电缆在房屋，医院和工业仓库中很普遍，容易纠结。本文通过引入新颖的不确定性定量指标和与电缆相互作用以减少感知不确定性相互作用的新型不确定性定量指标和动作，扩展了对自动释放长电缆的先前工作。我们为Tangle操纵2.0（SGTM 2.0）提供了滑动和握力，该系统使用双边机器人自动解开大约3米长的电缆，并使用每个步骤的不确定性估算值估计，以告知动作。通过互动降低不确定性，缠结操作2.0（SGTM 2.0）的滑动和握住可以减少其必须采用的状态排列动作的数量，从而大大加快运行时间。实验表明，SGTM 2.0可以在1或2台上和图8节的电缆上取得83％的脱节成功，并且在这些配置中的70％终止检测成功，在无障碍精度上优于SGTM 1.0，超过43％，在全部推出速度上超过200％ 。可以在sites.google.com/view/sgtm2上找到补充材料，可视化和视频。

我们的方法从单个RGB-D观察中研究了以对象为中心的3D理解的复杂任务。由于这是一个不适的问题，因此现有的方法在3D形状和6D姿势和尺寸估计中都遭受了遮挡的复杂多对象方案的尺寸估计。我们提出了Shapo，这是一种联合多对象检测的方法，3D纹理重建，6D对象姿势和尺寸估计。 Shapo的关键是一条单杆管道，可回归形状，外观和构成潜在的代码以及每个对象实例的口罩，然后以稀疏到密集的方式进一步完善。首先学到了一种新颖的剖面形状和前景数据库，以将对象嵌入各自的形状和外观空间中。我们还提出了一个基于OCTREE的新颖的可区分优化步骤，使我们能够以分析的方式进一步改善对象形状，姿势和外观。我们新颖的联合隐式纹理对象表示使我们能够准确地识别和重建新颖的看不见的对象，而无需访问其3D网格。通过广泛的实验，我们表明我们的方法在模拟的室内场景上进行了训练，可以准确地回归现实世界中新颖物体的形状，外观和姿势，并以最小的微调。我们的方法显着超过了NOCS数据集上的所有基准，对于6D姿势估计，MAP的绝对改进为8％。项目页面：https：//zubair-irshad.github.io/projects/shapo.html

最近的工作表明，2臂“ Fling”运动对于服装平滑可能是有效的。我们考虑单臂弹性运动。与几乎不需要机器人轨迹参数调整的2臂fling运动不同，单臂fling运动对轨迹参数很敏感。我们考虑一个单一的6多机器人臂，该机器人臂学习跨越轨迹以实现高衣覆盖率。给定服装抓握点，机器人在物理实验中探索了不同的参数化fling轨迹。为了提高学习效率，我们提出了一种粗到精细的学习方法，该方法首先使用多军匪徒（MAB）框架有效地找到候选动作，然后通过连续优化方法来完善。此外，我们提出了基于Fling Fall结果不确定性的新颖培训和执行时间停止标准。与基线相比，我们表明所提出的方法显着加速学习。此外，由于通过自学人员收集的类似服装的先前经验，新服装的MAB学习时间最多减少了87％。我们评估了6种服装类型：毛巾，T恤，长袖衬衫，礼服，汗衫和牛仔裤。结果表明，使用先前的经验，机器人需要30分钟以下的时间才能为达到60-94％覆盖率的新型服装学习一项动作。

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.