arXiv subjects

Rui Chen

Publications and source records attributed to Rui Chen.

At least 109 records · Page 6Linked to original sources

arXiv2026

A soft and lightweight fabric-based pneumatic interface for multimodal fingertip tactile feedback

Wearable fingertip haptic devices are critical for realistic interaction in virtual reality, augmented reality, and teleoperation, yet existing approaches struggle to simultaneously achieve adequate tactile output, low mass, simple fabrication, and untethered portability. Here we show that fabric-based pneumatic actuation can address this gap. Our device comprises four pneumatic chambers fabricated from thermoplastic polyurethane-coated fabric via computer numerical control heat-sealing, yielding a soft, conformable interface weighing 2.1 g that operates untethered with a wrist-mounted control unit. Mechanical and dynamic characterization confirms that the fabric actuators produce sufficient force, displacement, and bandwidth for fingertip tactile rendering. A psychophysical study with 15 participants demonstrates classification accuracy exceeding 90% across three distinct tactile modes -- contact configuration, directional sliding, and vibrotactile frequency. These findings establish fabric-based pneumatic actuation as a viable technology route for lightweight, low-cost, and multimodal fingertip haptic interfaces.

cs.RO↗

arXiv2026

Tightening CVaR Approximations via Scenario-Wise Scaling for Chance-Constrained Programming

Chance-constrained programs (CCPs) provide a powerful modeling framework for decision-making under uncertainty, but their nonconvex feasible regions make them computationally challenging. A widely used convex inner approximation replaces chance constraints with Conditional Value-at-Risk (CVaR) constraints; however, the resulting solutions can be overly conservative and suboptimal. We propose a scenario-wise scaling approach that strengthens CVaR approximations for CCPs with finitely supported uncertainty. The method introduces scaling factors that reweight individual scenarios within the CVaR constraint, yielding a family of potentially tighter inner approximations. We establish sufficient conditions under which, for a suitable choice of scaling factors, the scaled CVaR approximation attains the same optimal value as the original CCP and admits a (near-)optimal solution of the CCP. We show that these conditions are tight and further relax them in the convex setting. We also show that optimizing over scenario-wise scaling factors is NP-hard. To address this computational challenge, we develop efficient heuristic and sequential convex approximation algorithms that iteratively update the scaling factors and generate improved feasible solutions. Numerical experiments demonstrate that the proposed methods consistently improve upon standard CVaR and state-of-the-art convex approximations, often reducing conservativeness while maintaining tractability.

math.OC↗

arXiv2026

Channel Estimation for 6G Near-Field Wireless Communications: A Comprehensive Survey

The sixth-generation (6G) wireless systems are expected to adopt extremely large aperture arrays (ELAAs), novel antenna architectures, and operate in extremely high-frequency bands to meet growing data demands. ELAAs significantly increase the number of antennas, enabling finer spatial resolution and improved beamforming. At high frequencies, ELAAs shift communication from the conventional far-field to near-field regime, where spherical wavefronts dominate and the channel response depends on both angle and distance, increasing channel dimensionality. Conventional far-field channel estimation methods, which rely on angular information, struggle in near-field scenarios due to increased pilot overhead and computational complexity. This paper presents a comprehensive survey of recent advances in near-field channel estimation. It first defines the near- and far-field boundary from an electromagnetic perspective and discusses key propagation differences, alongside a brief review of ELAA developments. Then, it introduces mainstream near-field channel models and compares them with far-field models. Major estimation techniques are reviewed under different configurations (single/multi-user, single/multi-carrier), including both direct estimation and RIS-assisted cascaded estimation. These techniques reveal trade-offs among estimation accuracy, complexity, and overhead. This survey aims to provide insights and foundations for efficient and scalable near-field channel estimation in 6G systems, while identifying key challenges and future research directions.

eess.SP↗

arXiv2026

From Scale to Speed: Adaptive Test-Time Scaling for Image Editing

Image Chain-of-Thought (Image-CoT) is a test-time scaling paradigm that improves image generation by extending inference time. Most Image-CoT methods focus on text-to-image (T2I) generation. Unlike T2I generation, image editing is goal-directed: the solution space is constrained by the source image and instruction. This mismatch causes three challenges when applying Image-CoT to editing: inefficient resource allocation with fixed sampling budgets, unreliable early-stage verification using general MLLM scores, and redundant edited results from large-scale sampling. To address this, we propose ADaptive Edit-CoT (ADE-CoT), an on-demand test-time scaling framework to enhance editing efficiency and performance. It incorporates three key strategies: (1) a difficulty-aware resource allocation that assigns dynamic budgets based on estimated edit difficulty; (2) edit-specific verification in early pruning that uses region localization and caption consistency to select promising candidates; and (3) depth-first opportunistic stopping, guided by an instance-specific verifier, that terminates when intent-aligned results are found. Extensive experiments on three SOTA editing models (Step1X-Edit, BAGEL, FLUX.1 Kontext) across three benchmarks show that ADE-CoT achieves superior performance-efficiency trade-offs. With comparable sampling budgets, ADE-CoT obtains better performance with more than 2x speedup over Best-of-N.

cs.CV↗

arXiv2026

TopoMesh: High-Fidelity Mesh Autoencoding via Topological Unification

The dominant paradigm for high-fidelity 3D generation relies on a VAE-Diffusion pipeline, where the VAE's reconstruction capability sets a firm upper bound on generation quality. A fundamental challenge limiting existing VAEs is the representation mismatch between ground-truth meshes and network predictions: GT meshes have arbitrary, variable topology, while VAEs typically predict fixed-structure implicit fields (\eg, SDF on regular grids). This inherent misalignment prevents establishing explicit mesh-level correspondences, forcing prior work to rely on indirect supervision signals such as SDF or rendering losses. Consequently, fine geometric details, particularly sharp features, are poorly preserved during reconstruction. To address this, we introduce TopoMesh, a sparse voxel-based VAE that unifies both GT and predicted meshes under a shared Dual Marching Cubes (DMC) topological framework. Specifically, we convert arbitrary input meshes into DMC-compliant representations via a remeshing algorithm that preserves sharp edges using an L$\infty$ distance metric. Our decoder outputs meshes in the same DMC format, ensuring that both predicted and target meshes share identical topological structures. This establishes explicit correspondences at the vertex and face level, allowing us to derive explicit mesh-level supervision signals for topology, vertex positions, and face orientations with clear gradients. Our sparse VAE architecture employs this unified framework and is trained with Teacher Forcing and progressive resolution training for stable and efficient convergence. Extensive experiments demonstrate that TopoMesh significantly outperforms existing VAEs in reconstruction fidelity, achieving superior preservation of sharp features and geometric details.

cs.CV↗

arXiv2026

Exploring two-body strong decay properties for possible single charm molecular pentaquarks with strangeness $|S|=1,2$

The exploration of exotic hadrons provides a crucial testing ground for quantum chromodynamics in its non-perturbative regime. In this work, we perform a systematic study of the two-body strong decay properties of single-charm molecular pentaquarks in the $Y_c\bar{K}^{(*)}$ systems, where $Y_c = Λ_c$, $Σ_c$, $Ξ_c$, and $Ξ_c'$. Employing an effective Lagrangian approach combined with hadronic molecular wave functions derived from the one-boson-exchange model, we compute the decay widths and branching ratios for a series of predicted states with strangeness $|S| = 1$ and $|S| = 2$. Our calculations reveal distinctive decay patterns that serve as fingerprints for molecular identification. The total decay widths vary dramatically, from less than 1 MeV for the narrow $Σ_c\bar{K}$ $(I(J^P)=1/2(1/2^-))$ state to several tens of MeV for broader coupled-channel molecules like $Λ_c\bar{K}^*/Σ_c\bar{K}^*$. A key finding is the stability of the predicted branching ratios against variations in the binding energy. The decay dynamics are dominated by light meson (particularly pion) exchange, leading to a strong preference for final states containing a charmed baryon and a strange meson. Furthermore, coupled-channel effects and isospin-related interference play essential roles in both the formation and decay mechanisms of specific candidates. The results provide concrete, testable predictions for future experimental searches at facilities such as LHCb and Belle II.

hep-ph↗

arXiv2026

The Conformal Fractional--Logarithmic Laplacian on the Sphere: Yamabe Problems and Sharp Inequalities

In this paper, we introduce the conformal fractional--logarithmic Laplacian on the unit sphere, defined as the derivative of the conformal fractional Laplacian with respect to the order parameter $s\in(0,1)$. We investigate its fundamental analytic and spectral properties, including its relation to the conformal logarithmic Laplacian, its spectral representation, and the explicit form of its eigenvalues and eigenfunctions. We further establish its conformal covariance law and derive the associated Yamabe-type equation, proving its equivalence to the corresponding conformal equation in $\mathbb R^N$ through stereographic projection. Finally, we apply this framework to sharp Sobolev-type inequalities, recovering the sharp logarithmic Sobolev inequality, revealing the failure of a naive fractional--logarithmic analogue, and establishing new sharp fractional--logarithmic inequalities.

math.AP↗

arXiv2026

Video-CoE: Reinforcing Video Event Prediction via Chain of Events

Despite advances in the application of MLLMs for various video tasks, video event prediction (VEP) remains relatively underexplored. VEP requires the model to perform fine-grained temporal modeling of videos and establish logical relationships between videos and future events, which current MLLMs still struggle with. In this work, we first present a comprehensive evaluation of current leading MLLMs on the VEP task, revealing the reasons behind their inaccurate predictions, including lack of logical reasoning ability for future events prediction and insufficient utilization of visual information. To address these challenges, we propose \textbf{C}hain \textbf{o}f \textbf{E}vents (\textbf{CoE}) paradigm, which constructs temporal event chains to implicitly enforce MLLM focusing on the visual content and the logical connections between videos and future events, incentivizing model's reasoning capability with multiple training protocols. Experimental results on public benchmarks demonstrate that our method outperforms both leading open-source and commercial MLLMs, establishing a new state-of-the-art on the VEP task. Codes and models will be released soon.

cs.CV↗

arXiv2026

Discovering an Unquenched Dynamics Mechanism for Charmonium Scattering

In this work, we propose an unquenched mechanism for charmonium scattering that utilizes the internal structure of charmonium. By capturing light flavor quarks and anti-quarks from the vacuum, charm and anti-charm quarks form virtual charmed mesons, which mediate an effective one-boson exchange process. This approach accurately reproduces the di-$J/ψ$ invariant mass spectrum observed by CMS and LHCb, demonstrating its validity. Our mechanism offers a comprehensive framework for understanding charmonium scattering and is applicable to the scattering problems involving all fully heavy hadrons, an area of increasing interest.

hep-ph↗

arXiv2026

UniView: Enhancing Novel View Synthesis From A Single Image By Unifying Reference Features

The task of synthesizing novel views from a single image is highly ill-posed due to multiple explanations for unobserved areas. Most current methods tend to generate unseen regions from ambiguity priors and interpolation near input views, which often lead to severe distortions. To address this limitation, we propose a novel model dubbed as UniView, which can leverage reference images from a similar object to provide strong prior information during view synthesis. More specifically, we construct a retrieval and augmentation system and employ a multimodal large language model (MLLM) to assist in selecting reference images that meet our requirements. Additionally, a plug-and-play adapter module with multi-level isolation layers is introduced to dynamically generate reference features for the target views. Moreover, in order to preserve the details of an original input image, we design a decoupled triple attention mechanism, which can effectively align and integrate multi-branch features into the synthesis process. Extensive experiments have demonstrated that our UniView significantly improves novel view synthesis performance and outperforms state-of-the-art methods on the challenging datasets.

cs.CV↗

arXiv2026

Easier Painting Than Thinking: Can Text-to-Image Models Set the Stage, but Not Direct the Play?

Text-to-image (T2I) generation aims to synthesize images from textual prompts, which jointly specify what must be shown and imply what can be inferred, which thus correspond to two core capabilities: \textbf{\textit{composition}} and \textbf{\textit{reasoning}}. Despite recent advances of T2I models in both composition and reasoning, existing benchmarks remain limited in evaluation. They not only fail to provide comprehensive coverage across and within both capabilities, but also largely restrict evaluation to low scene density and simple one-to-one reasoning. To address these limitations, we propose \textbf{\textsc{T2I-CoReBench}}, a comprehensive and complex benchmark that evaluates both composition and reasoning capabilities of T2I models. To ensure comprehensiveness, we structure composition around scene graph elements (\textit{instance}, \textit{attribute}, and \textit{relation}) and reasoning around the philosophical framework of inference (\textit{deductive}, \textit{inductive}, and \textit{abductive}), formulating a 12-dimensional evaluation taxonomy. To increase complexity, driven by the inherent real-world complexities, we curate each prompt with higher compositional density for composition and greater reasoning intensity for reasoning. To facilitate fine-grained and reliable evaluation, we also pair each evaluation prompt with a checklist that specifies individual \textit{yes/no} questions to assess each intended element independently. In statistics, our benchmark comprises 1,080 challenging prompts and around 13,500 checklist questions. Experiments across 38 current T2I models reveal that their composition capability still remains limited in high compositional scenarios, while the reasoning capability lags even further behind as a critical bottleneck, with all models struggling to infer implicit elements from prompts.

cs.CV↗

arXiv2026

Superconductivity in the A15-type V3(Os1-2xSixGex) medium-entropy alloys

Cubic A15-type superconducting alloys continue to fascinate the academic and industrial fields because they mainly support the largest market for low-temperature superconducting applications and show exotic physical properties. Medium-/high-entropy alloys (MEAs-HEAs) can be employed stably under extreme conditions due to their high mechanical hardness and excellent irradiation tolerance. Combining with the features of the A15-type superconductor and MEAs-HEAs, we design a series of previously unreported A15-type V3(Os1-2xSixGex) (x = 0.333, 0.375, 0.425) MEA superconductors, which can be obtained by an arc melting method. Resistivity, magnetic susceptibility, and specific heat measurements indicate that all of them are type-II bulk superconductors. The superconducting transition temperature (Tc) exhibits an upward trend with the systematic reduction of Os concentration. Additionally, the upper critical field of the V3(Os0.333Si0.333Ge0.333) sample is larger than the Pauli limit, suggesting it may be robust against magnetic fields due to spin-orbit coupling induced by the heavy Os atoms. These findings not only advance our understanding of emergent phenomena in entropy-stabilized A15-type alloys but also expand the members of new superconductors.

cond-mat.supr-con↗

arXiv2026

Geometry-based pneumatic actuators for soft robotics

Soft pneumatic actuators enable safe human-machine interaction with lightweight and powerful applied parts. On the other side, they suffer design limitations as regards complex actuation patterns, including minimum bending radii, multi-states capabilities and structural stability. We present geometry-based pneumatic actuators (GPAs), a design and implementation approach that introduces constraint layers with configurable CNC heat-sealed chambers. The approach achieves predictable deformation, near-zero bending radii, multi-states actuation, and enables customizable and repeatable complex actuated geometries. Mathematical modeling reveals predictable linear angle transformations and validates nonlinear torque-angle relationships across diverse configurations. We demonstrate versatility of the GPAs approach through three applications: a 49 g wrist exoskeleton reducing muscle activity by up to 51%, a 30.8 g haptic interface delivering 8 N force feedback with fast response, and a 208 g bipedal robot achieving multi-gait locomotion. GPAs establish a configurable platform for next-generation wearable robotics, haptic systems, and soft locomotion devices.

cs.RO↗

arXiv2026

Deep Learning-Based Meat Freshness Detection with Segmentation and OOD-Aware Classification

In this study, we present a meat freshness classification framework from Red-Green-Blue (RGB) images that supports both packaged and unpackaged meat datasets. The system classifies four in-distribution (ID) meat classes and uses an out-of-distribution (OOD)-aware abstention mechanism that flags low-confidence samples as No Result. The pipeline combines U-Net-based segmentation with deep feature classifiers. Segmentation is used as a preprocessing step to isolate the meat region and reduce background, producing more consistent inputs for classification. The segmentation module achieved an Intersection over Union (IoU) of 75% and a Dice coefficient of 82%, producing standardized inputs for the classification stage. For classification, we benchmark five backbones: Residual Network-50 (ResNet-50), Vision Transformer-Base/16 (ViT-B/16), Swin Transformer-Tiny (Swin-T), EfficientNet-B0, and MobileNetV3-Small. We use nested 5x3 cross-validation (CV) for model selection and hyperparameter tuning. On the held-out ID test set, EfficientNet-B0 achieves the highest accuracy (98.10%), followed by ResNet-50 and MobileNetV3-Small (both 97.63%) and Swin-T (97.51%), while ViT-B/16 is lower (94.42%). We additionally evaluate OOD scoring and thresholding using standard OOD metrics and sensitivity analysis over the abstention threshold. Finally, we report on-device latency using TensorFlow Lite (TFLite) on a smartphone, highlighting practical accuracy-latency trade-offs for future deployment.

cs.LG↗

arXiv2026

Oxygen permeability and stability in the entropy-stabilized Co-based Perovskite oxygen permeable membranes

Oxygen transport membranes (OTMs), enabling catalytic reaction and gas separation, support crucial chemical engineering processes and decarbonization technologies, but their applications are hindered by limited oxygen permeation fluxes and inadequate long-term stability during operation. Here, a series of high-entropy perovskite OTMs based on La0.5Sr0.5CoO3 were designed and synthesized by the simple sol-gel method. The impact of varying doping ratios on the structure, surface morphology, oxygen permeability, and stability of these high-entropy OTMs was thoroughly examined. At 950 °C, the optimal composition, La0.25Sr0.25Gd0.2Nd0.2Pr0.1CoO3, achieved oxygen permeation fluxes of 1.62 mL min-1 cm-2 under air/He gradient and 1.46 mL min-1 cm-2 under air/CO2, respectively. Remarkably, all high-entropy OTMs demonstrated stable operation for over 100 h in a pure CO2 environment without a significant decline in performance. This finding paves a new way to enhance the structural and oxygen permeation stability of OTMs, and further promotes the application of OTMs in oxy-fuel combustion technologies aimed at improving CO2 capture and storage efficiency.

cond-mat.mtrl-sci↗

arXiv2026

Adaptive Multi-Objective Tiered Storage Configuration for KV Cache in LLM Service

The memory-for-computation paradigm of KV caching is essential for accelerating large language model (LLM) inference service, but limited GPU high-bandwidth memory (HBM) capacity motivates offloading the KV cache to cheaper external storage tiers. While this expands capacity, it introduces the challenge of dynamically managing heterogeneous storage resources to balance cost, throughput, and latency under varying workloads. We formulate this as a multi-objective optimization problem: identifying the Pareto frontier across these metrics within the storage configuration space. Using a high-fidelity end-to-end simulator, we observe that the objective functions are non-analytic and exhibit complex variable coupling, making the Pareto frontier difficult to approximate analytically. To obtain the frontier, we introduce Kareto, a KV-cache Adaptive REsource managemenT Optimizer. Kareto leverages a diminishing-return-guided pruning method to efficiently navigate the large configuration space and approximate the Pareto frontier. Additionally, it incorporates a fine-grained adaptive tuner that uses eviction policies in tier storage and KV block access patterns for group-specific cache management, improving cache efficiency. Experiments on real-world traces show that Kareto adapts to workload and can identify configurations of better cost efficiency, covering static strategies. Compared to the fixed setup with 1024 GB DRAM, Kareto can improve throughput by up to 9.3%, or reduce latency by up to 58.3%, or lower cost by up to 20.2% under respective optimization objectives.

cs.AR↗

arXiv2026

Design and Characterization of a Dual-DOF Soft Shoulder Exosuit with Volume-Optimized Pneumatic Actuator

Portable pneumatic systems for 2 degree-of-freedom (DOF) soft shoulder exosuits remain underexplored, and face fundamental trade-offs between torque output and dynamic response that are further compounded by the need for multiple actuators to support complex shoulder movement. This work addresses these constraints through a volume-optimized spindle-shaped angled actuator (SSAA) geometry: by reducing actuator volume by 35.7% (357mL vs. 555mL), the SSAA maintains 94.2% of output torque while achieving 35.2% faster dynamic response compared to uniform cylindrical designs. Building on the SSAA, we develop a curved abduction actuator (CAA) based on the SSAA geometry and a horizontal adduction actuator (HAA) based on the pouch motor principle, integrating both into a dual-DOF textile-based shoulder exosuit (390 g). The exosuit delivers multi-modal assistance spanning shoulder abduction, flexion, and horizontal adduction, depending on the actuation. User studies with 10 healthy participants reveal that the exosuit substantially reduces electromyographic (EMG) activity across both shoulder abduction and flexion tasks. For abduction with HAA only, the exosuit achieved up to 59% muscle activity reduction across seven muscles. For flexion, both the single-actuator configuration (HAA only) and the dual-actuator configuration (HAA,+,CAA) reduced EMG activity by up to 63.7% compared to no assistance. However, the incremental benefit of adding the CAA to existing HAA support was limited in healthy users during flexion, with statistically significant additional reductions observed only in pectoralis major. These experimental findings characterize actuator contributions in healthy users and provide design guidance for multi-DOF exosuit systems.

cs.RO↗

arXiv2026

Large-Time Asymptotics for Heat and Fractional Heat Equations on the Lattice and General Finite Subgraphs

In this paper, we study large-time asymptotics for heat and fractional heat equations in two discrete settings: the full lattice $\mathbb Z^d$ and finite connected subgraphs with Dirichlet boundary condition. These results provide a unified discrete theory of long-time asymptotics for local and nonlocal diffusions. For $d\ge1$ and $s\in(0,1]$, we consider on $\mathbb Z^d$ the Cauchy problem \[ \partial_t u+(-Δ)^s u=0,\qquad u(0)=u_0\in \ell^1(\mathbb Z^d), \] and derive a precise first-order asymptotic expansion toward the lattice fractional heat kernel $G_t^{(s)}$. The main technical input is a pair of sharp translation-increment bounds for $G_t^{(s)}$: a pointwise estimate and an $\ell^1$-estimate. As consequences, under finite first moment we obtain the optimal decay rate $t^{-1/(2s)}$ in $\ell^p$-asymptotics ($1\le p\le\infty$), and we prove sharpness by explicit shifted-kernel examples. Without moment assumptions, we still establish convergence in the full $\ell^1$-class, and we show that no universal quantitative rate can hold in general. We also analyze fractional Dirichlet diffusion on finite connected subgraphs (restricted fractional setting, including $s=1$ as the local case). In this finite-dimensional framework, solutions admit spectral decomposition and exhibit exponential large-time behavior governed by the principal eigenvalue and the spectral gap. In addition, we study positivity improving properties of the associated semigroups for both the lattice and Dirichlet evolutions.

math.AP↗