Few-Shot Classification with Contrastive Learning

A two-stage training paradigm consisting of sequential pre-training and meta-training stages has been widely used in current few-shot learning (FSL) research. Many of these methods use self-supervised learning and contrastive learning to achieve new state-of-the-art results. However, the potential of contrastive learning in both stages of FSL training paradigm is still not fully exploited. In this paper, we propose a novel contrastive learning-based framework that seamlessly integrates contrastive learning into both stages to improve the performance of few-shot classification. In the pre-training stage, we propose a self-supervised contrastive loss in the forms of feature vector vs. feature map and feature map vs. feature map, which uses global and local information to learn good initial representations. In the meta-training stage, we propose a cross-view episodic training mechanism to perform the nearest centroid classification on two different views of the same episode and adopt a distance-scaled contrastive loss based on them. These two strategies force the model to overcome the bias between views and promote the transferability of representations. Extensive experiments on three benchmark datasets demonstrate that our method achieves competitive results.Comment: To appear in ECCV 202

Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction

Magnetic resonance (MR) image acquisition is an inherently prolonged process, whose acceleration by obtaining multiple undersampled images simultaneously through parallel imaging has always been the subject of research. In this paper, we propose the Dual-Octave Convolution (Dual-OctConv), which is capable of learning multi-scale spatial-frequency features from both real and imaginary components, for fast parallel MR image reconstruction. By reformulating the complex operations using octave convolutions, our model shows a strong ability to capture richer representations of MR images, while at the same time greatly reducing the spatial redundancy. More specifically, the input feature maps and convolutional kernels are first split into two components (i.e., real and imaginary), which are then divided into four groups according to their spatial frequencies. Then, our Dual-OctConv conducts intra-group information updating and inter-group information exchange to aggregate the contextual information across different groups. Our framework provides two appealing benefits: (i) it encourages interactions between real and imaginary components at various spatial frequencies to achieve richer representational capacity, and (ii) it enlarges the receptive field by learning multiple spatial-frequency features of both the real and imaginary components. We evaluate the performance of the proposed model on the acceleration of multi-coil MR image reconstruction. Extensive experiments are conducted on an {in vivo} knee dataset under different undersampling patterns and acceleration factors. The experimental results demonstrate the superiority of our model in accelerated parallel MR image reconstruction. Our code is available at: github.com/chunmeifeng/Dual-OctConv.Comment: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI) 202

Impact Time Control Cooperative Guidance Law Design Based on Modified Proportional Navigation

The paper proposes a two-dimensional impact time control cooperative guidance law under constant velocity and a three-dimensional impact time control cooperative guidance law under time-varying velocity, which can both improve the penetration ability and combat effectiveness of multi-missile systems and adapt to the complex and variable future warfare. First, a more accurate time-to-go estimation method is proposed, and based on which a modified proportional navigational guidance (MPNG) law with impact time constraint is designed in this paper, which is also effective when the initial leading angle is zero. Second, adopting cooperative guidance architecture with centralized coordination, using the MPNG law as the local guidance, and the desired impact time as the coordination variables, a two-dimensional impact time control cooperative guidance law under constant velocity is designed. Finally, a method of solving the expression of velocity is derived, and the analytic function of velocity with respect to time is given, a three-dimensional impact time control cooperative guidance law under time-varying velocity based on desired impact time is designed. Numerical simulation results verify the feasibility and applicability of the methods

Highly Efficient CuInSe2 Sensitized TiO2 Nanotube Films for Photocathodic Protection of 316 Stainless Steel

CuInSe2 nanoparticles were successfully deposited on the surface of TiO2 nanotube arrays (NTAs) by a solvothermal method for the photocathodic protection (PCP) of metals. Compared with TiO2 NTAs, the CuInSe2/TiO2 composites exhibited stronger visible light absorption and higher photoelectric conversion efficiency. After 316 Stainless Steel (SS) was coupled with CuInSe2/TiO2, the potential of 316 SS could drop to −0.90 V. The photocurrent density of CuInSe2/TiO2 connected to 316 SS reached 140 μA cm−2, which was four times that of TiO2 NTAs. The composites exhibited a protective effect in the dark state for more than 8 h after 4 h of visible light illumination. The above could be attributed to increased visible light absorption, the extended lifetime of photogenerated electrons, and generation of oxygen vacancies

Carbonate fractured gas reservoir prediction based on P-wave azimuthal anisotropy and dispersion

Carbonate fractured gas reservoir detection is very significant for the process of oil-gas exploration. It is difficult to characterize the reservoirs properly by traditional post-stack seismic attributes, because of the complexity of mineralogical composition and fluid type. Based on rock physics, it becomes possible to effectively solve this problem. In this paper, we combine seismic azimuthal anisotropy analysis and P-wave (primary wave) dispersion inversion based on the appropriate rock physics model in order to provide a method for carbonate fractured gas reservoir prediction. Firstly, referring to the geology and logging data of a carbonate fractured reservoir in the S area of Tarim basin in western China, we introduce the Voigt–Reuss–Hill theory into the Chapman model and set up an appropriate model which includes the influences of lithology and physical and fluid properties. Then, through seismic forward modeling and inversion based on this model, we find that attenuation azimuthal anisotropy is very sensitive to fracture density, and P-wave dispersion is closely linked to fluid type. By comprehensive analysis of these two attributes, we can characterize the reservoirs well. Finally, both attributes were applied to analysis of seismic field data for carbonate gas reservoir discrimination in the S area of the Tarim basin. The results show that zones with strong attenuation anisotropy and intense P-wave dispersion are likely to be favorable gas reservoirs. This is consistent with trial production data, hence demonstrating the great advantages of our method in carbonate gas exploration

Overexpression of <em>AtDREB1A</em> Causes a Severe Dwarf Phenotype by Decreasing Endogenous Gibberellin Levels in Soybean [<em>Glycine max</em> (L.) Merr.]

<div><p>Gibberellic acids (GAs) are plant hormones that play fundamental roles in plant growth and developmental processes. Previous studies have demonstrated that three key enzymes of GA20ox, GA3ox, and GA2ox are involved in GA biosynthesis. In this study, the <em>Arabidopsis DREB1A</em> gene driven by the CaMV 35S promoter was introduced into soybean plants by <em>Agrobacterium</em>- mediated transformation. The results showed that the transgenic soybean plants exhibited a typical phenotype of GA-deficient mutants, such as severe dwarfism, small and dark-green leaves, and late flowering compared to those of the non-transgenic plants. The dwarfism phenotype was rescued by the application of exogenous GA₃ once a week for three weeks with the concentrations of 144 µM or three times in one week with the concentrations of 60 µM. Quantitative RT-PCR analysis revealed that the transcription levels of the GA synthase genes were higher in the transgenic soybean plants than those in controls, whereas GA-deactivated genes except <em>GmGA2ox4</em> showed lower levels of expression. The transcript level of <em>GmGA2ox4</em> encoding the only deactivation enzyme using C₂₀-GAs as the substrates in soybean was dramatically enhanced in transgenic plants compared to that of wide type. Furthermore, the contents of endogenous bioactive GAs were significantly decreased in transgenic plants than those of wide type. The results suggested that <em>AtDREB1A</em> could cause dwarfism mediated by GA biosynthesis pathway in soybean.</p> </div