2,444 research outputs found
Contrastive Masked Autoencoders for Self-Supervised Video Hashing
Self-Supervised Video Hashing (SSVH) models learn to generate short binary
representations for videos without ground-truth supervision, facilitating
large-scale video retrieval efficiency and attracting increasing research
attention. The success of SSVH lies in the understanding of video content and
the ability to capture the semantic relation among unlabeled videos. Typically,
state-of-the-art SSVH methods consider these two points in a two-stage training
pipeline, where they firstly train an auxiliary network by instance-wise
mask-and-predict tasks and secondly train a hashing model to preserve the
pseudo-neighborhood structure transferred from the auxiliary network. This
consecutive training strategy is inflexible and also unnecessary. In this
paper, we propose a simple yet effective one-stage SSVH method called ConMH,
which incorporates video semantic information and video similarity relationship
understanding in a single stage. To capture video semantic information for
better hashing learning, we adopt an encoder-decoder structure to reconstruct
the video from its temporal-masked frames. Particularly, we find that a higher
masking ratio helps video understanding. Besides, we fully exploit the
similarity relationship between videos by maximizing agreement between two
augmented views of a video, which contributes to more discriminative and robust
hash codes. Extensive experiments on three large-scale video datasets (i.e.,
FCVID, ActivityNet and YFCC) indicate that ConMH achieves state-of-the-art
results. Code is available at https://github.com/huangmozhi9527/ConMH.Comment: This work is accepted by the AAAI 2023. 9 pages, 6 figures, 6 table
Terahertz Wave Guiding by Femtosecond Laser Filament in Air
Femtosecond laser filament generates strong terahertz (THz) pulse in air. In
this paper, THz pulse waveform generated by femtosecond laser filament has been
experimentally investigated as a function of the length of the filament.
Superluminal propagation of THz pulse has been uncovered, indicating that the
filament creates a THz waveguide in air. Numerical simulation has confirmed
that the waveguide is formed because of the radially non-uniform refractive
index distribution inside the filament. The underlying physical mechanisms and
the control techniques of this type THz pulse generation method might be
revisited based on our findings. It might also potentially open a new approach
for long-distance propagation of THz wave in air.Comment: 5 pages, 6 figure
Constraint-free Natural Image Reconstruction from fMRI Signals Based on Convolutional Neural Network
In recent years, research on decoding brain activity based on functional
magnetic resonance imaging (fMRI) has made remarkable achievements. However,
constraint-free natural image reconstruction from brain activity is still a
challenge. The existing methods simplified the problem by using semantic prior
information or just reconstructing simple images such as letters and digitals.
Without semantic prior information, we present a novel method to reconstruct
nature images from fMRI signals of human visual cortex based on the computation
model of convolutional neural network (CNN). Firstly, we extracted the units
output of viewed natural images in each layer of a pre-trained CNN as CNN
features. Secondly, we transformed image reconstruction from fMRI signals into
the problem of CNN feature visualizations by training a sparse linear
regression to map from the fMRI patterns to CNN features. By iteratively
optimization to find the matched image, whose CNN unit features become most
similar to those predicted from the brain activity, we finally achieved the
promising results for the challenging constraint-free natural image
reconstruction. As there was no use of semantic prior information of the
stimuli when training decoding model, any category of images (not constraint by
the training set) could be reconstructed theoretically. We found that the
reconstructed images resembled the natural stimuli, especially in position and
shape. The experimental results suggest that hierarchical visual features can
effectively express the visual perception process of human brain
Quantitative analysis of the genes affecting development of the hypopharyngeal gland in honey bees (Apis mellifera L.)
Royal jelly has many important biological functions, however the molecular mechanism of royal jelly secretion in hypopharyngeal gland (HG) is still not well understood. In our previously study, six genes (SV2C, eIF-4E, PDK1, IMP, cell growth-regulating nucleolar protein and TGF-βR1) have been shown to might be associated with royal jelly secretion. In this study, the relative expression levels of these genes were examined in the hypopharyngeal gland of workers at different developmental stages (nurse, forager and reversed nurse stages). The results indicated that the relative expression levels of SV2C, eIF-4E, IMP, cell growth-regulating nucleolar protein and TGF-βR1 were reversed at reversed nurse stage compared to forager stage. We concluded that these genes are possibly candidates related to hypopharyngeal gland development or royal jelly secretion
Robust H
The robust H∞ filtering problem for a class of network-based systems with random sensor delay is investigated. The sensor delay is supposed to be a stochastic variable satisfying Bernoulli binary distribution. Using the Lyapunov function and Wirtinger’s inequality approach, the sufficient conditions are derived to ensure that the filtering error systems are exponentially stable with a prescribed H∞ disturbance attenuation level and the filter design method is proposed in terms of linear matrix inequalities. The effectiveness of the proposed method is illustrated by a numerical example
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