1,155 research outputs found
Serum cystatin C and chemerin levels in diabetic retinopathy
Peer reviewedPublisher PD
Acceleration of Histogram-Based Contrast Enhancement via Selective Downsampling
In this paper, we propose a general framework to accelerate the universal
histogram-based image contrast enhancement (CE) algorithms. Both spatial and
gray-level selective down- sampling of digital images are adopted to decrease
computational cost, while the visual quality of enhanced images is still
preserved and without apparent degradation. Mapping function calibration is
novelly proposed to reconstruct the pixel mapping on the gray levels missed by
downsampling. As two case studies, accelerations of histogram equalization (HE)
and the state-of-the-art global CE algorithm, i.e., spatial mutual information
and PageRank (SMIRANK), are presented detailedly. Both quantitative and
qualitative assessment results have verified the effectiveness of our proposed
CE acceleration framework. In typical tests, computational efficiencies of HE
and SMIRANK have been speeded up by about 3.9 and 13.5 times, respectively.Comment: accepted by IET Image Processin
Unified Language Representation for Question Answering over Text, Tables, and Images
When trying to answer complex questions, people often rely on multiple
sources of information, such as visual, textual, and tabular data. Previous
approaches to this problem have focused on designing input features or model
structure in the multi-modal space, which is inflexible for cross-modal
reasoning or data-efficient training. In this paper, we call for an alternative
paradigm, which transforms the images and tables into unified language
representations, so that we can simplify the task into a simpler textual QA
problem that can be solved using three steps: retrieval, ranking, and
generation, all within a language space. This idea takes advantage of the power
of pre-trained language models and is implemented in a framework called Solar.
Our experimental results show that Solar outperforms all existing methods by
10.6-32.3 pts on two datasets, MultimodalQA and MMCoQA, across ten different
metrics. Additionally, Solar achieves the best performance on the WebQA
leaderboardComment: Findings of ACL 202
Gain Scheduling Control of Nonlinear Shock Motion Based on Equilibrium Manifold Linearization Model
AbstractThe equilibrium manifold linearization model of nonlinear shock motion is of higher accuracy and lower complexity over other models such as the small perturbation model and the piecewise-linear model. This paper analyzes the physical significance of the equilibrium manifold linearization model, and the self-feedback mechanism of shock motion is revealed. This helps to describe the stability and dynamics of shock motion. Based on the model, the paper puts forwards a gain scheduling control method for nonlinear shock motion. Simulation has shown the validity of the control scheme
Improving Question Generation with Multi-level Content Planning
This paper addresses the problem of generating questions from a given context
and an answer, specifically focusing on questions that require multi-hop
reasoning across an extended context. Previous studies have suggested that key
phrase selection is essential for question generation (QG), yet it is still
challenging to connect such disjointed phrases into meaningful questions,
particularly for long context. To mitigate this issue, we propose MultiFactor,
a novel QG framework based on multi-level content planning. Specifically,
MultiFactor includes two components: FA-model, which simultaneously selects key
phrases and generates full answers, and Q-model which takes the generated full
answer as an additional input to generate questions. Here, full answer
generation is introduced to connect the short answer with the selected key
phrases, thus forming an answer-aware summary to facilitate QG. Both FA-model
and Q-model are formalized as simple-yet-effective Phrase-Enhanced
Transformers, our joint model for phrase selection and text generation.
Experimental results show that our method outperforms strong baselines on two
popular QG datasets. Our code is available at
https://github.com/zeaver/MultiFactor.Comment: Camera-ready. Accepted by EMNLP 2023 Finding
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