Search for: All records

A brain–computer interface (BCI) provides a direct communication pathway between the human brain and external devices, enabling users to control them through thought. It records brain signals and classifies them into specific commands for external devices. Among various classifiers used in BCI, those directly classifying covariance matrices using Riemannian geometry find broad applications not only in BCI, but also in diverse fields such as computer vision, natural language processing, domain adaption, and remote sensing. However, the existing Riemannian-based methods exhibit limitations, including time-intensive computations, susceptibility to disturbances, and convergence challenges in scenarios involving high-dimensional matrices. In this paper, we tackle these issues by introducing the Bures–Wasserstein (BW) distance for covariance matrices analysis and demonstrating its advantages in BCI applications. Both theoretical and computational aspects of BW distance are investigated, along with algorithms for Fréchet Mean (or barycenter) estimation using BW distance. Extensive simulations are conducted to evaluate the effectiveness, efficiency, and robustness of the BW distance and barycenter. Additionally, by integrating BW barycenter into the Minimum Distance to Riemannian Mean classifier, we showcase its superior classification performance through evaluations on five real datasets. 

Abstract Applications of RNA-based molecular logic have been hampered by sequence constraints imposed on the input and output of the circuits. Here we show that the sequence constraints can be substantially reduced by appropriately encoded multi-arm junctions of single-stranded RNA structures. To conditionally activate RNA translation, we integrated multi-arm junctions, self-assembled upstream of a regulated gene and designed to unfold sequentially in response to different RNA inputs, with motifs of loop-initiated RNA activators that function independently of the sequence of the input RNAs and that reduce interference with the output gene. We used the integrated RNA system and sequence-independent input RNAs to execute two-input and three-input OR and AND logic in Escherichia coli , and designed paper-based cell-free colourimetric assays that accurately identified two human immunodeficiency virus (HIV) subtypes (by executing OR logic) in amplified synthetic HIV RNA as well as severe acute respiratory syndrome coronavirus-2 (via two-input AND logic) in amplified RNA from saliva samples. The sequence-independent molecular logic enabled by the integration of multi-arm junction RNAs with motifs for loop-initiated RNA activators may be broadly applicable in biotechnology. 

Abstract The Calaveras Fault (CF) branches from the San Andreas Fault (SAF) near San Benito, extending sub‐parallel to the SAF for about 50 km with only 2–6 km separation and diverging northeastward. Both the SAF and CF are partially coupled, exhibit spatially variable aseismic creep and have hosted moderate to large earthquakes in recent decades. Understanding how slip partitions among the main fault strands of the SAF system and establishing their degree of coupling is crucial for seismic hazard evaluation. We perform a timeseries analysis using more than 5 years of Sentinel‐1 data covering the Bay Area (May 2015–October 2020), specifically targeting the spatiotemporal variations of creep rates around the SAF‐CF junction. We derive the surface creep rates from cross‐fault InSAR timeseries differences along the SAF and CF including adjacent Sargent and Quien Sabe Faults. We show that the variable creep rates (0–20 mm/yr) at the SAF‐CF junction are to first order controlled by the angle between the fault strike and the background stress orientation. We further examine the spatiotemporal variation of creep rates along the SAF and CF and find a multi‐annual coupling increase during 2016–2018 the subparallel sections of both faults, with the CF coupling change lagging behind the SAF by 3–6 months. Similar temporal variations are also observed in bothb‐values inferred from declustered seismicity and aseismic slip rates inferred from characteristic repeating earthquakes.