在这项工作中，我们探讨了对物体在看不见的世界中同时本地化和映射中的使用，并提出了一个对象辅助系统（OA-Slam）。更确切地说，我们表明，与低级点相比，物体的主要好处在于它们的高级语义和歧视力。相反，要点比代表对象（Cuboid或椭圆形）的通用粗模型具有更好的空间定位精度。我们表明，将点和对象组合非常有趣，可以解决相机姿势恢复的问题。我们的主要贡献是：（1）我们使用高级对象地标提高了SLAM系统的重新定位能力； （2）我们构建了一个能够使用3D椭圆形识别，跟踪和重建对象的自动系统； （3）我们表明，基于对象的本地化可用于重新初始化或恢复相机跟踪。我们的全自动系统允许对象映射和增强姿势跟踪恢复，我们认为这可以极大地受益于AR社区。我们的实验表明，可以从经典方法失败的视点重新定位相机。我们证明，尽管跟踪损失损失，但这种本地化使SLAM系统仍可以继续工作，而这种损失可能会经常发生在不理会的用户中。我们的代码和测试数据在gitlab.inria.fr/tangram/oa-slam上发布。

接受经验风险最小化（ERM）训练的机器学习模型的预测性能可以大大降解分配变化。在训练数据集中存在虚假相关性的存在导致ERM训练的模型在对不存在此类相关性的少数群体评估时表现出很高的损失。已经进行了广泛的尝试来开发改善最差的鲁棒性的方法。但是，他们需要每个培训输入的组信息，或者至少需要一个带有组标签的验证设置来调整其超参数，这可能是昂贵的或未知的。在本文中，我们应对在培训或验证期间没有小组注释的情况下提高组鲁棒性的挑战。为此，我们建议根据``识别''模型提取的特征的革兰氏集矩阵将训练数据集分为组，并根据这些伪组应用强大的优化。在不可用的小组标签的现实情况下，我们的实验表明，我们的方法不仅可以改善对ERM的稳健性，而且还优于所有最近的基线

在计算机视觉中，从3D几何实体之间的对应关系及其对图像的投影进行了摄影姿势估计已被广泛研究。尽管大多数最先进的方法利用了诸如点或线之类的低级原始方法，但近年来非常有效的基于CNN的对象探测器的出现为使用具有有意义语义有意义的高级功能铺平了道路信息。开拓性朝这个方向起作用，表明通过椭圆形对3D对象进行建模，而椭圆检测2D检测则提供了方便的方式来链接2D和3D数据。但是，相关垃圾中最常使用的数学形式主义不能轻易将椭圆形和椭圆形和其他四边形和圆锥形区分开，从而导致某些发展中可能有害的特异性丧失。此外，投影方程的线性化过程产生了相机参数的过度代表，也可能导致效率损失。因此，在本文中，我们引入了一个特定于椭圆形的理论框架，并在姿势估计的背景下证明了其有益的特性。更确切地说，我们首先表明拟议的形式主义使椭圆形姿势估计问题将其减少到仅位置或方向估计问题，其中剩余未知数可以以封闭形式得出。然后，我们证明它可以进一步简化为1个自由度（1DOF）问题，并提供姿势的分析表达，这是该唯一标量未知的函数。我们通过视觉示例说明了我们的理论考虑。最后，我们发布了这项工作，以便为更有效的椭圆形相关姿势估计问题做出贡献。

在本文中，我们提出了一个基于对象的摄像头姿势效果估计，并从单个RGB图像和以椭圆形模型表示的对象图中构建图。我们表明，与点对应关系相反，表征3D对象在2D对象检测上的投影的成本函数的定义并不简单。我们根据水平集采样开发了椭圆形成本，展示了其处理部分可见对象并将其性能与其他常见指标进行比较的良好属性。最后，我们表明，在检测到的椭圆上使用预测性不确定性允许对对应关系的贡献进行公平的权衡，从而改善了计算的姿势。该代码在https://gitlab.inria.fr/tangram/level-set基于camera-pose-Estimation上发布。

View-dependent effects such as reflections pose a substantial challenge for image-based and neural rendering algorithms. Above all, curved reflectors are particularly hard, as they lead to highly non-linear reflection flows as the camera moves. We introduce a new point-based representation to compute Neural Point Catacaustics allowing novel-view synthesis of scenes with curved reflectors, from a set of casually-captured input photos. At the core of our method is a neural warp field that models catacaustic trajectories of reflections, so complex specular effects can be rendered using efficient point splatting in conjunction with a neural renderer. One of our key contributions is the explicit representation of reflections with a reflection point cloud which is displaced by the neural warp field, and a primary point cloud which is optimized to represent the rest of the scene. After a short manual annotation step, our approach allows interactive high-quality renderings of novel views with accurate reflection flow. Additionally, the explicit representation of reflection flow supports several forms of scene manipulation in captured scenes, such as reflection editing, cloning of specular objects, reflection tracking across views, and comfortable stereo viewing. We provide the source code and other supplemental material on https://repo-sam.inria.fr/ fungraph/neural_catacaustics/

New architecture GPUs like A100 are now equipped with multi-instance GPU (MIG) technology, which allows the GPU to be partitioned into multiple small, isolated instances. This technology provides more flexibility for users to support both deep learning training and inference workloads, but efficiently utilizing it can still be challenging. The vision of this paper is to provide a more comprehensive and practical benchmark study for MIG in order to eliminate the need for tedious manual benchmarking and tuning efforts. To achieve this vision, the paper presents MIGPerf, an open-source tool that streamlines the benchmark study for MIG. Using MIGPerf, the authors conduct a series of experiments, including deep learning training and inference characterization on MIG, GPU sharing characterization, and framework compatibility with MIG. The results of these experiments provide new insights and guidance for users to effectively employ MIG, and lay the foundation for further research on the orchestration of hybrid training and inference workloads on MIGs. The code and results are released on https://github.com/MLSysOps/MIGProfiler. This work is still in progress and more results will be published soon.

Tobacco origin identification is significantly important in tobacco industry. Modeling analysis for sensor data with near infrared spectroscopy has become a popular method for rapid detection of internal features. However, for sensor data analysis using traditional artificial neural network or deep network models, the training process is extremely time-consuming. In this paper, a novel broad learning system with Takagi-Sugeno (TS) fuzzy subsystem is proposed for rapid identification of tobacco origin. Incremental learning is employed in the proposed method, which obtains the weight matrix of the network after a very small amount of computation, resulting in much shorter training time for the model, with only about 3 seconds for the extra step training. The experimental results show that the TS fuzzy subsystem can extract features from the near infrared data and effectively improve the recognition performance. The proposed method can achieve the highest prediction accuracy (95.59 %) in comparison to the traditional classification algorithms, artificial neural network, and deep convolutional neural network, and has a great advantage in the training time with only about 128 seconds.

Accurate modeling of ship performance is crucial for the shipping industry to optimize fuel consumption and subsequently reduce emissions. However, predicting the speed-power relation in real-world conditions remains a challenge. In this study, we used in-service monitoring data from multiple vessels with different hull shapes to compare the accuracy of data-driven machine learning (ML) algorithms to traditional methods for assessing ship performance. Our analysis consists of two main parts: (1) a comparison of sea trial curves with calm-water curves fitted on operational data, and (2) a benchmark of multiple added wave resistance theories with an ML-based approach. Our results showed that a simple neural network outperformed established semi-empirical formulas following first principles. The neural network only required operational data as input, while the traditional methods required extensive ship particulars that are often unavailable. These findings suggest that data-driven algorithms may be more effective for predicting ship performance in practical applications.

As a common appearance defect of concrete bridges, cracks are important indices for bridge structure health assessment. Although there has been much research on crack identification, research on the evolution mechanism of bridge cracks is still far from practical applications. In this paper, the state-of-the-art research on intelligent theories and methodologies for intelligent feature extraction, data fusion and crack detection based on data-driven approaches is comprehensively reviewed. The research is discussed from three aspects: the feature extraction level of the multimodal parameters of bridge cracks, the description level and the diagnosis level of the bridge crack damage states. We focus on previous research concerning the quantitative characterization problems of multimodal parameters of bridge cracks and their implementation in crack identification, while highlighting some of their major drawbacks. In addition, the current challenges and potential future research directions are discussed.

Two approaches to AI, neural networks and symbolic systems, have been proven very successful for an array of AI problems. However, neither has been able to achieve the general reasoning ability required for human-like intelligence. It has been argued that this is due to inherent weaknesses in each approach. Luckily, these weaknesses appear to be complementary, with symbolic systems being adept at the kinds of things neural networks have trouble with and vice-versa. The field of neural-symbolic AI attempts to exploit this asymmetry by combining neural networks and symbolic AI into integrated systems. Often this has been done by encoding symbolic knowledge into neural networks. Unfortunately, although many different methods for this have been proposed, there is no common definition of an encoding to compare them. We seek to rectify this problem by introducing a semantic framework for neural-symbolic AI, which is then shown to be general enough to account for a large family of neural-symbolic systems. We provide a number of examples and proofs of the application of the framework to the neural encoding of various forms of knowledge representation and neural network. These, at first sight disparate approaches, are all shown to fall within the framework's formal definition of what we call semantic encoding for neural-symbolic AI.