Learning from Future: A Novel Self-Training Framework for Semantic Segmentation

Self-training has shown great potential in semi-supervised learning. Its core idea is to use the model learned on labeled data to generate pseudo-labels for unlabeled samples, and in turn teach itself. To obtain valid supervision, active attempts typically employ a momentum teacher for pseudo-label prediction yet observe the confirmation bias issue, where the incorrect predictions may provide wrong supervision signals and get accumulated in the training process. The primary cause of such a drawback is that the prevailing self-training framework acts as guiding the current state with previous knowledge, because the teacher is updated with the past student only. To alleviate this problem, we propose a novel self-training strategy, which allows the model to learn from the future. Concretely, at each training step, we first virtually optimize the student (i.e., caching the gradients without applying them to the model weights), then update the teacher with the virtual future student, and finally ask the teacher to produce pseudo-labels for the current student as the guidance. In this way, we manage to improve the quality of pseudo-labels and thus boost the performance. We also develop two variants of our future-self-training (FST) framework through peeping at the future both deeply (FST-D) and widely (FST-W). Taking the tasks of unsupervised domain adaptive semantic segmentation and semi-supervised semantic segmentation as the instances, we experimentally demonstrate the effectiveness and superiority of our approach under a wide range of settings. Code will be made publicly available.Comment: Accepted to NeurIPS 202

The Ninth Visual Object Tracking VOT2021 Challenge Results

acceptedVersionPeer reviewe

Numerical study of the hydrodynamic performance of a point-absorbing wave energy converter

 As fossil energy is depleting and global warming effect is worsening rapidly, developing renewable energies becomes the top priority in most countries. In recent years, wave energy has attracted more and more attention due to its high energy density and enormous global capacity. The goal of this study is to carry out a numerical study of the hydrodynamic performance of a point-absorbing wave energy converter. In this study, an accurate and efficient numerical wave fume was established first. Commercial software code FLUENT?, which is a state-of-the-art computer program package for modeling fluid flow and heat transfer, was used for the numerical simulation. Based on the Navier-Stokes equations for viscous, incompressible fluid and Volume of fluid (VOF) method, a numerical wave tank was developed. Dynamic meshing method was used to simulate the wavemaker, and Geo-Reconstruct scheme was used to capture and reconstruct the free surface. A wave-absorbing method employing porous medium model was proposed to act as the wave absorbing beach, which can absorb the wave energy efficiently. A series of regular waves were simulated using the proposed numerical method. Validation has been made by physical experiments. After developing the wave flume model, a cylinder, which represents the point-absorbing wave energy converter (WEC), was added into the wave flume. The hydrodynamic behavior of the WEC was studied. The numerical results were also compared with physical experiments. Based on the numerical simulation results, suggestions on optimizing the point-absorber are provided. In this study, eight wave cases, with different wave period and wave length were simulated. The results show that the numerical simulation can match well with the physical wave tank result. Both the wave height and wave period in different cases can match well between the numerical simulation and physical wave tank results. In the wave-cylinder simulation, the results also show a good match in the numerical study and physical study. This numerical model is very significant in ocean structure design. The cylinder tested in this study can be easily changed to a ship or an offshore-platform. Compared with the physical experiment, numerical simulation is more flexible. The simulation can be carried on a large time span and spatial scale. The geometry can be changed easily. Also the cost of numerical simulation is relatively cheap compared with the physical experiment.published_or_final_versionMechanical EngineeringMasterMaster of Philosoph

What People Talk About Multi-Channel Purchasing Behavior and What They Intend to do: Related Perspective From ESG Evaluation System

With the development of the Internet economy, online shopping has become the main way for consumers to obtain goods, especially for organic infant milk. Do millennials who grew up in the era of Internet prefer online purchasing channel? Or are they stickier to online channel than offline channel? To solve these issues, we conduct the regression analysis of a latent class and the model of Quadratic Engel Almost Ideal Demand System aimed at the user stickiness in China. Moreover, we further analyze the environmental social governance effect of multi-channel stickiness, which is able to further explore the impact of environmental social governance investment strategy on consumers’ purchasing behavior. Through these analyses, we confirm the online channel stickiness and platform stickiness of Taobao. Results also indicate that (i) The primary factor influencing the inertia of consumption and trade volume is the channel and platform stickiness, the latter positively affects the former. (ii) The ESG rating index plays a positively moderating role in the consumers’ user stickiness. (iii) Environment and Social Score have a significant positive impact on online platform stickiness

Genome-Wide Identification and Chilling Stress Analysis of the NF-Y Gene Family in Melon

The nuclear factor Y (NF-Y) transcription factor contains three subfamilies: NF-YA, NF-YB, and NF-YC. The NF-Y family have been reported to be key regulators in plant growth and stress responses. However, little attention has been given to these genes in melon (Cucumis melo L.). In this study, twenty-five NF-Ys were identified in the melon genome, including six CmNF-YAs, eleven CmNF-YBs, and eight CmNF-YCs. Their basic information (gene location, protein characteristics, and subcellular localization), conserved domains and motifs, and phylogeny and gene structure were subsequently analyzed. Results showed highly conserved motifs exist in each subfamily, which are distinct between subfamilies. Most CmNF-Ys were expressed in five tissues and exhibited distinct expression patterns. However, CmNF-YA6, CmNF-YB1/B2/B3/B8, and CmNF-YC6 were not expressed and might be pseudogenes. Twelve CmNF-Ys were induced by cold stress, indicating the NF-Y family plays a key role in melon cold tolerance. Taken together, our findings provide a comprehensive understanding of CmNF-Y genes in the development and stress response of melon and provide genetic resources for solving the practical problems of melon production

Assessment of Wave Energy Resources in China

The evolution of renewable energy technologies may surmount fossil fuel disadvantages. Wave energy is considered one of the best alternatives to fossil energy due to its advantages. The 40-year (1979–2018) spatio-temporal distribution of wave energy is presented using European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) datasets. In addition, the common formula used for wave energy evaluation is derived from the linear wave theory in deep water, which is not applicable in shallow water. Therefore, a new equation that is suitable for both shallow and deep water is derived, which improves the accuracy of the wave energy evaluation for various water depths. The main aim is to investigate the spatio-temporal wave energy for offshore China from 1979 to 2018 and combine the new standard of classification to recommend the optimal area. The nearshore zone from Zhejiang province to Guandong province is considered the ideal zone, and the average annual wave energy density in this area is above 10 kW/m

H2O2 mediates ALA-induced glutathione and ascorbate accumulation in the perception and resistance to oxidative stress in Solanum lycopersicum at low temperatures

Abstract Background Low temperature is a crucial factor influencing plant growth and development. The chlorophyll precursor, 5-aminolevulinic acid (ALA) is widely used to improve plant cold tolerance. However, the interaction between H2O2 and cellular redox signaling involved in ALA-induced resistance to low temperature stress in plants remains largely unknown. Here, the roles of ALA in perceiving and regulating low temperature-induced oxidative stress in tomato plants, together with the roles of H2O2 and cellular redox states, were characterized. Results Low concentrations (10–25 mg·L− 1) of ALA enhanced low temperature-induced oxidative stress tolerance of tomato seedlings. The most effective concentration was 25 mg·L− 1, which markedly increased the ratio of reduced glutathione and ascorbate (GSH and AsA), and enhanced the activities of superoxide dismutase, catalase, ascorbate peroxidase, dehydroascorbate reductase, and glutathione reductase. Furthermore, gene expression of respiratory burst oxidase homolog1 and H2O2 content were upregulated with ALA treatment under normal conditions. Treatment with exogenous H2O2, GSH, and AsA also induced plant tolerance to oxidative stress at low temperatures, while inhibition of GSH and AsA syntheses significantly decreased H2O2-induced oxidative stress tolerance. Meanwhile, scavenging or inhibition of H2O2 production weakened, but did not eliminate, GSH- or AsA- induced tomato plant tolerance to oxidative stress at low temperatures. Conclusions Appropriate concentrations of ALA alleviated the low temperature-induced oxidative stress in tomato plants via an antioxidant system. The most effective concentration was 25 mg·L− 1. The results showed that H2O2 induced by exogenous ALA under normal conditions is crucial and may be the initial step for perception and signaling transmission, which then improves the ratio of GSH and AsA. GSH and AsA may then interact with H2O2 signaling, resulting in enhanced antioxidant capacity in tomato plants at low temperatures