基于合奏的大规模模拟动态系统对于广泛的科学和工程问题至关重要。模拟中使用的常规数值求解器受到时间整合的步长显着限制，这会阻碍效率和可行性，尤其是在需要高精度的情况下。为了克服这一限制，我们提出了一种数据驱动的校正方法，该方法允许使用大型步骤，同时补偿了积分误差以提高精度。该校正器以矢量值函数的形式表示，并通过神经网络建模以回归相空间中的误差。因此，我们将校正神经矢量（Neurvec）命名。我们表明，Neurvec可以达到与传统求解器具有更大步骤尺寸的传统求解器相同的准确性。我们从经验上证明，Neurvec可以显着加速各种数值求解器，并克服这些求解器的稳定性限制。我们关于基准问题的结果，从高维问题到混乱系统，表明Neurvec能够捕获主要的误差项并保持整体预测的统计数据。

最近，提出了许多插件自我发场模块（SAM），以通过利用深卷积神经网络（CNN）的内部信息来增强模型的概括。通常，先前的作品忽略了插入SAM的位置，因为它们将SAM与整个CNN主链的每个块分别连接在一起，从而导致逐步计算成本和随着网络深度增长的参数数量。但是，我们从经验上找到并验证了一些违反直觉现象，这些现象：（a）将SAM连接到所有块可能并不总是带来最大的性能提升，并且连接到部分块将更好； （b）将SAMS添加到CNN中可能并不总是带来性能提升，相反，它甚至可能会损害原始CNN骨架的性能。因此，我们阐明并演示了自我发挥网络的彩票票证假设：一个完整​​的自我发项网络包含一个带有稀疏自我注意连接的子网络，可以（1）加速推理，（2）减少额外的参数增量，（3） ）保持准确性。除经验证据外，我们的理论证据还支持这一假设。此外，我们提出了一种简单而有效的基于加强学习的方法来搜索机票，即满足上述三个条件的连接方案。广泛使用的基准数据集和流行的自我注意力网络的广泛实验显示了我们方法的有效性。此外，我们的实验表明，我们的搜索机票具有转移到某些视觉任务（例如人群计数和细分）的能力。

设计高维偏微分方程（PDE）的高效和准确的数值求解器仍然是计算科学和工程中的一个具有挑战性且重要的主题，这主要是由于“设计数字方案”在设计中的“维度诅咒”。一种新方法，在具有有限的分析表达式的功能空间中寻求近似PDE解决方案，因此，该方法被命名为有限的表达方法（FEX）。在近似理论中证明，FEX可以避免维克斯的诅咒。作为概念的证明，提出了一种深入的增强学习方法，以在不同维度上为各种高维PDE实施FEX，以在维度和可依式的时间复杂性中具有内存复杂性多项式的高度甚至机器的精度。具有有限解决方案的近似解决方案分析表达式还提供了对地面真相PDE解决方案的可解释见解，这可以进一步帮助提高对物理系统的理解，并为精制解决方案设计后处理技术。

本文提出了一个无网格的计算框架和机器学习理论，用于在未知的歧管上求解椭圆形PDE，并根据扩散地图（DM）和深度学习确定点云。 PDE求解器是作为监督的学习任务制定的，以解决最小二乘回归问题，该问题施加了近似PDE的代数方程（如果适用）。该代数方程涉及通过DM渐近扩展获得的图形拉平型矩阵，该基质是二阶椭圆差差算子的一致估计器。最终的数值方法是解决受神经网络假设空间解决方案的高度非凸经验最小化问题。在体积良好的椭圆PDE设置中，当假设空间由具有无限宽度或深度的神经网络组成时，我们表明，经验损失函数的全球最小化器是大型训练数据极限的一致解决方案。当假设空间是一个两层神经网络时，我们表明，对于足够大的宽度，梯度下降可以识别经验损失函数的全局最小化器。支持数值示例证明了解决方案的收敛性，范围从具有低和高共限度的简单歧管到具有和没有边界的粗糙表面。我们还表明，所提出的NN求解器可以在具有概括性误差的新数据点上稳健地概括PDE解决方案，这些误差几乎与训练错误相同，从而取代了基于Nystrom的插值方法。

量化城市道路网络（URNS）不同部分的拓扑相似之处使我们能够了解城市成长模式。虽然传统统计信息提供有关单个节点的直接邻居或整个网络的特性的有用信息，但是这种度量无法衡量考虑本地间接邻域关系的子网的相似性。在这项研究中，我们提出了一种基于图的机器学习方法来量化子网的空间均匀性。我们将该方法应用于全球30个城市的11,790个城市道路网络，以衡量每个城市和不同城市的道路网络的空间均匀性。我们发现，城市内的空间均匀性与诸如GDP和人口增长的社会经济地位高度相关。此外，通过在不同城市转移模型获得的城市间空间均匀性揭示了欧洲的城市网络结构的城市网络结构间相似性，传递给美国和亚洲的城市。可以利用使用我们的方法揭示的社会经济发展和城市间相似性，以了解和转移城市的洞察力。它还使我们能够解决城市政策挑战，包括在迅速城市化地区的网络规划，并打击区域不平等。

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.

Decompilation aims to transform a low-level program language (LPL) (eg., binary file) into its functionally-equivalent high-level program language (HPL) (e.g., C/C++). It is a core technology in software security, especially in vulnerability discovery and malware analysis. In recent years, with the successful application of neural machine translation (NMT) models in natural language processing (NLP), researchers have tried to build neural decompilers by borrowing the idea of NMT. They formulate the decompilation process as a translation problem between LPL and HPL, aiming to reduce the human cost required to develop decompilation tools and improve their generalizability. However, state-of-the-art learning-based decompilers do not cope well with compiler-optimized binaries. Since real-world binaries are mostly compiler-optimized, decompilers that do not consider optimized binaries have limited practical significance. In this paper, we propose a novel learning-based approach named NeurDP, that targets compiler-optimized binaries. NeurDP uses a graph neural network (GNN) model to convert LPL to an intermediate representation (IR), which bridges the gap between source code and optimized binary. We also design an Optimized Translation Unit (OTU) to split functions into smaller code fragments for better translation performance. Evaluation results on datasets containing various types of statements show that NeurDP can decompile optimized binaries with 45.21% higher accuracy than state-of-the-art neural decompilation frameworks.

Image Virtual try-on aims at replacing the cloth on a personal image with a garment image (in-shop clothes), which has attracted increasing attention from the multimedia and computer vision communities. Prior methods successfully preserve the character of clothing images, however, occlusion remains a pernicious effect for realistic virtual try-on. In this work, we first present a comprehensive analysis of the occlusions and categorize them into two aspects: i) Inherent-Occlusion: the ghost of the former cloth still exists in the try-on image; ii) Acquired-Occlusion: the target cloth warps to the unreasonable body part. Based on the in-depth analysis, we find that the occlusions can be simulated by a novel semantically-guided mixup module, which can generate semantic-specific occluded images that work together with the try-on images to facilitate training a de-occlusion try-on (DOC-VTON) framework. Specifically, DOC-VTON first conducts a sharpened semantic parsing on the try-on person. Aided by semantics guidance and pose prior, various complexities of texture are selectively blending with human parts in a copy-and-paste manner. Then, the Generative Module (GM) is utilized to take charge of synthesizing the final try-on image and learning to de-occlusion jointly. In comparison to the state-of-the-art methods, DOC-VTON achieves better perceptual quality by reducing occlusion effects.

In recent years, the Transformer architecture has shown its superiority in the video-based person re-identification task. Inspired by video representation learning, these methods mainly focus on designing modules to extract informative spatial and temporal features. However, they are still limited in extracting local attributes and global identity information, which are critical for the person re-identification task. In this paper, we propose a novel Multi-Stage Spatial-Temporal Aggregation Transformer (MSTAT) with two novel designed proxy embedding modules to address the above issue. Specifically, MSTAT consists of three stages to encode the attribute-associated, the identity-associated, and the attribute-identity-associated information from the video clips, respectively, achieving the holistic perception of the input person. We combine the outputs of all the stages for the final identification. In practice, to save the computational cost, the Spatial-Temporal Aggregation (STA) modules are first adopted in each stage to conduct the self-attention operations along the spatial and temporal dimensions separately. We further introduce the Attribute-Aware and Identity-Aware Proxy embedding modules (AAP and IAP) to extract the informative and discriminative feature representations at different stages. All of them are realized by employing newly designed self-attention operations with specific meanings. Moreover, temporal patch shuffling is also introduced to further improve the robustness of the model. Extensive experimental results demonstrate the effectiveness of the proposed modules in extracting the informative and discriminative information from the videos, and illustrate the MSTAT can achieve state-of-the-art accuracies on various standard benchmarks.

It has been observed in practice that applying pruning-at-initialization methods to neural networks and training the sparsified networks can not only retain the testing performance of the original dense models, but also sometimes even slightly boost the generalization performance. Theoretical understanding for such experimental observations are yet to be developed. This work makes the first attempt to study how different pruning fractions affect the model's gradient descent dynamics and generalization. Specifically, this work considers a classification task for overparameterized two-layer neural networks, where the network is randomly pruned according to different rates at the initialization. It is shown that as long as the pruning fraction is below a certain threshold, gradient descent can drive the training loss toward zero and the network exhibits good generalization performance. More surprisingly, the generalization bound gets better as the pruning fraction gets larger. To complement this positive result, this work further shows a negative result: there exists a large pruning fraction such that while gradient descent is still able to drive the training loss toward zero (by memorizing noise), the generalization performance is no better than random guessing. This further suggests that pruning can change the feature learning process, which leads to the performance drop of the pruned neural network. Up to our knowledge, this is the \textbf{first} generalization result for pruned neural networks, suggesting that pruning can improve the neural network's generalization.