Timing and Single-pulse Study of Pulsar J1909+0122 Discovered by CRAFTS

We report the discovery of PSR J1909+0122 by the Five-hundred-meter Aperture Spherical Radio Telescope(FAST)as part of the Commensal Radio Astronomy FAST Survey.PSR J1909+0122 has a spin period of 1.257 s and a dispersion measure of 186.2 pc cm^(-3).The averaged pulse profile shows two distinct components.We performed a single-pulse study based on a one-hour observation at 1.25 GHz on 2021 August 23.We used a threshold of 5σ_(ep) to measure the nulling fraction(NF)as 63%±1.5%.The longitude-resolved fluctuation spectra and fast Fourier transform spectra of the binary sequences revealed the quasi-periodicity of nulling with a period of 30 rotation periods.We examined the reliability of the periodicity by comparing it to random noise injection.The NF,E,and modulation periodicity P_(M) of PSR J1909+0122 were compared with other periodic nulling pulsars,showing that the source of J1909+0122 has the second largest NF in the population.Long-term timing observations over six months were used to derive the phase-connected ephemeris of this pulsar.The measured P and P values disfavor dipolar geometry for polar gap models,and the prediction for a space-charge-limited flow model in the case of inverse Compton scattering is only just above the death line.In this work,PSR J1909+0122 has revealed possible correlations between nulling behavior and pulsar properties,which will help to shed light on the pulsar emission mechanism and its temporal evolution in future observations.

Reciprocating Magnetic Fields in the Pulsar Wind Observed from the Black Widow Pulsar J1720-0534

We report the radio observations of the eclipsing black widow pulsar J1720-0534, a 3.26 ms pulsar in orbit with a low mass companion of mass 0.029 to 0.034 M⊙. We obtain the phase-connected timing ephemeris and polarization profile of this millisecond pulsar(MSP) using the Five-hundred-meter Aperture Spherical radio Telescope(FAST), the Green Bank Telescope(GBT), and the Parkes Telescope. For the first time from such a system, an oscillatory polarization angle change was observed from a particular eclipse egress with partial depolarization, indicating 10-milliGauss-level reciprocating magnetic fields oscillating in a length scale of 5 ×10^(3)km(assuming an orbital inclination angle of 90°) outside the companion's magnetosphere. The dispersion measure variation observed during the ingresses and egresses shows the rapid raising of the electron density in the shock boundary between the companion's magnetosphere and the surrounding pulsar wind. We suggest that the observed oscillatory magnetic fields originate from the pulsar wind outside the companion's magnetosphere.

双标签监督的几何约束对抗训练

近年来的研究表明,对抗训练是一种有效的防御对抗样本攻击的方法.然而,现有的对抗训练策略在提升模型鲁棒性的同时会造成模型的泛化能力下降.现阶段主流的对抗训练方法通常都是独立地处理每个训练样本,而忽略了样本之间的关系,这使得模型无法充分挖掘样本间的几何关系来学习更鲁棒的模型,以便更好地防御对抗攻击.因此,重点研究如何在对抗训练过程中保持样本间的几何结构稳定性,达到提升模型鲁棒性的目的.具体而言,在对抗训练中,设计了一种新的几何结构约束方法,其目的是保持自然样本与对抗样本的特征空间分布一致性.此外,提出了一种基于双标签的监督学习方法,该方法同时采用自然样本和对抗样本的标签对模型进行联合监督训练.最后,分析了双标签监督学习方法的特性,试图从理论上解释对抗样本的工作机理.多个基准数据集上的实验结果表明:相比于已有方法,该方法有效地提升了模型的鲁棒性且保持了较好的泛化精度.相关代码已经开源:https://github.com/SkyKuang/DGCAT.

Survey on depth and RGB image-based 3D hand shape and pose estimation

The field of vision-based human hand three-dimensional(3D)shape and pose estimation has attracted significant attention recently owing to its key role in various applications,such as natural human computer interactions.With the availability of large-scale annotated hand datasets and the rapid developments of deep neural networks(DNNs),numerous DNN-based data-driven methods have been proposed for accurate and rapid hand shape and pose estimation.Nonetheless,the existence of complicated hand articulation,depth and scale ambiguities,occlusions,and finger similarity remain challenging.In this study,we present a comprehensive survey of state-of-the-art 3D hand shape and pose estimation approaches using RGB-D cameras.Related RGB-D cameras,hand datasets,and a performance analysis are also discussed to provide a holistic view of recent achievements.We also discuss the research potential of this rapidly growing field.

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 pa-per,we provide a comprehensive review of deep learning-based image anomaly detection techniques,from the perspectives of neural net-work architectures,levels of supervision,loss functions,metrics and datasets.In addition,we extract the promising setting from indus-trial manufacturing and review the current IAD approaches under our proposed setting.Moreover,we highlight several opening chal-lenges 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.

A discovery of two slow pulsars with FAST:“Ronin”from the globular cluster M15

Globular clusters harbor numerous millisecond pulsars,but long-period pulsars(P 100 ms)are rarely found.In this study,we employed a fast folding algorithm to analyze observational data from multiple globular clusters obtained by the Five-hundredmeter Aperture Spherical radio Telescope(FAST),aiming to detect the existence of long-period pulsars.We estimated the impact of the median filtering algorithm in eliminating red noise on the minimum detectable flux density(S_(min))of pulsars.Subsequently,we successfully discovered two isolated long-period pulsars in M15 with periods approximately equal to 1.928451 and3.960716 s,respectively.On the P-˙P diagram,both pulsars are positioned below the spin-up line,suggesting a possible history of partial recycling in X-ray binary systems disrupted by dynamical encounters later on.According to timing results,these two pulsars exhibit remarkably strong magnetic fields.If the magnetic fields were weakened during the accretion process,then a short duration of accretion might explain the strong magnetic fields of these pulsars.

Transformers in computational visual media:A survey

Transformers,the dominant architecture for natural language processing,have also recently attracted much attention from computational visual media researchers due to their capacity for long-range representation and high performance.Transformers are sequence-to-sequence models,which use a selfattention mechanism rather than the RNN sequential structure.Thus,such models can be trained in parallel and can represent global information.This study comprehensively surveys recent visual transformer works.We categorize them according to task scenario:backbone design,high-level vision,low-level vision and generation,and multimodal learning.Their key ideas are also analyzed.Differing from previous surveys,we mainly focus on visual transformer methods in low-level vision and generation.The latest works on backbone design are also reviewed in detail.For ease of understanding,we precisely describe the main contributions of the latest works in the form of tables.As well as giving quantitative comparisons,we also present image results for low-level vision and generation tasks.Computational costs and source code links for various important works are also given in this survey to assist further development.

Learning to assess visual aesthetics of food images

Distinguishing aesthetically pleasing food photos from others is an important visual analysis task for social media and ranking systems related to food.Nevertheless,aesthetic assessment of food images remains a challenging and relatively unexplored task,largely due to the lack of related food image datasets and practical knowledge.Thus,we present the Gourmet Photography Dataset(GPD),the first largescale dataset for aesthetic assessment of food photos.It contains 24,000 images with corresponding binary aesthetic labels,covering a large variety of foods and scenes.We also provide a non-stationary regularization method to combat over-fitting and enhance the ability of tuned models to generalize.Quantitative results from extensive experiments,including a generalization ability test,verify that neural networks trained on the GPD achieve comparable performance to human experts on the task of aesthetic assessment.We reveal several valuable findings to support further research and applications related to visual aesthetic analysis of food images.To encourage further research,we have made the GPD publicly available at https://github.com/Openning07/GPA.