Robust Signal Extraction Based on Time-Frequency Joint Analysis and GRNN for …
In pursuit of vibration recovery, with subwavelength precision based on laser self-mixing interferometry (SMI), the estimation of two key parameters, i.e., the optical feedback parameter C and line…
Increased Spurious-Free Dynamic Range in Frequency Sweeping Interferometry by…
We propose an envelope detrending method to correct parasitic amplitude modulation (AM) in frequency sweeping interferometry (FSI). Parasitic AM arises from effects such as device spectral response…
LoNAS : Low-Cost Neural Architecture Search Using a Three-Stage Evolutionary …
Neural architecture search (NAS) has been widely studied to design high-performance network architectures automatically. However, existing approaches require more search time and substantial resour…
Prioritizing Variables for Observational Study Design using the Joint Variabl…
Observational studies of treatment effects require adjustment for confounding variables. However, causal inference methods typically cannot deliver perfect adjustment on all measured baseline varia…
Graphical Assistant Grouped Network Autoregression Model : A Bayesian Nonpara…
Vector autoregression model is ubiquitous in classical time series data analysis. With the rapid advance of social network sites, time series data over latent graph is becoming increasingly popular…
Learning Human Activity Patterns Using Clustered Point Processes With Active …
Modeling event patterns is a central task in a wide range of disciplines. In applications such as studying human activity patterns, events often arrive clustered with sporadic and long periods of i…
Random Forest Adjustment for Approximate Bayesian Computation
We propose a novel method for regression adjustment in approximate Bayesian computation to help improve the accuracy and computational efficiency of the posterior inference. The proposed method use…
Least-Square Approximation for a Distributed System
In this work, we develop a distributed least-square approximation (DLSA) method that is able to solve a large family of regression problems (e.g., linear regression, logistic regression, and Cox’…
Scalable Bayesian Nonparametric Clustering and Classification
We develop a scalable multistep Monte Carlo algorithm for inference under a large class of nonparametric Bayesian models for clustering and classification. Each step is “embarrassingly parallel�…
Ultra-Wideband OAM Mode Generator Based on a Helical Grating Written in a Gra…
An ultra-wideband orbital-angular-momentum (OAM) mode generator has been proposed and demonstrated both theoretically and experimentally, which is based on a helical long period fiber grating (HLPG…
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