674 research outputs found
Aquaculture genomics, genetics and breeding in the United States: current status, challenges, and priorities for future research
Advancing the production efficiency and profitability of aquaculture is dependent upon the ability to utilize a diverse array of genetic resources. The ultimate goals of aquaculture genomics, genetics and breeding research are to enhance aquaculture production efficiency, sustainability, product quality, and profitability in support of the commercial sector and for the benefit of consumers. In order to achieve these goals, it is important to understand the genomic structure and organization of aquaculture species, and their genomic and phenomic variations, as well as the genetic basis of traits and their interrelationships. In addition, it is also important to understand the mechanisms of regulation and evolutionary conservation at the levels of genome, transcriptome, proteome, epigenome, and systems biology. With genomic information and information between the genomes and phenomes, technologies for marker/causal mutation-assisted selection, genome selection, and genome editing can be developed for applications in aquaculture. A set of genomic tools and resources must be made available including reference genome sequences and their annotations (including coding and non-coding regulatory elements), genome-wide polymorphic markers, efficient genotyping platforms, high-density and high-resolution linkage maps, and transcriptome resources including non-coding transcripts. Genomic and genetic control of important performance and production traits, such as disease resistance, feed conversion efficiency, growth rate, processing yield, behaviour, reproductive characteristics, and tolerance to environmental stressors like low dissolved oxygen, high or low water temperature and salinity, must be understood. QTL need to be identified, validated across strains, lines and populations, and their mechanisms of control understood. Causal gene(s) need to be identified. Genetic and epigenetic regulation of important aquaculture traits need to be determined, and technologies for marker-assisted selection, causal gene/mutation-assisted selection, genome selection, and genome editing using CRISPR and other technologies must be developed, demonstrated with applicability, and application to aquaculture industries. Major progress has been made in aquaculture genomics for dozens of fish and shellfish species including the development of genetic linkage maps, physical maps, microarrays, single nucleotide polymorphism (SNP) arrays, transcriptome databases and various stages of genome reference sequences. This paper provides a general review of the current status, challenges and future research needs of aquaculture genomics, genetics, and breeding, with a focus on major aquaculture species in the United States: catfish, rainbow trout, Atlantic salmon, tilapia, striped bass, oysters, and shrimp. While the overall research priorities and the practical goals are similar across various aquaculture species, the current status in each species should dictate the next priority areas within the species. This paper is an output of the USDA Workshop for Aquaculture Genomics, Genetics, and Breeding held in late March 2016 in Auburn, Alabama, with participants from all parts of the United States
Evidence of Vocal Tract Articulation in Self-Supervised Learning of Speech
Recent self-supervised learning (SSL) models have proven to learn rich
representations of speech, which can readily be utilized by diverse downstream
tasks. To understand such utilities, various analyses have been done for speech
SSL models to reveal which and how information is encoded in the learned
representations. Although the scope of previous analyses is extensive in
acoustic, phonetic, and semantic perspectives, the physical grounding by speech
production has not yet received full attention. To bridge this gap, we conduct
a comprehensive analysis to link speech representations to articulatory
trajectories measured by electromagnetic articulography (EMA). Our analysis is
based on a linear probing approach where we measure articulatory score as an
average correlation of linear mapping to EMA. We analyze a set of SSL models
selected from the leaderboard of the SUPERB benchmark and perform further
layer-wise analyses on two most successful models, Wav2Vec 2.0 and HuBERT.
Surprisingly, representations from the recent speech SSL models are highly
correlated with EMA traces (best: r = 0.81), and only 5 minutes are sufficient
to train a linear model with high performance (r = 0.77). Our findings suggest
that SSL models learn to align closely with continuous articulations, and
provide a novel insight into speech SSL
Generation and collective interaction of giant magnetic dipoles in laser cluster plasma
Interaction of circularly polarized laser pulses with spherical nano-droplets generates nanometer-size magnets with lifetime on the order of hundreds of femtoseconds. Such magnetic dipoles are close enough in a cluster target and magnetic interaction takes place. We investigate such system of several magnetic dipoles and describe their rotation in the framework of Lagrangian formalism. The semi-analytical results are compared to particle-in-cell simulations, which confirm the theoretically obtained terrahertz frequency of the dipole oscillation
D3Former: Debiased Dual Distilled Transformer for Incremental Learning
In class incremental learning (CIL) setting, groups of classes are introduced
to a model in each learning phase. The goal is to learn a unified model
performant on all the classes observed so far. Given the recent popularity of
Vision Transformers (ViTs) in conventional classification settings, an
interesting question is to study their continual learning behaviour. In this
work, we develop a Debiased Dual Distilled Transformer for CIL dubbed
. The proposed model leverages a hybrid nested ViT
design to ensure data efficiency and scalability to small as well as large
datasets. In contrast to a recent ViT based CIL approach, our
does not dynamically expand its architecture when
new tasks are learned and remains suitable for a large number of incremental
tasks. The improved CIL behaviour of owes to two
fundamental changes to the ViT design. First, we treat the incremental learning
as a long-tail classification problem where the majority samples from new
classes vastly outnumber the limited exemplars available for old classes. To
avoid the bias against the minority old classes, we propose to dynamically
adjust logits to emphasize on retaining the representations relevant to old
tasks. Second, we propose to preserve the configuration of spatial attention
maps as the learning progresses across tasks. This helps in reducing
catastrophic forgetting by constraining the model to retain the attention on
the most discriminative regions. obtains
favorable results on incremental versions of CIFAR-100, MNIST, SVHN, and
ImageNet datasets. Code is available at https://tinyurl.com/d3formerComment: Accepted to CLVision at CVPR 202
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