Lobster eye telescopes are ideal monitors to detect X-ray transients, because they could observe celestial objects over a wide field of view in X-ray band. However, images obtained by lobster eye telescopes are modified by their unique point spread functions, making it hard to design a high efficiency target detection algorithm. In this paper, we integrate several machine learning algorithms to build a target detection framework for data obtained by lobster eye telescopes. Our framework would firstly generate two 2D images with different pixel scales according to positions of photons on the detector. Then an algorithm based on morphological operations and two neural networks would be used to detect candidates of celestial objects with different flux from these 2D images. At last, a random forest algorithm will be used to pick up final detection results from candidates obtained by previous steps. Tested with simulated data of the Wide-field X-ray Telescope onboard the Einstein Probe, our detection framework could achieve over 94% purity and over 90% completeness for targets with flux more than 3 mCrab (9.6 * 10-11 erg/cm2/s) and more than 94% purity and moderate completeness for targets with lower flux at acceptable time cost. The framework proposed in this paper could be used as references for data processing methods developed for other lobster eye X-ray telescopes.

Multilingual end-to-end models have shown great improvement over monolingual systems. With the development of pre-training methods on speech, self-supervised multilingual speech representation learning like XLSR has shown success in improving the performance of multilingual automatic speech recognition (ASR). However, similar to the supervised learning, multilingual pre-training may also suffer from language interference and further affect the application of multilingual system. In this paper, we introduce several techniques for improving self-supervised multilingual pre-training by leveraging auxiliary language information, including the language adversarial training, language embedding and language adaptive training during the pre-training stage. We conduct experiments on a multilingual ASR task consisting of 16 languages. Our experimental results demonstrate 14.3% relative gain over the standard XLSR model, and 19.8% relative gain over the no pre-training multilingual model.

This paper describes the submission of the RoyalFlush neural machine translation system for the WMT 2022 translation efficiency task. Unlike the commonly used autoregressive translation system, we adopted a two-stage translation paradigm called Hybrid Regression Translation (HRT) to combine the advantages of autoregressive and non-autoregressive translation. Specifically, HRT first autoregressively generates a discontinuous sequence (e.g., make a prediction every $k$ tokens, $k>1$) and then fills in all previously skipped tokens at once in a non-autoregressive manner. Thus, we can easily trade off the translation quality and speed by adjusting $k$. In addition, by integrating other modeling techniques (e.g., sequence-level knowledge distillation and deep-encoder-shallow-decoder layer allocation strategy) and a mass of engineering efforts, HRT improves 80\% inference speed and achieves equivalent translation performance with the same-capacity AT counterpart. Our fastest system reaches 6k+ words/second on the GPU latency setting, estimated to be about 3.1x faster than the last year's winner.

Motion planning is challenging for autonomous systems in multi-obstacle environments due to nonconvex collision avoidance constraints. Directly applying numerical solvers to these nonconvex formulations fails to exploit the constraint structures, resulting in excessive computation time. In this paper, we present an accelerated collision-free motion planner, namely regularized dual alternating direction method of multipliers (RDADMM or RDA for short), for the model predictive control (MPC) based motion planning problem. The proposed RDA addresses nonconvex motion planning via solving a smooth biconvex reformulation via duality and allows the collision avoidance constraints to be computed in parallel for each obstacle to reduce computation time significantly. We validate the performance of the RDA planner through path-tracking experiments with car-like robots in simulation and real world setting. Experimental results show that the proposed methods can generate smooth collision-free trajectories with less computation time compared with other benchmarks and perform robustly in cluttered environments.

由于自我批判性和歧义，了解动态的手动运动和动态动作是一项基本而又具有挑战性的任务。为了解决遮挡和歧义，我们开发了一个基于变压器的框架来利用时间信息以进行稳健的估计。注意到手部姿势估计和动作识别之间的不同时间粒度和语义相关性，我们建立了一个网络层次结构，其中有两个级联变压器编码器，其中第一个利用了短期的时间cue进行手姿势估算，而后者则每次聚集物，后者每次聚集体 - 帧姿势和对象信息在更长的时间范围内识别动作。我们的方法在两个第一人称手动作基准（即FPHA和H2O）上取得了竞争成果。广泛的消融研究验证了我们的设计选择。我们将开放源代码和数据以促进未来的研究。

深度强化学习在基于激光的碰撞避免有效的情况下取得了巨大的成功，因为激光器可以感觉到准确的深度信息而无需太多冗余数据，这可以在算法从模拟环境迁移到现实世界时保持算法的稳健性。但是，高成本激光设备不仅很难为大型机器人部署，而且还表现出对复杂障碍的鲁棒性，包括不规则的障碍，例如桌子，桌子，椅子和架子，以及复杂的地面和特殊材料。在本文中，我们提出了一个新型的基于单眼相机的复杂障碍避免框架。特别是，我们创新地将捕获的RGB图像转换为伪激光测量，以进行有效的深度强化学习。与在一定高度捕获的传统激光测量相比，仅包含距离附近障碍的一维距离信息，我们提议的伪激光测量融合了捕获的RGB图像的深度和语义信息，这使我们的方法有效地有效障碍。我们还设计了一个功能提取引导模块，以加重输入伪激光测量，并且代理对当前状态具有更合理的关注，这有利于提高障碍避免政策的准确性和效率。

本文回顾了AIM 2022上压缩图像和视频超级分辨率的挑战。这项挑战包括两条曲目。轨道1的目标是压缩图像的超分辨率，轨迹〜2靶向压缩视频的超分辨率。在轨道1中，我们使用流行的数据集DIV2K作为培训，验证和测试集。在轨道2中，我们提出了LDV 3.0数据集，其中包含365个视频，包括LDV 2.0数据集（335个视频）和30个其他视频。在这一挑战中，有12支球队和2支球队分别提交了赛道1和赛道2的最终结果。所提出的方法和解决方案衡量了压缩图像和视频上超分辨率的最先进。提出的LDV 3.0数据集可在https://github.com/renyang-home/ldv_dataset上找到。此挑战的首页是在https://github.com/renyang-home/aim22_compresssr。

图形神经网络（GNN）在许多预测任务中表现出优于图形的优越性，因为它们在图形结构数据中捕获非线性关系的令人印象深刻。但是，对于节点分类任务，通常只观察到GNN在线性对应物上的边际改进。以前的作品对这种现象的理解很少。在这项工作中，我们求助于贝叶斯学习，以深入研究GNNS在节点分类任务中非线性的功能。鉴于从统计模型CSBM生成的图，我们观察到，给定其自身和邻居的属性的节点标签的最大a-后方估计包括两种类型的非线性，可能是节点属性和节点属性的非线性转换和来自邻居的重新激活特征聚合。后者令人惊讶地与许多GNN模型中使用的非线性类型匹配。通过进一步对节点属性施加高斯假设，我们证明，当节点属性比图形结构更具信息性时，这些relu激活的优越性才是显着的，该图与许多以前的经验观察非常匹配。当训练和测试数据集之间的节点属性分布变化时，可以实现类似的参数。最后，我们验证了关于合成和现实世界网络的理论。

最佳执行是算法交易中节省成本的顺序决策问题。研究发现，加强学习（RL）可以帮助确定订单分类的大小。但是，问题尚未解决：如何以适当的限制价格下达限额订单？关键挑战在于动作空间的“连续折叠双重性”。一方面，使用价格变化百分比变化的连续行动空间是概括。另一方面，交易者最终需要离散地选择限制价格，这是由于tick尺寸的存在，这需要对每个具有不同特征（例如流动性和价格范围）的单人进行专业化。因此，我们需要连续控制进行概括和离散控制以进行专业化。为此，我们提出了一种混合RL方法来结合两者的优势。我们首先使用连续的控制代理来范围范围，然后部署细粒代理以选择特定的限制价格。广泛的实验表明，与现有的RL算法相比，我们的方法具有更高的样本效率和更好的训练稳定性，并且显着优于先前基于学习的方法的订单执行方法。

当机器人在城市环境中导航时，大量动态物体的出现将使空间结构多样化。因此，在线删除动态对象至关重要。在本文中，我们为高度动态的城市环境介绍了一个新颖的在线拆除框架。该框架由扫描到图的前端和地图对后端模块组成。前端和后端都深入整合了基于可见性的方法和基于地图的方法。该实验在高度动态的模拟方案和现实世界数据集中验证了框架。