arXiv subjects

Jun Shi

Publications and source records attributed to Jun Shi.

At least 19 recordsLinked to original sources

arXiv2026

Direct lattice QCD calculation of the $\theta$-induced CP-violating pion-nucleon coupling

We present the first direct lattice QCD determination of the $\theta$-induced CP-violating pion-nucleon-nucleon coupling $\tilde g_{\pi NN}$. Using overlap valence fermions on three $2+1$-flavor domain-wall ensembles at a single lattice spacing, we calculate the forward proton matrix element of the isovector pseudoscalar density in the $\theta$-vacuum to first order in $\bar\theta$. The parity-mixing effect in the external nucleon states is included in the extraction. The cluster-decomposition error-reduction method is used to improve the statistical precision. A simultaneous extrapolation in the valence- and sea-pion masses, with model averaging over 7 forms based on the Akaike information criterion, gives $\tilde g_{\pi NN}=0.0306(56)(124)\,\bar\theta$ at the physical point. The first uncertainty includes the statistical and matrix-element-fit systematic uncertainties, whereas the second reflects the spread among the extrapolation forms. Within these uncertainties, the result is consistent with the indirect determination based on the strong neutron-proton mass splitting. The present precision is limited primarily by the extrapolation from the relatively heavy sea-pion masses.

hep-lat↗

arXiv2026

TERRA: A Hierarchical Parallel Training and Memory Orchestration Framework for High-Resolution AI-based Earth Modeling

Training high-resolution AI-based Earth forecasting models is memory-intensive. Window-based Swin Transformers reduce the quadratic cost of global attention, but existing distributed systems such as AERIS primarily target pixel-level models and do not jointly support convolutional sampling modules and shifted-window execution. Long-lead rollout finetuning further increases activation memory. To address these challenges, we present TERRA, a hierarchical parallel training framework for high-resolution Earth forecasting. TERRA introduces Sampling-Aware Window, Sequence, and Tensor Parallelism (SAWSTP), which preserves spatially contiguous layouts for sampling modules and routes tokens into topology-aware ragged window layouts for Transformer execution. For long-lead finetuning, Memory Orchestration (MO) provides rollout-aware checkpoint planning and combines input buffering with budget-constrained activation offloading. Experiments on the $1/12^\circ$ GLORYS-based Wenhai workload show that TERRA supports models with up to 11.4B parameters on 96 H200 GPUs and sustains up to $39.76$ PFLOPS, achieving $65.0\%$ strong-scaling and $94.1\%$ weak-scaling efficiency. Compared with checkpoint-only policies, MO further reduces peak allocated GPU memory by $32.2\%$--$51.8\%$ with at most $20.0\%$ step-time overhead, which makes finetuning with smaller patch sizes and longer rollouts feasible for improved forecasting accuracy.

cs.CV↗

arXiv2026

Spectral Consistent Flow for One-step 3D Medical Image Translation

We present Spectral Consistent Flow (SC-Flow), a 3D medical image translation framework with a single function evaluation (1-NFE) in the latent space. This approach reformulates medical image translation as a stochastic Brownian bridge process that directly constructs a mapping between source and target modalities by predicting the support regularized mean velocity field. To mitigate modality entanglement, over-smoothing, and artifacts induced by the implicit low-pass modulation of the latent average velocity, we introduce a Spectral Consistency Corrector that dynamically regularizes the evolution of the power spectral density via learnable frequency-domain gain modulation. This mechanism establishes an explicit bridge between spatial textures and spectral energy flow, enabling the model to recover fine-grained anatomical fidelity while maintaining global structural coherence. Extensive experiments on four datasets demonstrate that SC-Flow delivers significantly more accurate, consistent, and robust performance across various translation scenarios.

cs.CV↗

arXiv2026

Summary of the First Year of the Space Weather Around Young Suns Program: 900 Hours of Low-frequency Radio and Optical Data Dedicated to Young, Solar-type Stars

The Space Weather Around Young Suns (SWAYS) program was introduced in \citet{Davis2025} as a multi-wavelength monitoring program for studying the activity and particle environments of nearby, young, solar-type stars. The SWAYS program currently includes the Owens Valley Radio Observatory Long Wavelength Array (OVRO-LWA) operating between 13--87\,MHz to search for stellar equivalents of solar type~II and III bursts, which are associated with bulk plasma motion in the corona and interplanetary medium. These observations are accompanied by simultaneous photometric data from the high-precision, optical instrument Flarescope to identify associated flare events. These two instruments have collectively acquired nearly 900\,hr of data with $\approx70\%$ overlap between November 2023--June 2024, dedicated to six stars. Here, we present the results of this first season of the SWAYS observing campaign, which include a superflare from the star EK~Draconis with no accompanying low-frequency particle-flux signal. The novelty of the coordination at these specific parts of the spectrum allow us to uniquely evaluate the conditions that may have inhibited a radio detection. We find that the exceptionally hot, dense coronae of incredibly active stars may not be conducive to the development of the instabilities required for type~II and III bursts, or else inspire new expectations for when we should expect to observe a signal relative to the time of the flare. This may represent the plasma-density complement to the magnetospheric limitations to observing space-weather signatures at low frequencies.

astro-ph.SR↗

arXiv2026

All-Sky Ultra-Narrowband Spectral Imaging with the OVRO-LWA: Technosignature Constraints and Axion-Like Particle Prospects

We present an imaging-domain search for technosignatures at decametric wavelengths with the OVRO-LWA, targeting ultra-narrowband continuous-wave signals between 50 and 86 MHz. We implement an offline GPU pipeline that processes raw voltage data with upchannelization to approximately 10 Hz frequency resolution, producing all-sky images for each fine channel and totaling more than 3 x 10^6 images for a single 30 s epoch. Candidate selection is performed using multi-kernel matched filtering across frequency, empirical noise standardization, and false-discovery-rate control. After applying quality cuts that remove extended sources, corrupted images, and obvious RFI, three narrowband candidates with signal-to-noise ratios above 10 sigma were selected for detailed analysis. By re-imaging these candidates with finer temporal and spectral resolution, we resolved their structure and found them to be inconsistent with compact celestial narrowband emitters. Consequently, we report no detection of extraterrestrial technosignatures. The representative sensitivity of the search is ~100 Jy per channel across the entire visible hemisphere. For an unresolved emitter, this corresponds to 10 sigma equivalent isotropic radiated power (EIRP) limits of about 10^14 W at a distance of 10 pc and 10^18 W at 1 kpc. The wide field of view and ultra-fine spectral resolution of this approach enable simultaneous probing of technosignature signals from millions of stellar systems. This method further establishes a scalable framework for deeper integrations and stacked searches toward neutron-star targets relevant to axion-like particle (ALP) line conversion.

astro-ph.IM↗

arXiv2026

Clore: Interactive Pathology Image Segmentation with Click-based Local Refinement

Recent advancements in deep learning-based interactive segmentation methods have significantly improved pathology image segmentation. Most existing approaches utilize user-provided positive and negative clicks to guide the segmentation process. However, these methods primarily rely on iterative global updates for refinement, which lead to redundant re-prediction and often fail to capture fine-grained structures or correct subtle errors during localized adjustments. To address this limitation, we propose the Click-based Local Refinement (Clore) pipeline, a simple yet efficient method designed to enhance interactive segmentation. The key innovation of Clore lies in its hierarchical interaction paradigm: the initial clicks drive global segmentation to rapidly outline large target regions, while subsequent clicks progressively refine local details to achieve precise boundaries. This approach not only improves the ability to handle fine-grained segmentation tasks but also achieves high-quality results with fewer interactions. Experimental results on four datasets demonstrate that Clore achieves the best balance between segmentation accuracy and interaction cost, making it an effective solution for efficient and accurate interactive pathology image segmentation.

cs.CV↗

arXiv2026

Implementation of a Near-Realtime Recording and Reporting System of Solar Radio Bursts

Strong solar activity is often accompanied by a variety of radio bursts. These bursts are valuable diagnostics of coronal and heliospheric processes and also have potential applications in space weather monitoring and forecasting. However, space weather applications require low-latency, high-sensitivity radio burst recording and reporting capabilities, which have remained limited. In this work, we present the development of a near-realtime radio burst recording and reporting system using the Owens Valley Radio Observatory Long Wavelength Array. The system directly clips data from a realtime buffer and streams them as a live radio dynamic spectrogram. These spectrograms are then processed by a deep-learning-based burst identification module for type III radio bursts. The identifier is based on a YOLO (You Only Look Once) architecture and is trained on synthetic type III radio bursts generated using a physics-based model to achieve accurate and robust detection. This system enables continuous realtime radio spectrum streaming and automatic reporting of type III radio bursts within approximately 10 seconds of their occurrence.

astro-ph.SR↗

arXiv2026

A Commensal Radio-Only Cosmic Ray Detector at the Owens Valley Radio Observatory Long Wavelength Array

The brief (10 nanoseconds) transient radio emission from cosmic ray air showers carries key information about the energy and mass composition of high energy cosmic rays, but anthropogenic radio frequency interference has historically prevented radio-based cosmic ray studies from being carried out independently from other types of detectors. We describe a cosmic ray detection system for the Owens Valley Radio Observatory Long Wavelength Array that searches for radio emission from cosmic ray air showers without relying on an external trigger, and runs alongside the other observing modes of the array. The OVRO-LWA, located in Eastern California, recently completed an expansion to 352 dual-polarization antennas and new signal processing infrastructure. In order to detect cosmic rays in the presence of radio frequency interference (RFI), initial event classification and RFI rejection is performed on Field Programmable Gate Array boards, which each process a sampled voltage timeseries from both polarizations of a subarray of 32 antennas. Each board uses dedicated RFI veto antennas outside the air shower radio footprint to reject RFI events. We present the trigger design, RFI flagging strategy, and candidate cosmic rays.

astro-ph.IM↗

arXiv2026

Estimating Electron Densities in the Middle Solar Corona using White-light and Radio Observations

The electron density of the solar corona is a fundamental parameter in many areas of solar physics. Traditionally, routine estimates of coronal density have relied exclusively on white-light observations. However, these density estimates, obtained by inverting the white-light data, require simplifying assumptions, which may affect the robustness of the measurements. Hence, to improve the reliability of coronal density measurements, it is highly desirable to explore other complementary methods. In this study, we estimate the coronal electron densities in the middle corona, between approximately $1.7-3.5R_\odot$, using low-frequency radio observations from the recently commissioned Long Wavelength Array at the Owens Valley Radio Observatory (OVRO-LWA). The results demonstrate consistency with those derived from white-light coronagraph data and predictions from theoretical models. We also derive a density model valid between 1.7--3.5 $r_\odot$ and is given by $\rho (r')=1.27r'^{-2}+29.02r'^{-4}+71.18r'^{-6}$, where $r'=r/R_\odot$, and $r$ is the heliocentric distance. OVRO-LWA is a solar-dedicated radio interferometer that provides science-ready images with low latency, making it well-suited for generating regular and independent estimates of coronal densities to complement existing white-light techniques.

astro-ph.SR↗

arXiv2026

Radiative decay of heavy-light mesons from lattice QCD

We present the first systematic study of the radiative decays of charmed mesons using $2+1$-flavor clover fermion gauge ensembles generated by the CLQCD collaboration. One of the ensembles is at the physical pion mass, and one has a fine lattice spacing $a\sim 0.05 ~\text{fm}$. We determine the coupling constants to be $g_{D^{\ast+} D^+ \gamma} = -0.204(22)$ GeV$^{-1}$, $g_{D^{\ast0} D^0 \gamma} = 1.73(37)$ GeV$^{-1}$, and $g_{D_s^{\ast+} D_s^+ \gamma} =-0.120(14)$ GeV$^{-1}$, respectively. Compared with previous studies, our results demonstrate significant improvements in precision. In particular, we carefully estimate the systematic uncertainty arising from matrix element fits, momentum transfer extrapolations, and chiral and continuum limit extrapolations, which are included in the reported total uncertainties. These couplings yield the following predictions of decay widths: $\Gamma_{D^{\ast+} \rightarrow D^+ \gamma} = 0.253(55)$ keV, $\Gamma_{D^{\ast0} \rightarrow D^0 \gamma} = 18.2(7.8)$ keV, and $\Gamma_{D_s^{\ast+}\rightarrow D_s^+ \gamma} = 0.094(22)$ keV. This work establishes first-principles results of the charmed meson radiative transitions and provides inputs for understanding the structure and properties of heavy-light mesons.

hep-lat↗

arXiv2026

Frequency Error-Guided Under-sampling Optimization for Multi-Contrast MRI Reconstruction

Magnetic resonance imaging (MRI) plays a vital role in clinical diagnostics, yet it remains hindered by long acquisition times and motion artifacts. Multi-contrast MRI reconstruction has emerged as a promising direction by leveraging complementary information from fully-sampled reference scans. However, existing approaches suffer from three major limitations: (1) superficial reference fusion strategies, such as simple concatenation, (2) insufficient utilization of the complementary information provided by the reference contrast, and (3) fixed under-sampling patterns. We propose an efficient and interpretable frequency error-guided reconstruction framework to tackle these issues. We first employ a conditional diffusion model to learn a Frequency Error Prior (FEP), which is then incorporated into a unified framework for jointly optimizing both the under-sampling pattern and the reconstruction network. The proposed reconstruction model employs a model-driven deep unfolding framework that jointly exploits frequency- and image-domain information. In addition, a spatial alignment module and a reference feature decomposition strategy are incorporated to improve reconstruction quality and bridge model-based optimization with data-driven learning for improved physical interpretability. Comprehensive validation across multiple imaging modalities, acceleration rates (4-30x), and sampling schemes demonstrates consistent superiority over state-of-the-art methods in both quantitative metrics and visual quality. All codes are available at https://github.com/fangxinming/JUF-MRI.

cs.CV↗

arXiv2026

MiMo-V2-Flash Technical Report

We present MiMo-V2-Flash, a Mixture-of-Experts (MoE) model with 309B total parameters and 15B active parameters, designed for fast, strong reasoning and agentic capabilities. MiMo-V2-Flash adopts a hybrid attention architecture that interleaves Sliding Window Attention (SWA) with global attention, with a 128-token sliding window under a 5:1 hybrid ratio. The model is pre-trained on 27 trillion tokens with Multi-Token Prediction (MTP), employing a native 32k context length and subsequently extended to 256k. To efficiently scale post-training compute, MiMo-V2-Flash introduces a novel Multi-Teacher On-Policy Distillation (MOPD) paradigm. In this framework, domain-specialized teachers (e.g., trained via large-scale reinforcement learning) provide dense and token-level reward, enabling the student model to perfectly master teacher expertise. MiMo-V2-Flash rivals top-tier open-weight models such as DeepSeek-V3.2 and Kimi-K2, despite using only 1/2 and 1/3 of their total parameters, respectively. During inference, by repurposing MTP as a draft model for speculative decoding, MiMo-V2-Flash achieves up to 3.6 acceptance length and 2.6x decoding speedup with three MTP layers. We open-source both the model weights and the three-layer MTP weights to foster open research and community collaboration.

cs.CL↗

arXiv2025

MiMo-Audio: Audio Language Models are Few-Shot Learners

Existing audio language models typically rely on task-specific fine-tuning to accomplish particular audio tasks. In contrast, humans are able to generalize to new audio tasks with only a few examples or simple instructions. GPT-3 has shown that scaling next-token prediction pretraining enables strong generalization capabilities in text, and we believe this paradigm is equally applicable to the audio domain. By scaling MiMo-Audio's pretraining data to over one hundred million of hours, we observe the emergence of few-shot learning capabilities across a diverse set of audio tasks. We develop a systematic evaluation of these capabilities and find that MiMo-Audio-7B-Base achieves SOTA performance on both speech intelligence and audio understanding benchmarks among open-source models. Beyond standard metrics, MiMo-Audio-7B-Base generalizes to tasks absent from its training data, such as voice conversion, style transfer, and speech editing. MiMo-Audio-7B-Base also demonstrates powerful speech continuation capabilities, capable of generating highly realistic talk shows, recitations, livestreaming and debates. At the post-training stage, we curate a diverse instruction-tuning corpus and introduce thinking mechanisms into both audio understanding and generation. MiMo-Audio-7B-Instruct achieves open-source SOTA on audio understanding benchmarks (MMSU, MMAU, MMAR, MMAU-Pro), spoken dialogue benchmarks (Big Bench Audio, MultiChallenge Audio) and instruct-TTS evaluations, approaching or surpassing closed-source models. Model checkpoints and full evaluation suite are available at https://github.com/XiaomiMiMo/MiMo-Audio.

cs.CL↗

arXiv2025

Mamba-Based Modality Disentanglement Network for Multi-Contrast MRI Reconstruction

Magnetic resonance imaging (MRI) is a cornerstone of modern clinical diagnosis, offering unparalleled soft-tissue contrast without ionizing radiation. However, prolonged scan times remain a major barrier to patient throughput and comfort. Existing accelerated MRI techniques often struggle with two key challenges: (1) failure to effectively utilize inherent K-space prior information, leading to persistent aliasing artifacts from zero-filled inputs; and (2) contamination of target reconstruction quality by irrelevant information when employing multi-contrast fusion strategies. To overcome these challenges, we present MambaMDN, a dual-domain framework for multi-contrast MRI reconstruction. Our approach first employs fully-sampled reference K-space data to complete the undersampled target data, generating structurally aligned but modality-mixed inputs. Subsequently, we develop a Mamba-based modality disentanglement network to extract and remove reference-specific features from the mixed representation. Furthermore, we introduce an iterative refinement mechanism to progressively enhance reconstruction accuracy through repeated feature purification. Extensive experiments demonstrate that MambaMDN can significantly outperform existing multi-contrast reconstruction methods.

cs.CV↗

arXiv2025

Searches for Prompt Low-Frequency Radio Counterparts to Gravitational Wave Event S250206dm with the OVRO-LWA Time Machine

We report on a search for prompt, low-frequency radio emission from the gravitational-wave (GW) merger S250206dm using the Owens Valley Radio Observatory Long Wavelength Array (OVRO-LWA). Early alerts favored a neutron-star-containing merger, making this a compelling target. Motivated by theoretical predictions of coherent radio bursts from mergers involving a neutron star, we utilized the OVRO-LWA Time Machine system to analyze voltage data recorded around the time of the event. The Time Machine is a two-stage voltage buffer and processing pipeline that continuously buffers raw data from all antennas across the array's nearly full-hemisphere instantaneous field of view, enabling retrospective beamforming, dedispersion, and fast-transient candidate identification. For this event, we analyzed a 30-minute interval beginning 3.5 minutes after the merger, which included two minutes of pre-alert data recovered by the ring buffer. We searched the 50% localization probability region with millisecond time resolution in the 69-86 MHz frequency band. No radio counterpart was detected above a 7-sigma fluence detection threshold of ~150 Jy ms. Using Bayesian analysis, we place a 95% confidence upper limit on the source luminosity of L95 = 4 x 10^41 erg s^-1. These constraints start to probe the bright end of the coherent-emission parameter space predicted by jet-ISM shock processes, magnetar and blitzar-like mechanisms, and recent simulation-based scenarios for neutron-star-containing mergers. This study presents the first sensitive, large-area, millisecond-timescale search for prompt low-frequency radio emission from a GW merger with the OVRO-LWA, establishing a framework in which about ten additional events will yield stringent population-level constraints.

astro-ph.HE↗

arXiv2025

Traffic Image Restoration under Adverse Weather via Frequency-Aware Mamba

Traffic image restoration under adverse weather conditions remains a critical challenge for intelligent transportation systems. Existing methods primarily focus on spatial-domain modeling but neglect frequency-domain priors. Although the emerging Mamba architecture excels at long-range dependency modeling through patch-wise correlation analysis, its potential for frequency-domain feature extraction remains unexplored. To address this, we propose Frequency-Aware Mamba (FAMamba), a novel framework that integrates frequency guidance with sequence modeling for efficient image restoration. Our architecture consists of two key components: (1) a Dual-Branch Feature Extraction Block (DFEB) that enhances local-global interaction via bidirectional 2D frequency-adaptive scanning, dynamically adjusting traversal paths based on sub-band texture distributions; and (2) a Prior-Guided Block (PGB) that refines texture details through wavelet-based high-frequency residual learning, enabling high-quality image reconstruction with precise details. Meanwhile, we design a novel Adaptive Frequency Scanning Mechanism (AFSM) for the Mamba architecture, which enables the Mamba to achieve frequency-domain scanning across distinct subgraphs, thereby fully leveraging the texture distribution characteristics inherent in subgraph structures. Extensive experiments demonstrate the efficiency and effectiveness of FAMamba.

cs.CV↗

arXiv2025

Huizhou Hadron Spectrometer -- a Proposed High-rate Experimental Setup at the High Intensity Heavy-ion Accelerator Facility

The High-Intensity Heavy-Ion Accelerator Facility (HIAF), currently under construction in Huizhou, Guangdong Province, China, is projected to be completed by 2025. This facility will be capable of producing proton and heavy-ion beams with energies reaching several GeV, thereby offering a versatile platform for advanced fundamental physics research. Key scientific objectives include exploring physics beyond the Standard Model through the search for novel particles and interactions, testing fundamental symmetries, investigating exotic hadronic states such as di-baryons, pentaquark states and multi-strange hypernuclei, conducting precise measurements of hadron and hypernucleus properties, and probing the phase boundary and critical point of nuclear matter. To facilitate these investigations, we propose the development of a dedicated experimental apparatus at HIAF - the Huizhou Hadron Spectrometer (HHaS). This paper presents the conceptual design of HHaS, comprising a solenoid magnet, a five-dimensional silicon pixel tracker, a Low-Gain Avalanche Detector (LGAD) for time-of-flight measurements, and a Cherenkov-scintillation dual-readout electromagnetic calorimeter. The design anticipates an unprecedented event rate of 1-100 MHz, extensive particle acceptance, a track momentum resolution at 1% level, an electromagnetic energy resolution of ~3% @ 1 GeV and multi-particle identification capabilities. Such capabilities position HHaS as a powerful instrument for advancing experimental studies in particle and nuclear physics. The successful realization of HHaS is expected to significantly bolster the development of medium- and high-energy physics research within China.

hep-ex↗

arXiv2025

Large Language Model Aided Birt-Hogg-Dube Syndrome Diagnosis with Multimodal Retrieval-Augmented Generation

Deep learning methods face dual challenges of limited clinical samples and low inter-class differentiation among Diffuse Cystic Lung Diseases (DCLDs) in advancing Birt-Hogg-Dube syndrome (BHD) diagnosis via Computed Tomography (CT) imaging. While Multimodal Large Language Models (MLLMs) demonstrate diagnostic potential fo such rare diseases, the absence of domain-specific knowledge and referable radiological features intensify hallucination risks. To address this problem, we propose BHD-RAG, a multimodal retrieval-augmented generation framework that integrates DCLD-specific expertise and clinical precedents with MLLMs to improve BHD diagnostic accuracy. BHDRAG employs: (1) a specialized agent generating imaging manifestation descriptions of CT images to construct a multimodal corpus of DCLDs cases. (2) a cosine similarity-based retriever pinpointing relevant imagedescription pairs for query images, and (3) an MLLM synthesizing retrieved evidence with imaging data for diagnosis. BHD-RAG is validated on the dataset involving four types of DCLDs, achieving superior accuracy and generating evidence-based descriptions closely aligned with expert insights.

cs.CV↗