136 research outputs found
Blowup dynamics for smooth equivariant solutions to energy critical Landau-Lifschitz flow
In this paper, we study the energy critical equivariant Landau-Lifschitz flow
with target manifold . We prove that there exists a codimension
one smooth well-localized set of initial data which generates finite-time
type-II blowup solutions, and then give a precise description of the
corresponding singularity formation. In our proof, both the Schr\"odinger part
and the heat part play important roles in the construction of approximate
solutions and the mixed Energy-Morawetz arguments. However, the blowup rate is
independent of their coefficients.Comment: 84 page
New Trends in Machine Translation using Large Language Models: Case Examples with ChatGPT
Machine Translation (MT) has made significant progress in recent years using
deep learning, especially after the emergence of large language models (LLMs)
such as GPT-3 and ChatGPT. This brings new challenges and opportunities for MT
using LLMs. In this paper, we brainstorm some interesting directions for MT
using LLMs, including stylized MT, interactive MT, and Translation Memory-based
MT, as well as a new evaluation paradigm using LLMs. We also discuss the
privacy concerns in MT using LLMs and a basic privacy-preserving method to
mitigate such risks. To illustrate the potential of our proposed directions, we
present several examples for the new directions mentioned above, demonstrating
the feasibility of the proposed directions and highlight the opportunities and
challenges for future research in MT using LLMs
Finding and Exploring Promising Search Space for the 0-1 Multidimensional Knapsack Problem
The 0-1 multidimensional knapsack problem(MKP) is a classical NP-hard
combinatorial optimization problem. In this paper, we propose a novel heuristic
algorithm simulating evolutionary computation and large neighbourhood search
for the MKP. It maintains a set of solutions and abstracts information from the
solution set to generate good partial assignments. To find high-quality
solutions, integer programming is employed to explore the promising search
space specified by the good partial assignments. Extensive experimentation with
commonly used benchmark sets shows that our approach outperforms the state of
the art heuristic algorithms, TPTEA and DQPSO, in solution quality. It finds
new lower bound for 8 large and hard instance
BiSync: A Bilingual Editor for Synchronized Monolingual Texts
In our globalized world, a growing number of situations arise where people
are required to communicate in one or several foreign languages. In the case of
written communication, users with a good command of a foreign language may find
assistance from computer-aided translation (CAT) technologies. These
technologies often allow users to access external resources, such as
dictionaries, terminologies or bilingual concordancers, thereby interrupting
and considerably hindering the writing process. In addition, CAT systems assume
that the source sentence is fixed and also restrict the possible changes on the
target side. In order to make the writing process smoother, we present BiSync,
a bilingual writing assistant that allows users to freely compose text in two
languages, while maintaining the two monolingual texts synchronized. We also
include additional functionalities, such as the display of alternative prefix
translations and paraphrases, which are intended to facilitate the authoring of
texts. We detail the model architecture used for synchronization and evaluate
the resulting tool, showing that high accuracy can be attained with limited
computational resources. The interface and models are publicly available at
https://github.com/jmcrego/BiSync and a demonstration video can be watched on
YouTube at https://youtu.be/_l-ugDHfNgU .Comment: ACL 2023 System Dem
Attention, Please! Adversarial Defense via Attention Rectification and Preservation
This study provides a new understanding of the adversarial attack problem by
examining the correlation between adversarial attack and visual attention
change. In particular, we observed that: (1) images with incomplete attention
regions are more vulnerable to adversarial attacks; and (2) successful
adversarial attacks lead to deviated and scattered attention map. Accordingly,
an attention-based adversarial defense framework is designed to simultaneously
rectify the attention map for prediction and preserve the attention area
between adversarial and original images. The problem of adding iteratively
attacked samples is also discussed in the context of visual attention change.
We hope the attention-related data analysis and defense solution in this study
will shed some light on the mechanism behind the adversarial attack and also
facilitate future adversarial defense/attack model design
Improved Visual Fine-tuning with Natural Language Supervision
Fine-tuning a visual pre-trained model can leverage the semantic information
from large-scale pre-training data and mitigate the over-fitting problem on
downstream vision tasks with limited training examples. While the problem of
catastrophic forgetting in pre-trained backbone has been extensively studied
for fine-tuning, its potential bias from the corresponding pre-training task
and data, attracts less attention. In this work, we investigate this problem by
demonstrating that the obtained classifier after fine-tuning will be close to
that induced by the pre-trained model. To reduce the bias in the classifier
effectively, we introduce a reference distribution obtained from a fixed text
classifier, which can help regularize the learned vision classifier. The
proposed method, Text Supervised fine-tuning (TeS), is evaluated with diverse
pre-trained vision models including ResNet and ViT, and text encoders including
BERT and CLIP, on 11 downstream tasks. The consistent improvement with a clear
margin over distinct scenarios confirms the effectiveness of our proposal. Code
is available at \url{https://github.com/idstcv/TeS}.Comment: accepted by ICCV'2
- …