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。

我们通过在计算图的空间中搜索计算基于值的无模型RL代理以优化的计算函数来提出一种用于元学习增强学习算法的方法。学到的算法是域 - 不可思议的，可以推广到训练期间未见的新环境。我们的方法既可以从头开始学习，又可以从已知的现有算法（例如DQN）学习，从而实现可解释的修改，从而改善性能。从头开始学习简单的经典控制和网格世界任务，我们的方法重新发现了时间差异（TD）算法。我们从DQN进行了引导，我们重点介绍了两种学到的算法，这些算法比其他经典控制任务，GridWorld类型任务和Atari游戏获得了良好的概括性能。对学习算法行为的分析表明，与最近提出的RL算法相似，该算法解决了基于价值的方法的高估。

即使互联网和社交媒体增加了人们可能会消耗的新闻和信息量，大多数用户才会暴露于加强其职位的内容，并将其与其他思想社区隔离。这种环境对我们的生活产生了极大的影响，如严重的政治极化，轻松传播的假新闻，政治极端主义，仇恨团体以及缺乏丰富的辩论等。因此，鼓励不同的用户组之间的对话并打破封闭的社区对健康社会的重要性。在本文中，我们使用自然语言处理技术和图形机学习算法来表征和研究在Twitter上打破社区的用户。特别是，我们从150万用户收集了900万个Twitter消息，并构建了转发网络。我们确定了他们的社区和与他们相关的讨论主题。通过这些数据，我们为社交媒体用户分类提供了一种机器学习框架，该分类检测到“社区分手”，即从他们的封闭社区到另一个用户的用户。三个Twitter极化政治数据集中的一个特征重要性分析表明，这些用户的PageRank值低，表明改变是推动的，因为他们的消息在其社区中没有响应。这种方法还允许我们确定其特定的兴趣主题，提供了这种用户的全面表征。

Recent advances in neural architecture search (NAS) demand tremendous computational resources, which makes it difficult to reproduce experiments and imposes a barrier-to-entry to researchers without access to large-scale computation. We aim to ameliorate these problems by introducing NAS-Bench-101, the first public architecture dataset for NAS research. To build NAS-Bench-101, we carefully constructed a compact, yet expressive, search space, exploiting graph isomorphisms to identify 423k unique convolutional architectures. We trained and evaluated all of these architectures multiple times on CIFAR-10 and compiled the results into a large dataset of over 5 million trained models. This allows researchers to evaluate the quality of a diverse range of models in milliseconds by querying the precomputed dataset. We demonstrate its utility by analyzing the dataset as a whole and by benchmarking a range of architecture optimization algorithms.

The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically. Although evolutionary algorithms have been repeatedly applied to neural network topologies, the image classifiers thus discovered have remained inferior to human-crafted ones. Here, we evolve an image classifier-AmoebaNet-A-that surpasses hand-designs for the first time.To do this, we modify the tournament selection evolutionary algorithm by introducing an age property to favor the younger genotypes. Matching size, AmoebaNet-A has comparable accuracy to current state-of-the-art ImageNet models discovered with more complex architecture-search methods. Scaled to larger size, AmoebaNet-A sets a new state-of-theart 83.9% top-1 / 96.6% top-5 ImageNet accuracy. In a controlled comparison against a well known reinforcement learning algorithm, we give evidence that evolution can obtain results faster with the same hardware, especially at the earlier stages of the search. This is relevant when fewer compute resources are available. Evolution is, thus, a simple method to effectively discover high-quality architectures. Related WorkReview papers provide informative surveys of earlier [18,49] and more recent [15] literature on image classifier architecture search, including successful RL studies [2,6,29,[52][53][54] and evolutionary studies like those mentioned in 1 After our submission, a recent preprint has further scaled up and retrained AmoebaNet-A to reach 84.3% top-1 / 97.0% top-5 ImageNet accuracy [25].

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.

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.

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。