We exploit cutting-edge deep learning methodologies to explore the
predictability of high-frequency Limit Order Book mid-price changes for a
heterogeneous set of stocks traded on the NASDAQ exchange. In so doing, we
release `LOBFrame', an open-source code base, to efficiently process
large-scale Limit Order Book data and quantitatively assess state-of-the-art
deep learning models' forecasting capabilities. Our results are twofold. We
demonstrate that the stocks' microstructural characteristics influence the
efficacy of deep learning methods and that their high forecasting power does
not necessarily correspond to actionable trading signals. We argue that
traditional machine learning metrics fail to adequately assess the quality of
forecasts in the Limit Order Book context. As an alternative, we propose an
innovative operational framework that assesses predictions' practicality by
focusing on the probability of accurately forecasting complete transactions.
This work offers academics and practitioners an avenue to make informed and
robust decisions on the application of deep learning techniques, their scope
and limitations, effectively exploiting emergent statistical properties of the
Limit Order Book.

利用先进的深度学习方法，研究预测在纳斯达克交易所上交易的异构股票的高频限价挂单簿中价位变动的可预测性。通过释放开源代码 'LOBFrame'，高效处理大规模的限价挂单簿数据，定量评估最新的深度学习模型的预测能力。研究结果有两个方面：我们证明股票的微观结构特征影响深度学习方法的有效性，而高预测能力并不一定对应可执行的交易信号。我们认为，传统的机器学习指标未能充分评估限价挂单簿上的预测质量。作为替代，我们提出了一种创新的操作框架，通过关注准确预测完整交易的概率来评估预测的实用性。这项工作为学术界和实践者们提供了一个应用深度学习技术、了解其范围和限制以及有效利用限价挂单簿的新统计特性的途径，从而做出明智而稳健的决策。

深度委托簿预测

Deep Limit Order Book Forecasting

As a beloved sport worldwide, dancing is getting integrated into traditional
and virtual reality-based gaming platforms nowadays. It opens up new
opportunities in the technology-mediated dancing space. These platforms
primarily rely on passive and continuous human pose estimation as an input
capture mechanism. Existing solutions are mainly based on RGB or RGB-Depth
cameras for dance games. The former suffers in low-lighting conditions due to
the motion blur and low sensitivity, while the latter is too power-hungry, has
a low frame rate, and has limited working distance. With ultra-low latency,
energy efficiency, and wide dynamic range characteristics, the event camera is
a promising solution to overcome these shortcomings. We propose YeLan, an event
camera-based 3-dimensional high-frequency human pose estimation(HPE) system
that survives low-lighting conditions and dynamic backgrounds. We collected the
world's first event camera dance dataset and developed a fully customizable
motion-to-event physics-aware simulator. YeLan outperforms the baseline models
in these challenging conditions and demonstrated robustness against different
types of clothing, background motion, viewing angle, occlusion, and lighting
fluctuations.

本文介绍了一种基于事件相机的高频人体姿态估计系统，名为 YeLan，它能够在低光和动态背景下工作，并且在具有挑战性的条件下表现出色，且对于不同类型的衣服，背景运动，观察角度，遮挡和照明波动具有抗干扰性。

动态环境下的神经形态高频 3D 舞蹈姿态估计

Neuromorphic High-Frequency 3D Dancing Pose Estimation in Dynamic Environment

Recent studies show that Transformer has strong capability of building
long-range dependencies, yet is incompetent in capturing high frequencies that
predominantly convey local information. To tackle this issue, we present a
novel and general-purpose Inception Transformer, or iFormer for short, that
effectively learns comprehensive features with both high- and low-frequency
information in visual data. Specifically, we design an Inception mixer to
explicitly graft the advantages of convolution and max-pooling for capturing
the high-frequency information to Transformers. Different from recent hybrid
frameworks, the Inception mixer brings greater efficiency through a channel
splitting mechanism to adopt parallel convolution/max-pooling path and
self-attention path as high- and low-frequency mixers, while having the
flexibility to model discriminative information scattered within a wide
frequency range. Considering that bottom layers play more roles in capturing
high-frequency details while top layers more in modeling low-frequency global
information, we further introduce a frequency ramp structure, i.e. gradually
decreasing the dimensions fed to the high-frequency mixer and increasing those
to the low-frequency mixer, which can effectively trade-off high- and
low-frequency components across different layers. We benchmark the iFormer on a
series of vision tasks, and showcase that it achieves impressive performance on
image classification, COCO detection and ADE20K segmentation. For example, our
iFormer-S hits the top-1 accuracy of 83.4% on ImageNet-1K, much higher than
DeiT-S by 3.6%, and even slightly better than much bigger model Swin-B (83.3%)
with only 1/4 parameters and 1/3 FLOPs. Code and models will be released at
this https URL.

文章提出了一种名为 iFormer 的基于 Inception 的 Transformer 架构，通过一种名为 Inception mixer 的操作使得网络能够更好地捕捉高频信息和低频信息，加入了渐进降低高频分量和增加低频分量的结构，并在图像分类等任务上取得了非常优秀的表现。