ICECUBE是一种用于检测1 GEV和1 PEV之间大气和天体中微子的光学传感器的立方公斤阵列，该阵列已部署1.45 km至2.45 km的南极的冰盖表面以下1.45 km至2.45 km。来自ICE探测器的事件的分类和重建在ICeCube数据分析中起着核心作用。重建和分类事件是一个挑战，这是由于探测器的几何形状，不均匀的散射和冰中光的吸收，并且低于100 GEV的光，每个事件产生的信号光子数量相对较少。为了应对这一挑战，可以将ICECUBE事件表示为点云图形，并将图形神经网络（GNN）作为分类和重建方法。 GNN能够将中微子事件与宇宙射线背景区分开，对不同的中微子事件类型进行分类，并重建沉积的能量，方向和相互作用顶点。基于仿真，我们提供了1-100 GEV能量范围的比较与当前ICECUBE分析中使用的当前最新最大似然技术，包括已知系统不确定性的影响。对于中微子事件分类，与当前的IceCube方法相比，GNN以固定的假阳性速率（FPR）提高了信号效率的18％。另外，GNN在固定信号效率下将FPR的降低超过8（低于半百分比）。对于能源，方向和相互作用顶点的重建，与当前最大似然技术相比，分辨率平均提高了13％-20％。当在GPU上运行时，GNN能够以几乎是2.7 kHz的中位数ICECUBE触发速率的速率处理ICECUBE事件，这打开了在在线搜索瞬态事件中使用低能量中微子的可能性。

由于非平稳性，现实世界多变量时间序列（MTS）的分布会随着时间而变化，称为分布漂移。大多数现有的MT预测模型都会极大地遭受分销漂移的影响，并随着时间的推移降低了预测性能。现有方法通过适应最新到达数据或根据未来数据得出的元知识进行自我纠正来解决分布漂移。尽管在MT的预测中取得了巨大的成功，但这些方法几乎无法捕获固有的分布变化，尤其是从分布的角度来看。因此，我们提出了一个新型的框架时间条件变化自动编码器（TCVAE），以对MTS中历史观察结果和未来数据之间的动态分布依赖性进行建模，并将依赖性作为时间条件分布推断为利用潜在变量。具体而言，新型的颞鹰注意机制代表了随后馈入馈送前网络的时间因素，以估计潜在变量的先前高斯分布。时间因素的表示进一步动态地调整了基于变压器的编码器和解码器的结构，以利用门控注意机制来变化。此外，我们引入条件连续归一化流量，以将先前的高斯转化为复杂且无形式的分布，以促进对时间条件分布的灵活推断。在六个现实世界MTS数据集上进行的广泛实验表明，与最先进的MTS预测基线相比，TCVAE的出色鲁棒性和有效性。我们进一步说明了TCVAE通过多方面的案例研究和现实情况下的可视化来说明TCVAE的适用性。

微调可能容易受到对抗攻击的影响。现有有关对微调模型（BAFT）的黑盒攻击的作品受到强有力的假设的限制。为了填补空白，我们提出了两个新型的BAFT设置，即跨域和跨域交叉结构BAFT，这仅假设（1）攻击的目标模型是微调模型，以及（2）源域数据是已知和可访问的。为了成功攻击两种设置下的微调模型，我们建议先训练针对源模型的对抗发电机，该模型采用编码器架构体系结构并将干净的输入映射到对抗性示例。然后，我们在对抗发电机的编码器产生的低维潜在空间中搜索。搜索是根据从源模型获得的替代梯度的指导进行的。对不同域和不同网络体系结构的实验结果表明，提出的攻击方法可以有效，有效地攻击微调模型。

对标签噪声的学习是一个至关重要的话题，可以保证深度神经网络的可靠表现。最近的研究通常是指具有模型输出概率和损失值的动态噪声建模，然后分离清洁和嘈杂的样本。这些方法取得了显着的成功。但是，与樱桃挑选的数据不同，现有方法在面对不平衡数据集时通常无法表现良好，这是现实世界中常见的情况。我们彻底研究了这一现象，并指出了两个主要问题，这些问题阻碍了性能，即\ emph {类间损耗分布差异}和\ emph {由于不确定性而引起的误导性预测}。第一个问题是现有方法通常执行类不足的噪声建模。然而，损失分布显示在类失衡下的类别之间存在显着差异，并且类不足的噪声建模很容易与少数族裔类别中的嘈杂样本和样本混淆。第二个问题是指该模型可能会因认知不确定性和不确定性而导致的误导性预测，因此仅依靠输出概率的现有方法可能无法区分自信的样本。受我们的观察启发，我们提出了一个不确定性的标签校正框架〜（ULC）来处理不平衡数据集上的标签噪声。首先，我们执行认识不确定性的班级特异性噪声建模，以识别可信赖的干净样本并精炼/丢弃高度自信的真实/损坏的标签。然后，我们在随后的学习过程中介绍了不确定性，以防止标签噪声建模过程中的噪声积累。我们对几个合成和现实世界数据集进行实验。结果证明了提出的方法的有效性，尤其是在数据集中。

POI推荐是旅游信息系统的关键任务。然而，与传统的兴趣点（POI）推荐系统相比，可用数据非常稀疏;大多数旅游访问一些观光点一次，大部分景点都没有来自新游客的登记数据。大多数传统系统基于他们的受欢迎程度，声誉和基于类别的类似价值与用户的偏好等级等级。他们不澄清用户可以在这些斑点中体验的内容，这使得难以满足各种旅游需求。为此，在这项工作中，我们提出了一种推荐游客的机制。我们的机制包括两个组件：一个是一个概率模型，揭示了旅游业的用户行为;另一个是伪评级机制，用于处理POIS建议中的冷启动问题。我们对从Flickr收集的两个数据集进行了广泛的实验。实验结果表明，我们的方法优于推荐表演（精确，召回和F测量）和公平性的最先进的方法。实验结果还验证了所提出的方法的鲁棒性，即，我们的方法可以处理数据稀疏性问题。

Due to their ability to offer more comprehensive information than data from a single view, multi-view (multi-source, multi-modal, multi-perspective, etc.) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality becomes more apparent, limiting the potential benefits of multi-view data. Although recent deep neural network (DNN) based models can learn the weight of data adaptively, a lack of research on explicitly quantifying the data quality of each view when fusing them renders these models inexplicable, performing unsatisfactorily and inflexible in downstream remote sensing tasks. To fill this gap, in this paper, evidential deep learning is introduced to the task of aerial-ground dual-view remote sensing scene classification to model the credibility of each view. Specifically, the theory of evidence is used to calculate an uncertainty value which describes the decision-making risk of each view. Based on this uncertainty, a novel decision-level fusion strategy is proposed to ensure that the view with lower risk obtains more weight, making the classification more credible. On two well-known, publicly available datasets of aerial-ground dual-view remote sensing images, the proposed approach achieves state-of-the-art results, demonstrating its effectiveness. The code and datasets of this article are available at the following address: https://github.com/gaopiaoliang/Evidential.

Dynamic Graph Neural Networks (DGNNs) have been broadly applied in various real-life applications, such as link prediction and pandemic forecast, to capture both static structural information and temporal characteristics from dynamic graphs. Combining both time-dependent and -independent components, DGNNs manifest substantial parallel computation and data reuse potentials, but suffer from severe memory access inefficiency and data transfer overhead under the canonical one-graph-at-a-time training pattern. To tackle the challenges, we propose PiPAD, a $\underline{\textbf{Pi}}pelined$ and $\underline{\textbf{PA}}rallel$ $\underline{\textbf{D}}GNN$ training framework for the end-to-end performance optimization on GPUs. From both the algorithm and runtime level, PiPAD holistically reconstructs the overall training paradigm from the data organization to computation manner. Capable of processing multiple graph snapshots in parallel, PiPAD eliminates the unnecessary data transmission and alleviates memory access inefficiency to improve the overall performance. Our evaluation across various datasets shows PiPAD achieves $1.22\times$-$9.57\times$ speedup over the state-of-the-art DGNN frameworks on three representative models.

We propose a novel teacher-student model for semi-supervised multi-organ segmentation. In teacher-student model, data augmentation is usually adopted on unlabeled data to regularize the consistent training between teacher and student. We start from a key perspective that fixed relative locations and variable sizes of different organs can provide distribution information where a multi-organ CT scan is drawn. Thus, we treat the prior anatomy as a strong tool to guide the data augmentation and reduce the mismatch between labeled and unlabeled images for semi-supervised learning. More specifically, we propose a data augmentation strategy based on partition-and-recovery N$^3$ cubes cross- and within- labeled and unlabeled images. Our strategy encourages unlabeled images to learn organ semantics in relative locations from the labeled images (cross-branch) and enhances the learning ability for small organs (within-branch). For within-branch, we further propose to refine the quality of pseudo labels by blending the learned representations from small cubes to incorporate local attributes. Our method is termed as MagicNet, since it treats the CT volume as a magic-cube and $N^3$-cube partition-and-recovery process matches with the rule of playing a magic-cube. Extensive experiments on two public CT multi-organ datasets demonstrate the effectiveness of MagicNet, and noticeably outperforms state-of-the-art semi-supervised medical image segmentation approaches, with +7% DSC improvement on MACT dataset with 10% labeled images.

The current optical communication systems minimize bit or symbol errors without considering the semantic meaning behind digital bits, thus transmitting a lot of unnecessary information. We propose and experimentally demonstrate a semantic optical fiber communication (SOFC) system. Instead of encoding information into bits for transmission, semantic information is extracted from the source using deep learning. The generated semantic symbols are then directly transmitted through an optical fiber. Compared with the bit-based structure, the SOFC system achieved higher information compression and a more stable performance, especially in the low received optical power regime, and enhanced the robustness against optical link impairments. This work introduces an intelligent optical communication system at the human analytical thinking level, which is a significant step toward a breakthrough in the current optical communication architecture.

The task of referring video object segmentation aims to segment the object in the frames of a given video to which the referring expressions refer. Previous methods adopt multi-stage approach and design complex pipelines to obtain promising results. Recently, the end-to-end method based on Transformer has proved its superiority. In this work, we draw on the advantages of the above methods to provide a simple and effective pipeline for RVOS. Firstly, We improve the state-of-the-art one-stage method ReferFormer to obtain mask sequences that are strongly correlated with language descriptions. Secondly, based on a reliable and high-quality keyframe, we leverage the superior performance of video object segmentation model to further enhance the quality and temporal consistency of the mask results. Our single model reaches 70.3 J &F on the Referring Youtube-VOS validation set and 63.0 on the test set. After ensemble, we achieve 64.1 on the final leaderboard, ranking 1st place on CVPR2022 Referring Youtube-VOS challenge. Code will be available at https://github.com/Zhiweihhh/cvpr2022-rvos-challenge.git.