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.

Copy-Paste is a simple and effective data augmentation strategy for instance segmentation. By randomly pasting object instances onto new background images, it creates new training data for free and significantly boosts the segmentation performance, especially for rare object categories. Although diverse, high-quality object instances used in Copy-Paste result in more performance gain, previous works utilize object instances either from human-annotated instance segmentation datasets or rendered from 3D object models, and both approaches are too expensive to scale up to obtain good diversity. In this paper, we revisit Copy-Paste at scale with the power of newly emerged zero-shot recognition models (e.g., CLIP) and text2image models (e.g., StableDiffusion). We demonstrate for the first time that using a text2image model to generate images or zero-shot recognition model to filter noisily crawled images for different object categories is a feasible way to make Copy-Paste truly scalable. To make such success happen, we design a data acquisition and processing framework, dubbed "X-Paste", upon which a systematic study is conducted. On the LVIS dataset, X-Paste provides impressive improvements over the strong baseline CenterNet2 with Swin-L as the backbone. Specifically, it archives +2.6 box AP and +2.1 mask AP gains on all classes and even more significant gains with +6.8 box AP +6.5 mask AP on long-tail classes.

Time series forecasting is a long-standing challenge due to the real-world information is in various scenario (e.g., energy, weather, traffic, economics, earthquake warning). However some mainstream forecasting model forecasting result is derailed dramatically from ground truth. We believe it's the reason that model's lacking ability of capturing frequency information which richly contains in real world datasets. At present, the mainstream frequency information extraction methods are Fourier transform(FT) based. However, use of FT is problematic due to Gibbs phenomenon. If the values on both sides of sequences differ significantly, oscillatory approximations are observed around both sides and high frequency noise will be introduced. Therefore We propose a novel frequency enhanced channel attention that adaptively modelling frequency interdependencies between channels based on Discrete Cosine Transform which would intrinsically avoid high frequency noise caused by problematic periodity during Fourier Transform, which is defined as Gibbs Phenomenon. We show that this network generalize extremely effectively across six real-world datasets and achieve state-of-the-art performance, we further demonstrate that frequency enhanced channel attention mechanism module can be flexibly applied to different networks. This module can improve the prediction ability of existing mainstream networks, which reduces 35.99% MSE on LSTM, 10.01% on Reformer, 8.71% on Informer, 8.29% on Autoformer, 8.06% on Transformer, etc., at a slight computational cost ,with just a few line of code. Our codes and data are available at https://github.com/Zero-coder/FECAM.

The accurate detection and grasping of transparent objects are challenging but of significance to robots. Here, a visual-tactile fusion framework for transparent object grasping under complex backgrounds and variant light conditions is proposed, including the grasping position detection, tactile calibration, and visual-tactile fusion based classification. First, a multi-scene synthetic grasping dataset generation method with a Gaussian distribution based data annotation is proposed. Besides, a novel grasping network named TGCNN is proposed for grasping position detection, showing good results in both synthetic and real scenes. In tactile calibration, inspired by human grasping, a fully convolutional network based tactile feature extraction method and a central location based adaptive grasping strategy are designed, improving the success rate by 36.7% compared to direct grasping. Furthermore, a visual-tactile fusion method is proposed for transparent objects classification, which improves the classification accuracy by 34%. The proposed framework synergizes the advantages of vision and touch, and greatly improves the grasping efficiency of transparent objects.

实时音乐伴奏的生成在音乐行业（例如音乐教育和现场表演）中具有广泛的应用。但是，自动实时音乐伴奏的产生仍在研究中，并且经常在逻辑延迟和暴露偏见之间取决于权衡。在本文中，我们提出了Song Driver，这是一种无逻辑延迟或暴露偏见的实时音乐伴奏系统。具体而言，Songdriver将一个伴奏的生成任务分为两个阶段：1）安排阶段，其中变压器模型首先安排了和弦，以实时进行输入旋律，并在下一阶段加速了和弦，而不是播放它们。 2）预测阶段，其中CRF模型基于先前缓存的和弦生成了即将到来的旋律的可播放的多轨伴奏。通过这种两相策略，歌手直接生成即将到来的旋律的伴奏，从而达到了零逻辑延迟。此外，在预测时间步的和弦时，歌手是指第一阶段的缓存和弦，而不是其先前的预测，这避免了暴露偏见问题。由于输入长度通常在实时条件下受到限制，因此另一个潜在的问题是长期顺序信息的丢失。为了弥补这一缺点，我们在当前时间步骤作为全球信息之前从长期音乐作品中提取了四个音乐功能。在实验中，我们在一些开源数据集上训练歌手，以及由中国风格的现代流行音乐得分构建的原始\```````'''aisong数据集。结果表明，歌手在客观和主观指标上均优于现有的SOTA（最先进）模型，同时大大降低了物理潜伏期。

由于在建模相互依存系统中，由于其高效用，多层图已经在许多领域获得了大量的研究。然而，多层图的聚类，其旨在将图形节点划分为类别或社区，仍处于新生阶段。现有方法通常限于利用MultiView属性或多个网络，并忽略更复杂和更丰富的网络框架。为此，我们向多层图形聚类提出了一种名为Multidayer agal对比聚类网络（MGCCN）的多层图形聚类的通用和有效的AutoEncoder框架。 MGCCN由三个模块组成：（1）应用机制以更好地捕获节点与邻居之间的相关性以获得更好的节点嵌入。 （2）更好地探索不同网络中的一致信息，引入了对比融合策略。 （3）MGCCN采用自我监督的组件，可迭代地增强节点嵌入和聚类。对不同类型的真实图数据数据的广泛实验表明我们所提出的方法优于最先进的技术。

人类的持续学习（CL）能力与稳定性与可塑性困境密切相关，描述了人类如何实现持续的学习能力和保存的学习信息。自发育以来，CL的概念始终存在于人工智能（AI）中。本文提出了对CL的全面审查。与之前的评论不同，主要关注CL中的灾难性遗忘现象，本文根据稳定性与可塑性机制的宏观视角来调查CL。类似于生物对应物，“智能”AI代理商应该是I）记住以前学到的信息（信息回流）; ii）不断推断新信息（信息浏览:); iii）转移有用的信息（信息转移），以实现高级CL。根据分类学，评估度量，算法，应用以及一些打开问题。我们的主要贡献涉及I）从人工综合情报层面重新检查CL; ii）在CL主题提供详细和广泛的概述; iii）提出一些关于CL潜在发展的新颖思路。

记住和遗忘机制是人类学习记忆系统中同一硬币的两侧。灵感来自人类脑记忆机制，现代机器学习系统一直在努力通过更好地记住终身学习能力的机器，同时推动遗忘为敌人来克服。尽管如此，这个想法可能只能看到半张图片。直到最近，越来越多的研究人员认为，大脑出生忘记，即忘记是抽象，丰富和灵活的陈述的自然和积极的过程。本文通过人工神经网络积极遗忘机制提出了一种学习模型。主动遗忘机制（AFM）通过“即插即用”遗忘层（P \＆PF）引入神经网络，由具有内部调节策略（IRS）的抑制神经元组成，以调整自己的消光率通过横向抑制机制和外部调节策略（ERS）通过抑制机制调节兴奋性神经元的消光速率。实验研究表明，P \＆PF提供了令人惊讶的益处：自适应结构，强大的泛化，长期学习和记忆，以及对数据和参数扰动的鲁棒性。这项工作阐明了忘记学习过程的重要性，并提供了新的视角，了解神经网络的潜在机制。

拟合科学数据的部分微分方程（PDE）可以用可解释的机制来代表各种以数学为导向的受试者的物理定律。从科学数据中发现PDE的数据驱动的发现蓬勃发展，作为对自然界中复杂现象进行建模的新尝试，但是当前实践的有效性通常受数据的稀缺性和现象的复杂性的限制。尤其是，从低质量数据中发现具有高度非线性系数的PDE在很大程度上已经不足。为了应对这一挑战，我们提出了一种新颖的物理学指导学习方法，该方法不仅可以编码观察知识，例如初始和边界条件，而且还包含了基本的物理原理和法律来指导模型优化。我们从经验上证明，所提出的方法对数据噪声和稀疏性更为强大，并且可以将估计误差较大。此外，我们第一次能够发现具有高度非线性系数的PDE。凭借有希望的性能，提出的方法推动了PDE的边界，这可以通过机器学习模型来进行科学发现。

Representing and synthesizing novel views in real-world dynamic scenes from casual monocular videos is a long-standing problem. Existing solutions typically approach dynamic scenes by applying geometry techniques or utilizing temporal information between several adjacent frames without considering the underlying background distribution in the entire scene or the transmittance over the ray dimension, limiting their performance on static and occlusion areas. Our approach $\textbf{D}$istribution-$\textbf{D}$riven neural radiance fields offers high-quality view synthesis and a 3D solution to $\textbf{D}$etach the background from the entire $\textbf{D}$ynamic scene, which is called $\text{D}^4$NeRF. Specifically, it employs a neural representation to capture the scene distribution in the static background and a 6D-input NeRF to represent dynamic objects, respectively. Each ray sample is given an additional occlusion weight to indicate the transmittance lying in the static and dynamic components. We evaluate $\text{D}^4$NeRF on public dynamic scenes and our urban driving scenes acquired from an autonomous-driving dataset. Extensive experiments demonstrate that our approach outperforms previous methods in rendering texture details and motion areas while also producing a clean static background. Our code will be released at https://github.com/Luciferbobo/D4NeRF.