Deep neural networks have emerged as the workhorse for a large section of robotics and control applications, especially as models for dynamical systems. Such data-driven models are in turn used for designing and verifying autonomous systems. This is particularly useful in modeling medical systems where data can be leveraged to individualize treatment. In safety-critical applications, it is important that the data-driven model is conformant to established knowledge from the natural sciences. Such knowledge is often available or can often be distilled into a (possibly black-box) model $M$. For instance, the unicycle model for an F1 racing car. In this light, we consider the following problem - given a model $M$ and state transition dataset, we wish to best approximate the system model while being bounded distance away from $M$. We propose a method to guarantee this conformance. Our first step is to distill the dataset into few representative samples called memories, using the idea of a growing neural gas. Next, using these memories we partition the state space into disjoint subsets and compute bounds that should be respected by the neural network, when the input is drawn from a particular subset. This serves as a symbolic wrapper for guaranteed conformance. We argue theoretically that this only leads to bounded increase in approximation error; which can be controlled by increasing the number of memories. We experimentally show that on three case studies (Car Model, Drones, and Artificial Pancreas), our constrained neurosymbolic models conform to specified $M$ models (each encoding various constraints) with order-of-magnitude improvements compared to the augmented Lagrangian and vanilla training methods.

对抗性训练（AT）及其变体在过去几年来改善对对抗性扰动和常见腐败的神经网络的鲁棒性方面取得了长足的进步。 AT及其变体的算法设计集中在指定的扰动强度$ \ epsilon $上，并且仅利用该$ \ epsilon $ -Robust模型的性能的反馈来改善算法。在这项工作中，我们专注于在$ \ epsilon $值的频谱上训练的模型。我们分析了三个观点：模型性能，中间特征精度和卷积滤波器灵敏度。在每种情况下，我们都会确定AT的替代改进，否则在单个$ \ epsilon $中并不明显。具体来说，我们发现，对于以某种强度$ \ delta $的pgd攻击，有一个型号以某种稍大的强度$ \ epsilon $，但没有更大的范围，可以概括它。因此，我们建议过度设计鲁棒性，我们建议以$ \ epsilon $略高于$ \ delta $的培训模型。其次，我们观察到（在各种$ \ epsilon $值中），鲁棒性对中间特征的精度，尤其是在第一层和第二层之后的精度高度敏感。因此，我们建议在防御措施中添加简单的量化，以提高可见和看不见的适应性攻击的准确性。第三，我们分析了增加$ \ epsilon $的每一层模型的卷积过滤器，并注意到第一和第二层的卷积过滤器可能完全负责放大输入扰动。我们通过在CIFAR-10和CIFAR-10-C数据集上使用Resnet和WideSnet模型进行实验，介绍我们的发现并证明我们的技术。

长期的Horizo​​n机器人学习任务稀疏的奖励对当前的强化学习算法构成了重大挑战。使人类能够学习挑战的控制任务的关键功能是，他们经常获得专家干预，使他们能够在掌握低级控制动作之前了解任务的高级结构。我们为利用专家干预来解决长马增强学习任务的框架。我们考虑\ emph {选项模板}，这是编码可以使用强化学习训练的潜在选项的规格。我们将专家干预提出，因为允许代理商在学习实施之前执行选项模板。这使他们能够使用选项，然后才能为学习成本昂贵的资源学习。我们在三个具有挑战性的强化学习问题上评估了我们的方法，这表明它的表现要优于最先进的方法。训练有素的代理商和我们的代码视频可以在以下网址找到：https：//sites.google.com/view/stickymittens

由于机器学习技术在新域中被广泛采用，特别是在诸如自主车辆的安全关键系统中，这是提供准确的输出不确定性估计至关重要。因此，已经提出了许多方法来校准神经网络以准确估计错误分类的可能性。但是，虽然这些方法实现了低校准误差，但有空间以进一步改进，尤其是在大维设置（如想象成）中。在本文中，我们介绍了一个名为Hoki的校准算法，它通过将随机转换应用于神经网络编程来工作。我们为基于应用转换后观察到的标签预测变化的数量提供了足够的条件。我们在多个数据集上执行实验，并表明所提出的方法通常优于多个数据集和模型的最先进的校准算法，尤其是在充满挑战的ImageNet数据集上。最后，Hoki也是可扩展的，因为它需要可比较的执行时间到温度缩放的执行时间。

Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive. The Gittins Index (GI) is a solution to the MABP that can simultaneously attain optimality and computationally efficiency goals, and it has been recently used in experiments with Bernoulli and Gaussian rewards. For the first time, we present a modification of the GI rule that can be used in experiments with exponentially-distributed rewards. We report its performance in simulated 2- armed and 3-armed experiments. Compared to traditional non-adaptive designs, our novel GI modified design shows operating characteristics comparable in learning (e.g. statistical power) but substantially better in earning (e.g. direct benefits). This illustrates the potential that designs using a GI approach to allocate participants have to improve participant benefits, increase efficiencies, and reduce experimental costs in adaptive multi-armed experiments with exponential rewards.

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.

Extracting complex structures from grid-based data is a common key step in automated medical image analysis. The conventional solution to recovering tree-structured geometries typically involves computing the minimal cost path through intermediate representations derived from segmentation masks. However, this methodology has significant limitations in the context of projective imaging of tree-structured 3D anatomical data such as coronary arteries, since there are often overlapping branches in the 2D projection. In this work, we propose a novel approach to predicting tree connectivity structure which reformulates the task as an optimization problem over individual steps of a recursive process. We design and train a two-stage model which leverages the UNet and Transformer architectures and introduces an image-based prompting technique. Our proposed method achieves compelling results on a pair of synthetic datasets, and outperforms a shortest-path baseline.

Cashews are grown by over 3 million smallholders in more than 40 countries worldwide as a principal source of income. As the third largest cashew producer in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings. However, a lack of information on where and how cashew trees grow across the country hinders decision-making that could support increased cashew production and poverty alleviation. By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, newly developed deep learning algorithms, and large-scale ground truth datasets, we successfully produced the first national map of cashew in Benin and characterized the expansion of cashew plantations between 2015 and 2021. In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season. We further developed a Clustering Augmented Self-supervised Temporal Classification (CASTC) model to distinguish high-density versus low-density cashew plantations by automatic feature extraction and optimized clustering. Results show that the STCA model has an overall accuracy of 80% and the CASTC model achieved an overall accuracy of 77.9%. We found that the cashew area in Benin has doubled from 2015 to 2021 with 60% of new plantation development coming from cropland or fallow land, while encroachment of cashew plantations into protected areas has increased by 70%. Only half of cashew plantations were high-density in 2021, suggesting high potential for intensification. Our study illustrates the power of combining high-resolution remote sensing imagery and state-of-the-art deep learning algorithms to better understand tree crops in the heterogeneous smallholder landscape.

Grasping is an incredible ability of animals using their arms and limbs in their daily life. The human hand is an especially astonishing multi-fingered tool for precise grasping, which helped humans to develop the modern world. The implementation of the human grasp to virtual reality and telerobotics is always interesting and challenging at the same time. In this work, authors surveyed, studied, and analyzed the human hand-grasping behavior for the possibilities of haptic grasping in the virtual and remote environment. This work is focused on the motion and force analysis of fingers in human hand grasping scenarios and the paper describes the transition of the human hand grasping towards a tripod haptic grasp model for effective interaction in virtual reality.

Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i.e., the forecasts should satisfy the hierarchical aggregation constraints. Moreover, the disparities of statistical characteristics between levels can be huge, worsened by non-Gaussian distributions and non-linear correlations. To this extent, we propose a novel end-to-end hierarchical time series forecasting model, based on conditioned normalizing flow-based autoregressive transformer reconciliation, to represent complex data distribution while simultaneously reconciling the forecasts to ensure coherency. Unlike other state-of-the-art methods, we achieve the forecasting and reconciliation simultaneously without requiring any explicit post-processing step. In addition, by harnessing the power of deep model, we do not rely on any assumption such as unbiased estimates or Gaussian distribution. Our evaluation experiments are conducted on four real-world hierarchical datasets from different industrial domains (three public ones and a dataset from the application servers of Alipay's data center) and the preliminary results demonstrate efficacy of our proposed method.