Flow field segmentation and classification help researchers to understand
vortex structure and thus turbulent flow. Existing deep learning methods mainly
based on global information and focused on 2D circumstance. Based on flow field
theory, we propose novel flow field segmentation and classification deep
learning methods in three-dimensional space. We construct segmentation
criterion based on local velocity information and classification criterion
based on the relationship between local vorticity and vortex wake, to identify
vortex structure in 3D flow field, and further classify the type of vortex
wakes accurately and rapidly. Simulation experiment results showed that,
compared with existing methods, our segmentation method can identify the vortex
area more accurately, while the time consumption is reduced more than 50\%; our
classification method can reduce the time consumption by more than 90\% while
maintaining the same classification accuracy level.

基于三维流场理论，提出了一种基于局部速度信息的分割标准和基于局部涡量与涡尾之间关系的分类标准的流场分割及分类深度学习方法，能更准确地识别涡旋结构，并在准确分类涡尾类型的同时，大幅度减少时间消耗。

3D 流场分割和分类的新型深度学习方法

Novel deep learning methods for 3D flow field segmentation and  classification

The security of deep neural networks (DNNs) has attracted increasing
attention due to their widespread use in various applications. Recently, the
deployed DNNs have been demonstrated to be vulnerable to Trojan attacks, which
manipulate model parameters with bit flips to inject a hidden behavior and
activate it by a specific trigger pattern. However, all existing Trojan attacks
adopt noticeable patch-based triggers (e.g., a square pattern), making them
perceptible to humans and easy to be spotted by machines. In this paper, we
present a novel attack, namely hardly perceptible Trojan attack (HPT). HPT
crafts hardly perceptible Trojan images by utilizing the additive noise and per
pixel flow field to tweak the pixel values and positions of the original
images, respectively. To achieve superior attack performance, we propose to
jointly optimize bit flips, additive noise, and flow field. Since the weight
bits of the DNNs are binary, this problem is very hard to be solved. We handle
the binary constraint with equivalent replacement and provide an effective
optimization algorithm. Extensive experiments on CIFAR-10, SVHN, and ImageNet
datasets show that the proposed HPT can generate hardly perceptible Trojan
images, while achieving comparable or better attack performance compared to the
state-of-the-art methods. The code is available at:
this https URL

本文提出了一种利用加性噪声和像素流场手段，针对深度神经网络中存在的隐蔽行为的 Trojan 攻击，并通过等效替换处理二进制约束提高了攻击效率。在 CIFAR-10、SVHN 和 ImageNet 数据集上进行了广泛的实验验证，并在攻击效果和图像隐蔽性方面做了对比分析。

针对神经网络的几乎不可感知的比特翻转特洛伊攻击

Hardly Perceptible Trojan Attack against Neural Networks with Bit Flips

Both pedestrian and robot comfort are of the highest priority whenever a
robot is placed in an environment containing human beings. In the case of
pedestrian-unaware mobile robots this desire for safety leads to the freezing
robot problem, where a robot confronted with a large dynamic group of obstacles
(such as a crowd of pedestrians) would determine all forward navigation unsafe
causing the robot to stop in place. In order to navigate in a socially
compliant manner while avoiding the freezing robot problem we are interested in
understanding the flow of pedestrians in crowded scenarios. By treating the
pedestrians in the crowd as particles moved along by the crowd itself we can
model the system as a time dependent flow field. From this flow field we can
extract different flow segments that reflect the motion patterns emerging from
the crowd. These motion patterns can then be accounted for during the control
and navigation of a mobile robot allowing it to move safely within the flow of
the crowd to reach a desired location within or beyond the flow.
We combine flow-field extraction with a discrete heuristic search to create
Flow-Informed path planning (FIPP). We provide empirical results showing that
when compared against a trajectory-rollout local path planner, a robot using
FIPP was able not only to reach its goal more quickly but also was shown to be
more socially compliant than a robot using traditional techniques both in
simulation and on real robots.

针对机器人在人群中出现的冻结问题，提出了基于人流场的路径规划算法，实现了机器人在人流中更加安全、高效和符合社交规范的移动。

密集人群流动感知路径规划

Dense Crowd Flow-Informed Path Planning

Deep convolutional neutral networks have achieved great success on image
recognition tasks. Yet, it is non-trivial to transfer the state-of-the-art
image recognition networks to videos as per-frame evaluation is too slow and
unaffordable. We present deep feature flow, a fast and accurate framework for
video recognition. It runs the expensive convolutional sub-network only on
sparse key frames and propagates their deep feature maps to other frames via a
flow field. It achieves significant speedup as flow computation is relatively
fast. The end-to-end training of the whole architecture significantly boosts
the recognition accuracy. Deep feature flow is flexible and general. It is
validated on two recent large scale video datasets. It makes a large step
towards practical video recognition.

该研究提出了一种针对视频识别的研究框架，Deep feature flow，使用卷积神经网络在关键帧上运行，通过流场传播其特征图到其他帧，大大提高了视频识别的速度和准确性，同时为实际应用打下坚实的基础。