This paper introduces HPC-Net, a high-precision and rapidly convergent object
detection network.

本文介绍了 HPC-Net，一种高精度、快速收敛的目标检测网络。

混合池化卷积网络对物体检测的精度和训练收敛速度的改进

Hybrid Pooling and Convolutional Network for Improving Accuracy and  Training Convergence Speed in Object Detection

Neural network solvers represent an innovative and promising approach for
tackling time-fractional partial differential equations by utilizing deep
learning techniques. L1 interpolation approximation serves as the standard
method for addressing time-fractional derivatives within neural network
solvers. However, we have discovered that neural network solvers based on L1
interpolation approximation are unable to fully exploit the benefits of neural
networks, and the accuracy of these models is constrained to interpolation
errors. In this paper, we present the high-precision Hermite Neural Solver
(HNS) for solving time-fractional partial differential equations. Specifically,
we first construct a high-order explicit approximation scheme for fractional
derivatives using Hermite interpolation techniques, and rigorously analyze its
approximation accuracy. Afterward, taking into account the infinitely
differentiable properties of deep neural networks, we integrate the high-order
Hermite interpolation explicit approximation scheme with deep neural networks
to propose the HNS. The experimental results show that HNS achieves higher
accuracy than methods based on the L1 scheme for both forward and inverse
problems, as well as in high-dimensional scenarios. This indicates that HNS has
significantly improved accuracy and flexibility compared to existing L1-based
methods, and has overcome the limitations of explicit finite difference
approximation methods that are often constrained to function value
interpolation. As a result, the HNS is not a simple combination of numerical
computing methods and neural networks, but rather achieves a complementary and
mutually reinforcing advantages of both approaches. The data and code can be
found at https://github.com/hsbhc/HNS.

基于高阶 Hermite 插值和深度神经网络的高精度 Hermite Neural Solver（HNS）用于解决时间分数阶偏微分方程，取得较 L1 插值近似方法更高的准确性和灵活性。

HNS: 解决时间分数阶偏微分方程的高效 Hermite 神经求解器

HNS: An Efficient Hermite Neural Solver for Solving Time-Fractional  Partial Differential Equations

We present a high-precision real-time facial animation pipeline suitable for
animators to use on their desktops. This pipeline is about to be launched in
FACEGOOD's Avatary\footnote{this https URL} software, which will
accelerate animators' productivity. The pipeline differs from professional
head-mounted facial capture solutions in that it only requires the use of a
consumer-grade 3D camera on the desk to achieve high-precision real-time facial
capture. The system enables animators to create high-quality facial animations
with ease and speed, while reducing the cost and complexity of traditional
facial capture solutions. Our approach has the potential to revolutionize the
way facial animation is done in the entertainment industry.

本文介绍了一种适用于动画师在桌面上高精度实时进行面部动画的工作流程，该工作流程将被用于 FACEGOOD 的 Avatary 软件中，仅需使用消费级 3D 摄像头，便可以达到高精度面部捕捉，并极大地提高动画师的生产效率，减少了传统面部捕捉方案的成本和复杂性，具有革命性的潜力，将改变娱乐产业的面部动画制作方式。

桌面场景下的 4D 普适实时人脸动画制作流程

4D Agnostic Real-Time Facial Animation Pipeline for Desktop Scenarios

One of the main tasks in argument mining is the retrieval of argumentative
content pertaining to a given topic. Most previous work addressed this task by
retrieving a relatively small number of relevant documents as the initial
source for such content. This line of research yielded moderate success, which
is of limited use in a real-world system. Furthermore, for such a system to
yield a comprehensive set of relevant arguments, over a wide range of topics,
it requires leveraging a large and diverse corpus in an appropriate manner.
Here we present a first end-to-end high-precision, corpus-wide argument mining
system. This is made possible by combining sentence-level queries over an
appropriate indexing of a very large corpus of newspaper articles, with an
iterative annotation scheme. This scheme addresses the inherent label bias in
the data and pinpoints the regions of the sample space whose manual labeling is
required to obtain high-precision among top-ranked candidates.

本文介绍了一个全面的、高精度的基于话语级查询的语料库广泛的 Argument Mining 系统，通过将适当索引的大型语料库上的句子级查询与迭代式注释方案结合起来，解决了数据中的固有标签偏差，标注手册所需的空间样本的区域，从而获得高精度的排名前几的候选项。