arXiv subjects

Rui Chen

Publications and source records attributed to Rui Chen.

At least 145 records · Page 8Linked to original sources

arXiv2025

ViTaMIn-B: A Reliable and Efficient Visuo-Tactile Bimanual Manipulation Interface

Handheld devices have opened up unprecedented opportunities to collect large-scale, high-quality demonstrations efficiently. However, existing systems often lack robust tactile sensing or reliable pose tracking to handle complex interaction scenarios, especially for bimanual and contact-rich tasks. In this work, we propose ViTaMIn-B, a more capable and efficient handheld data collection system for such tasks. We first design DuoTact, a novel compliant visuo-tactile sensor built with a flexible frame to withstand large contact forces during manipulation while capturing high-resolution contact geometry. To enhance the cross-sensor generalizability, we propose reconstructing the sensor's global deformation as a 3D point cloud and using it as the policy input. We further develop a robust, unified 6-DoF bimanual pose acquisition process using Meta Quest controllers, which eliminates the trajectory drift issue in common SLAM-based methods. Comprehensive user studies confirm the efficiency and high usability of ViTaMIn-B among novice and expert operators. Furthermore, experiments on four bimanual manipulation tasks demonstrate its superior task performance relative to existing systems. Project page: https://chuanyune.github.io/ViTaMIn-B_page/

cs.RO↗

arXiv2025

Two-phase flow in porous metal foam flow fields of PEM fuel cells

Porous metal foam (PMF) flow field is a potential option for proton exchange membrane fuel cells (PEMFCs) due to its excellent capabilities in gas distribution and water drainage. However, the gas-liquid two-phase flow in the PMF flow field on the pore scale is still unclear. In this study, we investigate the gas-liquid two-phase flow in the PMF flow field. Film, plug, and ligament flows are found in the hydrophilic PMF flow field, while slug and droplet flows are found in the hydrophobic PMF flow field. The results suggest that optimizing the pore size, increasing the metal foam surface hydrophobicity, and optimizing the operating condition are helpful for the water management of the PMF flow field. The frequency analysis of the pressure drop also shows that the dominant frequency can be used as an indicator to analyze the transition between different flow patterns.

physics.flu-dyn↗

arXiv2025

Mass transfer and water management in proton exchange membrane fuel cells with a composite foam-rib flow field

Mass transfer capability of reactants and hydrothermal management is important for the performance and durability of proton exchange membrane fuel cells. In the conventional rib flow field, the oxygen transport is affected by the accumulation of under-rib liquid water which causes excessive concentration loss and limits cell performance. To improve the cell performance, a composite foam-rib flow field structure is proposed by combining the metal foam flow field and the conventional rib flow field. The proposed design is simulated by using a three-dimensional homogeneous non-isothermal numerical model. The results show that the composite foam-rib flow field, by improving the oxygen transfer and water removal capabilities under the ribs, can improve the oxygen concentration and current density without increasing the pumping power, thus improving the cell performance under different conditions. The key parameters of the composite foam-rib flow field are optimized. With the optimal metal foam filling ratio of 0.75 and porosity of 0.85, the peak power density and the limiting current density for the composite foam-rib flow field are higher than the conventional rib flow field by 5.20% and 22.68%.

physics.flu-dyn↗

arXiv2025

Chain-of-Query: Unleashing the Power of LLMs in SQL-Aided Table Understanding via Multi-Agent Collaboration

Table understanding requires structured, multi-step reasoning. Large Language Models (LLMs) struggle with it due to the structural complexity of tabular data. Recently, multi-agent frameworks for SQL generation have shown promise in tackling the challenges of understanding tabular data, but existing approaches often suffer from limitations such as the inability to comprehend table structure for reliable SQL generation, error propagation that results in invalid queries, and over-reliance on execution correctness. To address these issues, we propose Chain-of-Query (CoQ), a novel multi-agent framework for SQL-aided table understanding. CoQ adopts natural-language-style representations of table schemas to abstract away structural noise and enhance understanding. It employs a clause-by-clause SQL generation strategy to improve query quality and introduces a hybrid reasoning division that separates SQL-based mechanical reasoning from LLM-based logical inference, thereby reducing reliance on execution outcomes. Extensive experiments across four models and five widely used benchmarks demonstrate that CoQ achieves substantial accuracy improvements and significantly lowers invalid SQL rates compared to prior generic LLM-based, SQL-aided, and hybrid baselines, confirming its superior effectiveness in table understanding. The code is available at https://github.com/SongyuanSui/ChainofQuery.

cs.CL↗

arXiv2025

Exploring pion emission properties of (strange) hidden-charm molecular pentaquarks in a chiral quark model

The internal structure of the exotic $P_c$ and $P_{cs}$ pentaquarks remains an open question. To address this, we demonstrate that pion emission serves as a sensitive probe by calculating its properties within a molecular scenario using the chiral quark model and coupled-channel effects. Our results reveal a strong dependence of the decay widths on the internal structure and spatial wave functions. We therefore expect this study to stimulate experimental measurements of these decays, which are crucial for determining the nature of these states and guiding the search for further molecular partners.

hep-ph↗

arXiv2025

FusionFM: All-in-One Multi-Modal Image Fusion with Flow Matching

Current multi-modal image fusion methods typically rely on task-specific models, leading to high training costs and limited scalability. While generative methods provide a unified modeling perspective, they often suffer from slow inference due to the complex sampling trajectories from noise to image. To address this, we formulate image fusion as a direct probabilistic transport from source modalities to the fused image distribution, leveraging the flow matching paradigm to improve sampling efficiency and structural consistency. To mitigate the lack of high-quality fused images for supervision, we collect fusion results from multiple state-of-the-art models as priors, and employ a task-aware selection function to select the most reliable pseudo-labels for each task. We further introduce a Fusion Refiner module that employs a divide-and-conquer strategy to systematically identify, decompose, and enhance degraded components in selected pseudo-labels. For multi-task scenarios, we integrate elastic weight consolidation and experience replay mechanisms to preserve cross-task performance and enhance continual learning ability from both parameter stability and memory retention perspectives. Our approach achieves competitive performance across diverse fusion tasks, while significantly improving sampling efficiency and maintaining a lightweight model design. The code will be available at: https://github.com/Ist-Zhy/FusionFM.

cs.CV↗

arXiv2025

Imbalance in Balance: Online Concept Balancing in Generation Models

In visual generation tasks, the responses and combinations of complex concepts often lack stability and are error-prone, which remains an under-explored area. In this paper, we attempt to explore the causal factors for poor concept responses through elaborately designed experiments. We also design a concept-wise equalization loss function (IMBA loss) to address this issue. Our proposed method is online, eliminating the need for offline dataset processing, and requires minimal code changes. In our newly proposed complex concept benchmark Inert-CompBench and two other public test sets, our method significantly enhances the concept response capability of baseline models and yields highly competitive results with only a few codes released at https://github.com/KwaiVGI/IMBA-Loss.

cs.CV↗

arXiv2025

MOSAIC: Generating Consistent, Privacy-Preserving Scenes from Multiple Depth Views in Multi-Room Environments

We introduce a diffusion-based approach for generating privacy-preserving digital twins of multi-room indoor environments from depth images only. Central to our approach is a novel Multi-view Overlapped Scene Alignment with Implicit Consistency (MOSAIC) model that explicitly considers cross-view dependencies within the same scene in the probabilistic sense. MOSAIC operates through a multi-channel inference-time optimization that avoids error accumulation common in sequential or single-room constraints in panorama-based approaches. MOSAIC scales to complex scenes with zero extra training and provably reduces the variance during denoising process when more overlapping views are added, leading to improved generation quality. Experiments show that MOSAIC outperforms state-of-the-art baselines on image fidelity metrics in reconstructing complex multi-room environments. Resources and code are at https://mosaic-cmubig.github.io

cs.CV↗

arXiv2025

2D Addressable Mid-infrared Metasurface Spatial Light Modulator

Active metasurfaces enable dynamic control of light for applications in beam steering, pixelated holography, and adaptive optics, but demonstrations of two-dimensional (2D) electrically addressable arrays have so far been limited. Here we introduce a scalable 2D architecture based on phase-change materials (PCMs) integrated metasurfaces and apply it to realize the first transmissive mid-infrared (mid-IR) spatial light modulator (SLM). The device is fabricated through standard silicon photonic foundry processing combined with backend-of-line (BEOL) integration and employs multilayer backend metal interconnects to implement a crossbar addressing scheme. Each pixel is integrated with a silicon diode selector to suppress sneak-path currents, a feature essential for scaling to large arrays. The result establishes a foundry-compatible route to high-density, large-area active metasurfaces with independently tunable pixels.

physics.optics↗

arXiv2025

On the Convexification of a Class of Mixed-Integer Conic Sets

We investigate mixed-integer second-order conic (SOC) sets with a nonlinear right-hand side in the SOC constraint, a structure frequently arising in mixed-integer quadratically constrained programming (MIQCP). Under mild assumptions, we show that the convex hull can be exactly described by replacing the right-hand side with its concave envelope. This characterization enables strong relaxations for MIQCPs via reformulations and cutting planes. Computational experiments on distributionally robust chance-constrained knapsack variants demonstrate the efficacy of our reformulation techniques.

math.OC↗

arXiv2025

Solving 0-1 Integer Programs with Unknown Knapsack Constraints Using Membership Oracles

We consider solving a combinatorial optimization problem with unknown knapsack constraints using a membership oracle for each unknown constraint such that, given a solution, the oracle determines whether the constraint is satisfied or not with absolute certainty. The goal of the decision maker is to find the best possible solution subject to a budget on the number of oracle calls. Inspired by active learning for binary classification based on Support Vector Machines (SVMs), we devise a framework to solve the problem by learning and exploiting surrogate linear constraints. The framework includes training linear separators on the labeled points and selecting new points to be labeled, which is achieved by applying a sampling strategy and solving a 0-1 integer linear program. Following the active learning literature, a natural choice would be SVM as a linear classifier and the information-based sampling strategy known as simple margin, for each unknown constraint. We improve on both sides: we propose an alternative sampling strategy based on mixed-integer quadratic programming and a linear separation method inspired by an algorithm for convex optimization in the oracle model. We conduct experiments on classical problems and variants inspired by realistic applications to show how different linear separation methods and sampling strategies influence the quality of the results in terms of several metrics including objective value, dual bound and running time.

cs.LG↗

arXiv2025

Seed3D 1.0: From Images to High-Fidelity Simulation-Ready 3D Assets

Developing embodied AI agents requires scalable training environments that balance content diversity with physics accuracy. World simulators provide such environments but face distinct limitations: video-based methods generate diverse content but lack real-time physics feedback for interactive learning, while physics-based engines provide accurate dynamics but face scalability limitations from costly manual asset creation. We present Seed3D 1.0, a foundation model that generates simulation-ready 3D assets from single images, addressing the scalability challenge while maintaining physics rigor. Unlike existing 3D generation models, our system produces assets with accurate geometry, well-aligned textures, and realistic physically-based materials. These assets can be directly integrated into physics engines with minimal configuration, enabling deployment in robotic manipulation and simulation training. Beyond individual objects, the system scales to complete scene generation through assembling objects into coherent environments. By enabling scalable simulation-ready content creation, Seed3D 1.0 provides a foundation for advancing physics-based world simulators. Seed3D 1.0 is now available on https://console.volcengine.com/ark/region:ark+cn-beijing/experience/vision?modelId=doubao-seed3d-1-0-250928&tab=Gen3D

eess.IV↗

arXiv2025

The Complexity of Recognizing Facets for the Knapsack Polytope

The complexity class DP is the class of all languages that are the intersection of a language in NP and a language in coNP. It was conjectured that recognizing a facet for the knapsack polytope is DP-complete. We provide a positive answer to this conjecture. Moreover, despite the \DP-hardness of the recognition problem, we give a polynomial time algorithm for deciding if an inequality with a fixed number of distinct coefficients defines a facet of a knapsack polytope.

math.OC↗

arXiv2025

Emergent Weyl-like points in periodically modulated systems

We investigate a three-dimensional (3D) topological phase resembling a Weyl semimetal, modulated by a periodic potential and engineered through Floquet dynamics. This system is constructed by stacking two-dimensional Chern insulators and hosts Weyl-like points defined in the parameter space $(k_x, k_y, z)$, distinct from conventional Weyl points in momentum space $(k_x, k_y, k_z)$. The Weyl-semimetal-like phase exhibits characteristics akin to those of Weyl semimetals, including linear dispersion near the Weyl-like points, nontrivial bulk topology, the presence of Fermi arcs connecting the Weyl-like points, and the Berry monopoles. Unlike traditional Weyl semimetals, these features manifest in real space rather than momentum space. Furthermore, we calculate the local density of states, the layer Hall conductance, and the total 3D Hall conductivity, demonstrating that the Weyl-semimetal-like phase remains stable under weak and moderate interlayer couplings. The influence of disorder is also examined: Beyond a critical disorder strength, the Weyl-like points destabilize and the topological phase collapses. Moreover, by computing the Floquet Chern number, we demonstrate that the locations of the Weyl-like points can be tuned via high-frequency laser pumping. Finally, we show that both type-I and II Weyl-like behaviors can arise in a tilted Weyl-like model.

cond-mat.mes-hall↗

arXiv2025

The recoil corrections, correlation functions and possible double-strange hadronic molecules

In this work, we perform a systematic investigation of ${K}^{(*)}{K}^{(*)}$ interactions within a one-boson-exchange model.The framework incorporates both $S-D$ wave mixing and coupled-channel effects, with effective potentials retained up to order $\mathcal{O}(1/M^2)$. By solving the coupled channel Schrödinger equations, we can predict two double-strange molecular candidates: a $KK^*$ molecule with $I(J^P)=0(1^+)$ and a $K^*K^*$ molecule with $0(1^+)$. Our results also show that the recoil corrections play a crucial role in the formation of these two molecular candidates. Furthermore, the $S-D$ wave mixing effects contribute positively to the formation process. As a byproduct of this analysis, we extend our study to $K^{(*)}\bar{K}^{(*)}$ interactions with the same model. Our findings suggest that the $K\bar{K}^{*}$ states with $0(1^{+-}, 1^{++})$ and $K^{*}\bar{K}^{*}$ states with $0(0^{++}, 1^{+-}, 2^{++})$ can be promising molecular candidates. Additionally, we analyze the correlations between the constituent mesons, the resulting correlation functions provide additional support for our predictions of both double-strange and strangonium-like molecular states.

hep-ph↗

arXiv2025

LLM Hallucination Detection: A Fast Fourier Transform Method Based on Hidden Layer Temporal Signals

Hallucination remains a critical barrier for deploying large language models (LLMs) in reliability-sensitive applications. Existing detection methods largely fall into two categories: factuality checking, which is fundamentally constrained by external knowledge coverage, and static hidden-state analysis, that fails to capture deviations in reasoning dynamics. As a result, their effectiveness and robustness remain limited. We propose HSAD (Hidden Signal Analysis-based Detection), a novel hallucination detection framework that models the temporal dynamics of hidden representations during autoregressive generation. HSAD constructs hidden-layer signals by sampling activations across layers, applies Fast Fourier Transform (FFT) to obtain frequency-domain representations, and extracts the strongest non-DC frequency component as spectral features. Furthermore, by leveraging the autoregressive nature of LLMs, HSAD identifies optimal observation points for effective and reliable detection. Across multiple benchmarks, including TruthfulQA, HSAD achieves over 10 percentage points improvement compared to prior state-of-the-art methods. By integrating reasoning-process modeling with frequency-domain analysis, HSAD establishes a new paradigm for robust hallucination detection in LLMs.

cs.CL↗

arXiv2025

Inexact Column Generation for Bayesian Network Structure Learning via Difference-of-Submodular Optimization

In this paper, we consider a score-based Integer Programming (IP) approach for solving the Bayesian Network Structure Learning (BNSL) problem. State-of-the-art BNSL IP formulations suffer from the exponentially large number of variables and constraints. A standard approach in IP to address such challenges is to employ row and column generation techniques, which dynamically generate rows and columns, while the complex pricing problem remains a computational bottleneck for BNSL. For the general class of $\ell_0$-penalized likelihood scores, we show how the pricing problem can be reformulated as a difference of submodular optimization problem, and how the Difference of Convex Algorithm (DCA) can be applied as an inexact method to efficiently solve the pricing problems. Empirically, we show that, for continuous Gaussian data, our row and column generation approach yields solutions with higher quality than state-of-the-art score-based approaches, especially when the graph density increases, and achieves comparable performance against benchmark constraint-based and hybrid approaches, even when the graph size increases.

stat.ML↗

arXiv2025

HapMorph: A Pneumatic Framework for Multi-Dimensional Haptic Property Rendering

Haptic interfaces that can simultaneously modulate multiple physical properties remain a fundamental challenge in human-robot interaction. Existing systems typically allow the rendering of either geometric features or mechanical properties, but rarely both, within wearable form factors. Here, we introduce HapMorph, a pneumatic framework that enables continuous, simultaneous modulation of object size and stiffness through antagonistic fabric-based pneumatic actuators (AFPAs). We implemented a HapMorph protoytpe designed for hands interaction achieving size variation from 50 to 104 mm, stiffness modulation up to 4.7 N/mm and mass of the wearable parts of just 21 g. Through systematic characterization, we demonstrate decoupled control of size and stiffness properties via dual-chamber pressure regulation. Human perception studies with 10 participants reveal that users can distinguish nine discrete states across three size categories and three stiffness levels with 89.4% accuracy and 6.7 s average response time. We further demonstrate extended architectures that combine AFPAs with complementary pneumatic structures to enable shape or geometry morphing with concurrent stiffness control. Our results establish antagonistic pneumatic principle as a pathway toward next-generation haptic interfaces, capable of multi-dimensiona rendering properties within practical wearable constraints.

cs.RO↗