arXiv subjects

Yuan Shi

Publications and source records attributed to Yuan Shi.

At least 37 records · Page 2Linked to original sources

arXiv2024

Accurate Kappa Reconstruction Algorithm for masked shear catalog (AKRA)

Weak gravitational lensing is an invaluable tool for understanding fundamental cosmological physics. An unresolved issue in weak lensing cosmology is to accurately reconstruct the lensing convergence $κ$ maps from discrete shear catalog with survey masks, which the seminal Kaiser-Squire (KS) method is not designed to address. We present the Accurate Kappa Reconstruction Algorithm for masked shear catalog (AKRA) to address the issue of mask. AKRA is built upon the prior-free maximum likelihood mapmaking method (or the unbiased minimum variance linear estimator). It is mathematically robust in dealing with mask, numerically stable to implement, and practically effective in improving the reconstruction accuracy. Using simulated maps with mask fractions ranging from 10\% to 50\% and various mask shapes, we demonstrate that AKRA outperforms KS at both the map level and summary statistics such as the auto power spectrum $C_κ$ of the reconstructed map, its cross-correlation coefficient $r_\ell$ with the true $κ$ map, the scatter plot and the localization measure. Unlike the Wiener filter method, it adopts no priors on the signal power spectrum, and therefore avoids the Wiener filter related biases at both the map level and cross-correlation statistics. If we only use the reconstructed map in the unmasked regions, the reconstructed $C_κ$ is accurate to $1\%$ or better and $1-r_\ell \lesssim 1\%$ (excluding $\ell$ at the smallest scales investigated), even for extreme cases of mask fraction and shape. As the first step, the current version of AKRA only addresses the mask issue and therefore ignores complexities such as curved sky and inhomogeneous shape measurement noise. AKRA is capable of dealing with these issues straightfowrardly, and will be addressed in the next version.

astro-ph.CO↗

arXiv2024

VmambaIR: Visual State Space Model for Image Restoration

Image restoration is a critical task in low-level computer vision, aiming to restore high-quality images from degraded inputs. Various models, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), transformers, and diffusion models (DMs), have been employed to address this problem with significant impact. However, CNNs have limitations in capturing long-range dependencies. DMs require large prior models and computationally intensive denoising steps. Transformers have powerful modeling capabilities but face challenges due to quadratic complexity with input image size. To address these challenges, we propose VmambaIR, which introduces State Space Models (SSMs) with linear complexity into comprehensive image restoration tasks. We utilize a Unet architecture to stack our proposed Omni Selective Scan (OSS) blocks, consisting of an OSS module and an Efficient Feed-Forward Network (EFFN). Our proposed omni selective scan mechanism overcomes the unidirectional modeling limitation of SSMs by efficiently modeling image information flows in all six directions. Furthermore, we conducted a comprehensive evaluation of our VmambaIR across multiple image restoration tasks, including image deraining, single image super-resolution, and real-world image super-resolution. Extensive experimental results demonstrate that our proposed VmambaIR achieves state-of-the-art (SOTA) performance with much fewer computational resources and parameters. Our research highlights the potential of state space models as promising alternatives to the transformer and CNN architectures in serving as foundational frameworks for next-generation low-level visual tasks.

cs.CV↗

arXiv2024

LLMRA: Multi-modal Large Language Model based Restoration Assistant

Multi-modal Large Language Models (MLLMs) have a significant impact on various tasks, due to their extensive knowledge and powerful perception and generation capabilities. However, it still remains an open research problem on applying MLLMs to low-level vision tasks. In this paper, we present a simple MLLM-based Image Restoration framework to address this gap, namely Multi-modal Large Language Model based Restoration Assistant (LLMRA). We exploit the impressive capabilities of MLLMs to obtain the degradation information for universal image restoration. By employing a pretrained multi-modal large language model and a vision language model, we generate text descriptions and encode them as context embedding with degradation information for the degraded image. Through the proposed Context Enhance Module (CEM) and Degradation Context based Transformer Network (DC-former), we integrate these context embedding into the restoration network, contributing to more accurate and adjustable image restoration. Based on the dialogue with the users, our method leverages image degradation priors from MLLMs, providing low-level attributes descriptions of the input low-quality images and the restored high-quality images simultaneously. Extensive experiments demonstrate the superior performance of our LLMRA in universal image restoration tasks.

cs.CV↗

arXiv2023

DSR-Diff: Depth Map Super-Resolution with Diffusion Model

Color-guided depth map super-resolution (CDSR) improve the spatial resolution of a low-quality depth map with the corresponding high-quality color map, benefiting various applications such as 3D reconstruction, virtual reality, and augmented reality. While conventional CDSR methods typically rely on convolutional neural networks or transformers, diffusion models (DMs) have demonstrated notable effectiveness in high-level vision tasks. In this work, we present a novel CDSR paradigm that utilizes a diffusion model within the latent space to generate guidance for depth map super-resolution. The proposed method comprises a guidance generation network (GGN), a depth map super-resolution network (DSRN), and a guidance recovery network (GRN). The GGN is specifically designed to generate the guidance while managing its compactness. Additionally, we integrate a simple but effective feature fusion module and a transformer-style feature extraction module into the DSRN, enabling it to leverage guided priors in the extraction, fusion, and reconstruction of multi-model images. Taking into account both accuracy and efficiency, our proposed method has shown superior performance in extensive experiments when compared to state-of-the-art methods. Our codes will be made available at https://github.com/shiyuan7/DSR-Diff.

cs.CV↗

arXiv2023

On Measuring the 21 cm Global Spectrum of the Cosmic Dawn with an Interferometer Array

We theoretically investigate the recovery of global spectrum (monopole) from visibilities (cross-correlation only) measured by the interferometer array and the feasibility of extracting 21 cm signal of cosmic dawn. In our approach, the global spectrum is obtained by solving the monopole and higher-order components simultaneously from the visibilities measured with up to thousands of baselines. Using this algorithm, the monopole of both foreground and the 21 cm signal can be correctly recovered in a broad range of conditions. We find that a 3D baseline distribution can have much better performance than a 2D (planar) baseline distribution, particularly when there is a lack of shorter baselines. We simulate for ground-based 2D and 3D array configurations, and a cross-shaped space array located at the Sun-Earth L2 point that can form 3D baselines through orbital precession. In all simulations we obtain good recovered global spectrum, and successfully extract the 21 cm signal from it, with reasonable number of antennas and observation time.

astro-ph.CO↗

arXiv2022

Surgical Scheduling via Optimization and Machine Learning with Long-Tailed Data

Using data from cardiovascular surgery patients with long and highly variable post-surgical lengths of stay (LOS), we develop a modeling framework to reduce recovery unit congestion. We estimate the LOS and its probability distribution using machine learning models, schedule procedures on a rolling basis using a variety of optimization models, and estimate performance with simulation. The machine learning models achieved only modest LOS prediction accuracy, despite access to a very rich set of patient characteristics. Compared to the current paper-based system used in the hospital, most optimization models failed to reduce congestion without increasing wait times for surgery. A conservative stochastic optimization with sufficient sampling to capture the long tail of the LOS distribution outperformed the current manual process and other stochastic and robust optimization approaches. These results highlight the perils of using oversimplified distributional models of LOS for scheduling procedures and the importance of using optimization methods well-suited to dealing with long-tailed behavior.

cs.LG↗

arXiv2022

A new method of reconstructing Galactic three-dimensional structures using ultralong-wavelength radio observations

The free-free absorption of low frequency radio waves by thermal electrons in the warm ionized medium of our Galaxy becomes very significant at $\lesssim 10$ MHz (ultralong-wavelength), and the absorption strength depends on the radio frequency. Upcoming space experiments such as the Discovering Sky at the Longest wavelength (DSL) and Farside Array for Radio Science Investigations of the Dark ages and Exoplanets (FARSIDE) will produce high-resolution multi-frequency sky maps at the ultralong-wavelength, providing a new window to observe the Universe. In this paper we propose that from these ultralong-wavelength multi-frequency maps, the three-dimensional distribution of the Galactic electrons can be reconstructed. This novel and robust reconstruction of the Galactic electron distribution will be a key science case of those space missions. Ultralong-wavelength observations will be a powerful tool for studying the astrophysics relevant to the Galactic electron distribution, for example, the impacts of supernova explosions on electron distribution, and the interaction between interstellar atoms and ionizing photons escaped from the HII regions around massive stars. An animation shows the reconstructed results using {\tt NE2001} model as input test. On ArXiv, it is given in the directory: Ancillary files. In the paper the animation is linked to Fig. 5.

astro-ph.GA↗

arXiv2022

Benchmarking magnetized three-wave coupling for laser backscattering: Analytic solutions and kinetic simulations

Understanding magnetized laser-plasma interactions is important for controlling magneto-inertial fusion experiments and developing magnetically assisted radiation and particle sources. In the long-pulse regime, interactions are dominated by coherent three-wave interactions, whose nonlinear coupling coefficients become known only recently when waves propagate at oblique angles with the magnetic field. In this paper, backscattering coupling coefficients predicted by warm-fluid theory is benchmarked using particle-in-cell simulations in one spatial dimension, and excellent agreements are found for a wide range of plasma temperatures, magnetic field strengths, and laser propagation angles, when the interactions are mediated by electron-dominant hybrid waves. Systematic comparisons between theory and simulations are made possible by a rigorous protocol: On the theory side, the initial boundary value problem of linearized three-wave equations is solved, and the transient-time solutions allow effects of growth and damping to be distinguished. On the simulation side, parameters are carefully chosen and calibration runs are performed to ensure that stimulated runs are well controlled. Fitting simulation data to analytical solutions yields numerical growth rates, which match theory predictions within error bars. Although warm-fluid theory is found to be valid for a wide parameter range, genuine kinetic effects have also been observed.

physics.plasm-ph↗

arXiv2022

Enhanced collisionless laser absorption in strongly magnetized plasmas

Strongly magnetizing a plasma adds a range of waves that do not exist in unmagnetized plasmas and enlarges the laser-plasma interaction (LPI) landscape. In this paper, we use particle-in-cell (PIC) simulations to investigate strongly magnetized LPI in one dimension under conditions relevant for magneto-inertial fusion experiments, focusing on a regime where the electron-cyclotron frequency is greater than the plasma frequency and the magnetic field is at an oblique angle with respect to the wave vectors. We show that when electron-cyclotron-like hybrid wave frequency is about half the laser frequency, the laser light resonantly decays to magnetized plasma waves via primary and secondary instabilities with large growth rates. These distinct magnetic-field-controlled instabilities, which we collectively call two-magnon decays, are analogous to two-plasmon decays in unmagnetized plasmas. Since additional phase mixing mechanisms are introduced by the oblique magnetic field, collisionless damping of large-amplitude magnetized waves substantially broadens the electron distribution function, especially along the direction of the magnetic field. During this process, energy is transferred efficiently from the laser to plasma waves and then to electrons, leading to a large overall absorptivity when strong resonances are present. The enhanced laser energy absorption may explain hotter-than-expected temperatures observed in magnetized laser implosion experiments and may also be exploited to develop more efficient laser-driven x-ray sources.

physics.plasm-ph↗

arXiv2022

Using Domain Knowledge for Low Resource Named Entity Recognition

In recent years, named entity recognition has always been a popular research in the field of natural language processing, while traditional deep learning methods require a large amount of labeled data for model training, which makes them not suitable for areas where labeling resources are scarce. In addition, the existing cross-domain knowledge transfer methods need to adjust the entity labels for different fields, so as to increase the training cost. To solve these problems, enlightened by a processing method of Chinese named entity recognition, we propose to use domain knowledge to improve the performance of named entity recognition in areas with low resources. The domain knowledge mainly applied by us is domain dictionary and domain labeled data. We use dictionary information for each word to strengthen its word embedding and domain labeled data to reinforce the recognition effect. The proposed model avoids large-scale data adjustments in different domains while handling named entities recognition with low resources. Experiments demonstrate the effectiveness of our method, which has achieved impressive results on the data set in the field of scientific and technological equipment, and the F1 score has been significantly improved compared with many other baseline methods.

cs.CL↗

arXiv2022

Lunar Orbit Measurement of Cosmic Dawn 21 cm Global Spectrum

A redshifted 21 cm line absorption signature is commonly expected from the cosmic dawn era, when the first stars and galaxies formed. The detailed traits of this signal can provide important insight on the cosmic history. However, high precision measurement of this signal is hampered by the ionosphere refraction and absorption, as well as radio frequency interference (RFI). A space observation can solve the problem of the ionosphere, and the Moon can shield the RFI from the Earth. In this paper, we present simulations of the global spectrum measurement in the 30 -- 120 MHz frequency band on the lunar orbit, from the proposed Discovering the Sky at the Longest wavelength (DSL) project. In particular, we consider how the measured signal varies as the satellite moves along the orbit, take into account the blockage of different parts of the sky by the Moon and the antenna response. We estimate the sensitivity for such a 21 cm global spectrum experiment. An RMS noise level of $\le 0.05$ K is expected at 75 MHz after 10 orbits ($\sim$ 1 day) observation, for a frequency channel width of 0.4 MHz. We also study the influence of a frequency-dependent beam, which may generate complex spectral structures in the spectrum. Estimates of the uncertainties in the foreground and 21 cm model parameters are obtained.

astro-ph.IM↗

arXiv2021

Imaging sensitivity of a linear interferometer array on lunar orbit

Ground-based observation at frequencies below 30 MHz is hindered by the ionosphere of the Earth and radio frequency interference. To map the sky at these low frequencies, we have proposed the Discovering the Sky at the Longest wavelength mission (DSL, also known as the "Hongmeng" mission, which means "Primordial Universe" in Chinese) concept, which employs a linear array of micro-satellites orbiting the Moon. Such an array can be deployed to the lunar orbit by a single rocket launch, and it can make interferometric observations achieving good angular resolutions despite the small size of the antennas. However, it differs from the conventional ground-based interferometer array or even the previous orbital interferometers in many aspects, new data-processing methods need to be developed. In this work, we make a series of simulations to assess the imaging quality and sensitivity of such an array. We start with an input sky model and a simple orbit model, generate mock interferometric visibilities, and then reconstruct the sky map. We consider various observational effects and practical issues, such as the system noise, antenna response, and Moon blockage. Based on the quality of the recovered image, we quantify the imaging capability of the array for different satellite numbers and array configurations. For the first time, we make practical estimates of the point source sensitivity for such a lunar orbit array, and predict the expected number of detectable sources for the mission. Depending on the radio source number distribution which is still very uncertain at these frequencies, the proposed mission can detect $10^2 \sim 10^4$ sources during its operation.

astro-ph.IM↗

arXiv2021

Force, metric, or mass: Disambiguating causes of uniform gravity

In addition to nonzero forces and nontrivial metrics, here I show that a nonconstant Higgs expectation value, which endows elementary particles with their masses, also leads to apparent universal particle accelerations and photon frequency shifts. When effects of the Higgs is attributed to spacetime curvatures, a spurious stress-energy tensor is required in Einstein's equation. On cosmological scales, the spurious density coincides with the observed dark energy density. On smaller scales, effects of the Standard Model Higgs gradients are unlikely observable except near compact astrophysical bodies. To estimate the experimental precision required to disambiguate causes of apparent accelerations, I compare distinct effects of the force, metric, and Higgs profiles that cause uniform acceleration of a test particle. When the acceleration is caused by a force, the motion of all particles are hyperbolic with the same acceleration. However, when the cause is a metric, only a one-parameter family of particles undergo hyperbolic motion. In comparison, when the cause is a Higgs gradient, the trajectory of all particles are hyperbolic, but the acceleration is larger when the particle's energy is higher. The discrepancies among the three causes are minuscule on laboratory scales, which makes experimental tests very challenging.

gr-qc↗

arXiv2021

Nonperturbative $ϕ^4$ potentials: Phase transitions and light horizons

It is commonly believed that a massive real scalar field $ϕ$ only mediates short-range interactions on the scale of its Compton wavelength via the Yukawa potential. However, in the nonperturbative regime of nonlinear self coupling, $ϕ$ can also mediate larger scale interactions. Moreover, the classical potential, namely, the static configuration of $ϕ$ in the presence of an external source, is not always unique for given boundary conditions. In this paper, a complete set of finite-energy potentials (FEPs) induced by a Gaussian source is identified in one, two, and three spatial dimensions when the nonlinearity is of the Mexican-hat type, which is often prescribed to induce spontaneous symmetry breaking. In the size-strength parameter space of the source, phase boundaries are mapped out, across which the number of FEPs differ. Additionally, softer phase transitions are delineated according to whether the potential exhibits a light horizon at which $ϕ$ vanishes. The light horizon is of physical significance when $ϕ$ couples with other particles. For example, when $ϕ$ is the Higgs field, all elementary particles become massless at the light horizon. It is remarkable that white dwarfs and neutron stars are potentially in a phase where light horizons exist, whose outer radii are a few times the star sizes. Moreover, suppose elementary fermions of mass greater than $\sim10^3$ GeV exist, then they may also be surrounded by light horizons with radii comparable to the Higgs Compton wavelength. Finally, nonperturbative states may also be realized in condensed matter systems, wherein phase transitions are controllable using localized sources.

hep-ph↗

arXiv2021

Simulating nonnative cubic interactions on noisy quantum machines

As a milestone for general-purpose computing machines, we demonstrate that quantum processors can be programmed to efficiently simulate dynamics that are not native to the hardware. Moreover, on noisy devices without error correction, we show that simulation results are significantly improved when the quantum program is compiled using modular gates instead of a restricted set of standard gates. We demonstrate the general methodology by solving a cubic interaction problem, which appears in nonlinear optics, gauge theories, as well as plasma and fluid dynamics. To encode the nonnative Hamiltonian evolution, we decompose the Hilbert space into a direct sum of invariant subspaces in which the nonlinear problem is mapped to a finite-dimensional Hamiltonian simulation problem. In a three-states example, the resultant unitary evolution is realized by a product of ~20 standard gates, using which ~10 simulation steps can be carried out on state-of-the-art quantum hardware before results are corrupted by decoherence. In comparison, the simulation depth is improved by more than an order of magnitude when the unitary evolution is realized as a single cubic gate, which is compiled directly using optimal control. Alternatively, parametric gates may also be compiled by interpolating control pulses. Modular gates thus obtained provide high-fidelity building blocks for quantum Hamiltonian simulations.

quant-ph↗

arXiv2020

Plasma physics in strong-field regimes: theories and simulations

In strong electromagnetic fields, unique plasma phenomena and applications emerge, whose description requires recently developed theories and simulations [Y. Shi, Ph.D. thesis, Princeton University (2018)]. In the classical regime, to quantify effects of strong magnetic fields on three-wave interactions, a convenient formula is derived by solving the fluid model to the second order in general geometry. As an application, magnetic resonances are exploited to mediate laser pulse compression, using which higher intensity pulses can be produced in wider frequency ranges, as confirmed by particle-in-cell simulations. In even stronger fields, relativistic-quantum effects become important, and a plasma model based on scalar quantum electrodynamics (QED) is developed, which unveils observable corrections to Faraday rotation and cyclotron absorption in strongly magnetized plasmas. Beyond the perturbative regime, lattice QED is extended as a numerical tool for plasma physics, using which the transition from wakefield acceleration to electron-positron pair production is captured when laser intensity exceeds the Schwinger threshold.

physics.plasm-ph↗

arXiv2019

Amplification of mid-infrared lasers via magnetized-plasma coupling

Plasmas may be used as gain media for amplifying intense lasers, and external magnetic fields may be applied to improve the performance. For mid-infrared lasers, the requisite magnetic field is on megagauss scale, which can already be provided by current technologies. Designing the laser amplifier requires knowing the magnetized three-wave coupling coefficient, which is mapped out systematically in this paper. By numerically evaluating its formula, we demonstrate how the coupling depends on angle of wave propagation, laser polarization, magnetic field strength, plasma temperature, and plasma density in the backscattering geometry. Since the mediation is now provided by magnetized plasma waves, the coupling can differ significantly from unmagnetized Raman and Brillouin scatterings.

physics.plasm-ph↗

arXiv2019

Three-wave interactions in magnetized warm-fluid plasmas: general theory with evaluable coupling coefficient

Resonant three-wave coupling is an important mechanism via which waves interact in a nonlinear medium. When the medium is a magnetized warm-fluid plasma, a previously-unknown formula for the coupling coefficients is derived by solving the fluid-Maxwell's equations to second order using multiscale perturbative expansions. The formula is not only general but also evaluable, whereby numerical values of the coupling coefficient can be determined for any three resonantly interacting waves propagating at arbitrary angles. As one example, coupling coefficient governing laser scattering is evaluated. In conditions relevant to magnetized inertial confinement fusion experiments, lasers scatter from magnetized plasma waves and the growth rates are modified at oblique angles. As another example, coupling coefficient between two Alfvén waves via a sound wave is evaluated. In conditions relevant to solar corona, the decay of a parallel Alfvén wave only slightly prefers exact backward geometry.

physics.plasm-ph↗