最佳控制问题自然出现在许多科学应用中，希望将动态系统从某个初始状态引导动态系统$ \ mathbf {x} _0 $到所需的目标状态$ \ mathbf {x}^*$有限时间$ t $ t $ 。深度学习和基于神经网络的优化的最新进展有助于开发可以帮助解决涉及高维动力系统的控制问题的方法。特别是，神经普通微分方程（神经ODE）的框架为迭代近似于与分析性棘手和计算要求的控制任务相关的连续时间控制功能提供了有效的手段。尽管神经ODE控制器在解决复杂的控制问题方面表现出了巨大的潜力，但对网络结构和优化器等超参数的影响的理解仍然非常有限。我们的工作旨在解决其中一些知识差距，以进行有效的超参数优化。为此，我们首先分析了如何通过时间进行截断和未截断的反向传播影响运行时性能以及神经网络学习最佳控制功能的能力。然后，我们使用分析和数值方法，然后研究参数初始化，优化器和神经网络体系结构的作用。最后，我们将结果与神经控制器隐式正规化控制能量的能力联系起来。

可持续消费旨在最大限度地减少使用服务和产品的环境和社会影响。服务和产品的过度消耗导致潜在的自然资源耗尽和社会不平等，因为对商品和服务的访问变得更具挑战性。在日常生活中，一个人可以通过大大改变他们的生活方式选择并可能违背其个人价值观或愿望来实现更可持续的购买。相反，实现可持续消费，同时考虑个人价值观是一个更复杂的任务，因为在努力满足环境和个人目标时出现潜在的权衡。本文重点介绍了推荐系统的价值敏感设计，使消费者能够在尊重其个人价值观的同时提高购物的可持续性。可持续消费的价值敏感建议被形式化为多目标优化问题，每个目标都代表不同的可持续性目标和个人价值。新颖和现有的多目标算法计算解决此问题的解决方案。该解决方案被提出为消费者的个性化可持续篮子建议。这些建议在合成数据集中进行了评估，其中包括来自相关科学和组织报告的三个建立的现实数据集。合成数据集包含有关产品价格，营养价值和环境影响指标的定量数据，例如温室气体排放和水占地面积。推荐的篮子与消费者购买的篮子高度相似，并与可持续发展目标和与健康，支出和品味相关的个人价值观对齐。即使消费者只接受一小部分建议，也观察到环境影响的相当大降低。

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/

Edge computing is changing the face of many industries and services. Common edge computing models offload computing which is prone to security risks and privacy violation. However, advances in deep learning enabled Internet of Things (IoTs) to take decisions and run cognitive tasks locally. This research introduces a decentralized-control edge model where most computation and decisions are moved to the IoT level. The model aims at decreasing communication to the edge which in return enhances efficiency and decreases latency. The model also avoids data transfer which raises security and privacy risks. To examine the model, we developed SAFEMYRIDES, a scene-aware ridesharing monitoring system where smart phones are detecting violations at the runtime. Current real-time monitoring systems are costly and require continuous network connectivity. The system uses optimized deep learning that run locally on IoTs to detect violations in ridesharing and record violation incidences. The system would enhance safety and security in ridesharing without violating privacy.

Cognitive Computing (COC) aims to build highly cognitive machines with low computational resources that respond in real-time. However, scholarly literature shows varying research areas and various interpretations of COC. This calls for a cohesive architecture that delineates the nature of COC. We argue that if Herbert Simon considered the design science is the science of artificial, cognitive systems are the products of cognitive science or 'the newest science of the artificial'. Therefore, building a conceptual basis for COC is an essential step into prospective cognitive computing-based systems. This paper proposes an architecture of COC through analyzing the literature on COC using a myriad of statistical analysis methods. Then, we compare the statistical analysis results with previous qualitative analysis results to confirm our findings. The study also comprehensively surveys the recent research on COC to identify the state of the art and connect the advances in varied research disciplines in COC. The study found that there are three underlaying computing paradigms, Von-Neuman, Neuromorphic Engineering and Quantum Computing, that comprehensively complement the structure of cognitive computation. The research discuss possible applications and open research directions under the COC umbrella.

Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets. To address this challenge, we introduce the Merger Agreement Understanding Dataset (MAUD), an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study, with over 39,000 examples and over 47,000 total annotations. Our fine-tuned Transformer baselines show promising results, with models performing well above random on most questions. However, on a large subset of questions, there is still room for significant improvement. As the only expert-annotated merger agreement dataset, MAUD is valuable as a benchmark for both the legal profession and the NLP community.

The application of deep learning algorithms to financial data is difficult due to heavy non-stationarities which can lead to over-fitted models that underperform under regime changes. Using the Numerai tournament data set as a motivating example, we propose a machine learning pipeline for trading market-neutral stock portfolios based on tabular data which is robust under changes in market conditions. We evaluate various machine-learning models, including Gradient Boosting Decision Trees (GBDTs) and Neural Networks with and without simple feature engineering, as the building blocks for the pipeline. We find that GBDT models with dropout display high performance, robustness and generalisability with relatively low complexity and reduced computational cost. We then show that online learning techniques can be used in post-prediction processing to enhance the results. In particular, dynamic feature neutralisation, an efficient procedure that requires no retraining of models and can be applied post-prediction to any machine learning model, improves robustness by reducing drawdown in volatile market conditions. Furthermore, we demonstrate that the creation of model ensembles through dynamic model selection based on recent model performance leads to improved performance over baseline by improving the Sharpe and Calmar ratios. We also evaluate the robustness of our pipeline across different data splits and random seeds with good reproducibility of results.

In this work, we address the problem of unsupervised moving object segmentation (MOS) in 4D LiDAR data recorded from a stationary sensor, where no ground truth annotations are involved. Deep learning-based state-of-the-art methods for LiDAR MOS strongly depend on annotated ground truth data, which is expensive to obtain and scarce in existence. To close this gap in the stationary setting, we propose a novel 4D LiDAR representation based on multivariate time series that relaxes the problem of unsupervised MOS to a time series clustering problem. More specifically, we propose modeling the change in occupancy of a voxel by a multivariate occupancy time series (MOTS), which captures spatio-temporal occupancy changes on the voxel level and its surrounding neighborhood. To perform unsupervised MOS, we train a neural network in a self-supervised manner to encode MOTS into voxel-level feature representations, which can be partitioned by a clustering algorithm into moving or stationary. Experiments on stationary scenes from the Raw KITTI dataset show that our fully unsupervised approach achieves performance that is comparable to that of supervised state-of-the-art approaches.

Automated text analysis has become a widely used tool in political science. In this research, we use a BERT model trained on German party manifestos to identify the individual parties' contribution to the coalition agreement of 2021.

When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of black-box learned components in the modern robot autonomy stack. Therefore, coping with OOD data is an important challenge on the path towards trustworthy learning-enabled open-world autonomy. In this paper, we aim to demystify the topic of OOD data and its associated challenges in the context of data-driven robotic systems, drawing connections to emerging paradigms in the ML community that study the effect of OOD data on learned models in isolation. We argue that as roboticists, we should reason about the overall system-level competence of a robot as it performs tasks in OOD conditions. We highlight key research questions around this system-level view of OOD problems to guide future research toward safe and reliable learning-enabled autonomy.