Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Services

Time series anomaly detection is crucial for industrial monitoring services that handle a large volume of data, aiming to ensure reliability and optimize system performance. Existing methods often require extensive labeled resources and manual parameter selection, highlighting the need for automation. This paper proposes a comprehensive framework for automatic parameter optimization in time series anomaly detection models. The framework introduces three optimization targets: prediction score, shape score, and sensitivity score, which can be easily adapted to different model backbones without prior knowledge or manual labeling efforts. The proposed framework has been successfully applied online for over six months, serving more than 50,000 time series every minute. It simplifies the user's experience by requiring only an expected sensitive value, offering a user-friendly interface, and achieving desired detection results. Extensive evaluations conducted on public datasets and comparison with other methods further confirm the effectiveness of the proposed framework.Comment: Accepted by 2023 IJCAI Worksho

Scaling laws for the (de-)polarization time of relativistic particle beams in strong fields

The acceleration of polarized electrons and protons in strong laser and plasma fields is a very attractive option to obtain polarized beams in the GeV range. We investigate the feasibility of particle acceleration in strong fields without destroying an initial polarization, taking into account all relevant mechanisms that could cause polarization losses, i.e. the spin precession described by the T-BMT equation, the Sokolov-Ternov effect and the Stern-Gerlach force. Scaling laws for the (de-)polarization time caused by these effects reveal that the dominant polarization limiting effect is the rotation of the single particle spins around the local electromagnetic fields. We compare our findings to test-particle simulations for high energetic electrons moving in a homogeneous electric field. For high particle energies the observed depolarization times are in good agreement with the analytically estimated ones.Comment: 17 pages and 4 figure

Polarized electron-beam acceleration driven by vortex laser pulses

We propose a new approach based on an all-optical set-up for generating relativistic polarized electron beams via vortex Laguerre-Gaussian (LG) laser-driven wakefield acceleration. Using a pre-polarized gas target, we find that the topology of the vortex wakefield resolves the depolarization issue of the injected electrons. In full three-dimensional particle-in-cell simulations, incorporating the spin dynamics via the Thomas-Bargmann Michel Telegdi equation, the LG laser preserves the electron spin polarization by more than 80% at high beam charge and flux. The method releases the limit on beam flux for polarized electron acceleration and promises more than an order of magnitude boost in peak flux, as compared to Gaussian beams. These results suggest a promising table-top method to produce energetic polarized electron beams.Comment: We replace some results and revise some description

A two-branch cloud detection algorithm based on the fusion of a feature enhancement module and Gaussian mixture model

Accurate cloud detection is an important step to improve the utilization rate of remote sensing (RS). However, existing cloud detection algorithms have difficulty in identifying edge clouds and broken clouds. Therefore, based on the channel data of the Himawari-8 satellite, this work proposes a method that combines the feature enhancement module with the Gaussian mixture model (GMM). First, statistical analysis using the probability density functions (PDFs) of spectral data from clouds and underlying surface pixels was conducted, selecting cluster features suitable for daytime and nighttime. Then, in this work, the Laplacian operator is introduced to enhance the spectral features of cloud edges and broken clouds. Additionally, enhanced spectral features are input into the debugged GMM model for cloud detection. Validation against visual interpretation shows promising consistency, with the proposed algorithm outperforming other methods such as RF, KNN and GMM in accuracy metrics, demonstrating its potential for high-precision cloud detection in RS images

Using age information as a soft biometric trait for face image analysis

Soft biometrics refers to a group of traits that can provide some information about an individual but are inadequate for identification or recognition purposes. Age, as an important soft biometric trait, can be inferred based on the appearance of human faces. However, compared to other facial attributes like race and gender, age is rather subtle due to the underlying conditions of individuals (i.e., their upbringing environment and genes). These uncertainties make age-related face image analysis (including age estimation, age synthesis and age-invariant face recognition) still unsolved. Specifically, age estimation is concerned with inferring the specific age from human face images. Age synthesis is concerned with the rendering of face images with natural ageing or rejuvenating effects. Age-invariant face recognition involves the recognition of the identity of subjects correctly regardless of their age. Recently, thanks to the rapid development of machine learning, especially deep learning, age-related face image analysis has gained much more attention from the research community than ever before. Deep learning based models that deal with age-related face image analysis have also significantly boosted performance compared to models that only use traditional machine learning methods, such as decision trees or boost algorithms. In this chapter, we first introduce the concepts and theory behind the three main areas of age-related face image analysis and how they can be used in practical biometric applications. Then, we analyse the difficulties involved in these applications and summarise the recent progress by reviewing the state-of-the-art methods involving deep learning. Finally, we discuss the future research trends and the issues that are not addressed by existing works. We also discuss the relationship among these three areas and show how solutions within one area can help to tackle issues in the others

Spin Filter for Polarized Electron Acceleration in Plasma Wakefields

We propose a filter method to generate electron beams of high polarization from bubble and blow-out wakefield accelerators. The mechanism is based on the idea of identifying all electron-beam subsets with low polarization and filtering them out with an X-shaped slit placed immediately behind the plasma accelerator. To find these subsets we investigate the dependence between the initial azimuthal angle and the spin of single electrons during the trapping process. This dependence shows that transverse electron spins preserve their orientation during injection if they are initially aligned parallel or antiparallel to the local magnetic field. We derive a precise correlation of the local beam polarization as a function of the coordinate and the electron phase angle. Three-dimensional particle-in-cell simulations, incorporating classical spin dynamics, show that the beam polarization can be increased from 35% to about 80% after spin filtering. The injected flux is strongly restricted to preserve the beam polarization; for example, less than 1 kA in Wen et al. [Phys. Rev. Lett. 122, 214801 (2019)]. This limitation is removed by use of the proposed filter mechanism. The robustness of the method is discussed in terms of drive-beam fluctuations, jitters, the thickness of the filter, and the initial temperature. This idea marks an efficient and simple strategy to generate energetic polarized electron beams on the basis of wakefield acceleration

Polarized electron acceleration in beam-driven plasma wakefield based on density down-ramp injection

We investigate the precession of electron spins during beam-driven plasma-wakefield acceleration based on density down-ramp injection by means of full three-dimensional (3D) particle-in-cell (PIC) simulations. A relativistic electron beam generated via, e.g., laser wakefield acceleration, serves as the driving source. It traverses the prepolarized gas target and accelerates polarized electrons via the excited wakefield. We derive the criteria for the driving beam parameters and the limitation on the injected beam flux to preserve a high degree of polarization for the accelerated electrons, which are confirmed by our 3D PIC simulations and single-particle modeling. The electron-beam driver is free of the prepulse issue associated with a laser driver, thus eliminating possible depolarization of the prepolarized gas due to ionization by the prepulse. These results provide guidance for future experiments towards generating a source of polarized electrons based on wakefield acceleration