A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation

Due to the abundant neurophysiological information in the electroencephalogram (EEG) signal, EEG signals integrated with deep learning methods have gained substantial traction across numerous real-world tasks. However, the development of supervised learning methods based on EEG signals has been hindered by the high cost and significant label discrepancies to manually label large-scale EEG datasets. Self-supervised frameworks are adopted in vision and language fields to solve this issue, but the lack of EEG-specific theoretical foundations hampers their applicability across various tasks. To solve these challenges, this paper proposes a knowledge-driven cross-view contrastive learning framework (KDC2), which integrates neurological theory to extract effective representations from EEG with limited labels. The KDC2 method creates scalp and neural views of EEG signals, simulating the internal and external representation of brain activity. Sequentially, inter-view and cross-view contrastive learning pipelines in combination with various augmentation methods are applied to capture neural features from different views. By modeling prior neural knowledge based on homologous neural information consistency theory, the proposed method extracts invariant and complementary neural knowledge to generate combined representations. Experimental results on different downstream tasks demonstrate that our method outperforms state-of-the-art methods, highlighting the superior generalization of neural knowledge-supported EEG representations across various brain tasks.Comment: 14pages,7 figure

Recalibrating Features and Regression for Oriented Object Detection

The objects in remote sensing images are normally densely packed, arbitrarily oriented, and surrounded by complex backgrounds. Great efforts have been devoted to developing oriented object detection models to accommodate such data characteristics. We argue that an effective detection model hinges on three aspects: feature enhancement, feature decoupling for classification and localization, and an appropriate bounding box regression scheme. In this article, we instantiate the three aspects on top of the classical Faster R-CNN, with three novel components proposed. First, we propose a weighted fusion and refinement (WFR) module, which adaptively weighs multi-level features and leverages the attention mechanism to refine the fused features. Second, we decouple the RoI (region of interest) features for the subsequent classification and localization via a lightweight affine transformation-based feature decoupling (ATFD) module. Third, we propose a post-classification regression (PCR) module for generating the desired quadrilateral bounding boxes. Specifically, PCR predicts the precise vertex location on each side of a predicted horizontal box, by simply learning the following: (i) classify the discretized regression range of the vertex, and (ii) revise the vertex location with an offset. We conduct extensive experiments on the DOTA, DIOR-R, and HRSC2016 datasets to evaluate our method

Direct observation of spreading precursor liquids in a corner

Precursor liquid is a nanoscale liquid creeping ahead of the macroscopic edge of spreading liquids, whose behaviors tightly correlate with the three-phase reaction efficiency and patterning accuracy. However, the important spatial-temporal characteristic of the precursor liquid still remains obscure because its real-time spreading process has not been directly observed. Here, we report that the spreading ionic liquid precursors in a silicon corner can be directly captured on video using in situ scanning electron microscopy. In situ spreading videos show that the precursor liquid spreads linearly over time (Delta L similar to Delta T) rather than obeying the classic Lucas-Washburn law (l similar to t(1/2)) and possesses a characteristic width of similar to 250-310 nm. Theoretical analyses and molecular dynamics simulations demonstrate that the unique behaviors of precursor liquids originate from the competing effect of van der Waals force and surface energy. These findings provide avenues for directly observing liquid/solid interfacial phenomena on a microscopic level

Direct observation of spreading precursor liquids in a corner

Precursor liquid is a nanoscale liquid creeping ahead of the macroscopic edge of spreading liquids, whose behaviors tightly correlate with the three-phase reaction efficiency and patterning accuracy. However, the important spatial-temporal characteristic of the precursor liquid still remains obscure because its real-time spreading process has not been directly observed. Here, we report that the spreading ionic liquid precursors in a silicon corner can be directly captured on video using in situ scanning electron microscopy. In situ spreading videos show that the precursor liquid spreads linearly over time (Delta L similar to Delta T) rather than obeying the classic Lucas-Washburn law (l similar to t(1/2)) and possesses a characteristic width of similar to 250-310 nm. Theoretical analyses and molecular dynamics simulations demonstrate that the unique behaviors of precursor liquids originate from the competing effect of van der Waals force and surface energy. These findings provide avenues for directly observing liquid/solid interfacial phenomena on a microscopic level

Foolproof Method for Fast and Reversible Switching of Water-Droplet Adhesion by Magnetic Gradients

Reversible switching of water-droplet adhesion on solid surfaces is of great significance for smart devices, such as microfluidics. In this work, we designed a foolproof method for fast and reversible magnet-controlled switching of water-droplet adhesion surfaces by doping iron powders in soft poly­(dimethylsiloxane). The water adhesion is adjusted by magnetic field-induced structure changes, avoiding complex chemical or physical surface design. The regulation process is so convenient that only tens of milliseconds are needed. The on-site responsive mechanism extends its use to unusual curved surfaces. Moreover, the excellent reversibility and stability make the film an ideal candidate for real-time applications