272 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
UPTON: Preventing Authorship Leakage from Public Text Release via Data Poisoning
Consider a scenario where an author-e.g., activist, whistle-blower, with many
public writings wishes to write "anonymously" when attackers may have already
built an authorship attribution (AA) model based off of public writings
including those of the author. To enable her wish, we ask a question "Can one
make the publicly released writings, T, unattributable so that AA models
trained on T cannot attribute its authorship well?" Toward this question, we
present a novel solution, UPTON, that exploits black-box data poisoning methods
to weaken the authorship features in training samples and make released texts
unlearnable. It is different from previous obfuscation works-e.g., adversarial
attacks that modify test samples or backdoor works that only change the model
outputs when triggering words occur. Using four authorship datasets (IMDb10,
IMDb64, Enron, and WJO), we present empirical validation where UPTON
successfully downgrades the accuracy of AA models to the impractical level
(~35%) while keeping texts still readable (semantic similarity>0.9). UPTON
remains effective to AA models that are already trained on available clean
writings of authors
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
A positivity preserving scheme for Poisson-Nernst-Planck Navier-Stokes equations and its error analysis
We consider in this paper a numerical approximation of
Poisson-Nernst-Planck-Navier- Stokes (PNP-NS) system. We construct a decoupled
semi-discrete and fully discrete scheme that enjoys the properties of
positivity preserving, mass conserving, and unconditionally energy stability.
Then, we establish the well-posedness and regularity of the initial and
(periodic) boundary value problem of the PNP-NS system under suitable
assumptions on the initial data, and carry out a rigorous convergence analysis
for the fully discretized scheme. We also present some numerical results to
validate the positivity-preserving property and the accuracy of our scheme
Learning from Incomplete Features by Simultaneous Training of Neural Networks and Sparse Coding
In this paper, the problem of training a classifier on a dataset with incomplete features is addressed. We assume that different subsets of features (random or structured) are available at each data instance. This situation typically occurs in the applications when not all the features are collected for every data sample. A new supervised learning method is developed to train a general classifier, such as a logistic regression or a deep neural network, using only a subset of features per sample, while assuming sparse representations of data vectors on an unknown dictionary. Sufficient conditions are identified, such that, if it is possible to train a classifier on incomplete observations so that their reconstructions are well separated by a hyperplane, then the same classifier also correctly separates the original (unobserved) data samples. Extensive simulation results on synthetic and well-known datasets are presented that validate our theoretical findings and demonstrate the effectiveness of the proposed method compared to traditional data imputation approaches and one state-of-the-art algorithm.Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Wang, Ziyao. South East University; ChinaFil: Sole Casals, Jordi. University of Vic; EspañaFil: Zhao, Qibin. Center for Advanced Intelligence Project; JapónIEEE Computer Society Conference on Computer Vision and Pattern Recognition 2021New YorkEstados UnidosIEE
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 “InMut-Finder”), 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
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