arXiv subjects

Qiang Li

Publications and source records attributed to Qiang Li.

At least 199 records · Page 11Linked to original sources

arXiv2025

Industrial-Grade Sensor Simulation via Gaussian Splatting: A Modular Framework for Scalable Editing and Full-Stack Validation

Sensor simulation is pivotal for scalable validation of autonomous driving systems, yet existing Neural Radiance Fields (NeRF) based methods face applicability and efficiency challenges in industrial workflows. This paper introduces a Gaussian Splatting (GS) based system to address these challenges: We first break down sensor simulator components and analyze the possible advantages of GS over NeRF. Then in practice, we refactor three crucial components through GS, to leverage its explicit scene representation and real-time rendering: (1) choosing the 2D neural Gaussian representation for physics-compliant scene and sensor modeling, (2) proposing a scene editing pipeline to leverage Gaussian primitives library for data augmentation, and (3) coupling a controllable diffusion model for scene expansion and harmonization. We implement this framework on a proprietary autonomous driving dataset supporting cameras and LiDAR sensors. We demonstrate through ablation studies that our approach reduces frame-wise simulation latency, achieves better geometric and photometric consistency, and enables interpretable explicit scene editing and expansion. Furthermore, we showcase how integrating such a GS-based sensor simulator with traffic and dynamic simulators enables full-stack testing of end-to-end autonomy algorithms. Our work provides both algorithmic insights and practical validation, establishing GS as a cornerstone for industrial-grade sensor simulation.

cs.CV↗

arXiv2025

Temporal variability and obscuration effects in the X-ray emission of classical nova V339 Delphini (Nova Delphini 2013)

In this study, we present a detailed analysis of public archival soft X-ray data on the classical nova V339 Delphini (Nova Del 2013) during its outburst, obtained using the {\it Chandra} High-Resolution Camera Spectrometer (HRC-S) and Low Energy Transmission Grating (LETG), as well as {\it XMM-Newton} in 2013. The observations, spanning from day 85.2 to day 112.0 after the optical maximum, capture the nova during its luminous supersoft X-ray source (SSS) phase. The spectra reveal numerous absorption features with blue-shifted velocities ranging from $\sim$ 724 to $\sim$ 1474 km s$^{-1}$, with the majority of lines blue-shifted by approximately 1200 km s$^{-1}$. We confirm the presence of a short-period modulation of the X-ray flux with a period of approximately 54 seconds, as well as the drift of this period, which was detected on days 97.0 and 112.0 during the outburst with both {\it XMM-Newton} and {\it Chandra}. This period modulation is transient in nature, with significant variations in amplitude and pulse profile over timescales of a few thousand seconds, likely due to temporary obscuration events that affect the emission from the central hot source. The pulse profiles exhibit substantial deviations from a pure sinusoidal shape, which may be related to the period drift. Additionally, the modulation amplitude shows a possible anti-correlation with the count rates on day 97.0, likely also caused by temporary obscuration events influencing the central source's emission.

astro-ph.HE↗

arXiv2025

CoStoDet-DDPM: Collaborative Training of Stochastic and Deterministic Models Improves Surgical Workflow Anticipation and Recognition

Anticipating and recognizing surgical workflows are critical for intelligent surgical assistance systems. However, existing methods rely on deterministic decision-making, struggling to generalize across the large anatomical and procedural variations inherent in real-world surgeries.In this paper, we introduce an innovative framework that incorporates stochastic modeling through a denoising diffusion probabilistic model (DDPM) into conventional deterministic learning for surgical workflow analysis. At the heart of our approach is a collaborative co-training paradigm: the DDPM branch captures procedural uncertainties to enrich feature representations, while the task branch focuses on predicting surgical phases and instrument usage.Theoretically, we demonstrate that this mutual refinement mechanism benefits both branches: the DDPM reduces prediction errors in uncertain scenarios, and the task branch directs the DDPM toward clinically meaningful representations. Notably, the DDPM branch is discarded during inference, enabling real-time predictions without sacrificing accuracy.Experiments on the Cholec80 dataset show that for the anticipation task, our method achieves a 16% reduction in eMAE compared to state-of-the-art approaches, and for phase recognition, it improves the Jaccard score by 1.0%. Additionally, on the AutoLaparo dataset, our method achieves a 1.5% improvement in the Jaccard score for phase recognition, while also exhibiting robust generalization to patient-specific variations. Our code and weight are available at https://github.com/kk42yy/CoStoDet-DDPM.

cs.CV↗

arXiv2025

DreamerV3 for Traffic Signal Control: Hyperparameter Tuning and Performance

Reinforcement learning (RL) has evolved into a widely investigated technology for the development of smart TSC strategies. However, current RL algorithms necessitate excessive interaction with the environment to learn effective policies, making them impractical for large-scale tasks. The DreamerV3 algorithm presents compelling properties for policy learning. It summarizes general dynamics knowledge about the environment and enables the prediction of future outcomes of potential actions from past experience, reducing the interaction with the environment through imagination training. In this paper, a corridor TSC model is trained using the DreamerV3 algorithm to explore the benefits of world models for TSC strategy learning. In RL environment design, to manage congestion levels effectively, both the state and reward functions are defined based on queue length, and the action is designed to manage queue length efficiently. Using the SUMO simulation platform, the two hyperparameters (training ratio and model size) of the DreamerV3 algorithm were tuned and analyzed across different OD matrix scenarios. We discovered that choosing a smaller model size and initially attempting several medium training ratios can significantly reduce the time spent on hyperparameter tuning. Additionally, we found that the approach is generally applicable as it can solve two TSC task scenarios with the same hyperparameters. Regarding the claimed data-efficiency of the DreamerV3 algorithm, due to the significant fluctuation of the episode reward curve in the early stages of training, it can only be confirmed that larger model sizes exhibit modest data-efficiency, and no evidence was found that increasing the training ratio accelerates convergence.

cs.LG↗

arXiv2025

Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging

Vision foundation models (VFMs) are pre-trained on extensive image datasets to learn general representations for diverse types of data. These models can subsequently be fine-tuned for specific downstream tasks, significantly boosting performance across a broad range of applications. However, existing vision foundation models that claim to be applicable to various clinical tasks are mostly pre-trained on 3D computed tomography (CT), which benefits from the availability of extensive 3D CT databases. Significant differences between CT and magnetic resonance imaging (MRI) in imaging principles, signal characteristics, and data distribution may hinder their practical performance and versatility in MRI-specific applications. Here, we propose Triad, a vision foundation model for 3D MRI. Triad adopts a widely used autoencoder architecture to learn robust representations from 131,170 3D MRI volumes and uses organ-independent imaging descriptions to constrain the semantic distribution of the visual modality. The above pre-training dataset is called Triad-131K, which is currently the largest 3D MRI pre-training dataset. We evaluate Triad across three tasks, namely, organ/tumor segmentation, organ/cancer classification, and medical image registration, in two data modalities (within-domain and out-of-domain) settings using 25 downstream datasets. By initializing models with Triad's pre-trained weights, nnUNet-Triad improves segmentation performance by 2.51% compared to nnUNet-Scratch across 17 datasets. Swin-B-Triad achieves a 3.97% improvement over Swin-B-Scratch in classification tasks across five datasets. SwinUNETR-Triad improves by 4.00% compared to SwinUNETR-Scratch in registration tasks across two datasets. Our study demonstrates that pre-training can improve performance when the data modalities and organs of upstream and downstream tasks are consistent.

cs.CV↗

arXiv2025

Timing and spectral analysis of GK Persei during the 2010 dwarf nova outburst

GK Persei, an old nova and intermediate polar (IP), exhibited a dwarf nova (DN) outburst in 2010. This outburst was extensively observed by the Neil Gehrels Swift Observatory, beginning 1.95 days after the eruption and continuing until 13.9 days before the maximum of the outburst in the optical. In this paper, we present timing and spectral analyses, comparing the results with those of other outbursts. We confirm the spin modulation in the 2 $-$ 10 keV X-ray range with a period of $P_{\rm WD} = 351.325(9)$ s. Additionally, we detected spin modulation in the 0.3 $-$ 2 keV band during the second half of the observations, a feature not seen in the 2015 and 2018 outbursts. This finding suggests that the soft X-ray emission in GK Per may originate partly near the magnetic poles and partly from a wind or circumstellar material.

astro-ph.HE↗

arXiv2025

Charge density waves and pinning by lattice anisotropy in 214 cuprates

The detection of static charge density waves (CDWs) in La$_{2-x}$Sr$_x$CuO$_4$ (LSCO) with x $\sim$ 0.12 at relatively high temperatures has raised the question of what lattice feature pins the CDWs. Some recent structural studies have concluded that some form of monoclinic distortion, indicated by the appearance of certain weak Bragg peaks (type M peaks) at otherwise forbidden positions, are responsible for CDW pinning. As a test of this idea, we present neutron diffraction results for a single crystal of La$_{2-x}$Ba$_x$CuO$_4$ (LBCO) with x = 1/8, which is known to undergo two structural transitions on cooling, from high-temperature tetragonal (HTT) to low-temperature orthorhombic (LTO) near 240 K, involving a collective tilt pattern of the corner-sharing CuO$_6$ octahedra, and from LTO to low temperature tetragonal (LTT) near 56 K, involving a new tilt pattern and the appearance of intensity at peaks of type T. We observe both type M and type T peaks in the LTT phase, while the type M peaks (but not type T) are still present in the LTO phase. Given that CDW order is observed only in the LTT phase of LBCO, it is apparent that the in-plane Cu-O bond anisotropy associated with the octahedral tilt pattern is responsible for charge pinning. We point out that evidence for a similar, but weaker, bond anisotropy has been observed previously in LSCO and should be responsible for CDW pinning there. In the case of LBCO, the monoclinic distortion may help to explain previously-reported magneto-optical evidence for gyrotropic order.

cond-mat.supr-con↗

arXiv2025

Testing spooky action between free-traveling electron-positron pairs

Quantum entanglement is a cornerstone of quantum mechanics. While the entanglement of confined electron pairs has been established early on, the entanglement of free-traveling electron pairs, particularly at high energies, remains largely unexplored due to the substantial challenges involved in measuring the spins of free-traveling electrons. In this study, we investigate the entanglement and the Bell inequality violation of free-traveling electron-positron pairs generated in a fixed-target experiment. This experimental setup facilitates the creation of a controllable source of entangled electron-positron pairs, where entangled events are produced in specific phase spaces. Based on this source and the prior knowledge of the entangled state, we demonstrate the feasibility of measuring the polarization correlations of the entangled $e^+e^-$ pairs through their individual secondary scatterings off two separate additional targets.

hep-ph↗

arXiv2025

Clipped SGD Algorithms for Performative Prediction: Tight Bounds for Clipping Bias and Remedies

This paper studies the convergence of clipped stochastic gradient descent (SGD) algorithms with decision-dependent data distribution. Our setting is motivated by privacy preserving optimization algorithms that interact with performative data where the prediction models can influence future outcomes. This challenging setting involves the non-smooth clipping operator and non-gradient dynamics due to distribution shifts. We make two contributions in pursuit for a performative stable solution using clipped SGD algorithms. First, we characterize the clipping bias with projected clipped SGD (PCSGD) algorithm which is caused by the clipping operator that prevents PCSGD from reaching a stable solution. When the loss function is strongly convex, we quantify the lower and upper bounds for this clipping bias and demonstrate a bias amplification phenomenon with the sensitivity of data distribution. When the loss function is non-convex, we bound the magnitude of stationarity bias. Second, we propose remedies to mitigate the bias either by utilizing an optimal step size design for PCSGD, or to apply the recent DiceSGD algorithm [Zhang et al., 2024]. Our analysis is also extended to show that the latter algorithm is free from clipping bias in the performative setting. Numerical experiments verify our findings.

math.OC↗

arXiv2025

Unveiling Symmetry Instability induced by Topological Phase Transitions

The symmetry-topology interplay dictates how to define order parameters and classify material ordered phases. However, current understanding of this interplay has been predominately approached from a one-sided perspective, with topological states being classified within the constraints imposed by specific fixed symmetries. Here we complete this full circle by demonstrating spontaneous symmetry breaking that results from a periodic alteration of topological phases induced by light in a centrosymmetric Dirac material ZrTe$_5$. The distinguishing feature is the observation of robust correlation and striking anomalies in the fluence and temperature dependence of key transport parameters.First, both shift current $J_{\text{s}}$ and displacement current $J_{\text{d}}$, arising from interband transition and infrared phonon driving, respectively, along with charge carrier pumping, exhibit similar behaviors. Second, they all peak at similar low pump fluence, followed by a subsequent reduction as the fluence further increases. This behavior cannot be explained by conventional energetically allowed, direct excitations. Third, all the three observables exhibit anomalies when they approach the topological phase transition temperature. These results highlight the unique low-energy pumping behaviors in ZrTe$_5$, characterized by reversible fluence dependence and a 'hinge-like' interaction that connects various electronic and lattice observables, including phonons, charge carriers, and currents. Our findings, supported by model analysis, provide key insights into the fragility of crystalline (inversion) and time-reversal symmetries during the dynamics of topological phase transitions. This fragility drives spontaneous symmetry breaking, evidenced by the synchronized emergence of off-resonant infrared phonons and broken-symmetry photocurrents.

cond-mat.mtrl-sci↗

arXiv2025

Stochastic Optimization Schemes for Performative Prediction with Nonconvex Loss

This paper studies a risk minimization problem with decision dependent data distribution. The problem pertains to the performative prediction setting in which a trained model can affect the outcome estimated by the model. Such dependency creates a feedback loop that influences the stability of optimization algorithms such as stochastic gradient descent (SGD). We present the first study on performative prediction with smooth but possibly non-convex loss. We analyze a greedy deployment scheme with SGD (SGD-GD). Note that in the literature, SGD-GD is often studied with strongly convex loss. We first propose the definition of stationary performative stable (SPS) solutions through relaxing the popular performative stable condition. We then prove that SGD-GD converges to a biased SPS solution in expectation. We consider two conditions of sensitivity on the distribution shifts: (i) the sensitivity is characterized by Wasserstein-1 distance and the loss is Lipschitz w.r.t.~data samples, or (ii) the sensitivity is characterized by total variation (TV) divergence and the loss is bounded. In both conditions, the bias levels are proportional to the stochastic gradient's variance and sensitivity level. Our analysis is extended to a lazy deployment scheme where models are deployed once per several SGD updates, and we show that it converges to an SPS solution with reduced bias. Numerical experiments corroborate our theories.

math.OC↗

arXiv2025

An Immersive Virtual Reality Bimanual Telerobotic System With Haptic Feedback

In robotic bimanual teleoperation, multimodal sensory feedback plays a crucial role, providing operators with a more immersive operating experience, reducing cognitive burden, and improving operating efficiency. In this study, we develop an immersive bilateral isomorphic bimanual telerobotic system, which comprises dual arm and dual dexterous hands, with visual and haptic force feedback. To assess the performance of this system, we carried out a series of experiments and investigated the user's teleoperation experience. The results demonstrate that haptic force feedback enhances physical perception capabilities and complex task operating abilities. In addition, it compensates for visual perception deficiencies and reduces the operator's work burden. Consequently, our proposed system achieves more intuitive, realistic and immersive teleoperation, improves operating efficiency, and expands the complexity of tasks that robots can perform through teleoperation.

cs.HC↗

arXiv2024

Multi-Grained Preference Enhanced Transformer for Multi-Behavior Sequential Recommendation

Sequential recommendation (SR) aims to predict the next purchasing item according to users' dynamic preference learned from their historical user-item interactions. To improve the performance of recommendation, learning dynamic heterogeneous cross-type behavior dependencies is indispensable for recommender system. However, there still exists some challenges in Multi-Behavior Sequential Recommendation (MBSR). On the one hand, existing methods only model heterogeneous multi-behavior dependencies at behavior-level or item-level, and modelling interaction-level dependencies is still a challenge. On the other hand, the dynamic multi-grained behavior-aware preference is hard to capture in interaction sequences, which reflects interaction-aware sequential pattern. To tackle these challenges, we propose a Multi-Grained Preference enhanced Transformer framework (M-GPT). First, M-GPT constructs a interaction-level graph of historical cross-typed interactions in a sequence. Then graph convolution is performed to derive interaction-level multi-behavior dependency representation repeatedly, in which the complex correlation between historical cross-typed interactions at specific orders can be well learned. Secondly, a novel multi-scale transformer architecture equipped with multi-grained user preference extraction is proposed to encode the interaction-aware sequential pattern enhanced by capturing temporal behavior-aware multi-grained preference . Experiments on the real-world datasets indicate that our method M-GPT consistently outperforms various state-of-the-art recommendation methods.

cs.IR↗

arXiv2024

First-frame Supervised Video Polyp Segmentation via Propagative and Semantic Dual-teacher Network

Automatic video polyp segmentation plays a critical role in gastrointestinal cancer screening, but the cost of frameby-frame annotations is prohibitively high. While sparse-frame supervised methods have reduced this burden proportionately, the cost remains overwhelming for long-duration videos and large-scale datasets. In this paper, we, for the first time, reduce the annotation cost to just a single frame per polyp video, regardless of the video's length. To this end, we introduce a new task, First-Frame Supervised Video Polyp Segmentation (FSVPS), and propose a novel Propagative and Semantic Dual-Teacher Network (PSDNet). Specifically, PSDNet adopts a teacher-student framework but employs two distinct types of teachers: the propagative teacher and the semantic teacher. The propagative teacher is a universal object tracker that propagates the first-frame annotation to subsequent frames as pseudo labels. However, tracking errors may accumulate over time, gradually degrading the pseudo labels and misguiding the student model. To address this, we introduce the semantic teacher, an exponential moving average of the student model, which produces more stable and time-invariant pseudo labels. PSDNet merges the pseudo labels from both teachers using a carefully-designed back-propagation strategy. This strategy assesses the quality of the pseudo labels by tracking them backward to the first frame. High-quality pseudo labels are more likely to spatially align with the firstframe annotation after this backward tracking, ensuring more accurate teacher-to-student knowledge transfer and improved segmentation performance. Benchmarking on SUN-SEG, the largest VPS dataset, demonstrates the competitive performance of PSDNet compared to fully-supervised approaches, and its superiority over sparse-frame supervised state-of-the-arts with a minimum improvement of 4.5% in Dice score.

cs.CV↗

arXiv2024

MonoPCC: Photometric-invariant Cycle Constraint for Monocular Depth Estimation of Endoscopic Images

Photometric constraint is indispensable for self-supervised monocular depth estimation. It involves warping a source image onto a target view using estimated depth&pose, and then minimizing the difference between the warped and target images. However, the endoscopic built-in light causes significant brightness fluctuations, and thus makes the photometric constraint unreliable. Previous efforts only mitigate this relying on extra models to calibrate image brightness. In this paper, we propose MonoPCC to address the brightness inconsistency radically by reshaping the photometric constraint into a cycle form. Instead of only warping the source image, MonoPCC constructs a closed loop consisting of two opposite forward-backward warping paths: from target to source and then back to target. Thus, the target image finally receives an image cycle-warped from itself, which naturally makes the constraint invariant to brightness changes. Moreover, MonoPCC transplants the source image's phase-frequency into the intermediate warped image to avoid structure lost, and also stabilizes the training via an exponential moving average (EMA) strategy to avoid frequent changes in the forward warping. The comprehensive and extensive experimental results on four endoscopic datasets demonstrate that our proposed MonoPCC shows a great robustness to the brightness inconsistency, and exceeds other state-of-the-arts by reducing the absolute relative error by at least 7.27%, 9.38%, 9.90% and 3.17%, respectively.

cs.CV↗

arXiv2024

$η_{c2}(^1D_2)$ and its electromagnetic decays

The spin-singlet state $η_{c2}(^1D_2)$ has not been discovered in experiment and it is the only missing low-excited $D$-wave charmonium, so in this paper, we like to study its properties. Using the Bethe-Salpeter equation method, we obtain its mass as $3828.2$ MeV and its electromagnetic decay widths as $Γ[η_{c2}(1D)\rightarrow h_{c}(1P)γ]=284$ keV, $Γ[η_{c2}(1D)\rightarrow J/ψγ]=1.04$ keV, $Γ[η_{c2}(1D)\rightarrowψ(2S)γ]=3.08$ eV, and $Γ[η_{c2}(1D)\rightarrowψ(3770)γ]=0.143$ keV. {Considering the strong decay widths are estimated to be $Γ(η_{c2}(1D)\toη_c ππ)=144~\rm{keV}$ and $Γ(η_{c2}(1D)\to gg)= 46.1~\rm{keV}$, we obtain the total decay width of $475$ keV for $η_{c2}(1D)$, and point out that the full width is very sensitive to the mass $M_{η_{c2}}$.} In our calculation, the emphasis is put on the relativistic corrections. Our results show that $η_{c2}\rightarrow h_{c}γ$ is the nonrelativistic $E1$ transition dominated $E1+M2+E3$ decay, and $η_{c2}\rightarrow ψγ$ is the $M1+E2+M3+E4$ decay but the relativistic $E2$ transition contributes the most.

hep-ph↗

arXiv2024

Temporal Action Localization with Cross Layer Task Decoupling and Refinement

Temporal action localization (TAL) involves dual tasks to classify and localize actions within untrimmed videos. However, the two tasks often have conflicting requirements for features. Existing methods typically employ separate heads for classification and localization tasks but share the same input feature, leading to suboptimal performance. To address this issue, we propose a novel TAL method with Cross Layer Task Decoupling and Refinement (CLTDR). Based on the feature pyramid of video, CLTDR strategy integrates semantically strong features from higher pyramid layers and detailed boundary-aware boundary features from lower pyramid layers to effectively disentangle the action classification and localization tasks. Moreover, the multiple features from cross layers are also employed to refine and align the disentangled classification and regression results. At last, a lightweight Gated Multi-Granularity (GMG) module is proposed to comprehensively extract and aggregate video features at instant, local, and global temporal granularities. Benefiting from the CLTDR and GMG modules, our method achieves state-of-the-art performance on five challenging benchmarks: THUMOS14, MultiTHUMOS, EPIC-KITCHENS-100, ActivityNet-1.3, and HACS. Our code and pre-trained models are publicly available at: https://github.com/LiQiang0307/CLTDR-GMG.

cs.CV↗

arXiv2024

MonoBox: Tightness-free Box-supervised Polyp Segmentation using Monotonicity Constraint

We propose MonoBox, an innovative box-supervised segmentation method constrained by monotonicity to liberate its training from the user-unfriendly box-tightness assumption. In contrast to conventional box-supervised segmentation, where the box edges must precisely touch the target boundaries, MonoBox leverages imprecisely-annotated boxes to achieve robust pixel-wise segmentation. The 'linchpin' is that, within the noisy zones around box edges, MonoBox discards the traditional misguiding multiple-instance learning loss, and instead optimizes a carefully-designed objective, termed monotonicity constraint. Along directions transitioning from the foreground to background, this new constraint steers responses to adhere to a trend of monotonically decreasing values. Consequently, the originally unreliable learning within the noisy zones is transformed into a correct and effective monotonicity optimization. Moreover, an adaptive label correction is introduced, enabling MonoBox to enhance the tightness of box annotations using predicted masks from the previous epoch and dynamically shrink the noisy zones as training progresses. We verify MonoBox in the box-supervised segmentation task of polyps, where satisfying box-tightness is challenging due to the vague boundaries between the polyp and normal tissues. Experiments on both public synthetic and in-house real noisy datasets demonstrate that MonoBox exceeds other anti-noise state-of-the-arts by improving Dice by at least 5.5% and 3.3%, respectively. Codes are at https://github.com/Huster-Hq/MonoBox.

cs.CV↗