城市化及其问题需要对城市动态，尤其是现代城市复杂而多样化的生活方式的深入和全面的了解。数字化的数据可以准确捕获复杂的人类活动，但缺乏人口统计数据的解释性。在本文中，我们研究了美国11个都会区的120万人到110万个地方的出行探访模式的隐私增强数据集，以检测美国最大的美国城市中的潜在行动行为和生活方式。尽管出行访问的复杂性很大，但我们发现生活方式可以自动分解为12种潜在的可解释的活动行为，人们如何将购物，饮食，工作或利用空闲时间结合起来。我们没有描述具有单一生活方式的人，而是发现城市居民的行为是这些行为的混合。那些被检测到的潜在活动行为同样存在于城市之间，无法通过主要人口特征来完全解释。最后，我们发现这些潜在行为与在控制人口特征之后，即使在控制人口特征之后，这些潜在行为也与经验丰富的收入隔离，运输或健康行为有关。我们的结果表明，与活动行为相辅相成，以了解城市动态的重要性。

从移动设备收集的位置数据代表个人和社会水平的移动性行为。这些数据具有从运输计划到疫情建模的重要应用。但是，必须克服最佳服务的问题：数据通常代表有限的人口样本和数据危害隐私的数据。为了解决这些问题，我们展示并评估用于使用在实际位置数据上培训的深频复制神经网络（RNN）来生成合成移动数据的系统。该系统将群体分发作为输入，为相应的合成群生成移动性跟踪。相关的生成方法尚未解决在较长时间内捕获个人移动行为中的模式和变异性的挑战，同时还平衡了具有隐私的现实数据的产生。我们的系统利用RNNS的能力生成复杂和新序列的能力，同时保留训练数据的模式。此外，该模型引入了用于校准各个级别的合成和实际数据之间的变化的随机性。这是捕获人类移动性的可变性，并保护用户隐私。基于位置的服务（LBS）来自22,700多种移动设备的数据用于实用程序和隐私度量的实验评估。我们示出了生成的移动数据保留了实际数据的特征，同时从个人级别的实际数据变化，并且在此变化量匹配真实数据内的变化。

Recently, Person Re-Identification (Re-ID) has received a lot of attention. Large datasets containing labeled images of various individuals have been released, allowing researchers to develop and test many successful approaches. However, when such Re-ID models are deployed in new cities or environments, the task of searching for people within a network of security cameras is likely to face an important domain shift, thus resulting in decreased performance. Indeed, while most public datasets were collected in a limited geographic area, images from a new city present different features (e.g., people's ethnicity and clothing style, weather, architecture, etc.). In addition, the whole frames of the video streams must be converted into cropped images of people using pedestrian detection models, which behave differently from the human annotators who created the dataset used for training. To better understand the extent of this issue, this paper introduces a complete methodology to evaluate Re-ID approaches and training datasets with respect to their suitability for unsupervised deployment for live operations. This method is used to benchmark four Re-ID approaches on three datasets, providing insight and guidelines that can help to design better Re-ID pipelines in the future.

Early recognition of clinical deterioration (CD) has vital importance in patients' survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network's performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models' performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data.

A link stream is a set of triplets $(t, u, v)$ indicating that $u$ and $v$ interacted at time $t$. Link streams model numerous datasets and their proper study is crucial in many applications. In practice, raw link streams are often aggregated or transformed into time series or graphs where decisions are made. Yet, it remains unclear how the dynamical and structural information of a raw link stream carries into the transformed object. This work shows that it is possible to shed light into this question by studying link streams via algebraically linear graph and signal operators, for which we introduce a novel linear matrix framework for the analysis of link streams. We show that, due to their linearity, most methods in signal processing can be easily adopted by our framework to analyze the time/frequency information of link streams. However, the availability of linear graph methods to analyze relational/structural information is limited. We address this limitation by developing (i) a new basis for graphs that allow us to decompose them into structures at different resolution levels; and (ii) filters for graphs that allow us to change their structural information in a controlled manner. By plugging-in these developments and their time-domain counterpart into our framework, we are able to (i) obtain a new basis for link streams that allow us to represent them in a frequency-structure domain; and (ii) show that many interesting transformations to link streams, like the aggregation of interactions or their embedding into a euclidean space, can be seen as simple filters in our frequency-structure domain.

Scenario-based probabilistic forecasts have become a vital tool to equip decision-makers to address the uncertain nature of renewable energies. To that end, this paper presents a recent promising deep learning generative approach called denoising diffusion probabilistic models. It is a class of latent variable models which have recently demonstrated impressive results in the computer vision community. However, to the best of our knowledge, there has yet to be a demonstration that they can generate high-quality samples of load, PV, or wind power time series, crucial elements to face the new challenges in power systems applications. Thus, we propose the first implementation of this model for energy forecasting using the open data of the Global Energy Forecasting Competition 2014. The results demonstrate this approach is competitive with other state-of-the-art deep learning generative models, including generative adversarial networks, variational autoencoders, and normalizing flows.

This work is on vision-based planning strategies for legged robots that separate locomotion planning into foothold selection and pose adaptation. Current pose adaptation strategies optimize the robot's body pose relative to given footholds. If these footholds are not reached, the robot may end up in a state with no reachable safe footholds. Therefore, we present a Vision-Based Terrain-Aware Locomotion (ViTAL) strategy that consists of novel pose adaptation and foothold selection algorithms. ViTAL introduces a different paradigm in pose adaptation that does not optimize the body pose relative to given footholds, but the body pose that maximizes the chances of the legs in reaching safe footholds. ViTAL plans footholds and poses based on skills that characterize the robot's capabilities and its terrain-awareness. We use the 90 kg HyQ and 140 kg HyQReal quadruped robots to validate ViTAL, and show that they are able to climb various obstacles including stairs, gaps, and rough terrains at different speeds and gaits. We compare ViTAL with a baseline strategy that selects the robot pose based on given selected footholds, and show that ViTAL outperforms the baseline.

人重新识别（RE-ID）旨在在相机网络中寻找感兴趣的人（查询）。在经典的重新设置中，查询查询在包含整个身体的正确裁剪图像的画廊中。最近，引入了实时重新ID设置，以更好地代表Re-ID的实际应用上下文。它包括在简短的视频中搜索查询，其中包含整个场景帧。最初的实时重新ID基线使用行人探测器来构建大型搜索库和经典的重新ID模型，以在画廊中找到查询。但是，产生的画廊太大，包含低质量的图像，从而降低了现场重新ID性能。在这里，我们提出了一种称为贸易的新现场重新ID方法，以产生较低的高质量画廊。贸易首先使用跟踪算法来识别画廊中同一个人的图像序列。随后，使用异常检测模型选择每个轨道的单个良好代表。贸易已在PRID-2011数据集的实时重新ID版本上进行了验证，并显示出比基线的显着改进。

我们提出了一种使用平滑数值方法来构建大型数据集的模糊簇的新方法。通常会放宽方面的标准，因此在连续的空间上进行了良好的模糊分区的搜索，而不是像经典方法\ cite {hartigan}那样的组合空间。平滑性可以通过使用无限类别的可区分函数，从强烈的非差异问题转换为优化的可区别子问题。为了实现算法，我们使用了统计软件$ r $，并将获得的结果与Bezdek提出的传统模糊$ C $ - 表示方法进行了比较。

在此贡献中，我们使用一种合奏深度学习方法来组合两个单个单阶段探测器（即Yolov4和Yolact）的预测，目的是检测内窥镜图像中的伪像。这种整体策略使我们能够改善各个模型的鲁棒性，而无需损害其实时计算功能。我们通过训练和测试两个单独的模型和各种集合配置在“内窥镜伪影检测挑战”数据集中证明了方法的有效性。广泛的实验表明，在平均平均精度方面，合奏方法比单个模型和以前的作品的优越性。