arXiv subjects

Bing Li

Publications and source records attributed to Bing Li.

At least 127 records · Page 7Linked to original sources

arXiv2025

Intersecting well approximable and missing digit sets

Let $b\geq3$ be an integer and $C(b,D)$ be the set of real numbers in $[0,1]$ whose $b$-ary expansion consists of digits restricted to a given set $D\subseteq\{0,\ldots,b-1\}$. Given an integer $t\geq2$ and a real, positive function $ψ$, let $W_{t}(ψ)$ denote the set of $x$ in $[0,1]$ for which $|x-p/t^{n}|<ψ(n)$ for infinitely many $(p,n)\in\mathbb{Z}\times\mathbb{N}$. We prove a general Hausdorff dimension result concerning the intersection of $W_{t}(ψ)$ with an arbitrary self similar set which implies that $\dim_{\rm H}(W_{t}(ψ)\cap C(b,D))\le\dim_{\rm H}W_{t}(ψ)\times \dim_{\rm H}C(b,D)$. When $b$ and $t$ have the same prime divisors, under certain restrictions on the digit set $D$, we give a sufficient condition for the Hausdorff measure of $W_{t}(ψ)\cap C(b,D)$ to be zero. This closes a gap in a result of Li, Li and Wu \cite{LLW2025} and shows that the dimension of the intersection can be strictly less than the product of the dimensions. The latter disproves the product conjecture of Li, Li and Wu.

math.NT↗

arXiv2025

MMhops-R1: Multimodal Multi-hop Reasoning

The ability to perform multi-modal multi-hop reasoning by iteratively integrating information across various modalities and external knowledge is critical for addressing complex real-world challenges. However, existing Multi-modal Large Language Models (MLLMs) are predominantly limited to single-step reasoning, as existing benchmarks lack the complexity needed to evaluate and drive multi-hop abilities. To bridge this gap, we introduce MMhops, a novel, large-scale benchmark designed to systematically evaluate and foster multi-modal multi-hop reasoning. MMhops dataset comprises two challenging task formats, Bridging and Comparison, which necessitate that models dynamically construct complex reasoning chains by integrating external knowledge. To tackle the challenges posed by MMhops, we propose MMhops-R1, a novel multi-modal Retrieval-Augmented Generation (mRAG) framework for dynamic reasoning. Our framework utilizes reinforcement learning to optimize the model for autonomously planning reasoning paths, formulating targeted queries, and synthesizing multi-level information. Comprehensive experiments demonstrate that MMhops-R1 significantly outperforms strong baselines on MMhops, highlighting that dynamic planning and multi-modal knowledge integration are crucial for complex reasoning. Moreover, MMhops-R1 demonstrates strong generalization to tasks requiring fixed-hop reasoning, underscoring the robustness of our dynamic planning approach. In conclusion, our work contributes a challenging new benchmark and a powerful baseline model, and we will release the associated code, data, and weights to catalyze future research in this critical area.

cs.CV↗

arXiv2025

Dimension theory of inhomogeneous Diophantine approximation with matrix sequences

In this paper, we investigate the Hausdorff dimension of naturally occurring sets of inhomogeneous well-approximable points with a sequence of real invertible matrices $\mathcal{A}=(A_n)_{n\in\mathbb{N}}$. Specifically, for a given point $\mathbf{y}\in [0,1)^d$ and a function $ψ: \mathbb{N} \to \mathbb{R}^+$, we study the limsup set \[ W\big(\mathcal{A},ψ,{\bf y}\big) =\Big\{\mathbf{x}\in [0,1)^d\colon A_n\mathbf{x}~(\bmod~1)\in B\big(\mathbf{y}, ψ(n)\big) {\rm ~ for~ infinitely ~many}~n\in\mathbb{N}\Big\}.\] The upper and lower bounds on the Hausdorff dimension of $W\big(\mathcal{A},ψ,{\bf y}\big)$ are determined by involving the singular values of $A_n$ and the successive minima of the lattice $A_n^{-1}\mathbb{Z}^d$, and both bounds are shown to be attainable for some matrices. Within this framework, we unify the problem of shrinking target sets and recurrence sets, establishing the Hausdorff dimensions for such limsup sets. As applications, our corresponding upper bounds for shrinking target and recurrence sets essentially improve those appearing in the present literature. Furthermore, explicit Hausdorff dimension formulas are derived for shrinking targets and recurrence sets associated with concrete classes of matrices. We extend the Mass Transference Principle for rectangles of Li-Liao-Velani-Wang-Zorin (Adv. Math., 2025) to rectangles under local isometries. This generalization yields a general lower bound for the Hausdorff dimension of $W\big(\mathcal{A},ψ,{\bf y}\big)$.

math.NT↗

arXiv2025

Advancing Identification method of Gamma-Ray Bursts with Data and Feature Enhancement

Gamma-ray bursts (GRBs) are challenging to identify due to their transient nature, complex temporal profiles, and limited observational datasets. We address this with a one-dimensional convolutional neural network integrated with an Adaptive Frequency Feature Enhancement module and physics-informed data augmentation. Our framework generates 100,000 synthetic GRB samples, expanding training data diversity and volume while preserving physical fidelity-especially for low-significance events. The model achieves 97.46% classification accuracy, outperforming all tested variants with conventional enhancement modules, highlighting enhanced domain-specific feature capture. Feature visualization shows model focuses on deep-seated morphological features and confirms the capability of extracting physically meaningful burst characteristics. Dimensionality reduction and clustering reveal GRBs with similar morphologies or progenitor origins cluster in the feature space, linking learned features to physical properties. This perhaps offers a novel diagnostic tool for identifying kilonova- and supernova-associated GRB candidates, establishing criteria to enhance multi-messenger early-warning systems. The framework aids current time-domain surveys, generalizes to other rare transients, and advances automated detection in large-volume observational data.

astro-ph.HE↗

arXiv2025

VFM-ISRefiner: Towards Better Adapting Vision Foundation Models for Interactive Segmentation of Remote Sensing Images

Interactive image segmentation(IIS) plays a critical role in generating precise annotations for remote sensing imagery, where objects often exhibit scale variations, irregular boundaries and complex backgrounds. However, existing IIS methods, primarily designed for natural images, struggle to generalize to remote sensing domains due to limited annotated data and computational overhead. To address these challenges, we proposed RS-ISRefiner, a novel click-based IIS framework tailored for remote sensing images. The framework employs an adapter-based tuning strategy that preserves the general representations of Vision Foundation Models while enabling efficient learning of remote sensing-specific spatial and boundary characteristics. A hybrid attention mechanism integrating convolutional local modeling with Transformer-based global reasoning enhances robustness against scale diversity and scene complexity. Furthermore, an improved probability map modulation scheme effectively incorporates historical user interactions, yielding more stable iterative refinement and higher boundary accuracy. Comprehensive experiments on six remote sensing datasets, including iSAID, ISPRS Potsdam, SandBar, NWPU, LoveDA Urban and WHUBuilding, demonstrate that RS-ISRefiner consistently outperforms state-of-the-art IIS methods in terms of segmentation accuracy, efficiency and interaction cost. These results confirm the effectiveness and generalizability of our framework, making it highly suitable for high-quality instance segmentation in practical remote sensing scenarios. The codes are available at https://github.com/wondelyan/VFM-ISRefiner .

cs.CV↗

arXiv2025

A winning approach to the intersections of twisted non-recurrent sets with fractals

In this paper, we prove that if $S\subseteq\mathbb{R}^d$ is hyperplane absolute winning on a closed hyperplane diffuse set $L\subseteq\mathbb{R}^d$, then $\mathrm{dim}_H S\cap K=\mathrm{dim}_H K$ for any irreducible self-conformal set $K\subseteq L$ without assuming any separation condition on $K$. The result is then applied to obtain the Hausdorff dimension of intersections between irreducible self-conformal sets and twisted non-recurrent sets $\mathrm{N}(T,\mathcal{G})$ defined as $$ \mathrm{N}(T,\mathcal{G}):=\left\{\mathbf{x}\in[0,1]^d:\liminf_{n\to\infty}\|T^n(\mathbf{x})-g_n(\mathbf{x})\|>0\right\}, $$ where $T:[0,1]^d\to[0,1]^d$ belongs to a broad class of product maps, $\mathcal{G}:=\{g_n\}_{n\in\mathbb{N}}$ is a sequence of self-maps on $[0,1]^d$ with uniform Lipschitz constant and $\|\cdot\|$ denotes the maximal norm in $\mathbb{R}^d$. When $T$ is the $β$-transformation on $[0,1]$, it provides a positive answer to a question raised informally by Broderick, Bugeaud, Fishman, Kleinbock and Weiss (Math. Res. Lett., 2010). For the case $T$ is a $d\times d$ diagonal matrix transformations, our results provide a partial answer asked in a paper of Li, Liao, Velani and Zorin (Adv. Math., 2023). A natural generalization to non-autonomous setting is also obtained.

math.DS↗

arXiv2025

GRB 240715A: Revealing Novel Intrinsic Mechanism by Different Individual Pulse

The Space-based multiband astronomical Variable Objects Monitor (SVOM), detected its first short gamma-ray burst, GRB 240715A, in-flight, which was jointly observed by Fermi. Based on observational data of SVOM/GRM and Fermi/GBM, we perform a comprehensive temporal and spectral analysis for individual pulse in the prompt emission of this burst, and novel characteristics are revealed. Firstly, opposite evolutions of spectral lag are found in the first and third pulse of this burst. Second, the large negative lag of the first pulse is an outlier in short GRB sample, especially when the pulse duration is considered. Spectral analysis shows that the negative lag of the first pulse is caused by the evolution of spectrum index, and is irrelevant to Epeak, which is inconsistent with the previous study. The intrinsic mechanism is probably attributed to electron cooling in the decaying magnetic field, which leads to the continuous hardening of the spectrum index and results in negative lag. Furthermore, spectral analysis also shows that the third pulse is more likely to be described by a quasi-thermal spectrum, indicating the existence of photospheric emission. It is difficult to explain how the synchrotron radiation appears before photospheric emission in a single GRB and some assumptions are discussed.

astro-ph.HE↗

arXiv2025

CorrectHDL: Agentic HDL Design with LLMs Leveraging High-Level Synthesis as Reference

Large Language Models (LLMs) have demonstrated remarkable potential in hardware front-end design using hardware description languages (HDLs). However, their inherent tendency toward hallucination often introduces functional errors into the generated HDL designs. To address this issue, we propose the framework CorrectHDL that leverages high-level synthesis (HLS) results as functional references to correct potential errors in LLM-generated HDL designs.The input to the proposed framework is a C/C++ program that specifies the target circuit's functionality. The program is provided to an LLM to directly generate an HDL design, whose syntax errors are repaired using a Retrieval-Augmented Generation (RAG) mechanism. The functional correctness of the LLM-generated circuit is iteratively improved by comparing its simulated behavior with an HLS reference design produced by conventional HLS tools, which ensures the functional correctness of the result but can lead to suboptimal area and power efficiency. Experimental results demonstrate that circuits generated by the proposed framework achieve significantly better area and power efficiency than conventional HLS designs and approach the quality of human-engineered circuits. Meanwhile, the correctness of the resulting HDL implementation is maintained, highlighting the effectiveness and potential of agentic HDL design leveraging the generative capabilities of LLMs and the rigor of traditional correctness-driven IC design flows.

cs.AI↗

arXiv2025

Root Cause Analysis for Microservice Systems via Cascaded Conditional Learning with Hypergraphs

Root cause analysis in microservice systems typically involves two core tasks: root cause localization (RCL) and failure type identification (FTI). Despite substantial research efforts, conventional diagnostic approaches still face two key challenges. First, these methods predominantly adopt a joint learning paradigm for RCL and FTI to exploit shared information and reduce training time. However, this simplistic integration neglects the causal dependencies between tasks, thereby impeding inter-task collaboration and information transfer. Second, these existing methods primarily focus on point-to-point relationships between instances, overlooking the group nature of inter-instance influences induced by deployment configurations and load balancing. To overcome these limitations, we propose CCLH, a novel root cause analysis framework that orchestrates diagnostic tasks based on cascaded conditional learning. CCLH provides a three-level taxonomy for group influences between instances and incorporates a heterogeneous hypergraph to model these relationships, facilitating the simulation of failure propagation. Extensive experiments conducted on datasets from three microservice benchmarks demonstrate that CCLH outperforms state-of-the-art methods in both RCL and FTI.

cs.LG↗

arXiv2025

Hidden symmetry-breaking in a kagome Ising ferromagnet

Kagome metals can host unconventional electronic phenomena that emerge from their frustrated lattice geometry and associated band topology. Correlated electronic orders, such as charge-density waves and superconductivity, are observed to intertwine with subtle time-reversal symmetry breaking whose microscopic origin is not currently understood. Here, we provide evidence for such time-reversal symmetry breaking in the kagome metal TbV$_6$Sn$_6$ arising from staggered magnetic moments within the kagome layers. TbV$_6$Sn$_6$ consists of metallic V kagome layers separated by Tb triangular layers that host Ising ferromagnetic order. Deep in the ferromagnetic state, the Tb Ising doublet ground state should display a single, dispersionless spin-flip excitation. Instead, inelastic neutron scattering reveals two sharp excitations associated with inequivalent Tb sites, demonstrating that a symmetry-broken phase coexists with Ising ferromagnetism. No additional structural or magnetic phase transitions are detected, and first-principles calculations rule out lattice distortions as the origin of the splitting. We attribute this effect to time-reversal symmetry breaking encoded by small V moments that couple to the Tb sublattice and leave a measurable spectral fingerprint. Our results establish rare-earth local moment spectroscopy as a sensitive probe of subtle broken symmetries and highlight an unexpected interplay between kagome magnetism and rare-earth local moment magnetism.

cond-mat.str-el↗

arXiv2025

Nature of quantum criticality in the Ising ferromagnet TbV$_6$Sn$_6$

TbV$_6$Sn$_6$ is a topological metal where ferromagnetic Tb ions with strong uniaxial magnetic anisotropy interact with V kagome layers. Inelastic neutron scattering (INS) measurements show that the Tb ions adopt an Ising doublet ground state. Here, we consider whether a transverse magnetic field can drive TbV$_6$Sn$_6$ toward a quantum critical point, providing a rare example of transverse-field Ising criticality in a metallic compound. High-field magnetization measurements reveal a first-order-like spin-reorientation transition at 25.6 T. Our INS-based magnetic model finds that this is caused by an avoided crossing of an excited-state singlet with the ground-state doublet. Surprisingly, our model predicts that quantum critical and tricritical points are accessible within the range of experimentally determined model parameters and may be reached by varying the direction of an applied magnetic field.

cond-mat.str-el↗

arXiv2025

Collapsing Categories for Regression with Mixed Predictors

Categorical predictors are omnipresent in everyday regression practice: in fact, most regression data involve some categorical predictors, and this tendency is increasing in modern applications with more complex structures and larger data sizes. However, including too many categories in a regression model would seriously hamper accuracy, as the information in the data is fragmented by the multitude of categories. In this paper, we introduce a systematic method to reduce the complexity of categorical predictors by adaptively collapsing categories in regressions, so as to enhance the performance of regression estimation. Our method is based on the {\em pairwise vector fused LASSO}, which automatically fuses the categories that bear a similar regression relation with the response. We develop our method under a wide class of regression models defined by a general loss function, which includes linear models and generalized linear models as special cases. We rigorously established the category collapsing consistency of our method, developed an Inexact Proximal Gradient Descent algorithm to implement it, and proved the feasibility and convergence of our algorithm. Through simulations and an application to Spotify music data, we demonstrate that our method can effectively reduce categorical complexity while improving prediction performance, making it a powerful tool for regression with mixed predictors.

stat.ME↗

arXiv2025

A comprehensive search for Long and Short Periodic Features from an Extremely Active Cycle of FRB 20240114A

Possible periodic features in fast radio bursts (FRBs) may provide insights into their astrophysical origins. Using extensive observations from the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we conduct a multi-timescale periodicity search for the exceptionally active repeater FRB~20240114A. Our analysis is based on different datasets for different timescales: for short-timescale periodicity in Time of Arrivals (TOAs), we use 57 observations from January to August 2024; for long-timescale periodicity, we employ an extended TOA dataset comprising 111 observations spanning from January 2024 to October 2025; and for burst time series analysis, we utilize individual burst data from the 57 FAST observations. We identify three candidate short-timescale periodic signals (0.673~s, 0.635~s, and 0.536~s) with significances of $3.2σ$--$6σ$, each detected in two independent observations. On longer timescales, we detect a significant $143.40\pm7.19$-day periodicity with $5.2σ$ significance, establishing FRB~20240114A as a periodic repeater. In burst time series, we find quasi-periodic oscillations in the few hundred Hz range ($3.4σ$ and $3.7σ$) and periodic burst trains with periods of several to tens of milliseconds ($3σ$--$3.9σ$), though these periodic features appear transient and short-lived. The detection of periodic signals at these different time scales indicates that FRB 20240114A exhibits intriguing periodic self-similar characteristics. Despite the comprehensive dataset, no definitive periodicity linked to the source's rotation is confirmed, placing stringent constraints on the intrinsic source properties and the modulation mechanisms. All data are available via the Science Data Bank.

astro-ph.HE↗

arXiv2025

Every Activation Boosted: Scaling General Reasoner to 1 Trillion Open Language Foundation

We introduce Ling 2.0, a series reasoning-oriented language foundation built upon the principle that every activation boosts reasoning capability. Designed to scale from tens of billions to one trillion parameters under a unified Mixture-of-Experts (MoE) paradigm, Ling 2.0 emphasizes high sparsity, cross-scale consistency, and efficiency guided by empirical scaling laws. The series includes three non-thinking (instruct) models - Ling-mini-2.0, Ling-flash-2.0, and Ling-1T - ranging from 16B to 1T total parameters and achieving up to 7-fold active-compute efficiency compared with dense counterparts. Ling 2.0 integrates coordinated innovations across model architecture, pre-training, post-training, and infrastructure: a high-sparsity MoE with MTP for efficient reasoning, reasoning-oriented data and mid-training CoT activation, reinforcement-based fine-tuning (DFT, Evo-CoT), and full-scale FP8 training with fine-grained heterogeneous pipelines. At the trillion scale, Ling-1T establishes a new Pareto frontier of reasoning accuracy versus computational efficiency, demonstrating that sparse activation, when properly aligned with reasoning objectives, enables scalable and efficient intelligence. Collectively, Ling 2.0 provides a coherent, open, and efficient foundation for advancing future reasoning and thinking models, including the Ring series built upon the same base.

cs.CL↗

arXiv2025

VFocus: Better Verilog Generation from Large Language Model via Focused Reasoning

Large Language Models (LLMs) have shown impressive potential in generating Verilog codes, but ensuring functional correctness remains a challenge. Existing approaches often rely on self-consistency or simulation feedback to select the best candidate, but they miss opportunities to focus LLM reasoning on the most informative parts of the design. We propose VFocus, a three-stage framework that enhances Verilog generation by sharpening the focus of LLM reasoning onto critical decision points in the code generation process. In the \textbf{pre-ranking stage}, VFocus generates multiple code candidates through LLM prompting, retries for syntactically valid outputs, and introduces a \textit{Density-guided Filtering} to retain candidates that fall within the "reasoning sweet spot" for functional correctness. In the \textbf{ranking stage}, we simulate each code candidate using an automatically generated testbench and apply self-consistency-based clustering to identify the most consistent outputs. Finally, in the \textbf{post-ranking refinement stage}, VFocus performs inconsistency mining on top-ranked candidates and invokes reasoning-augmented LLM prompts for candidate refinement. Experiments on the VerilogEval-Human benchmark show that VFocus significantly improves the pass@1 correctness across multiple reasoning LLMs, demonstrating its effectiveness in enhancing Verilog generation for complex hardware design tasks.

cs.AR↗

arXiv2025

Shrinking Targets versus Recurrence: a brief survey

Let $(X,d)$ be a compact metric space and $(X,\mathcal{A},μ,T)$ a measure preserving dynamical system. Furthermore, given a real, positive function $ψ$, let $W(T, ψ)$ and $ R(T,ψ) $ respectively denote the shrinking target set and the recurrent set associated with the dynamical system. Under certain mixing properties it is known that if the natural measure sum diverges then the recurrent and shrinking target sets are of full $μ$-measure. The purpose of this survey is to provide a brief overview of such results, to discuss the potential quantitative strengthening of the full measure statements and to bring to the forefront key differences in the theory.

math.DS↗

arXiv2025

Exchange operation of Majorana zero modes in topological insulator-based Josephson trijunctions

Majorana zero modes are anyons obeying non-Abelian exchange statistics distinct from fermions or bosons. While significant progresses have been achieved in the past two decades in searching for these exotic excitations in solid-state systems, their non-Abelian nature remains unverified, as definitive proof requires braiding operations. Here, we report preliminarily experimental advances in creating, manipulating, and exchanging the presumed Majorana zero modes in an envelope-shaped Josephson device composed of multiple trijunctions on a topological insulator surface. We observed the signatures of in-gap states migration consistent with the expectations of the Fu-Kane model, supporting the realization of an exchange operation. This work would establish a critical pathway toward ultimately braiding Majorana zero modes in the Fu-Kane scheme of topological quantum computation.

cond-mat.mes-hall↗

arXiv2025

Multiplexed Catheter-Integrated Pressure Sensing System for Endoluminal Interventions

Advances in flexible catheters pave the way for minimally invasive diagnosis and treatment of luminal organs and tubular structures through endoluminal interventions. A key challenge is in establishing non-constraining pressure monitoring at the interfaces between medical catheters and intraluminal anatomy exhibiting curvilinear contours, structural variability, and time-dependent physiological motion. This work presents a scalable and multi-purpose pressure sensing system for multidirectional monitoring of tissue interactions, establishing a robust solution for deploying diagnostic and therapeutic instruments in various types of endoluminal interventions. This approach provides an integrated system encompassing pressure sensors, catheters, and signal acquisition devices. A poly (vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) film is miniaturized and configured into a multiplexed piezoelectric-based pressure sensor, providing flexibility and scalability in conforming to medical catheters with curved surfaces. The catheter is fabricated with a cost-effective and highly scalable fiber drawing technology, establishing a means of fast prototyping catheters with bespoke structures for sensor integration and medical instrument integration. The system achieves enhanced pressure detection sensitivity and a comparable sensing range, compared with state-of-the-art catheter-integrated sensors. Through in-vitro phantom studies, the system performs precise multi-directional sensing within various clinical endoluminal scenarios, showing its potential in digitalizing tissue interactions during endoluminal interventions.

physics.med-ph↗