A Differential Private Method for Distributed Optimization in Directed Networks via State Decomposition

In this paper, we study the problem of consensus-based distributed optimization where a network of agents, abstracted as a directed graph, aims to minimize the sum of all agents' cost functions collaboratively. In existing distributed optimization approaches (Push-Pull/AB) for directed graphs, all agents exchange their states with neighbors to achieve the optimal solution with a constant stepsize, which may lead to the disclosure of sensitive and private information. For privacy preservation, we propose a novel state-decomposition based gradient tracking approach (SD-Push-Pull) for distributed optimzation over directed networks that preserves differential privacy, which is a strong notion that protects agents' privacy against an adversary with arbitrary auxiliary information. The main idea of the proposed approach is to decompose the gradient state of each agent into two sub-states. Only one substate is exchanged by the agent with its neighbours over time, and the other one is kept private. That is to say, only one substate is visible to an adversary, protecting the privacy from being leaked. It is proved that under certain decomposition principles, a bound for the sub-optimality of the proposed algorithm can be derived and the differential privacy is achieved simultaneously. Moreover, the trade-off between differential privacy and the optimization accuracy is also characterized. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed approach

Safety and efficacy of short-term dual antiplatelet therapy combined with intensive rosuvastatin in acute ischemic stroke

Objective: To investigate the safety and efficacy of short-term (7-day) Dual Antiplatelet Therapy (DAPT) with intensive rosuvastatin in Acute Ischemic Stroke (AIS). Methods: In this study, patients with AIS in the emergency department of the hospital from October 2016 to December 2019 were registered and divided into the control group (Single Antiplatelet Therapy [SAPT] + rosuvastatin) and the study group (7-day DAPT + intensive rosuvastatin) according to the therapy regimens. The generalized linear model was used to compare the National Institute of Health Stroke Scale (NIHSS) scores between the two groups during the 21-day treatment. A Cox regression model was used to compare recurrent ischemic stroke, bleeding events, Statin-Induced Liver Injury (SILI), and Statin-Associated Myopathy (SAM) between the two groups during the 90-day follow-up. Results: Comparison of NIHSS scores after 21-day treatment: NIHSS scores in the study group decreased significantly, 0.273-times as much as that in the control group (Odds Ratio [OR] 0.273; 95% Confidence Interval [95% CI] 0.208–0.359; p < 0.001). Comparison of recurrent ischemic stroke during the 90-day follow-up: The therapy of the study group reduced the risk of recurrent stroke by 65% (7.76% vs. 22.82%, Hazard Ratio [HR] 0.350; 95% CI 0.167–0.730; p = 0.005). Comparison of bleeding events: There was no statistical difference between the two groups (7.79% vs. 6.71%, HR = 1.076; 95% CI 0.424–2.732; p = 0.878). No cases of SILI and SAM were found. Conclusions: Short-term DAPT with intensive rosuvastatin effectively relieved the clinical symptoms and significantly reduced the recurrent stroke for patients with mild-to-moderate AIS within 90 days, without increasing bleeding events, SILI and SAM

Enhancing Generalizable 6D Pose Tracking of an In-Hand Object with Tactile Sensing

While holding and manipulating an object, humans track the object states through vision and touch so as to achieve complex tasks. However, nowadays the majority of robot research perceives object states just from visual signals, hugely limiting the robotic manipulation abilities. This work presents a tactile-enhanced generalizable 6D pose tracking design named TEG-Track to track previously unseen in-hand objects. TEG-Track extracts tactile kinematic cues of an in-hand object from consecutive tactile sensing signals. Such cues are incorporated into a geometric-kinematic optimization scheme to enhance existing generalizable visual trackers. To test our method in real scenarios and enable future studies on generalizable visual-tactile tracking, we collect a real visual-tactile in-hand object pose tracking dataset. Experiments show that TEG-Track significantly improves state-of-the-art generalizable 6D pose trackers in both synthetic and real cases

Design and real-time implementation of data-driven adaptive wide-area damping controller for back-to-back VSC-HVDC

This paper proposes a data-driven adaptive wide-area damping controller (D-WADC) for back-to-back VSC-HVDC to suppress the low frequency oscillation in a large-scale interconnected power system. The proposed D-WADC adopts a dual-loop control structure to make full use of the active and reactive power control of VSC-HVDC to improve the damping of the power system. A data-driven algorithm named the goal representation heuristic dynamic programming is employed to design the proposed D-WADC, which means the design procedure only requires the input and output data rather than the mathematic model of the concerned power system. Thus, the D-WADC can adapt to the change of operating condition through online weight modification. Besides, the adaptive delay compensator (ADC) is added to effectively compensate the stochastic delay involved in the wide-area feedback signal. Case studies are conducted based on the simplified model of a practical power system and the 16-machine system with a back-to-back VSC-HVDC. Both the simulation and hardware-in-loop experiment results verify that the proposed D-WADC can effectively suppress the low-frequency oscillation under a wide range of operating conditions, disturbances, and stochastic communication delays

GAMMA: Graspability-Aware Mobile MAnipulation Policy Learning based on Online Grasping Pose Fusion

Mobile manipulation constitutes a fundamental task for robotic assistants and garners significant attention within the robotics community. A critical challenge inherent in mobile manipulation is the effective observation of the target while approaching it for grasping. In this work, we propose a graspability-aware mobile manipulation approach powered by an online grasping pose fusion framework that enables a temporally consistent grasping observation. Specifically, the predicted grasping poses are online organized to eliminate the redundant, outlier grasping poses, which can be encoded as a grasping pose observation state for reinforcement learning. Moreover, on-the-fly fusing the grasping poses enables a direct assessment of graspability, encompassing both the quantity and quality of grasping poses

Advances in COVID-19 research until November 2020:Update from the UNCOVER registry

No abstract availabl

An observational and Mendelian randomisation study on vitamin D and COVID-19 risk in UK Biobank

A growing body of evidence suggests that vitamin D deficiency has been associated with an increased susceptibility to viral and bacterial respiratory infections. In this study, we aimed to examine the association between vitamin D and COVID-19 risk and outcomes. We used logistic regression to identify associations between vitamin D variables and COVID-19 (risk of infection, hospitalisation and death) in 417,342 participants from UK Biobank. We subsequently performed a Mendelian Randomisation (MR) study to look for evidence of a causal effect. In total, 1746 COVID-19 cases (399 deaths) were registered between March and June 2020. We found no significant associations between COVID-19 infection risk and measured 25-OHD levels after adjusted for covariates, but this finding is limited by the fact that the vitamin D levels were measured on average 11 years before the pandemic. Ambient UVB was strongly and inversely associated with COVID-19 hospitalization and death overall and consistently after stratification by BMI and ethnicity. We also observed an interaction that suggested greater protective effect of genetically-predicted vitamin D levels when ambient UVB radiation is stronger. The main MR analysis did not show that genetically-predicted vitamin D levels are causally associated with COVID-19 risk (OR = 0.77, 95% CI 0.55–1.11, P = 0.160), but MR sensitivity analyses indicated a potential causal effect (weighted mode MR: OR = 0.72, 95% CI 0.55–0.95, P = 0.021; weighted median MR: OR = 0.61, 95% CI 0.42–0.92, P = 0.016). Analysis of MR-PRESSO did not find outliers for any instrumental variables and suggested a potential causal effect (OR = 0.80, 95% CI 0.66–0.98, p-val = 0.030). In conclusion, the effect of vitamin D levels on the risk or severity of COVID-19 remains controversial, further studies are needed to validate vitamin D supplementation as a means of protecting against worsened COVID-19