Chao Yang
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View article: Street view-derived city built environment and vulnerability to temperature extremes: a nationally representative population-based cohort study
Street view-derived city built environment and vulnerability to temperature extremes: a nationally representative population-based cohort study Open
Our findings provide insights for evidence-based urban planning and public health strategies, emphasizing the need for adaptive, context-specific urban design that balances the potentially competing demands of population heat resilience an…
View article: SwinMR: A Mutual Refinement Enhanced SwinTrack Framework
SwinMR: A Mutual Refinement Enhanced SwinTrack Framework Open
The task of tracking weak targets in low-altitude UAV scenarios requires high robustness and generalization ability of the model. Against this backdrop, this paper proposes a novel annotation and training mechanism based on SwinTrack. To i…
View article: QuGStep: Refining step size selection in gradient estimation for variational quantum algorithms
QuGStep: Refining step size selection in gradient estimation for variational quantum algorithms Open
Variational quantum algorithms (VQAs) offer a promising approach to solving computationally demanding problems by combining parameterized quantum circuits with classical optimization. Estimating probabilistic outcomes on quantum hardware r…
View article: Predicting kidney replacement therapy, cardiovascular disease and all-cause mortality in advanced chronic kidney disease among the Chinese population
Predicting kidney replacement therapy, cardiovascular disease and all-cause mortality in advanced chronic kidney disease among the Chinese population Open
The Grams model, designed to predict adverse event risks in advanced chronic kidney disease (CKD) patients, was evaluated in a Chinese cohort of 1,333 patients with eGFR below 30 mL/min/1.73 m2. The model demonstrated moderate to good disc…
View article: SRaFTE: Super-Resolution and Future Time Extrapolation for Time-Dependent PDEs
SRaFTE: Super-Resolution and Future Time Extrapolation for Time-Dependent PDEs Open
We present SRaFTE (Super-Resolution and Future Time Extrapolation), a two-phase learning framework that couples coarse grid solvers with neural operators to super-resolve and forecast fine grid dynamics for time-dependent partial different…
View article: PeTTO: Leveraging GPUs to Accelerate Topology Optimization with the Pseudo-Transient Methods
PeTTO: Leveraging GPUs to Accelerate Topology Optimization with the Pseudo-Transient Methods Open
We present a Pseudo-Transient Topology Optimization (PeTTO) approach that can leverage graphics processing units (GPUs) to efficiently solve single-material and multi-material topology optimization problems. By integrating PeTTO with phase…
View article: GPU acceleration of non-equilibrium Green's function calculation using OpenACC and CUDA FORTRAN
GPU acceleration of non-equilibrium Green's function calculation using OpenACC and CUDA FORTRAN Open
The numerical solution of the Kadanoff-Baym nonlinear integro-differential equations, which yields the non-equilibrium Green's functions (NEGFs) of quantum many-body systems, poses significant computational challenges due to its high compu…
View article: Dynamic mode decomposition for gyrokinetic eigenmode analysis
Dynamic mode decomposition for gyrokinetic eigenmode analysis Open
Dynamic mode decomposition (DMD) is a post-processing approach to decompose a complex time series into a set of modes via spectral analysis. DMD provides a new and powerful method to recover gyrokinetic drift-wave eigenfrequencies and eige…
View article: Exploring the nexus of many-body theories through neural network techniques: the tangent model
Exploring the nexus of many-body theories through neural network techniques: the tangent model Open
In this paper, we present a physically informed neural network (NN) representation of the effective interactions associated with coupled-cluster downfolding models to describe chemical systems and processes. The NN representation not only …
View article: Quantum Chemical Density Matrix Renormalization Group Method Boosted by Machine Learning
Quantum Chemical Density Matrix Renormalization Group Method Boosted by Machine Learning Open
The use of machine learning (ML) to refine low-level theoretical calculations to achieve higher accuracy is a promising and actively evolving approach known as Δ-ML. The density matrix renormalization group (DMRG) is a powerful variational…
View article: Inexact subspace projection methods for low-rank tensor eigenvalue problems
Inexact subspace projection methods for low-rank tensor eigenvalue problems Open
We propose inexact subspace iteration for solving high-dimensional eigenvalue problems with low-rank structure. Inexactness stems from low-rank compression, enabling efficient representation of high-dimensional vectors in a low-rank tensor…
View article: Predicting nonequilibrium Green's function dynamics and photoemission spectra via nonlinear integral operator learning
Predicting nonequilibrium Green's function dynamics and photoemission spectra via nonlinear integral operator learning Open
Understanding the dynamics of nonequilibrium quantum many-body systems is an important research topic in a wide range of fields across condensed matter physics, quantum optics, and high-energy physics. However, numerical studies of large-s…
View article: Effective many-body interactions in reduced-dimensionality spaces through neural network models
Effective many-body interactions in reduced-dimensionality spaces through neural network models Open
Accurately describing properties of challenging problems in physical sciences often requires complex mathematical models that are unmanageable to tackle head on. Therefore, developing reduced-dimensionality representations that encapsulate…
View article: The Effect of Post Heat Treatment on the Microstructure and Mechanical Properties of Cold-Sprayed Zn-6Cu Deposits
The Effect of Post Heat Treatment on the Microstructure and Mechanical Properties of Cold-Sprayed Zn-6Cu Deposits Open
To explore the feasibility of preparing Zn alloy bulk, Zn-6Cu deposit was prepared by cold-spraying additive manufacturing. Microstructure, tensile and wear behavior were investigated before and after heat treatment. Cold-sprayed Zn-6Cu de…
View article: Artificial-intelligence-driven shot reduction in quantum measurement
Artificial-intelligence-driven shot reduction in quantum measurement Open
Variational Quantum Eigensolver (VQE) provides a powerful solution for approximating molecular ground state energies by combining quantum circuits and classical computers. However, estimating probabilistic outcomes on quantum hardware requ…
View article: Research on Pseudo-Label Improvement Based on Probabilistic Uncertainty
Research on Pseudo-Label Improvement Based on Probabilistic Uncertainty Open
The pedestrian re-identification task explored in this study has strong application scenarios in real life. Generally, in actual scenarios, the pedestrian samples taken by the camera are unlabeled, so pseudo-labeling is used. However, low-…
View article: Denoising of imaginary time response functions with Hankel projections
Denoising of imaginary time response functions with Hankel projections Open
Imaginary-time response functions of finite-temperature quantum systems are often obtained with methods that exhibit stochastic or systematic errors. Reducing these errors comes at a large computational cost—in quantum Monte Carlo simulati…
View article: Solving a class of infinite‐dimensional tensor eigenvalue problems by translational invariant tensor ring approximations
Solving a class of infinite‐dimensional tensor eigenvalue problems by translational invariant tensor ring approximations Open
We examine a method for solving an infinite‐dimensional tensor eigenvalue problem , where the infinite‐dimensional symmetric matrix exhibits a translational invariant structure. We provide a formulation of this type of problem from a numer…
View article: Artificial-Intelligence-Driven Shot Reduction in Quantum Measurement
Artificial-Intelligence-Driven Shot Reduction in Quantum Measurement Open
Variational Quantum Eigensolver (VQE) provides a powerful solution for approximating molecular ground state energies by combining quantum circuits and classical computers. However, estimating probabilistic outcomes on quantum hardware requ…
View article: Denoising and Extension of Response Functions in the Time Domain
Denoising and Extension of Response Functions in the Time Domain Open
Response functions of quantum systems, such as electron Green's functions, magnetic, or charge susceptibilities, describe the response of a system to an external perturbation. They are the central objects of interest in field theories and …
View article: Denoising of Imaginary Time Response Functions with Hankel projections
Denoising of Imaginary Time Response Functions with Hankel projections Open
Imaginary-time response functions of finite-temperature quantum systems are often obtained with methods that exhibit stochastic or systematic errors. Reducing these errors comes at a large computational cost -- in quantum Monte Carlo simul…
View article: Explicit Quantum Circuits for Block Encodings of Certain Sparse Matrices
Explicit Quantum Circuits for Block Encodings of Certain Sparse Matrices Open
Many standard linear algebra problems can be solved on a quantum computer by using recently developed quantum linear algebra algorithms that make use of block encodings and quantum eigenvalue/singular value transformations. A block encodin…
View article: HOSCF: Efficient decoupling algorithms for finding the best rank-one approximation of higher-order tensors
HOSCF: Efficient decoupling algorithms for finding the best rank-one approximation of higher-order tensors Open
Best rank-one approximation is one of the most fundamental tasks in tensor computation. In order to fully exploit modern multi-core parallel computers, it is necessary to develop decoupling algorithms for computing the best rank-one approx…
View article: A deep-ultraviolet nonlinear-optical material with a wide bandgap and large static dielectric polarizability coefficient: Na<sub>6</sub>Si<sub>3</sub>F<sub>18</sub>
A deep-ultraviolet nonlinear-optical material with a wide bandgap and large static dielectric polarizability coefficient: Na<sub>6</sub>Si<sub>3</sub>F<sub>18</sub> Open
A silicon-based NC DUV NLO material Na 6 Si 3 F 18 has large bandgap of 10.45 eV and static dielectric polarizability coefficient 4.3 times that of quartz, and is potential for manufacturing an all-solid-state laser with a wavelength short…
View article: Learning nonlinear integral operators via recurrent neural networks and its application in solving integro-differential equations
Learning nonlinear integral operators via recurrent neural networks and its application in solving integro-differential equations Open
In this paper, we propose using LSTM-RNNs (Long Short-Term Memory-Recurrent Neural Networks) to learn and represent nonlinear integral operators that appear in nonlinear integro-differential equations (IDEs). The LSTM-RNN representation of…