arXiv subjects

Haifeng Wang

Publications and source records attributed to Haifeng Wang.

At least 109 records · Page 6Linked to original sources

arXiv2024

Inflight Performance and Calibrations of the Lyman-alpha Solar Telescope on board the Advanced Space-based Solar Observatory

The Lyman-alpha Solar Telescope (LST) on board the Advanced Space-based Solar Observatory (ASO-S) is the first payload to image the full solar disk and the solar corona in both white-light (WL) and ultraviolet (UV) H I Lya, extending up to 2.5 solar radii (Rs). Since the launch of the ASO-S on 9 October 2022, LST has captured various significant solar activities including flares, prominences, coronal mass ejections (CMEs). LST covers different passbands of 121.6 nm, 360 nm and 700 nm. The Lya Solar Disk Imager (SDI) has a field of view (FOV) of 38.4 arcmin and a spatial resolution of around 9.5 arcsec, while the White-Light Solar Telescope (WST) has a FOV of 38.43 arcmin and a spatial resolution of around 3.0 arcsec. The FOV of the Lya Solar Corona Imager (SCI) reaches 81.1 arcmin and its spatial resolution is 4.3 arcsec. The stray-light level in the 700 nm waveband is about 7.8e-6 MSB (mean solar brightness) at 1.1 Rs and 7.6e-7 MSB at 2.5 Rs, and in the Lya waveband it is around 4.3e-3 MSB at 1.1 Rs and 4.1e-4 MSB at 2.5 Rs. This article will detail the results from on-orbit tests and calibrations.

astro-ph.SR↗

arXiv2024

Exploring the Causality of End-to-End Autonomous Driving

Deep learning-based models are widely deployed in autonomous driving areas, especially the increasingly noticed end-to-end solutions. However, the black-box property of these models raises concerns about their trustworthiness and safety for autonomous driving, and how to debug the causality has become a pressing concern. Despite some existing research on the explainability of autonomous driving, there is currently no systematic solution to help researchers debug and identify the key factors that lead to the final predicted action of end-to-end autonomous driving. In this work, we propose a comprehensive approach to explore and analyze the causality of end-to-end autonomous driving. First, we validate the essential information that the final planning depends on by using controlled variables and counterfactual interventions for qualitative analysis. Then, we quantitatively assess the factors influencing model decisions by visualizing and statistically analyzing the response of key model inputs. Finally, based on the comprehensive study of the multi-factorial end-to-end autonomous driving system, we have developed a strong baseline and a tool for exploring causality in the close-loop simulator CARLA. It leverages the essential input sources to obtain a well-designed model, resulting in highly competitive capabilities. As far as we know, our work is the first to unveil the mystery of end-to-end autonomous driving and turn the black box into a white one. Thorough close-loop experiments demonstrate that our method can be applied to end-to-end autonomous driving solutions for causality debugging. Code will be available at https://github.com/bdvisl/DriveInsight.

cs.CV↗

arXiv2024

A review on vision-based motion estimation

Compared to contact sensors-based motion measurement, vision-based motion measurement has advantages of low cost and high efficiency and have been under active development in the past decades. This paper provides a review on existing motion measurement methods. In addition to the development of each branch of vision-based motion measurement methods, this paper also discussed the advantages and disadvantages of existing methods. Based on this discussion, it was identified that existing methods have a common limitation in optimally balancing accuracy and robustness. To address issue, we developed the Gaussian kernel-based motion measurement method. Preliminary study shows that the developed method can achieve high accuracy on simple synthesized images.

cs.CV↗

arXiv2024

Joint Diffusion: Mutual Consistency-Driven Diffusion Model for PET-MRI Co-Reconstruction

Positron Emission Tomography and Magnetic Resonance Imaging (PET-MRI) systems can obtain functional and anatomical scans. PET suffers from a low signal-to-noise ratio. Meanwhile, the k-space data acquisition process in MRI is time-consuming. The study aims to accelerate MRI and enhance PET image quality. Conventional approaches involve the separate reconstruction of each modality within PET-MRI systems. However, there exists complementary information among multi-modal images. The complementary information can contribute to image reconstruction. In this study, we propose a novel PET-MRI joint reconstruction model employing a mutual consistency-driven diffusion mode, namely MC-Diffusion. MC-Diffusion learns the joint probability distribution of PET and MRI for utilizing complementary information. We conducted a series of contrast experiments about LPLS, Joint ISAT-net and MC-Diffusion by the ADNI dataset. The results underscore the qualitative and quantitative improvements achieved by MC-Diffusion, surpassing the state-of-the-art method.

eess.IV↗

arXiv2024

Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model

MRI and PET are crucial diagnostic tools for brain diseases, as they provide complementary information on brain structure and function. However, PET scanning is costly and involves radioactive exposure, resulting in a lack of PET. Moreover, simultaneous PET and MRI at ultra-high-field are currently hardly infeasible. Ultra-high-field imaging has unquestionably proven valuable in both clinical and academic settings, especially in the field of cognitive neuroimaging. These motivate us to propose a method for synthetic PET from high-filed MRI and ultra-high-field MRI. From a statistical perspective, the joint probability distribution (JPD) is the most direct and fundamental means of portraying the correlation between PET and MRI. This paper proposes a novel joint diffusion attention model which has the joint probability distribution and attention strategy, named JDAM. JDAM has a diffusion process and a sampling process. The diffusion process involves the gradual diffusion of PET to Gaussian noise by adding Gaussian noise, while MRI remains fixed. JPD of MRI and noise-added PET was learned in the diffusion process. The sampling process is a predictor-corrector. PET images were generated from MRI by JPD of MRI and noise-added PET. The predictor is a reverse diffusion process and the corrector is Langevin dynamics. Experimental results on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that the proposed method outperforms state-of-the-art CycleGAN for high-field MRI (3T MRI). Finally, synthetic PET images from the ultra-high-field (5T MRI and 7T MRI) be attempted, providing a possibility for ultra-high-field PET-MRI imaging.

cs.LG↗

arXiv2024

Generation of multi-component super integrable hierarchy associated with higher-dimensional Lie superalgebra spo(2N,2) and osp(2N,2)

A new type of high-dimensional Lie superalgebras is constructed, including Lie superalgebras spo(4,2) and osp(4,2). Based on it, two different coupled nonisospectral super AKNS hierarchies and their bi-Hamiltonian structures are obtained. Then, the Lie superalgebras spo(4,2) and osp(4,2) are expanded to infinite-dimensional Lie superalgebras spo(2N,2) and osp(2N,2). It follows that we present the generation of multi-component super integrable hierarchy, and deduce two different multi-component super integrable AKNS hierarchies and their Hamiltonian structures.

nlin.SI↗

arXiv2024

Efficient Surgical Tool Recognition via HMM-Stabilized Deep Learning

Recognizing various surgical tools, actions and phases from surgery videos is an important problem in computer vision with exciting clinical applications. Existing deep-learning-based methods for this problem either process each surgical video as a series of independent images without considering their dependence, or rely on complicated deep learning models to count for dependence of video frames. In this study, we revealed from exploratory data analysis that surgical videos enjoy relatively simple semantic structure, where the presence of surgical phases and tools can be well modeled by a compact hidden Markov model (HMM). Based on this observation, we propose an HMM-stabilized deep learning method for tool presence detection. A wide range of experiments confirm that the proposed approaches achieve better performance with lower training and running costs, and support more flexible ways to construct and utilize training data in scenarios where not all surgery videos of interest are extensively labelled. These results suggest that popular deep learning approaches with over-complicated model structures may suffer from inefficient utilization of data, and integrating ingredients of deep learning and statistical learning wisely may lead to more powerful algorithms that enjoy competitive performance, transparent interpretation and convenient model training simultaneously.

cs.CV↗

arXiv2024

BASES: Large-scale Web Search User Simulation with Large Language Model based Agents

Due to the excellent capacities of large language models (LLMs), it becomes feasible to develop LLM-based agents for reliable user simulation. Considering the scarcity and limit (e.g., privacy issues) of real user data, in this paper, we conduct large-scale user simulation for web search, to improve the analysis and modeling of user search behavior. Specially, we propose BASES, a novel user simulation framework with LLM-based agents, designed to facilitate comprehensive simulations of web search user behaviors. Our simulation framework can generate unique user profiles at scale, which subsequently leads to diverse search behaviors. To demonstrate the effectiveness of BASES, we conduct evaluation experiments based on two human benchmarks in both Chinese and English, demonstrating that BASES can effectively simulate large-scale human-like search behaviors. To further accommodate the research on web search, we develop WARRIORS, a new large-scale dataset encompassing web search user behaviors, including both Chinese and English versions, which can greatly bolster research in the field of information retrieval. Our code and data will be publicly released soon.

cs.IR↗

arXiv2024

Towards Boosting Many-to-Many Multilingual Machine Translation with Large Language Models

The training paradigm for machine translation has gradually shifted, from learning neural machine translation (NMT) models with extensive parallel corpora to instruction finetuning on multilingual large language models (LLMs) with high-quality translation pairs. In this paper, we focus on boosting many-to-many multilingual translation of LLMs with an emphasis on zero-shot translation directions. We demonstrate that prompt strategies adopted during finetuning are crucial to zero-shot translation and introduce a cross-lingual consistency regularization, XConST, to bridge the representation gap among different languages and improve zero-shot translation performance. XConST is not a new method, but a version of CrossConST (Gao et al., 2023a) adapted for translation instruction finetuning with LLMs. Experimental results on ALMA (Xu et al., 2023), Tower (Team, 2024), and LLaMA-2 (Touvron et al., 2023) show that our approach consistently improves translation performance. Our implementations are available at https://github.com/gpengzhi/CrossConST-LLM.

cs.CL↗

arXiv2024

Simultaneous Blind Demixing and Super-resolution via Vectorized Hankel Lift

In this work, we investigate the problem of simultaneous blind demixing and super-resolution. Leveraging the subspace assumption regarding unknown point spread functions, this problem can be reformulated as a low-rank matrix demixing problem. We propose a convex recovery approach that utilizes the low-rank structure of each vectorized Hankel matrix associated with the target matrix. Our analysis reveals that for achieving exact recovery, the number of samples needs to satisfy the condition $n\gtrsim Ksr \log (sn)$. Empirical evaluations demonstrate the recovery capabilities and the computational efficiency of the convex method.

cs.IT↗

arXiv2024

Rapid Estimation of Left Ventricular Contractility with a Physics-Informed Neural Network Inverse Modeling Approach

Physics-based computer models based on numerical solution of the governing equations generally cannot make rapid predictions, which in turn, limits their applications in the clinic. To address this issue, we developed a physics-informed neural network (PINN) model that encodes the physics of a closed-loop blood circulation system embedding a left ventricle (LV). The PINN model is trained to satisfy a system of ordinary differential equations (ODEs) associated with a lumped parameter description of the circulatory system. The model predictions have a maximum error of less than 5% when compared to those obtained by solving the ODEs numerically. An inverse modeling approach using the PINN model is also developed to rapidly estimate model parameters (in $\sim$ 3 mins) from single-beat LV pressure and volume waveforms. Using synthetic LV pressure and volume waveforms generated by the PINN model with different model parameter values, we show that the inverse modeling approach can recover the corresponding ground truth values, which suggests that the model parameters are unique. The PINN inverse modeling approach is then applied to estimate LV contractility indexed by the end-systolic elastance $E_{es}$ using waveforms acquired from 11 swine models, including waveforms acquired before and after administration of dobutamine (an inotropic agent) in 3 animals. The estimated $E_{es}$ is about 58% to 284% higher for the data associated with dobutamine compared to those without, which implies that this approach can be used to estimate LV contractility using single-beat measurements. The PINN inverse modeling can potentially be used in the clinic to simultaneously estimate LV contractility and other physiological parameters from single-beat measurements.

cs.CE↗

arXiv2023

The accretion history of the Milky Way. II. Internal kinematics of globular clusters and of dwarf galaxies

We study how structural properties of globular clusters and dwarf galaxies are linked to their orbits in the Milky Way halo. From the inner to the outer halo, orbital energy increases and stellar-systems gradually move out of internal equilibrium: in the inner halo, high-surface brightness globular clusters are at pseudo-equilibrium, while further away, low-surface brightness clusters and dwarfs appear more tidally disturbed. Dwarf galaxies are the latest to arrive into the halo as indicated by their large orbital energies and pericenters, and have no time for more than one orbit. Their (gas-rich) progenitors likely lost their gas during their recent arrival in the Galactic halo. If dwarfs are at equilibrium with their dark matter (DM) content, the DM density should anti-correlate with pericenter. However, the transformation of DM dominated dwarfs from gas-rich rotation-supported into gas-poor dispersion-supported systems is unlikely accomplished during a single orbit. We suggest instead that the above anti-correlation is brought by the combination of ram-pressure stripping and of Galactic tidal shocks. Recent gas removal leads to an expansion of their stellar content caused by the associated gravity loss, making them sufficiently fragile to be transformed near pericenter passage. Out of equilibrium dwarfs would explain the observed anti-correlation of kinematics-based DM density with pericenter without invoking DM density itself, questioning its previous estimates. Ram-pressure stripping and tidal shocks may contribute to the dwarf velocity dispersion excess. It predicts the presence of numerous stars in their outskirts and a few young stars in their cores.

astro-ph.GA↗

arXiv2023

The Accretion History of the Milky Way: III. Hydrodynamical Simulations of Galactic Dwarf Galaxies at First Infall

Most Milky Way dwarf galaxies are much less bound to their host than are relics of Gaia-Sausage-Enceladus and Sgr. These dwarfs are expected to have fallen into the Galactic halo less than 3 Gyr ago, and will therefore have undergone no more than one full orbit. Here, we have performed hydrodynamical simulations of this process, assuming that their progenitors are gas-rich, rotation-supported dwarfs. We follow their transformation through interactions with the hot corona and gravitational field of the Galaxy. Our dedicated simulations reproduce the structural properties of three dwarf galaxies: Sculptor, Antlia II and, with somewhat a lower accuracy, Crater II. This includes reproducing their large velocity dispersions, which are caused by ram-pressure stripping and Galactic tidal shocks. Differences between dwarfs can be interpreted as due to different orbital paths, as well as to different initial conditions for their progenitor gas and stellar contents. However, we failed to suppress in a single orbit the rotational support of our Sculptor analog if it is fully dark-matter dominated. In addition, we have found that classical dwarf galaxies like Sculptor may have stellar cores sufficiently dense to survive the pericenter passage through adiabatic contraction. On the contrary, our Antlia II and Crater II analogs are tidally stripped, explaining their large sizes, extremely low surface brightnesses, and velocity dispersion. This modeling explains differences between dwarf galaxies by reproducing them as being at different stages of out-of-equilibrium stellar systems.

astro-ph.GA↗

arXiv2023

Generation of higher-dimensional isospectral-nonisospectral integrable hierarchies associated with a new class of higher-dimensional column-vector loop algebras

We construct a new class of higher-dimensional column-vector loop algebras. Based on it, a method for generating higher-dimensional isospectral-nonisospectral integrable hierarchies is proposed. As an application, we derive a generalized nonisospectral integrable Schrödinger hierarchy which can be reduced to the famous derivative nonlinear Schrödinger equation. By using the higher-dimensional column-vector loop algebras, we obtain an expanded isospectral-nonisospectral integrable Schrödinger hierarchy which can be reduced to many classical and new equations, such as the expanded nonisospectral derivative nonlinear Schrödinger system, the heat equation, the Fokker-Plank equation which has a wide range of applications in stochastic dynamic systems. Furthermore, we deduce a ZN nonisospectral integrable Schrödinger hierarchy, which means that the coupling results are extended to an arbitrary number of components. Additionally, the Hamiltonian structures of these hierarchies are discussed by using the quadratic form trace identity.

nlin.SI↗

arXiv2023

A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection

Accurate detection and segmentation of brain tumors is critical for medical diagnosis. However, current supervised learning methods require extensively annotated images and the state-of-the-art generative models used in unsupervised methods often have limitations in covering the whole data distribution. In this paper, we propose a novel framework Two-Stage Generative Model (TSGM) that combines Cycle Generative Adversarial Network (CycleGAN) and Variance Exploding stochastic differential equation using joint probability (VE-JP) to improve brain tumor detection and segmentation. The CycleGAN is trained on unpaired data to generate abnormal images from healthy images as data prior. Then VE-JP is implemented to reconstruct healthy images using synthetic paired abnormal images as a guide, which alters only pathological regions but not regions of healthy. Notably, our method directly learned the joint probability distribution for conditional generation. The residual between input and reconstructed images suggests the abnormalities and a thresholding method is subsequently applied to obtain segmentation results. Furthermore, the multimodal results are weighted with different weights to improve the segmentation accuracy further. We validated our method on three datasets, and compared with other unsupervised methods for anomaly detection and segmentation. The DSC score of 0.8590 in BraTs2020 dataset, 0.6226 in ITCS dataset and 0.7403 in In-house dataset show that our method achieves better segmentation performance and has better generalization.

eess.IV↗

arXiv2023

Analysis of osteoporotic tissue using combination nonlinear optical imaging

Currently, a large number of stored tissue samples are unavailable for spectroscopic study without the time consuming and destructive process of paraffin removal. Instead, a structurally sensitive technique, sum frequency generation, and a chemically sensitive technique, coherent anti-Stokes Raman scattering enables imaging through the paraffin. This method is demonstrated by imaging collagen in mouse tibia. We introduce a statistical method for separating images by quality and, with the aid of machine learning, distinguish osteoporotic and healthy bone. This method has the potential to verify the results of previous studies and reduce new sample production by allowing retesting results with spectroscopy.

physics.med-ph↗

arXiv2023

Detection of the Keplerian decline in the Milky Way rotation curve

Our position inside the Galactic disc had prevented us from establishing an accurate rotation curve, until the advent of Gaia, whose third data release (Gaia DR3) made it possible to specify it up to twice the optical radius. We aim to establish a new rotation curve of the Galaxy from the Gaia DR3, by drastically reducing uncertainties and systematics, and with the goal to provide a new estimate of the mass of the Galaxy. We have compared different estimates, established a robust assessment of the systematic uncertainties, and addressed differences in methodologies, particularly regarding distance estimates. This results in a sharply decreasing rotation curve for the Milky Way, the decrease in velocity between 19.5 and 26.5 kpc is approximately 30 km s$^{-1}$. We have identified, for the first time, a Keplerian decline of the rotation curve, starting at $\sim$ 19 kpc and up to $\sim$ 26.5 kpc from the Galaxy center, while a flat rotation curve is rejected with a significance of 3$σ$. The total mass is revised downwards to $2.06^{+0.24}_{-0.13}\times 10^{11}\ M_{\odot}$, in agreement with an absence of significant mass increase at radii larger than 19 kpc. The upper limit of the total mass was evaluated by considering the upper values of velocity measurements, which leads to a strict, unsurpassable, limit of $5.4\times 10^{11}\ M_{\odot}$.

astro-ph.GA↗

arXiv2023

GLS-CSC: A Simple but Effective Strategy to Mitigate Chinese STM Models' Over-Reliance on Superficial Clue

Pre-trained models have achieved success in Chinese Short Text Matching (STM) tasks, but they often rely on superficial clues, leading to a lack of robust predictions. To address this issue, it is crucial to analyze and mitigate the influence of superficial clues on STM models. Our study aims to investigate their over-reliance on the edit distance feature, commonly used to measure the semantic similarity of Chinese text pairs, which can be considered a superficial clue. To mitigate STM models' over-reliance on superficial clues, we propose a novel resampling training strategy called Gradually Learn Samples Containing Superficial Clue (GLS-CSC). Through comprehensive evaluations of In-Domain (I.D.), Robustness (Rob.), and Out-Of-Domain (O.O.D.) test sets, we demonstrate that GLS-CSC outperforms existing methods in terms of enhancing the robustness and generalization of Chinese STM models. Moreover, we conduct a detailed analysis of existing methods and reveal their commonality.

cs.CL↗