420 research outputs found
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Visualizing Morphogenesis through Instability Formation in 4-D Printing.
Heterogeneous growth in a myriad of biological systems can lead to the formation of distinct morphologies during the maturation processes of different species. We demonstrate that the distinct circumferential buckling observed in pumpkins can be reproduced by a core-shell barrel structure using four-dimensional (4D) printing, taking advantage of digital light processing (DLP)-based three-dimensional (3D) printing and stimulus-responsive hydrogels. The mechanical mismatch between the stiff core and compliant shell results in buckling instability on the surface. The initiation and development of the buckling are governed by the ratio of core/shell radius, the ratio of core/shell swelling ratios, and the mismatch between the core and shell in stiffness. Furthermore, the rigid core not only acts as a source of circumferential confinement but also sets a boundary at the poles of the entire structure. The heterogeneous structures with controllable buckling geometrically and structurally behave much like plants' fruits. This replicates the biological morphologic change and elucidates the general mechanism and dynamics of the complex instability formation of heterogeneous 3D objects
Neuronal Circuitry Mechanisms Regulating Adult Mammalian Neurogenesis
The adult mammalian brain is a dynamic structure, capable of remodeling in response to various physiological and pathological stimuli. One dramatic example of brain plasticity is the birth and subsequent integration of newborn neurons into the existing circuitry. This process, termed adult neurogenesis, recapitulates neural developmental events in two specialized adult brain regions: the lateral ventricles of the forebrain. Recent studies have begun to delineate how the existing neuronal circuits influence the dynamic process of adult neurogenesis, from activation of quiescent neural stem cells (NSCs) to the integration and survival of newborn neurons. Here, we review recent progress toward understanding the circuit-based regulation of adult neurogenesis in the hippocampus and olfactory bulb
FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems
Bird's eye view (BEV) perception is becoming increasingly important in the
field of autonomous driving. It uses multi-view camera data to learn a
transformer model that directly projects the perception of the road environment
onto the BEV perspective. However, training a transformer model often requires
a large amount of data, and as camera data for road traffic are often private,
they are typically not shared. Federated learning offers a solution that
enables clients to collaborate and train models without exchanging data but
model parameters. In this paper, we introduce FedBEVT, a federated transformer
learning approach for BEV perception. In order to address two common data
heterogeneity issues in FedBEVT: (i) diverse sensor poses, and (ii) varying
sensor numbers in perception systems, we propose two approaches -- Federated
Learning with Camera-Attentive Personalization (FedCaP) and Adaptive
Multi-Camera Masking (AMCM), respectively. To evaluate our method in real-world
settings, we create a dataset consisting of four typical federated use cases.
Our findings suggest that FedBEVT outperforms the baseline approaches in all
four use cases, demonstrating the potential of our approach for improving BEV
perception in autonomous driving.Comment: Accepted by IEEE T-IV. Code: https://github.com/rruisong/FedBEV
Dual-mode adaptive-SVD ghost imaging
In this paper, we present a dual-mode adaptive singular value decomposition
ghost imaging (A-SVD GI), which can be easily switched between the modes of
imaging and edge detection. It can adaptively localize the foreground pixels
via a threshold selection method. Then only the foreground region is
illuminated by the singular value decomposition (SVD) - based patterns,
consequently retrieving high-quality images with fewer sampling ratios. By
changing the selecting range of foreground pixels, the A-SVD GI can be switched
to the mode of edge detection to directly reveal the edge of objects, without
needing the original image. We investigate the performance of these two modes
through both numerical simulations and experiments. We also develop a
single-round scheme to halve measurement numbers in experiments, instead of
separately illuminating positive and negative patterns in traditional methods.
The binarized SVD patterns, generated by the spatial dithering method, are
modulated by a digital micromirror device (DMD) to speed up the data
acquisition. This dual-mode A-SVD GI can be applied in various applications,
such as remote sensing or target recognition, and could be further extended for
multi-modality functional imaging/detection
DualMix: Unleashing the Potential of Data Augmentation for Online Class-Incremental Learning
Online Class-Incremental (OCI) learning has sparked new approaches to expand
the previously trained model knowledge from sequentially arriving data streams
with new classes. Unfortunately, OCI learning can suffer from catastrophic
forgetting (CF) as the decision boundaries for old classes can become
inaccurate when perturbated by new ones. Existing literature have applied the
data augmentation (DA) to alleviate the model forgetting, while the role of DA
in OCI has not been well understood so far. In this paper, we theoretically
show that augmented samples with lower correlation to the original data are
more effective in preventing forgetting. However, aggressive augmentation may
also reduce the consistency between data and corresponding labels, which
motivates us to exploit proper DA to boost the OCI performance and prevent the
CF problem. We propose the Enhanced Mixup (EnMix) method that mixes the
augmented samples and their labels simultaneously, which is shown to enhance
the sample diversity while maintaining strong consistency with corresponding
labels. Further, to solve the class imbalance problem, we design an Adaptive
Mixup (AdpMix) method to calibrate the decision boundaries by mixing samples
from both old and new classes and dynamically adjusting the label mixing ratio.
Our approach is demonstrated to be effective on several benchmark datasets
through extensive experiments, and it is shown to be compatible with other
replay-based techniques.Comment: 10 pages, 7 figures and 3 table
Interfacing Nickel Nitride and Nickel Boosts Both Electrocatalytic Hydrogen Evolution and Oxidation Reactions
Electrocatalysts of the hydrogen evolution and oxidation reactions (HER and HOR) are of critical importance for the realization of future hydrogen economy. In order to make electrocatalysts economically competitive for large-scale applications, increasing attention has been devoted to developing noble metal-free HER and HOR electrocatalysts especially for alkaline electrolytes due to the promise of emerging hydroxide exchange membrane fuel cells. Herein, we report that interface engineering of Ni3N and Ni results in a unique Ni3N/Ni electrocatalyst which exhibits exceptional HER/HOR activities in aqueous electrolytes. A systematic electrochemical study was carried out to investigate the superior hydrogen electrochemistry catalyzed by Ni3N/Ni, including nearly zero overpotential of catalytic onset, robust long-term durability, unity Faradaic efficiency, and excellent CO tolerance. Density functional theory computations were performed to aid the understanding of the electrochemical results and suggested that the real active sites are located at the interface between Ni3N and Ni
Interfacing Nickel Nitride and Nickel Boosts Both Electrocatalytic Hydrogen Evolution and Oxidation Reactions
Electrocatalysts of the hydrogen evolution and oxidation reactions (HER and HOR) are of critical importance for the realization of future hydrogen economy. In order to make electrocatalysts economically competitive for large-scale applications, increasing attention has been devoted to developing noble metal-free HER and HOR electrocatalysts especially for alkaline electrolytes due to the promise of emerging hydroxide exchange membrane fuel cells. Herein, we report that interface engineering of Ni3N and Ni results in a unique Ni3N/Ni electrocatalyst which exhibits exceptional HER/HOR activities in aqueous electrolytes. A systematic electrochemical study was carried out to investigate the superior hydrogen electrochemistry catalyzed by Ni3N/Ni, including nearly zero overpotential of catalytic onset, robust long-term durability, unity Faradaic efficiency, and excellent CO tolerance. Density functional theory computations were performed to aid the understanding of the electrochemical results and suggested that the real active sites are located at the interface between Ni3N and Ni
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