SMMix: Self-Motivated Image Mixing for Vision Transformers

CutMix is a vital augmentation strategy that determines the performance and generalization ability of vision transformers (ViTs). However, the inconsistency between the mixed images and the corresponding labels harms its efficacy. Existing CutMix variants tackle this problem by generating more consistent mixed images or more precise mixed labels, but inevitably introduce heavy training overhead or require extra information, undermining ease of use. To this end, we propose an efficient and effective Self-Motivated image Mixing method (SMMix), which motivates both image and label enhancement by the model under training itself. Specifically, we propose a max-min attention region mixing approach that enriches the attention-focused objects in the mixed images. Then, we introduce a fine-grained label assignment technique that co-trains the output tokens of mixed images with fine-grained supervision. Moreover, we devise a novel feature consistency constraint to align features from mixed and unmixed images. Due to the subtle designs of the self-motivated paradigm, our SMMix is significant in its smaller training overhead and better performance than other CutMix variants. In particular, SMMix improves the accuracy of DeiT-T/S, CaiT-XXS-24/36, and PVT-T/S/M/L by more than +1% on ImageNet-1k. The generalization capability of our method is also demonstrated on downstream tasks and out-of-distribution datasets. Code of this project is available at https://github.com/ChenMnZ/SMMix

Effects of different cooling methods on the carbon footprint of cooked rice

peer-reviewedGlobal warming has become a serious problem facing the international community. All countries strive to reduce greenhouse gas (GHG) emissions. The food system produces a large amount of GHGs, and thus study of the carbon footprint (CF) in the food industry has attracted the attention of researchers. Based on the lifecycle assessment (LCA) method, the present study calculated CFs of cooling of cooked rice, as a unit operation under different operational conditions. The results showed that the carbon footprints for cooling 200 g cooked rice were 54.36 ± 1.07 gCO2eq for refrigerator cooling at 0 °C, 66.05 ± 2.00 g CO2eq for refrigerator cooling at 8 °C, 741.55 ± 27.26 g CO2eq for vacuum cooling, 1914.10 ± 141.24 g CO2eq for air blast cooling at 0 °C, 2463.61 ± 221.21 g CO2eq for air blast cooling at 3 °C, and 3916.54 ± 202.28 g CO2eq for air blast cooling at 8 °C. In addition, the CF for the cooling process was positively correlated with the output power of equipment and the cooling time. The carbon emissions arising from electricity consumption contributed to most of the CF for the cooling process. Sensitivity analysis of the parameters for the CF for the cooling process revealed that the CF of cooling process was stable for the applied equipment emission factor, but sensitive to the efficiency of electricity use and the extent of load. Improving the efficiency of electricity use and increasing cooling load could reduce the final CF of a product

Simultaneously suppressing the dendritic lithium growth and polysulfides migration by a polyethyleneimine grafted bacterial cellulose membrane in lithium-sulfur batteries

Owing to the ultrahigh theoretical energy density and low-cost, lithium-sulfur (Li-S) batteries hold broad prospects as one of the promising substitutes for commercial lithium-ion batteries. The polysulfides shuttling originated from sulfur cathode and the lithium dendrite growth from lithium anode are the main challenges that hinder the commercial survival of Li-S batteries. Herein, thermal stable bacterial cellulose (BC) separator is successfully fixed with polyethyleneimine (PEI) by a scalable chemical grafting. The hydroxyl groups and amino groups in PEI grafted BC (PEI@BC) separator can participate in the formation of Li2O and Li3N, respectively, contributing to robust solid electrolyte interface with high ionic conductivity. Therefore, the lithium deposition is well regulated, resulting in a spherical and dendrite-free Li deposit pattern. The Li/Li symmetrical cell assembled with PEI@BC separator exhibits excellent cyclic stability, which can continuously plate/stripe for more than 820 h with an overpotential of ∼ 40 mV at 2 mA cm−2. Meanwhile, the polar amino group can restrain the polysulfides migration via chemosorption. As a consequence of these merits, ultrahigh initial capacity (1402 mAh g−1 at 0.1C) and excellent rate performance (440.5 mAh g−1 at 2C) for Li-S full cell are achieved, presenting new insights into the fabrication of multifunctional separators for Li-S batteries

Synthetic Datasets for Autonomous Driving: A Survey

Autonomous driving techniques have been flourishing in recent years while thirsting for huge amounts of high-quality data. However, it is difficult for real-world datasets to keep up with the pace of changing requirements due to their expensive and time-consuming experimental and labeling costs. Therefore, more and more researchers are turning to synthetic datasets to easily generate rich and changeable data as an effective complement to the real world and to improve the performance of algorithms. In this paper, we summarize the evolution of synthetic dataset generation methods and review the work to date in synthetic datasets related to single and multi-task categories for to autonomous driving study. We also discuss the role that synthetic dataset plays the evaluation, gap test, and positive effect in autonomous driving related algorithm testing, especially on trustworthiness and safety aspects. Finally, we discuss general trends and possible development directions. To the best of our knowledge, this is the first survey focusing on the application of synthetic datasets in autonomous driving. This survey also raises awareness of the problems of real-world deployment of autonomous driving technology and provides researchers with a possible solution.Comment: 19 pages, 5 figure

A Deep Learning Approach to Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data

Solar activity is usually caused by the evolution of solar magnetic fields. Magnetic field parameters derived from photospheric vector magnetograms of solar active regions have been used to analyze and forecast eruptive events such as solar flares and coronal mass ejections. Unfortunately, the most recent solar cycle 24 was relatively weak with few large flares, though it is the only solar cycle in which consistent time-sequence vector magnetograms have been available through the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) since its launch in 2010. In this paper, we look into another major instrument, namely the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO) from 1996 to 2010. The data archive of SOHO/MDI covers more active solar cycle 23 with many large flares. However, SOHO/MDI data only has line-of-sight (LOS) magnetograms. We propose a new deep learning method, named MagNet, to learn from combined LOS magnetograms, Bx and By taken by SDO/HMI along with H-alpha observations collected by the Big Bear Solar Observatory (BBSO), and to generate vector components Bx' and By', which would form vector magnetograms with observed LOS data. In this way, we can expand the availability of vector magnetograms to the period from 1996 to present. Experimental results demonstrate the good performance of the proposed method. To our knowledge, this is the first time that deep learning has been used to generate photospheric vector magnetograms of solar active regions for SOHO/MDI using SDO/HMI and H-alpha data.Comment: 15 pages, 6 figure