我们为视频中的无监督对象细分提出了一种简单而强大的方法。我们引入了一个目标函数，其最小值代表输入序列上主要显着对象的掩码。它仅依赖于独立的图像特征和光流，可以使用现成的自我监督方法获得。它以序列的长度缩放，不需要超级像素或稀疏，并且在没有任何特定培训的情况下将其推广到不同的数据集。该目标函数实际上可以从应用于整个视频的光谱群集形式得出。我们的方法通过标准基准（Davis2016，segtrack-v2，fbms59）实现了PAR的性能，同时在概念上且实际上更简单。代码可从https://ponimatkin.github.io/ssl-vos获得。
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The Internet of Things (IoT) is a system that connects physical computing devices, sensors, software, and other technologies. Data can be collected, transferred, and exchanged with other devices over the network without requiring human interactions. One challenge the development of IoT faces is the existence of anomaly data in the network. Therefore, research on anomaly detection in the IoT environment has become popular and necessary in recent years. This survey provides an overview to understand the current progress of the different anomaly detection algorithms and how they can be applied in the context of the Internet of Things. In this survey, we categorize the widely used anomaly detection machine learning and deep learning techniques in IoT into three types: clustering-based, classification-based, and deep learning based. For each category, we introduce some state-of-the-art anomaly detection methods and evaluate the advantages and limitations of each technique.
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Predicting the physical interaction of proteins is a cornerstone problem in computational biology. New classes of learning-based algorithms are actively being developed, and are typically trained end-to-end on protein complex structures extracted from the Protein Data Bank. These training datasets tend to be large and difficult to use for prototyping and, unlike image or natural language datasets, they are not easily interpretable by non-experts. We present Dock2D-IP and Dock2D-IF, two "toy" datasets that can be used to select algorithms predicting protein-protein interactions$\unicode{x2014}$or any other type of molecular interactions. Using two-dimensional shapes as input, each example from Dock2D-IP ("interaction pose") describes the interaction pose of two shapes known to interact and each example from Dock2D-IF ("interaction fact") describes whether two shapes form a stable complex or not. We propose a number of baseline solutions to the problem and show that the same underlying energy function can be learned either by solving the interaction pose task (formulated as an energy-minimization "docking" problem) or the fact-of-interaction task (formulated as a binding free energy estimation problem).
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The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.
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本文开发了一种协作人类机器人探索的方法，该方法利用了隐式协调。大多数自动的单机器人和多机器人勘探系统都要求远程操作员为机器人团队提供明确的指导。很少有人考虑如何将人类合作伙伴与机器人一起嵌入到该领域的指导。对人类机器人探索的剩下的挑战是从人类到机器人的目标有效沟通。在本文中，我们开发了一种方法论，该方法从人的头上的头盔深度相机到机器人的头盔深度摄像头，以及一个基于信息增益的探索目标，并在人类提供的观点中偏向运动计划。结果是一个安全访问感兴趣区域的空中系统，该区域可能无法立即被人类查看或无法触及。该方法在模拟和运动捕获场中的硬件实验中进行了评估。仿真和硬件实验的视频可在以下网址提供：https：//youtu.be/7jgkbpvfioe。
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本文通过开发一种层次碰撞避免方法来改善基于安全的多旋转器的近电视，该方法根据环境复杂性和感知约束来调节最大速度。在表现出不同混乱的环境中，安全速度调制具有挑战性。现有方法固定了最大速度和地图分辨率，该方法可防止车辆进入狭窄的空间，并将认知负荷置于操作员上的速度。我们通过提出一种高速公路（10 Hz）的远程操作方法来解决这些差距，该方法通过分层碰撞检查调节最大车辆速度。分层碰撞检查器同时适应当地地图的体素尺寸和最大车辆速度，以确保运动计划安全。在模拟和现实世界实验中评估了所提出的方法，并将其与基于非自适应运动原语的远程操作方法进行了比较。结果证明了所提出的详细方法方法的优势以及完成任务的能力，而无需用户指定最大车辆速度。
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本文介绍了一种使用旨在解决现实世界应用中CDSS的低适用性和可扩展性问题的数据驱动的预测模型来构建一致和适用的临床决策支持系统（CDSS）的方法。该方法基于域特定和数据驱动的支持程序的三种特定于域和数据驱动的支持程序，该程序将被纳入临床业务流程，具有更高的信任和预测结果和建议的解释性。在考虑的三个阶段，监管策略，数据驱动模式和解释程序被集成，以实现与决策者的自然域特定的互动，具有智能决策支持焦点的连续缩小。该提出的方法能够实现更高水平的自动化，可扩展性和CDSS的语义解释性。该方法是在软件解决方案中实现的，并在T2DM预测中进行了测试，使我们能够改善已知的临床尺度（例如FindRisk），同时保持与现有应用程序类似的特定问题的推理界面。这种继承与三分阶段的方法一起提供了更高的解决方案兼容性，并导致数据驱动的解决方案在现实案件中的信任，有效和解释应用。
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