可以使用X射线自由电子激光器的强脉冲和短脉冲直接通过单次相干衍射成像直接观察到自由飞行中孤立的纳米样品的结构和动力学。广角散射图像甚至编码样品的三维形态信息，但是该信息的检索仍然是一个挑战。到目前为止，只有通过与高度约束模型拟合，需要对单镜头实现有效的三维形态重建，这需要有关可能的几何形状的先验知识。在这里，我们提出了一种更通用的成像方法。依赖于允许凸多面体描述的任何样品形态的模型，我们从单个银纳米颗粒中重建广角衍射模式。除了具有高对称性的已知结构动机外，我们还检索了以前无法访问的不完美形状和聚集物。我们的结果为单个纳米颗粒的真实3D结构确定以及最终的超快纳米级动力学的3D电影开辟了新的途径。

多代理系统（质量）可以通过每个代理商的个人智能以及合作和利用集体智能来自主学会解决先前未知的任务。本文考虑了一组自治代理，学习在可能少量的试验中跟踪相同的给定参考轨迹。我们提出了一种新颖的集体学习控制方法，将迭代学习控制（ILC）与集体更新策略相结合。我们推导了这种系统的理想收敛性质的条件。我们表明，该方法允许集体结合代理商的个人学习策略的优势，从而克服单股ILC的权衡和局限性。通过设计异构集体，即，各代理商分配了不同的学习法，实现了这种益处。所有理论结果都在模拟和实验中确认，两轮倒立摆机器人（TWIPR）共同学会执行所需的机动。

基于惯性传感器的姿态估计是各种应用中的重要技术，来自人类运动跟踪到自主空中和地面车辆。应用场景在执行运动的特征，扰动和环境条件的存在方面不同。由于最先进的态度估计器不概括在这些特征上，因此必须对其参数进行调整以用于各个运动特性和情况。我们提出了RIANN，即立即使用，基于神经网络，无参数，实时功能的惯性态度估计器，其横跨不同的运动动态，环境和采样率概括，而无需特定于应用程序适应。我们收集六个公开的数据集，其中我们利用了两个数据集进行了方法开发和培训，并使用四个数据集进行三种不同的测试场景评估培训的估计，不同的实际相关性。结果表明，RIANN优于最先进的态度估算过滤器，以至于它在不同应用中的各种动作和条件上遍历了更好的方式，具有不同的传感器硬件和不同的采样频率。即使在每个单独的测试数据集上调整过滤器，也是如此，而RANN在完全分开的数据上培训，并且从未见过任何这些测试数据集。 RIANN可以直接应用，没有适应或培训，预计将在许多应用中启用即插即用解决方案，特别是当准确性至关重要时，没有地理数据可以调整或运动和扰动特性不确定。我们宣传了Riann。

评估数据流是否是从相同分布中绘制的是各种机器学习问题的核心。这与动态系统生成的数据尤其重要，因为这种系统对于生物医学，经济或工程系统的许多实际过程至关重要。虽然内核两样本测试对于比较独立和相同分布的随机变量具有强大的功能，但没有建立的方法来比较动态系统。主要问题是固有的违反独立假设。我们通过解决三个核心挑战提出了针对动态系统的两样本测试：我们（i）引入了一种新颖的混合概念，该概念在相关度量标准中捕获自相关，（ii）提出了一种有效的方法来估计混合速度纯粹依赖于纯粹依赖混合的速度。数据，（iii）将它们集成到已建立的核两样本测试中。结果是一种数据驱动的方法，可直接在实践中使用，并具有合理的理论保证。在从人类步行数据中进行异常检测的示例应用程序中，我们表明该测试很容易适用，没有任何人类的专家知识和功能工程。

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.