RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series

Decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting. Although numerous methods have been proposed, there are still many time series characteristics exhibiting in real-world data which are not addressed properly, including 1) ability to handle seasonality fluctuation and shift, and abrupt change in trend and reminder; 2) robustness on data with anomalies; 3) applicability on time series with long seasonality period. In the paper, we propose a novel and generic time series decomposition algorithm to address these challenges. Specifically, we extract the trend component robustly by solving a regression problem using the least absolute deviations loss with sparse regularization. Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component. This process is repeated until accurate decomposition is obtained. Experiments on different synthetic and real-world time series datasets demonstrate that our method outperforms existing solutions.Comment: Accepted to the thirty-third AAAI Conference on Artificial Intelligence (AAAI 2019), 9 pages, 5 figure

Simultaneous CTEQ-TEA extraction of PDFs and SMEFT parameters from jet and ttˉt{\bar t} data

Recasting phenomenological Lagrangians in terms of SM effective field theory (SMEFT) provides a valuable means of connecting potential BSM physics at momenta well above the electroweak scale to experimental signatures at lower energies. In this work we jointly fit the Wilson coefficients of SMEFT operators as well as the PDFs in an extension of the CT18 global analysis framework, obtaining self-consistent constraints to possible BSM physics effects. Global fits are boosted with machine-learning techniques in the form of neural networks to ensure efficient scans of the full PDF+SMEFT parameter space. We focus on several operators relevant for top-quark pair and jet production at hadron colliders and obtain constraints on the Wilson coefficients with Lagrange Multiplier scans. We find mild correlations between the extracted Wilson coefficients, PDFs, and other QCD parameters, and see indications that these correlations may become more prominent in future analyses based on data of higher precision. This work serves as a new platform for joint analyses of SM and BSM physics based on the CTEQ-TEA framework.Comment: 39 pages, 18 figure

Self-supervised phase unwrapping in fringe projection profilometry

Fast-speed and high-accuracy three-dimensional (3D) shape measurement has been the goal all along in fringe projection profilometry (FPP). The dual-frequency temporal phase unwrapping method (DF-TPU) is one of the prominent technologies to achieve this goal. However, the period number of the high-frequency pattern of existing DF-TPU approaches is usually limited by the inevitable phase errors, setting a limit to measurement accuracy. Deep-learning-based phase unwrapping methods for single-camera FPP usually require labeled data for training. In this letter, a novel self-supervised phase unwrapping method for single-camera FPP systems is proposed. The trained network can retrieve the absolute fringe order from one phase map of 64-period and overperform DF-TPU approaches in terms of depth accuracy. Experimental results demonstrate the validation of the proposed method on real scenes of motion blur, isolated objects, low reflectivity, and phase discontinuity

Preliminary screening, identification and biological characteristic analysis of Bacillus probiotics isolated from Cynoglossus semilaevis

To screen local probiotic strains to promote antibiotic-free farming, two potential probiotic strains (S3, S5) were recognized among 89 cultivable bacterial strains isolated from the intestine of healthy Cynoglossus semilaevis. The two potential probiotic isolates were analyzed in terms of their morphology, physiology, biochemistry, the similarity of 16S rDNA sequences, growth characteristics, enzyme production capacity, bacterial antagonism, and safety in C. semilaevis. The results revealed that the bacterial morphology and physiological and biochemical characteristics of S3 and S5 were similar to those of Bacillus subtilis. The 16S rDNA sequences had 99.9 % similarity to that of Bacillus subtilis MH 145363.1. Therefore, S3 and S5 were identified as B. subtilis. In addition, we found that S3 and S5 had a strong ability to secrete amylase, protease, and lipase. During the safety tests of S3 and S5 in C. semilaevis with high concentrations, C. semilaevis in immersion, injection, and feeding groups remained in good condition without falling ill or dying. Moreover, we found that S3 and S5 exhibited superior growth at 25~50℃, salinities of 10 to 40, and pH values of 5 to 9. Furthermore, S3 and S5 had significant bacteriostatic activity against Vibrio anguillarum, Aeromonas salmonicida, and Shewanella algae, which are the main pathogenic bacteria of mariculture fish. In summary, S3 and S5 showed superb inhibition of the pathogenic bacteria of marine fish, rapid growth, eurythermal and euryhaline features, and suitability for the intestinal environment of C. semilaevis. Thus, strains S3 and S5 have excellent commercial development potential. These results provide a basis for ecological disease prevention strategies and are also valuable for developing and utilizing probiotics

A Super-resolution Reconstruction Method of Remotely Sensed Image Based on Sparse Representation

The traditional method of image super-resolution reconstruction uses the sub-pixel displacement information between multi-frame low-resolution images to reconstruct a high-resolution image. Image super-resolution reconstruction is a typical mathematical inverse problem, and it is ill-posed problem [1]. To solve this problem, prior knowledge of data or question should be added. As the latest development achievements of signal priori or modeling, sparse representation of the signal has been studied in depth in the field of image processing. Super-resolution reconstruction based on sparse representation can improve the image quality and get richer image details [8]. Due to the sparse representation of image reconstruction has strong priority, this paper focuses on super-resolution reconstruction of the single frame remotely sensed image based on sparse representation. Compared with other algorithms, it is proved that the super-resolution reconstruction algorithm based on sparse representation has advantages in remotely sensed image reconstruction