TimeSQL: Improving Multivariate Time Series Forecasting with Multi-Scale Patching and Smooth Quadratic Loss

Time series is a special type of sequence data, a sequence of real-valued random variables collected at even intervals of time. The real-world multivariate time series comes with noises and contains complicated local and global temporal dynamics, making it difficult to forecast the future time series given the historical observations. This work proposes a simple and effective framework, coined as TimeSQL, which leverages multi-scale patching and smooth quadratic loss (SQL) to tackle the above challenges. The multi-scale patching transforms the time series into two-dimensional patches with different length scales, facilitating the perception of both locality and long-term correlations in time series. SQL is derived from the rational quadratic kernel and can dynamically adjust the gradients to avoid overfitting to the noises and outliers. Theoretical analysis demonstrates that, under mild conditions, the effect of the noises on the model with SQL is always smaller than that with MSE. Based on the two modules, TimeSQL achieves new state-of-the-art performance on the eight real-world benchmark datasets. Further ablation studies indicate that the key modules in TimeSQL could also enhance the results of other models for multivariate time series forecasting, standing as plug-and-play techniques

Genomics of post-bottleneck recovery in the northern elephant seal.

Populations and species are threatened by human pressure, but their fate is variable. Some depleted populations, such as that of the northern elephant seal (Mirounga angustirostris), recover rapidly even when the surviving population was small. The northern elephant seal was hunted extensively and taken by collectors between the early 1800s and 1892, suffering an extreme population bottleneck as a consequence. Recovery was rapid and now there are over 200,000 individuals. We sequenced 260 modern and 8 historical northern elephant seal nuclear genomes to assess the impact of the population bottleneck on individual northern elephant seals and to better understand their recovery. Here we show that inbreeding, an increase in the frequency of alleles compromised by lost function, and allele frequency distortion, reduced the fitness of breeding males and females, as well as the performance of adult females on foraging migrations. We provide a detailed investigation of the impact of a severe bottleneck on fitness at the genomic level and report on the role of specific gene systems. [Abstract copyright: © 2024. The Author(s).

Runs of homozygosity in killer whale genomes provide a global record of demographic histories

Runs of homozygosity (ROH) occur when offspring inherit haplotypes that are identical by descent from each parent. Length distributions of ROH are informative about population history; specifically, the probability of inbreeding mediated by mating system and/or population demography. Here, we investigated whether variation in killer whale (Orcinus orca) demographic history is reflected in genome-wide heterozygosity and ROH length distributions, using a global data set of 26 genomes representative of geographic and ecotypic variation in this species, and two F1 admixed individuals with Pacific-Atlantic parentage. We first reconstructed demographic history for each population as changes in effective population size through time using the pairwise sequential Markovian coalescent (PSMC) method. We found a subset of populations declined in effective population size during the Late Pleistocene, while others had more stable demography. Genomes inferred to have undergone ancestral declines in effective population size, were autozygous at hundreds of short ROH (1.5 Mb) were found in low latitude populations, and populations of known conservation concern. These include a Scottish killer whale, for which 37.8% of the autosomes were comprised of ROH >1.5 Mb in length. The fate of this population, in which only two adult males have been sighted in the past five years, and zero fecundity over the last two decades, may be inextricably linked to its demographic history and consequential inbreeding depression

Research on Deep Defect Detection Method of Cable Lead Sealing Based on Improved Pulsed Eddy Current Excitation

In order to reduce power failures caused by lead sealing defects, it is necessary to carry out nondestructive testing of cable lead sealings. However, previous studies have focused on the detection of surface and near-surface defects of lead sealings. Thus, an improved pulsed eddy current detection (IPECD) method is introduced to detect the deep defects of cable lead sealings (with depths ranging from 6 to 12 mm), and the frequency range selection principle and the optimization method of initial phase angles of different frequency components of IPECD, used to maximize the peak value of the excitation signal, are first explained in detail. Then, the detection sensitivities of the deep defects before and after the optimization are compared and analyzed based on a simulation. Finally, using the IPECD method, experiments are conducted to study the effects of the defect depth on features of the lift-off point of intersection and the zero-crossing time, enhancing the foundation for the prediction or rapid detection of the depth of lead sealing defects

Research on Deep Defect Detection Method of Cable Lead Sealing Based on Improved Pulsed Eddy Current Excitation

In order to reduce power failures caused by lead sealing defects, it is necessary to carry out nondestructive testing of cable lead sealings. However, previous studies have focused on the detection of surface and near-surface defects of lead sealings. Thus, an improved pulsed eddy current detection (IPECD) method is introduced to detect the deep defects of cable lead sealings (with depths ranging from 6 to 12 mm), and the frequency range selection principle and the optimization method of initial phase angles of different frequency components of IPECD, used to maximize the peak value of the excitation signal, are first explained in detail. Then, the detection sensitivities of the deep defects before and after the optimization are compared and analyzed based on a simulation. Finally, using the IPECD method, experiments are conducted to study the effects of the defect depth on features of the lift-off point of intersection and the zero-crossing time, enhancing the foundation for the prediction or rapid detection of the depth of lead sealing defects

Fault identification of double-circuit transmission lines on the same pole based on EEMD energy ratio

In order to improve the sensitivity and reliability of traveling wave protection, on the basis of analyzing the relationship of the anti-traveling wave current amplitude in the window after the internal/external failure of the double circuit line on the same tower, a fault identification method based on EEMD energy ratio is proposed. Use EEMD decomposition to decompose the anti-traveling wave current in a time window after the fault into 7 scales, and extracts the EEMD energy ratio at each scale at both ends to form a feature vector. Then it is sent to the particle swarm optimization support vector machine (PSO-SVM) for training and testing, and the internal and external faults are identified. Experiments show that the algorithm has good fault identification ability, the fault accuracy is 95% and the method sensitivity is high

On Applications of Spiking Neural P Systems

Over the years, spiking neural P systems (SNPS) have grown into a popular model in membrane computing because of their diverse range of applications. In this paper, we give a comprehensive summary of applications of SNPS and its variants, especially highlighting power systems fault diagnoses with fuzzy reasoning SNPS. We also study the structure and workings of these models, their comparisons along with their advantages and disadvantages. We also study the implementation of these models in hardware. Finally, we discuss some new ideas which can further expand the scope of applications of SNPS models as well as their implementations

Infrared image-based detection method of electrical equipment overheating area in substation

For the detection of overheated areas of electrical equipment, in order to accurately segment out the overheated areas and reduce the fault detection range, this paper proposes a new overheated area detection algorithm. Firstly, the Ostu algorithm is used to remove the background and segment the general area of the electrical equipment area; secondly, the active contour model is used to refine the edge of the target area to remove the redundant edge points; finally, FCM clustering algorithm is used to suppress over segmentation and accurately divide the overheated area. The experiment proves that the algorithm can accurately divide the overheated area, and has certain practical value

A Review of Power System Fault Diagnosis with Spiking Neural P Systems

With the advancement of technologies it is becoming imperative to have a stable, secure and uninterrupted supply of power to electronic systems as well as to ensure the identification of faults occurring in these systems quickly and efficiently in case of any accident. Spiking neural P system (SNPS) is a popular parallel distributed computing model. It is inspired by the structure and functioning of spiking neurons. It belongs to the category of neural-like P systems and is well-known as a branch of the third generation neural networks. SNPS and its variants can perform the task of fault diagnosis in power systems efficiently. In this paper, we provide a comprehensive survey of these models, which can perform the task of fault diagnosis in transformers, power transmission networks, traction power supply systems, metro traction power supply systems, and electric locomotive systems. Furthermore, we discuss the use of these models in fault section estimation of power systems, fault location identification in distribution network, and fault line detection. We also discuss a software tool which can perform the task of fault diagnosis automatically. Finally, we discuss future research lines related to this topic