185 research outputs found
Free electron emission in vacuum assisted by photonic time crystals
The Cerenkov radiation and the Smith-Purcell effect state that free electron
emission occurs exclusively in dielectrics when the velocity of the particles
exceeds the speed of light in the medium or in the vicinity of periodic
gratings close to each other within a vacuum. We demonstrate that free
electrons in a vacuum can also emit highly directional monochromatic waves when
they are in close proximity to a medium that is periodically modulated
temporally, suggesting the existence of temporal Smith-Purcell effect. The
momentum band gaps of time-varying media, such as photonic time crystals
(PTCs), create new pathways for the injection of external energy, allowing the
frequency, intensity, and spatial distribution of the electromagnetic fields to
be controlled. Moreover, the PTC substrate enables the conversion of localized
evanescent fields into amplified, highly directional propagating plane waves
that are only sensitive to the velocity of particles and the modulation
frequency, which allows us to observe and utilize Cerenkov-like radiation in
free space. Our work exhibits significant opportunities for the utilization of
time-varying structures in various fields, including particle identification,
ultraweak signal detection, and improved radiation source design
Analysis and design of transition radiation in layered uniaxial crystals using Tandem neural networks
With the flourishing development of nanophotonics, Cherenkov radiation
pattern can be designed to achieve superior performance in particle detection
by fine-tuning the properties of metamaterials such as photonic crystals (PCs)
surrounding the swift particle. However, the radiation pattern can be sensitive
to the geometry and material properties of PCs, such as periodicity, unit
thickness, and dielectric fraction, making direct analysis and inverse design
difficult. In this article, we propose a systematic method to analyze and
design PC-based transition radiation, which is assisted by deep learning neural
networks. By matching boundary conditions at the interfaces, Cherenkov-like
radiation of multilayered structures can be resolved analytically using the
cascading scattering matrix method, despite the optical axes not being aligned
with the swift electron trajectory. Once well trained, forward deep learning
neural networks can be utilized to predict the radiation pattern without
further direct electromagnetic simulations; moreover, Tandem neural networks
have been proposed to inversely design the geometry and/or material properties
for desired Cherenkov radiation pattern. Our proposal demonstrates a promising
strategy for dealing with layered-medium-based Cherenkov radiation detectors,
and it can be extended for other emerging metamaterials, such as photonic time
crystals
Privacy Preserving Text Recognition with Gradient-Boosting for Federated Learning
Typical machine learning approaches require centralized data for model
training, which may not be possible where restrictions on data sharing are in
place due to, for instance, privacy protection. The recently proposed Federated
Learning (FL) frame-work allows learning a shared model collaboratively without
data being centralized or data sharing among data owners. However, we show in
this paper that the generalization ability of the joint model is poor on
Non-Independent and Non-Identically Dis-tributed (Non-IID) data, particularly
when the Federated Averaging (FedAvg) strategy is used in this collaborative
learning framework thanks to the weight divergence phenomenon. We propose a
novel boosting algorithm for FL to address this generalisation issue, as well
as achieving much faster convergence in gradient based optimization. We
demonstrate our Federated Boosting (FedBoost) method on privacy-preserved text
recognition, which shows significant improvements in both performance and
efficiency. The text images are based on publicly available datasets for fair
comparison and we intend to make our implementation public to ensure
reproducibility.Comment: The paper has been submitted to BMVC2020 on April 30t
Multiple Partial Regularized Nonnegative Matrix Factorization for Predicting Ontological Functions of lncRNAs
Long non-coding RNAs (LncRNA) are critical regulators for biological processes, which are highly related to complex diseases. Even though the next generation sequence technology facilitates the discovery of a great number of lncRNAs, the knowledge about the functions of lncRNAs is limited. Thus, it is promising to predict the functions of lncRNAs, which shed light on revealing the mechanisms of complex diseases. The current algorithms predict the functions of lncRNA by using the features of protein-coding genes. Generally speaking, these algorithms fuse heterogeneous genomic data to construct lncRNA-gene associations via a linear combination, which cannot fully characterize the function-lncRNA relations. To overcome this issue, we present an nonnegative matrix factorization algorithm with multiple partial regularization (aka MPrNMF) to predict the functions of lncRNAs without fusing the heterogeneous genomic data. In details, for each type of genomic data, we construct the lncRNA-gene associations, resulting in multiple associations. The proposed method integrates separately them via regularization strategy, rather than fuse them into a single type of associations. The results demonstrate that the proposed algorithm outperforms state-of-the-art methods based network-analysis. The model and algorithm provide an effective way to explore the functions of lncRNAs
Image restoration with group sparse representation and low‐rank group residual learning
Image restoration, as a fundamental research topic of image processing, is to reconstruct the original image from degraded signal using the prior knowledge of image. Group sparse representation (GSR) is powerful for image restoration; it however often leads to undesirable sparse solutions in practice. In order to improve the quality of image restoration based on GSR, the sparsity residual model expects the representation learned from degraded images to be as close as possible to the true representation. In this article, a group residual learning based on low-rank self-representation is proposed to automatically estimate the true group sparse representation. It makes full use of the relation among patches and explores the subgroup structures within the same group, which makes the sparse residual model have better interpretation furthermore, results in high-quality restored images. Extensive experimental results on two typical image restoration tasks (image denoising and deblocking) demonstrate that the proposed algorithm outperforms many other popular or state-of-the-art image restoration methods
Identifying noncoding risk variants using disease-relevant gene regulatory networks.
Identifying noncoding risk variants remains a challenging task. Because noncoding variants exert their effects in the context of a gene regulatory network (GRN), we hypothesize that explicit use of disease-relevant GRNs can significantly improve the inference accuracy of noncoding risk variants. We describe Annotation of Regulatory Variants using Integrated Networks (ARVIN), a general computational framework for predicting causal noncoding variants. It employs a set of novel regulatory network-based features, combined with sequence-based features to infer noncoding risk variants. Using known causal variants in gene promoters and enhancers in a number of diseases, we show ARVIN outperforms state-of-the-art methods that use sequence-based features alone. Additional experimental validation using reporter assay further demonstrates the accuracy of ARVIN. Application of ARVIN to seven autoimmune diseases provides a holistic view of the gene subnetwork perturbed by the combinatorial action of the entire set of risk noncoding mutations. Nat Commun 2018 Feb 16; 9(1):702
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