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The rapid changes in the global environment have led to an unprecedented decline in biodiversity, with over 28% of species facing extinction. This includes snakes, which are key to ecological balance. Detecting snakes is challenging due to their camouflage and elusive nature, causing data loss and feature extraction difficulties in ecological monitoring.

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Visual attention is intrinsically rhythmic and oscillates based on the discrete sampling of either single or multiple objects. Recently, studies have found that the early visual cortex (V1/V2) modulates attentional rhythms. Both monocular and binocular cells are present in the early visual cortex, which acts as a transfer station for transformation of the monocular visual pathway into the binocular visual pathway.

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Identity Model Transformation for boosting performance and efficiency in object detection network.

Neural Netw

December 2024

The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan, 430079, China. Electronic address:

Modifying the structure of an existing network is a common method to further improve the performance of the network. However, modifying some layers in network often results in pre-trained weight mismatch, and fine-tune process is time-consuming and resource-inefficient. To address this issue, we propose a novel technique called Identity Model Transformation (IMT), which keep the output before and after transformation in an equal form by rigorous algebraic transformations.

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Frictional motion is mediated by rapidly propagating ruptures that detach the ensemble of contacts forming the frictional interface between contacting bodies. These ruptures are similar to shear cracks. When this process takes place in natural faults, these rapid ruptures are essentially earthquakes.

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Task relevant autoencoding enhances machine learning for human neuroscience.

Sci Rep

January 2025

Department of Cognitive Sciences, University of California, 2201 Social & Behavioral Sciences Gateway, Irvine, CA, 92697, USA.

In human neuroscience, machine learning can help reveal lower-dimensional neural representations relevant to subjects' behavior. However, state-of-the-art models typically require large datasets to train, and so are prone to overfitting on human neuroimaging data that often possess few samples but many input dimensions. Here, we capitalized on the fact that the features we seek in human neuroscience are precisely those relevant to subjects' behavior rather than noise or other irrelevant factors.

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