222 research outputs found
Deep Planar Parallax for Monocular Depth Estimation
Recent research has highlighted the utility of Planar Parallax Geometry in
monocular depth estimation. However, its potential has yet to be fully realized
because networks rely heavily on appearance for depth prediction. Our in-depth
analysis reveals that utilizing flow-pretrain can optimize the network's usage
of consecutive frame modeling, leading to substantial performance enhancement.
Additionally, we propose Planar Position Embedding (PPE) to handle dynamic
objects that defy static scene assumptions and to tackle slope variations that
are challenging to differentiate. Comprehensive experiments on autonomous
driving datasets, namely KITTI and the Waymo Open Dataset (WOD), prove that our
Planar Parallax Network (PPNet) significantly surpasses existing learning-based
methods in performance
Adversarial Purification of Information Masking
Adversarial attacks meticulously generate minuscule, imperceptible
perturbations to images to deceive neural networks. Counteracting these,
adversarial purification methods seek to transform adversarial input samples
into clean output images to defend against adversarial attacks. Nonetheless,
extent generative models fail to effectively eliminate adversarial
perturbations, yielding less-than-ideal purification results. We emphasize the
potential threat of residual adversarial perturbations to target models,
quantitatively establishing a relationship between perturbation scale and
attack capability. Notably, the residual perturbations on the purified image
primarily stem from the same-position patch and similar patches of the
adversarial sample. We propose a novel adversarial purification approach named
Information Mask Purification (IMPure), aims to extensively eliminate
adversarial perturbations. To obtain an adversarial sample, we first mask part
of the patches information, then reconstruct the patches to resist adversarial
perturbations from the patches. We reconstruct all patches in parallel to
obtain a cohesive image. Then, in order to protect the purified samples against
potential similar regional perturbations, we simulate this risk by randomly
mixing the purified samples with the input samples before inputting them into
the feature extraction network. Finally, we establish a combined constraint of
pixel loss and perceptual loss to augment the model's reconstruction
adaptability. Extensive experiments on the ImageNet dataset with three
classifier models demonstrate that our approach achieves state-of-the-art
results against nine adversarial attack methods. Implementation code and
pre-trained weights can be accessed at
\textcolor{blue}{https://github.com/NoWindButRain/IMPure}
Network analysis on cortical morphometry in first-episode schizophrenia
First-episode schizophrenia (FES) results in abnormality of brain
connectivity at different levels. Despite some successful findings on
functional and structural connectivity of FES, relatively few studies have been
focused on morphological connectivity, which may provide a potential biomarker
for FES. In this study, we aim to investigate cortical morphological
connectivity in FES. T1-weighted magnetic resonance image data from 92 FES
patients and 106 healthy controls (HCs) are analyzed.We parcellate brain into
68 cortical regions, calculate the averaged thickness and surface area of each
region, construct undirected networks by correlating cortical thickness or
surface area measures across 68 regions for each group, and finally compute a
variety of network-related topology characteristics. Our experimental results
show that both the cortical thickness network and the surface area network in
two groups are small-world networks; that is, those networks have high
clustering coefficients and low characteristic path lengths. At certain network
sparsity levels, both the cortical thickness network and the surface area
network of FES have significantly lower clustering coefficients and local
efficiencies than those of HC, indicating FES-related abnormalities in local
connectivity and small-worldness. These abnormalities mainly involve the
frontal, parietal, and temporal lobes. Further regional analyses confirm
significant group differences in the node betweenness of the posterior
cingulate gyrus for both the cortical thickness network and the surface area
network. Our work supports that cortical morphological connectivity, which is
constructed based on correlations across subjects' cortical thickness, may
serve as a tool to study topological abnormalities in neurological disorders
Deep learning acceleration of iterative model-based light fluence correction for photoacoustic tomography
Photoacoustic tomography (PAT) is a promising imaging technique that can
visualize the distribution of chromophores within biological tissue. However,
the accuracy of PAT imaging is compromised by light fluence (LF), which hinders
the quantification of light absorbers. Currently, model-based iterative methods
are used for LF correction, but they require significant computational
resources due to repeated LF estimation based on differential light transport
models. To improve LF correction efficiency, we propose to use Fourier neural
operator (FNO), a neural network specially designed for solving differential
equations, to learn the forward projection of light transport in PAT. Trained
using paired finite-element-based LF simulation data, our FNO model replaces
the traditional computational heavy LF estimator during iterative correction,
such that the correction procedure is significantly accelerated. Simulation and
experimental results demonstrate that our method achieves comparable LF
correction quality to traditional iterative methods while reducing the
correction time by over 30 times
Cascaded Detail-Preserving Networks for Super-Resolution of Document Images
The accuracy of OCR is usually affected by the quality of the input document
image and different kinds of marred document images hamper the OCR results.
Among these scenarios, the low-resolution image is a common and challenging
case. In this paper, we propose the cascaded networks for document image
super-resolution. Our model is composed by the Detail-Preserving Networks with
small magnification. The loss function with perceptual terms is designed to
simultaneously preserve the original patterns and enhance the edge of the
characters. These networks are trained with the same architecture and different
parameters and then assembled into a pipeline model with a larger
magnification. The low-resolution images can upscale gradually by passing
through each Detail-Preserving Network until the final high-resolution images.
Through extensive experiments on two scanning document image datasets, we
demonstrate that the proposed approach outperforms recent state-of-the-art
image super-resolution methods, and combining it with standard OCR system lead
to signification improvements on the recognition results
Linoleic acid participates in the response to ischemic brain injury through oxidized metabolites that regulate neurotransmission.
Linoleic acid (LA; 18:2 n-6), the most abundant polyunsaturated fatty acid in the US diet, is a precursor to oxidized metabolites that have unknown roles in the brain. Here, we show that oxidized LA-derived metabolites accumulate in several rat brain regions during CO2-induced ischemia and that LA-derived 13-hydroxyoctadecadienoic acid, but not LA, increase somatic paired-pulse facilitation in rat hippocampus by 80%, suggesting bioactivity. This study provides new evidence that LA participates in the response to ischemia-induced brain injury through oxidized metabolites that regulate neurotransmission. Targeting this pathway may be therapeutically relevant for ischemia-related conditions such as stroke
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