217 research outputs found
FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent
The theoretical landscape of federated learning (FL) undergoes rapid
evolution, but its practical application encounters a series of intricate
challenges, and hyperparameter optimization is one of these critical
challenges. Amongst the diverse adjustments in hyperparameters, the adaptation
of the learning rate emerges as a crucial component, holding the promise of
significantly enhancing the efficacy of FL systems. In response to this
critical need, this paper presents FedHyper, a novel hypergradient-based
learning rate adaptation algorithm specifically designed for FL. FedHyper
serves as a universal learning rate scheduler that can adapt both global and
local rates as the training progresses. In addition, FedHyper not only
showcases unparalleled robustness to a spectrum of initial learning rate
configurations but also significantly alleviates the necessity for laborious
empirical learning rate adjustments. We provide a comprehensive theoretical
analysis of FedHyper's convergence rate and conduct extensive experiments on
vision and language benchmark datasets. The results demonstrate that FEDHYPER
consistently converges 1.1-3x faster than FedAvg and the competing baselines
while achieving superior final accuracy. Moreover, FedHyper catalyzes a
remarkable surge in accuracy, augmenting it by up to 15% compared to FedAvg
under suboptimal initial learning rate settings
A Method to Judge the Style of Classical Poetry Based on Pre-trained Model
One of the important topics in the research field of Chinese classical poetry
is to analyze the poetic style. By examining the relevant works of previous
dynasties, researchers judge a poetic style mostly by their subjective
feelings, and refer to the previous evaluations that have become a certain
conclusion. Although this judgment method is often effective, there may be some
errors. This paper builds the most perfect data set of Chinese classical poetry
at present, trains a BART-poem pre -trained model on this data set, and puts
forward a generally applicable poetry style judgment method based on this
BART-poem model, innovatively introduces in-depth learning into the field of
computational stylistics, and provides a new research method for the study of
classical poetry. This paper attempts to use this method to solve the problem
of poetry style identification in the Tang and Song Dynasties, and takes the
poetry schools that are considered to have a relatively clear and consistent
poetic style, such as the Hongzheng Qizi and Jiajing Qizi, Jiangxi poetic
school and Tongguang poetic school, as the research object, and takes the poems
of their representative poets for testing. Experiments show that the judgment
results of the tested poetry work made by the model are basically consistent
with the conclusions given by critics of previous dynasties, verify some
avant-garde judgments of Mr. Qian Zhongshu, and better solve the task of poetry
style recognition in the Tang and Song dynasties.Comment: 4 pages, 2 figure
Generation of Chinese classical poetry based on pre-trained model
In order to test whether artificial intelligence can create qualified
classical poetry like humans, the author proposes a study of Chinese classical
poetry generation based on a pre-trained model. This paper mainly tries to use
BART and other pre training models, proposes FS2TEXT and RR2TEXT to generate
metrical poetry text and even specific style poetry text, and solves the
problem that the user's writing intention gradually reduces the relevance of
the generated poetry text.
In order to test the model's results, the authors selected ancient poets, by
combining it with BART's poetic model work, developed a set of AI poetry Turing
problems, it was reviewed by a group of poets and poetry writing researchers.
There were more than 600 participants, and the final results showed that,
high-level poetry lovers can't distinguish between AI activity and human
activity, this indicates that the author's working methods are not
significantly different from human activities. The model of poetry generation
studied by the author generalizes works that cannot be distinguished from those
of advanced scholars.
The number of modern Chinese poets has reached 5 million. However, many
modern Chinese poets lack language ability and skills as a result of their
childhood learning. However, many modern poets have no creative inspiration,
and the author's model can help them. They can look at this model when they
choose words and phrases and they can write works based on the poems they
already have, and they can write their own poems. The importance of poetry lies
in the author's thoughts and reflections. It doesn't matter how good AI poetry
is. The only thing that matters is for people to see and inspire them.Comment: 8 pages,2 figure
InMut-finder: a software tool for insertion identification in mutagenesis using Nanopore long reads
Background: Biological mutagens (such as transposon) with sequences inserted, play a crucial role to link observed phenotype and genotype in reverse genetic studies. For this reason, accurate and efficient software tools for identifying insertion sites based on the analysis of sequencing reads are desired. Results: We developed a bioinformatics tool, a Finder, to identify genome-wide Insertions in Mutagenesis (named as âInMut-Finderâ), based on target sequences and flanking sequences from long reads, such as Oxford Nanopore Sequencing. InMut-Finder succeeded in identify \u3e 100 insertion sites in Medicago truncatula and soybean mutants based on sequencing reads of whole-genome DNA or enriched insertion-site DNA fragments. Insertion sites discovered by InMut-Finder were validated by PCR experiments. Conclusion: InMut-Finder is a comprehensive and powerful tool for automated insertion detection from Nanopore long reads. The simplicity, efficiency, and flexibility of InMut-Finder make it a valuable tool for functional genomics and forward and reverse genetics. InMut-Finder was implemented with Perl, R, and Shell scripts, which are independent of the OS. The source code and instructions can be accessed at https:// github. com/ jsg20 0830/ InMut- Finder
Optical Trapping and Separation of Metal Nanoparticles by Cylindrical Metalenses With Phase Gradients
We proposed a method for driving metal nanoparticles in the focal field by cylindrical metalens with phase gradient. It was found that the introduced gradient phase would not affect the formation of the focal line, where metal nanoparticles can be trapped. While being driven along the direction with the phase gradient, Ag nanoparticles with different sizes, and nanoparticles with different materials (Au and Ag) were successfully separated, respectively. The induced driving force has an approximately linear relationship with the phase gradient. This kind of planar thin structure can be combined with a microfluidic chip to form a miniaturized system for label-free and non-contact sorting of particles or biological cells, and it may find potential applications in biomedicine
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