249 research outputs found
Efficient HDR Reconstruction from Real-World Raw Images
High dynamic range (HDR) imaging is still a significant yet challenging
problem due to the limited dynamic range of generic image sensors. Most
existing learning-based HDR reconstruction methods take a set of
bracketed-exposure sRGB images to extend the dynamic range, and thus are
computational- and memory-inefficient by requiring the Image Signal Processor
(ISP) to produce multiple sRGB images from the raw ones. In this paper, we
propose to broaden the dynamic range from the raw inputs and perform only one
ISP processing for the reconstructed HDR raw image. Our key insights are
threefold: (1) we design a new computational raw HDR data formation pipeline
and construct the first real-world raw HDR dataset, RealRaw-HDR; (2) we develop
a lightweight-efficient HDR model, RepUNet, using the structural
re-parameterization technique; (3) we propose a plug-and-play motion alignment
loss to mitigate motion misalignment between short- and long-exposure images.
Extensive experiments demonstrate that our approach achieves state-of-the-art
performance in both visual quality and quantitative metrics
Event-triggered communication for passivity and synchronisation of multi-weighted coupled neural networks with and without parameter uncertainties
A multi-weighted coupled neural networks (MWCNNs) model with event-triggered communication is studied here. On the one hand, the passivity of the presented network model is studied by utilising Lyapunov stability theory and some inequality techniques, and a synchronisation criterion based on the obtained output-strict passivity condition of MWCNNs with eventtriggered communication is derived. On the other hand, some robust passivity and robust synchronisation criteria based on output-strict passivity of the proposed network with uncertain parameters are presented. At last, two numerical examples are provided to testify the effectiveness of the output-strict passivity and robust synchronisation results
Towards Mitigating Spurious Correlations in the Wild: A Benchmark and a more Realistic Dataset
Deep neural networks often exploit non-predictive features that are
spuriously correlated with class labels, leading to poor performance on groups
of examples without such features. Despite the growing body of recent works on
remedying spurious correlations, the lack of a standardized benchmark hinders
reproducible evaluation and comparison of the proposed solutions. To address
this, we present SpuCo, a python package with modular implementations of
state-of-the-art solutions enabling easy and reproducible evaluation of current
methods. Using SpuCo, we demonstrate the limitations of existing datasets and
evaluation schemes in validating the learning of predictive features over
spurious ones. To overcome these limitations, we propose two new vision
datasets: (1) SpuCoMNIST, a synthetic dataset that enables simulating the
effect of real world data properties e.g. difficulty of learning spurious
feature, as well as noise in the labels and features; (2) SpuCoAnimals, a
large-scale dataset curated from ImageNet that captures spurious correlations
in the wild much more closely than existing datasets. These contributions
highlight the shortcomings of current methods and provide a direction for
future research in tackling spurious correlations. SpuCo, containing the
benchmark and datasets, can be found at https://github.com/BigML-CS-UCLA/SpuCo,
with detailed documentation available at
https://spuco.readthedocs.io/en/latest/.Comment: Package: https://github.com/BigML-CS-UCLA/SpuC
Realization of a three-dimensional photonic topological insulator
Confining photons in a finite volume is in high demand in modern photonic
devices. This motivated decades ago the invention of photonic crystals,
featured with a photonic bandgap forbidding light propagation in all
directions. Recently, inspired by the discoveries of topological insulators
(TIs), the confinement of photons with topological protection has been
demonstrated in two-dimensional (2D) photonic structures known as photonic TIs,
with promising applications in topological lasers and robust optical delay
lines. However, a fully three-dimensional (3D) topological photonic bandgap has
never before been achieved. Here, we experimentally demonstrate a 3D photonic
TI with an extremely wide (> 25% bandwidth) 3D topological bandgap. The sample
consists of split-ring resonators (SRRs) with strong magneto-electric coupling
and behaves as a 'weak TI', or a stack of 2D quantum spin Hall insulators.
Using direct field measurements, we map out both the gapped bulk bandstructure
and the Dirac-like dispersion of the photonic surface states, and demonstrate
robust photonic propagation along a non-planar surface. Our work extends the
family of 3D TIs from fermions to bosons and paves the way for applications in
topological photonic cavities, circuits, and lasers in 3D geometries
Valley-Hall photonic topological insulators with dual-band kink states
Extensive researches have revealed that valley, a binary degree of freedom
(DOF), can be an excellent candidate of information carrier. Recently, valley
DOF has been introduced into photonic systems, and several valley-Hall photonic
topological insulators (PTIs) have been experimentally demonstrated. However,
in the previous valley-Hall PTIs, topological kink states only work at a single
frequency band, which limits potential applications in multiband waveguides,
filters, communications, and so on. To overcome this challenge, here we
experimentally demonstrate a valley-Hall PTI, where the topological kink states
exist at two separated frequency bands, in a microwave substrate-integrated
circuitry. Both the simulated and experimental results demonstrate the
dual-band valley-Hall topological kink states are robust against the sharp
bends of the internal domain wall with negligible inter-valley scattering. Our
work may pave the way for multi-channel substrate-integrated photonic devices
with high efficiency and high capacity for information communications and
processing
Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in dilated cardiomyopathy with heart failure
The etiologies and pathogenesis of dilated cardiomyopathy (DCM) with heart failure (HF) remain to be defined. Thus, exploring specific diagnosis biomarkers and mechanisms is urgently needed to improve this situation. In this study, three gene expression profiling datasets (GSE29819, GSE21610, GSE17800) and one single-cell RNA sequencing dataset (GSE95140) were obtained from the Gene Expression Omnibus (GEO) database. GSE29819 and GSE21610 were combined into the training group, while GSE17800 was the test group. We used the weighted gene co-expression network analysis (WGCNA) and identified fifteen driver genes highly associated with DCM with HF in the module. We performed the least absolute shrinkage and selection operator (LASSO) on the driver genes and then constructed five machine learning classifiers (random forest, gradient boosting machine, neural network, eXtreme gradient boosting, and support vector machine). Random forest was the best-performing classifier established on five Lasso-selected genes, which was utilized to select out NPPA, OMD, and PRELP for diagnosing DCM with HF. Moreover, we observed the up-regulation mRNA levels and robust diagnostic accuracies of NPPA, OMD, and PRELP in the training group and test group. Single-cell RNA-seq analysis further demonstrated their stable up-regulation expression patterns in various cardiomyocytes of DCM patients. Besides, through gene set enrichment analysis (GSEA), we found TGF-β signaling pathway, correlated with NPPA, OMD, and PRELP, was the underlying mechanism of DCM with HF. Overall, our study revealed NPPA, OMD, and PRELP serving as diagnostic biomarkers for DCM with HF, deepening the understanding of its pathogenesis
Protect Federated Learning Against Backdoor Attacks via Data-Free Trigger Generation
As a distributed machine learning paradigm, Federated Learning (FL) enables
large-scale clients to collaboratively train a model without sharing their raw
data. However, due to the lack of data auditing for untrusted clients, FL is
vulnerable to poisoning attacks, especially backdoor attacks. By using poisoned
data for local training or directly changing the model parameters, attackers
can easily inject backdoors into the model, which can trigger the model to make
misclassification of targeted patterns in images. To address these issues, we
propose a novel data-free trigger-generation-based defense approach based on
the two characteristics of backdoor attacks: i) triggers are learned faster
than normal knowledge, and ii) trigger patterns have a greater effect on image
classification than normal class patterns. Our approach generates the images
with newly learned knowledge by identifying the differences between the old and
new global models, and filters trigger images by evaluating the effect of these
generated images. By using these trigger images, our approach eliminates
poisoned models to ensure the updated global model is benign. Comprehensive
experiments demonstrate that our approach can defend against almost all the
existing types of backdoor attacks and outperform all the seven
state-of-the-art defense methods with both IID and non-IID scenarios.
Especially, our approach can successfully defend against the backdoor attack
even when 80\% of the clients are malicious
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