This paper presents an approach that reconstructs a hand-held object from a monocular video. In contrast to many recent methods that directly predict object geometry by a trained network, the proposed approach does not require any learned prior about the object and is able to recover more accurate and detailed object geometry. The key idea is that the hand motion naturally provides multiple views of the object and the motion can be reliably estimated by a hand pose tracker. Then, the object geometry can be recovered by solving a multi-view reconstruction problem. We devise an implicit neural representation-based method to solve the reconstruction problem and address the issues of imprecise hand pose estimation, relative hand-object motion, and insufficient geometry optimization for small objects. We also provide a newly collected dataset with 3D ground truth to validate the proposed approach.

本文解决了从多视频视频中重建动画人类模型的挑战。最近的一些作品提出，将一个非刚性变形的场景分解为规范的神经辐射场和一组变形场，它们映射观察空间指向规范空间，从而使它们能够从图像中学习动态场景。但是，它们代表变形场作为转换矢量场或SE（3）字段，这使得优化高度不受限制。此外，这些表示无法通过输入动议明确控制。取而代之的是，我们基于线性混合剥皮算法引入了一个姿势驱动的变形场，该算法结合了混合重量场和3D人类骨架，以产生观察到的对应对应。由于3D人类骨骼更容易观察到，因此它们可以正规化变形场的学习。此外，可以通过输入骨骼运动来控制姿势驱动的变形场，以生成新的变形字段来动画规范人类模型。实验表明，我们的方法显着优于最近的人类建模方法。该代码可在https://zju3dv.github.io/animatable_nerf/上获得。

股票运动预测（SMP）旨在预测上市公司的股份量股份，由于金融市场的挥发性，这是一个具有挑战性的任务。最近的财务研究表明，动量溢出效应在股票波动中发挥着重要作用。然而，以前的研究通常只学习相关公司之间的简单连接信息，这不可避免地未能模仿真实金融市场中上市公司的复杂关系。为了解决这个问题，我们首先建立一个更全面的市场知识图（MKG），其中包含有限的公司，包括上市公司及其相关的高管，以及包括明确关系和隐性关系的混合关系。之后，我们提出了一种新颖的双重关注网络，以了解基于构造的MKG用于库存预测的势头溢出信号。对九个SOTA基线构建数据集的实证实验表明，所提出的丹林公司能够改善与构造的MKG的库存预测。

随着电子商务行业的爆炸性增长，检测现实世界应用中的在线交易欺诈对电子商务平台的发展越来越重要。用户的顺序行为历史提供有用的信息，以区分从常规支付的欺诈性付款。最近，已经提出了一些方法来解决基于序列的欺诈检测问题。然而，这些方法通常遭受两个问题：预测结果难以解释，并且对行为的内部信息的利用不足。为了解决上述两个问题，我们提出了一个分层可解释的网络（母鸡）来模拟用户的行为序列，这不仅可以提高欺诈检测的性能，还可以使推理过程解释。同时，随着电子商务业务扩展到新域名，例如新的国家或新市场，在欺诈检测系统中建模用户行为的一个主要问题是数据收集的限制，例如，非常少的数据/标签。因此，在本文中，我们进一步提出了一种转移框架来解决跨域欺诈检测问题，其旨在从现有域（源域）的知识传输足够的域（源域），以提高新域中的性能（目标域）。我们所提出的方法是一般的转移框架，不仅可以应用于母鸡而且可以在嵌入和MLP范例中应用各种现有模型。基于90个转移任务实验，我们还表明，我们的转移框架不仅可以促进母鸡的跨域欺诈检测任务，而且对于各种现有模型也是普遍的和可扩展的。

许多真实应用程序的预测任务需要在用户的事件序列中模拟多阶特征交互以获得更好的检测性能。然而，现有的流行解决方案通常遭受两个关键问题：1）仅关注特征交互并无法捕获序列影响;2）仅关注序列信息，但忽略每个事件的内部特征关系，因此无法提取更好的事件表示。在本文中，我们考虑使用用户的事件顺序捕获分层信息的两级结构：1）基于基于事件表示的学习有效特征交互;2）建模用户历史事件的序列表示。工业和公共数据集的实验结果清楚地表明，与最先进的基线相比，我们的模式实现了更好的性能。

本文回顾了关于压缩视频质量增强质量的第一个NTIRE挑战，重点是拟议的方法和结果。在此挑战中，采用了新的大型不同视频（LDV）数据集。挑战有三个曲目。Track 1和2的目标是增强HEVC在固定QP上压缩的视频，而Track 3旨在增强X265压缩的视频，以固定的位速率压缩。此外，轨道1和3的质量提高了提高保真度（PSNR）的目标，以及提高感知质量的2个目标。这三个曲目完全吸引了482个注册。在测试阶段，分别提交了12个团队，8支球队和11支球队，分别提交了轨道1、2和3的最终结果。拟议的方法和解决方案衡量视频质量增强的最先进。挑战的首页：https：//github.com/renyang-home/ntire21_venh

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.

For Prognostics and Health Management (PHM) of Lithium-ion (Li-ion) batteries, many models have been established to characterize their degradation process. The existing empirical or physical models can reveal important information regarding the degradation dynamics. However, there is no general and flexible methods to fuse the information represented by those models. Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical or physical dynamic models with data-driven models. To take full advantage of various information sources, we propose a model fusion scheme based on PINN. It is implemented by developing a semi-empirical semi-physical Partial Differential Equation (PDE) to model the degradation dynamics of Li-ion-batteries. When there is little prior knowledge about the dynamics, we leverage the data-driven Deep Hidden Physics Model (DeepHPM) to discover the underlying governing dynamic models. The uncovered dynamics information is then fused with that mined by the surrogate neural network in the PINN framework. Moreover, an uncertainty-based adaptive weighting method is employed to balance the multiple learning tasks when training the PINN. The proposed methods are verified on a public dataset of Li-ion Phosphate (LFP)/graphite batteries.

Hybrid unmanned aerial vehicles (UAVs) integrate the efficient forward flight of fixed-wing and vertical takeoff and landing (VTOL) capabilities of multicopter UAVs. This paper presents the modeling, control and simulation of a new type of hybrid micro-small UAVs, coined as lifting-wing quadcopters. The airframe orientation of the lifting wing needs to tilt a specific angle often within $ 45$ degrees, neither nearly $ 90$ nor approximately $ 0$ degrees. Compared with some convertiplane and tail-sitter UAVs, the lifting-wing quadcopter has a highly reliable structure, robust wind resistance, low cruise speed and reliable transition flight, making it potential to work fully-autonomous outdoor or some confined airspace indoor. In the modeling part, forces and moments generated by both lifting wing and rotors are considered. Based on the established model, a unified controller for the full flight phase is designed. The controller has the capability of uniformly treating the hovering and forward flight, and enables a continuous transition between two modes, depending on the velocity command. What is more, by taking rotor thrust and aerodynamic force under consideration simultaneously, a control allocation based on optimization is utilized to realize cooperative control for energy saving. Finally, comprehensive Hardware-In-the-Loop (HIL) simulations are performed to verify the advantages of the designed aircraft and the proposed controller.