彼此接触的任何两个物体都会仅仅是由于重力或机械接触而引起的力，例如机器人手臂抓住一个物体，甚至是我们膝关节处的两个骨头之间的接触。自然测量和监视这些接触力的能力允许从仓库管理（基于重量检测错误包装）到机器人技术（使机器人臂的抓地力与人类皮肤一样敏感）和医疗保健（膝关节植入物）的大量应用。设计一个无处不在的力传感器是充满挑战的，该传感器可自然地用于所有这些应用。首先，传感器应足够小，以适合狭窄的空间。接下来，我们不想铺设笨重的电缆来读取传感器的力值。最后，我们需要进行无电池设计以满足体内应用程序。我们开发了WiforCesticker，这是一种无线，无电池，类似贴纸的力传感器，可以在任何表面上都可以无处不在，例如所有仓库包装，机器人手臂和膝关节。 WiforCesticker首先设计一个$ 4 $ 〜mm〜 $ \ $ \ times $〜$〜$ 2 $ 〜mm〜 $ \ $ \ times $〜$〜$〜$ 0.4 $〜毫米电容传感器设计，配备了$ 10 $〜$〜$〜$〜$〜$〜$〜$ 〜mm〜mm 〜mm 〜mm 〜mm在灵活的PCB基材上设计。其次，它引入了一种新的机制，可以通过将传感器与COTS RFID系统插入传感器，从而无线读取器无线读取器可以通过无线读取器读取力信息。该传感器可以在多个测试环境中检测到$ 0 $ -6 $ 〜n的力量，感应精度为$ <0.5 $ 〜n，并在传感器上使用超过10,000美元的$ 10,000 $变化的力级按下。我们还通过设计传感器展示了两个应用程序案例研究，称量仓库包和骨接头施加的传感力。

预测以过去观察和电动机命令为条件的未来视觉观察的能力可以使体现的代理能够计划复杂环境中各种任务的解决方案。这项工作表明，我们可以通过掩盖的视觉建模预训练变压器来创建良好的视频预测模型。我们的方法名为MaskVit，基于两个简单的设计决策。首先，为了记忆和训练效率，我们使用两种类型的窗户注意力：时空和时空。其次，在训练期间，我们掩盖了一个可变百分比的令牌，而不是固定蒙版比率。对于推断，MaskVit通过迭代改进生成所有令牌，在该迭代中，我们会在掩码调度函数后逐步降低掩蔽率。在几个数据集上，我们证明了MaskVit优于视频预测中的先前作品，这是参数有效的，并且可以生成高分辨率视频（256x256）。此外，我们通过使用MaskVit在真实机器人上进行计划，证明了推理加速器的好处（最高512x）。我们的工作表明，我们可以通过利用最小的域知识的掩盖视觉建模的一般框架来赋予体现的代理具有强大的预测模型。

Progress on object detection is enabled by datasets that focus the research community's attention on open challenges. This process led us from simple images to complex scenes and from bounding boxes to segmentation masks. In this work, we introduce LVIS (pronounced 'el-vis'): a new dataset for Large Vocabulary Instance Segmentation. We plan to collect ∼2 million high-quality instance segmentation masks for over 1000 entry-level object categories in 164k images. Due to the Zipfian distribution of categories in natural images, LVIS naturally has a long tail of categories with few training samples. Given that state-of-the-art deep learning methods for object detection perform poorly in the low-sample regime, we believe that our dataset poses an important and exciting new scientific challenge. LVIS is available at http://www.lvisdataset.org.

To truly understand the visual world our models should be able not only to recognize images but also generate them. To this end, there has been exciting recent progress on generating images from natural language descriptions. These methods give stunning results on limited domains such as descriptions of birds or flowers, but struggle to faithfully reproduce complex sentences with many objects and relationships. To overcome this limitation we propose a method for generating images from scene graphs, enabling explicitly reasoning about objects and their relationships. Our model uses graph convolution to process input graphs, computes a scene layout by predicting bounding boxes and segmentation masks for objects, and converts the layout to an image with a cascaded refinement network. The network is trained adversarially against a pair of discriminators to ensure realistic outputs. We validate our approach on Visual Genome and COCO-Stuff, where qualitative results, ablations, and user studies demonstrate our method's ability to generate complex images with multiple objects.

Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments. This is challenging because human motion is inherently multimodal: given a history of human motion paths, there are many socially plausible ways that people could move in the future. We tackle this problem by combining tools from sequence prediction and generative adversarial networks: a recurrent sequence-to-sequence model observes motion histories and predicts future behavior, using a novel pooling mechanism to aggregate information across people. We predict socially plausible futures by training adversarially against a recurrent discriminator, and encourage diverse predictions with a novel variety loss. Through experiments on several datasets we demonstrate that our approach outperforms prior work in terms of accuracy, variety, collision avoidance, and computational complexity.

Existing federated classification algorithms typically assume the local annotations at every client cover the same set of classes. In this paper, we aim to lift such an assumption and focus on a more general yet practical non-IID setting where every client can work on non-identical and even disjoint sets of classes (i.e., client-exclusive classes), and the clients have a common goal which is to build a global classification model to identify the union of these classes. Such heterogeneity in client class sets poses a new challenge: how to ensure different clients are operating in the same latent space so as to avoid the drift after aggregation? We observe that the classes can be described in natural languages (i.e., class names) and these names are typically safe to share with all parties. Thus, we formulate the classification problem as a matching process between data representations and class representations and break the classification model into a data encoder and a label encoder. We leverage the natural-language class names as the common ground to anchor the class representations in the label encoder. In each iteration, the label encoder updates the class representations and regulates the data representations through matching. We further use the updated class representations at each round to annotate data samples for locally-unaware classes according to similarity and distill knowledge to local models. Extensive experiments on four real-world datasets show that the proposed method can outperform various classical and state-of-the-art federated learning methods designed for learning with non-IID data.

The rise in data has led to the need for dimension reduction techniques, especially in the area of non-scalar variables, including time series, natural language processing, and computer vision. In this paper, we specifically investigate dimension reduction for time series through functional data analysis. Current methods for dimension reduction in functional data are functional principal component analysis and functional autoencoders, which are limited to linear mappings or scalar representations for the time series, which is inefficient. In real data applications, the nature of the data is much more complex. We propose a non-linear function-on-function approach, which consists of a functional encoder and a functional decoder, that uses continuous hidden layers consisting of continuous neurons to learn the structure inherent in functional data, which addresses the aforementioned concerns in the existing approaches. Our approach gives a low dimension latent representation by reducing the number of functional features as well as the timepoints at which the functions are observed. The effectiveness of the proposed model is demonstrated through multiple simulations and real data examples.

Landing an unmanned aerial vehicle unmanned aerial vehicle (UAV) on top of an unmanned surface vehicle (USV) in harsh open waters is a challenging problem, owing to forces that can damage the UAV due to a severe roll and/or pitch angle of the USV during touchdown. To tackle this, we propose a novel model predictive control (MPC) approach enabling a UAV to land autonomously on a USV in these harsh conditions. The MPC employs a novel objective function and an online decomposition of the oscillatory motion of the vessel to predict, attempt, and accomplish the landing during near-zero tilt of the landing platform. The nonlinear prediction of the motion of the vessel is performed using visual data from an onboard camera. Therefore, the system does not require any communication with the USV or a control station. The proposed method was analyzed in numerous robotics simulations in harsh and extreme conditions and further validated in various real-world scenarios.

Multiple studies have focused on predicting the prospective popularity of an online document as a whole, without paying attention to the contributions of its individual parts. We introduce the task of proactively forecasting popularities of sentences within online news documents solely utilizing their natural language content. We model sentence-specific popularity forecasting as a sequence regression task. For training our models, we curate InfoPop, the first dataset containing popularity labels for over 1.7 million sentences from over 50,000 online news documents. To the best of our knowledge, this is the first dataset automatically created using streams of incoming search engine queries to generate sentence-level popularity annotations. We propose a novel transfer learning approach involving sentence salience prediction as an auxiliary task. Our proposed technique coupled with a BERT-based neural model exceeds nDCG values of 0.8 for proactive sentence-specific popularity forecasting. Notably, our study presents a non-trivial takeaway: though popularity and salience are different concepts, transfer learning from salience prediction enhances popularity forecasting. We release InfoPop and make our code publicly available: https://github.com/sayarghoshroy/InfoPopularity

The ability for an agent to continuously learn new skills without catastrophically forgetting existing knowledge is of critical importance for the development of generally intelligent agents. Most methods devised to address this problem depend heavily on well-defined task boundaries, and thus depend on human supervision. Our task-agnostic method, Self-Activating Neural Ensembles (SANE), uses a modular architecture designed to avoid catastrophic forgetting without making any such assumptions. At the beginning of each trajectory, a module in the SANE ensemble is activated to determine the agent's next policy. During training, new modules are created as needed and only activated modules are updated to ensure that unused modules remain unchanged. This system enables our method to retain and leverage old skills, while growing and learning new ones. We demonstrate our approach on visually rich procedurally generated environments.