Ptychography is a powerful imaging technique that is used in a variety of
fields, including materials science, biology, and nanotechnology. However, the
accuracy of the reconstructed ptychography image is highly dependent on the
accuracy of the recorded probe positions which often contain errors. These
errors are typically corrected jointly with phase retrieval through numerical
optimization approaches. When the error accumulates along the scan path or when
the error magnitude is large, these approaches may not converge with
satisfactory result. We propose a fundamentally new approach for ptychography
probe position prediction for data with large position errors, where a neural
network is used to make single-shot phase retrieval on individual diffraction
patterns, yielding the object image at each scan point. The pairwise offsets
among these images are then found using a robust image registration method, and
the results are combined to yield the complete scan path by constructing and
solving a linear equation. We show that our method can achieve good position
prediction accuracy for data with large and accumulating errors on the order of
$10^2$ pixels, a magnitude that often makes optimization-based algorithms fail
to converge. For ptychography instruments without sophisticated position
control equipment such as interferometers, our method is of significant
practical potential.

利用神经网络进行密集分析，基于图像配准以及线性方程的构建和求解，实现 ptychography 探针位置的准确预测并对大尺度和累积性位置误差进行有效校正。

利用单次相位恢复神经网络预测相衬成像探针位置

Predicting ptychography probe positions using single-shot phase  retrieval neural network

The RoboCup competitions hold various leagues, and the Soccer Simulation 2D
League is a major among them. Soccer Simulation 2D (SS2D) match involves two
teams, including 11 players and a coach for each team, competing against each
other. The players can only communicate with the Soccer Simulation Server
during the game. Several code bases are released publicly to simplify team
development. So researchers can easily focus on decision-making and
implementing machine learning methods. SS2D actions and behaviors are only
partially accurate due to different challenges, such as noise and partial
observation. Therefore, one strategy is to implement alternative denoising
methods to tackle observation inaccuracy. Our idea is to predict opponent
positions while they have yet to be seen in a finite number of cycles using
machine learning methods to make more accurate actions such as pass. We will
explain our position prediction idea powered by Long Short-Term Memory models
(LSTM) and Deep Neural Networks (DNN). The results show that the LSTM and DNN
predict the opponents' position more accurately than the standard algorithm,
such as the last-seen method.

我们通过使用长短期记忆模型（LSTM）和深度神经网络（DNN）预测对手的位置，以实现比传统算法更准确的行动，例如传球。结果表明，LSTM 和 DNN 比最后可见方法更准确地预测了对手的位置。

在部分观测环境中去噪对手位置

Denoising Opponents Position in Partial Observation Environment

In implant prosthesis treatment, the design of surgical guide requires lots
of manual labors and is prone to subjective variations. When deep learning
based methods has started to be applied to address this problem, the space
between teeth are various and some of them might present similar texture
characteristic with the actual implant region. Both problems make a big
challenge for the implant position prediction. In this paper, we develop a
two-stream implant position regression framework (TSIPR), which consists of an
implant region detector (IRD) and a multi-scale patch embedding regression
network (MSPENet), to address this issue. For the training of IRD, we extend
the original annotation to provide additional supervisory information, which
contains much more rich characteristic and do not introduce extra labeling
costs. A multi-scale patch embedding module is designed for the MSPENet to
adaptively extract features from the images with various tooth spacing. The
global-local feature interaction block is designed to build the encoder of
MSPENet, which combines the transformer and convolution for enriched feature
representation. During inference, the RoI mask extracted from the IRD is used
to refine the prediction results of the MSPENet. Extensive experiments on a
dental implant dataset through five-fold cross-validation demonstrated that the
proposed TSIPR achieves superior performance than existing methods.

本文提出了一种基于深度学习的两流植入体位置回归框架，使用 IRP 作为植入区域探测器和 MSPENet 作为多尺度嵌入回归网络，并在牙种间各种间隙中采用了多尺度图像块来建立编码器以适应植入体位置预测问题。实验结果表明，该框架在牙种植入领域中表现优异。

用于牙种植物位置预测的双流回归网络

Two-Stream Regression Network for Dental Implant Position Prediction

Modern online multiple object tracking (MOT) methods usually focus on two
directions to improve tracking performance. One is to predict new positions in
an incoming frame based on tracking information from previous frames, and the
other is to enhance data association by generating more discriminative identity
embeddings. Some works combined both directions within one framework but
handled them as two individual tasks, thus gaining little mutual benefits. In
this paper, we propose a novel unified model with synergy between position
prediction and embedding association. The two tasks are linked by
temporal-aware target attention and distractor attention, as well as
identity-aware memory aggregation model. Specifically, the attention modules
can make the prediction focus more on targets and less on distractors,
therefore more reliable embeddings can be extracted accordingly for
association. On the other hand, such reliable embeddings can boost
identity-awareness through memory aggregation, hence strengthen attention
modules and suppress drifts. In this way, the synergy between position
prediction and embedding association is achieved, which leads to strong
robustness to occlusions. Extensive experiments demonstrate the superiority of
our proposed model over a wide range of existing methods on MOTChallenge
benchmarks. Our code and models are publicly available at
this https URL

本研究提出了一种新的联合模型，将位置预测和嵌入关联相结合，以尽量从目标信息中提取更可靠的嵌入，从而提高数据关联的准确性和鲁棒性。实验结果表明，该方法在 MOT 挑战基准测试中的性能优于现有方法。