10,066 research outputs found
Existence and asymptotic behavior of least energy sign-changing solutions for Schrodinger-Poisson systems with doubly critical exponents
In this paper, we are concerned with the following Schr\"{o}dinger-Poisson
system with critical nonlinearity and critical nonlocal term due to the
Hardy-Littlewood-Sobolev inequality \begin{equation}\begin{cases}
-\Delta u+u+\lambda\phi |u|^3u =|u|^4u+ |u|^{q-2}u,\ \ &\ x \in
\mathbb{R}^{3},\\[2mm]
-\Delta \phi=|u|^5, \ \ &\ x \in \mathbb{R}^{3}, \end{cases} \end{equation}
where is a parameter and . If and , the above system has no nontrivial
solution. If for some , we obtain a
least energy radial sign-changing solution to the above system.
Furthermore, we consider as a parameter and analyze the asymptotic
behavior of as
Identical and Fraternal Twins: Fine-Grained Semantic Contrastive Learning of Sentence Representations
The enhancement of unsupervised learning of sentence representations has been
significantly achieved by the utility of contrastive learning. This approach
clusters the augmented positive instance with the anchor instance to create a
desired embedding space. However, relying solely on the contrastive objective
can result in sub-optimal outcomes due to its inability to differentiate subtle
semantic variations between positive pairs. Specifically, common data
augmentation techniques frequently introduce semantic distortion, leading to a
semantic margin between the positive pair. While the InfoNCE loss function
overlooks the semantic margin and prioritizes similarity maximization between
positive pairs during training, leading to the insensitive semantic
comprehension ability of the trained model. In this paper, we introduce a novel
Identical and Fraternal Twins of Contrastive Learning (named IFTCL) framework,
capable of simultaneously adapting to various positive pairs generated by
different augmentation techniques. We propose a \textit{Twins Loss} to preserve
the innate margin during training and promote the potential of data enhancement
in order to overcome the sub-optimal issue. We also present proof-of-concept
experiments combined with the contrastive objective to prove the validity of
the proposed Twins Loss. Furthermore, we propose a hippocampus queue mechanism
to restore and reuse the negative instances without additional calculation,
which further enhances the efficiency and performance of the IFCL. We verify
the IFCL framework on nine semantic textual similarity tasks with both English
and Chinese datasets, and the experimental results show that IFCL outperforms
state-of-the-art methods.Comment: This article has been accepted for publication in European Conference
on Artificial Intelligence (ECAI2023). 9 pages, 4 figure
Topic-DPR: Topic-based Prompts for Dense Passage Retrieval
Prompt-based learning's efficacy across numerous natural language processing
tasks has led to its integration into dense passage retrieval. Prior research
has mainly focused on enhancing the semantic understanding of pre-trained
language models by optimizing a single vector as a continuous prompt. This
approach, however, leads to a semantic space collapse; identical semantic
information seeps into all representations, causing their distributions to
converge in a restricted region. This hinders differentiation between relevant
and irrelevant passages during dense retrieval. To tackle this issue, we
present Topic-DPR, a dense passage retrieval model that uses topic-based
prompts. Unlike the single prompt method, multiple topic-based prompts are
established over a probabilistic simplex and optimized simultaneously through
contrastive learning. This encourages representations to align with their topic
distributions, improving space uniformity. Furthermore, we introduce a novel
positive and negative sampling strategy, leveraging semi-structured data to
boost dense retrieval efficiency. Experimental results from two datasets affirm
that our method surpasses previous state-of-the-art retrieval techniques.Comment: Findings of EMNLP 202
- …