In this paper, we propose the first-ever real benchmark thought for evaluating Neural Radiance Fields (NeRFs) and, in general, Neural Rendering (NR) frameworks. We design and implement an effective pipeline for scanning real objects in quantity and effortlessly. Our scan station is built with less than 500$ hardware budget and can collect roughly 4000 images of a scanned object in just 5 minutes. Such a platform is used to build ScanNeRF, a dataset characterized by several train/val/test splits aimed at benchmarking the performance of modern NeRF methods under different conditions. Accordingly, we evaluate three cutting-edge NeRF variants on it to highlight their strengths and weaknesses. The dataset is available on our project page, together with an online benchmark to foster the development of better and better NeRFs.

尽管最近的自动文本识别取得了进步，但在历史手稿方面，该性能仍然保持温和。这主要是因为缺乏可用的标记数据来训练渴望数据的手写文本识别（HTR）模型。由于错误率的降低，关键字发现系统（KWS）提供了HTR的有效替代方案，但通常仅限于封闭的参考词汇。在本文中，我们提出了一些学习范式，用于发现几个字符（n-gram）的序列，这些序列需要少量标记的训练数据。我们表明，对重要的n-gram的认识可以减少系统对词汇的依赖。在这种情况下，输入手写线图像中的vocabulary（OOV）单词可能是属于词典的n-gram序列。对我们提出的多代表方法进行了广泛的实验评估。

许多微体系式优化为深度神经网络解锁了巨大的处理能力，从而促进了AI革命。随着这种优化的精疲力尽，现代AI的增长现在是通过培训系统的性能，尤其是其数据流动的。我们没有专注于单个加速器，而是研究了全系统规模的大规模培训的数据移动特征。基于我们的工作量分析，我们设计了HammingMesh，这是一种新颖的网络拓扑，以低成本提供高的带宽，并具有很高的工作计划灵活性。具体而言，HammingMesh可以支持具有两个并行性的两个维度的深度学习培训工作的完整带宽和隔离。此外，它还为通用流量的高全球带宽提供支持。因此，HammingMesh将为未来的大规模深度学习系统供电，并具有极端的带宽要求。

单词是基本的语言单位，通过含义将思想和事物联系起来。但是，单词在文本序列中并未独立出现。句法规则的存在导致相邻单词之间的相关性。此外，单词不是均匀分布的，而是遵循幂定律，因为带有纯语义内容的术语似乎比指定语法关系的术语要少得多。使用序数模式方法，我们对11种主要语言的词汇统计连接进行了分析。我们发现，语言用来表达单词关系的各种举止产生了独特的模式分布。值得注意的是，我们发现这些关系可以用马尔可夫2的模型建模，并且该结果对所有研究的语言都有普遍有效。此外，模式分布的波动可以使我们能够确定文本及其作者的历史时期。综上所述，这些结果强调了时间序列分析和信息理论方法的相关性，以理解自然语言的统计相关性。

本文使用基于实例分割和图形匹配的LIDAR点云进行了极强和轻量级的定位。我们将3D点云建模为在语义上识别的组件的完全连接图，每个顶点对应于对象实例并编码其形状。跨图的最佳顶点关联允许通过测量相似性进行完整的6度自由（DOF）姿势估计和放置识别。这种表示非常简洁，将地图的大小缩合为25倍，而最先进的图像仅需要3KB代表1.4MB激光扫描。我们验证了系统在Semantickitti数据集中的功效，在该数据集中，我们获得了新的最新识别，平均召回了88.4％的召回，而下一个最接近的竞争对手则为64.9％。我们还显示了准确的度量姿势估计性能 - 估计中位误差为10 cm和0.33度的6 -DOF姿势。

本文提出了一种利用车辆运动限制来完善基于点的雷达辐射系统中的数据关联的方法。通过对非整体机器人如何限制在环境中平稳移动的强大先验，我们开发了必要的框架，以估算单个地标关联的自我运动，而不是一次考虑所有这些对应关系。这允许对差异不佳的匹配的明智异常检测，这是姿势估计误差的主要来源。通过完善匹配地标的子集，我们看到翻译误差的绝对降低2.15％（从4.68％到2.53％），大约比使用完整的对应关系时的探空仪（降低45.94％）的误差（减少45.94％）。该贡献与依赖范围传感器的其他基于点的探针计实现有关，并提供了一种轻巧且可解释的方法，用于将车辆动力学纳入自我动态估计。

在本文中，我们介绍了基于变化自动编码器（VAES）的卫星数据在卫星数据中改变检测的重量轻，无人监督的方法，具体用途。灾害管理等诸如诸如卫星观测的快速可用性的灾害。传统上，在将所有数据转移到地面后，在地面上执行数据分析 - 向地面站进行。因此，对下行链路功能的约束会影响任何下游应用程序。相比之下，Ravaen直接在卫星上预处理采样的数据，并标志改变了下行链路的优先级，缩短了响应时间。我们验证了我们的系统对由时间赛事的时间系列组成的效果 - 我们计划与本出版物一起发布 - 证明拉韦突出了像素明智的基准。最后，我们在资源限制硬件上测试了我们的方法，以评估计算和内存限制。

Advances in computer vision and machine learning techniques have led to significant development in 2D and 3D human pose estimation from RGB cameras, LiDAR, and radars. However, human pose estimation from images is adversely affected by occlusion and lighting, which are common in many scenarios of interest. Radar and LiDAR technologies, on the other hand, need specialized hardware that is expensive and power-intensive. Furthermore, placing these sensors in non-public areas raises significant privacy concerns. To address these limitations, recent research has explored the use of WiFi antennas (1D sensors) for body segmentation and key-point body detection. This paper further expands on the use of the WiFi signal in combination with deep learning architectures, commonly used in computer vision, to estimate dense human pose correspondence. We developed a deep neural network that maps the phase and amplitude of WiFi signals to UV coordinates within 24 human regions. The results of the study reveal that our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches, by utilizing WiFi signals as the only input. This paves the way for low-cost, broadly accessible, and privacy-preserving algorithms for human sensing.

Due to the environmental impacts caused by the construction industry, repurposing existing buildings and making them more energy-efficient has become a high-priority issue. However, a legitimate concern of land developers is associated with the buildings' state of conservation. For that reason, infrared thermography has been used as a powerful tool to characterize these buildings' state of conservation by detecting pathologies, such as cracks and humidity. Thermal cameras detect the radiation emitted by any material and translate it into temperature-color-coded images. Abnormal temperature changes may indicate the presence of pathologies, however, reading thermal images might not be quite simple. This research project aims to combine infrared thermography and machine learning (ML) to help stakeholders determine the viability of reusing existing buildings by identifying their pathologies and defects more efficiently and accurately. In this particular phase of this research project, we've used an image classification machine learning model of Convolutional Neural Networks (DCNN) to differentiate three levels of cracks in one particular building. The model's accuracy was compared between the MSX and thermal images acquired from two distinct thermal cameras and fused images (formed through multisource information) to test the influence of the input data and network on the detection results.

In the last years, the number of IoT devices deployed has suffered an undoubted explosion, reaching the scale of billions. However, some new cybersecurity issues have appeared together with this development. Some of these issues are the deployment of unauthorized devices, malicious code modification, malware deployment, or vulnerability exploitation. This fact has motivated the requirement for new device identification mechanisms based on behavior monitoring. Besides, these solutions have recently leveraged Machine and Deep Learning techniques due to the advances in this field and the increase in processing capabilities. In contrast, attackers do not stay stalled and have developed adversarial attacks focused on context modification and ML/DL evaluation evasion applied to IoT device identification solutions. This work explores the performance of hardware behavior-based individual device identification, how it is affected by possible context- and ML/DL-focused attacks, and how its resilience can be improved using defense techniques. In this sense, it proposes an LSTM-CNN architecture based on hardware performance behavior for individual device identification. Then, previous techniques have been compared with the proposed architecture using a hardware performance dataset collected from 45 Raspberry Pi devices running identical software. The LSTM-CNN improves previous solutions achieving a +0.96 average F1-Score and 0.8 minimum TPR for all devices. Afterward, context- and ML/DL-focused adversarial attacks were applied against the previous model to test its robustness. A temperature-based context attack was not able to disrupt the identification. However, some ML/DL state-of-the-art evasion attacks were successful. Finally, adversarial training and model distillation defense techniques are selected to improve the model resilience to evasion attacks, without degrading its performance.