1,539 research outputs found
LenSiam: Self-Supervised Learning on Strong Gravitational Lens Images
Self-supervised learning has been known for learning good representations
from data without the need for annotated labels. We explore the simple siamese
(SimSiam) architecture for representation learning on strong gravitational lens
images. Commonly used image augmentations tend to change lens properties; for
example, zoom-in would affect the Einstein radius. To create image pairs
representing the same underlying lens model, we introduce a lens augmentation
method to preserve lens properties by fixing the lens model while varying the
source galaxies. Our research demonstrates this lens augmentation works well
with SimSiam for learning the lens image representation without labels, so we
name it LenSiam. We also show that a pre-trained LenSiam model can benefit
downstream tasks. We open-source our code and datasets at
https://github.com/kuanweih/LenSiam .Comment: 5 pages, 2 figures. Accepted by NeurIPS 2023 AI for Science Worksho
SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers
We investigate Siamese networks for learning related embeddings for augmented
samples of molecular conformers. We find that a non-contrastive (positive-pair
only) auxiliary task aids in supervised training of Euclidean neural networks
(E3NNs) and increases manifold smoothness (MS) around point-cloud geometries.
We demonstrate this property for multiple drug-activity prediction tasks while
maintaining relevant performance metrics, and propose an extension of MS to
probabilistic and regression settings. We provide an analysis of representation
collapse, finding substantial effects of task-weighting, latent dimension, and
regularization. We expect the presented protocol to aid in the development of
reliable E3NNs from molecular conformers, even for small-data drug discovery
programs.Comment: Submitted to the MLDD workshop, ICLR 202
Strong Gravitational Lensing Parameter Estimation with Vision Transformer
Quantifying the parameters and corresponding uncertainties of hundreds of
strongly lensed quasar systems holds the key to resolving one of the most
important scientific questions: the Hubble constant () tension. The
commonly used Markov chain Monte Carlo (MCMC) method has been too
time-consuming to achieve this goal, yet recent work has shown that convolution
neural networks (CNNs) can be an alternative with seven orders of magnitude
improvement in speed. With 31,200 simulated strongly lensed quasar images, we
explore the usage of Vision Transformer (ViT) for simulated strong
gravitational lensing for the first time. We show that ViT could reach
competitive results compared with CNNs, and is specifically good at some
lensing parameters, including the most important mass-related parameters such
as the center of lens and , the ellipticities
and , and the radial power-law slope . With this promising
preliminary result, we believe the ViT (or attention-based) network
architecture can be an important tool for strong lensing science for the next
generation of surveys. The open source of our code and data is in
\url{https://github.com/kuanweih/strong_lensing_vit_resnet}.Comment: Accepted by ECCV 2022 AI for Space Worksho
Detailed Analysis of a Contiguous 22-Mb Region of the Maize Genome
Most of our understanding of plant genome structure and evolution has come from the careful annotation of small (e.g., 100 kb) sequenced genomic regions or from automated annotation of complete genome sequences. Here, we sequenced and carefully annotated a contiguous 22 Mb region of maize chromosome 4 using an improved pseudomolecule for annotation. The sequence segment was comprehensively ordered, oriented, and confirmed using the maize optical map. Nearly 84% of the sequence is composed of transposable elements (TEs) that are mostly nested within each other, of which most families are low-copy. We identified 544 gene models using multiple levels of evidence, as well as five miRNA genes. Gene fragments, many captured by TEs, are prevalent within this region. Elimination of gene redundancy from a tetraploid maize ancestor that originated a few million years ago is responsible in this region for most disruptions of synteny with sorghum and rice. Consistent with other sub-genomic analyses in maize, small RNA mapping showed that many small RNAs match TEs and that most TEs match small RNAs. These results, performed on ∼1% of the maize genome, demonstrate the feasibility of refining the B73 RefGen_v1 genome assembly by incorporating optical map, high-resolution genetic map, and comparative genomic data sets. Such improvements, along with those of gene and repeat annotation, will serve to promote future functional genomic and phylogenomic research in maize and other grasses
The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals
To dissect the genetic architecture of blood pressure and assess effects on target-organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure loci, of which 17 were novel and 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target-organ damage in multiple tissues, with minor effects in the kidney. Our findings expand current knowledge of blood pressure pathways and highlight tissues beyond the classic renal system in blood pressure regulation
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