Properties of charge transport in a novel holographic quantum phase transition model

We study the properties of charge transport in a novel holographic QPT (quantum phase transition) model, which has two different metallic phases: the normal metallic phase and the novel metallic one. We numerically work out the scaling behaviors of DC (direct current) resistivity at low temperatures in both different metallic phases. The numerical results are solidly in agreement with the analytical ones determined by the near horizon geometry. Then, we mainly explore the properties of the low-frequency AC (alternating current) conductivity. A remarkable characteristic is that the normal metallic phase is a coherent system with vanishing intrinsic conductivity $\sigma_Q$, which is independent of the strength of the momentum dissipation. This result is in contrast with the common belief that with the strength of the momentum dissipation increasing, the system changes from a coherent phase to an incoherent one. But the novel metallic phase is an incoherent system with non-vanishing $\sigma_Q$. Away from the QCP (quantum critical point), $\sigma_Q$ increases, which indicates that the incoherent behavior becomes stronger.Comment: 16 pages, 5 figure

Holographic p-wave superconductivity from higher derivative theory

We construct a holographic SU(2) p-wave superconductor model with Weyl corrections. The high derivative (HD) terms do not seem to spoil the generation of the p-wave superconducting phase. We mainly study the properties of AC conductivity, which is absent in holographic SU(2) p-wave superconductor with Weyl corrections. The conductivities in superconducting phase exhibit obvious anisotropic behaviors. Along $y$ direction, the conductivity $\sigma_{yy}$ is similar to that of holographic s-wave superconductor. The superconducting energy gap exhibits a wide extension. For the conductivity $\sigma_{xx}$ along $x$ direction, the behaviors of the real part in the normal state are closely similar to that of $\sigma_{yy}$. However, the anisotropy of the conductivity obviously shows up in the superconducting phase. A Drude-like peak at low frequency emerges in $Re\sigma_{xx}$ once the system enters into the superconducting phase, regardless of the behaviors in normal state.Comment: 19 pages, 7 figure

Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation

The ability to scene understanding in adverse visual conditions, e.g., nighttime, has sparked active research for RGB-Thermal (RGB-T) semantic segmentation. However, it is essentially hampered by two critical problems: 1) the day-night gap of RGB images is larger than that of thermal images, and 2) the class-wise performance of RGB images at night is not consistently higher or lower than that of thermal images. we propose the first test-time adaptation (TTA) framework, dubbed Night-TTA, to address the problems for nighttime RGBT semantic segmentation without access to the source (daytime) data during adaptation. Our method enjoys three key technical parts. Firstly, as one modality (e.g., RGB) suffers from a larger domain gap than that of the other (e.g., thermal), Imaging Heterogeneity Refinement (IHR) employs an interaction branch on the basis of RGB and thermal branches to prevent cross-modal discrepancy and performance degradation. Then, Class Aware Refinement (CAR) is introduced to obtain reliable ensemble logits based on pixel-level distribution aggregation of the three branches. In addition, we also design a specific learning scheme for our TTA framework, which enables the ensemble logits and three student logits to collaboratively learn to improve the quality of predictions during the testing phase of our Night TTA. Extensive experiments show that our method achieves state-of-the-art (SoTA) performance with a 13.07% boost in mIoU

IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing

Image anomaly detection (IAD) is an emerging and vital computer vision task in industrial manufacturing (IM). Recently many advanced algorithms have been published, but their performance deviates greatly. We realize that the lack of actual IM settings most probably hinders the development and usage of these methods in real-world applications. As far as we know, IAD methods are not evaluated systematically. As a result, this makes it difficult for researchers to analyze them because they are designed for different or special cases. To solve this problem, we first propose a uniform IM setting to assess how well these algorithms perform, which includes several aspects, i.e., various levels of supervision (unsupervised vs. semi-supervised), few-shot learning, continual learning, noisy labels, memory usage, and inference speed. Moreover, we skillfully build a comprehensive image anomaly detection benchmark (IM-IAD) that includes 16 algorithms on 7 mainstream datasets with uniform settings. Our extensive experiments (17,017 in total) provide in-depth insights for IAD algorithm redesign or selection under the IM setting. Next, the proposed benchmark IM-IAD gives challenges as well as directions for the future. To foster reproducibility and accessibility, the source code of IM-IAD is uploaded on the website, https://github.com/M-3LAB/IM-IAD

Peculiar properties in quasi-normal spectra from loop quantum gravity effect

We investigate the quasi-normal mode (QNM) spectra for scalar and electromagnetic fields over a covairant loop quantum gravity black hole (LQG-BH). For the fundamental modes, the LQG effect reduces the oscillations in the scalar field, however it induces stronger oscillations in the electromagnetic field, comparing to the classical case. Under the scalar field perturbation, the system enjoys faster decaying modes with more oscillations than the electromagnetic field. Some peculiar phenomena emerge in the scalar field's QNM spectra with high overtones for the angular quantum numbers $l>0$. It is that the LQG-BH has a larger real part of QNM with high overtones than the Schwarzschild black hole (SS-BH). Such an anomalous phenomenon results in the oscillation of the scalar field in the LQG-BH to be nearly identical to that in the SS-BH. Therefore, the high overtone modes of the scalar field in LQG-BH play an important role in the modes with $l>0$. This anomalous phenomenon, however, does not occur in the electromagnetic field's QNM spectra.Comment: 28 pages,10 figure

Real3D-AD: A Dataset of Point Cloud Anomaly Detection

High-precision point cloud anomaly detection is the gold standard for identifying the defects of advancing machining and precision manufacturing. Despite some methodological advances in this area, the scarcity of datasets and the lack of a systematic benchmark hinder its development. We introduce Real3D-AD, a challenging high-precision point cloud anomaly detection dataset, addressing the limitations in the field. With 1,254 high-resolution 3D items from forty thousand to millions of points for each item, Real3D-AD is the largest dataset for high-precision 3D industrial anomaly detection to date. Real3D-AD surpasses existing 3D anomaly detection datasets available regarding point cloud resolution (0.0010mm-0.0015mm), 360 degree coverage and perfect prototype. Additionally, we present a comprehensive benchmark for Real3D-AD, revealing the absence of baseline methods for high-precision point cloud anomaly detection. To address this, we propose Reg3D-AD, a registration-based 3D anomaly detection method incorporating a novel feature memory bank that preserves local and global representations. Extensive experiments on the Real3D-AD dataset highlight the effectiveness of Reg3D-AD. For reproducibility and accessibility, we provide the Real3D-AD dataset, benchmark source code, and Reg3D-AD on our website:https://github.com/M-3LAB/Real3D-AD

EasyNet: An Easy Network for 3D Industrial Anomaly Detection

3D anomaly detection is an emerging and vital computer vision task in industrial manufacturing (IM). Recently many advanced algorithms have been published, but most of them cannot meet the needs of IM. There are several disadvantages: i) difficult to deploy on production lines since their algorithms heavily rely on large pre-trained models; ii) hugely increase storage overhead due to overuse of memory banks; iii) the inference speed cannot be achieved in real-time. To overcome these issues, we propose an easy and deployment-friendly network (called EasyNet) without using pre-trained models and memory banks: firstly, we design a multi-scale multi-modality feature encoder-decoder to accurately reconstruct the segmentation maps of anomalous regions and encourage the interaction between RGB images and depth images; secondly, we adopt a multi-modality anomaly segmentation network to achieve a precise anomaly map; thirdly, we propose an attention-based information entropy fusion module for feature fusion during inference, making it suitable for real-time deployment. Extensive experiments show that EasyNet achieves an anomaly detection AUROC of 92.6% without using pre-trained models and memory banks. In addition, EasyNet is faster than existing methods, with a high frame rate of 94.55 FPS on a Tesla V100 GPU

Is the COVID-19 epidemic affecting the body mass of Chinese teenagers? – A longitudinal follow-up study

BackgroundAfter the spread and outbreak of COVID-19 worldwide, the learning, lifestyle, and health level of young individuals have been immensely impacted. With regard to the existing studies, the development trend of adolescents’ body shape in the late COVID-19 period is not sufficiently analyzed, and relevant targeted investigation is lacking. This study aimed to explore the body mass index (BMI) changes of 6–14 years-old adolescents before and after the COVID-19 epidemic, and to provide a reference for promoting the continuous enhancement of adolescent health.MethodsThe BMI and related data pertaining to 93,046 individuals from 2019 to 2022 were collected by cluster sampling, and changes in the BMI Z score and detection rate of overweight and obese adolescents before and after the epidemic were analyzed. Furthermore, the trend of obesity rates among adolescents in Jinan from 2019 to 2022 was analyzed using a logistic regression analysis model.ResultsThe one-way ANOVA models indicated that the BMI Z score of 6–14 years-old adolescents in 2020 significantly increased compared to 2019 (p < 0.01), and decreased in 2021 and 2022; in 2020, the obesity rate of adolescents exhibited a significant increase; however, the rate decreased after being controlled in 2021 and 2022. The normal-body size proportion continued to rise (p < 0.01), and adolescents of different age groups and genders exhibited similar development trends; the results of the logistic regression analysis indicate that there was a significant increase in obesity rates in 2020, adolescents of different age groups and genders exhibited similar development trends (p < 0.05).ConclusionThis study demonstrated that the COVID-19 epidemic impacts the BMI and obesity detection rate of adolescents. Adolescents from different age groups and genders exhibited similar development trends

Deep Industrial Image Anomaly Detection: A Survey

The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the new setting from industrial manufacturing and review the current IAD approaches under our proposed our new setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection