Regression Spline via Penalizing Derivatives.

Master'sMASTER OF SCIENC

UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms

Recent technological advancements in space, air and ground components have made possible a new network paradigm called "space-air-ground integrated network" (SAGIN). Unmanned aerial vehicles (UAVs) play a key role in SAGINs. However, due to UAVs' high dynamics and complexity, the real-world deployment of a SAGIN becomes a major barrier for realizing such SAGINs. Compared to the space and terrestrial components, UAVs are expected to meet performance requirements with high flexibility and dynamics using limited resources. Therefore, employing UAVs in various usage scenarios requires well-designed planning in algorithmic approaches. In this paper, we provide a comprehensive review of recent learning-based algorithmic approaches. We consider possible reward functions and discuss the state-of-the-art algorithms for optimizing the reward functions, including Q-learning, deep Q-learning, multi-armed bandit (MAB), particle swarm optimization (PSO) and satisfaction-based learning algorithms. Unlike other survey papers, we focus on the methodological perspective of the optimization problem, which can be applicable to various UAV-assisted missions on a SAGIN using these algorithms. We simulate users and environments according to real-world scenarios and compare the learning-based and PSO-based methods in terms of throughput, load, fairness, computation time, etc. We also implement and evaluate the 2-dimensional (2D) and 3-dimensional (3D) variations of these algorithms to reflect different deployment cases. Our simulation suggests that the $3$D satisfaction-based learning algorithm outperforms the other approaches for various metrics in most cases. We discuss some open challenges at the end and our findings aim to provide design guidelines for algorithm selections while optimizing the deployment of UAV-assisted SAGINs.Comment: Submitted to the IEEE Internet of Things Journal in June 202

An Anomaly Detection Method for Satellites Using Monte Carlo Dropout

Recently, there has been a significant amount of interest in satellite telemetry anomaly detection (AD) using neural networks (NN). For AD purposes, the current approaches focus on either forecasting or reconstruction of the time series, and they cannot measure the level of reliability or the probability of correct detection. Although the Bayesian neural network (BNN)-based approaches are well known for time series uncertainty estimation, they are computationally intractable. In this paper, we present a tractable approximation for BNN based on the Monte Carlo (MC) dropout method for capturing the uncertainty in the satellite telemetry time series, without sacrificing accuracy. For time series forecasting, we employ an NN, which consists of several Long Short-Term Memory (LSTM) layers followed by various dense layers. We employ the MC dropout inside each LSTM layer and before the dense layers for uncertainty estimation. With the proposed uncertainty region and by utilizing a post-processing filter, we can effectively capture the anomaly points. Numerical results show that our proposed time series AD approach outperforms the existing methods from both prediction accuracy and AD perspectives

Discovery of a Unique Structural Motif in Lanthipeptide Synthetases for Substrate Binding and Interdomain Interactions

Class III lanthipeptide synthetases catalyze the formation of lanthionine/methyllanthionine and labionin crosslinks. We present here the 2.40 Å resolution structure of the kinase domain of a class III lanthipeptide synthetase CurKC from the biosynthesis of curvopeptin. A unique structural subunit for leader binding, named leader recognition domain (LRD), was identified. The LRD of CurKC is responsible for the recognition of the leader peptide and for mediating interactions between the lyase and kinase domains. LRDs are highly conserved among the kinase domains of class III and class IV lanthipeptide synthetases. The discovery of LRDs provides insight into the substrate recognition and domain organization in multidomain lanthipeptide synthetases

Simultaneous Maximum Likelihood Estimation for Piecewise Linear Instrumental Variable Models

Analysis of instrumental variables is an effective approach to dealing with endogenous variables and unmeasured confounding issue in causal inference. We propose using the piecewise linear model to fit the relationship between the continuous instrumental variable and the continuous explanatory variable, as well as the relationship between the continuous explanatory variable and the outcome variable, which generalizes the traditional linear instrumental variable models. The two-stage least square and limited information maximum likelihood methods are used for the simultaneous estimation of the regression coefficients and the threshold parameters. Furthermore, we study the limiting distribution of the estimators in the correctly specified and misspecified models and provide a robust estimation of the variance-covariance matrix. We illustrate the finite sample properties of the estimation in terms of the Monte Carlo biases, standard errors, and coverage probabilities via the simulated data. Our proposed model is applied to an education-salary data, which investigates the causal effect of children’s years of schooling on estimated hourly wage with father’s years of schooling as the instrumental variable

Learning Heterogeneity in Causal Inference Using Sufficient Dimension Reduction

Often the research interest in causal inference is on the regression causal effect, which is the mean difference in the potential outcomes conditional on the covariates. In this paper, we use sufficient dimension reduction to estimate a lower dimensional linear combination of the covariates that is sufficient to model the regression causal effect. Compared with the existing applications of sufficient dimension reduction in causal inference, our approaches are more efficient in reducing the dimensionality of covariates, and avoid estimating the individual outcome regressions. The proposed approaches can be used in three ways to assist modeling the regression causal effect: to conduct variable selection, to improve the estimation accuracy, and to detect the heterogeneity. Their usefulness are illustrated by both simulation studies and a real data example

A Kernel-Based Metric for Balance Assessment

An important goal in causal inference is to achieve balance in the covariates among the treatment groups. In this article, we introduce the concept of distributional balance preserving which requires the distribution of the covariates to be the same in different treatment groups. We also introduce a new balance measure called kernel distance, which is the empirical estimate of the probability metric defined in the reproducing kernel Hilbert spaces. Compared to the traditional balance metrics, the kernel distance measures the difference in the two multivariate distributions instead of the difference in the finite moments of the distributions. Simulation results show that the kernel distance is the best indicator of bias in the estimated casual effect compared to several commonly used balance measures. We then incorporate kernel distance into genetic matching, the state-of-the-art matching procedure and apply the proposed approach to analyze the Early Dieting in Girls study. The study indicates that mothers’ overall weight concern increases the likelihood of daughters’ early dieting behavior, but the causal effect is not significant

Monitoring the in vivo siRNA release from lipid nanoparticles based on the fluorescence resonance energy transfer principle

The siRNA-loaded lipid nanoparticles have attracted much attention due to its significant gene silencing effect and successful marketization. However, the in vivo distribution and release of siRNA still cannot be effectively monitored. In this study, based on the fluorescence resonance energy transfer (FRET) principle, a fluorescence dye Cy5-modified survivin siRNA was conjugated to nanogolds (Au-DR-siRNA), which were then wrapped with lipid nanoparticles (LNPs) for monitoring the release behaviour of siRNA in vivo. The results showed that once Au-DR-siRNA was released from the LNPs and cleaved by the Dicer enzyme to produce free siRNA in cells, the fluorescence of Cy5 would change from quenched state to activated state, showing the location and time of siRNA release. Besides, the LNPs showed a significant antitumor effect by silencing the survivin gene and a CT imaging function superior to iohexol by nanogolds. Therefore, this work provided not only an effective method for monitoring the pharmacokinetic behaviour of LNP-based siRNA, but also a siRNA delivery system for treating and diagnosing tumors