我们提出了一种算法，以（i）在线学习具有带有激光雷达的机器人的深度签名距离功能（SDF），以代表3D环境几何形状，以及（ii）鉴于此深度学习的地图，（ii）计划无碰撞轨迹。我们的算法采用了传入的激光扫描，并不断优化神经网络，以代表其当前附近环境的SDF。当SDF网络质量饱和时，我们将缓存网络的副本，以及学习的置信度指标，并初始化新的SDF网络以继续映射环境的新区域。然后，我们通过信心加权的计划来串联所有缓存的本地SDF，以提供全球SDF进行计划。为了计划，我们使用顺序凸模型预测控制（MPC）算法。 MPC规划师优化了机器人动态可行的轨迹，同时没有与全局SDF中映射的障碍物相撞。我们表明，与现有在线SDF培训的现有方法相比，我们的在线映射算法产生的地图更高。在Webots Simulator中，我们进一步展示了在线运行的组合映射器和计划者 - 自动导航，并且在未知环境中没有碰撞。

Adversarial attacks hamper the decision-making ability of neural networks by perturbing the input signal. The addition of calculated small distortion to images, for instance, can deceive a well-trained image classification network. In this work, we propose a novel attack technique called Sparse Adversarial and Interpretable Attack Framework (SAIF). Specifically, we design imperceptible attacks that contain low-magnitude perturbations at a small number of pixels and leverage these sparse attacks to reveal the vulnerability of classifiers. We use the Frank-Wolfe (conditional gradient) algorithm to simultaneously optimize the attack perturbations for bounded magnitude and sparsity with $O(1/\sqrt{T})$ convergence. Empirical results show that SAIF computes highly imperceptible and interpretable adversarial examples, and outperforms state-of-the-art sparse attack methods on the ImageNet dataset.

Unhealthy dietary habits are considered as the primary cause of multiple chronic diseases such as obesity and diabetes. The automatic food intake monitoring system has the potential to improve the quality of life (QoF) of people with dietary related diseases through dietary assessment. In this work, we propose a novel contact-less radar-based food intake monitoring approach. Specifically, a Frequency Modulated Continuous Wave (FMCW) radar sensor is employed to recognize fine-grained eating and drinking gestures. The fine-grained eating/drinking gesture contains a series of movement from raising the hand to the mouth until putting away the hand from the mouth. A 3D temporal convolutional network (3D-TCN) is developed to detect and segment eating and drinking gestures in meal sessions by processing the Range-Doppler Cube (RD Cube). Unlike previous radar-based research, this work collects data in continuous meal sessions. We create a public dataset that contains 48 meal sessions (3121 eating gestures and 608 drinking gestures) from 48 participants with a total duration of 783 minutes. Four eating styles (fork & knife, chopsticks, spoon, hand) are included in this dataset. To validate the performance of the proposed approach, 8-fold cross validation method is applied. Experimental results show that our proposed 3D-TCN outperforms the model that combines a convolutional neural network and a long-short-term-memory network (CNN-LSTM), and also the CNN-Bidirectional LSTM model (CNN-BiLSTM) in eating and drinking gesture detection. The 3D-TCN model achieves a segmental F1-score of 0.887 and 0.844 for eating and drinking gestures, respectively. The results of the proposed approach indicate the feasibility of using radar for fine-grained eating and drinking gesture detection and segmentation in meal sessions.

患者特异性的心脏计算模型对于使用数字双胞胎的精密医学和silico临床试验的有效实现至关重要。心脏数字双胞胎可以为个别患者提供心脏功能的非侵入性特征，因此对于患者特定的诊断和治疗分层有希望。然而，目前的解剖学和功能性孪生阶段的工作流，指的是模型解剖结构和临床数据的参数的推断，并不足够有效，稳健且准确。在这项工作中，我们提出了一个基于深度学习的特定于患者的计算模型，该模型可以融合解剖学和电生理信息，以推理心室激活特性，即传导速度和根节点。激活特性可以提供对心脏电生理功能的定量评估，以指导介入。我们采用Eikonal模型来生成具有地面真实特性的模拟心电图（ECG），以训练推理模型，在此还考虑了特定的患者信息。为了进行评估，我们在模拟数据上测试模型，并以快速的计算时间获得通常有希望的结果。

有限的作品显示无监督的分布（OOD）方法对复杂的医疗数据的功效。在这里，我们展示了我们无监督的OOD检测算法，SIMCLR-LOF的初步调查结果，以及在医学图像上应用的最近现实方法（SSD）的最新状态。SIMCLR-LOF使用SIMCLR学习语义有意义的功能，如果测试样本是ood的，则使用LOF进行评分。我们在多源国际皮肤成像协作（ISIC）2019数据集上进行了评估，并显示与SSD竞争的结果以及应用于同一数据的最近监督方法。