140 research outputs found
Stability of Soft Quasicrystals in a Coupled-Mode Swift-Hohenberg Model for Three-Component Systems
In this article, we discuss the stability of soft quasicrystalline phases in
a coupled-mode Swift-Hohenberg model for three-component systems, where the
characteristic length scales are governed by the positive-definite gradient
terms. Classic two-mode approximation method and direct numerical minimization
are applied to the model. In the latter approach, we apply the projection
method to deal with the potentially quasiperiodic ground states. A variable
cell method of optimizing the shape and size of higher-dimensional periodic
cell is developed to minimize the free energy with respect to the order
parameters. Based on the developed numerical methods, we rediscover decagonal
and dodecagonal quasicrystalline phases, and find diverse periodic phases and
complex modulated phases. Furthermore, phase diagrams are obtained in various
phase spaces by comparing the free energies of different candidate structures.
It does show not only the important roles of system parameters, but also the
effect of optimizing computational domain. In particular, the optimization of
computational cell allows us to capture the ground states and phase behavior
with higher fidelity. We also make some discussions on our results and show the
potential of applying our numerical methods to a larger class of mean-field
free energy functionals.Comment: 26 pages, 13 figures; accepted by Communications in Computational
Physic
Stability of Two-Dimensional Soft Quasicrystals
The relative stability of two-dimensional soft quasicrystals is examined
using a recently developed projection method which provides a unified numerical
framework to compute the free energy of periodic crystal and quasicrystals.
Accurate free energies of numerous ordered phases, including dodecagonal,
decagonal and octagonal quasicrystals, are obtained for a simple model, i.e.
the Lifshitz-Petrich free energy functional, of soft quasicrystals with two
length-scales. The availability of the free energy allows us to construct phase
diagrams of the system, demonstrating that, for the Lifshitz-Petrich model, the
dodecagonal and decagonal quasicrystals can become stable phases, whereas the
octagonal quasicrystal stays as a metastable phase.Comment: 11 pages, 7 figure
Geometric Properties of the 2-D Peskin Problem
The 2-D Peskin problem describes a 1-D closed elastic string immersed and
moving in a 2-D Stokes flow that is induced by its own elastic force. The
geometric shape of the string and its internal stretching configuration evolve
in a coupled way, and they combined govern the dynamics of the system. In this
paper, we show that certain geometric quantities of the moving string satisfy
extremum principles and decay estimates. As a result, we can prove that the 2-D
Peskin problem admits a unique global solution when the initial data satisfies
a medium-size geometric condition on the string shape, while no assumption on
the size of stretching is needed
Boosting Commit Classification with Contrastive Learning
Commit Classification (CC) is an important task in software maintenance,
which helps software developers classify code changes into different types
according to their nature and purpose. It allows developers to understand
better how their development efforts are progressing, identify areas where they
need improvement, and make informed decisions about when and how to release new
software versions. However, existing models need lots of manually labeled data
for fine-tuning processes, and ignore sentence-level semantic information,
which is often essential for discovering the difference between diverse
commits. Therefore, it is still challenging to solve CC in fewshot scenario.
To solve the above problems, we propose a contrastive learning-based commit
classification framework. Firstly, we generate sentences and pseudo-labels
according to the labels of the dataset, which aims to enhance the dataset.
Secondly, we randomly group the augmented data times to compare their
similarity with the positive and negative samples. We
utilize individual pretrained sentence transformers (ST)s to efficiently obtain
the sentence-level embeddings from different features respectively. Finally, we
adopt the cosine similarity function to limit the distribution of vectors,
similar vectors are more adjacent. The light fine-tuned model is then applied
to the label prediction of incoming commits.
Extensive experiments on two open available datasets demonstrate that our
framework can solve the CC problem simply but effectively in fewshot scenarios,
while achieving state-of-the-art(SOTA) performance and improving the
adaptability of the model without requiring a large number of training samples
for fine-tuning. The code, data, and trained models are available at
https://github.com/AppleMax1992/CommitFit
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