arXiv subjects

Chen Yang

Publications and source records attributed to Chen Yang.

At least 181 records · Page 10Linked to original sources

arXiv2025

Provable Zero-Shot Generalization in Offline Reinforcement Learning

In this work, we study offline reinforcement learning (RL) with zero-shot generalization property (ZSG), where the agent has access to an offline dataset including experiences from different environments, and the goal of the agent is to train a policy over the training environments which performs well on test environments without further interaction. Existing work showed that classical offline RL fails to generalize to new, unseen environments. We propose pessimistic empirical risk minimization (PERM) and pessimistic proximal policy optimization (PPPO), which leverage pessimistic policy evaluation to guide policy learning and enhance generalization. We show that both PERM and PPPO are capable of finding a near-optimal policy with ZSG. Our result serves as a first step in understanding the foundation of the generalization phenomenon in offline reinforcement learning.

cs.LG↗

arXiv2025

WcDT: World-centric Diffusion Transformer for Traffic Scene Generation

In this paper, we introduce a novel approach for autonomous driving trajectory generation by harnessing the complementary strengths of diffusion probabilistic models (a.k.a., diffusion models) and transformers. Our proposed framework, termed the "World-Centric Diffusion Transformer"(WcDT), optimizes the entire trajectory generation process, from feature extraction to model inference. To enhance the scene diversity and stochasticity, the historical trajectory data is first preprocessed into "Agent Move Statement" and encoded into latent space using Denoising Diffusion Probabilistic Models (DDPM) enhanced with Diffusion with Transformer (DiT) blocks. Then, the latent features, historical trajectories, HD map features, and historical traffic signal information are fused with various transformer-based encoders that are used to enhance the interaction of agents with other elements in the traffic scene. The encoded traffic scenes are then decoded by a trajectory decoder to generate multimodal future trajectories. Comprehensive experimental results show that the proposed approach exhibits superior performance in generating both realistic and diverse trajectories, showing its potential for integration into automatic driving simulation systems. Our code is available at \url{https://github.com/yangchen1997/WcDT}.

cs.CV↗

arXiv2025

Vortex Motion Induced Losses in Tantalum Resonators

Tantalum (Ta) based superconducting circuits have been demonstrated to enable record qubit coherence times and quality factors, motivating a careful study of the microscopic origin of the remaining losses that limit their performance. We have recently shown that the losses in Ta-based resonators are dominated by two-level systems (TLSs) at low microwave powers and millikelvin temperatures. We also observe that some devices exhibit loss that is exponentially activated at a lower temperature inconsistent with the superconducting critical temperature (Tc) of the constituent film. Specifically, dc resistivity measurements show a Tc of over 4 K, while microwave measurements of resonators fabricated from these films show losses that increase exponentially with temperature with an activation energy as low as 0.3 K. Here, we present a comparative study of the structural and thermodynamic properties of Ta-based resonators and identify vortex motion-induced loss as the source of thermally activated microwave loss. Through careful magnetoresistance and x-ray diffraction measurements, we observe that the increased loss occurs for films that are in the clean limit, where the superconducting coherence length is shorter than the mean free path. Vortex motion-induced losses are suppressed for films in the dirty limit, which show evidence of structural defects that can pin vortices. We verify this hypothesis by explicitly pinning vortices via patterning and find that we can suppress the loss by microfabrication.

cond-mat.supr-con↗

arXiv2025

Hysteretic responses of nanomechanical resonators based on crumpled few-layer graphene

Manipulating two-dimensional materials occasionally results in crumpled membranes. Their complicated morphologies feature an abundance of folds, creases and wrinkles that make each crumpled membrane unique. Here, we prepare four nanomechanical resonators based on crumpled membranes of few-layer graphene and measure their static response and the spectrum of their dynamic response. We tune both responses with a dc voltage applied between the membrane and an underlying gate electrode. Surprisingly, we find that all four resonators exhibit hysteretic responses as the gate voltage is increased and then decreased. Concomitant discontinuities in the static response and in the vibrational resonant frequencies indicate a sudden change in the shape and in the tensile strain of the membranes. We also find that the hystereses can be removed and regular responses can be restored by annealing the resonators. We hypothesize that the hysteretic nature of the responses may originate from an interplay between the rugged morphology of the membranes and adsorbates trapped within the confine of the folds.

cond-mat.mes-hall↗

arXiv2025

A few-layer graphene nanomechanical resonator driven by digitally modulated video signals

Nanomechanical resonators driven by multifrequency signals combine the physics of mesoscopic vibrations and the technologies of radio communication. Their simplest property stems from their resonant response: they behave as filters, responding only to driving signals whose frequency range is contained within that of the mechanical response. While the response is routinely probed with a single tone drive, a multifrequency drive offers the possibility of inducing richer vibrational dynamics. In this case, all the frequency components of the drive are simultaneously transduced into vibrations with different amplitudes and phases that superimpose and interfere. Here, we employ a few-layer graphene nanomechanical resonator as a filter for broadband, digitally modulated video signals. We transduce the modulated drive into modulated vibrations, which we demodulate into a nanomechanical video. Our work reveals distinctive features in vibrations driven by a coherent, multifrequency drive unseen in vibrations actuated by single tone drives or by a force noise.

cond-mat.mes-hall↗

arXiv2025

Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition

This paper introduces a new approach to address the issue of class imbalance in graph neural networks (GNNs) for learning on graph-structured data. Our approach integrates imbalanced node classification and Bias-Variance Decomposition, establishing a theoretical framework that closely relates data imbalance to model variance. We also leverage graph augmentation technique to estimate the variance, and design a regularization term to alleviate the impact of imbalance. Exhaustive tests are conducted on multiple benchmarks, including naturally imbalanced datasets and public-split class-imbalanced datasets, demonstrating that our approach outperforms state-of-the-art methods in various imbalanced scenarios. This work provides a novel theoretical perspective for addressing the problem of imbalanced node classification in GNNs.

cs.LG↗

arXiv2025

Soliton microcombs in X-cut LiNbO3 microresonators

Chip-scale integration of optical frequency combs, particularly soliton microcombs, enables miniaturized instrumentation for timekeeping, ranging, and spectroscopy. Although soliton microcombs have been demonstrated on various material platforms, realizing complete comb functionality on photonic chips requires the co-integration of high-speed modulators and efficient frequency doublers, features that are available in a monolithic form on X-cut thin-film lithium niobate (TFLN). However, the pronounced Raman nonlinearity associated with extraordinary light in this platform has so far precluded soliton microcomb generation. Here, we report the generation of transverse-electric-polarized soliton microcombs with a 25 GHz repetition rate in high-Q microresonators on X-cut TFLN chips. By precisely orienting the racetrack microresonator relative to the optical axis, we mitigate Raman nonlinearity and enable soliton formation under continuous-wave laser pumping. Moreover, the soliton microcomb spectra are extended to 350 nm with pulsed laser pumping. This work expands the capabilities of TFLN photonics and paves the way for the monolithic integration of fast-tunable, self-referenced microcombs.

physics.optics↗

arXiv2025

Spatial-offset pump-probe imaging of nonradiative dynamics at optical resolution

Nonradiative photothermal (PT) and photoacoustic (PA) processes have found widespread applications in imaging, stimulation, and therapy. Mapping the generation and propagation of PA and PT waves with resolution is important to elucidate how these fields interact with biological systems. To this end, we introduce spatial offset pump-probe imaging (SOPPI). By spatially offsetting the pump beam and the probe beam, SOPPI can image simultaneously PA and PT wave propagation with nanosecond temporal resolution, micrometer spatial resolution, 65 MHz detection bandwidth, and a sensitivity of 9.9 Pa noise equivalent pressure. We first map the PA and PT evolution from a fiber emitter, and how the wave interacting with a mouse skull and brain slices. SOPPI imaging of PA waves from a tapered fiber with water as an absorber shows a wavelength-dependent generation, evanescent wave generated PA, and back-propagated acoustic Mach Cone. At last, a SOPPI-PACT is developed to reconstruct the pigment distribution inside a zebrafish larva with high precision and signal-to-noise ratio.

physics.optics↗

arXiv2025

Learning Universal Multi-level Market Irrationality Factors to Improve Stock Return Forecasting

Recent years have witnessed the perfect encounter of deep learning and quantitative trading has achieved great success in stock investment. Numerous deep learning-based models have been developed for forecasting stock returns, leveraging the powerful representation capabilities of neural networks to identify patterns and factors influencing stock prices. These models can effectively capture general patterns in the market, such as stock price trends, volume-price relationships, and time variations. However, the impact of special irrationality factors -- such as market sentiment, speculative behavior, market manipulation, and psychological biases -- have not been fully considered in existing deep stock forecasting models due to their relative abstraction as well as lack of explicit labels and data description. To fill this gap, we propose UMI, a Universal multi-level Market Irrationality factor model to enhance stock return forecasting. The UMI model learns factors that can reflect irrational behaviors in market from both individual stock and overall market levels. For the stock-level, UMI construct an estimated rational price for each stock, which is cointegrated with the stock's actual price. The discrepancy between the actual and the rational prices serves as a factor to indicate stock-level irrational events. Additionally, we define market-level irrational behaviors as anomalous synchronous fluctuations of stocks within a market. Using two self-supervised representation learning tasks, i.e., sub-market comparative learning and market synchronism prediction, the UMI model incorporates market-level irrationalities into a market representation vector, which is then used as the market-level irrationality factor.

cs.LG↗

arXiv2025

Segment Any 3D Gaussians

This paper presents SAGA (Segment Any 3D GAussians), a highly efficient 3D promptable segmentation method based on 3D Gaussian Splatting (3D-GS). Given 2D visual prompts as input, SAGA can segment the corresponding 3D target represented by 3D Gaussians within 4 ms. This is achieved by attaching an scale-gated affinity feature to each 3D Gaussian to endow it a new property towards multi-granularity segmentation. Specifically, a scale-aware contrastive training strategy is proposed for the scale-gated affinity feature learning. It 1) distills the segmentation capability of the Segment Anything Model (SAM) from 2D masks into the affinity features and 2) employs a soft scale gate mechanism to deal with multi-granularity ambiguity in 3D segmentation through adjusting the magnitude of each feature channel according to a specified 3D physical scale. Evaluations demonstrate that SAGA achieves real-time multi-granularity segmentation with quality comparable to state-of-the-art methods. As one of the first methods addressing promptable segmentation in 3D-GS, the simplicity and effectiveness of SAGA pave the way for future advancements in this field. Our code will be released.

cs.CV↗

arXiv2025

Three-dimensional DtN-FEM scattering analysis of Lamb and SH guided waves by a symmetric cavity defect in an isotropic infinite plate

In this paper, a three-dimensional Dirichlet-to-Neumann (DtN) finite element method (FEM) is developed to analyze the scattering of Lamb and SH guided waves due to a symmetric cavity defect in an isotropic infinite plate. During the finite element analysis, it is necessary to determine the far-field DtN conditions at virtual boundaries where both displacements and tractions are unknown. In this study, firstly, the scattered waves at the virtual boundaries are represented by a superposition of guided waves with unknown scattered coefficients. Secondly, utilizing the mode orthogonality, the unknown tractions at virtual boundaries are expressed in terms of the unknown scattered displacements at virtual boundaries via scattered coefficients. Thirdly, this relationship at virtual boundaries can be finally assembled into the global DtN-FEM matrix to solve the problem. This method is simple and elegant, which has advantages on dimension reduction and needs no absorption medium or perfectly matched layer to suppress the reflected waves compared to traditional FEM. Furthermore, the reflection and transmission coefficients of each guided mode can be directly obtained without post-processing. This proposed 3D DtN-FEM will be compared with boundary element method (BEM) and finally validated for several benchmark problems.

math.NA↗

arXiv2024

Pairing correlation of the Kagome-lattice Hubbard model with the nearest-neighbor interaction

A recently discovered family of Kagome lattice materials, $\emph{A}\mathrm{V}_{3}\mathrm{Sb}_{5}$($\emph{A}$= $\mathrm{K,Rb,Cs}$), has attracted great interest, especially in the debate over its dominant superconducting pairing symmetry. To explore this issue, we study the superconducting pairing behavior within the Kagome-Hubbard model through the constrained path Monte Carlo method. It is found that doping around the Dirac point generates a dominant next-nearest-neighbour-$d$ pairing symmetry driven by on-site Coulomb interaction $U$. However, when considering the nearest-neighbor interaction $V$, it may induce nearest-neighbor-$p$ pairing to become the preferred pairing symmetry. Our results provide useful information to identify the dominant superconducting pairing symmetry in $\emph{A}\mathrm{V}_{3}\mathrm{Sb}_{5}$ family.

cond-mat.str-el↗

arXiv2024

Toward ultimate-efficiency frequency conversion in nonlinear optical microresonators

Integrated nonlinear photonics has emerged as a transformative platform, enabling nanoscale nonlinear optical processes with significant implications for sensing, computation, and metrology. Achieving efficient nonlinear frequency conversion in optical microresonators is paramount to fully unlocking this potential, yet the absolute conversion efficiency (ACE) of many processes, such as second-harmonic generation (SHG), remains fundamentally constrained by dissipative losses and intrinsic nonlinear effects in the device. In this work, we establish a unified theoretical framework for SHG in microresonators, identifying a decisive factor M that predicts the upper limit of ACE under the nonlinear critical coupling (NCC) condition. Using this framework, we fabricate integrated periodically poled lithium niobate microresonators and address the dispersive and dissipative suppression to approach the NCC condition. We achieve a record-high experimental ACE of 61.3% with milliwatt-level pump powers toward the ultimate efficiency, with the potential for even higher efficiency as the M factor increases. These results provide a versatile paradigm for high-efficiency nonlinear optical devices, offering new opportunities for advancements across classical and quantum photonic applications.

physics.optics↗

arXiv2024

LiftImage3D: Lifting Any Single Image to 3D Gaussians with Video Generation Priors

Single-image 3D reconstruction remains a fundamental challenge in computer vision due to inherent geometric ambiguities and limited viewpoint information. Recent advances in Latent Video Diffusion Models (LVDMs) offer promising 3D priors learned from large-scale video data. However, leveraging these priors effectively faces three key challenges: (1) degradation in quality across large camera motions, (2) difficulties in achieving precise camera control, and (3) geometric distortions inherent to the diffusion process that damage 3D consistency. We address these challenges by proposing LiftImage3D, a framework that effectively releases LVDMs' generative priors while ensuring 3D consistency. Specifically, we design an articulated trajectory strategy to generate video frames, which decomposes video sequences with large camera motions into ones with controllable small motions. Then we use robust neural matching models, i.e. MASt3R, to calibrate the camera poses of generated frames and produce corresponding point clouds. Finally, we propose a distortion-aware 3D Gaussian splatting representation, which can learn independent distortions between frames and output undistorted canonical Gaussians. Extensive experiments demonstrate that LiftImage3D achieves state-of-the-art performance on two challenging datasets, i.e. LLFF, DL3DV, and Tanks and Temples, and generalizes well to diverse in-the-wild images, from cartoon illustrations to complex real-world scenes.

cs.CV↗

arXiv2024

Realistic Surgical Simulation from Monocular Videos

This paper tackles the challenge of automatically performing realistic surgical simulations from readily available surgical videos. Recent efforts have successfully integrated physically grounded dynamics within 3D Gaussians to perform high-fidelity simulations in well-reconstructed simulation environments from static scenes. However, they struggle with the geometric inconsistency in reconstructing simulation environments and unrealistic physical deformations in simulations of soft tissues when it comes to dynamic and complex surgical processes. In this paper, we propose SurgiSim, a novel automatic simulation system to overcome these limitations. To build a surgical simulation environment, we maintain a canonical 3D scene composed of 3D Gaussians coupled with a deformation field to represent a dynamic surgical scene. This process involves a multi-stage optimization with trajectory and anisotropic regularization, enhancing the geometry consistency of the canonical scene, which serves as the simulation environment. To achieve realistic physical simulations in this environment, we implement a Visco-Elastic deformation model based on the Maxwell model, effectively restoring the complex deformations of tissues. Additionally, we infer the physical parameters of tissues by minimizing the discrepancies between the input video and simulation results guided by estimated tissue motion, ensuring realistic simulation outcomes. Experiments on various surgical scenarios and interactions demonstrate SurgiSim's ability to perform realistic simulation of soft tissues among surgical procedures, showing its enormous potential for enhancing surgical training, planning, and robotic surgery systems. The project page is at https://namaenashibot.github.io/SurgiSim/.

cs.CV↗

arXiv2024

Facial Features Matter: a Dynamic Watermark based Proactive Deepfake Detection Approach

Current passive deepfake face-swapping detection methods encounter significance bottlenecks in model generalization capabilities. Meanwhile, proactive detection methods often use fixed watermarks which lack a close relationship with the content they protect and are vulnerable to security risks. Dynamic watermarks based on facial features offer a promising solution, as these features provide unique identifiers. Therefore, this paper proposes a Facial Feature-based Proactive deepfake detection method (FaceProtect), which utilizes changes in facial characteristics during deepfake manipulation as a novel detection mechanism. We introduce a GAN-based One-way Dynamic Watermark Generating Mechanism (GODWGM) that uses 128-dimensional facial feature vectors as inputs. This method creates irreversible mappings from facial features to watermarks, enhancing protection against various reverse inference attacks. Additionally, we propose a Watermark-based Verification Strategy (WVS) that combines steganography with GODWGM, allowing simultaneous transmission of the benchmark watermark representing facial features within the image. Experimental results demonstrate that our proposed method maintains exceptional detection performance and exhibits high practicality on images altered by various deepfake techniques.

cs.CV↗

arXiv2024

3D-Consistent Human Avatars with Sparse Inputs via Gaussian Splatting and Contrastive Learning

Existing approaches for human avatar generation--both NeRF-based and 3D Gaussian Splatting (3DGS) based--struggle with maintaining 3D consistency and exhibit degraded detail reconstruction, particularly when training with sparse inputs. To address this challenge, we propose CHASE, a novel framework that achieves dense-input-level performance using only sparse inputs through two key innovations: cross-pose intrinsic 3D consistency supervision and 3D geometry contrastive learning. Building upon prior skeleton-driven approaches that combine rigid deformation with non-rigid cloth dynamics, we first establish baseline avatars with fundamental 3D consistency. To enhance 3D consistency under sparse inputs, we introduce a Dynamic Avatar Adjustment (DAA) module, which refines deformed Gaussians by leveraging similar poses from the training set. By minimizing the rendering discrepancy between adjusted Gaussians and reference poses, DAA provides additional supervision for avatar reconstruction. We further maintain global 3D consistency through a novel geometry-aware contrastive learning strategy. While designed for sparse inputs, CHASE surpasses state-of-the-art methods across both full and sparse settings on ZJU-MoCap and H36M datasets, demonstrating that our enhanced 3D consistency leads to superior rendering quality.

cs.CV↗

arXiv2024

Topology-aware Human Avatars with Semantically-guided Gaussian Splatting

Reconstructing photo-realistic and topology-aware animatable human avatars from monocular videos remains challenging in computer vision and graphics. Recently, methods using 3D Gaussians to represent the human body have emerged, offering faster optimization and real-time rendering. However, due to ignoring the crucial role of human body semantic information which represents the explicit topological and intrinsic structure within human body, they fail to achieve fine-detail reconstruction of human avatars. To address this issue, we propose SG-GS, which uses semantics-embedded 3D Gaussians, skeleton-driven rigid deformation, and non-rigid cloth dynamics deformation to create photo-realistic human avatars. We then design a Semantic Human-Body Annotator (SHA) which utilizes SMPL's semantic prior for efficient body part semantic labeling. The generated labels are used to guide the optimization of semantic attributes of Gaussian. To capture the explicit topological structure of the human body, we employ a 3D network that integrates both topological and geometric associations for human avatar deformation. We further implement three key strategies to enhance the semantic accuracy of 3D Gaussians and rendering quality: semantic projection with 2D regularization, semantic-guided density regularization and semantic-aware regularization with neighborhood consistency. Extensive experiments demonstrate that SG-GS achieves state-of-the-art geometry and appearance reconstruction performance.

cs.CV↗