187 research outputs found
Unsupervised Low Light Image Enhancement Using SNR-Aware Swin Transformer
Image captured under low-light conditions presents unpleasing artifacts,
which debilitate the performance of feature extraction for many upstream visual
tasks. Low-light image enhancement aims at improving brightness and contrast,
and further reducing noise that corrupts the visual quality. Recently, many
image restoration methods based on Swin Transformer have been proposed and
achieve impressive performance. However, On one hand, trivially employing Swin
Transformer for low-light image enhancement would expose some artifacts,
including over-exposure, brightness imbalance and noise corruption, etc. On the
other hand, it is impractical to capture image pairs of low-light images and
corresponding ground-truth, i.e. well-exposed image in same visual scene. In
this paper, we propose a dual-branch network based on Swin Transformer, guided
by a signal-to-noise ratio prior map which provides the spatial-varying
information for low-light image enhancement. Moreover, we leverage unsupervised
learning to construct the optimization objective based on Retinex model, to
guide the training of proposed network. Experimental results demonstrate that
the proposed model is competitive with the baseline models
Towards Robust Text Retrieval with Progressive Learning
Retrieval augmentation has become an effective solution to empower large
language models (LLMs) with external and verified knowledge sources from the
database, which overcomes the limitations and hallucinations of LLMs in
handling up-to-date and domain-specific information. However, existing
embedding models for text retrieval usually have three non-negligible
limitations. First, the number and diversity of samples in a batch are too
restricted to supervise the modeling of textual nuances at scale. Second, the
high proportional noise are detrimental to the semantic correctness and
consistency of embeddings. Third, the equal treatment to easy and difficult
samples would cause sub-optimum convergence of embeddings with poorer
generalization. In this paper, we propose the PEG, a progressively learned
embeddings for robust text retrieval. Specifically, we increase the training
in-batch negative samples to 80,000, and for each query, we extracted five hard
negatives. Concurrently, we incorporated a progressive learning mechanism,
enabling the model to dynamically modulate its attention to the samples
throughout the entire training process. Additionally, PEG is trained on more
than 100 million data, encompassing a wide range of domains (e.g., finance,
medicine, and tourism) and covering various tasks (e.g., question-answering,
machine reading comprehension, and similarity matching). Extensive experiments
conducted on C-MTEB and DuReader demonstrate that PEG surpasses
state-of-the-art embeddings in retrieving true positives, highlighting its
significant potential for applications in LLMs. Our model is publicly available
at https://huggingface.co/TownsWu/PEG
kTrans: Knowledge-Aware Transformer for Binary Code Embedding
Binary Code Embedding (BCE) has important applications in various reverse
engineering tasks such as binary code similarity detection, type recovery,
control-flow recovery and data-flow analysis. Recent studies have shown that
the Transformer model can comprehend the semantics of binary code to support
downstream tasks. However, existing models overlooked the prior knowledge of
assembly language. In this paper, we propose a novel Transformer-based
approach, namely kTrans, to generate knowledge-aware binary code embedding. By
feeding explicit knowledge as additional inputs to the Transformer, and fusing
implicit knowledge with a novel pre-training task, kTrans provides a new
perspective to incorporating domain knowledge into a Transformer framework. We
inspect the generated embeddings with outlier detection and visualization, and
also apply kTrans to 3 downstream tasks: Binary Code Similarity Detection
(BCSD), Function Type Recovery (FTR) and Indirect Call Recognition (ICR).
Evaluation results show that kTrans can generate high-quality binary code
embeddings, and outperforms state-of-the-art (SOTA) approaches on downstream
tasks by 5.2%, 6.8%, and 12.6% respectively. kTrans is publicly available at:
https://github.com/Learner0x5a/kTrans-releas
Soluble epoxide hydrolase inhibitor promotes the healing of oral ulcers
Objective: Oral ulcers are a lesion in the oral mucosa that impacts chewing or drinking. Epoxyeicosatrienoic Acids (EETs) have enhanced angiogenic, regenerative, anti-inflammatory, and analgesic effects. The present study aims to evaluate the effects of 1-Trifluoromethoxyphenyl-3-(1-Propionylpiperidin-4-yl) Urea (TPPU), a soluble epoxide hydrolase inhibitor for increasing EETs level, on the healing of oral ulcers.
Methods: The chemically-induced oral ulcers were established in Sprague Dawley rats. The ulcer area was treated with TPPU to evaluate the healing time and pain threshold of ulcers. The expression of angiogenesis and cell proliferation-related protein in the ulcer area was detected using immunohistochemical staining. The effects of TPPU on migration and angiogenesis capability were measured with scratch assay and tube formation.
Results: Compared with the control group, TPPU promoted wound healing of oral ulcers with a shorter healing time, and raised pain thresholds. Immunohistochemical staining showed that TPPU increased the expression of angiogenesis and cell proliferation-related protein with reduced inflammatory cell infiltration in the ulcer area. TPPU enhanced cell migration and tube-forming potential in vitro.
Conclusions: The present results support the potential of TPPU with multiple biological effects for the treatment of oral ulcers by targeting soluble epoxide hydrolase
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