In person re-identification (ReID) tasks, many works explore the learning of part features to improve the performance over global image features. Existing methods extract part features in an explicit manner, by either using a hand-designed image division or keypoints obtained with external visual systems. In this work, we propose to learn Discriminative implicit Parts (DiPs) which are decoupled from explicit body parts. Therefore, DiPs can learn to extract any discriminative features that can benefit in distinguishing identities, which is beyond predefined body parts (such as accessories). Moreover, we propose a novel implicit position to give a geometric interpretation for each DiP. The implicit position can also serve as a learning signal to encourage DiPs to be more position-equivariant with the identity in the image. Lastly, a set of attributes and auxiliary losses are introduced to further improve the learning of DiPs. Extensive experiments show that the proposed method achieves state-of-the-art performance on multiple person ReID benchmarks.

Anomaly Detection (AD), as a critical problem, has been widely discussed. In this paper, we specialize in one specific problem, Visual Defect Detection (VDD), in many industrial applications. And in practice, defect image samples are very rare and difficult to collect. Thus, we focus on the unsupervised visual defect detection and localization tasks and propose a novel framework based on the recent score-based generative models, which synthesize the real image by iterative denoising through stochastic differential equations (SDEs). Our work is inspired by the fact that with noise injected into the original image, the defects may be changed into normal cases in the denoising process (i.e., reconstruction). First, based on the assumption that the anomalous data lie in the low probability density region of the normal data distribution, we explain a common phenomenon that occurs when reconstruction-based approaches are applied to VDD: normal pixels also change during the reconstruction process. Second, due to the differences in normal pixels between the reconstructed and original images, a time-dependent gradient value (i.e., score) of normal data distribution is utilized as a metric, rather than reconstruction loss, to gauge the defects. Third, a novel $T$ scales approach is developed to dramatically reduce the required number of iterations, accelerating the inference process. These practices allow our model to generalize VDD in an unsupervised manner while maintaining reasonably good performance. We evaluate our method on several datasets to demonstrate its effectiveness.

Oriented normals are common pre-requisites for many geometric algorithms based on point clouds, such as Poisson surface reconstruction. However, it is not trivial to obtain a consistent orientation. In this work, we bridge orientation and reconstruction in implicit space and propose a novel approach to orient point cloud normals by incorporating isovalue constraints to the Poisson equation. Our key observation is that when using a point cloud with consistently oriented normals as the input for implicit surface reconstruction, the indicator function values of the sample points should be close to the isovalue of the surface. Based on this observation and the Poisson equation, we propose an optimization formulation that combines isovalue constraints with local consistency requirements for normals. We optimize normals and implicit functions simultaneously and solve for a globally consistent orientation. Thanks to the sparsity of the linear system, our method can work on an average laptop with reasonable computational time. Experiments show that our method can achieve high performance in non-uniform and noisy data and manage varying sampling densities, artifacts, multiple connected components, and nested surfaces.

微型机构中的一个重要问题是如何控制具有全局控制信号的大型微型机器人。本文着重于控制大规模的微棉机器人，并使用车载物理有限状态机器来控制。我们介绍了基于组的控制的概念，这使得可以扩大群体大小，同时降低机器人制造和群体控制的复杂性。我们证明，基于组的控制系统可以从机器人位置上进行本地访问。我们进一步基于广泛的模拟，即该系统在全球可控。提出了一种非线性优化策略，以最大程度地减少控制努力来控制群体。我们还提出了一种适合在线使用的概率完整的避免碰撞方法。本文以对模拟中提出的方法的评估结束。

通过将熵编解码器应用于学习的数据分布，神经压缩机在压缩比方面显着优于传统编解码器。但是，神经网络的高推断潜伏期阻碍了实际应用中神经压缩机的部署。在这项工作中，我们提出了仅整数离散流（IODF），这是一种具有仅整数算术的有效神经压缩机。我们的工作建立在整数离散流的基础上，该流程包括离散随机变量之间的可逆转换。我们提出了基于8位量化的纯整数算术的有效可逆转换。我们的可逆转换配备了可学习的二进制门，以在推理过程中去除冗余过滤器。我们在GPU上使用Tensorrt部署IODF，与现有最快的神经压缩机相比，达到10倍推理的速度，同时保留了Imagenet32和Imagenet64上的高压缩率。

Transfer learning increasingly becomes an important tool in handling data scarcity often encountered in machine learning. In the application of high-throughput thickness as a downstream process of the high-throughput optimization of optoelectronic thin films with autonomous workflows, data scarcity occurs especially for new materials. To achieve high-throughput thickness characterization, we propose a machine learning model called thicknessML that predicts thickness from UV-Vis spectrophotometry input and an overarching transfer learning workflow. We demonstrate the transfer learning workflow from generic source domain of generic band-gapped materials to specific target domain of perovskite materials, where the target domain data only come from limited number (18) of refractive indices from literature. The target domain can be easily extended to other material classes with a few literature data. Defining thickness prediction accuracy to be within-10% deviation, thicknessML achieves 92.2% (with a deviation of 3.6%) accuracy with transfer learning compared to 81.8% (with a deviation of 3.6%) 11.7% without (lower mean and larger standard deviation). Experimental validation on six deposited perovskite films also corroborates the efficacy of the proposed workflow by yielding a 10.5% mean absolute percentage error (MAPE).

我们训练一个神经网络模型，以预测宇宙N体模拟的全相空间演化。它的成功表明，神经网络模型正在准确地近似绿色的功能扩展，该功能将模拟的初始条件与其在深层非线性方向上的后期结合到结果。我们通过评估其在具有已知精确解决方案或充分理解扩展的简单情况下的良好理解的简单案例上的表现来测试这种近似值的准确性。这些场景包括球形构型，隔离平面波和两个相互作用的平面波：与用于训练的高斯随机场有很大不同的初始条件。我们发现我们的模型可以很好地推广到这些良好理解的方案，这表明网络已经推断了一般的物理原理，并从复杂的随机高斯训练数据中学习了非线性模式耦合。这些测试还为查找模型的优势和劣势以及确定改进模型的策略提供了有用的诊断。我们还测试了仅包含横向模式的初始条件，该模式的模式不仅在其相位上有所不同，而且还与训练集中使用的纵向生长模式相比。当网络遇到与训练集正交的这些初始条件时，该模型将完全失败。除了这些简单的配置外，我们还评估了模型对N体模拟的标准初始条件的密度，位移和动量功率谱的预测。我们将这些摘要统计数据与N体结果和称为COLA的近似快速模拟方法进行了比较。我们的模型在$ k \ sim 1 \ \ mathrm {mpc}^{ - 1} \，h $的非线性尺度上达到百分比精度，代表了对COLA的显着改进。

使用神经网络代表3D对象已变得流行。但是，许多以前的作品采用具有固定体系结构和大小的神经网络来表示不同的3D对象，这导致简单对象的网络参数过多，并且对复杂对象的重建精度有限。对于每个3D模型，希望拥有尽可能少的参数以实现高保真重建的端到端神经网络。在本文中，我们提出了一种利用神经体系结构搜索（NAS）和二进制分类的高效体素重建方法。以层数，每一层的节点数量以及每一层的激活函数为搜索空间，可以根据强化学习技术获得特定的网络体系结构。此外，为了摆脱网络推理后使用的传统表面重建算法（例如，行进立方体），我们通过对二进制体素进行分类来完成端到端网络。与其他签名的距离字段（SDF）预测或二进制分类网络相比，我们的方法使用更少的网络参数获得了更高的重建精度。

预先接受的语言模型实现了最先进的导致各种自然语言处理（NLP）任务。 GPT-3表明，缩放预先训练的语言模型可以进一步利用它们的巨大潜力。最近提出了一个名为Ernie 3.0的统一框架，以预先培训大型知识增强型号，并培训了具有10亿参数的模型。 Ernie 3.0在各种NLP任务上表现出最先进的模型。为了探讨缩放的表现，我们培养了百卢比的3.0泰坦参数型号，在PaddlePaddle平台上有高达260亿参数的泰坦。此外，我们设计了一种自我监督的对抗性损失和可控语言建模损失，以使ERNIE 3.0 TITAN产生可信和可控的文本。为了减少计算开销和碳排放，我们向Ernie 3.0泰坦提出了一个在线蒸馏框架，教师模型将同时教授学生和培训。埃塞尼3.0泰坦是迄今为止最大的中国密集预训练模型。经验结果表明，Ernie 3.0泰坦在68个NLP数据集中优于最先进的模型。

主动学习（AL）是应选择的数据用于注释。现有的工作试图选择高度不确定或信息性的注释数据。尽管如此，它仍然不清楚所选择的数据如何影响AL中使用的任务模型的测试性能。在这项工作中，我们通过理论上证明，选择更高梯度规范的未标记数据导致测试损失的较低的上限，从而探讨了这种影响，从而产生更好的测试性能。但是，由于缺乏标签信息，直接计算未标记数据的梯度标准是不可行的。为了解决这一挑战，我们提出了两种计划，即预期的Gradnorm和熵 - Gradnorm。前者通过构建预期的经验损失来计算梯度规范，而后者用熵构造无监督的损失。此外，我们将这两个方案集成在通用AL框架中。我们在古典图像分类和语义分割任务中评估我们的方法。为了展示其域应用程序的能力及其对噪声的鲁棒性，我们还在蜂窝成像分析任务中验证了我们的方法，即Cryo-Collecton Subtom图分类。结果表明，我们的方法达到了最先进的卓越性能。我们的源代码可在https://github.com/xulabs/aitom提供