Exploring the Equivalence of Siamese Self-Supervised Learning via A Unified Gradient Framework

Self-supervised learning has shown its great potential to extract powerful visual representations without human annotations. Various works are proposed to deal with self-supervised learning from different perspectives: (1) contrastive learning methods (e.g., MoCo, SimCLR) utilize both positive and negative samples to guide the training direction; (2) asymmetric network methods (e.g., BYOL, SimSiam) get rid of negative samples via the introduction of a predictor network and the stop-gradient operation; (3) feature decorrelation methods (e.g., Barlow Twins, VICReg) instead aim to reduce the redundancy between feature dimensions. These methods appear to be quite different in the designed loss functions from various motivations. The final accuracy numbers also vary, where different networks and tricks are utilized in different works. In this work, we demonstrate that these methods can be unified into the same form. Instead of comparing their loss functions, we derive a unified formula through gradient analysis. Furthermore, we conduct fair and detailed experiments to compare their performances. It turns out that there is little gap between these methods, and the use of momentum encoder is the key factor to boost performance. From this unified framework, we propose UniGrad, a simple but effective gradient form for self-supervised learning. It does not require a memory bank or a predictor network, but can still achieve state-of-the-art performance and easily adopt other training strategies. Extensive experiments on linear evaluation and many downstream tasks also show its effectiveness. Code is released at https://github.com/fundamentalvision/UniGrad.Comment: CVPR202

Parameter Calibration Method of Microscopic Traffic Flow Simulation Models based on Orthogonal Genetic Algorithm

Abstract-Traffic microscopic traffic simulation models have become extensively used in both transportation operations and management analyses, which are very useful in reflecting the dynamic nature of transportation system in a stochastic manner. As far as the microscopic traffic flow simulation users are concerned, the one of the major concerns would be the appropriate calibration of the simulation models. In this paper a parameter calibration method of microscopic traffic flow simulation models based on orthogonal genetic algorithm is presented. In order to improve the capacity of locating a possible solution in solution space, the proposed method incorporates the orthogonal experimental design method into the genetic algorithm. The proposed method is applied to an arterial section of Ronghua Road in Beijing. Through comparing with the parameter calibration method based on genetic algorithm, the advantage of the proposed method is shown

Can We Transfer Noise Patterns? An Multi-environment Spectrum Analysis Model Using Generated Cases

Spectrum analysis systems in online water quality testing are designed to detect types and concentrations of pollutants and enable regulatory agencies to respond promptly to pollution incidents. However, spectral data-based testing devices suffer from complex noise patterns when deployed in non-laboratory environments. To make the analysis model applicable to more environments, we propose a noise patterns transferring model, which takes the spectrum of standard water samples in different environments as cases and learns the differences in their noise patterns, thus enabling noise patterns to transfer to unknown samples. Unfortunately, the inevitable sample-level baseline noise makes the model unable to obtain the paired data that only differ in dataset-level environmental noise. To address the problem, we generate a sample-to-sample case-base to exclude the interference of sample-level noise on dataset-level noise learning, enhancing the system's learning performance. Experiments on spectral data with different background noises demonstrate the good noise-transferring ability of the proposed method against baseline systems ranging from wavelet denoising, deep neural networks, and generative models. From this research, we posit that our method can enhance the performance of DL models by generating high-quality cases. The source code is made publicly available online at https://github.com/Magnomic/CNST

JB-UIDS -- An interactive UIMS based on OSF/Motif

This paper presents the principle, functions, features and implementation of a general graphical user interface management system based on OSF/Motif--JB-UIDS, which is a part of the integrated software engineering environment CASE( Computer Aided Software Engineering ), named JB( Jade Bird ). The visual and interactive UIMS can help the interface designer to generate user interface automatically and then refine it interactively. It adopts a new method of describing internal application interface based on Object-Oriented ideas to support the separation of user interface component from computational component. JB-UIDS has been implemented on SCO-ODT and has good portability and flexibility

Experimental Investigations on the Deformation and Breakup of Hundred-Micron Droplet Driven by Shock Wave

This study examines the process of a 240 µm droplet breakup under a shock wave through experiments using a double-pulse laser holographic test technique on a shock tube. The technique allowed for high-resolution data to be obtained at the micron-nanosecond level, including the Weber number distribution of deformation and breakup modes for droplets of different sizes and loads. Results were compared with larger droplets at the same Weber number, revealing that higher Weber numbers result in more difficulty in droplet breakup, longer deformation times, and increased deformation behavior. At low Weber numbers within the critical range, changes in droplet diameter affect the Rayleigh–Taylor waves and alter the droplet’s characteristics. The study also investigates the laws and reasons behind windward displacement variation for hundred-micron droplets at different Weber numbers over time

A Disturbed Siderophore Transport Inhibits Myxobacterial Predation

Background: Understanding the intrinsic mechanisms of bacterial competition is a fundamental question. Iron is an essential trace nutrient that bacteria compete for. The most prevalent manner for iron scavenging is through the secretion of siderophores. Although tremendous efforts have focused on elucidating the molecular mechanisms of siderophores biosynthesis, export, uptake, and regulation of siderophores, the ecological aspects of siderophore-mediated competition are not well understood. Methods: We performed predation and bacterial competition assays to investigate the function of siderophore transport on myxobacterial predation. Results: Deletion of msuB, which encodes an iron chelate uptake ABC transporter family permease subunit, led to a reduction in myxobacterial predation and intracellular iron, but iron deficiency was not the predominant reason for the decrease in the predation ability of the ∆msuB mutant. We further confirmed that obstruction of siderophore transport decreased myxobacterial predation by investigating the function of a non-ribosomal peptide synthetase for siderophore biosynthesis, a TonB-dependent receptor, and a siderophore binding protein in M. xanthus. Our results showed that the obstruction of siderophores transport decreased myxobacterial predation ability through the downregulation of lytic enzyme genes, especially outer membrane vesicle (OMV)-specific proteins. Conclusions: This work provides insight into the mechanism of siderophore-mediated competition in myxobacteria

A K-means Algorithm Based On Feature Weighting

Cluster analysis is a statistical analysis technique that divides the research objects into relatively homogeneous groups. The core of cluster analysis is to find useful clusters of objects. K-means clustering algorithm has been receiving much attention from scholars because of its excellent speed and good scalability. However, the traditional K-means algorithm does not consider the influence of each attribute on the final clustering result, which makes the accuracy of clustering have a certain impact. In response to the above problems, this paper proposes an improved feature weighting algorithm. The improved algorithm uses the information gain and ReliefF feature selection algorithm to weight the features and correct the distance function between clustering objects, so that the algorithm can achieve more accurate and efficient clustering effect. The simulation results show that compared with the traditional K-means algorithm, the improved algorithm clustering results are stable, and the accuracy of clustering is significantly improved

Advances on the application of biochar in radioactive wastewater treatment

The domestic and foreign scholars found that biochar prepared from waste crops, animal manure, and tissues has a good adsorption effect on radioactive pollutants. Biochar is an ideal material for the adsorbent. This article summarizes the preparation methods and modification methods of biochar, and the adsorption characteristics of biochar to heavy metals and radioactive elements. This article also points out the problems and development trends in the current research of biochar