arXiv subjects

Heng Li

Publications and source records attributed to Heng Li.

At least 37 records · Page 2Linked to original sources

arXiv2026

Endoscopic Depth Estimation Based on Deep Learning: A Survey

Endoscopic depth estimation is a critical technology for improving the safety and precision of minimally invasive surgery. It has attracted considerable attention from researchers in medical imaging, computer vision, and robotics. Over the past decade, a large number of methods have been developed. Despite the existence of several related surveys, a comprehensive overview focusing on recent deep learning-based techniques is still limited. This paper endeavors to bridge this gap by comprehensively reviewing the state-of-the-art literature. Specifically, we provide a thorough survey of the field from three key perspectives: data, methods, and applications. Firstly, at the data level, we describe the acquisition process of publicly available datasets. Secondly, at the methodological level, we introduce both monocular and stereo deep learning-based approaches for endoscopic depth estimation. Thirdly, at the application level, we identify the specific challenges and corresponding solutions for the clinical implementation of depth estimation technology, situated within concrete clinical scenarios. Finally, we outline potential directions for future research, such as domain adaptation, real-time implementation, and the synergistic fusion of depth information with sensor technologies, thereby providing a valuable starting point for researchers to engage with and advance the field toward clinical translation.

cs.CV↗

arXiv2026

Low-Rank Velocity Fields as a Structural Prior for Unsupervised 4D Medical Image Interpolation

Endpoint-only unsupervised 4D medical image interpolation synthesizes intermediate volumes from sparsely sampled sequences with only the start and end volumes available for training; however, this weakly constrained setting often yields intermediates with unstable boundaries and non-physiological motion, limiting interpretability and downstream analysis. We propose low-rank velocity fields as a structural prior, constraining motion to a structured Tucker low-rank velocity field space that decomposes motion into globally shared spatial bases and a compact sample-specific core, thereby encouraging spatially correlated, anatomy-consistent deformation while suppressing voxel-wise high-frequency artifacts. To capture global coordination and local non-rigid details, we model motion in a coarse-to-fine multi-scale scheme and compose scale-wise deformations at inference to synthesize volumes at arbitrary times. We further provide a theoretical analysis showing that, under Tucker parameterization, low-rank parameters control the smoothness energy of the velocity field, explaining why low-rank modeling promotes smoother motion. Experiments on ACDC and 4D-Lung demonstrate state-of-the-art performance, remaining competitive with methods trained with intermediate-frame supervision, and producing intermediates with improved structural coherence and more stable anatomical contours.

cs.CV↗

arXiv2026

Phase-Aligned Finite-Fourier Periodic Deformation for 4D Medical Image Interpolation

4D medical image interpolation aims to recover missing volumes from sparsely observed time points and is important for dynamic anatomical analysis in applications such as cardiac MRI and thoracic CT, where motion is often repetitive or near-periodic over clinically relevant intervals. A key challenge is that this structure is not always encoded directly in deformation representations for interpolation. In addition, physiological motion is often non-uniform, so equal temporal intervals do not necessarily correspond to equal amounts of anatomical change. To address these issues, we formulate interpolation as learning a continuous deformation process with a phase-structured prior. Given two endpoint volumes, we parameterize a phase-conditioned velocity field with a finite Fourier basis, which embeds near-periodic motion patterns directly into the deformation space and supports continuous querying at arbitrary target times. We further introduce a phase-aligned temporal reparameterization that maps normalized within-interval time to a latent motion phase according to deformation variation intensity, thereby better modeling non-uniform motion progression. Intermediate volumes are then synthesized by continuously warping both endpoints, followed by bidirectional fusion and lightweight residual refinement. Experiments on ACDC and 4D-Lung show that the proposed method achieves state-of-the-art performance over existing baselines while producing anatomically plausible and coherent intermediate volumes from sparse observations.

cs.CV↗

arXiv2026

HA-VLN 2.0: An Open Benchmark and Leaderboard for Human-Aware Navigation in Discrete and Continuous Environments with Dynamic Multi-Human Interactions

Vision-and-Language Navigation (VLN) has been studied mainly in either discrete or continuous spaces, with little attention to dynamic, crowded environments. We present HA-VLN 2.0, a unified benchmark introducing explicit social-awareness constraints. Our contributions are: (i) a standardized task and metrics capturing both goal accuracy and personal-space adherence; (ii) HAPS 2.0 dataset and simulators modeling multi-human interactions, outdoor contexts, and finer language-motion alignment; (iii) benchmarks on 16,844 socially grounded instructions, revealing sharp performance drops of leading agents under human dynamics and partial observability; and (iv) real-world robot experiments validating sim-to-real transfer, with an open leaderboard enabling transparent comparison. Results show that explicit social modeling improves navigation robustness and reduces collisions, underscoring necessity of human-centric approaches. By releasing datasets, simulators, baselines, and protocols, HA-VLN 2.0 provides a strong foundation for safe, human-aware navigation research.

cs.AI↗

arXiv2026

A multi-center analysis of deep learning methods for video polyp detection and segmentation

Colonic polyps are well-recognized precursors to colorectal cancer (CRC), typically detected during colonoscopy. However, the variability in appearance, location, and size of these polyps complicates their detection and removal, leading to challenges in effective surveillance, intervention, and subsequently CRC prevention. The processes of colonoscopy surveillance and polyp removal are highly reliant on the expertise of gastroenterologists and occur within the complexities of the colonic structure. As a result, there is a high rate of missed detections and incomplete removal of colonic polyps, which can adversely impact patient outcomes. Recently, automated methods that use machine learning have been developed to enhance polyps detection and segmentation, thus helping clinical processes and reducing missed rates. These advancements highlight the potential for improving diagnostic accuracy in real-time applications, which ultimately facilitates more effective patient management. Furthermore, integrating sequence data and temporal information could significantly enhance the precision of these methods by capturing the dynamic nature of polyp growth and the changes that occur over time. To rigorously investigate these challenges, data scientists and experts gastroenterologists collaborated to compile a comprehensive dataset that spans multiple centers and diverse populations. This initiative aims to underscore the critical importance of incorporating sequence data and temporal information in the development of robust automated detection and segmentation methods. This study evaluates the applicability of deep learning techniques developed in real-time clinical colonoscopy tasks using sequence data, highlighting the critical role of temporal relationships between frames in improving diagnostic precision.

cs.CV↗

arXiv2026

Superlinear Lower Bounds for Monochromatic Path Partitions

In 1989, Gyárfás conjectured that the vertex set of every $r$-edge-coloured complete graph can be partitioned into at most $r$ vertex-disjoint monochromatic paths. Erdős, Gyárfás, and Pyber subsequently proposed the analogous conjecture for monochromatic cycles. Pokrovskiy proved Gyárfás's conjecture for $r=3$, while disproving the conjecture of Erdős, Gyárfás, and Pyber for every $r\ge3$ by constructing colourings that require at least $r+1$ monochromatic cycles. In this paper, we disprove Gyárfás's conjecture in a quantitatively strong superlinear form: for every sufficiently large $r$, there exists an $r$-edge-coloured complete graph that requires at least $(1-o(1))r\log\log r$ vertex-disjoint monochromatic paths. Consequently, the monochromatic cycle-partition number is also superlinear in $r$. Our construction also disproves two conjectures of Pokrovskiy: one on monochromatic cycle coverings and the other on path coverings in the balanced bipartite setting.

math.CO↗

arXiv2026

Nearly Sharp Bounds for Lattice Coverings by Convex Bodies

For an $n$-dimensional convex body $K$, let $θ_L(K)$ denote its lattice covering density, and let $Θ_L^{\mathrm{conv}}(n)$ and $Θ_L^{\mathrm{sym}}(n)$ be the corresponding worst-case quantities over all convex bodies and over origin-symmetric convex bodies, respectively. Before this work, these quantities were known only to lie between a lower bound of order $n$ and an upper bound of order $n^2$, so even their polynomial order was undetermined. We prove that there are absolute constants $c,C>0$ such that \[ c n\log n \le Θ_L^{\mathrm{sym}}(n) \le Θ_L^{\mathrm{conv}}(n) \le Cn\log n\,(\log\log n)^{10/3+o(1)}. \] Thus both worst-case quantities are $n\log n\,(\log n)^{o(1)}$, and the upper and lower bounds differ by a factor at most $(\log\log n)^{10/3+o(1)}$. For the upper bound, a vertical--horizontal amplification based on weighted Boolean cubes combines covering estimates for low-codimensional sections into an exact lattice covering of an arbitrary convex body. For the lower bound, a random-slab construction and Poisson witnesses on flat tori show, with positive probability, that the resulting body admits no lattice covering of density below $c n\log n$.

math.MG↗

arXiv2026

Forcing Quasirandomness via Rooted F-Densities

Let $F$ be a finite graph with at least one edge, and let $W$ be a graphon. We show that if the density of $F$ rooted at each edge is almost everywhere constant, then either $t(F,W)=0$ or $W$ is constant. For edge-transitive $F$, one rooted equation suffices. This recovers the edge-rooted triangle theorem of Reiher and Schacht. In their terminology, our result also shows that every clique is $2$-forcing, answering a question they posed. We give an explicit stability estimate when $W$ is bounded away from zero. Our proof has two steps: an entropy argument turns constant rooted densities into an additive identity for $\log W$, and a Hoeffding decomposition determines all solutions of that identity. The same method gives exact classifications and quantitative stability estimates for symmetric uniform hyperkernels, dissociated Aldous--Hoover hypergraphons, directed kernels, and tournamentons.

math.CO↗

arXiv2026

Understanding the Energy Impact of Software Refactoring: A Workload-Aware Study of Controlled Examples and Real-World Commits

Refactoring improves software maintainability while preserving functional behavior, yet behavior preservation does not imply energy neutrality. Existing studies primarily examine isolated refactorings under fixed or simple workloads, leaving the effects of workload variation, real-world refactoring practices, explanatory factors, and energy regression identification insufficiently understood. We present the first large-scale empirical study of the energy impact of refactoring across two complementary Java benchmarks: a Micro-benchmark, comprising 68 refactoring types evaluated under diverse workloads, and a Practical-benchmark, containing 481 real-world refactoring commits from 430 GitHub projects. Using repeated paired energy measurements, we analyze workload sensitivity, refactoring patterns, explanatory factors, and the effectiveness of metric- and LLM-based regression identification. In the Micro-benchmark, 199 of 384 refactoring-workload pairs (51.8%) exhibit statistically significant energy differences, and 45.3% of refactoring instances change energy-impact classification across workloads. In the Practical-benchmark, only 36 commits (7.5%) show significant energy changes, although two-thirds differ by at least 10%. Refactoring type alone is insufficient to predict energy outcomes, while certain recurring refactoring combinations are associated with energy reductions. Changes in execution time consistently explain energy variation in the controlled benchmark but correlate weakly with energy changes in real-world commits. Our findings highlight the need for workload-diverse evaluation of the energy impact of refactoring; neither existing metric-based approaches nor LLM-based predictors can reliably identify refactoring-induced energy regressions, motivating the development of more accurate techniques for predicting the energy impact of refactoring.

cs.SE↗

arXiv2026

Intervals of uniform Turán densities

We prove that the set $Π_{\therefore,\infty}$ of uniform Turán densities of possibly infinite families of $3$-graphs contains a terminal interval: there exists $δ>0$ such that $[1-δ,1]\subseteqΠ_{\therefore,\infty}$. Consequently, $Π_{\therefore,\infty}$ has positive Lebesgue measure and Hausdorff dimension $1$.

math.CO↗

arXiv2026

LLM Serving in the Wild: An Empirical Study of Frameworks, Methods, and System Designs

Large Language Models (LLMs) are integrated into software systems and AI services, making efficient LLM serving a concern for software engineering. Serving LLMs is challenging because inference requires computation, memory, GPU resources, and execution while maintaining latency and throughput. Although prior research has proposed LLM inference, optimization, and serving techniques and frameworks, little is known about how they are adopted in practice. In this study, we investigate the use of LLM serving frameworks and serving methods in open-source software systems. We identify and analyze five LLM-specific frameworks: vLLM, SGLang, TensorRT-LLM, LMDeploy, and FlashInfer. We examine how these frameworks and techniques are adopted individually and in combination, how adoption varies across categories of LLMs, and how repositories differ in intent, focus, use case, and architectural design. Our results show that vLLM is the most visible framework in popularity and adoption, while parallel computation, memory management, and network pruning are the most frequently used serving-method categories. Multi-framework usage is limited, suggesting that developers rely on a single serving framework; however, combined frameworks connect complementary capabilities across the serving stack. Framework adoption varies across model families, modalities, model sizes, domain specializations, and deployment settings. Repository-level analysis shows that LLM serving frameworks support applications and architectures, including Reinforcement Learning (RL)-based reasoning, multimodal generation and understanding, microservices, and cloud infrastructure. Overall, this study provides a large-scale empirical characterization of LLM serving framework adoption in practice and offers insights for researchers, framework maintainers, and practitioners working on LLM systems.

cs.SE↗

arXiv2026

Intraday Gas Fee Heterogeneity on Ethereum: Evidence from Operational Firms

Ethereum's EIP-1559 fee mechanism was designed under the assumption of homogeneous, myopic agents responding to a single congestion signal. We examine how this assumption interacts with the heterogeneous demand structure of real-world Ethereum users. Analyzing 62,142 confirmed transactions from seven operational firms across seven industries (January--March 2026), we document significant intraday gas-fee variation: fees peak at hour~12 UTC (7\,AM ET, $\hatβ_{12}=\$0.054$ above the U.S.\ evening baseline, $p<0.001$) and are associated with periods of elevated speculative-arbitrage activity. Operational firms exhibit heterogeneous scheduling responses moderated by transaction deferrability and gas intensity. Residual cost floors, i.e. the gap between observed expenditure and the counterfactual under perfect off-peak scheduling, range from 40.7\% to 92.5\% of actual expenditure, and persist even during the lowest-cost hours ($h\in\{20,21,22,23\}$ UTC, 3--6\,PM ET). We introduce an On-Chain Scheduling Matrix that maps firms to four scheduling regimes as a practical framework for managing gas-fee exposure under the current mechanism.

econ.EM↗

arXiv2026

Backend-Aware Graph Learning for Denoising Outcome Distributions in Quantum Program Testing

Testing quantum programs on NISQ (Noisy Intermediate-Scale Quantum) backends is challenging because the noise disturbs outcome distributions and can affect pass/fail decisions. We present Q-BRIDGE, a graph learning-based approach that converts noisy observations into denoised distributions suitable for oracle-based verification. Q-BRIDGE uses a graph transformer architecture to encode a transpiled quantum circuit, capturing the characteristics of its gates and their connectivity; the physical backend information is encoded together with the logical structure of the circuit. An additional conditioning layer, based on FiLM (Feature-Wise Linear Modulation), takes the encoding as input and integrates noisy observations to produce denoised outcomes. We evaluate Q-BRIDGE on 23 IBM noise backends and 6 circuit families representative of practical workloads. In the first setting, we train a separate Q-BRIDGE model for each backend; in the second setting, we train a single general model shared across all backends. Across both settings, Q-BRIDGE outperforms the state-of-the-art baseline in noise mitigation by a large margin. In testing scenarios with noisy executions, Q-BRIDGE achieves 93.97%-94.90% precision and 82.50%-83.51% recall in detecting bug-induced test failures, significantly outperforming the state-of-the-art baseline. These results indicate that considering the graph structure of the transpiled circuits and the physical characteristics of specific quantum backends is a practical route to more reliable noise-aware quantum program testing.

cs.SE↗

arXiv2026

FSE: Continual Learning for Named Entity Recognition by Fast-Slow Experts

Continual Learning for Named Entity Recognition (CLNER) enable models to incrementally learn new entity types without forgetting previously acquired ones. However, existing methods suffer from catastrophic forgetting and insufficient exploitation of shared information across tasks. This paper proposes FSE, a Fast-Slow Experts enhanced span-based NER model for CLNER. The shared fast expert learns token-level links to efficiently filter out unlikely spans, while the task-specific slow expert performs span classification only on the remaining candidates. It stabilizes learning by promoting knowledge sharing across tasks and maintains plasticity by reducing learning burden at each task. A length-decay negative sampling strategy to mitigate span imbalance is also introduced. Extensive experiments on OntoNotes and FewNERD synthestic datasets demonstrate that FSE achieves state-of-the-art performance in CLNER scenarios, with effectiveness of each component, empirical evidence of faster convergence and expected functionality of both experts.

cs.CL↗

arXiv2026

Grasp, Handover, Rotate: Bimanual Object Reorientation via Compositional Diffusion and Energy-Based Optimization

Bimanual object reorientation - picking an object, handing it over between two arms, and placing it in a desired target pose - is valuable when direct placement from the initial grasp is infeasible due to collisions, kinematic constraints, or poor final orientation. However, achieving this under multiple competing objectives remains challenging. We introduce BiCompoDiff, a compositional diffusion and energy-based framework that jointly optimizes grasp selection, handover, regrasp, and motion planning under multiple constraints. By combining a pretrained grasp diffusion model with bimanual planning energy-based models (EBMs), our method injects gradient guidance during reverse diffusion to enforce collision avoidance, trajectory smoothness (via differentiable inverse kinematics), handover feasibility, and regrasp safety. Annealed MCMC sampling further refines grasp poses over the composite energy landscape. Experiments across diverse simulated household reorientation tasks demonstrate that BiCompoDiff achieves over 20% higher success rates and up to 37% smoother trajectories (measured by joint displacement) compared to strong sampling-based baselines. Real-world validation confirms effective sim-to-real transfer and robust performance on challenging scenes.

cs.RO↗

arXiv2026

Glob3R: Global Structure-from-Motion with 3D Foundation Models

Recent 3D geometric foundation models, such as VGGT, provide robust feed-forward 3D reconstruction by directly predicting camera poses and 3D scene points from input images. However, their results remain inaccurate, and scaling them to long sequences or large unordered image sets typically requires chunk-wise processing, which can introduce drift and inconsistency. We present Glob3R, a global SfM-style reconstruction built on 3D foundation models. Our key idea is to explicitly optimize feed-forward geometric predictions. To this end, we augment a frozen Pi3X backbone with a lightweight dense matching head that predicts image warps between selected reference frames and neighboring views. These dense warps are converted into sparse but reliable multi-view feature tracks, which provide correspondence constraints for global optimization. We further introduce a keyframe-based sliding-window association strategy that propagates tracks and relative poses across overlapping windows, enabling scalable reconstruction. Finally, we perform global motion averaging and bundle adjustment to refine camera poses, reduce scale inconsistencies, and recover dense scene geometry. Extensive experiments on indoor, outdoor, large-scale driving, and unordered SfM benchmarks demonstrate that Glob3R achieves robust and accurate reconstruction. It consistently improves over feed-forward foundation-model baselines and recent scalable reconstruction methods, while being more robust than classical SfM pipelines. The refined poses also lead to higher-quality neural rendering, validating the benefit of combining foundation-model priors with global geometric optimization. Project page: https://junyuandeng.github.io/Glob3r

cs.CV↗

arXiv2026

A Higher-Order Clique Density Theorem

Reiher's clique density theorem determines the sharp lower envelope for the density of $K_r$ at fixed edge density. We prove a higher-order version in which the prescribed quantity is itself a clique density. For every $3\le s<r$, we determine the minimum possible $K_r$-density among graphons with prescribed $K_s$-density. For $s\ge3$ the constraint is genuinely nonlinear and leaves the edge density undetermined; nevertheless, on the positive range the sharp lower boundary is the classical multipartite edge-to-clique profile, reparametrised by $K_s$-density. We also prove stability on the positive branches of this profile: at every interior point, near extremality forces cut-distance closeness to the corresponding extremal family at the induced edge density.

math.CO↗

arXiv2026

PTCOG Treatment Efficiency Subcommittee Risk Assessment Report on Patient-Specific Quality Assurance

Patient-specific quality assurance (PSQA) in pencil beam scanning proton therapy (PBS-PT) is often treated as a purely technical verification task. This PTCOG Treatment Efficiency Subcommittee White Paper instead frames PSQA as a workflow-embedded risk-control strategy and asks how different PSQA approaches reshape the same clinical risk landscape. Using a generic PBS-PT process-driven Failure Mode and Effects Analysis (pFMEA), 44 validated PSQA-relevant failure modes across 20 process steps were scored under a common no-PSQA baseline and three PSQA pathways: measurement-based PSQA, log file-based PSQA, and independent secondary dose calculation. A staged mathematical formalism separates preparatory data-stage effects, method-specific full-stage verification, cumulative endstate effects, and a Data-to-Cum bridge that quantifies additional verification benefit on the baseline scale. In this expert-scored, baseline-anchored model, log file-based PSQA produced the largest cumulative workflow-level risk-score reduction, followed by measurement-based PSQA and independent secondary dose calculation. The ranking is not a winner-takes-all rule or probability-calibrated risk estimate; instead, each method shows distinct risk-control strengths in different workflow regions. The White Paper therefore supports a risk-informed hybrid PSQA architecture, where log file-based PSQA, measurement-based PSQA, and independent secondary dose calculation are assigned to the workflow segments in which their signatures are strongest. It provides a transparent, semi-quantitative, stage-resolved framework for institutions seeking to evaluate, implement, or evolve PSQA in PBS-PT and emphasizes that log file-based PSQA must itself be supported by validated and governed log data and treatment records.

physics.med-ph↗