Two Comments on "Laplace comparison on Kahler Ricci flow and convergence (arXiv:2509.14820v1)"
We clarify the definition of polarized canonical radius in the study of Kahler Ricci flow.
math.DG↗
arXiv subjects
Bing Wang
Publications and source records attributed to Bing Wang.
Pixel-Perfect Depth with Semantics-Prompted Diffusion Transformers
This paper presents Pixel-Perfect Depth, a monocular depth estimation model based on pixel-space diffusion generation that produces high-quality, flying-pixel-free point clouds from estimated depth maps. Current generative depth estimation models fine-tune Stable Diffusion and achieve impressive performance. However, they require a VAE to compress depth maps into latent space, which inevitably introduces \textit{flying pixels} at edges and details. Our model addresses this challenge by directly performing diffusion generation in the pixel space, avoiding VAE-induced artifacts. To overcome the high complexity associated with pixel-space generation, we introduce two novel designs: 1) Semantics-Prompted Diffusion Transformers (SP-DiT), which incorporate semantic representations from vision foundation models into DiT to prompt the diffusion process, thereby preserving global semantic consistency while enhancing fine-grained visual details; and 2) Cascade DiT Design that progressively increases the number of tokens to further enhance efficiency and accuracy. Our model achieves the best performance among all published generative models across five benchmarks, and significantly outperforms all other models in edge-aware point cloud evaluation.
cs.CV↗
U-DECN: End-to-End Underwater Object Detection ConvNet with Improved DeNoising Training
Underwater object detection has higher requirements of running speed and deployment efficiency for the detector due to its specific environmental challenges. NMS of two- or one-stage object detectors and transformer architecture of query-based end-to-end object detectors are not conducive to deployment on underwater embedded devices with limited processing power. As for the detrimental effect of underwater color cast noise, recent underwater object detectors make network architecture or training complex, which also hinders their application and deployment on unmanned underwater vehicles. In this paper, we propose the Underwater DECO with improved deNoising training (U-DECN), the query-based end-to-end object detector (with ConvNet encoder-decoder architecture) for underwater color cast noise that addresses the above problems. We integrate advanced technologies from DETR variants into DECO and design optimization methods specifically for the ConvNet architecture, including Deformable Convolution in SIM and Separate Contrastive DeNoising Forward methods. To address the underwater color cast noise issue, we propose an Underwater Color DeNoising Query method to improve the generalization of the model for the biased object feature information by different color cast noise. Our U-DECN, with ResNet-50 backbone, achieves the best 64.0 AP on DUO and the best 58.1 AP on RUOD, and 21 FPS (5 times faster than Deformable DETR and DINO 4 FPS) on NVIDIA AGX Orin by TensorRT FP16, outperforming the other state-of-the-art query-based end-to-end object detectors. The code is available at https://github.com/LEFTeyex/U-DECN.
cs.CV↗
Physics-informed machine learning for combustion: A review
Physics-informed machine learning (PIML) represents an emerging paradigm that integrates various forms of physical knowledge into machine learning (ML) components, thereby enhancing the physical consistency of ML models compared to purely data-driven paradigms. The field of combustion, characterized by a rich foundation of physical laws and abundant data, is undergoing a transformation due to PIML. This paper aims to provide a comprehensive overview of PIML for combustion, systematically outlining fundamental principles, significant contributions, key advancements, and available resources. The application of PIML in combustion is categorized into three domains: combustion chemical kinetics, combustion reacting flows, and other combustion-related scenarios. Additionally, current challenges, potential solutions, and practical guidelines for researchers and engineers will be discussed. A primary focus of this review is to demonstrate how combustion laws can be integrated into ML, either through soft or hard constraints, via loss functions or representation models, and within coordinate-to-variable or field-to-field paradigms. This paper shows that PIML offers a unified framework linking physics, model, and data in combustion--integrating physical knowledge in model-to-data simulation and reconstruction tasks, as well as data-to-model modeling tasks--resulting in enhanced data, improved physical models, and more reliable ML models. PIML for combustion presents significant opportunities for both the combustion and ML communities, encouraging greater collaboration and cross-disciplinary engagement.
physics.chem-ph↗
ViSE: A Systematic Approach to Vision-Only Street-View Extrapolation
Realistic view extrapolation is critical for closed-loop simulation in autonomous driving, yet it remains a significant challenge for current Novel View Synthesis (NVS) methods, which often produce distorted and inconsistent images beyond the original trajectory. This report presents our winning solution which ctook first place in the RealADSim Workshop NVS track at ICCV 2025. To address the core challenges of street view extrapolation, we introduce a comprehensive four-stage pipeline. First, we employ a data-driven initialization strategy to generate a robust pseudo-LiDAR point cloud, avoiding local minima. Second, we inject strong geometric priors by modeling the road surface with a novel dimension-reduced SDF termed 2D-SDF. Third, we leverage a generative prior to create pseudo ground truth for extrapolated viewpoints, providing auxilary supervision. Finally, a data-driven adaptation network removes time-specific artifacts. On the RealADSim-NVS benchmark, our method achieves a final score of 0.441, ranking first among all participants.
cs.CV↗
Stacking-tunable multiferroic states in bilayer ScI2
Two-dimensional(2D) multiferroic materials hold significant promise for advancing the miniaturization and integration of nanodevices. In this study, we demonstrate that 2D bilayer ScI2, which exhibits ferromagnetic(FM) ordering within each layer, enables the tuning of interlayer magnetic coupling, ferroelectricity, and valley polarization through interlayer sliding and rotation. Our first-principles calculations show that the AA stacking configuration induces antiferromagnetic (AFM) interlayer coupling, while a 180 rotation of one layer (resulting in the antialigned AA stacking) leads to FM interlayer coupling. Moreover, the interlayer magnetic coupling can be switched between AFM and FM by translating the stacking configuration: FM in the aligned AB and BA configurations, and AFM in the antialigned AB and BA configurations. This switching behavior is driven by variations in superexchange interactions due to orbital hopping between layers. Notably, the aligned stacking exhibits ferroelectricity upon sliding, which is induced by interlayer orbital hybridization and the resulting asymmetric charge redistribution, with maximal ferroelectric behavior occurring at the AB and BA stacking configurations. Additionally, for the AB and BA stackings, spontaneous valley polarization emerges from the manipulation of the spin orientation toward the out-of-plane direction. This valley polarization arises due to inversion symmetry breaking, either through ferroelectricity (in the AB and BA stackings) or AFM interlayer coupling , in combination with spin-orbit coupling. These results highlight the intricate interplay between magnetism, ferroelectricity, and valley polarization in bilayer ScI2, with each property being tunable via stacking configuration.
cond-mat.mtrl-sci↗
HANDO: Hierarchical Autonomous Navigation and Dexterous Omni-loco-manipulation
Seamless loco-manipulation in unstructured environments requires robots to leverage autonomous exploration alongside whole-body control for physical interaction. In this work, we introduce HANDO (Hierarchical Autonomous Navigation and Dexterous Omni-loco-manipulation), a two-layer framework designed for legged robots equipped with manipulators to perform human-centered mobile manipulation tasks. The first layer utilizes a goal-conditioned autonomous exploration policy to guide the robot to semantically specified targets, such as a black office chair in a dynamic environment. The second layer employs a unified whole-body loco-manipulation policy to coordinate the arm and legs for precise interaction tasks-for example, handing a drink to a person seated on the chair. We have conducted an initial deployment of the navigation module, and will continue to pursue finer-grained deployment of whole-body loco-manipulation.
cs.RO↗
The $B^-\to J/ψη^\prime K^-$ reaction and the $Y(4710)$ and $K_0^*(1430)$ contributions
Motivated by the recent LHCb Collaboration analysis, we have investigated the process $B^-\to J/ψη^\prime K^-$ by considering the contributions from the resonances $Y(4710)$ and $K_0^*(1430)$. Our results are in good agreement with the measured invariant mass distributions and the Dalitz plot of LHCb, which supports the existence of the $K_0^*(1430)$ and the $Y(4710)$ in the process $B^-\to J/ψη^\prime K^-$. Since the decay mode of $Y(4710)\to J/ψη'$ is Okubo-Zweig-Iizuka (OZI) suppressed for the charmonium state, the measurements of the $\mathcal{B}(Y(4710)\to J/ψη')$ through the process $B^-\to J/ψη' K^-$ or other processes is helpful to understanding the nature of the $Y(4710)$. Thus, we advocate that Belle~II and LHCb Collaborations could perform the more precise analysis to confirm the evidence of the $Y(4710)$ state, which could be helpful to reduce the experimental uncertainties of its mass and width, and to explore its nature.
hep-ph↗
Microscopic origin of the magnetic easy-axis switching in Fe3GaTe2 under pressure
The two-dimensional layered ferromagnet Fe3GaTe2, composed of a Te-FeI-FeII/Ga-FeI-Te stacking sequence, hosts two inequivalent Fe sites and exhibits a high Curie temperature and strong out-of-plane magneticanisotropy, making it a promising platform for spintronic applications. Recent experiments have observed a pressure-induced switching of the magnetic easy axis from out-of-plane to in-plane near 10 GPa, though its microscopic origin remains unclear. Here, we employ first-principles calculations to investigate the pressure dependence of the magnetocrystalline anisotropy energy in Fe3GaTe2. Our results reveal a clear easy-axis switching at a critical pressure of approximately 10 GPa, accompanied by a sharp decrease in the magnetic moments arising from FeI and FeII atoms. As pressure increases, spin-up and spin-down bands broaden and shift oppositely due to band dispersion effects, leading to a reduction in net magnetization. Simultaneously, the SOC contribution from FeI, which initially favors an out-of-plane easy axis, diminishes and ultimately changes sign, thereby promoting in-plane anisotropy. The SOC contribution from the outer-layer Te atoms also decreases steadily with pressure, although it retains its original sign; this additional reduction further reinforces the in-plane magnetic easy axis. In contrast, FeII atoms continue to favor an out-of-plane orientation, but their contribution is insufficient to counterbalance the dominant in-plane preference at high pressure. These findings elucidate the origin of magnetic easy-axis switching in Fe3GaTe2 and provide insights for tuning magnetic anisotropy in layered materials for spintronic applications.
cond-mat.mtrl-sci↗
ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving
Recent studies have explored leveraging the world knowledge and cognitive capabilities of Vision-Language Models (VLMs) to address the long-tail problem in end-to-end autonomous driving. However, existing methods typically formulate trajectory planning as a language modeling task, where physical actions are output in the language space, potentially leading to issues such as format-violating outputs, infeasible actions, and slow inference speeds. In this paper, we propose ReCogDrive, a novel Reinforced Cognitive framework for end-to-end autonomous Driving, unifying driving understanding and planning by integrating an autoregressive model with a diffusion planner. First, to instill human driving cognition into the VLM, we introduce a hierarchical data pipeline that mimics the sequential cognitive process of human drivers through three stages: generation, refinement, and quality control. Building on this cognitive foundation, we then address the language-action mismatch by injecting the VLM's learned driving priors into a diffusion planner to efficiently generate continuous and stable trajectories. Furthermore, to enhance driving safety and reduce collisions, we introduce a Diffusion Group Relative Policy Optimization (DiffGRPO) stage, reinforcing the planner for enhanced safety and comfort. Extensive experiments on the NAVSIM and Bench2Drive benchmarks demonstrate that ReCogDrive achieves state-of-the-art performance. Additionally, qualitative results across diverse driving scenarios and DriveBench highlight the model's scene comprehension. All code, model weights, and datasets will be made publicly available to facilitate subsequent research.
cs.CV↗
Continual Learning to Generalize Forwarding Strategies for Diverse Mobile Wireless Networks
Deep reinforcement learning (DRL) has been successfully used to design forwarding strategies for multi-hop mobile wireless networks. While such strategies can be used directly for networks with varied connectivity and dynamic conditions, developing generalizable approaches that are effective on scenarios significantly different from the training environment remains largely unexplored. In this paper, we propose a framework to address the challenge of generalizability by (i) developing a generalizable base model considering diverse mobile network scenarios, and (ii) using the generalizable base model for new scenarios, and when needed, fine-tuning the base model using a small amount of data from the new scenarios. To support this framework, we first design new features to characterize network variation and feature quality, thereby improving the information used in DRL-based forwarding decisions. We then develop a continual learning (CL) approach able to train DRL models across diverse network scenarios without ``catastrophic forgetting.'' Using extensive evaluation, including real-world scenarios in two cities, we show that our approach is generalizable to unseen mobility scenarios. Compared to a state-of-the-art heuristic forwarding strategy, it leads to up to 78% reduction in delay, 24% improvement in delivery rate, and comparable or slightly higher number of forwards.
cs.NI↗
Quantum Public Key Encryption for NISQ Devices
Quantum public-key encryption (PKE), where public-keys and/or ciphertexts can be quantum states, is an important primitive in quantum cryptography. Unlike classical PKE (e.g., RSA or ECC), quantum PKE can leverage quantum-secure cryptographic assumptions or the principles of quantum mechanics for security. It has great potential in providing for secure cryptographic systems under potentially weaker assumptions than is possible classically. In addition, it is of both practical and theoretical interest, opening the door to novel cryptographic systems not possible with classical information alone. While multiple quantum PKE schemes have been proposed, they require a large number of qubits acting coherently, and are not practical on current noisy quantum devices. In this paper, we design a practical quantum PKE scheme, taking into account the constraints of current NISQ devices. Specifically, we design a PKE scheme with quantum-classical public keys and classical ciphertexts, that is noise-resilient and only requires a small number of qubits acting coherently. In addition, our design provides tradeoffs in terms of efficiency and the number of qubits that are required.
quant-ph↗
Accurate and Complete Surface Reconstruction from 3D Gaussians via Direct SDF Learning
3D Gaussian Splatting (3DGS) has recently emerged as a powerful paradigm for photorealistic view synthesis, representing scenes with spatially distributed Gaussian primitives. While highly effective for rendering, achieving accurate and complete surface reconstruction remains challenging due to the unstructured nature of the representation and the absence of explicit geometric supervision. In this work, we propose DiGS, a unified framework that embeds Signed Distance Field (SDF) learning directly into the 3DGS pipeline, thereby enforcing strong and interpretable surface priors. By associating each Gaussian with a learnable SDF value, DiGS explicitly aligns primitives with underlying geometry and improves cross-view consistency. To further ensure dense and coherent coverage, we design a geometry-guided grid growth strategy that adaptively distributes Gaussians along geometry-consistent regions under a multi-scale hierarchy. Extensive experiments on standard benchmarks, including DTU, Mip-NeRF 360, and Tanks& Temples, demonstrate that DiGS consistently improves reconstruction accuracy and completeness while retaining high rendering fidelity.
cs.CV↗
CETUS: Causal Event-Driven Temporal Modeling With Unified Variable-Rate Scheduling
Event cameras capture asynchronous pixel-level brightness changes with microsecond temporal resolution, offering unique advantages for high-speed vision tasks. Existing methods often convert event streams into intermediate representations such as frames, voxel grids, or point clouds, which inevitably require predefined time windows and thus introduce window latency. Meanwhile, pointwise detection methods face computational challenges that prevent real-time efficiency due to their high computational cost. To overcome these limitations, we propose the Variable-Rate Spatial Event Mamba, a novel architecture that directly processes raw event streams without intermediate representations. Our method introduces a lightweight causal spatial neighborhood encoder to efficiently capture local geometric relations, followed by Mamba-based state space models for scalable temporal modeling with linear complexity. During inference, a controller adaptively adjusts the processing speed according to the event rate, achieving an optimal balance between window latency and inference latency.
cs.CV↗
Contrasting magnetic anisotropy in CrCl3 and CrBr3: A first-principles study
We present a first-principles study of the contrasting easy magnetization axes(EMAs) in the layered chromium trihalides CrCl3 and CrBr3, which exhibit in-plane and out-of-plane EMAs, respectively. Using density-functional theory calculations, we show that the EMA is determined by the interplay between spin-orbit coupling-induced magnetocrystalline anisotropy energy (SOC-MAE) and shape magnetic anisotropy energy(shape-MAE) arising from dipole-dipole interactions. While the Cr d orbitals contribute similarly to the SOC-MAE in both compounds, the key difference stems from the halogen p orbitals. In CrCl3, the localized Cl 3p orbitals favor spin-flip SOC interactions, particularly between the (px, py) and (py, pz) channels. These channels contribute with opposite signs-negative and positive, respectively-leading to partial cancellation and a small net SOC-MAE. As a result, the shape-MAE exceeds the SOC-MAE in magnitude, favoring an in-plane EMA. In contrast, CrBr3 features more delocalized Br 4p orbitals, enhanced p-d hybridization, and stronger SOC. This leads to stronger spin-conserving SOC interactions, with dominant contributions from both the (px, py) and (py, pz) channels. In this case, the positive contribution from the (px, py) channel outweighs the smaller negative contribution from the (py, pz) channel, resulting in a sizable net SOC-MAE. The SOC-MAE thus surpasses the shape-MAE and stabilizes an out-of-plane EMA. These findings demonstrate that the contrasting magnetic anisotropies in CrCl3 and CrBr3 originate from differences in the spatial distribution, SOC strength, and hybridization of the halogen p orbitals, highlighting the critical role of orbital anisotropy and spin selection rules in governing magnetic behavior in layered semiconductors.
cond-mat.mtrl-sci↗
FAGC:Feature Augmentation on Geodesic Curve in the Pre-Shape Space
Due to the constraints on model performance imposed by the size of the training data, data augmentation has become an essential technique in deep learning. However, most existing data augmentation methods are affected by information loss and perform poorly in small-sample scenarios, which limits their application. To overcome the limitation, we propose a Feature Augmentation method on Geodesic Curve in the pre-shape space, called the FAGC. First, a pre-trained neural network model is employed to extract features from the input images. Then, the image features as a vector is projected into the pre-shape space by removing its position and scale information. In the pre-shape space, an optimal Geodesic curve is constructed to fit the feature vectors. Finally, new feature vectors are generated for model learning by interpolating along the constructed Geodesic curve. We conducted extensive experiments to demonstrate the effectiveness and versatility of the FAGC. The results demonstrate that applying the FAGC to deep learning or machine learning methods can significantly improve their performance in small-sample tasks.
cs.CV↗
Self-consistent clustering analysis for homogenisation of heterogeneous plates
This work introduces a reduced-order model for plate structures with periodic micro-structures by coupling self-consistent clustering analysis (SCA) with the Lippmann-Schwinger equation, enabling rapid multiscale homogenisation of heterogeneous plates. A plate-specific SCA scheme is derived for the first time and features two key elements: (i) an offline-online strategy that combines Green's functions with k-means data compression, and (ii) an online self-consistent update that exploits the weak sensitivity of the reference medium. The framework handles both linear and nonlinear problems in classical plate theory and first-order shear deformation theory, and its performance is verified on linear isotropic perforated plates and woven composites, as well as on non-linear elasto-plastic perforated plates and woven composites with damage. Across all cases the proposed model matches the accuracy of FFT-based direct numerical simulation while reducing computational cost by over an order of magnitude.
physics.comp-ph↗
ExtraGS: Geometric-Aware Trajectory Extrapolation with Uncertainty-Guided Generative Priors
Synthesizing extrapolated views from recorded driving logs is critical for simulating driving scenes for autonomous driving vehicles, yet it remains a challenging task. Recent methods leverage generative priors as pseudo ground truth, but often lead to poor geometric consistency and over-smoothed renderings. To address these limitations, we propose ExtraGS, a holistic framework for trajectory extrapolation that integrates both geometric and generative priors. At the core of ExtraGS is a novel Road Surface Gaussian(RSG) representation based on a hybrid Gaussian-Signed Distance Function (SDF) design, and Far Field Gaussians (FFG) that use learnable scaling factors to efficiently handle distant objects. Furthermore, we develop a self-supervised uncertainty estimation framework based on spherical harmonics that enables selective integration of generative priors only where extrapolation artifacts occur. Extensive experiments on multiple datasets, diverse multi-camera setups, and various generative priors demonstrate that ExtraGS significantly enhances the realism and geometric consistency of extrapolated views, while preserving high fidelity along the original trajectory.
cs.CV↗