Reliability Assessment Commitment (RAC) Optimization is increasingly important in grid operations due to larger shares of renewable generations in the generation mix and increased prediction errors. Independent System Operators (ISOs) also aim at using finer time granularities, longer time horizons, and possibly stochastic formulations for additional economic and reliability benefits. The goal of this paper is to address the computational challenges arising in extending the scope of RAC formulations. It presents RACLEARN that (1) uses Graph Neural Networks (GNN) to predict generator commitments and active line constraints, (2) associates a confidence value to each commitment prediction, (3) selects a subset of the high-confidence predictions, which are (4) repaired for feasibility, and (5) seeds a state-of-the-art optimization algorithm with the feasible predictions and the active constraints. Experimental results on exact RAC formulations used by the Midcontinent Independent System Operator (MISO) and an actual transmission network (8965 transmission lines, 6708 buses, 1890 generators, and 6262 load units) show that the RACLEARN framework can speed up RAC optimization by factors ranging from 2 to 4 with negligible loss in solution quality.

在具有可再生生成的大量份额的网格中，由于负载和发电的波动性增加，运营商将需要其他工具来评估运营风险。正向不确定性传播问题的计算要求必须解决众多安全受限的经济调度（SCED）优化，是这种实时风险评估的主要障碍。本文提出了一个即时风险评估学习框架（Jitralf）作为替代方案。 Jitralf训练风险代理，每天每小时一个，使用机器学习（ML）来预测估计风险所需的数量，而无需明确解决SCED问题。这大大减轻了正向不确定性传播的计算负担，并允许快速，实时的风险估计。本文还提出了一种新颖的，不对称的损失函数，并表明使用不对称损失训练的模型的性能优于使用对称损耗函数的模型。在法国传输系统上评估了Jitralf，以评估运营储量不足的风险，减轻负载的风险和预期的运营成本。

本文考虑了最佳功率流（OPF）的优化代理，即近似于OPF的输入/输出关系的机器学习模型。最近的工作重点是表明此类代理可能具有高忠诚。但是，他们的培训需要大量数据，每个实例都需要（离线）解决输入分布样本的OPF。为了满足市场清除应用程序的要求，本文提出了积极的桶装采样（ABS），这是一个新型的活跃学习框架，旨在培训在一个时间限制内培训最佳OPF代理。ABS将输入分布分配到存储桶中，并使用采集函数来确定接下来的何处。它依靠自适应学习率，随着时间的推移会增加和降低。实验结果证明了ABS的好处。

安全约束的经济调度（SCED）是传输系统运营商（TSO）的基本优化模型，以清除实时能源市场，同时确保电网的可靠操作。在不断增长的运营不确定性的背景下，由于可再生发电机和分布式能源资源的渗透率增加，运营商必须实时监控风险，即，他们必须在负载和可再生生产的各种变化下快速评估系统的行为。遗憾的是，鉴于实时操作的严格约束，系统地解决了每个这样的场景的优化问题。为了克服这种限制，本文提出了学习SCED，即机器学习（ML）模型的优化代理，其可以预测用于以毫秒为单位的最佳解决方案。本文提出了对MISO市场清算优化优化的原则性分析，提出了一种新颖的ML管道，解决了学习SCES解决方案的主要挑战，即负载，可再生产量和生产成本以及组合结构的变化，以及组合结构承诺决定。还提出了一种新的分类 - 然后回归架构，以进一步捕获SCED解决方案的行为。在法国传输系统上报告了数值实验，并展示了该方法在与实时操作兼容的时间范围内生产的能力，精确的优化代理产生相对误差低于0.6 \％$。

For long-term simultaneous planning, localization and mapping (SPLAM), a robot should be able to continuously update its map according to the dynamic changes of the environment and the new areas explored. With limited onboard computation capabilities, a robot should also be able to limit the size of the map used for online localization and mapping. This paper addresses these challenges using a memory management mechanism, which identifies locations that should remain in a Working Memory (WM) for online processing from locations that should be transferred to a Long-Term Memory (LTM). When revisiting previously mapped areas that are in LTM, the mechanism can retrieve these locations and place them back in WM for online SPLAM. The approach is tested on a robot equipped with a short-range laser rangefinder and a RGB-D camera, patrolling autonomously 10.5 km in an indoor environment over 11 sessions while having encountered 139 people.

Vision transformers have emerged as powerful tools for many computer vision tasks. It has been shown that their features and class tokens can be used for salient object segmentation. However, the properties of segmentation transformers remain largely unstudied. In this work we conduct an in-depth study of the spatial attentions of different backbone layers of semantic segmentation transformers and uncover interesting properties. The spatial attentions of a patch intersecting with an object tend to concentrate within the object, whereas the attentions of larger, more uniform image areas rather follow a diffusive behavior. In other words, vision transformers trained to segment a fixed set of object classes generalize to objects well beyond this set. We exploit this by extracting heatmaps that can be used to segment unknown objects within diverse backgrounds, such as obstacles in traffic scenes. Our method is training-free and its computational overhead negligible. We use off-the-shelf transformers trained for street-scene segmentation to process other scene types.

Unpaired exemplar-based image-to-image (UEI2I) translation aims to translate a source image to a target image domain with the style of a target image exemplar, without ground-truth input-translation pairs. Existing UEI2I methods represent style using either a global, image-level feature vector, or one vector per object instance/class but requiring knowledge of the scene semantics. Here, by contrast, we propose to represent style as a dense feature map, allowing for a finer-grained transfer to the source image without requiring any external semantic information. We then rely on perceptual and adversarial losses to disentangle our dense style and content representations, and exploit unsupervised cross-domain semantic correspondences to warp the exemplar style to the source content. We demonstrate the effectiveness of our method on two datasets using standard metrics together with a new localized style metric measuring style similarity in a class-wise manner. Our results evidence that the translations produced by our approach are more diverse and closer to the exemplars than those of the state-of-the-art methods while nonetheless preserving the source content.

The optimal layout of a complex system such as aerospace vehicles consists in placing a given number of components in a container in order to minimize one or several objectives under some geometrical or functional constraints. This paper presents an extended formulation of this problem as a variable-size design space (VSDS) problem to take into account a large number of architectural choices and components allocation during the design process. As a representative example of such systems, considering the layout of a satellite module, the VSDS aspect translates the fact that the optimizer has to choose between several subdivisions of the components. For instance, one large tank of fuel might be placed as well as two smaller tanks or three even smaller tanks for the same amount of fuel. In order to tackle this NP-hard problem, a genetic algorithm enhanced by an adapted hidden-variables mechanism is proposed. This latter is illustrated on a toy case and an aerospace application case representative to real world complexity to illustrate the performance of the proposed algorithms. The results obtained using the proposed mechanism are reported and analyzed.

Automatic differentiation (AD) is a technique for computing the derivative of a function represented by a program. This technique is considered as the de-facto standard for computing the differentiation in many machine learning and optimisation software tools. Despite the practicality of this technique, the performance of the differentiated programs, especially for functional languages and in the presence of vectors, is suboptimal. We present an AD system for a higher-order functional array-processing language. The core functional language underlying this system simultaneously supports both source-to-source forward-mode AD and global optimisations such as loop transformations. In combination, gradient computation with forward-mode AD can be as efficient as reverse mode, and the Jacobian matrices required for numerical algorithms such as Gauss-Newton and Levenberg-Marquardt can be efficiently computed.

With the rise of task-specific pre-training objectives, abstractive summarization models like PEGASUS offer appealing zero-shot performance on downstream summarization tasks. However, the performance of such unsupervised models still lags significantly behind their supervised counterparts. Similarly to the supervised setup, we notice a very high variance in quality among summary candidates from these models whereas only one candidate is kept as the summary output. In this paper, we propose to re-rank summary candidates in an unsupervised manner, aiming to close the performance gap between unsupervised and supervised models. Our approach improves the pre-trained unsupervised PEGASUS by 4.37% to 7.27% relative mean ROUGE across four widely-adopted summarization benchmarks, and achieves relative gains of 7.51% (up to 23.73%) averaged over 30 transfer setups.