arXiv subjects

Xiaofei Huang

Publications and source records attributed to Xiaofei Huang.

At least 19 recordsLinked to original sources

arXiv2026

Earth Matter Enhanced Axion Dark Matter Search

Laboratory searches for ultralight axion dark matter (DM) have traditionally assumed the terrestrial density of axions is equal to the average density of DM in the solar system. However, quadratic couplings to matter introduce a non-trivial field profile near the Earth. In this work, we present the first dedicated experimental implementation of this environment-aware axion DM wind search framework. Leveraging the extreme sensitivity of a K--Rb--$^{21}$Ne comagnetometer to pseudo-magnetic fields induced by axion DM, we analyzed our data in the context of the massively enhanced local gradient of axions due to interactions with matter, though no signal candidates were found. Consequently, we have set the most stringent limits on axion-neutron derivative interactions for masses $m_a \in [0.041, ~28.9]~\rm feV$, improving from previous experiments that ignore terrestrial matter effects by as much as three orders of magnitude for certain masses. Our work highlights the necessity of accounting for environmental modifications in precision frontier experiments and demonstrates how geophysical variations can be harnessed to act as a natural amplifier for DM possibly enabling future detection in parts of the parameter space that were previously beyond reach.

hep-ph↗

arXiv2025

Search for Ultralight Dark Matter with Quantum Magnetometry in the Earth's Cavity

Ultralight dark matter candidates, such as axions and dark photons, are leading dark matter candidates. They may couple feebly to photons, sourcing oscillating electromagnetic signals in the Earth's conducting cavity formed between the ground and the ionosphere, providing detectable magnetic field signatures at wavelengths above the Earth's size. We carry out a project aiming to search for new physics using an unshielded high-sensitivity atomic magnetometer, termed the Geomagnetic Probe for nEw physiCS (GPEX). In this work, we report our first search for axion and dark photon dark matter, conducted in the desert of XiaoDushan in Gansu Province, China. Analysis of the collection of one-hour data shows no robust evidence for axion- or dark photon-induced magnetic signals. Correspondingly, we set the constraints on the axion-photon coupling with $g_{a\gamma\gamma} < 7\times10^{-10}\, \mathrm{GeV^{-1}}$ and the dark photon kinetic-mixing parameter $\epsilon < 2\times10^{-6}$ in the mass range $3.5 \times 10^{-16}\, \mathrm{eV} \sim 1.8 \times 10^{-14}\, \mathrm{eV}$. Our findings demonstrate the feasibility of using ground-based quantum magnetic sensors for ultralight dark matter searches. Future networks of such detectors operating over extended periods could improve the sensitivity by about three orders of magnitude.

hep-ph↗

arXiv2025

Dynamically Polarized SERF Atomic Comagnetometer

Atomic spin sensors are essential for beyond-the-standard-model exploration, biomagnetic measurement, and quantum navigation. While the traditional DC mode spin-exchange relaxation-free (SERF) comagnetometer achieves ultrahigh sensitivity, further improvements require suppressing technical noise and surpassing standard quantum limit. In this work, we develop a K-Rb-$^{21}$Ne SERF atomic comagnetometer that dynamically polarizes the electron and nuclear spins, shielding signals from direct interference by pump light. We establish a three-phase evolutionary model for hybrid spin ensemble dynamics, yielding a complete analytical solution, and analyze the responses to various spin perturbations. Additionally, we achieve an averaged 38.5 $\%$ suppression of the polarization noise and identify the key factors that limit sensitivity improvements. The dynamically polarized comagnetometer exhibits effective suppression of technical noise and holds the potential to overcome quantum noise limit, while offering promising applications in exploring new physics and precise magnetic field measurements.

physics.atom-ph↗

arXiv2025

Search for a parity-violating long-range spin-dependent interaction

High-sensitivity quantum sensors are a promising tool for experimental searches for beyond-Standard-Model interactions. Here, we demonstrate an atomic comagnetometer operating under a resonantly-coupled hybrid spin-resonance (HSR) regime to probe P-odd, T-even interactions. The HSR regime enables robust nuclear-electron spin coupling, enhancing measurement bandwidth and stability without compromising the high sensitivity of spin-exchange relaxation-free magnetometers. To minimize vibration noise from velocity-modulated sources, we implement a multistage vibration isolation system, achieving a vibration noise reduction exceeding 700-fold. We establish new constraints on vector-boson-mediated parity-violating interactions, improving experimental sensitivity by three orders of magnitude compared to previous limits. The new constraints complement existing astrophysical and laboratory studies of potential extensions to the Standard Model.

quant-ph↗

arXiv2024

DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics.

cs.CV↗

arXiv2024

Axion-Like Particle Detection in Alkali-Noble-Gas Haloscopes

Revealing the essence of dark matter (DM) and dark energy is essential for understanding our universe. Ultralight (rest energy $<$10 eV) bosonic particles, including pseudoscalar axions and axion-like particles (ALPs) have emerged among leading candidates to explain the composition of DM and searching for them has become an important part of precision-measurement science. Ultrahigh-sensitivity alkali-noble-gas based comagnetometers and magnetometers are being used as powerful haloscopes, i.e., devices designed to search for DM present in the galactic halo. A broad variety of such devices include clock-comparison comagnetometers, self-compensating comagnetometers, hybrid-spin-resonance magnetometer, spin-exchange-relaxation-free magnetometers, nuclear magnetic-resonance magnetometers, Floquet magnetometers, masers, as well as devices like the cosmic axion spin-precession experiment (CASPEr) using liquid $^{129}$Xe, prepolarized via spin-exchange optical pumping with rubidium atoms. The combination of alkali metal and noble gas allows one to take the best advantage of the complementary properties of the two spin systems. This review summarizes the operational principles, experimental setups and the successful explorations of new physics using these haloscopes. Additionally, some limiting factors are pointed out for further improvement.

hep-ex↗

arXiv2023

Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the Art

Automated human action recognition, a burgeoning field within computer vision, boasts diverse applications spanning surveillance, security, human-computer interaction, tele-health, and sports analysis. Precise action recognition in infants serves a multitude of pivotal purposes, encompassing safety monitoring, developmental milestone tracking, early intervention for developmental delays, fostering parent-infant bonds, advancing computer-aided diagnostics, and contributing to the scientific comprehension of child development. This paper delves into the intricacies of infant action recognition, a domain that has remained relatively uncharted despite the accomplishments in adult action recognition. In this study, we introduce a groundbreaking dataset called ``InfActPrimitive'', encompassing five significant infant milestone action categories, and we incorporate specialized preprocessing for infant data. We conducted an extensive comparative analysis employing cutting-edge skeleton-based action recognition models using this dataset. Our findings reveal that, although the PoseC3D model achieves the highest accuracy at approximately 71%, the remaining models struggle to accurately capture the dynamics of infant actions. This highlights a substantial knowledge gap between infant and adult action recognition domains and the urgent need for data-efficient pipeline models.

cs.CV↗

arXiv2023

Constraining Ultralight Dark Matter through an Accelerated Resonant Search

Experiments aimed at detecting ultralight dark matter typically rely on resonant effects, which are sensitive to the dark matter mass that matches the resonance frequency. In this study, we investigate the nucleon couplings of ultralight axion dark matter using a magnetometer operating in a nuclear magnetic resonance (NMR) mode. Our approach involves the use of a $^{21}$Ne spin-based sensor, which features the lowest nuclear magnetic moment among noble-gas spins. This configuration allows us to achieve an ultrahigh sensitivity of 0.73 fT/Hz$^{1/2}$ at around 5 Hz, corresponding to energy resolution of approximately 1.5$\times 10^{-23}\,\rm{eV/Hz^{1/2}}$. Our analysis reveals that under certain conditions it is beneficial to scan the frequency with steps significantly larger than the resonance width. The analytical results are in agreement with experimental data and the scan strategy is potentially applicable to other resonant searches. Further, our study establishes stringent constraints on axion-like particles (ALP) in the 4.5--15.5 Hz Compton-frequency range coupling to neutrons and protons, improving on prior work by several-fold. Within a band around 4.6--6.6 Hz and around 7.5 Hz, our laboratory findings surpass astrophysical limits derived from neutron-star cooling. Hence, we demonstrate an accelerated resonance search for ultralight dark matter, achieving an approximately 30-fold increase in scanning step while maintaining competitive sensitivity.

hep-ph↗

arXiv2023

Dark Matter Search with a Resonantly-Coupled Hybrid Spin System

Recent advances in tabletop quantum sensor technology have enabled searches for nongravitational interactions of dark matter (DM). Traditional axion DM experiments rely on sharp resonance, resulting in extensive scanning time to cover a wide mass range. In this work, we present a broadband approach in an alkali-${}^{21}$Ne spin system. We identify two distinct hybrid spin-coupled regimes: a self-compensation (SC) regime at low frequencies and a hybrid spin resonance (HSR) regime at higher frequencies. By utilizing these two distinct regimes, we significantly enhance the bandwidth of ${}^{21}$Ne nuclear spin compared to conventional nuclear magnetic resonance, while maintaining competitive sensitivity. We present a comprehensive broadband search for axion-like dark matter, covering 5 orders of magnitude of Compton frequencies range within $[10^{-2}, \, 10^3]$ Hz. We set new constraints on the axion dark matter interactions with neutrons and protons, accounting for the effects of DM stochasticity. For the axion-neutron coupling, our results reach a low value of $|g_{ann}|\le 3\times 10^{-10}$ in the frequency range $[2\times 10^{-2}, \, 4]$ Hz surpassing astrophysical limits and providing the strongest laboratory constraints in the $[10, \, 100]$ Hz range. For the axion-proton coupling, we offer the best terrestrial constraints for the frequency ranges $[2\times 10^{-2}, \, 5]$Hz and $[16, \, 7\times 10^{2}]$ Hz.

hep-ph↗

arXiv2023

HST-MRF: Heterogeneous Swin Transformer with Multi-Receptive Field for Medical Image Segmentation

The Transformer has been successfully used in medical image segmentation due to its excellent long-range modeling capabilities. However, patch segmentation is necessary when building a Transformer class model. This process may disrupt the tissue structure in medical images, resulting in the loss of relevant information. In this study, we proposed a Heterogeneous Swin Transformer with Multi-Receptive Field (HST-MRF) model based on U-shaped networks for medical image segmentation. The main purpose is to solve the problem of loss of structural information caused by patch segmentation using transformer by fusing patch information under different receptive fields. The heterogeneous Swin Transformer (HST) is the core module, which achieves the interaction of multi-receptive field patch information through heterogeneous attention and passes it to the next stage for progressive learning. We also designed a two-stage fusion module, multimodal bilinear pooling (MBP), to assist HST in further fusing multi-receptive field information and combining low-level and high-level semantic information for accurate localization of lesion regions. In addition, we developed adaptive patch embedding (APE) and soft channel attention (SCA) modules to retain more valuable information when acquiring patch embedding and filtering channel features, respectively, thereby improving model segmentation quality. We evaluated HST-MRF on multiple datasets for polyp and skin lesion segmentation tasks. Experimental results show that our proposed method outperforms state-of-the-art models and can achieve superior performance. Furthermore, we verified the effectiveness of each module and the benefits of multi-receptive field segmentation in reducing the loss of structural information through ablation experiments.

cs.CV↗

arXiv2022

A Dual-Attention Learning Network with Word and Sentence Embedding for Medical Visual Question Answering

Research in medical visual question answering (MVQA) can contribute to the development of computeraided diagnosis. MVQA is a task that aims to predict accurate and convincing answers based on given medical images and associated natural language questions. This task requires extracting medical knowledge-rich feature content and making fine-grained understandings of them. Therefore, constructing an effective feature extraction and understanding scheme are keys to modeling. Existing MVQA question extraction schemes mainly focus on word information, ignoring medical information in the text. Meanwhile, some visual and textual feature understanding schemes cannot effectively capture the correlation between regions and keywords for reasonable visual reasoning. In this study, a dual-attention learning network with word and sentence embedding (WSDAN) is proposed. We design a module, transformer with sentence embedding (TSE), to extract a double embedding representation of questions containing keywords and medical information. A dualattention learning (DAL) module consisting of self-attention and guided attention is proposed to model intensive intramodal and intermodal interactions. With multiple DAL modules (DALs), learning visual and textual co-attention can increase the granularity of understanding and improve visual reasoning. Experimental results on the ImageCLEF 2019 VQA-MED (VQA-MED 2019) and VQA-RAD datasets demonstrate that our proposed method outperforms previous state-of-the-art methods. According to the ablation studies and Grad-CAM maps, WSDAN can extract rich textual information and has strong visual reasoning ability.

cs.CV↗

arXiv2022

Automatic Assessment of Infant Face and Upper-Body Symmetry as Early Signs of Torticollis

We apply computer vision pose estimation techniques developed expressly for the data-scarce infant domain to the study of torticollis, a common condition in infants for which early identification and treatment is critical. Specifically, we use a combination of facial landmark and body joint estimation techniques designed for infants to estimate a range of geometric measures pertaining to face and upper body symmetry, drawn from an array of sources in the physical therapy and ophthalmology research literature in torticollis. We gauge performance with a range of metrics and show that the estimates of most these geometric measures are successful, yielding strong to very strong Spearman's $ρ$ correlation with ground truth values. Furthermore, we show that these estimates, derived from pose estimation neural networks designed for the infant domain, cleanly outperform estimates derived from more widely known networks designed for the adult domain

eess.IV↗

arXiv2022

Computer Vision to the Rescue: Infant Postural Symmetry Estimation from Incongruent Annotations

Bilateral postural symmetry plays a key role as a potential risk marker for autism spectrum disorder (ASD) and as a symptom of congenital muscular torticollis (CMT) in infants, but current methods of assessing symmetry require laborious clinical expert assessments. In this paper, we develop a computer vision based infant symmetry assessment system, leveraging 3D human pose estimation for infants. Evaluation and calibration of our system against ground truth assessments is complicated by our findings from a survey of human ratings of angle and symmetry, that such ratings exhibit low inter-rater reliability. To rectify this, we develop a Bayesian estimator of the ground truth derived from a probabilistic graphical model of fallible human raters. We show that the 3D infant pose estimation model can achieve 68% area under the receiver operating characteristic curve performance in predicting the Bayesian aggregate labels, compared to only 61% from a 2D infant pose estimation model and 60% from a 3D adult pose estimation model, highlighting the importance of 3D poses and infant domain knowledge in assessing infant body symmetry. Our survey analysis also suggests that human ratings are susceptible to higher levels of bias and inconsistency, and hence our final 3D pose-based symmetry assessment system is calibrated but not directly supervised by Bayesian aggregate human ratings, yielding higher levels of consistency and lower levels of inter-limb assessment bias.

cs.CV↗

arXiv2022

InfAnFace: Bridging the infant-adult domain gap in facial landmark estimation in the wild

We lay the groundwork for research in the algorithmic comprehension of infant faces, in anticipation of applications from healthcare to psychology, especially in the early prediction of developmental disorders. Specifically, we introduce the first-ever dataset of infant faces annotated with facial landmark coordinates and pose attributes, demonstrate the inadequacies of existing facial landmark estimation algorithms in the infant domain, and train new state-of-the-art models that significantly improve upon those algorithms using domain adaptation techniques. We touch on the closely related task of facial detection for infants, and also on a challenging case study of infrared baby monitor images gathered by our lab as part of in-field research into the aforementioned developmental issues.

cs.CV↗

arXiv2021

Invariant Representation Learning for Infant Pose Estimation with Small Data

Infant motion analysis is a topic with critical importance in early childhood development studies. However, while the applications of human pose estimation have become more and more broad, models trained on large-scale adult pose datasets are barely successful in estimating infant poses due to the significant differences in their body ratio and the versatility of their poses. Moreover, the privacy and security considerations hinder the availability of adequate infant pose data required for training of a robust model from scratch. To address this problem, this paper presents (1) building and publicly releasing a hybrid synthetic and real infant pose (SyRIP) dataset with small yet diverse real infant images as well as generated synthetic infant poses and (2) a multi-stage invariant representation learning strategy that could transfer the knowledge from the adjacent domains of adult poses and synthetic infant images into our fine-tuned domain-adapted infant pose (FiDIP) estimation model. In our ablation study, with identical network structure, models trained on SyRIP dataset show noticeable improvement over the ones trained on the only other public infant pose datasets. Integrated with pose estimation backbone networks with varying complexity, FiDIP performs consistently better than the fine-tuned versions of those models. One of our best infant pose estimation performers on the state-of-the-art DarkPose model shows mean average precision (mAP) of 93.6.

cs.CV↗

arXiv2020

Simultaneously-Collected Multimodal Lying Pose Dataset: Towards In-Bed Human Pose Monitoring under Adverse Vision Conditions

Computer vision (CV) has achieved great success in interpreting semantic meanings from images, yet CV algorithms can be brittle for tasks with adverse vision conditions and the ones suffering from data/label pair limitation. One of this tasks is in-bed human pose estimation, which has significant values in many healthcare applications. In-bed pose monitoring in natural settings could involve complete darkness or full occlusion. Furthermore, the lack of publicly available in-bed pose datasets hinders the use of many successful pose estimation algorithms for this task. In this paper, we introduce our Simultaneously-collected multimodal Lying Pose (SLP) dataset, which includes in-bed pose images from 109 participants captured using multiple imaging modalities including RGB, long wave infrared, depth, and pressure map. We also present a physical hyper parameter tuning strategy for ground truth pose label generation under extreme conditions such as lights off and being fully covered by a sheet/blanket. SLP design is compatible with the mainstream human pose datasets, therefore, the state-of-the-art 2D pose estimation models can be trained effectively with SLP data with promising performance as high as 95% at PCKh@0.5 on a single modality. The pose estimation performance can be further improved by including additional modalities through collaboration.

cs.CV↗

arXiv2019

Infant Contact-less Non-Nutritive Sucking Pattern Quantification via Facial Gesture Analysis

Non-nutritive sucking (NNS) is defined as the sucking action that occurs when a finger, pacifier, or other object is placed in the baby's mouth, but there is no nutrient delivered. In addition to providing a sense of safety, NNS even can be regarded as an indicator of infant's central nervous system development. The rich data, such as sucking frequency, the number of cycles, and their amplitude during baby's non-nutritive sucking is important clue for judging the brain development of infants or preterm infants. Nowadays most researchers are collecting NNS data by using some contact devices such as pressure transducers. However, such invasive contact will have a direct impact on the baby's natural sucking behavior, resulting in significant distortion in the collected data. Therefore, we propose a novel contact-less NNS data acquisition and quantification scheme, which leverages the facial landmarks tracking technology to extract the movement signals of baby's jaw from recorded baby's sucking video. Since completion of the sucking action requires a large amount of synchronous coordination and neural integration of the facial muscles and the cranial nerves, the facial muscle movement signals accompanying baby's sucking pacifier can indirectly replace the NNS signal. We have evaluated our method on videos collected from several infants during their NNS behaviors and we have achieved the quantified NNS patterns closely comparable to results from visual inspection as well as contact-based sensor readings.

cs.CV↗

arXiv2013

Critical Missing Equation of Quantum Physics for Understanding Atomic Structures

This paper presents an optimization approach to explain why and how a quantum system evolves from an arbitrary initial state to a stationary state, satisfying the time-independent Schrödinger equation. It also points out the inaccuracy of this equation, which is critial important in quantum mechanics and quantum chemistry, due to a fundamental flaw in it conflicting with the physical reality. The some directions are suggested on how to modify the equation to fix the problem

physics.gen-ph↗