arXiv subjects

Xiaobo Li

Publications and source records attributed to Xiaobo Li.

At least 37 records · Page 2Linked to original sources

arXiv2025

Unveil A Peculiar Light Curve Pattern of Magnetar Burst with GECAM observations of SGR J1935+2154

Magnetar X-ray Burst (MXB) is usually composed of a single pulse or multiple pulses with rapid rise and brief duration mostly observed in hard X-ray (soft gamma-ray) band. Previous work studied the temporal behavior of some magnetar bursts and employed the Fast Rise Exponential Decay (FRED) model to fit pulses of MXB. However, whether there is other kind of pulse shape has not been explored. In this study, we systematically examined light curve of MXBs from SGR J1935+2154 detected by GECAM between 2021 and 2022. We find that there are different light curve morphologies. Especially, we discover a peculiar and new pattern, Exponential Rise and Cut-Off Decay (ERCOD), which is significantly different from FRED and could be well described by a mathematical function we proposed. We find that MXBs with ERCOD shape are generally longer in duration, brighter in the peak flux, and harder in spectrum. We note that the ERCOD shape is not unique to SGR J1935+2154 but also present in other magnetars. This new light curve pattern may imply a special burst and radiation mechanism of magnetar.

astro-ph.HE↗

arXiv2025

Development and validation of an AI foundation model for endoscopic diagnosis of esophagogastric junction adenocarcinoma: a cohort and deep learning study

The early detection of esophagogastric junction adenocarcinoma (EGJA) is crucial for improving patient prognosis, yet its current diagnosis is highly operator-dependent. This paper aims to make the first attempt to develop an artificial intelligence (AI) foundation model-based method for both screening and staging diagnosis of EGJA using endoscopic images. In this cohort and learning study, we conducted a multicentre study across seven Chinese hospitals between December 28, 2016 and December 30, 2024. It comprises 12,302 images from 1,546 patients; 8,249 of them were employed for model training, while the remaining were divided into the held-out (112 patients, 914 images), external (230 patients, 1,539 images), and prospective (198 patients, 1,600 images) test sets for evaluation. The proposed model employs DINOv2 (a vision foundation model) and ResNet50 (a convolutional neural network) to extract features of global appearance and local details of endoscopic images for EGJA staging diagnosis. Our model demonstrates satisfactory performance for EGJA staging diagnosis across three test sets, achieving an accuracy of 0.9256, 0.8895, and 0.8956, respectively. In contrast, among representative AI models, the best one (ResNet50) achieves an accuracy of 0.9125, 0.8382, and 0.8519 on the three test sets, respectively; the expert endoscopists achieve an accuracy of 0.8147 on the held-out test set. Moreover, with the assistance of our model, the overall accuracy for the trainee, competent, and expert endoscopists improves from 0.7035, 0.7350, and 0.8147 to 0.8497, 0.8521, and 0.8696, respectively. To our knowledge, our model is the first application of foundation models for EGJA staging diagnosis and demonstrates great potential in both diagnostic accuracy and efficiency.

cs.CV↗

arXiv2025

A molecular rotor driven by an electric field on graphene

We propose a scheme for driving a dipolar molecular rotor to rotate continuously by applying an external electric field: the dipolar rotor is fixed on a graphene sheet via a metal atom to facilitate the free rotation; it is in the meantime subjected to an electric field oriented parallel to the graphene sheet. We use computational modeling with density functional theory and Newtonian mechanics, similar to molecular dynamics simulations, to obtain the torque, angular velocity, and rotation period of the rotor. Our results show that the dipolar rotor designed here can rotate with a period of 2.96 ps by an alternating rectangular electric field with a strength of 0.5 V/Å. However, a cosine wave alternating electric field depending on time cannot drive the dipolar rotor to rotate regularly. Therefore, a cosine wave electric field depending on the rotation angle is suggested, as it can not only drive the rotor but also produce additional power. Machine learning molecular dynamics (MLMD) simulations further confirm that the rotor remains thermodynamically stable under an electric field. This work reveals the rotation mechanism of a dipolar molecular rotor in a transverse electric field, and we hope this work can open a new path for designing more diverse molecular machines in experiments.

physics.comp-ph↗

arXiv2025

Timing and spectral studies of SRGA J144459.2$-$604207 with NICER, Einstein Probe, IXPE, NuSTAR, Insight-HXMT and INTEGRAL during its 2024 outburst

SRGA J144459.2$-$604207 is a newly confirmed accreting millisecond X-ray pulsar and type I X-ray burster. We present the broadband X-ray timing and spectral behaviors of SRGA J144459.2$-$604207 during its 2024 outburst. The data were collected from NICER, Einstein Probe, IXPE, Insight-HXMT, NuSTAR and INTEGRAL observations. X-ray pulsations have been detected for the 1.5--90 keV energy range throughout the `ON' phase of the outburst from MJD $\sim 60355-60385$. We refined the orbital and spin ephemerides assuming a circular orbit, and found that the pulsar was in a spin-up state during MJD $\sim$ 60361--60377 showing a significant spin-up rate $\dotν$ of $(3.15\pm 0.36)\times10^{-13}~{\rm Hz~s^{-1}}$. Around MJD $\sim 60377$ a swing was detected in the spin evolution accompanied by significantly enhanced pulsed emission. We studied the pulse profile morphology during the X-ray bursts as observed by Insight-HXMT, IXPE and NuSTAR. During the bursts, pulsations were detected across the 2--60 keV with shapes broadly consistent with those observed for the persistent emission. We found, however, that the `burst' pulse profiles exhibit significant phase offsets relative to the pre- and post-burst profiles. These offsets systematically decrease with increasing energy, $Δϕ\approx0.15$, 0.11 and 0.02 for IXPE, Insight-HXMT ME and HE in 2--8, 5--30 and 20--60 keV, respectively, and $Δϕ\approx 0.21$, 0.10 and 0.07 for NuSTAR in 3--10, 20--35 and 35--60 keV, respectively, compared to the pre- and post-burst profiles. We performed a joint spectral analysis of quasi-simultaneous NICER, NuSTAR, and Insight-HXMT data for two epochs. The resulting spectra from both observations were consistent and well-described by an absorbed thermal Comptonization model, nthcomp, plus relativistic reflection, relxillCp.

astro-ph.HE↗

arXiv2025

Physics of Strong Magnetism with eXTP

In this paper we present the science potential of the enhanced X-ray Timing and Polarimetry (eXTP) mission, in its new configuration, for studies of strongly magnetized compact objects. We discuss the scientific potential of eXTP for quantum electrodynamic (QED) studies, especially leveraging on the recent observations made with the NASA IXPE mission. Given eXTP's unique combination of timing, spectroscopy, and polarimetry, we focus on the perspectives for physics and astrophysics studies of strongly magnetized compact objects, such as magnetars and accreting X-ray pulsars. Developed by an international Consortium led by the Institute of High Energy Physics of the Chinese Academy of Sciences, the eXTP mission is expected to launch in early 2030.

astro-ph.HE↗

arXiv2025

CPKD: Clinical Prior Knowledge-Constrained Diffusion Models for Surgical Phase Recognition in Endoscopic Submucosal Dissection

Gastrointestinal malignancies constitute a leading cause of cancer-related mortality worldwide, with advanced-stage prognosis remaining particularly dismal. Originating as a groundbreaking technique for early gastric cancer treatment, Endoscopic Submucosal Dissection has evolved into a versatile intervention for diverse gastrointestinal lesions. While computer-assisted systems significantly enhance procedural precision and safety in ESD, their clinical adoption faces a critical bottleneck: reliable surgical phase recognition within complex endoscopic workflows. Current state-of-the-art approaches predominantly rely on multi-stage refinement architectures that iteratively optimize temporal predictions. In this paper, we present Clinical Prior Knowledge-Constrained Diffusion (CPKD), a novel generative framework that reimagines phase recognition through denoising diffusion principles while preserving the core iterative refinement philosophy. This architecture progressively reconstructs phase sequences starting from random noise and conditioned on visual-temporal features. To better capture three domain-specific characteristics, including positional priors, boundary ambiguity, and relation dependency, we design a conditional masking strategy. Furthermore, we incorporate clinical prior knowledge into the model training to improve its ability to correct phase logical errors. Comprehensive evaluations on ESD820, Cholec80, and external multi-center demonstrate that our proposed CPKD achieves superior or comparable performance to state-of-the-art approaches, validating the effectiveness of diffusion-based generative paradigms for surgical phase recognition.

cs.CV↗

arXiv2025

SPRMamba: Surgical Phase Recognition for Endoscopic Submucosal Dissection with Mamba

Endoscopic Submucosal Dissection (ESD) is a minimally invasive procedure initially developed for early gastric cancer treatment and has expanded to address diverse gastrointestinal lesions. While computer-assisted surgery (CAS) systems enhance ESD precision and safety, their efficacy hinges on accurate real-time surgical phase recognition, a task complicated by ESD's inherent complexity, including heterogeneous lesion characteristics and dynamic tissue interactions. Existing video-based phase recognition algorithms, constrained by inefficient temporal context modeling, exhibit limited performance in capturing fine-grained phase transitions and long-range dependencies. To overcome these limitations, we propose SPRMamba, a novel framework integrating a Mamba-based architecture with a Scaled Residual TranMamba (SRTM) block to synergize long-term temporal modeling and localized detail extraction. SPRMamba further introduces the Hierarchical Sampling Strategy to optimize computational efficiency, enabling real-time processing critical for clinical deployment. Evaluated on the ESD385 dataset and the cholecystectomy benchmark Cholec80, SPRMamba achieves state-of-the-art performance (87.64% accuracy on ESD385, +1.0% over prior methods), demonstrating robust generalizability across surgical workflows. This advancement bridges the gap between computational efficiency and temporal sensitivity, offering a transformative tool for intraoperative guidance and skill assessment in ESD surgery. The code is accessible at https://github.com/Zxnyyyyy/SPRMamba.

cs.CV↗

arXiv2025

High-efficiency silicon LED with ultra-wideband emission from visible to infrared at room temperature

The primary challenge in silicon photonics is achieving efficient luminescence in the communication band, crucial for its large-scale application. Despite significant efforts, silicon light sources still suffer from low efficiency and limited emission wavelengths. We addressed this by achieving broadband luminescence from 600-1650 nm through femtosecond laser annealing of 220nm standard SOI, resulting in an external quantum efficiency exceeding 0.26% and an output optical power density greater than 20 W/cm2, several orders of magnitude higher than other silicon-based LEDs in performance. The broadband LED has potential applications in optical inspection, gas sensing, optical coherence tomography, optical communication, and more.

physics.optics↗

arXiv2025

Mapping ground-based coronagraphic images to Helioprojective-Cartesian coordinate system by image registration

A few ground-based solar coronagraphs have been installed in western China for observing the low-layer corona in recent years. However, determining the Helioprojective Coordinates for the coronagraphic data with high precision is an important but challenging step for further research with other multi-wavelength data. In this paper, we propose an automatic coronal image registration method that combines local statistical correlation and feature point matching to achieve accurate registration between ground-based coronal green-line images and space-based 211 Å images. Then, the accurate field of view information of the coronal green-line images can be derived, allowing the images to be mapped to the Helioprojective Cartesian Coordinates with an accuracy of no less than 0.1''. This method has been extensively validated using 100 days of coronal data spanning an 11-year period, demonstrating its broad applicability to ground-based coronagraphs equipped with green-line observations. It significantly enhances the scientific value of ground-based coronal data, enabling comprehensive studies of coronal transient activities and facilitating the joint analysis of data from multiple instruments. Additionally, it holds potential for future applications in improving the pointing accuracy of coronagraphs.

astro-ph.SR↗

arXiv2025

From Photospheric Footpoint Motion to Plasmoid Ejection: A Two-Stage Reconnection Process in a Small-scale Chromospheric Jet

Using high spatiotemporal resolution, multi-wavelength observations from the New Vacuum Solar Telescope (NVST) and the Solar Dynamics Observatory (SDO), we present a detailed analysis of a small-scale chromospheric jet driven by plasmoid-mediated magnetic reconnection. Our results reveal that the entire process is governed by the dynamic evolution of photospheric magnetic footpoints, which proceeds in two distinct stages. An initial separating motion of the footpoints corresponds to a mild reconnection phase, characterized by a short current sheet and the eruption of a cool H$α$ jet. Subsequently, a converging motion of the footpoints triggers an intense reconnection phase. During this intense stage, the current sheet rapidly elongates, and the resulting decrease in its aspect ratio initiates a tearing-mode instability, forming a plasmoid. The appearance of this plasmoid mediates the onset of fast magnetic reconnection, which produces a hot EUV jet and is concurrent with significant magnetic flux cancellation. We interpret this cancellation as the submergence of newly formed, post-reconnection loops. Furthermore, we identify a distinct, high-temperature plasma blob in the jet spire, significantly hotter than the surrounding jet plasma. We attribute this feature to a secondary heating process, likely caused by reconnection between the upward-propagating plasmoid and the overlying magnetic cusp structure. These observations provide a comprehensive, observationally driven picture (from the initial photospheric triggers to the multi-stage, plasmoid-mediated reconnection) that forms chromospheric jets, highlighting the critical role of footpoint motions in solar atmospheric dynamics.

astro-ph.SR↗

arXiv2025

Video Virtual Try-on with Conditional Diffusion Transformer Inpainter

Video virtual try-on aims to naturally fit a garment to a target person in consecutive video frames. It is a challenging task, on the one hand, the output video should be in good spatial-temporal consistency, on the other hand, the details of the given garment need to be preserved well in all the frames. Naively using image-based try-on methods frame by frame can get poor results due to severe inconsistency. Recent diffusion-based video try-on methods, though very few, happen to coincide with a similar solution: inserting temporal attention into image-based try-on model to adapt it for video try-on task, which have shown improvements but there still exist inconsistency problems. In this paper, we propose ViTI (Video Try-on Inpainter), formulate and implement video virtual try-on as a conditional video inpainting task, which is different from previous methods. In this way, we start with a video generation problem instead of an image-based try-on problem, which from the beginning has a better spatial-temporal consistency. Specifically, at first we build a video inpainting framework based on Diffusion Transformer with full 3D spatial-temporal attention, and then we progressively adapt it for video garment inpainting, with a collection of masking strategies and multi-stage training. After these steps, the model can inpaint the masked garment area with appropriate garment pixels according to the prompt with good spatial-temporal consistency. Finally, as other try-on methods, garment condition is added to the model to make sure the inpainted garment appearance and details are as expected. Both quantitative and qualitative experimental results show that ViTI is superior to previous works.

cs.CV↗

arXiv2025

MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention

We introduce MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model. MiniMax-M1 is powered by a hybrid Mixture-of-Experts (MoE) architecture combined with a lightning attention mechanism. The model is developed based on our previous MiniMax-Text-01 model, which contains a total of 456 billion parameters with 45.9 billion parameters activated per token. The M1 model natively supports a context length of 1 million tokens, 8x the context size of DeepSeek R1. Furthermore, the lightning attention mechanism in MiniMax-M1 enables efficient scaling of test-time compute. These properties make M1 particularly suitable for complex tasks that require processing long inputs and thinking extensively. MiniMax-M1 is trained using large-scale reinforcement learning (RL) on diverse problems including sandbox-based, real-world software engineering environments. In addition to M1's inherent efficiency advantage for RL training, we propose CISPO, a novel RL algorithm to further enhance RL efficiency. CISPO clips importance sampling weights rather than token updates, outperforming other competitive RL variants. Combining hybrid-attention and CISPO enables MiniMax-M1's full RL training on 512 H800 GPUs to complete in only three weeks, with a rental cost of just $534,700. We release two versions of MiniMax-M1 models with 40K and 80K thinking budgets respectively, where the 40K model represents an intermediate phase of the 80K training. Experiments on standard benchmarks show that our models are comparable or superior to strong open-weight models such as the original DeepSeek-R1 and Qwen3-235B, with particular strengths in complex software engineering, tool utilization, and long-context tasks. We publicly release MiniMax-M1 at https://github.com/MiniMax-AI/MiniMax-M1.

cs.CL↗

arXiv2025

GECAM Discovery of Peculiar Oscillating Particle Precipitation Events

Charged particle precipitation typically manifests as a gradual increase and decrease of flux observed by space detectors. Cases with rapidly flux variation are very rare. Periodic events are even more extraordinary. These oscillating particle precipitation (OPP) events are usually attributed to the bounce motion of electrons, which are induced by lightning. Owing to the observation limitations, there has been debate regarding whether these oscillations originate from temporal flux evolution or spatial structure evolution. Here we report three peculiar charged particle precipitation events detected by GECAM during a geomagnetic storm on March 21, 2024, with two exhibiting significant periodicity. These events were observed around the same region during three consecutive orbits. Through comprehensive temporal and spectral analyses, we revealed that one of the OPP events exhibited a transition in spectral lag of mini-pulses, shifting from "softer-earlier" to "softer-later" while showing no significant time evolution in overall frequency characteristics. And there is no association found between these two OPP events and lightning activity. Several possible scenarios are discussed to explain these charged particles with a life time of more than 3.5 hours, but the nature of these three events remains an enigma. We suggest that these GECAM-detected OPP events may represent a new type of particle precipitation event or a peculiar Lightning-induced Electron Precipitations (LEPs).

astro-ph.HE↗

arXiv2025

A Nonparametric Approach with Marginals for Modeling Consumer Choice

Given data on the choices made by consumers for different offer sets, a key challenge is to develop parsimonious models that describe and predict consumer choice behavior while being amenable to prescriptive tasks such as pricing and assortment optimization. The marginal distribution model (MDM) is one such model, which requires only the specification of marginal distributions of the random utilities. This paper aims to establish necessary and sufficient conditions for given choice data to be consistent with the MDM hypothesis, inspired by the usefulness of similar characterizations for the random utility model (RUM). This endeavor leads to an exact characterization of the set of choice probabilities that the MDM can represent. Verifying the consistency of choice data with this characterization is equivalent to solving a polynomial-sized linear program. Since the analogous verification task for RUM is computationally intractable and neither of these models subsumes the other, MDM is helpful in striking a balance between tractability and representational power. The characterization is then used with robust optimization for making data-driven sales and revenue predictions for new unseen assortments. When the choice data lacks consistency with the MDM hypothesis, finding the best-fitting MDM choice probabilities reduces to solving a mixed integer convex program. Numerical results using real world data and synthetic data demonstrate that MDM exhibits competitive representational power and prediction performance compared to RUM and parametric models while being significantly faster in computation than RUM.

stat.ML↗

arXiv2025

A comprehensive study of type I (thermonuclear) bursts in the new transient SRGA J144459.2$-$604207

We report an analysis of Insight-HXMT observations of the newly discovered accreting millisecond pulsar SRGA J144459.2$-$604207. During the outburst, detected in 2024 February by SRG/ART-XC, the broadband persistent spectrum was well fitted by an absorbed Comptonization model. We detected 60 type I X-ray bursts in the Insight-HXMT medium energy (ME) data, and 37 were also detected with the low-energy (LE) telescope. By superimposing the Insight-HXMT/LE/ME/HE light curves of 37 bursts with similar profiles and intensities, we measured a deficit of X-rays in the $40-70$ keV energy band. By analyzing the time-resolved X-ray burst spectra, we determine the mean ratio of persistent to burst flux of $α=71\pm7$. We estimate the average hydrogen mass fraction in the fuel at ignition, as $\bar{X} =0.342\pm0.033$, and constrain the burst fuel composition as $X_0\leq0.4$. We found that 14 out of 60 X-ray bursts exhibited photospheric expansion, and thus we estimated the distance to the source as $10.0\pm0.71$ kpc. Combined with IXPE observations, the burst recurrence time increased from 1.55 to 8 hr as the local mass accretion rate decreased, which can be described as $ΔT_{\rm rec}\sim \dot{m}^{-0.91\pm0.02}$.

astro-ph.HE↗

arXiv2025

5f Electron Induced Spin Transport by Sandwich-Type Phthalocyanine

In this study, we employed the non-equilibrium Green's function method combined with density functional theory to investigate the spin transport properties of the actinide sandwich phthalocyanine molecule U(Pc)2.This study aims to provide beneficial assistance for the development of actinide phthalocyanine molecular spintronic devices.

physics.comp-ph↗

arXiv2024

HOTVCOM: Generating Buzzworthy Comments for Videos

In the era of social media video platforms, popular ``hot-comments'' play a crucial role in attracting user impressions of short-form videos, making them vital for marketing and branding purpose. However, existing research predominantly focuses on generating descriptive comments or ``danmaku'' in English, offering immediate reactions to specific video moments. Addressing this gap, our study introduces \textsc{HotVCom}, the largest Chinese video hot-comment dataset, comprising 94k diverse videos and 137 million comments. We also present the \texttt{ComHeat} framework, which synergistically integrates visual, auditory, and textual data to generate influential hot-comments on the Chinese video dataset. Empirical evaluations highlight the effectiveness of our framework, demonstrating its excellence on both the newly constructed and existing datasets.

cs.CV↗

arXiv2024

ALSS-YOLO: An Adaptive Lightweight Channel Split and Shuffling Network for TIR Wildlife Detection in UAV Imagery

Unmanned aerial vehicles (UAVs) equipped with thermal infrared (TIR) cameras play a crucial role in combating nocturnal wildlife poaching. However, TIR images often face challenges such as jitter, and wildlife overlap, necessitating UAVs to possess the capability to identify blurred and overlapping small targets. Current traditional lightweight networks deployed on UAVs struggle to extract features from blurry small targets. To address this issue, we developed ALSS-YOLO, an efficient and lightweight detector optimized for TIR aerial images. Firstly, we propose a novel Adaptive Lightweight Channel Split and Shuffling (ALSS) module. This module employs an adaptive channel split strategy to optimize feature extraction and integrates a channel shuffling mechanism to enhance information exchange between channels. This improves the extraction of blurry features, crucial for handling jitter-induced blur and overlapping targets. Secondly, we developed a Lightweight Coordinate Attention (LCA) module that employs adaptive pooling and grouped convolution to integrate feature information across dimensions. This module ensures lightweight operation while maintaining high detection precision and robustness against jitter and target overlap. Additionally, we developed a single-channel focus module to aggregate the width and height information of each channel into four-dimensional channel fusion, which improves the feature representation efficiency of infrared images. Finally, we modify the localization loss function to emphasize the loss value associated with small objects to improve localization accuracy. Extensive experiments on the BIRDSAI and ISOD TIR UAV wildlife datasets show that ALSS-YOLO achieves state-of-the-art performance, Our code is openly available at https://github.com/helloworlder8/computer_vision.

cs.CV↗