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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 Gas extraction from the Groningen gas reservoir, located in the northeastern Netherlands, has led to a drop in pressure and drove compaction and induced seismicity. Stress-based models have shown success in forecasting induced seismicity in this particular context and elsewhere, but they generally assume that earthquake clustering is negligible. To assess earthquake clustering at Groningen, we generate an enhanced seismicity catalog using a deep-learning-based workflow. We identify and locate 1369 events between 2015 and 2022, including 660 newly detected events not previously identified by the standard catalog from the Royal Netherlands Meteorological Institute. Using the nearest-neighbor distance approach, we find that 72% of events are background independent events, whereas the remaining 28% belong to clusters. The 55% of the clustered events are swarm-like, whereas the rest are aftershock-like. Among the swarms include five newly identified sequences propagating at high velocities between 3 and 50 km/day along directions that do not follow mapped faults or existing structures and frequently exhibit a sharp turn in the middle of the sequence. The swarms occurred around the time of the maximum compaction rate between November 2016 and May 2017 in the Zechstein layer, above the anhydrite caprock, and well-above the directly induced earthquakes that occur within the reservoir and caprock. We suggest that these swarms are related to the aseismic deformation within the salt formation rather than fluids. This study suggests that the propagating swarms do not always signify fluid migration. 

Abstract A variety of geo‐energy operations involve extraction or injections of fluids, including hydrocarbon production or storage, hydrogen storage, CO₂sequestration, and geothermal energy production. The surface deformation resulting from such operations can be a source of information on reservoir geomechanical properties as we show in this study. We analyze the time‐dependent surface deformation in the Groningen region in northeastern Netherlands using a comprehensive geodetic data set, which includes InSAR (Radarsat2, TerraSAR‐X, Sentinel‐1), GNSS, and optical leveling spanning several decades. We resort to an Independent Component Analysis (ICA) to isolate deformation signals of various origins. The signals related to gas production from the Groningen gas field and from seasonal storage at Norg Underground Gas Storage are clearly revealed. Surface deformation associated to the Groningen reservoir show decadal subsidence, with spatially variable subsidence rates dictated by local compressibility. The ICA reveals distinct seasonal fluctuations at Norg, closely mirroring the variations of gas storage. By comparing the observed long‐term subsidence within the Groningen reservoir and seasonal oscillations at Norg from a linear poroelastic compaction model, we quantify the fraction of inelastic deformation of the reservoir in space and time and constrain the reservoir compressibility. In Groningen, increased compressibility indicates inelastic compaction that has built over time and might account for as much as 20% of the total compaction cumulated until 2021, while Norg shows no signs of inelastic deformation and a constant compressibility. This study provides a methodology to monitor and calibrate models of the subsurface deformation induced by geo‐energy operations or aquifer management. 

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.