Sampling-based Motion Planning for Active Multirotor System Identification

This paper reports on an algorithm for planning trajectories that allow a multirotor micro aerial vehicle (MAV) to quickly identify a set of unknown parameters. In many problems like self calibration or model parameter identification some states are only observable under a specific motion. These motions are often hard to find, especially for inexperienced users. Therefore, we consider system model identification in an active setting, where the vehicle autonomously decides what actions to take in order to quickly identify the model. Our algorithm approximates the belief dynamics of the system around a candidate trajectory using an extended Kalman filter (EKF). It uses sampling-based motion planning to explore the space of possible beliefs and find a maximally informative trajectory within a user-defined budget. We validate our method in simulation and on a real system showing the feasibility and repeatability of the proposed approach. Our planner creates trajectories which reduce model parameter convergence time and uncertainty by a factor of four.Comment: Published at ICRA 2017. Video available at https://www.youtube.com/watch?v=xtqrWbgep5

Nonlinear Model Predictive Control for Multi-Micro Aerial Vehicle Robust Collision Avoidance

Multiple multirotor Micro Aerial Vehicles sharing the same airspace require a reliable and robust collision avoidance technique. In this paper we address the problem of multi-MAV reactive collision avoidance. A model-based controller is employed to achieve simultaneously reference trajectory tracking and collision avoidance. Moreover, we also account for the uncertainty of the state estimator and the other agents position and velocity uncertainties to achieve a higher degree of robustness. The proposed approach is decentralized, does not require collision-free reference trajectory and accounts for the full MAV dynamics. We validated our approach in simulation and experimentally.Comment: Video available on: https://www.youtube.com/watch?v=Ot76i9p2ZZo&t=40

Exploring The Interactions Between SARS-CoV-2 and Host Proteins.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the current pandemic, Coronavirus Disease 2019 (COVID-19). SARS-CoV-2 is considered to be of zoonotic origin; it originated in non-human animals and was transmitted to humans. Since the early stage of the pandemic, however, the evidence of transmissions from humans to animals (reverse zoonoses) has been found in multiple animal species including mink, white-tailed deer, and pet and zoo animals. Furthermore, secondary zoonotic events of SARS-CoV-2, transmissions from animals to humans, have been also reported. It is suggested that non-human hosts can act as SARS-CoV-2 reservoirs where accumulated mutations in viral proteins could change the transmissibility and/or pathogenicity of the virus when it is spilled over again to human populations. Our goal, therefore, is to examine the SARS-CoV-2 genomic changes in non-human hosts and to identify the changes responsible for the adaptation of the virus in non-human hosts. Changes in the physicochemical properties of viral proteins potentially affect and influence their functions. Therefore, in this study, we compared SARS-CoV-2 proteins among human and non-human hosts and analyzed the differences in their physicochemical properties using the principal component analysis. In addition to the viral proteins from bat and pangolin, those from white-tailed deer and mink showed larger differences in the properties. Van der Waals volume, isoelectric point, charge, and thermostability index were found to be the main contributing factors. We next performed the comparisons of protein-protein interaction (PPI) prediction methods that use different features including physicochemical properties and those based on natural language processing. It showed that the Cross-attention PHV had slightly better performance scores than InterSPPI-HVPPI and LGCA-VHPPI. Finally, to examine the effect of changes in physicochemical properties in viral proteins against host proteins, PPI prediction was performed using the Cross-attention PHV between viral proteins from different SARS-CoV-2 variants and host proteins. The prediction scores between the different variants and host proteins from human and white-tailed deer were highly similar. The results showed that the analysis of physicochemical properties of viral proteins helps to understand how physicochemical properties of viral proteins affect viral-host PPIs and how viral proteins evolve to adapt different host cell environments

Structural and forecasting softwood lumber models with a time series approach

The development of cointegration theories and the presence of nonstationarity in time series raised serious concerns about possible spurious estimations in forest products models. Based on the results of Hsiao (1997a, 1997b), all the virtues of two-stage least square (2SLS) hold if there are sufficient cointegration relations. Stationary null and nonstationary null unit root tests and monthly seasonal unit root tests were applied to the time series used in this dissertation. Cointegration tests with exogenous variables were performed to justify the 2SLS. A regional error correction model (ECM) with four regional lumber supply and demand equations and a U.S.-Canada supply and demand ECM were estimated. CUSUM tests did not find any structural changes. Both estimated models showed that the imported Canadian lumber and the U.S. lumber are substitutes. The estimated long-run and short-run own-price elasticities for demand and supply are inelastic for all the equations but the short-run supply equation for the West Coast. The long-run lumber supply equations have significant trends: annually -3% for the Inland West and 2% for the other regions. The popular maximum likelihood estimation for the restricted ECM cannot pass the test for the restrictions and is, therefore, not used for the regional structural lumber model. A series of univariate and multi-equation models were used as forecasting models. A combination of univariate model were shown to be the best forecasting models for lumber prices, and a combination of univariate and multi-equation models were shown to be the best forecasting models for lumber quantities. The selected combinations of models were shown to be the best with additional observations. It was also shown that lumber quantities could be forecasted better than lumber prices

Molecular mechanism of influenza A NS1-mediated TRIM25 recognition and inhibition

RIG-I is a viral RNA sensor that induces the production of type I interferon (IFN) in response to infection with a variety of viruses. Modification of RIG-I with K63-linked poly-ubiquitin chains, synthesised by TRIM25, is crucial for activation of the RIG-I/MAVS signalling pathway. TRIM25 activity is targeted by influenza A virus non-structural protein 1 (NS1) to suppress IFN production and prevent an efficient host immune response. Here we present structures of the human TRIM25 coiled-coil-PRYSPRY module and of complexes between the TRIM25 coiled-coil domain and NS1. These structures show that binding of NS1 interferes with the correct positioning of the PRYSPRY domain of TRIM25 required for substrate ubiquitination and provide a mechanistic explanation for how NS1 suppresses RIG-I ubiquitination and hence downstream signalling. In contrast, the formation of unanchored K63-linked poly-ubiquitin chains is unchanged by NS1 binding, indicating that RING dimerisation of TRIM25 is not affected by NS1

Evaluation of surface EMG-based recognition algorithms for decoding hand movements

Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins\u27 set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands