8 research outputs found
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
Bioinspired Ultrafast-Responsive Nanofluidic System for Ion and Molecule Transport with Speed Control
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 polyÂ(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