Explore the Power of Dropout on Few-shot Learning

The generalization power of the pre-trained model is the key for few-shot deep learning. Dropout is a regularization technique used in traditional deep learning methods. In this paper, we explore the power of dropout on few-shot learning and provide some insights about how to use it. Extensive experiments on the few-shot object detection and few-shot image classification datasets, i.e., Pascal VOC, MS COCO, CUB, and mini-ImageNet, validate the effectiveness of our method.Comment: arXiv admin note: substantial text overlap with arXiv:2210.0640

Correlation between the strength of low-temperature T-linear normal-state resistivity and TcT_{\rm c} in overdoped electron-doped cuprate superconductors

The recently observed an intimate link between the nature of the strange metallic normal-state and superconductivity in the overdoped electron-doped cuprate superconductors is calling for an explanation. Here the intrinsic correlation between the strength of the low-temperature linear-in-temperature normal-state resistivity and superconducting transition temperature $T_{\rm c}$ in the overdoped electron-doped cuprate superconductors is studied within the framework of the kinetic-energy-driven superconductivity. On the one hand, the main ingredient is identified into a electron pairing mechanism involving {\it the spin excitation}, and then $T_{\rm c}$ has a dome-like shape doping dependence with the maximal $T_{\rm c}$ that occurs at around the optimal electron doping. On the other hand, in the normal-state above $T_{\rm c}$, the low-temperature linear-in-temperature normal-state resistivity in the overdoped regime arises from the momentum relaxation due to the electron umklapp scattering mediated by {\it the same spin excitation}. This {\it same spin excitation} that governs both the electron umklapp scattering responsible for the low-temperature linear-in-temperature normal-state resistivity and electron pairing responsible for superconductivity naturally generates a correlation between the strength of the low-temperature linear-in-temperature normal-state resistivity and $T_{\rm c}$ in the overdoped regime.Comment: 12 pages, 6 figures. arXiv admin note: text overlap with arXiv:2211.0308

T-linear resistivity in the strange-metal phase of cuprate superconductors due to umklapp scattering from a spin excitation

The strange-metal phase of cuprate superconductors exhibits a linear in temperature resistivity, however, the origin of this remarkable anomaly is still not well understood. Here the linear temperature dependence of the electrical resistivity in the strange-metal phase of cuprate superconductors is investigated from the underdoped to overdoped regimes. The momentum dependence of the transport scattering rate arising from the umklapp scattering between electrons by the exchange of the spin excitation is derived and employed to calculate the electrical resistivity by making use of the Boltzmann equation. It is shown that the antinodal umklapp scattering leads to the linear in temperature resistivity in the low-temperature with the temperature linear coefficient that decreases with the increase of the doping concentration, however, the nodal umklapp scattering induces a deviation from the linear in temperature resistivity in the far lower temperature, and then the quadratic in temperature resistivity in the far lower temperature is generated by both the antinodal and nodal umklapp scattering. The theory also shows that the same spin excitation that acts like a bosonic glue to hold the electron pairs together also mediates scattering of electrons in the strange-metal phase of cuprtae superconductors responsible for the linear in temperature resistivity and the associated electronic structure.Comment: 16 pages, 11 figure

Visual Exploration of 3D Shape Databases via Feature Selection

We present a visual analytics approach for constructing effective visual representations of 3D shape databases as projections of multidimensional feature vectors extracted from their shapes. We present several methods to construct effective projections in which different-class shapes are well separated from each other. First, we propose a greedy heuristic for searching for near-optimal projections in the space of feature combinations. Next, we show how human insight can improve the quality of the constructed projections by iteratively identifying and selecting a small subset features that are responsible for characterizing different classes. Our methods allow users to construct high-quality projections with low effort, to explain these projections in terms of the contribution of different features, and to identify both useful features and features that work adversely for the separation task. We demonstrate our approach on a real-world 3D shape database

The mechanism of the irradiation synergistic effect of Silicon bipolar junction transistors explained by multiscale simulations of Monte Carlo and excited-state first-principle calculations

Neutron and $\gamma$-ray irradiation damages to transistors are found to be non-additive, and this is denoted as the irradiation synergistic effect (ISE). Its mechanism is not well-understood. The recent defect-based model [ACS Appl. Electron. Mater. 2, 3783 (2020)] for Silicon bipolar junction transistors (BJT) achieve quantitative agreement with experiments, but its assumptions on the defect reactions are unverified. Going beyond the model requires directly representing the effect of $\gamma$-ray irradiation in first-principles calculations, which is not feasible previously. In this work, we examine the defect-based model of the ISE by developing a multiscale method for the simulation of the $\gamma$-ray irradiation, where the $\gamma$-ray-induced electronic excitations are treated explicitly in excited-state first-principles calculations. We find the calculations agree with experiments, and the effect of the $\gamma$-ray-induced excitation is significantly different from the effects of defect charge state and temperature. We propose a diffusion-based qualitative explanation of the mechanism of positive/negative ISE in NPN/PNP BJTs in the end.Comment: 11 pages, 7 figures. Accepted by J. Chem. Phy

Rethinking Pseudo-LiDAR Representation

The recently proposed pseudo-LiDAR based 3D detectors greatly improve the benchmark of monocular/stereo 3D detection task. However, the underlying mechanism remains obscure to the research community. In this paper, we perform an in-depth investigation and observe that the efficacy of pseudo-LiDAR representation comes from the coordinate transformation, instead of data representation itself. Based on this observation, we design an image based CNN detector named Patch-Net, which is more generalized and can be instantiated as pseudo-LiDAR based 3D detectors. Moreover, the pseudo-LiDAR data in our PatchNet is organized as the image representation, which means existing 2D CNN designs can be easily utilized for extracting deep features from input data and boosting 3D detection performance. We conduct extensive experiments on the challenging KITTI dataset, where the proposed PatchNet outperforms all existing pseudo-LiDAR based counterparts. Code has been made available at: https://github.com/xinzhuma/patchnet.Comment: ECCV2020. Supplemental Material attache