The Role of Time Delay in Sim2real Transfer of Reinforcement Learning for Cyber-Physical Systems

This paper analyzes the simulation to reality gap in reinforcement learning (RL) cyber-physical systems with fractional delays (i.e. delays that are non-integer multiple of the sampling period). The consideration of fractional delay has important implications on the nature of the cyber-physical system considered. Systems with delays are non-Markovian, and the system state vector needs to be extended to make the system Markovian. We show that this is not possible when the delay is in the output, and the problem would always be non-Markovian. Based on this analysis, a sampling scheme is proposed that results in efficient RL training and agents that perform well in realistic multirotor unmanned aerial vehicle simulations. We demonstrate that the resultant agents do not produce excessive oscillations, which is not the case with RL agents that do not consider time delay in the model.Comment: 6 pages,4 figures, Submitted to ICRA202

SLITRK2 variants associated with neurodevelopmental disorders impair excitatory synaptic function and cognition in mice

SLITRK2 is a single-pass transmembrane protein expressed at postsynaptic neurons that regulates neurite outgrowth and excitatory synapse maintenance. In the present study, we report on rare variants (one nonsense and six missense variants) in SLITRK2 on the X chromosome identified by exome sequencing in individuals with neurodevelopmental disorders. Functional studies showed that some variants displayed impaired membrane transport and impaired excitatory synapse-promoting effects. Strikingly, these variations abolished the ability of SLITRK2 wild-type to reduce the levels of the receptor tyrosine kinase TrkB in neurons. Moreover, Slitrk2 conditional knockout mice exhibited impaired long-term memory and abnormal gait, recapitulating a subset of clinical features of patients with SLITRK2 variants. Furthermore, impaired excitatory synapse maintenance induced by hippocampal CA1-specific cKO of Slitrk2 caused abnormalities in spatial reference memory. Collectively, these data suggest that SLITRK2 is involved in X-linked neurodevelopmental disorders that are caused by perturbation of diverse facets of SLITRK2 function

Real-time system identification using deep learning for linear processes with application to unmanned aerial vehicles

This paper proposes a novel parametric identification approach for linear systems using Deep Learning (DL) and the Modified Relay Feedback Test (MRFT). The proposed methodology utilizes MRFT to reveal distinguishing frequencies about an unknown process; which are then passed to a trained DL model to identify the underlying process parameters. The presented approach guarantees stability and performance in the identification and control phases respectively, and requires few seconds of observation data to infer the dynamic system parameters. Quadrotor Unmanned Aerial Vehicle (UAV) attitude and altitude dynamics were used in simulation and experimentation to verify the presented methodology. Results show the effectiveness and real-time capabilities of the proposed approach, which outperforms the conventional Prediction Error Method in terms of accuracy, robustness to biases, computational efficiency and data requirements.Comment: 13 pages, 9 figures. Submitted to IEEE access. A supplementary video for the work presented in this paper can be accessed at: https://www.youtube.com/watch?v=dz3WTFU7W7c. This version includes minor style edits for appendix and reference

Design of Dynamics Invariant LSTM for Touch Based Human-UAV Interaction Detection

The field of Unmanned Aerial Vehicles (UAVs) has reached a high level of maturity in the last few years. Hence, bringing such platforms from closed labs, to day-to-day interactions with humans is important for commercialization of UAVs. One particular human-UAV scenario of interest for this paper is the payload handover scheme, where a UAV hands over a payload to a human upon their request. In this scope, this paper presents a novel real-time human-UAV interaction detection approach, where Long short-term memory (LSTM) based neural network is developed to detect state profiles resulting from human interaction dynamics. A novel data pre-processing technique is presented; this technique leverages estimated process parameters of training and testing UAVs to build dynamics invariant testing data. The proposed detection algorithm is lightweight and thus can be deployed in real-time using off the shelf UAV platforms; in addition, it depends solely on inertial and position measurements present on any classical UAV platform. The proposed approach is demonstrated on a payload handover task between multirotor UAVs and humans. Training and testing data were collected using real-time experiments. The detection approach has achieved an accuracy of 96\%, giving no false positives even in the presence of external wind disturbances, and when deployed and tested on two different UAVs.Comment: 13 pages, 13 figures, submitted to IEEE access, A supplementary video for the work presented in this paper can be accessed from https://youtu.be/29N_OXBl1m

Homozygous single base deletion in TUSC3 causes intellectual disability with developmental delay in an Omani family

Intellectual disability (ID) is the term used to describe a diverse group of neurological conditions with congenital or juvenile onset, characterized by an IQ score of less than 70 and difficulties associated with limitations in cognitive function and adaptive behavior. The condition can be inherited or caused by environmental factors. The genetic forms are heterogeneous, with mutations in over 500 known genes shown to cause the disorder. We report a consanguineous Omani family in which multiple individuals have ID and developmental delay together with some variably present features including short stature, microcephaly, moderate facial dysmorphism, and congenital malformations of the toes or hands. Homozygosity mapping combined with whole exome next generation sequencing identified a novel homozygous single base pair deletion in TUSC3, c.222delA, p.R74 fs. The mutation segregates with the disease phenotype in a recessive manner and is absent in 60,706 unrelated individuals from various disease-specific and population genetic studies. TUSC3 mutations have been previously identified as causing either syndromic or non-syndromic ID in patients from France, Italy, Iran and Pakistan. This paper supports the previous clinical descriptions of the condition caused by TUSC3 mutations and describes the seventh family with mutations in this gene, thus contributing to the genetic spectrum of mutations. This is the first report of a family from the Arabian peninsula with this form of ID

Type I interferonopathy due to a homozygous loss-of-inhibitory-function mutation in STAT2

International audiencePurpose STAT2 is both an effector and negative regulator of type I interferon (IFN-I) signalling. We describe the characterization of a novel homozygous missense STAT2 substitution in a patient with a type I interferonopathy. Methods Whole-genome sequencing (WGS) was used to identify the genetic basis of disease in a patient with features of enhanced IFN-I signalling. After stable lentiviral reconstitution of STAT2-null human fibrosarcoma U6A cells with STAT2 wild type or p.(A219V), we performed quantitative polymerase chain reaction, western blotting, immunofluorescence, and co-immunoprecipitation to functionally characterize the p.(A219V) variant. Results WGS identified a rare homozygous single nucleotide transition in STAT2 (c.656C > T), resulting in a p.(A219V) substitution, in a patient displaying developmental delay, intracranial calcification, and up-regulation of interferon-stimulated gene (ISG) expression in blood. In vitro studies revealed that the STAT2 p.(A219V) variant retained the ability to transduce an IFN-I stimulus. Notably, STAT2 p.(A219V) failed to support receptor desensitization, resulting in sustained STAT2 phosphorylation and ISG up-regulation. Mechanistically, STAT2 p.(A219V) showed defective binding to ubiquitin specific protease 18 (USP18), providing a possible explanation for the chronic IFN-I pathway activation seen in the patient. Conclusion Our data indicate an impaired negative regulatory role of STAT2 p.(A219V) in IFN-I signalling and that mutations in STAT2 resulting in a type I interferonopathy state are not limited to the previously reported R148 residue. Indeed, structural modelling highlights at least 3 further residues critical to mediating a STAT2-USP18 interaction, in which mutations might be expected to result in defective negative feedback regulation of IFN-I signalling