The application of superconducting materials is becoming more and more widespread. Traditionally, the discovery of new superconducting materials relies on the experience of experts and a large number of "trial and error" experiments, which not only increases the cost of experiments but also prolongs the period of discovering new superconducting materials. In recent years, machine learning has been increasingly applied to materials science. Based on this, this manuscript proposes the use of XGBoost model to identify superconductors; the first application of deep forest model to predict the critical temperature of superconductors; the first application of deep forest to predict the band gap of materials; and application of a new sub-network model to predict the Fermi energy level of materials. Compared with our known similar literature, all the above algorithms reach state-of-the-art. Finally, this manuscript uses the above models to search the COD public dataset and identify 50 candidate superconducting materials with possible critical temperature greater than 90 K.

近年来，人群计数已成为计算机视觉中的重要问题。在大多数方法中，密度图是通过从地面图中与人头中心标记的地面图图中的高斯内核进行卷积而产生的。由于CNN中的固定几何结构和模糊的头尺度信息，因此无法完全获得头部特征。提出了可变形的卷积来利用头部中CNN特征的尺度自适应能力。通过学习采样点的坐标偏移，可以提高调整接受场的能力。但是，头部在可变形卷积中的采样点并不统一，从而导致头部信息丢失。为了处理不均匀的采样，在本文中提出了改进的规范性卷积（\ textit {i.e。受NDLOSS限制的采样点的偏移往往更加均匀。然后，更完整地获得了头部中的功能，从而获得更好的性能。尤其是，拟议的NDCONV是一个轻巧的模块，与可变形卷积具有相似的计算负担。在广泛的实验中，我们的方法优于上海A，Shanghaitech B，UCF \ _QNRF和UCF \ _CC \ _50数据集，分别实现61.4、7.8、91.2和167.2 MAE。该代码可从https://github.com/bingshuangzhuzi/ndconv获得

随着人工智能的快速发展，材料数据库和机器学习的结合促进了材料信息学的进步。因为铝合金在许多领域被广泛使用，因此预测铝合金的性质是很重要的。在本文中，使用Al-Cu-Mg-X（X：Zn，Zr等）合金的数据输入组成，衰老条件（时间和温度）并预测其硬度。分别提出了基于自动机器学习和引入深度神经网络二级学习者的注意机制的集合学习解决方案。实验结果表明，选择正确的二级学习者可以进一步提高模型的预测准确性。该手稿介绍了基于深神经网络的二级学习者的注意机制，并获得了具有更好性能的融合模型。最佳模型的R平方为0.9697，MAE为3.4518hv。

在不规则的几何结构和高维空间的情况下，三维点云学习被广泛应用，但是点云仍无法令人满意地处理分类和识别任务。在3D空间中，点云由于其密度而倾向于具有规则的欧几里得结构。相反，由于高维度，高维空间的空间结构更为复杂，而点云主要在非欧洲结构中呈现。此外，在当前的3D点云分类算法中，基于欧几里得距离的规范胶囊算法很难有效分解并有效地识别非欧几里得结构。因此，针对3D和高维空间中非欧国人结构的点云分类任务时，本文是指基于测量距离的LLE算法，以优化并提出了高维点云的无监督算法。在本文中，在提取过程中考虑了点云的几何特征，以便将高维的非欧几里得结构转变为具有保持空间几何特征的较低维度的欧几里得结构。为了验证高维点云胶囊的无监督算法的可行性，在瑞士滚动数据集，点云MNIST数据集和点云LFW数据集中进行了实验。结果表明，（1）可以在瑞士滚动数据集中有效地确定（1）非欧几里得结构； （2）在Point Clouds MNIST数据集中实现了重要的无监督学习效果。总之，本文提出的高维点云无监督算法有利于扩展当前点云分类和识别任务的应用程序方案。

视觉变压器（VIT）的几乎没有射击的学习能力很少进行，尽管有很大的需求。在这项工作中，我们从经验上发现，使用相同的少数学习框架，例如\〜元基线，用VIT模型代替了广泛使用的CNN特征提取器，通常严重损害了几乎没有弹药的分类性能。此外，我们的实证研究表明，在没有归纳偏见的情况下，VIT通常会在几乎没有射击的学习方面学习低资格的令牌依赖性，在这些方案下，只有几个标记的培训数据可获得，这在很大程度上会导致上述性能降级。为了减轻这个问题，我们首次提出了一个简单而有效的几杆培训框架，即自我推广的监督（Sun）。具体而言，除了对全球语义学习的常规监督外，太阳还进一步预处理了少量学习数据集的VIT，然后使用它来生成各个位置特定的监督，以指导每个补丁令牌。此特定于位置的监督告诉VIT哪个贴片令牌相似或不同，因此可以加速令牌依赖的依赖学习。此外，它将每个贴片令牌中的本地语义建模，以提高对象接地和识别能力，以帮助学习可概括的模式。为了提高特定于位置的监督的质量，我们进一步提出了两种技术：〜1）背景补丁过滤以滤掉背景补丁并将其分配为额外的背景类别； 2）空间一致的增强，以引入足够的多样性以增加数据，同时保持生成的本地监督的准确性。实验结果表明，使用VITS的太阳显着超过了其他VIT的少量学习框架，并且是第一个获得比CNN最先进的效果更高的性能。

学习和概括与少数样本（少量学习）的新概念仍然是对现实世界应用的重要挑战。实现少量学习的原则方法是实现一种可以快速适应给定任务的上下文的模型。已经显示动态网络能够有效地学习内容自适应参数，使其适用于几次学习。在本文中，我们建议将卷积网络的动态内核作为手掌的任务的函数学习，从而实现更快的泛化。为此，我们基于整个任务和每个样本获得我们的动态内核，并在每个单独的频道和位置进行进一步调节机制。这导致动态内核，同时考虑可用的微型信息。我们经验证明，我们的模型在几次拍摄分类和检测任务上提高了性能，实现了几种基线模型的切实改进。这包括最先进的结果，以4次拍摄分类基准：迷你想象，分层 - 想象成，幼崽和FC100以及少量检测数据集的竞争结果：Coco-Pascal-VOC。

While deep convolutional neural networks (CNNs) have achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy photographs. The main reason is that their learned models are easy to overfit on the simplified AWGN model which deviates severely from the complicated real-world noise model. In order to improve the generalization ability of deep CNN denoisers, we suggest training a convolutional blind denoising network (CBDNet) with more realistic noise model and real-world noisy-clean image pairs. On the one hand, both signaldependent noise and in-camera signal processing pipeline is considered to synthesize realistic noisy images. On the other hand, real-world noisy photographs and their nearly noise-free counterparts are also included to train our CBD-Net. To further provide an interactive strategy to rectify denoising result conveniently, a noise estimation subnetwork with asymmetric learning to suppress under-estimation of noise level is embedded into CBDNet. Extensive experimental results on three datasets of real-world noisy photographs clearly demonstrate the superior performance of CBDNet over state-of-the-arts in terms of quantitative metrics and visual quality. The code has been made available at https://github.com/GuoShi28/CBDNet.

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

Learning feature interactions is the key to success for the large-scale CTR prediction and recommendation. In practice, handcrafted feature engineering usually requires exhaustive searching. In order to reduce the high cost of human efforts in feature engineering, researchers propose several deep neural networks (DNN)-based approaches to learn the feature interactions in an end-to-end fashion. However, existing methods either do not learn both vector-wise interactions and bit-wise interactions simultaneously, or fail to combine them in a controllable manner. In this paper, we propose a new model, xDeepInt, based on a novel network architecture called polynomial interaction network (PIN) which learns higher-order vector-wise interactions recursively. By integrating subspace-crossing mechanism, we enable xDeepInt to balance the mixture of vector-wise and bit-wise feature interactions at a bounded order. Based on the network architecture, we customize a combined optimization strategy to conduct feature selection and interaction selection. We implement the proposed model and evaluate the model performance on three real-world datasets. Our experiment results demonstrate the efficacy and effectiveness of xDeepInt over state-of-the-art models. We open-source the TensorFlow implementation of xDeepInt: https://github.com/yanyachen/xDeepInt.