arXiv subjects

Rui Chen

Publications and source records attributed to Rui Chen.

At least 163 records · Page 9Linked to original sources

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↗

arXiv2025

Tunable corner states in topological insulators with long-range hoppings and diverse shapes

In this work we develop a theoretical framework for the control of corner modes in higher-order topological insulators (HOTIs) featuring long-range hoppings and diverse geometries, enabling precise tunability of their spatial positions. First, we demonstrate that the locations of corner states can be finely tuned by varying long-range hoppings in a circular HOTI, as revealed by a detailed edge theory analysis and the condition of vanishing Dirac mass. Moreover, we show that long-range hoppings in different directions (e.g., $x$ and $y$) have distinct effects on the positioning of corner states. Second, we investigate HOTIs with various polygonal geometries and find that the presence and location of corner modes depend sensitively on the shape. In particular, a corner hosts a localized mode if the Dirac masses of its two adjacent edges have opposite signs, while no corner mode emerges if the masses share the same sign. Our findings offer a versatile approach for the controlled manipulation of corner modes in HOTIs, opening avenues for the design and implementation of higher-order topological materials.

cond-mat.mes-hall↗

arXiv2025

Electrically reconfigurable nonvolatile transmissive metasurface in visible

The synergy between metasurfaces and non-volatile phase change materials (PCMs) has created many reconfigurable photonic devices for applications in optical memory, optical computing and optical communications. But these advances have been limited to the infrared wavelengths due to the high loss of PCMs in the visible regime. Here we demonstrate a nonvolatile visible metasurface that is electrically reconfigurable using wide bandgap PCM Sb2S3. Our device supports a resonant mode at 610 nm, a wavelength largely under-explored for PCM-based metasurfaces. By incorporating only a 20 nm thick layer of Sb2S3, we experimentally demonstrate a resonance tuning range of 16 nm. Reversible switching of the metasurface is accomplished in situ using a carefully engineered, ultrathin doped silicon micro-heater. Our work paves the way for integrating PCMs into visible-frequency systems, particularly for human-centric applications such as augmented and virtual reality displays.

physics.optics↗

arXiv2025

ViTaMIn: Learning Contact-Rich Tasks Through Robot-Free Visuo-Tactile Manipulation Interface

Tactile information plays a crucial role for humans and robots to interact effectively with their environment, particularly for tasks requiring the understanding of contact properties. Solving such dexterous manipulation tasks often relies on imitation learning from demonstration datasets, which are typically collected via teleoperation systems and often demand substantial time and effort. To address these challenges, we present ViTaMIn, an embodiment-free manipulation interface that seamlessly integrates visual and tactile sensing into a hand-held gripper, enabling data collection without the need for teleoperation. Our design employs a compliant Fin Ray gripper with tactile sensing, allowing operators to perceive force feedback during manipulation for more intuitive operation. Additionally, we propose a multimodal representation learning strategy to obtain pre-trained tactile representations, improving data efficiency and policy robustness. Experiments on seven contact-rich manipulation tasks demonstrate that ViTaMIn significantly outperforms baseline methods, demonstrating its effectiveness for complex manipulation tasks.

cs.RO↗

arXiv2025

A Soft Fabric-Based Thermal Haptic Device for VR and Teleoperation

This paper presents a novel fabric-based thermal-haptic interface for virtual reality and teleoperation. It integrates pneumatic actuation and conductive fabric with an innovative ultra-lightweight design, achieving only 2~g for each finger unit. By embedding heating elements within textile pneumatic chambers, the system delivers modulated pressure and thermal stimuli to fingerpads through a fully soft, wearable interface. Comprehensive characterization demonstrates rapid thermal modulation with heating rates up to 3$^{\circ}$C/s, enabling dynamic thermal feedback for virtual or teleoperation interactions. The pneumatic subsystem generates forces up to 8.93~N at 50~kPa, while optimization of fingerpad-actuator clearance enhances cooling efficiency with minimal force reduction. Experimental validation conducted with two different user studies shows high temperature identification accuracy (0.98 overall) across three thermal levels, and significant manipulation improvements in a virtual pick-and-place tasks. Results show enhanced success rates (88.5\% to 96.4\%, p = 0.029) and improved force control precision (p = 0.013) when haptic feedback is enabled, validating the effectiveness of the integrated thermal-haptic approach for advanced human-machine interaction applications.

cs.RO↗

arXiv2025

VoxHammer: Training-Free Precise and Coherent 3D Editing in Native 3D Space

3D local editing of specified regions is crucial for game industry and robot interaction. Recent methods typically edit rendered multi-view images and then reconstruct 3D models, but they face challenges in precisely preserving unedited regions and overall coherence. Inspired by structured 3D generative models, we propose VoxHammer, a novel training-free approach that performs precise and coherent editing in 3D latent space. Given a 3D model, VoxHammer first predicts its inversion trajectory and obtains its inverted latents and key-value tokens at each timestep. Subsequently, in the denoising and editing phase, we replace the denoising features of preserved regions with the corresponding inverted latents and cached key-value tokens. By retaining these contextual features, this approach ensures consistent reconstruction of preserved areas and coherent integration of edited parts. To evaluate the consistency of preserved regions, we constructed Edit3D-Bench, a human-annotated dataset comprising hundreds of samples, each with carefully labeled 3D editing regions. Experiments demonstrate that VoxHammer significantly outperforms existing methods in terms of both 3D consistency of preserved regions and overall quality. Our method holds promise for synthesizing high-quality edited paired data, thereby laying the data foundation for in-context 3D generation. See our project page at https://huanngzh.github.io/VoxHammer-Page/.

cs.CV↗

arXiv2025

Learning Large Neighborhood Search for Maritime Inventory Routing Optimization

Maritime inventory routing optimization is an important yet challenging combinatorial optimization problem. We propose a machine learning-based local search approach for finding feasible solutions of large-scale maritime inventory routing optimization problems. Given the combinatorial complexity of the problems, we integrate a graph neural network-based neighborhood selection method to enhance local search efficiency. Our approach enables a structured exploration of different neighborhoods by imitating an optimization-based expert neighborhood selection policy, improving solution quality while maintaining computational efficiency. Through extensive computational experiments on realistic instances, we demonstrate that our method outperforms direct mixed-integer programming as well as benchmark local search approaches in solution time and solution quality.

math.OC↗

arXiv2025

Disentanglement in T-space for Faster and Distributed Training of Diffusion Models with Fewer Latent-states

We challenge a fundamental assumption of diffusion models, namely, that a large number of latent-states or time-steps is required for training so that the reverse generative process is close to a Gaussian. We first show that with careful selection of a noise schedule, diffusion models trained over a small number of latent states (i.e. $T \sim 32$) match the performance of models trained over a much large number of latent states ($T \sim 1,000$). Second, we push this limit (on the minimum number of latent states required) to a single latent-state, which we refer to as complete disentanglement in T-space. We show that high quality samples can be easily generated by the disentangled model obtained by combining several independently trained single latent-state models. We provide extensive experiments to show that the proposed disentangled model provides 4-6$\times$ faster convergence measured across a variety of metrics on two different datasets.

cs.LG↗

arXiv2025

LET-US: Long Event-Text Understanding of Scenes

Event cameras output event streams as sparse, asynchronous data with microsecond-level temporal resolution, enabling visual perception with low latency and a high dynamic range. While existing Multimodal Large Language Models (MLLMs) have achieved significant success in understanding and analyzing RGB video content, they either fail to interpret event streams effectively or remain constrained to very short sequences. In this paper, we introduce LET-US, a framework for long event-stream--text comprehension that employs an adaptive compression mechanism to reduce the volume of input events while preserving critical visual details. LET-US thus establishes a new frontier in cross-modal inferential understanding over extended event sequences. To bridge the substantial modality gap between event streams and textual representations, we adopt a two-stage optimization paradigm that progressively equips our model with the capacity to interpret event-based scenes. To handle the voluminous temporal information inherent in long event streams, we leverage text-guided cross-modal queries for feature reduction, augmented by hierarchical clustering and similarity computation to distill the most representative event features. Moreover, we curate and construct a large-scale event-text aligned dataset to train our model, achieving tighter alignment of event features within the LLM embedding space. We also develop a comprehensive benchmark covering a diverse set of tasks -- reasoning, captioning, classification, temporal localization and moment retrieval. Experimental results demonstrate that LET-US outperforms prior state-of-the-art MLLMs in both descriptive accuracy and semantic comprehension on long-duration event streams. All datasets, codes, and models will be publicly available.

cs.CV↗

arXiv2025

Strongly correlated electronic superconductivity in the noncentrosymmetric Re-Os-based high/medium-entropy alloys

The class of unconventional superconductors, particularly noncentrosymmetric superconductors, has been highly considered as potential materials for understanding the complex properties of quantum materials. Here, five previously unreported Re3.5Os3.5Ta0.5Hf0.5Nb3, Re3Os3Ta0.5Hf0.5Nb3, Re3.5Os3.5Mo0.5Hf0.5Nb3, Re3.5Os3.5Mo0.5W0.5Nb3, and Re3Os3Mo0.5Hf0.5Nb3 Re-Os-based high/medium-entropy alloys (MEAs-HEAs) with valence electron count ranging from 6.45 to 6.81 were synthesized and investigated using x-ray diffraction, transport, magnetization, and specific heat measurements. Our analyses confirm that all five compounds crystallize in a noncentrosymmetric α-Mn-type structure and exhibit type-II superconductivity with Tc values from 4.20 K to 5.11 K, respectively. Unexpectedly, despite being immersed in an acidic environment for one month, the structures and superconducting properties of HEAs remain stable. Our findings indicate that the Tc increases with an increasing valence electron count in MEAs-HEAs. Furthermore, these noncentrosymmetric α-Mn-type HEA superconductors have large Kadowaki-Woods ratios (KWR), implying the presence of strong electronic correlations.

cond-mat.supr-con↗

arXiv2025

Mixed Precision Photonic Computing with 3D Electronic-Photonic Integrated Circuits

We propose advancing photonic in-memory computing through three-dimensional photonic-electronic integrated circuits using phase-change materials (PCM) and AlGaAs-CMOS technology. These circuits offer high precision (greater than 12 bits), scalability (greater than 1024 by 1024), and massive parallelism (greater than 1 million operations) across the wavelength, spatial, and temporal domains at ultra-low power (less than 1 watt per PetaOPS). Monolithically integrated hybrid PCM-AlGaAs memory resonators handle coarse-precision iterations (greater than 5-bit most significant bit precision) through reversible PCM phase transitions. Electro-optic memristive tuning enables fine-precision updates (greater than 8-bit least significant bit precision), resulting in over 12-bit precision for in-memory computing. The use of low-loss PCM (less than 0.01 dB per cm) and electro-optical tuning yields memristive optical resonators with high Q-factors (greater than 1 million), low insertion loss, and low tuning power. A W by W photonic tensor core composed of PCM-AlGaAs memresonators performs general matrix multiplication (GEMM) across W wavelengths from optical frequency combs, with minimal crosstalk and loss. Hierarchical scaling in the wavelength domain (K) and spatial domain (L) enables this system to address high-dimensional (N) scientific partial differential equation (PDE) problems in a single constant-time operation, compared to the conventional quadratic-time (N squared) computational complexity.

physics.optics↗

arXiv2025

Multibeam High Throughput Satellite: Hardware Foundation, Resource Allocation, and Precoding

With its wide coverage and uninterrupted service, satellite communication is a critical technology for next-generation 6G communications. High throughput satellite (HTS) systems, utilizing multipoint beam and frequency multiplexing techniques, enable satellite communication capacity of up to Tbps to meet the growing traffic demand. Therefore, it is imperative to review the-state-of-the-art of multibeam HTS systems and identify their associated challenges and perspectives. Firstly, we summarize the multibeam HTS hardware foundations, including ground station systems, on-board payloads, and user terminals. Subsequently, we review the flexible on-board radio resource allocation approaches of bandwidth, power, time slot, and joint allocation schemes of HTS systems to optimize resource utilization and cater to non-uniform service demand. Additionally, we survey multibeam precoding methods for the HTS system to achieve full-frequency reuse and interference cancellation, which are classified according to different deployments such as single gateway precoding, multiple gateway precoding, on-board precoding, and hybrid on-board/on-ground precoding. Finally, we disscuss the challenges related to Q/V band link outage, time and frequency synchronization of gateways, the accuracy of channel state information (CSI), payload light-weight development, and the application of deep learning (DL). Research on these topics will contribute to enhancing the performance of HTS systems and finally delivering high-speed data to areas underserved by terrestrial networks.

eess.SP↗

arXiv2025

Strong correlation behavior and Strong coupling superconductivity in (Ti1/4Hf1/4Nb1/4Ta1/4)1-xNix with the rich magnetic element Ni

Searching for new superconductors, especially unconventional superconductors, has been studied extensively for decades but remains one of the major outstanding challenges in condensed matter physics. Medium/high-entropy alloys (MEAs-HEAs) are new fertile soils of unconventional superconductors and generate widespread interest and questions on the existence of superconductivity in highly disordered materials. Here, we report on the effect of Ni-doped on the crystal structure and superconductivity properties of strongly coupled TiHfNbTa MEA. XRD results indicate that the maximum solid solution of (Ti1/4Hf1/4Nb1/4Ta1/4)1-xNix is about 7.7%. Resistivity, magnetic susceptibility, and specific heat measurements demonstrated that (Ti1/4Hf1/4Nb1/4Ta1/4)1-xNix HEAs are all bulk type-II superconductors and follow the trend of the increase of Tc with the increase of Ni-doped contents. The specific heat jump of all (Ti1/4Hf1/4Nb1/4Ta1/4)1-xNix are much larger than the BCS value of 1.43, suggesting all these HEAs are strongly coupled superconductors. Additionally, large Kadawaki-Woods ratio values suggest that there is a strong electron correlation effect in this system. The (Ti1/4Hf1/4Nb1/4Ta1/4)1-xNix HEA system is a new ideal material platform for the study of strong correlation behavior and strongly coupled superconductivity, which provides an insight into the physics of high-temperature superconductors or other unconventional superconductors.

cond-mat.supr-con↗

arXiv2025

The Logarithmic Laplacian on General Graphs

We establish, for the first time, a Bochner-type integral representation for the logarithmic Laplacian on weighted graphs. Assuming stochastic completeness of the underlying graph, we further derive an explicit pointwise formula for this operator: \[ \log(-Δ)\:u(x) =\frac{1}{μ(x)}\sum_{y\neq x}W_{\log}(x,y)\,(u(x)-u(y)) -\frac{1}{μ(x)}\sum_{y}W(x,y)\,u(y) +Γ'(1)\,u(x). \] In the case of weighted lattice graphs with uniformly positive vertex measures, we obtain sharp two-sided bounds for the associated logarithmic kernel. Additionally, we prove that the logarithmic Laplacian is unbounded on $\ell^{2}$, and we present an alternative derivation of its pointwise form. Moreover, for every $1 < p \leq \infty$ and all $u \in C_c(\mathbb{Z}^{d})$, we establish a strong convergence in $\ell^{p}$: \[\frac{(-Δ)^{s} u - u}{s} \longrightarrow \log(-Δ) \:u \quad \text{as } s \to 0^{+}.\]Finally, on the standard lattice $\mathbb{Z}^{d}$, we compute the Fourier multipliers corresponding to both the fractional Laplacian and the logarithmic Laplacian, and derive exact large-time behavior and off-diagonal asymptotics of the associated diffusion kernels, including all sharp asymptotic constants.

math.AP↗

arXiv2025

LLMControl: Grounded Control of Text-to-Image Diffusion-based Synthesis with Multimodal LLMs

Recent spatial control methods for text-to-image (T2I) diffusion models have shown compelling results. However, these methods still fail to precisely follow the control conditions and generate the corresponding images, especially when encountering the textual prompts that contain multiple objects or have complex spatial compositions. In this work, we present a LLM-guided framework called LLM\_Control to address the challenges of the controllable T2I generation task. By improving grounding capabilities, LLM\_Control is introduced to accurately modulate the pre-trained diffusion models, where visual conditions and textual prompts influence the structures and appearance generation in a complementary way. We utilize the multimodal LLM as a global controller to arrange spatial layouts, augment semantic descriptions and bind object attributes. The obtained control signals are injected into the denoising network to refocus and enhance attention maps according to novel sampling constraints. Extensive qualitative and quantitative experiments have demonstrated that LLM\_Control achieves competitive synthesis quality compared to other state-of-the-art methods across various pre-trained T2I models. It is noteworthy that LLM\_Control allows the challenging input conditions on which most of the existing methods

cs.CV↗

arXiv2025

BadVideo: Stealthy Backdoor Attack against Text-to-Video Generation

Text-to-video (T2V) generative models have rapidly advanced and found widespread applications across fields like entertainment, education, and marketing. However, the adversarial vulnerabilities of these models remain rarely explored. We observe that in T2V generation tasks, the generated videos often contain substantial redundant information not explicitly specified in the text prompts, such as environmental elements, secondary objects, and additional details, providing opportunities for malicious attackers to embed hidden harmful content. Exploiting this inherent redundancy, we introduce BadVideo, the first backdoor attack framework tailored for T2V generation. Our attack focuses on designing target adversarial outputs through two key strategies: (1) Spatio-Temporal Composition, which combines different spatiotemporal features to encode malicious information; (2) Dynamic Element Transformation, which introduces transformations in redundant elements over time to convey malicious information. Based on these strategies, the attacker's malicious target seamlessly integrates with the user's textual instructions, providing high stealthiness. Moreover, by exploiting the temporal dimension of videos, our attack successfully evades traditional content moderation systems that primarily analyze spatial information within individual frames. Extensive experiments demonstrate that BadVideo achieves high attack success rates while preserving original semantics and maintaining excellent performance on clean inputs. Overall, our work reveals the adversarial vulnerability of T2V models, calling attention to potential risks and misuse. Our project page is at https://wrt2000.github.io/BadVideo2025/.

cs.CV↗

arXiv2025

Emergence of charm-strange dibaryons with negative parity via baryon-baryon interactions

Within the framework of the one-boson-exchange model, we systematically perform a coupled channel analysis of the $P-$wave interactions between a charm baryon and light baryon, the involved channels include $Ξ_cN$, $Λ_cΣ$, $Ξ_c^{\prime}N$, $Σ_cΛ$, $Ξ_c^*N$, $Σ_c^*Λ$, $Σ_cΣ$, and $Σ_c^*Σ$. Our results can predict several possible molecular candidates, such as a $Ξ_c^{\prime}N$ molecule with $I(J^P)=0(1^-)$, $Ξ_c^{*}N$ molecules with $0(0^-, 1^-, 2^-)$, $Σ_cΣ$ molecules with $0(1^-)$ and $1(1^-)$, and $Σ_c^*Σ$ molecules with $0(0^-, 1^-, 2^-)$, and $1(2^-)$. The coupled channel effects significantly influence the formation of the $Σ_cΣ$ molecule with $1(1^-)$. Furthermore, we analyze the phase shifts for these coupled channel systems. Our analysis not only confirms the existence of the predicted molecules but also identifies potential resonant dibaryons, including a $Σ_cΣ$ shape-type resonance with $1(1^-)$, a $Σ_c^*Σ$ shape-type resonance with $1(0^-)$, a $Λ_cΣ/Σ_cΣ$ coupled Feshbach-type resonance with $1(1^-)$, and $Λ_cΣ/Σ_c^*Σ$ coupled Feshbach-type resonances with $1(0^-, 2^-)$.

hep-ph↗