem of dynamic orchestration over a NDN-based computing network, in which nodes can be equipped with communication, computation, and data producing resources. Given a set of services with function-chaining structures, we address the design of distributed online algorithm that controls each node to make adaptive decisions on flowing service requests, committing function implementations, and/or producing data. We design a Service Discovery Assisted Dynamic Orchestration (SDADO) algorithm that reduces the end-to-end (E2E) delay of delivering the services, while providing optimal throughput performance. The proposed algorithm hybrids queuing-based flexibility and topology-based discipline, where the topological information is not pre-available but obtained through our proposed service discovery mechanism. We provide throughput-optimality analysis for SDADO, and then provide numerical results that confirm our analysis and demonstrates reduced round-trip E2E delay.

cs.NI↗

arXiv2022

Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimation

Occlusion poses a great threat to monocular multi-person 3D human pose estimation due to large variability in terms of the shape, appearance, and position of occluders. While existing methods try to handle occlusion with pose priors/constraints, data augmentation, or implicit reasoning, they still fail to generalize to unseen poses or occlusion cases and may make large mistakes when multiple people are present. Inspired by the remarkable ability of humans to infer occluded joints from visible cues, we develop a method to explicitly model this process that significantly improves bottom-up multi-person human pose estimation with or without occlusions. First, we split the task into two subtasks: visible keypoints detection and occluded keypoints reasoning, and propose a Deeply Supervised Encoder Distillation (DSED) network to solve the second one. To train our model, we propose a Skeleton-guided human Shape Fitting (SSF) approach to generate pseudo occlusion labels on the existing datasets, enabling explicit occlusion reasoning. Experiments show that explicitly learning from occlusions improves human pose estimation. In addition, exploiting feature-level information of visible joints allows us to reason about occluded joints more accurately. Our method outperforms both the state-of-the-art top-down and bottom-up methods on several benchmarks.

cs.CV↗

arXiv2022

SOUL-Net: A Sparse and Low-Rank Unrolling Network for Spectral CT Image Reconstruction

Spectral computed tomography (CT) is an emerging technology, that generates a multienergy attenuation map for the interior of an object and extends the traditional image volume into a 4D form. Compared with traditional CT based on energy-integrating detectors, spectral CT can make full use of spectral information, resulting in high resolution and providing accurate material quantification. Numerous model-based iterative reconstruction methods have been proposed for spectral CT reconstruction. However, these methods usually suffer from difficulties such as laborious parameter selection and expensive computational costs. In addition, due to the image similarity of different energy bins, spectral CT usually implies a strong low-rank prior, which has been widely adopted in current iterative reconstruction models. Singular value thresholding (SVT) is an effective algorithm to solve the low-rank constrained model. However, the SVT method requires manual selection of thresholds, which may lead to suboptimal results. To relieve these problems, in this paper, we propose a Sparse and lOw-rank UnroLling Network for spectral CT image reconstruction (SOUL-Net), that learns the parameters and thresholds in a data-driven manner. Furthermore, a Taylor expansion-based neural network backpropagation method is introduced to improve the numerical stability. The qualitative and quantitative results demonstrate that the proposed method outperforms several representative state-of-the-art algorithms in terms of detail preservation and artifact reduction.

physics.med-ph↗