arXiv subjects

Fan Zhang

Publications and source records attributed to Fan Zhang.

At least 415 records · Page 23Linked to original sources

arXiv2025

The GECAM Ground Search System for Gamma-ray Transients

In the era of time-domain, multi-messenger astronomy, the detection of transient events on the high-energy electromagnetic sky has become more important than ever. The Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is a dedicated mission to monitor gamma-ray transients, launched in December, 2020. A real-time on-board trigger and location software, using the traditional signal-to-noise ratio (SNR) method for blind search, is constrained to relatively bright signals due to the limitations in on-board computing resources and the need for real-time search. In this work, we developed a ground-based pipeline for GECAM to search for various transients, especially for weak bursts missed by on-board software. This pipeline includes both automatic and manual mode, offering options for blind search and targeted search. The targeted search is specifically designed to search for interesting weak bursts, such as gravitational wave-associated gamma-ray bursts (GRBs). From the ground search of the data in the first year, GECAM has been triggered by 54 GRBs and other transients, including soft gamma-ray repeaters, X-ray binaries, solar flares, terrestrial gamma-ray flashes. We report the properties of each type of triggers,such as trigger time and light curves. With this search pipeline and assuming a soft Band spectrum, the GRB detection sensitivity of GECAM is increased to about 1.1E-08 erg cm-2 s-1 (10 keV - 1000 keV, burst duration of 20 s). These results demonstrate that the GECAM ground search system (both blind search and targeted search) is a versatile pipeline to recover true astrophysical signals which were too weak to be found in the on-board search.

astro-ph.HE↗

arXiv2025

A Dynamic Bus Lane Strategy for Integrated Management of Human-Driven and Autonomous Vehicles

This study introduces a dynamic bus lane (DBL) strategy, referred to as the dynamic bus priority lane (DBPL) strategy, designed for mixed traffic environments featuring both manual and automated vehicles. Unlike previous DBL strategies, this approach accounts for partially connected and autonomous vehicles (CAVs) capable of autonomous trajectory planning. By leveraging this capability, the strategy grants certain CAVs Right of Way (ROW) in bus lanes while utilizing their leading effects in general lanes to guide vehicle platoons through intersections, thereby indirectly influencing the trajectories of other vehicles. The ROW allocation is optimized using a mixed-integer linear programming (MILP) model, aimed at minimizing total vehicle travel time. Since different CAVs entering the bus lane affect other vehicles travel times, the model incorporates lane change effects when estimating the states of CAVs, human-driven vehicles (HDVs), and connected autonomous buses (CABs) as they approach the stop bar. A dynamic control framework with a rolling horizon procedure is established to ensure precise execution of the ROW optimization under varying traffic conditions. Simulation experiments across two scenarios assess the performance of the proposed DBPL strategy at different CAV market penetration rates (MPRs).

math.OC↗

arXiv2025

Robust Super-Moiré in Large Angle Single-Twist Bilayers

Forming long wavelength moiré superlattices (MSL) at small-angle twist van der Waals (vdW) bilayers has been a key approach to creating moiré flat bands. The small-angle twist, however, leads to strong lattice reconstruction, causing domain walls and moiré disorders, which pose considerable challenges in engineering such platforms. At large twist angles, the rigid lattices render a more robust, but shorter wavelength MSL, making it difficult to engineer flat bands. Here, we depict a novel approach to tailoring robust super-moiré (SM) structures that combines the advantages of both small-twist and large-twist transition metal dichalcogenides (TMDs) bilayers using only a single twist angle near a commensurate angle. Structurally, we unveil the spontaneous formation of a periodic arrangement of three inequivalent commensurate moiré (CM) stacking, where the angle deviation from the commensurate angle can tune the periodicity. Electronically, we reveal a large set of van Hove singularities (VHSs) that indicate strong band hybridization, leading to flat bands near the valence band maximum. Our study paves the way for a new platform of robust SM bilayers with structural rigidity and controllable wavelength, extending the investigation of the interplay among band topology, quantum geometry, and moiré superconductivity to the large twist angle regime.

cond-mat.mtrl-sci↗

arXiv2025

Quarter Metal Superconductivity

We investigate the recently discovered multiple superconducting states in rhombohedral graphene quarter metal. We demonstrate that one of these states features a single-spin, single-valley, single-band, single-Fermi-pocket parent state and is most likely a chiral topological pair-density wave, marked by a threefold symmetry that may not be spontaneously broken, unpaired Majorana zero modes at edges, vortices, and dislocations, and an anomalous intrinsic superconducting diode effect.

cond-mat.mes-hall↗

arXiv2025

AquaNeRF: Neural Radiance Fields in Underwater Media with Distractor Removal

Neural radiance field (NeRF) research has made significant progress in modeling static video content captured in the wild. However, current models and rendering processes rarely consider scenes captured underwater, which are useful for studying and filming ocean life. They fail to address visual artifacts unique to underwater scenes, such as moving fish and suspended particles. This paper introduces a novel NeRF renderer and optimization scheme for an implicit MLP-based NeRF model. Our renderer reduces the influence of floaters and moving objects that interfere with static objects of interest by estimating a single surface per ray. We use a Gaussian weight function with a small offset to ensure that the transmittance of the surrounding media remains constant. Additionally, we enhance our model with a depth-based scaling function to upscale gradients for near-camera volumes. Overall, our method outperforms the baseline Nerfacto by approximately 7.5\% and SeaThru-NeRF by 6.2% in terms of PSNR. Subjective evaluation also shows a significant reduction of artifacts while preserving details of static targets and background compared to the state of the arts.

cs.CV↗

arXiv2025

Bridging the Gap: Transforming Natural Language Questions into SQL Queries via Abstract Query Pattern and Contextual Schema Markup

Large language models have demonstrated excellent performance in many tasks, including Text-to-SQL, due to their powerful in-context learning capabilities. They are becoming the mainstream approach for Text-to-SQL. However, these methods still have a significant gap compared to human performance, especially on complex questions. As the complexity of questions increases, the gap between questions and SQLs increases. We identify two important gaps: the structural mapping gap and the lexical mapping gap. To tackle these two gaps, we propose PAS-SQL, an efficient SQL generation pipeline based on LLMs, which alleviates gaps through Abstract Query Pattern (AQP) and Contextual Schema Markup (CSM). AQP aims to obtain the structural pattern of the question by removing database-related information, which enables us to find structurally similar demonstrations. CSM aims to associate database-related text span in the question with specific tables or columns in the database, which alleviates the lexical mapping gap. Experimental results on the Spider and BIRD datasets demonstrate the effectiveness of our proposed method. Specifically, PAS-SQL + GPT-4o sets a new state-of-the-art on the Spider benchmark with an execution accuracy of 87.9\%, and achieves leading results on the BIRD dataset with an execution accuracy of 64.67\%.

cs.CL↗

arXiv2025

DaBiT: Depth and Blur informed Transformer for Video Focal Deblurring

In many real-world scenarios, recorded videos suffer from accidental focus blur, and while video deblurring methods exist, most specifically target motion blur or spatial-invariant blur. This paper introduces a framework optimized for the as yet unattempted task of video focal deblurring (refocusing). The proposed method employs novel map-guided transformers, in addition to image propagation, to effectively leverage the continuous spatial variance of focal blur and restore the footage. We also introduce a flow re-focusing module designed to efficiently align relevant features between blurry and sharp domains. Additionally, we propose a novel technique for generating synthetic focal blur data, broadening the model's learning capabilities and robustness to include a wider array of content. We have made a new benchmark dataset, DAVIS-Blur, available. This dataset, a modified extension of the popular DAVIS video segmentation set, provides realistic focal blur degradations as well as the corresponding blur maps. Comprehensive experiments demonstrate the superiority of our approach. We achieve state-of-the-art results with an average PSNR performance over 1.9dB greater than comparable existing video restoration methods. Our source code and the developed databases will be made available at https://github.com/crispianm/DaBiT

cs.CV↗

arXiv2025

"An Image of Ourselves in Our Minds": How College-educated Online Dating Users Construct Profiles for Effective Self Presentation

Online dating is frequently used by individuals looking for potential relationships and intimate connections. Central to dating apps is the creation and refinement of a dating profile, which represents the way individuals desire to present themselves to potential mates, while hiding information they do not care to share. To investigate the way frequent users of dating apps construct their online profiles and perceive the effectiveness of strategies taken in making profiles, we conducted semi-structured interviews with 20 experienced users who are Chinese college-educated young adults and uncovered the processes and rationales by which they make profiles for online dating, particularly in selecting images for inclusion. We found that participants used idealized photos that exaggerated their positive personality traits, sometimes traits that they do not possess but perceive others to desire, and sometimes even traits they wish they had possessed. Users also strategically used photos that show personality and habits without showing themselves, and often hid certain identifying information to reduce privacy risks. This analysis signals potential factors that are key in building online dating profiles, providing design implications for systems that limit the use of inaccurate information while still promoting self-expression in relationship platforms.

cs.HC↗

arXiv2025

Medical Image Registration Meets Vision Foundation Model: Prototype Learning and Contour Awareness

Medical image registration is a fundamental task in medical image analysis, aiming to establish spatial correspondences between paired images. However, existing unsupervised deformable registration methods rely solely on intensity-based similarity metrics, lacking explicit anatomical knowledge, which limits their accuracy and robustness. Vision foundation models, such as the Segment Anything Model (SAM), can generate high-quality segmentation masks that provide explicit anatomical structure knowledge, addressing the limitations of traditional methods that depend only on intensity similarity. Based on this, we propose a novel SAM-assisted registration framework incorporating prototype learning and contour awareness. The framework includes: (1) Explicit anatomical information injection, where SAM-generated segmentation masks are used as auxiliary inputs throughout training and testing to ensure the consistency of anatomical information; (2) Prototype learning, which leverages segmentation masks to extract prototype features and aligns prototypes to optimize semantic correspondences between images; and (3) Contour-aware loss, a contour-aware loss is designed that leverages the edges of segmentation masks to improve the model's performance in fine-grained deformation fields. Extensive experiments demonstrate that the proposed framework significantly outperforms existing methods across multiple datasets, particularly in challenging scenarios with complex anatomical structures and ambiguous boundaries. Our code is available at https://github.com/HaoXu0507/IPMI25-SAM-Assisted-Registration.

cs.CV↗

arXiv2025

Deep neural networks from the perspective of ergodic theory

The design of deep neural networks remains somewhat of an art rather than precise science. By tentatively adopting ergodic theory considerations on top of viewing the network as the time evolution of a dynamical system, with each layer corresponding to a temporal instance, we show that some rules of thumb, which might otherwise appear mysterious, can be attributed heuristics.

cs.LG↗

arXiv2025

TractShapeNet: Efficient Multi-Shape Learning with 3D Tractography Point Clouds

Brain imaging studies have demonstrated that diffusion MRI tractography geometric shape descriptors can inform the study of the brain's white matter pathways and their relationship to brain function. In this work, we investigate the possibility of utilizing a deep learning model to compute shape measures of the brain's white matter connections. We introduce a novel framework, TractShapeNet, that leverages a point cloud representation of tractography to compute five shape measures: length, span, volume, total surface area, and irregularity. We assess the performance of the method on a large dataset including 1065 healthy young adults. Experiments for shape measure computation demonstrate that our proposed TractShapeNet outperforms other point cloud-based neural network models in both the Pearson correlation coefficient and normalized error metrics. We compare the inference runtime results with the conventional shape computation tool DSI-Studio. Our results demonstrate that a deep learning approach enables faster and more efficient shape measure computation. We also conduct experiments on two downstream language cognition prediction tasks, showing that shape measures from TractShapeNet perform similarly to those computed by DSI-Studio. Our code will be available at: https://github.com/SlicerDMRI/TractShapeNet.

cs.CV↗

arXiv2025

The shape of the brain's connections is predictive of cognitive performance: an explainable machine learning study

The shape of the brain's white matter connections is relatively unexplored in diffusion MRI tractography analysis. While it is known that tract shape varies in populations and across the human lifespan, it is unknown if the variability in dMRI tractography-derived shape may relate to the brain's functional variability across individuals. This work explores the potential of leveraging tractography fiber cluster shape measures to predict subject-specific cognitive performance. We implement machine learning models to predict individual cognitive performance scores. We study a large-scale database from the HCP-YA study. We apply an atlas-based fiber cluster parcellation to the dMRI tractography of each individual. We compute 15 shape, microstructure, and connectivity features for each fiber cluster. Using these features as input, we train a total of 210 models to predict 7 different NIH Toolbox cognitive performance assessments. We apply an explainable AI technique, SHAP, to assess the importance of each fiber cluster for prediction. Our results demonstrate that shape measures are predictive of individual cognitive performance. The studied shape measures, such as irregularity, diameter, total surface area, volume, and branch volume, are as effective for prediction as microstructure and connectivity measures. The overall best-performing feature is a shape feature, irregularity, which describes how different a cluster's shape is from an idealized cylinder. Further interpretation using SHAP values suggest that fiber clusters with features highly predictive of cognitive ability are widespread throughout the brain, including fiber clusters from the superficial association, deep association, cerebellar, striatal, and projection pathways. This study demonstrates the strong potential of shape descriptors to enhance the study of the brain's white matter and its relationship to cognitive function.

q-bio.NC↗

arXiv2025

Efficient magnetohydrodynamic modelling of the time-evolving corona by COCONUT

Compared to quasi-steady-state corona models that are constrained by a time-invariant magnetogram over a CR period, time-evolving corona models driven by time-varying photospheric magnetograms are more realistic and can maintain more useful information to accurately describe solar wind evolution and forecast CME propagation. This paper demonstrate that time-evolving corona simulations can be performed efficiently and accurately using an implicit method with relatively large time steps. We also evaluate differences between coronal structures captured by time-evolving and quasi-steady simulations over a CR period during solar minimum. We used a series of hourly updated photospheric magnetograms to drive the evolution of coronal structures from the solar surface to $25\; R_s$ during two CRs around the 2019 eclipse in an inertial coordinate system. We compare the time-evolving and quasi-steady simulations to demonstrate that the differences in these two types of coronal modelling can be obvious even for a solar minimum. The relative differences in radial velocity and density can be over $15 \%$ and $25 \%$ at 20$\;R_s$ during one CR period. We also evaluated the impact of time steps on the simulation results. Using a time step of approximately 10 minutes balances efficiency and necessary numerical stability and accuracy for time-evolving corona simulations around solar minima, with coronal evolution during a full CR simulated within only 9 hours (using 1080 CPU cores for 1.5M grid cells). The simulation results demonstrate that time-evolving MHD coronal simulations can be performed efficiently and accurately using an implicit method, offering a more realistic alternative to quasi-steady-state simulations. The fully implicit time-evolving corona model thus promises to simulate the time-evolving corona accurately in practical space weather forecasting.

astro-ph.SR↗

arXiv2025

$\mathsf{CRATE}$: Cross-Rollup Atomic Transaction Execution

Blockchains have revolutionized decentralized applications, with composability enabling atomic, trustless interactions across smart contracts. However, layer 2 (L2) scalability solutions like rollups introduce fragmentation and hinder composability. Current cross-chain protocols, including atomic swaps, bridges, and shared sequencers, lack the necessary coordination mechanisms or rely on trust assumptions, and are thus not sufficient to support full cross-rollup composability. This paper presents $\mathsf{CRATE}$, a secure protocol for cross-rollup composability that ensures all-or-nothing and serializable execution of cross-rollup transactions (CRTs). $\mathsf{CRATE}$ supports rollups on distinct layer 1 (L1) chains, achieves finality in 4 rounds on L1, and only relies on the underlying L1s and the liveness of L2s. We introduce two formal models for CRTs, define atomicity within them, and formally prove the security of $\mathsf{CRATE}$. We also provide an implementation of $\mathsf{CRATE}$ along with a cross-rollup flash loan application; our experiments demonstrate that $\mathsf{CRATE}$ is practical in terms of gas usage on L1.

cs.CR↗

arXiv2025

Learning Street View Representations with Spatiotemporal Contrast

Street view imagery is extensively utilized in representation learning for urban visual environments, supporting various sustainable development tasks such as environmental perception and socio-economic assessment. However, it is challenging for existing image representations to specifically encode the dynamic urban environment (such as pedestrians, vehicles, and vegetation), the built environment (including buildings, roads, and urban infrastructure), and the environmental ambiance (such as the cultural and socioeconomic atmosphere) depicted in street view imagery to address downstream tasks related to the city. In this work, we propose an innovative self-supervised learning framework that leverages temporal and spatial attributes of street view imagery to learn image representations of the dynamic urban environment for diverse downstream tasks. By employing street view images captured at the same location over time and spatially nearby views at the same time, we construct contrastive learning tasks designed to learn the temporal-invariant characteristics of the built environment and the spatial-invariant neighborhood ambiance. Our approach significantly outperforms traditional supervised and unsupervised methods in tasks such as visual place recognition, socioeconomic estimation, and human-environment perception. Moreover, we demonstrate the varying behaviors of image representations learned through different contrastive learning objectives across various downstream tasks. This study systematically discusses representation learning strategies for urban studies based on street view images, providing a benchmark that enhances the applicability of visual data in urban science. The code is available at https://github.com/yonglleee/UrbanSTCL.

cs.CV↗

arXiv2025

Experimental signature of layer skyrmions and implications for band topology in twisted WSe2 bilayers

Transition metal dichalcogenide (TMD) twisted homobilayers have been established as an ideal platform for studying strong correlation phenomena, as exemplified by the recent discovery of fractional Chern insulator (FCI) states in twisted MoTe2 and Chern insulators (CI) and unconventional superconductivity in twisted WSe2 (tWSe2). In these systems, nontrivial topology in the strongly layer-hybridized regime can arise from a spatial patterning of interlayer tunneling amplitudes and layer-dependent potentials that yields a lattice of layer skyrmions. Here we report on experimental signatures of skyrmion textures in the layer degree of freedom of Rhombohedral-stacked (R-stacked) tWSe2 homobilayers. This observation is based on scanning tunneling spectroscopy that separately resolves the Gamma-valley and K-valley moiré electronic states. We show that Gamma-valley states are subjected to a moiré potential with an amplitude of ~ 120 meV. At ~150 meV above the Gamma-valley, the K-valley states are subjected to a weaker moiré potential of ~30 meV. Most significantly, we reveal opposite layer polarizations of the K-valley at the MX and XM sites within the moiré unit cell, confirming the theoretically predicted layer skyrmion texture. The dI/dV mappings allow the parameters that enter the continuum model of moiré bands in twisted TMD bilayers to be determined experimentally, further establishing a direct correlation between the shape of the LDOS profile in real space and the topology of topmost moiré band.

cond-mat.mes-hall↗

arXiv2025

LEAF: Unveiling Two Sides of the Same Coin in Semi-supervised Facial Expression Recognition

Semi-supervised learning has emerged as a promising approach to tackle the challenge of label scarcity in facial expression recognition (FER) task. However, current state-of-the-art methods primarily focus on one side of the coin, i.e., generating high-quality pseudo-labels, while overlooking the other side: enhancing expression-relevant representations. In this paper, we unveil both sides of the coin by proposing a unified framework termed hierarchicaL dEcoupling And Fusing (LEAF) to coordinate expression-relevant representations and pseudo-labels for semi-supervised FER. LEAF introduces a hierarchical expression-aware aggregation strategy that operates at three levels: semantic, instance, and category. (1) At the semantic and instance levels, LEAF decouples representations into expression-agnostic and expression-relevant components, and adaptively fuses them using learnable gating weights. (2) At the category level, LEAF assigns ambiguous pseudo-labels by decoupling predictions into positive and negative parts, and employs a consistency loss to ensure agreement between two augmented views of the same image. Extensive experiments on benchmark datasets demonstrate that by unveiling and harmonizing both sides of the coin, LEAF outperforms state-of-the-art semi-supervised FER methods, effectively leveraging both labeled and unlabeled data. Moreover, the proposed expression-aware aggregation strategy can be seamlessly integrated into existing semi-supervised frameworks, leading to significant performance gains. Our code is available at \url{https://github.com/zfkarl/LEAF}.

cs.CV↗

arXiv2025

Impact of Spin-Orbit Coupling on Superconductivity in Rhombohedral Graphene

Spin-orbit coupling (SOC) has played an important role in many topological and correlated electron materials. In graphene-based systems, SOC induced by transition metal dichalcogenide (TMD) at proximity was shown to drive topological states and strengthen superconductivity. However, in rhombohedral multilayer graphene, a robust platform for electron correlation and topology, superconductivity and the role of SOC remain largely unexplored. Here we report transport measurements of TMD-proximitized rhombohedral trilayer graphene (RTG). We observed a new hole-doped superconducting state SC4 with Tc = 230 mK. On the electron-doped side, we identified a new isospin-symmetry breaking three-quarter-metal (TQM) phase and observed the nearby weak superconducting state SC3 is significantly enhanced. Surprisingly, the original superconducting state SC1 in bare RTG is strongly suppressed in the presence of TMD - opposite to the effect of SOC on all other graphene superconductivities. Our observations form the basis of exploring superconductivity and non-Abelian quasiparticles in rhombohedral graphene devices.

cond-mat.supr-con↗