动态复制是一种广泛的多拷贝路由方法，用于有效地应对移动机会主义网络中的间歇性连接。根据它，节点基于禁用后者的适合将消息传送到目的地的实用程序值，将消息副本转发到遇到的节点。该方法的普及源于其灵活性，在不需要特殊定制的情况下有效地在网络中有效地运行。尽管如此，其缺点是产生大量副本的趋势，这些副本消耗有限的资源，如能量和存储。为了解决问题，我们使观察到网络节点可以根据其实用程序值分组到描绘不同递送能力的集群中。我们利用此查找来转换基本转发策略，该策略是使用越来越多的效用的节点移动数据包，并实际上通过增加传递能力的集群来转发。新策略在Synernery中使用基本动态复制算法，可完全可配置，因此它可以与几乎任何实用程序功能一起使用。我们还扩展了我们的方法，同时使用两个实用程序功能，该功能在展示社会特征的移动网络中特别有效。通过在广泛的现实生活网络中进行实验，我们经验证明我们的方法在通过不同的连接特性下减少网络中的副本数量的整体数量，而没有同时阻碍输送效率。

Smart City applications, such as traffic monitoring and disaster response, often use swarms of intelligent and cooperative drones to efficiently collect sensor data over different areas of interest and time spans. However, when the required sensing becomes spatio-temporally large and varying, a collective arrangement of sensing tasks to a large number of battery-restricted and distributed drones is challenging. To address this problem, we introduce a scalable and energy-aware model for planning and coordination of spatio-temporal sensing. The coordination model is built upon a decentralized multi-agent collective learning algorithm (EPOS) to ensure scalability, resilience, and flexibility that existing approaches lack of. Experimental results illustrate the outstanding performance of the proposed method compared to state-of-the-art methods. Analytical results contribute a deeper understanding of how coordinated mobility of drones influences sensing performance. This novel coordination solution is applied to traffic monitoring using real-world data to demonstrate a $46.45\%$ more accurate and $2.88\%$ more efficient detection of vehicles as the number of drones become a scarce resource.

In this paper, we present an adjustable-equilibrium parallel elastic actuator (AE-PEA). The actuator consists of a motor, an equilibrium adjusting mechanism, and a spring arranged into a cylindrical geometry, similar to a motor-gearbox assembly. The novel component of the actuator is the equilibrium adjusting mechanism which (i) does not require external energy to maintain the equilibrium position of the actuator even if the spring is deformed and (ii) enables equilibrium position control with low energy cost by rotating the spring while keeping it undeformed. Adjustable equilibrium parallel elastic actuators resolve the main limitation of parallel elastic actuators (PEAs) by enabling energy-efficient operation at different equilibrium positions, instead of being limited to energy-efficient operation at a single equilibrium position. We foresee the use of AE-PEAs in industrial robots, mobile robots, exoskeletons, and prostheses, where efficient oscillatory motion and gravity compensation at different positions are required.

Automatic fake news detection is a challenging problem in misinformation spreading, and it has tremendous real-world political and social impacts. Past studies have proposed machine learning-based methods for detecting such fake news, focusing on different properties of the published news articles, such as linguistic characteristics of the actual content, which however have limitations due to the apparent language barriers. Departing from such efforts, we propose FNDaaS, the first automatic, content-agnostic fake news detection method, that considers new and unstudied features such as network and structural characteristics per news website. This method can be enforced as-a-Service, either at the ISP-side for easier scalability and maintenance, or user-side for better end-user privacy. We demonstrate the efficacy of our method using data crawled from existing lists of 637 fake and 1183 real news websites, and by building and testing a proof of concept system that materializes our proposal. Our analysis of data collected from these websites shows that the vast majority of fake news domains are very young and appear to have lower time periods of an IP associated with their domain than real news ones. By conducting various experiments with machine learning classifiers, we demonstrate that FNDaaS can achieve an AUC score of up to 0.967 on past sites, and up to 77-92% accuracy on newly-flagged ones.

Timely and effective feedback within surgical training plays a critical role in developing the skills required to perform safe and efficient surgery. Feedback from expert surgeons, while especially valuable in this regard, is challenging to acquire due to their typically busy schedules, and may be subject to biases. Formal assessment procedures like OSATS and GEARS attempt to provide objective measures of skill, but remain time-consuming. With advances in machine learning there is an opportunity for fast and objective automated feedback on technical skills. The SimSurgSkill 2021 challenge (hosted as a sub-challenge of EndoVis at MICCAI 2021) aimed to promote and foster work in this endeavor. Using virtual reality (VR) surgical tasks, competitors were tasked with localizing instruments and predicting surgical skill. Here we summarize the winning approaches and how they performed. Using this publicly available dataset and results as a springboard, future work may enable more efficient training of surgeons with advances in surgical data science. The dataset can be accessed from https://console.cloud.google.com/storage/browser/isi-simsurgskill-2021.

3D gaze estimation is most often tackled as learning a direct mapping between input images and the gaze vector or its spherical coordinates. Recently, it has been shown that pose estimation of the face, body and hands benefits from revising the learning target from few pose parameters to dense 3D coordinates. In this work, we leverage this observation and propose to tackle 3D gaze estimation as regression of 3D eye meshes. We overcome the absence of compatible ground truth by fitting a rigid 3D eyeball template on existing gaze datasets and propose to improve generalization by making use of widely available in-the-wild face images. To this end, we propose an automatic pipeline to retrieve robust gaze pseudo-labels from arbitrary face images and design a multi-view supervision framework to balance their effect during training. In our experiments, our method achieves improvement of 30% compared to state-of-the-art in cross-dataset gaze estimation, when no ground truth data are available for training, and 7% when they are. We make our project publicly available at https://github.com/Vagver/dense3Deyes.

Fast timescale state estimation for a large power system can be challenging if the sensors producing the measurements are few in number. This is particularly true for doing time-synchronized state estimation for a transmission system that has minimal phasor measurement unit (PMU) coverage. This paper proposes a Deep Neural network-based State Estimator (DeNSE) to overcome this extreme unobservability problem. For systems in which the existing PMU infrastructure is not able to bring the estimation errors within acceptable limits using the DeNSE, a data-driven incremental PMU placement methodology is also introduced. The practical utility of the proposed approach is demonstrated by considering topology changes, non-Gaussian measurement noise, bad data detection and correction, and large system application.

This paper proposes Distributed Model Predictive Covariance Steering (DMPCS), a novel method for safe multi-robot control under uncertainty. The scope of our approach is to blend covariance steering theory, distributed optimization and model predictive control (MPC) into a single methodology that is safe, scalable and decentralized. Initially, we pose a problem formulation that uses the Wasserstein distance to steer the state distributions of a multi-robot team to desired targets, and probabilistic constraints to ensure safety. We then transform this problem into a finite-dimensional optimization one by utilizing a disturbance feedback policy parametrization for covariance steering and a tractable approximation of the safety constraints. To solve the latter problem, we derive a decentralized consensus-based algorithm using the Alternating Direction Method of Multipliers (ADMM). This method is then extended to a receding horizon form, which yields the proposed DMPCS algorithm. Simulation experiments on large-scale problems with up to hundreds of robots successfully demonstrate the effectiveness and scalability of DMPCS. Its superior capability in achieving safety is also highlighted through a comparison against a standard stochastic MPC approach. A video with all simulation experiments is available in https://youtu.be/Hks-0BRozxA.

This paper proposes embedded Gaussian Process Barrier States (GP-BaS), a methodology to safely control unmodeled dynamics of nonlinear system using Bayesian learning. Gaussian Processes (GPs) are used to model the dynamics of the safety-critical system, which is subsequently used in the GP-BaS model. We derive the barrier state dynamics utilizing the GP posterior, which is used to construct a safety embedded Gaussian process dynamical model (GPDM). We show that the safety-critical system can be controlled to remain inside the safe region as long as we can design a controller that renders the BaS-GPDM's trajectories bounded (or asymptotically stable). The proposed approach overcomes various limitations in early attempts at combining GPs with barrier functions due to the abstention of restrictive assumptions such as linearity of the system with respect to control, relative degree of the constraints and number or nature of constraints. This work is implemented on various examples for trajectory optimization and control including optimal stabilization of unstable linear system and safe trajectory optimization of a Dubins vehicle navigating through an obstacle course and on a quadrotor in an obstacle avoidance task using GP differentiable dynamic programming (GP-DDP). The proposed framework is capable of maintaining safe optimization and control of unmodeled dynamics and is purely data driven.

Dimensionality reduction has become an important research topic as demand for interpreting high-dimensional datasets has been increasing rapidly in recent years. There have been many dimensionality reduction methods with good performance in preserving the overall relationship among data points when mapping them to a lower-dimensional space. However, these existing methods fail to incorporate the difference in importance among features. To address this problem, we propose a novel meta-method, DimenFix, which can be operated upon any base dimensionality reduction method that involves a gradient-descent-like process. By allowing users to define the importance of different features, which is considered in dimensionality reduction, DimenFix creates new possibilities to visualize and understand a given dataset. Meanwhile, DimenFix does not increase the time cost or reduce the quality of dimensionality reduction with respect to the base dimensionality reduction used.