复杂的流量分析，例如加密的流量分析和未知的恶意软件检测，强调需要进行高级方法来分析网络流量。使用固定模式，签名匹配和检测网络流量中已知模式的规则的传统方法已被AI（人工智能）驱动算法取代。但是，缺乏高性能AI网络特定的框架使得不可能在网络工作负载中部署基于AI的实时处理。在本文中，我们描述了流量分析开发工具包（TADK）的设计，这是一个针对基于AI的网络工作负载处理的行业标准框架。 TADK可以在数据中心到边缘的网络设备中基于实时的AI网络工作负载处理，而无需专门硬件（例如GPU，神经处理单元等）。我们已经在商品WAF和5G UPF中部署了TADK，评估结果表明，Tadk可以在流量功能提取时达到每个核心最多35.3Gbps的吞吐量，每核6.5Gbps在流量分类中，并且可以减少SQLI/XSS检测到下降至4.5us每个请求的精度比固定模式解决方案更高。

We propose a model-data asymptotic-preserving neural network(MD-APNN) method to solve the nonlinear gray radiative transfer equations(GRTEs). The system is challenging to be simulated with both the traditional numerical schemes and the vanilla physics-informed neural networks(PINNs) due to the multiscale characteristics. Under the framework of PINNs, we employ a micro-macro decomposition technique to construct a new asymptotic-preserving(AP) loss function, which includes the residual of the governing equations in the micro-macro coupled form, the initial and boundary conditions with additional diffusion limit information, the conservation laws, and a few labeled data. A convergence analysis is performed for the proposed method, and a number of numerical examples are presented to illustrate the efficiency of MD-APNNs, and particularly, the importance of the AP property in the neural networks for the diffusion dominating problems. The numerical results indicate that MD-APNNs lead to a better performance than APNNs or pure data-driven networks in the simulation of the nonlinear non-stationary GRTEs.

Neural Radiance Field (NeRF) has revolutionized free viewpoint rendering tasks and achieved impressive results. However, the efficiency and accuracy problems hinder its wide applications. To address these issues, we propose Geometry-Aware Generalized Neural Radiance Field (GARF) with a geometry-aware dynamic sampling (GADS) strategy to perform real-time novel view rendering and unsupervised depth estimation on unseen scenes without per-scene optimization. Distinct from most existing generalized NeRFs, our framework infers the unseen scenes on both pixel-scale and geometry-scale with only a few input images. More specifically, our method learns common attributes of novel-view synthesis by an encoder-decoder structure and a point-level learnable multi-view feature fusion module which helps avoid occlusion. To preserve scene characteristics in the generalized model, we introduce an unsupervised depth estimation module to derive the coarse geometry, narrow down the ray sampling interval to proximity space of the estimated surface and sample in expectation maximum position, constituting Geometry-Aware Dynamic Sampling strategy (GADS). Moreover, we introduce a Multi-level Semantic Consistency loss (MSC) to assist more informative representation learning. Extensive experiments on indoor and outdoor datasets show that comparing with state-of-the-art generalized NeRF methods, GARF reduces samples by more than 25\%, while improving rendering quality and 3D geometry estimation.

We present MEM: Multi-view Exploration Maximization for tackling complex visual control tasks. To the best of our knowledge, MEM is the first approach that combines multi-view representation learning and intrinsic reward-driven exploration in reinforcement learning (RL). More specifically, MEM first extracts the specific and shared information of multi-view observations to form high-quality features before performing RL on the learned features, enabling the agent to fully comprehend the environment and yield better actions. Furthermore, MEM transforms the multi-view features into intrinsic rewards based on entropy maximization to encourage exploration. As a result, MEM can significantly promote the sample-efficiency and generalization ability of the RL agent, facilitating solving real-world problems with high-dimensional observations and spare-reward space. We evaluate MEM on various tasks from DeepMind Control Suite and Procgen games. Extensive simulation results demonstrate that MEM can achieve superior performance and outperform the benchmarking schemes with simple architecture and higher efficiency.

Recent years we have witnessed rapid development in NeRF-based image rendering due to its high quality. However, point clouds rendering is somehow less explored. Compared to NeRF-based rendering which suffers from dense spatial sampling, point clouds rendering is naturally less computation intensive, which enables its deployment in mobile computing device. In this work, we focus on boosting the image quality of point clouds rendering with a compact model design. We first analyze the adaption of the volume rendering formulation on point clouds. Based on the analysis, we simplify the NeRF representation to a spatial mapping function which only requires single evaluation per pixel. Further, motivated by ray marching, we rectify the the noisy raw point clouds to the estimated intersection between rays and surfaces as queried coordinates, which could avoid \textit{spatial frequency collapse} and neighbor point disturbance. Composed of rasterization, spatial mapping and the refinement stages, our method achieves the state-of-the-art performance on point clouds rendering, outperforming prior works by notable margins, with a smaller model size. We obtain a PSNR of 31.74 on NeRF-Synthetic, 25.88 on ScanNet and 30.81 on DTU. Code and data are publicly available at https://github.com/seanywang0408/RadianceMapping.

探索对于具有高维观察和稀疏奖励的复杂环境中的深度强化学习至关重要。为了解决这个问题，最新的方法旨在利用内在的奖励来改善勘探，例如基于新颖的探索和基于预测的探索。但是，许多固有的奖励模块需要复杂的结构和表示学习，从而导致了过度的计算复杂性和不稳定的性能。在本文中，我们提出了一种有益的情节访问差异（REVD），这是一种计算有效且量化的探索方法。更具体地说，REVD通过评估情节之间的基于R \'Enyi Divergence的访问差异来提供内在的奖励。为了进行有效的差异估计，使用随机定义状态编码器使用K-Nearest邻居估计器。最后，在Pybullet机器人环境和Atari游戏上测试了REVD。广泛的实验表明，REVD可以显着提高强化学习算法的样本效率，并优于基准测定方法。

亚马逊客户服务每年为数百万客户联系提供实时支持。尽管Bot-Resolver有助于自动化一些流量，但我们仍然看到对人类代理商的需求很高，也称为主题专家（SME）。客户在不同域中的问题（返回策略，设备故障排除等）进行宣传。根据他们的培训，并非所有中小型企业都有资格处理所有联系人。与合格的中小型企业的路由联系是一个非平凡的问题，因为中小企业的域名资格受训练质量的影响，并且可以随着时间的推移而改变。为了在同时学习真正的资格状态的同时，我们建议使用非参数上下文的强盗算法（K-Boot）以及资格控制（EC）算法来制定路由问题。 K-Boot模型以$ K $ -NN选择的类似样品和Bootstrap Thompson采样进行探索，并以类似的样本进行奖励。 EC通过最初符合系统的资格过滤武器（SME），并动态验证该信息的可靠性。提出的K-boot是一种通用匪徒算法，EC适用于其他土匪。我们的仿真研究表明，K-boot在最新的匪徒模型上进行性能，并且当存在随机弹性信号时，EC会提高K-Boot性能。

感知是自动驾驶系统的关键模块之一，最近取得了长足的进步。但是，单个车辆的能力有限，导致感知表现的瓶颈。为了突破个人感知的局限性，已经提出了协作感知，使车辆能够共享信息以了解超出视线和视野的环境。在本文中，我们对有关有前途的协作感知技术的相关工作进行了评论，包括介绍基本概念，推广协作模式并总结协作感知的关键成分和应用。最后，我们讨论了该研究领域的公开挑战和问题，并提供了一些潜在的方向。

视频中的多目标跟踪需要解决相邻帧中对象之间一对一分配的基本问题。大多数方法通过首先丢弃不可能的对距离大于阈值的不可能对解决问题，然后使用匈牙利算法将对象链接起来以最大程度地减少整体距离。但是，我们发现从重新ID特征计算出的距离的分布可能在不同的视频中有很大差异。因此，没有一个最佳阈值可以使我们安全丢弃不可能的对。为了解决该问题，我们提出了一种有效的方法来实时计算每对对象的边际概率。边际概率可以视为标准化距离，比原始特征距离明显稳定。结果，我们可以为所有视频使用一个阈值。该方法是一般的，可以应用于现有的跟踪器，以在IDF1度量方面获得大约一个点改进。它在MOT17和MOT20基准上取得了竞争成果。此外，计算的概率更容易解释，从而有助于后续后期处理操作。

尽管单眼3D姿势估计似乎在公共数据集上取得了非常准确的结果，但它们的概括能力在很大程度上被忽略了。在这项工作中，我们对现有方法进行系统评估，并发现在对不同的摄像机，人体姿势和外观进行测试时，它们会出现更大的错误。为了解决这个问题，我们介绍了VirtualPose，这是一个两阶段的学习框架，以利用该任务特定的隐藏的“免费午餐”，即免费生成无限数量的姿势和摄像头，以免费培训模型。为此，第一阶段将图像转换为抽象的几何表示（AGR），然后第二阶段将它们映射到3D姿势。它从两个方面解决了概括问题：（1）可以在不同的2D数据集上对第一阶段进行培训，以降低过度合适外观的风险； （2）第二阶段可以接受从大量虚拟摄像机和姿势合成的不同AGR训练。它的表现优于SOTA方法，而无需使用任何配对的图像和3D姿势，从而为实用应用铺平了道路。代码可从https://github.com/wkom/virtualpose获得。