The Ocular Biometry of Adult Cataract Patients on Lifeline Express Hospital Eye-Train in Rural China

Aims. To describe and explore the distribution of ocular biometric parameters of adult cataract patients in rural China. Methods. Three Lifeline Express Hospital Eye-Train missions of Peking University People’s Hospital in China were chosen. 3828 adult cataract patients aged 29 to 88 years with axial length (AL) less than 27.0 mm were enrolled. The ocular biometry including visual acuity (VA), intraocular pressure, AL, corneal power (K1 and K2), and corneal endothelial counting (CEC) were collected and analysis. Corneal radius (CR) was calculated from the corneal power. Results. The participants in Zhoukou of these three missions had the worse preoperative VA (p<0.001), the lowest K1 (p<0.001), K2 (p<0.001), and K (p<0.001) and the highest K1-K2 (p<0.001), moreover AL/CR more closely to 3.0. The AL, K1-K2, and AL/CR were normally distributed. But the K1, K2, K, and CEC were not normal distributions. Except K1, all parameters were positively skewed and peaked. Conclusion. Our study provides normative ocular biometry in a large, representative rural Chinese population. The AL is normally distributed with a positive skew and big kurtosis. The corneal powers are not normal distribution. The corneal astigmatism might have a significant effect on the visual acuity

Disentangled Diffusion-Based 3D Human Pose Estimation with Hierarchical Spatial and Temporal Denoiser

Recently, diffusion-based methods for monocular 3D human pose estimation have achieved state-of-the-art (SOTA) performance by directly regressing the 3D joint coordinates from the 2D pose sequence. Although some methods decompose the task into bone length and bone direction prediction based on the human anatomical skeleton to explicitly incorporate more human body prior constraints, the performance of these methods is significantly lower than that of the SOTA diffusion-based methods. This can be attributed to the tree structure of the human skeleton. Direct application of the disentangled method could amplify the accumulation of hierarchical errors, propagating through each hierarchy. Meanwhile, the hierarchical information has not been fully explored by the previous methods. To address these problems, a Disentangled Diffusion-based 3D Human Pose Estimation method with Hierarchical Spatial and Temporal Denoiser is proposed, termed DDHPose. In our approach: (1) We disentangle the 3D pose and diffuse the bone length and bone direction during the forward process of the diffusion model to effectively model the human pose prior. A disentanglement loss is proposed to supervise diffusion model learning. (2) For the reverse process, we propose Hierarchical Spatial and Temporal Denoiser (HSTDenoiser) to improve the hierarchical modeling of each joint. Our HSTDenoiser comprises two components: the Hierarchical-Related Spatial Transformer (HRST) and the Hierarchical-Related Temporal Transformer (HRTT). HRST exploits joint spatial information and the influence of the parent joint on each joint for spatial modeling, while HRTT utilizes information from both the joint and its hierarchical adjacent joints to explore the hierarchical temporal correlations among joints.Comment: Accepted by AAAI2

Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark

Gait depicts individuals' unique and distinguishing walking patterns and has become one of the most promising biometric features for human identification. As a fine-grained recognition task, gait recognition is easily affected by many factors and usually requires a large amount of completely annotated data that is costly and insatiable. This paper proposes a large-scale self-supervised benchmark for gait recognition with contrastive learning, aiming to learn the general gait representation from massive unlabelled walking videos for practical applications via offering informative walking priors and diverse real-world variations. Specifically, we collect a large-scale unlabelled gait dataset GaitLU-1M consisting of 1.02M walking sequences and propose a conceptually simple yet empirically powerful baseline model GaitSSB. Experimentally, we evaluate the pre-trained model on four widely-used gait benchmarks, CASIA-B, OU-MVLP, GREW and Gait3D with or without transfer learning. The unsupervised results are comparable to or even better than the early model-based and GEI-based methods. After transfer learning, our method outperforms existing methods by a large margin in most cases. Theoretically, we discuss the critical issues for gait-specific contrastive framework and present some insights for further study. As far as we know, GaitLU-1M is the first large-scale unlabelled gait dataset, and GaitSSB is the first method that achieves remarkable unsupervised results on the aforementioned benchmarks. The source code of GaitSSB will be integrated into OpenGait which is available at https://github.com/ShiqiYu/OpenGait

GPGait: Generalized Pose-based Gait Recognition

Recent works on pose-based gait recognition have demonstrated the potential of using such simple information to achieve results comparable to silhouette-based methods. However, the generalization ability of pose-based methods on different datasets is undesirably inferior to that of silhouette-based ones, which has received little attention but hinders the application of these methods in real-world scenarios. To improve the generalization ability of pose-based methods across datasets, we propose a \textbf{G}eneralized \textbf{P}ose-based \textbf{Gait} recognition (\textbf{GPGait}) framework. First, a Human-Oriented Transformation (HOT) and a series of Human-Oriented Descriptors (HOD) are proposed to obtain a unified pose representation with discriminative multi-features. Then, given the slight variations in the unified representation after HOT and HOD, it becomes crucial for the network to extract local-global relationships between the keypoints. To this end, a Part-Aware Graph Convolutional Network (PAGCN) is proposed to enable efficient graph partition and local-global spatial feature extraction. Experiments on four public gait recognition datasets, CASIA-B, OUMVLP-Pose, Gait3D and GREW, show that our model demonstrates better and more stable cross-domain capabilities compared to existing skeleton-based methods, achieving comparable recognition results to silhouette-based ones. Code is available at https://github.com/BNU-IVC/FastPoseGait.Comment: ICCV Camera Read

FastPoseGait: A Toolbox and Benchmark for Efficient Pose-based Gait Recognition

We present FastPoseGait, an open-source toolbox for pose-based gait recognition based on PyTorch. Our toolbox supports a set of cutting-edge pose-based gait recognition algorithms and a variety of related benchmarks. Unlike other pose-based projects that focus on a single algorithm, FastPoseGait integrates several state-of-the-art (SOTA) algorithms under a unified framework, incorporating both the latest advancements and best practices to ease the comparison of effectiveness and efficiency. In addition, to promote future research on pose-based gait recognition, we provide numerous pre-trained models and detailed benchmark results, which offer valuable insights and serve as a reference for further investigations. By leveraging the highly modular structure and diverse methods offered by FastPoseGait, researchers can quickly delve into pose-based gait recognition and promote development in the field. In this paper, we outline various features of this toolbox, aiming that our toolbox and benchmarks can further foster collaboration, facilitate reproducibility, and encourage the development of innovative algorithms for pose-based gait recognition. FastPoseGait is available at https://github.com//BNU-IVC/FastPoseGait and is actively maintained. We will continue updating this report as we add new features.Comment: 10 pages, 4 figure

QAGait: Revisit Gait Recognition from a Quality Perspective

Gait recognition is a promising biometric method that aims to identify pedestrians from their unique walking patterns. Silhouette modality, renowned for its easy acquisition, simple structure, sparse representation, and convenient modeling, has been widely employed in controlled in-the-lab research. However, as gait recognition rapidly advances from in-the-lab to in-the-wild scenarios, various conditions raise significant challenges for silhouette modality, including 1) unidentifiable low-quality silhouettes (abnormal segmentation, severe occlusion, or even non-human shape), and 2) identifiable but challenging silhouettes (background noise, non-standard posture, slight occlusion). To address these challenges, we revisit gait recognition pipeline and approach gait recognition from a quality perspective, namely QAGait. Specifically, we propose a series of cost-effective quality assessment strategies, including Maxmial Connect Area and Template Match to eliminate background noises and unidentifiable silhouettes, Alignment strategy to handle non-standard postures. We also propose two quality-aware loss functions to integrate silhouette quality into optimization within the embedding space. Extensive experiments demonstrate our QAGait can guarantee both gait reliability and performance enhancement. Furthermore, our quality assessment strategies can seamlessly integrate with existing gait datasets, showcasing our superiority. Code is available at https://github.com/wzb-bupt/QAGait.Comment: Accepted by AAAI 202

Physiological responses and transcriptome analyses of upland rice following exposure to arsenite and arsenate

Acknowledgements This research was financially supported by the National Natural Science Foundation of China (No.41471274) and the Scottish Government’s Rural and Environment Science and Analytical Service Division (RESAS).Peer reviewedPostprin

Observation of medium-induced yield enhancement and acoplanarity broadening of low-pTp_{T} jets in pp and Pb-Pb collisions at sNN\sqrt{s_{NN}} = 5.02 TeV with ALICE

International audienceWe present the measurements of the semi-inclusive distributions of charged-particle jets recoiling from a trigger hadron in proton–proton (pp) and 0–10% Pb–Pb collisions at $\sqrt{s_\mathrm{NN}}=5.02$ TeV, searching for medium-induced yield enhancement and acoplanarity broadening effects in low transverse momentum ($p_{\rm T}$) jets. This technique provides precise data-driven subtraction of the large uncorrelated background yield in jet measurements, enabling the measurement of recoil jet distributions to the large jet radius at low $p_{\rm T}$ in central Pb--Pb collisions. Trigger-normalized recoil jet distributions are reported as a function of $p_{\rm T,jet}$ and as a function of the azimuthal angle ($\Delta\varphi$) between trigger hadron axis and recoil jet. Comparisons of the jet yield distributions in pp and Pb–Pb collisions show a significant medium-induced yield enhancement at low $p_{\rm T}$ and at large-angle jet deflection for large radius. Comparisons to theoretical calculations incorporating jet quenching will also be discussed

System and event activity dependent inclusive jet production with ALICE

Jets are produced by processes involving high momentum transfer of initial partons at high energies. Comparing jet production in pp and nucleus-nucleus collisions will allow us to study the jet-quenching effect caused by the hot and dense QCD medium produced in nucleus-nucleus collisions when energetic partons traverse the medium. In particular, systematic studies of jet production in different multiplicity environments will provide in-depth understanding of the medium properties and their evolution from small to large systems. In small systems and high multiplicity events, the bulk properties extracted by the low transverse momentum particle production behaves as if a hot QCD medium was created, but such behaviour is not observed with hard probes. Study of jet production in different multiplicity proton-proton collisions then helps to explore the QGP existence in small systems. In this proceeding, the jet cross section measurements in different collision systems using the data taken by ALICE during the LHC Run 2 are presented. The nuclear modification factor of jets are presented to characterize the jet-quenching effect. We observe that more jets are produced in high multiplicity bins compared to the inclusive one, while the jet production enhancement in high multiplicity environments has weaker jet $p_{\rm T}$ or resolution parameter dependences. In order to study the jet collimation properties, the jet cross section ratios for different jet resolution parameters are also measured and compared to theoretical models. As expected, the jets get more collimated at high $p_{\rm T}$ in numerous multiplicity bins, with no collision energy or multiplicity dependence when compared to earlier results.Jets are produced by processes involving high momentum transfer of initial partons at high energies.Comparing jet production in pp and nucleus-nucleus collisions will allow us to studythe jet-quenching effect caused by the hot and dense QCD medium produced in nucleus-nucleus collisions when energetic partons traverse the medium. In particular, systematic studies of jet production in different multiplicity environments will provide in-depth understanding of the medium properties and their evolution from small to large systems. In small systems and high multiplicity events, the bulk properties extracted by the low transverse momentum particle production behaves as if a hot QCD medium was created, but such behaviour is not observed with hard probes. Study of jet production in different multiplicity proton-proton collisions then helps to explore the QGP existence in small systems.In this proceeding, the jet cross section measurements in different collision systemsusing the data taken by ALICE during the LHC Run 2 are presented. The nuclear modification factor of jets are presented to characterize the jet-quenching effect. We observe that more jets are produced in high multiplicity bins compared to the inclusive one, while the jet production enhancement in high multiplicity environments has weaker jet $p_{\rm T}$ or resolution parameter dependences. In order to study the jet collimation properties, the jet cross section ratios for different jet resolution parameters are also measured and compared to theoretical models. As expected, the jets get more collimated at high $p_{\rm T}$ in numerous multiplicity bins, with no collision energy or multiplicity dependence when compared to earlier results