arXiv subjects

Rong Li

Publications and source records attributed to Rong Li.

At least 37 records · Page 2Linked to original sources

arXiv2025

Phenomenological Scaling Crossovers in Non-Equilibrium Critical Dynamics

Dynamical universality plays a fundamental role in understanding the scaling properties of critical dynamics, including absorbing phase transitions and physical aging. Although individual universality classes have been extensively studied, the theoretical framework for scaling crossovers between distinct dynamical regimes remains underdeveloped. In this work, we propose a phenomenological approach to describe dynamic scaling crossovers in reaction-diffusion systems, utilizing a Bernoulli differential equation with time-dependent reaction coefficients. Under a new scaling hypothesis for spatial fluctuations, we analytically derive five universal power-law crossover functions. These solutions accurately describe experimental observations of absorbing phase transitions in turbulent liquid crystals and reaction-diffusion crossovers in exciton-exciton recombination, revealing possible mechanisms associated with many-body correlations and two-component dynamics. These findings suggest the existence of universal scaling laws governing the transition between different dynamical states, motivating further studies using field-theoretic approaches and renormalization group analyses.

cond-mat.stat-mech↗

arXiv2025

ConSense: Continually Sensing Human Activity with WiFi via Growing and Picking

WiFi-based human activity recognition (HAR) holds significant application potential across various fields. To handle dynamic environments where new activities are continuously introduced, WiFi-based HAR systems must adapt by learning new concepts without forgetting previously learned ones. Furthermore, retaining knowledge from old activities by storing historical exemplar is impractical for WiFi-based HAR due to privacy concerns and limited storage capacity of edge devices. In this work, we propose ConSense, a lightweight and fast-adapted exemplar-free class incremental learning framework for WiFi-based HAR. The framework leverages the transformer architecture and involves dynamic model expansion and selective retraining to preserve previously learned knowledge while integrating new information. Specifically, during incremental sessions, small-scale trainable parameters that are trained specifically on the data of each task are added in the multi-head self-attention layer. In addition, a selective retraining strategy that dynamically adjusts the weights in multilayer perceptron based on the performance stability of neurons across tasks is used. Rather than training the entire model, the proposed strategies of dynamic model expansion and selective retraining reduce the overall computational load while balancing stability on previous tasks and plasticity on new tasks. Evaluation results on three public WiFi datasets demonstrate that ConSense not only outperforms several competitive approaches but also requires fewer parameters, highlighting its practical utility in class-incremental scenarios for HAR.

eess.SP↗

arXiv2024

HR Human: Modeling Human Avatars with Triangular Mesh and High-Resolution Textures from Videos

Recently, implicit neural representation has been widely used to generate animatable human avatars. However, the materials and geometry of those representations are coupled in the neural network and hard to edit, which hinders their application in traditional graphics engines. We present a framework for acquiring human avatars that are attached with high-resolution physically-based material textures and triangular mesh from monocular video. Our method introduces a novel information fusion strategy to combine the information from the monocular video and synthesize virtual multi-view images to tackle the sparsity of the input view. We reconstruct humans as deformable neural implicit surfaces and extract triangle mesh in a well-behaved pose as the initial mesh of the next stage. In addition, we introduce an approach to correct the bias for the boundary and size of the coarse mesh extracted. Finally, we adapt prior knowledge of the latent diffusion model at super-resolution in multi-view to distill the decomposed texture. Experiments show that our approach outperforms previous representations in terms of high fidelity, and this explicit result supports deployment on common renderers.

cs.CV↗

arXiv2024

TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation

LiDAR semantic segmentation plays a crucial role in enabling autonomous driving and robots to understand their surroundings accurately and robustly. A multitude of methods exist within this domain, including point-based, range-image-based, polar-coordinate-based, and hybrid strategies. Among these, range-image-based techniques have gained widespread adoption in practical applications due to their efficiency. However, they face a significant challenge known as the ``many-to-one'' problem caused by the range image's limited horizontal and vertical angular resolution. As a result, around 20% of the 3D points can be occluded. In this paper, we present TFNet, a range-image-based LiDAR semantic segmentation method that utilizes temporal information to address this issue. Specifically, we incorporate a temporal fusion layer to extract useful information from previous scans and integrate it with the current scan. We then design a max-voting-based post-processing technique to correct false predictions, particularly those caused by the ``many-to-one'' issue. We evaluated the approach on two benchmarks and demonstrated that the plug-in post-processing technique is generic and can be applied to various networks.

cs.CV↗

arXiv2024

Next-to-leading-order relativistic and QCD corrections to prompt $\boldsymbol{J/ψ}$ pair photoproduction at future $\boldsymbol{e^+e^-}$ colliders

Within the framework of nonrelativistic-QCD factorization, we calculate both the next-to-leading-order relativistic and QCD corrections to prompt $J/ψ$ pair production, with feeddown from $ψ(2S)$ mesons, via photon-photon collisions at future $e^+e^-$ colliders including the Future Circular Lepton Collider (FCC-ee), the Circular Electron Positron Collider (CEPC), and the Compact Linear Collider (CLIC). We present total cross sections and distributions in single $J/ψ$ transverse momentum and rapidity, and in $J/ψ$ pair invariant mass. The relativistic and QCD corrections both turn out to be large and negative. Yet, the production rates are large enough for useful experimental studies.

hep-ph↗

arXiv2024

Holistic Inverse Rendering of Complex Facade via Aerial 3D Scanning

In this work, we use multi-view aerial images to reconstruct the geometry, lighting, and material of facades using neural signed distance fields (SDFs). Without the requirement of complex equipment, our method only takes simple RGB images captured by a drone as inputs to enable physically based and photorealistic novel-view rendering, relighting, and editing. However, a real-world facade usually has complex appearances ranging from diffuse rocks with subtle details to large-area glass windows with specular reflections, making it hard to attend to everything. As a result, previous methods can preserve the geometry details but fail to reconstruct smooth glass windows or verse vise. In order to address this challenge, we introduce three spatial- and semantic-adaptive optimization strategies, including a semantic regularization approach based on zero-shot segmentation techniques to improve material consistency, a frequency-aware geometry regularization to balance surface smoothness and details in different surfaces, and a visibility probe-based scheme to enable efficient modeling of the local lighting in large-scale outdoor environments. In addition, we capture a real-world facade aerial 3D scanning image set and corresponding point clouds for training and benchmarking. The experiment demonstrates the superior quality of our method on facade holistic inverse rendering, novel view synthesis, and scene editing compared to state-of-the-art baselines.

cs.CV↗

arXiv2024

An Examination of the Compositionality of Large Generative Vision-Language Models

With the success of Large Language Models (LLMs), many Generative Vision-Language Models (GVLMs) have been constructed via multimodal instruction tuning. However, the performance of GVLMs in multimodal compositional reasoning remains under-explored. In this paper, we examine both the evaluation metrics (VisualGPTScore, etc.) and current benchmarks for evaluating the compositionality of GVLMs. We identify the syntactical bias in current benchmarks, which is exploited by the linguistic capability of GVLMs. The bias renders VisualGPTScore an insufficient metric for assessing GVLMs. To combat this, we first introduce a SyntaxBias Score, leveraging LLMs to quantify such bias for mitigation. A challenging new task is subsequently added to evaluate the robustness of GVLMs against inherent inclination toward syntactical correctness. Using the bias-mitigated datasets and the new task, we propose a novel benchmark, namely SyntActically DE-biased benchmark (SADE). Our study provides an unbiased benchmark for the compositionality of GVLMs, facilitating future research in this direction (Code and dataset are available at https://github.com/TeleeMa/SADE).

cs.CL↗

arXiv2024

2L3: Lifting Imperfect Generated 2D Images into Accurate 3D

Reconstructing 3D objects from a single image is an intriguing but challenging problem. One promising solution is to utilize multi-view (MV) 3D reconstruction to fuse generated MV images into consistent 3D objects. However, the generated images usually suffer from inconsistent lighting, misaligned geometry, and sparse views, leading to poor reconstruction quality. To cope with these problems, we present a novel 3D reconstruction framework that leverages intrinsic decomposition guidance, transient-mono prior guidance, and view augmentation to cope with the three issues, respectively. Specifically, we first leverage to decouple the shading information from the generated images to reduce the impact of inconsistent lighting; then, we introduce mono prior with view-dependent transient encoding to enhance the reconstructed normal; and finally, we design a view augmentation fusion strategy that minimizes pixel-level loss in generated sparse views and semantic loss in augmented random views, resulting in view-consistent geometry and detailed textures. Our approach, therefore, enables the integration of a pre-trained MV image generator and a neural network-based volumetric signed distance function (SDF) representation for a single image to 3D object reconstruction. We evaluate our framework on various datasets and demonstrate its superior performance in both quantitative and qualitative assessments, signifying a significant advancement in 3D object reconstruction. Compared with the latest state-of-the-art method Syncdreamer~\cite{liu2023syncdreamer}, we reduce the Chamfer Distance error by about 36\% and improve PSNR by about 30\% .

cs.CV↗

arXiv2023

Assessing Estimation Uncertainty under Model Misspecification

Model misspecification is ubiquitous in data analysis because the data-generating process is often complex and mathematically intractable. Therefore, assessing estimation uncertainty and conducting statistical inference under a possibly misspecified working model is unavoidable. In such a case, classical methods such as bootstrap and asymptotic theory-based inference frequently fail since they rely heavily on the model assumptions. In this article, we provide a new bootstrap procedure, termed local residual bootstrap, to assess estimation uncertainty under model misspecification for generalized linear models. By resampling the residuals from the neighboring observations, we can approximate the sampling distribution of the statistic of interest accurately. Instead of relying on the score equations, the proposed method directly recreates the response variables so that we can easily conduct standard error estimation, confidence interval construction, hypothesis testing, and model evaluation and selection. It performs similarly to classical bootstrap when the model is correctly specified and provides a more accurate assessment of uncertainty under model misspecification, offering data analysts an easy way to guard against the impact of misspecified models. We establish desirable theoretical properties, such as the bootstrap validity, for the proposed method using the surrogate residuals. Numerical results and real data analysis further demonstrate the superiority of the proposed method.

stat.ME↗

arXiv2023

Beyond Newtonian Mechanics: An Exploration of Active Force in Complex Swing Motions

Life mechanics, an emerging field, focuses on the self-organizing forces and motions within living systems. This study introduces the novel concept of active force, generated by mind-body interactions, as an essential element in understanding self-organizing movements. We propose a new set of control equations to model the self-pumping swing motion by incorporating the active force into Newton's second law. With this new mechanical framework, we analyzed the child's complex swing motions with rapid standing and squatting movements. The results reveal a pulse-like pattern of the active force along the swing length, driving changes in the radial speed and swing length. This active force counteracts the resistance and propels the swing, which is not attainable by the stone. Thus, active force underlies self-organization in living systems, warranting further exploration of its mechanics in life movements.

physics.bio-ph↗

arXiv2023

Semantic RGB-D Image Synthesis

Collecting diverse sets of training images for RGB-D semantic image segmentation is not always possible. In particular, when robots need to operate in privacy-sensitive areas like homes, the collection is often limited to a small set of locations. As a consequence, the annotated images lack diversity in appearance and approaches for RGB-D semantic image segmentation tend to overfit the training data. In this paper, we thus introduce semantic RGB-D image synthesis to address this problem. It requires synthesising a realistic-looking RGB-D image for a given semantic label map. Current approaches, however, are uni-modal and cannot cope with multi-modal data. Indeed, we show that extending uni-modal approaches to multi-modal data does not perform well. In this paper, we therefore propose a generator for multi-modal data that separates modal-independent information of the semantic layout from the modal-dependent information that is needed to generate an RGB and a depth image, respectively. Furthermore, we propose a discriminator that ensures semantic consistency between the label maps and the generated images and perceptual similarity between the real and generated images. Our comprehensive experiments demonstrate that the proposed method outperforms previous uni-modal methods by a large margin and that the accuracy of an approach for RGB-D semantic segmentation can be significantly improved by mixing real and generated images during training.

cs.CV↗

arXiv2023

Towards Content-based Pixel Retrieval in Revisited Oxford and Paris

This paper introduces the first two pixel retrieval benchmarks. Pixel retrieval is segmented instance retrieval. Like semantic segmentation extends classification to the pixel level, pixel retrieval is an extension of image retrieval and offers information about which pixels are related to the query object. In addition to retrieving images for the given query, it helps users quickly identify the query object in true positive images and exclude false positive images by denoting the correlated pixels. Our user study results show pixel-level annotation can significantly improve the user experience. Compared with semantic and instance segmentation, pixel retrieval requires a fine-grained recognition capability for variable-granularity targets. To this end, we propose pixel retrieval benchmarks named PROxford and PRParis, which are based on the widely used image retrieval datasets, ROxford and RParis. Three professional annotators label 5,942 images with two rounds of double-checking and refinement. Furthermore, we conduct extensive experiments and analysis on the SOTA methods in image search, image matching, detection, segmentation, and dense matching using our pixel retrieval benchmarks. Results show that the pixel retrieval task is challenging to these approaches and distinctive from existing problems, suggesting that further research can advance the content-based pixel-retrieval and thus user search experience. The datasets can be downloaded from \href{https://github.com/anguoyuan/Pixel_retrieval-Segmented_instance_retrieval}{this link}.

cs.CV↗

arXiv2023

Regulation-incorporated Gene Expression Network-based Heterogeneity Analysis

Gene expression-based heterogeneity analysis has been extensively conducted. In recent studies, it has been shown that network-based analysis, which takes a system perspective and accommodates the interconnections among genes, can be more informative than that based on simpler statistics. Gene expressions are highly regulated. Incorporating regulations in analysis can better delineate the "sources" of gene expression effects. Although conditional network analysis can somewhat serve this purpose, it does render enough attention to the regulation relationships. In this article, significantly advancing from the existing heterogeneity analyses based only on gene expression networks, conditional gene expression network analyses, and regression-based heterogeneity analyses, we propose heterogeneity analysis based on gene expression networks (after accounting for or "removing" regulation effects) as well as regulations of gene expressions. A high-dimensional penalized fusion approach is proposed, which can determine the number of sample groups and parameter values in a single step. An effective computational algorithm is proposed. It is rigorously proved that the proposed approach enjoys the estimation, selection, and grouping consistency properties. Extensive simulations demonstrate its practical superiority over closely related alternatives. In the analysis of two breast cancer datasets, the proposed approach identifies heterogeneity and gene network structures different from the alternatives and with sound biological implications.

stat.ME↗

arXiv2023

Triangle singularity in the $J/ψ\to γ\bar{p} Δ$ decay

In this work, we study the role of triangle singularity in the $J/ψ\to γ\bar{p} Δ$ decay. We find that through a triangle mechanism, involving a triangle loop composed by $ω$, $π$ and $p$, this decay may develop a triangle singularity and produce a visible peak in the invariant mass $M_{γΔ}$ around 1.73 GeV with a width of 0.02 GeV. Such a triangle mechanism may also cause significant spin effects on the final $Δ$, which can be detected by measuring its spin density matrix elements. Our calculations show that the branching ratios due to the triangle mechanism is Br($J/ψ\to γ\bar pΔ,Δ\to πp$)=$1.058\times 10^{-6}$. Hopefully, this reaction can be investigated at BESIII and future experiments, e.g. Super Tau-Charm Facility, and the narrow width of the induced structure, the moving TS position and the distinct features of the spin density matrix elements of the $Δ$ may serve as signals for the triangle singularity mechanism.

hep-ph↗

arXiv2023

Numerical investigation of mixed-phase turbulence induced by a plunging jet

In nature and engineering applications, plunging jet acts as a key process causing interface breaking and generating mixed-phase turbulence. In this paper, high-resolution numerical simulation of water jet plunging into a quiescent pool was perform to investigate the statistical property of the mixed-phase turbulence, with special focus on the closure problem of the Reynolds-averaged equation. We conducted phase-resolved simulations, with the air--water interface captured using a coupled level-set and volume-of-fluid method. Nine cases were performed to analyse the effects of the Froude number and Reynolds number. The simulation results showed that the turbulent statistics are insensitive to the Reynolds number under investigation, while the Froude number influences the flow properties significantly. To investigate the closure problem of the mean momentum equation, the turbulent kinetic energy (TKE) and turbulent mass flux (TMF) and their transport equations were further investigated. It was discovered that the balance relationship of the TKE budget terms remained similar to many single-phase turbulent flows. The TMF is an additional unclosed term in the mixed-phase turbulence. Our simulation results showed that the production term of its transport equation was highly correlated to the TKE. Based on this finding, a closure model of its production term was further proposed.

physics.flu-dyn↗

arXiv2023

Improving the Gilbert-Varshamov Bound by Graph Spectral Method

We improve Gilbert-Varshamov bound by graph spectral method. Gilbert graph $G_{q,n,d}$ is a graph with all vectors in $\mathbb{F}_q^n$ as vertices where two vertices are adjacent if their Hamming distance is less than $d$. In this paper, we calculate the eigenvalues and eigenvectors of $G_{q,n,d}$ using the properties of Cayley graph. The improved bound is associated with the minimum eigenvalue of the graph. Finally we give an algorithm to calculate the bound and linear codes which satisfy the bound.

cs.IT↗

arXiv2022

WAIR-D: Wireless AI Research Dataset

It is a common sense that datasets with high-quality data samples play an important role in artificial intelligence (AI), machine learning (ML) and related studies. However, although AI/ML has been introduced in wireless researches long time ago, few datasets are commonly used in the research community. Without a common dataset, AI-based methods proposed for wireless systems are hard to compare with both the traditional baselines and even each other. The existing wireless AI researches usually rely on datasets generated based on statistical models or ray-tracing simulations with limited environments. The statistical data hinder the trained AI models from further fine-tuning for a specific scenario, and ray-tracing data with limited environments lower down the generalization capability of the trained AI models. In this paper, we present the Wireless AI Research Dataset (WAIR-D)1, which consists of two scenarios. Scenario 1 contains 10,000 environments with sparsely dropped user equipments (UEs), and Scenario 2 contains 100 environments with densely dropped UEs. The environments are randomly picked up from more than 40 cities in the real world map. The large volume of the data guarantees that the trained AI models enjoy good generalization capability, while fine-tuning can be easily carried out on a specific chosen environment. Moreover, both the wireless channels and the corresponding environmental information are provided in WAIR-D, so that extra-information-aided communication mechanism can be designed and evaluated. WAIR-D provides the researchers benchmarks to compare their different designs or reproduce results of others. In this paper, we show the detailed construction of this dataset and examples of using it.

cs.LG↗

arXiv2022

Semiparametric integrative interaction analysis for non-small-cell lung cancer

In the genomic analysis, it is significant while challenging to identify markers associated with cancer outcomes or phenotypes. Based on the biological mechanisms of cancers and the characteristics of datasets as well, this paper proposes a novel integrative interaction approach under the semiparametric model, in which the genetic factors and environmental factors are included as the parametric and nonparametric components, respectively. The goal of this approach is to identify the genetic factors and gene-gene interactions associated with cancer outcomes, and meanwhile, estimate the nonlinear effects of environmental factors. The proposed approach is based on the threshold gradient directed regularization (TGDR) technique. Simulation studies indicate that the proposed approach outperforms in the identification of main effects and interactions, and has favorable estimation and prediction accuracy compared with the alternative methods. The analysis of non-small-cell lung carcinomas (NSCLC) datasets from The Cancer Genome Atlas (TCGA) are conducted, showing that the proposed approach can identify markers with important implications and have favorable performance in prediction accuracy, identification stability, and computation cost.

stat.ME↗