自动识别系统（AIS）消息对于使用无线电链路和卫星收发器在全球范围内跨海的血管活动很有用。这样的数据在跟踪血管活性和映射迁移率模式（例如捕鱼中发现）中起着重要作用。因此，本文提出了一种几何驱动的半监督方法，用于从AIS数据中检测捕捞活动。通过提出的方法，我们展示了如何探索消息中包含的信息，以提取描述船舶路线几何形状的特征。为此，我们利用了聚类分析的无监督性质来标记轨迹几何形状，突出了往往表明捕鱼活动的容器运动模式的变化。建议的无监督方法获得的标签用于检测捕鱼活动，我们将其作为时间序列分类任务进行。在这种情况下，我们在AIS数据流上使用复发性神经网络提出了一个解决方案，该解决方案大约是50种不同看不见的渔船的整个轨迹的总$ F $分数的87％。此类结果伴随着广泛的基准研究，该研究评估了不同复发性神经网络（RNN）体系结构的性能。总之，这项工作通过提出一个详尽的过程来做出贡献，其中包括数据准备，标签，数据建模和模型验证。因此，我们提出了一种新颖的解决方案，用于迁移模式检测，该解决方案依赖于时间上展开轨迹并观察其固有的几何形状。

在联合学习（FL）中，许多客户或设备在不共享数据的情况下协作培训模型。模型在每个客户端进行了优化，并进一步通信到中央集线器进行聚合。尽管FL是一个吸引人的分散培训范式，但来自不同客户的数据之间的异质性可能会导致本地优化从全球目标中消失。为了估计并消除这种漂移，最近已将差异技术纳入了FL优化。但是，这些方法不准确地估计客户的漂移，最终无法正确删除它。在这项工作中，我们提出了一种自适应算法，该算法可以准确地估计客户端的漂移。与以前的工作相比，我们的方法需要更少的存储和通信带宽以及较低的计算成本。此外，我们提出的方法可以通过限制客户漂移的估计标准来诱导稳定性，从而使大规模fl更实用。实验发现表明，所提出的算法比在各种FL基准中的基准相比，收敛的速度明显更快，并且获得了更高的准确性。

海洋是令人印象深刻的复杂数据混合的来源，可用于发现尚未发现的关系。此类数据来自海洋及其表面，例如用于跟踪血管轨迹的自动识别系统（AIS）消息。 AIS消息以理想的定期时间间隔通过无线电或卫星传输，但随着时间的流逝而变化不规则。因此，本文旨在通过神经网络对AIS消息传输行为进行建模，以预测即将到来的AIS消息的内容，尤其是在同时方法的情况下，尽管消息的时间不规则性作为异常值。我们提出了一组实验，其中包含用于预测任务的多种算法，其长度不同。深度学习模型（例如，神经网络）表明自己可以充分地保留血管的空间意识，而不管时间不规则。我们展示了如何通过共同努力来改善此类任务的卷积层，进料网络和反复的神经网络。尝试短，中和大型消息序列，我们的模型达到了相对百分比差异的36/37/38％ - 越低，越好，而我们在Elman的RNN上观察到92/45/96％，51 /52/40％的GRU，LSTM的129/98/61％。这些结果支持我们的模型作为驱动器，以改善在时间噪声数据下同时分析多个分歧类型的血管时，可以改善船舶路线的预测。

本次调查绘制了用于分析社交媒体数据的生成方法的研究状态的广泛的全景照片（Sota）。它填补了空白，因为现有的调查文章在其范围内或被约会。我们包括两个重要方面，目前正在挖掘和建模社交媒体的重要性：动态和网络。社会动态对于了解影响影响或疾病的传播，友谊的形成，友谊的形成等，另一方面，可以捕获各种复杂关系，提供额外的洞察力和识别否则将不会被注意的重要模式。

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.

Proteins are fundamental biological entities that play a key role in life activities. The amino acid sequences of proteins can be folded into stable 3D structures in the real physicochemical world, forming a special kind of sequence-structure data. With the development of Artificial Intelligence (AI) techniques, Protein Representation Learning (PRL) has recently emerged as a promising research topic for extracting informative knowledge from massive protein sequences or structures. To pave the way for AI researchers with little bioinformatics background, we present a timely and comprehensive review of PRL formulations and existing PRL methods from the perspective of model architectures, pretext tasks, and downstream applications. We first briefly introduce the motivations for protein representation learning and formulate it in a general and unified framework. Next, we divide existing PRL methods into three main categories: sequence-based, structure-based, and sequence-structure co-modeling. Finally, we discuss some technical challenges and potential directions for improving protein representation learning. The latest advances in PRL methods are summarized in a GitHub repository https://github.com/LirongWu/awesome-protein-representation-learning.

This paper investigates Support Vector Regression (SVR) in the context of the fundamental risk quadrangle paradigm. It is shown that both formulations of SVR, $\varepsilon$-SVR and $\nu$-SVR, correspond to the minimization of equivalent regular error measures (Vapnik error and superquantile (CVaR) norm, respectively) with a regularization penalty. These error measures, in turn, give rise to corresponding risk quadrangles. Additionally, the technique used for the construction of quadrangles serves as a powerful tool in proving the equivalence between $\varepsilon$-SVR and $\nu$-SVR. By constructing the fundamental risk quadrangle, which corresponds to SVR, we show that SVR is the asymptotically unbiased estimator of the average of two symmetric conditional quantiles. Additionally, SVR is formulated as a regular deviation minimization problem with a regularization penalty by invoking Error Shaping Decomposition of Regression. Finally, the dual formulation of SVR in the risk quadrangle framework is derived.

We introduce MegaPose, a method to estimate the 6D pose of novel objects, that is, objects unseen during training. At inference time, the method only assumes knowledge of (i) a region of interest displaying the object in the image and (ii) a CAD model of the observed object. The contributions of this work are threefold. First, we present a 6D pose refiner based on a render&compare strategy which can be applied to novel objects. The shape and coordinate system of the novel object are provided as inputs to the network by rendering multiple synthetic views of the object's CAD model. Second, we introduce a novel approach for coarse pose estimation which leverages a network trained to classify whether the pose error between a synthetic rendering and an observed image of the same object can be corrected by the refiner. Third, we introduce a large-scale synthetic dataset of photorealistic images of thousands of objects with diverse visual and shape properties and show that this diversity is crucial to obtain good generalization performance on novel objects. We train our approach on this large synthetic dataset and apply it without retraining to hundreds of novel objects in real images from several pose estimation benchmarks. Our approach achieves state-of-the-art performance on the ModelNet and YCB-Video datasets. An extensive evaluation on the 7 core datasets of the BOP challenge demonstrates that our approach achieves performance competitive with existing approaches that require access to the target objects during training. Code, dataset and trained models are available on the project page: https://megapose6d.github.io/.

Solving partial differential equations is difficult. Recently proposed neural resolution-invariant models, despite their effectiveness and efficiency, usually require equispaced spatial points of data. However, sampling in spatial domain is sometimes inevitably non-equispaced in real-world systems, limiting their applicability. In this paper, we propose a Non-equispaced Fourier PDE Solver (\textsc{NFS}) with adaptive interpolation on resampled equispaced points and a variant of Fourier Neural Operators as its components. Experimental results on complex PDEs demonstrate its advantages in accuracy and efficiency. Compared with the spatially-equispaced benchmark methods, it achieves superior performance with $42.85\%$ improvements on MAE, and is able to handle non-equispaced data with a tiny loss of accuracy. Besides, to our best knowledge, \textsc{NFS} is the first ML-based method with mesh invariant inference ability to successfully model turbulent flows in non-equispaced scenarios, with a minor deviation of the error on unseen spatial points.

Federated learning has been predominantly concerned with collaborative training of deep networks from scratch, and especially the many challenges that arise, such as communication cost, robustness to heterogeneous data, and support for diverse device capabilities. However, there is no unified framework that addresses all these problems together. This paper studies the challenges and opportunities of exploiting pre-trained Transformer models in FL. In particular, we propose to efficiently adapt such pre-trained models by injecting a novel attention-based adapter module at each transformer block that both modulates the forward pass and makes an early prediction. Training only the lightweight adapter by FL leads to fast and communication-efficient learning even in the presence of heterogeneous data and devices. Extensive experiments on standard FL benchmarks, including CIFAR-100, FEMNIST and SpeechCommandsv2 demonstrate that this simple framework provides fast and accurate FL while supporting heterogenous device capabilities, efficient personalization, and scalable-cost anytime inference.