arXiv subjects

Bin Chen

Publications and source records attributed to Bin Chen.

At least 73 records · Page 4Linked to original sources

arXiv2026

Large Value Estimates for Dirichlet Polynomials with Characters and Zero Density of Dirichlet $L$-Functions

It is proved that \[ \sum_{χ\bmod q}N(σ,T,χ) \ll_ε (qT)^{7(1-σ)/3+ε}, \] where $N(σ,T,χ)$ denotes the number of zeros $ρ=β+it$ of $L(s,χ)$ in the rectangle $σ\leq β\leq 1$, $|t|\leq T$. The exponent $7/3$ improves upon Huxley's earlier exponent of $12/5$. The key innovation lies in deriving a sharp upper bound for sums over affine transformations of functions with a GCD twist, which arises from our adaptation of the Guth--Maynard method. As applications of the zero density estimates obtained in this paper, we derive a new upper bound for the least Goldbach number in arithmetic progressions modulo a prime and establish new results on primes in arithmetic progressions in short intervals, in particular for prime-power moduli.

math.NT↗

arXiv2026

Critical Behavior of Photon Rings in Kerr-Bertotti-Robinson Spacetime

In this work, we investigate the critical behavior of photon rings in the Kerr-Bertotti-Robinson spacetime, describing a rotating black hole immersed in a background magnetic field. We analyze the radial and angular motions of photons under the small magnetic field approximation. Focusing on unstable spherical orbits, we determine three key parameters, $γ$, $δ$, and $τ$, which characterize radial compression, azimuthal advancement, and time delay. We then examine how these parameters depend on the black hole spin, magnetic field strength, and observer inclination for both on-axis and off-axis observers, and we further analyze the properties of higher-order images through near-critical lens equations. The results show that the magnetic field modifies the geodesic structure, and leads to observable changes in the fine structure of photon rings, providing a useful framework for probing magnetized black hole environments.

gr-qc↗

arXiv2026

Structuring Line Ensembles with Path-Integrated Fidelity and Structural Inconsistency Fields

When visualizing large-scale line ensembles, trajectory continuity and visual scalability are inherently antagonistic. Trajectory-centric renderings preserve path information but rapidly degenerate into clutter as line density increases and mutual occlusion dominates. In contrast, field-based density representations enhance visibility while sacrificing structural coherence: density reflects accumulation rather than agreement, such that scalar aggregation alone cannot discriminate between consistent and conflicting configurations. Rather than replacing density-based views, we complement them with a path-integrated trajectory-fidelity measure that quantifies the agreement of each trajectory with a surrounding tensor field. By projecting this passage-centered structural support back into image space, we obtain what we call a Structural Inconsistency Field, which localizes regions where dense patterns correspond to coherent structure versus disagreement, outliers, or connectivity-induced ambiguity. Dynamic leave-one-out correction reduces self-bias in the path integral. Efficient fixed-grid updates combined with prefix-sum evaluation enable interactive analysis and iterative extraction of coherent structures. Synthetic benchmarks, scalability analyses, and real-world case studies demonstrate that, when paired with conventional density views, the proposed method disambiguates dense line patterns by exposing spatially localized coherence and structural breakdown that remain concealed in density-only representations.

cs.GR↗

arXiv2026

Finite palette endpoints and degree-square Turán problems

We study finite palette extremal problems motivated by uniform Turán densities of $3$-uniform hypergraphs. Given a self-converse tournament $T$ with at least two vertices, we determine the largest possible number of admissible triples in an $m$-color palette that avoids the left and right palettes associated with $T$. The answer is the one-sided degree-square Turán number \[ \operatorname{pal}_T(m) = \operatorname{ex}_2^+(m,T) = \max\left\{ \sum_{v\in V(D)} d_D^+(v)^2: |V(D)|=m,\ D\text{ is }T\text{-free} \right\}. \] Thus this palette problem is reduced to an extremal problem for digraph out-degrees. We then prove a prefix-majorization lemma for convex out-degree moments and apply it to directed cycles. In particular, $\operatorname{ex}_2^+(m,\overrightarrow C_3)=\frac{m(m^2-1)}3$, which gives the sharp $m$-color palette endpoint $\frac13-\frac{1}{3m^2}$ for the directed triangle. Combining this endpoint with the palette characterization of uniform Turán density and the palette separation theorem, we show that for every $m\ge2$ there is a finite $3$-graph $H_m$ such that \[ \frac13-\frac{1}{3m^2}\le π_u(H_m)\le \frac13. \] Hence there is a sequence of individual finite $3$-graphs whose uniform Turán densities converge to $1/3$. We also describe the extremal palettes, prove a qualitative edit-distance stability theorem, and compute the Lagrangian of the endpoint palette $\mathcal P_m^\star$. As a consequence, for every $m\ge2$ there is a finite family $\mathcal F_m$ of $3$-graphs with $π_u(\mathcal F_m)=\frac13-\frac{1}{3m^2}$, so $1/3$ is an accumulation point of uniform Turán densities of finite forbidden families.

math.CO↗

arXiv2026

LPM: Industrial-Scale Generative Video Restoration

We present the Large Processing Model (LPM), a diffusion-based generative framework for photorealistic video restoration under complex, in-the-wild degradations. To our knowledge, LPM is the first generative video restoration model deployed at industrial scale. LPM addresses the diverse degradations in user-generated content (UGC) through a unified system encompassing large-scale data engineering, foundation-model training, and efficient inference. Its enhanced architecture, progressive training strategy, and temporal-pyramid inference mechanism jointly enable high-fidelity, temporally consistent restoration of arbitrarily long videos across the broad content distribution encountered on UGC platforms. LPM has been deployed in production at Kuaishou, where videos processed by the model account for approximately 45% of total viewing time, delivering consistent improvements across key quality-of-experience metrics. Beyond perceptual enhancement, LPM delivers substantial system-level benefits: at comparable perceptual quality, it reduces bitrate by 20% relative to Kuaishou's in-house codec, yielding annual bandwidth cost savings on the order of hundreds of millions. Its low serving cost also enables integration into products such as Kling, demonstrating that generative restoration can be practical, scalable, and cost-effective for large-scale video processing.

cs.CV↗

arXiv2026

Symmetries and Critical Dimensions of Tensionless Branes

In this work, we investigate the worldsheet symmetry of bosonic brane theories and its quantum consistency in the tensionless limit. We find that the residual worldsheet symmetry after specific gauge fixing is generated by a novel algebra, denoted as $g^{(p)}_λ$. To achieve full quantization of the tensionless brane, we introduce a $bc$ ghost system and derive the overall BRST charge. Moreover, we calculate the quantum anomaly of the $g^{(p)}_λ$ algebra for general parameters $p$ and $λ$ in the framework of canonical quantization. After demanding that this quantum anomaly vanishes, we derive the critical dimensions of the bosonic brane theories. Especially, we obtain nontrivial solutions for the tensionless string: $D=14$ spacetime dimensions when $λ=-1$ and $D=26$ spacetime dimensions when $λ=1$. We also notice that the quantum anomaly automatically vanishes after the Riemann zeta function regularization for $p>1$. This is consistent with the mathematical fact that there is no scalar central extension of the algebra $g^{(p)}_λ$ for $p>1$.

hep-th↗

arXiv2026

Role of SKA in Advancing Remote Measurements of Magnetic Fields of Solar Coronal Mass Ejections

Coronal Mass Ejections (CMEs) are large expulsions of magnetized plasma from the Sun into interplanetary space and are the primary drivers of extreme space weather variations. The strength and topology of CME magnetic fields largely determine their impact on Earth. Although visible-light coronagraphs routinely observe CMEs and provide their geometric and kinematic properties, they cannot directly measure CME vector magnetic fields. These fields evolve from initiation through the inner heliosphere due to interactions with other CMEs, coronal structures, and the ambient solar wind, leading to significant structural deformation. Such evolution complicates predictions of the CME magnetic field at Earth. Accurate measurements of CME magnetic fields in the corona and heliosphere are therefore essential for advancing space weather forecasting. Radio observations spanning MHz to GHz frequencies provide a powerful remote-sensing approach for measuring CME magnetic fields from the ground. Recent observations with Square Kilometre Array (SKA) precursors and pathfinder instruments, as well as other new-generation facilities, have demonstrated the potential of these radio techniques for CME magnetic-field diagnostics. At the same time, these studies have highlighted several limitations of current instruments. The higher sensitivity, wider instantaneous bandwidth, and broader frequency coverage of the SKA will open a new observational window, enabling these techniques to be fully exploited for constraining SpWx models and improving predictive accuracy. However, such observations are non-standard and require special consideration in scheduling, calibration, and imaging. Developments achieved with SKA precursors and pathfinders are paving the way for robust CME magnetic-field measurements with the SKA.

astro-ph.SR↗

arXiv2026

Polarization Architecture of Steady GRMHD Jets from the Horizon to Infinity

We develop a semi-analytic framework for stationary, axisymmetric GRMHD jets that efficiently generates resolved polarized images from the near-horizon region out to $\sim 10^5\,r_g$ across a broad parameter space, enabling rapid exploration of how gravity and magnetohydrodynamic flows imprint scale-dependent signatures on jet morphology and polarization. We identify a new scale-dependent separation in polarimetric diagnostics. Outside the photon ring, plasma loading strongly modifies the polarization-angle profile of the integrated jet-layer emission through inertia-driven winding of the magnetic field. At large image-plane radii, the polarization angle follows a power-law in radius, with an index determined by the jet collimation profile. Near the horizon, in contrast, jets converge to a universal polarization pattern controlled solely by black hole spin. This convergence is hierarchical: differences in velocity and magnetic-field structure are erased first, whereas collimation-dependent differences persist to smaller radii, thereby allowing these effects to be disentangled. These results establish a largely achromatic polarimetric diagnostic that connects GRMHD jet dynamics to resolved image structure, with direct implications for high-resolution polarimetry and for constraining black hole spin and jet formation.

astro-ph.HE↗

arXiv2026

Non-thermal Sources from Stereoscopic Hard X-ray and Earth-based Microwave Observations in a Data-Constrained Magnetohydrodynamic Simulation

We analyze the X7.1 flare on 2024 October 1 from NOAA AR 13842 using hard X-ray (HXR) imaging, microwave observations by the Expanded Owens Valley Solar Array (EOVSA), and a three-dimensional Magnetohydrodynamic (MHD) simulation. The flare was observed from two vantage points, with Solar Orbiter/Spectrometer Telescope for Imaging X-rays viewing the flare near the limb and Advanced Space-based Solar Observatory/Hard X-ray Imager and EOVSA observing it on the disk. We carried out a data-constrained MHD simulation using a nonlinear force-free field extrapolation as the initial condition and constrained the height of the non-thermal looptop source from stereoscopic HXR and microwave observations. The height is consistent between the stereoscopic analysis and the MHD simulation. A secondary non-thermal microwave source aligned with a southward plasma ejection corresponds to an elongated current sheet. Although the current sheet grows in multiple directions, the secondary microwave emission is observed only from the southern segment. This localization suggests reconnection in regions with different magnetic field strengths. Reconnection in strong-field regions produces flare arcades with dominant looptop emission, whereas reconnection in weaker southern regions gives rise to secondary microwave emission at higher altitudes. The height of the secondary source is consistent between the stereoscopic analysis and the MHD simulation. Microwave spectral fitting suggests a higher low-energy cutoff for non-thermal electrons in the secondary microwave source than in the main looptop source. This may reflect the transport of electrons pre-accelerated near the looptop source by the southward plasma ejection.

astro-ph.SR↗

arXiv2026

Radio Diagnostics of Particle Acceleration in Solar Flares with SKAO Observations

Particle acceleration is a fundamental astrophysical process occurring across diverse systems and scales, producing electromagnetic emission across all wavelengths. Radio bursts from astrophysical systems like active galaxy jets, solar flares, pulsars, etc., provide a probe into the emission mechanism and particle acceleration processes. Among all astrophysical phenomena, magnetically driven solar flares provide unique diagnostics of nonthermal particles due to the advantage of multiple spatial-temporal and spectral measurements. The subsequent emitted radiation spans various sections of the radio spectrum. Based on brightness temperature and spectrum, one can distinguish between bright plasma emission or 'nonthermal' emission. Nonthermal radio bursts in meter and microwave bands arise from suprathermal particles, while the surrounding plasma produces fainter thermal emission. High spatial-temporal studies along with polarimetry will enable tracking of electron beams in evolving magnetic fields and constrain coronal properties such as magnetic strength, density, and temperature. Fine spectral structures from gyrosynchrotron bursts allow mapping of magnetic fields along acceleration tracks. However, emissions from the more numerous, weaker particle populations remain difficult to observe. Particles in solar flares follow a power-law energy distribution, with weaker populations being more common. The Square Kilometre Array Observatory (SKAO) will provide high-fidelity data that will enable detailed characterisation of these populations, statistical studies of particle beams, and insights into their interaction with magnetic topologies. Its high-resolution (especially SKA-Mid) and multi-wavelength synergy (EUV, X-rays) will refine diagnostics of coronal and acceleration-region properties. This chapter reviews particle acceleration models and expected advances from SKAO.

astro-ph.SR↗

arXiv2026

Towards Consistent and Efficient Dataset Distillation via Diffusion-Driven Selection

Dataset distillation provides an effective approach to reduce memory and computational costs by optimizing a compact dataset that achieves performance comparable to the full original. However, for large-scale datasets and complex deep networks (e.g., ImageNet-1K with ResNet-101), the vast optimization space hinders distillation effectiveness, limiting practical applications. Recent methods leverage pre-trained diffusion models to directly generate informative images, thereby bypassing pixel-level optimization and achieving promising results. Nonetheless, these approaches often suffer from distribution shifts between the pre-trained diffusion prior and target datasets, as well as the need for multiple distillation steps under varying settings. To overcome these challenges, we propose a novel framework that is orthogonal to existing diffusion-based distillation techniques by utilizing the diffusion prior for patch selection rather than generation. Our method predicts noise from the diffusion model conditioned on input images and optional text prompts (with or without label information), and computes the associated loss for each image-patch pair. Based on the loss differences, we identify distinctive regions within the original images. Furthermore, we apply intra-class clustering and ranking on the selected patches to enforce diversity constraints. This streamlined pipeline enables a one-step distillation process. Extensive experiments demonstrate that our approach consistently outperforms state-of-the-art methods across various metrics and settings.

cs.CV↗

arXiv2026

Spectral Evolution-Guided Token Pruning in Multimodal Large Language Models

Reducing visual token redundancy is critical for accelerating Multimodal Large Language Models (MLLMs) without degrading cross-modal reasoning performance. Existing token pruning methods typically rely on single-layer signals, such as attention scores or token similarities, which overlook the cross-layer transformation of visual representations and may exhibit positional bias in multimodal token sequences. To address this limitation, we propose a training-free token pruning framework based on Cross-Layer Spectral Evolution (CLSE). Instead of measuring token importance from single-layer feature magnitudes, CLSE quantifies how token representations evolve across Transformer layers in the frequency domain. This evolution reflects the transition from high-frequency structural details to low-frequency semantic abstractions. We observe that tokens with stronger spectral redistribution across layers are more likely to be semantically active and should therefore be preserved. By modeling cross-layer token dynamics, CLSE provides a stable importance criterion that mitigates positional bias. Extensive experiments on both image and video benchmarks demonstrate that CLSE achieves a superior trade-off between efficiency and accuracy under aggressive token reduction. Across multiple MLLMs, CLSE reduces FLOPs, KV cache memory, and latency while maintaining competitive or improved performance.

cs.CV↗

arXiv2026

Pocket-Dentist: On-Device Dental Image Understanding via Efficient Multimodal Large Language Models

Evaluations of dental vision-language models remain fragmented across datasets, task definitions and metrics, and often ignore their computational cost. This limits their widespread deployment for dental screening outside specialist centres, where timely inference, limited hardware, and local handling of patient images are vital for practical, privacy-preserving clinical prescreening. Here we present Pocket-Dentist, an efficiency-aware benchmark for dental multimodal question answering that brings together three datasets spanning approximately 1,159 patients from BRAR and MetaDent, five task types and seven metrics. Across 14 typical VLMs, our results reveal an interesting observation: compact VLMs, such as 2B-parameter models, become competitive with much larger VLMs on most metrics after lightweight adaptation while requiring substantially lower computational costs in dental image understanding. Deployed locally on an iPhone 17 Pro, our finetuned compact VLM Pocket-Dentist-2B processed each sample in 4.31 s, reducing latency by 4.9x and memory use by 2.3x compared with a 7B baseline. Our project page is available at https://2026-icml.github.io/pocket-dentist-icml.

cs.CV↗

arXiv2026

Efficient Multimodal Clinical Question Answering for Pulmonary Embolism Risk Assessment

Pulmonary embolism (PE) is a high risk cardiopulmonary condition whose management requires both timely diagnosis and reliable assessment of future clinical risk. Because PE care routinely combines computed tomography pulmonary angiography (CTPA), radiology interpretation, and longitudinal electronic health record (EHR) evidence, it provides a clinically meaningful setting for evaluating compact multimodal language models. In this work, we build a benchmark using efficient multimodal large language models (MLLMs) on INSPECT, a multimodal PE dataset containing 23,248 CTPA studies from 19,402 patients. We formulate eight diagnostic and prognostic tasks as structured clinical question answering problems and evaluate on typical efficient MLLMs under CTPA-Only, EHR-Only, and CTPA+EHR settings with zero-shot and few-shot prompting. Results show that Gemma4 E4B and Gemma4 E2B perform more strongly when EHR evidence is available, especially under CTPA+EHR input. Task level analysis further shows that PE diagnosis achieves higher performance than prognostic tasks, particularly readmission prediction. These observations suggest that compact multimodal models have the great potential in early stage PE risk detection and explanation.

cs.AI↗

arXiv2026

Enhancing Line Density Plots with Outlier Control and Bin-based Illumination

Density plots effectively summarize large numbers of points, which would otherwise lead to severe overplotting in, for example, a scatter plot. However, when applied to line-based datasets, such as trajectories or time series, density plots alone are insufficient, as they disrupt path continuity, obscuring smooth trends and rare anomalies. We propose a bin-based illumination model that decouples structure from density to enhance flow and reveal sparse outliers while preserving the original colormap. We introduce a bin-based outlierness metric to rank trajectories. Guided by this ranking, we construct a structural normal map and apply locally-adaptive lighting in the luminance channel to highlight chosen patterns -- from dominant trends to atypical paths -- with acceptable color distortion. Our interactive method enables analysts to prioritize main trends, focus on outliers, or strike a balance between the two. We demonstrate our method on several real-world datasets, showing it reveals details missed by simpler alternatives, achieves significantly lower CIEDE2000 color distortion than standard shading, and supports interactive updates for up to 10,000 lines.

cs.GR↗

arXiv2026

Radio Spectral Imaging and MHD Modeling of a CME-Driven Shock: Connecting Solar Type II Radio Bursts with Shock-Surface Magnetic Geometry

Solar type II radio bursts are widely regarded as signatures of shock waves propagating in the solar corona and are of particular importance for understanding shock-driven particle acceleration processes. Type II radio bursts often exhibit complex multi-lane and split-band features. The detailed spectral, temporal, and spatial structures carry key information about the shock properties and evolution. However, the physical origin of the multi-lane and split-band features remains unclear, largely due to a lack of spatially resolved data and understanding of the concurrent shock morphology and its magnetic-field context. In this work, we combine radio imaging spectroscopy of a multi-lane, split-band type II burst event with a three-dimensional global magnetohydrodynamic simulation of the associated coronal mass ejection-driven shock using the Alfvén Wave Solar atmosphere Model-Realtime. In this event, the burst intensity evolves from fundamental-emission dominated to harmonic-emission dominated. Meanwhile, the preferential emission source region moves from the Earth-facing side to the limb or far side, coinciding with quasi-perpendicular shock regions with enhanced Mach numbers. The observed spatial offset between the fundamental and harmonic sources is generally aligned with the projected shock-surface magnetic field from the simulation, consistent with anisotropic scattering in a magnetized turbulent plasma. These results establish a physical connection between type II radio sources and coronal shock magnetic geometry, providing new insight into the origin of the multi-lane features and their diagnostics of coronal shocks.

astro-ph.SR↗

arXiv2026

Distinct Near-Horizon Trend of Synchrotron Polarization in Kerr Spacetime

We show that the near-horizon expansion of the linear polarization vector for synchrotron emission in a Kerr background admits a distinct analytic form. For emission from a stationary, axisymmetric, degenerate electromagnetic field, the leading-order polarization pattern depends only on the Kerr spin and the source polar angle, while the next-to-leading-order correction further encodes the geometric and rotational structure of the electromagnetic field. Our result extends the equatorial analysis of [Hou et al. (2024)] and the off-equatorial leading-order result of [Chael et al. (2026)]. Near-horizon polarization thus offers a potential probe of the fundamental properties of rotating black holes and of gravito-electromagnetic interactions.

gr-qc↗

arXiv2026

IndustryBench-MIPU: Benchmarking Multi-Image Attribute Value Extraction for Industrial Products

Industrial products such as valves and circuit breakers are defined by dense technical specifications that govern procurement, compatibility, and safety across supply chains. These specifications are scattered across multiple heterogeneous product images, including specification tables, nameplates, and technical drawings, yet whether Multimodal Large Language Models (MLLMs) can reliably recover them remains underexplored. To fill this gap, we introduce IndustryBench-MIPU, the first large-scale benchmark for multi-image industrial product understanding, built around structured attribute extraction -- recovering property-value pairs from product images. This task jointly probes text recognition on specification tables and nameplates, visual reasoning over technical drawings, domain knowledge to decode industrial terminology, and cross-image evidence integration to assemble scattered specifications. Concretely, the benchmark comprises 4,559 products across 27,652 images with 103,703 annotations spanning 18 industrial categories, constructed through multi-model consensus and three-tier quality assurance. Evaluating nine MLLMs under both single-image and product-level multi-image settings reveals a stark completeness gap: models achieve high precision (86--94%) but the best recovers only 49.9% of product-level attributes; moving from single-image to multi-image extraction costs 15--34 percentage points of recall. Multi-image completeness, not single-image accuracy, is the core bottleneck. Dataset and code are publicly available.

cs.CV↗