ShaDocFormer: A Shadow-attentive Threshold Detector with Cascaded Fusion Refiner for document shadow removal

Document shadow is a common issue that arise when capturing documents using mobile devices, which significantly impacts the readability. Current methods encounter various challenges including inaccurate detection of shadow masks and estimation of illumination. In this paper, we propose ShaDocFormer, a Transformer-based architecture that integrates traditional methodologies and deep learning techniques to tackle the problem of document shadow removal. The ShaDocFormer architecture comprises two components: the Shadow-attentive Threshold Detector (STD) and the Cascaded Fusion Refiner (CFR). The STD module employs a traditional thresholding technique and leverages the attention mechanism of the Transformer to gather global information, thereby enabling precise detection of shadow masks. The cascaded and aggregative structure of the CFR module facilitates a coarse-to-fine restoration process for the entire image. As a result, ShaDocFormer excels in accurately detecting and capturing variations in both shadow and illumination, thereby enabling effective removal of shadows. Extensive experiments demonstrate that ShaDocFormer outperforms current state-of-the-art methods in both qualitative and quantitative measurements

UWFormer: Underwater Image Enhancement via a Semi-Supervised Multi-Scale Transformer

Underwater images often exhibit poor quality, imbalanced coloration, and low contrast due to the complex and intricate interaction of light, water, and objects. Despite the significant contributions of previous underwater enhancement techniques, there exist several problems that demand further improvement: (i) Current deep learning methodologies depend on Convolutional Neural Networks (CNNs) that lack multi-scale enhancement and also have limited global perception fields. (ii) The scarcity of paired real-world underwater datasets poses a considerable challenge, and the utilization of synthetic image pairs risks overfitting. To address the aforementioned issues, this paper presents a Multi-scale Transformer-based Network called UWFormer for enhancing images at multiple frequencies via semi-supervised learning, in which we propose a Nonlinear Frequency-aware Attention mechanism and a Multi-Scale Fusion Feed-forward Network for low-frequency enhancement. Additionally, we introduce a specialized underwater semi-supervised training strategy, proposing a Subaqueous Perceptual Loss function to generate reliable pseudo labels. Experiments using full-reference and non-reference underwater benchmarks demonstrate that our method outperforms state-of-the-art methods in terms of both quantity and visual quality

From Clozing to Comprehending: Retrofitting Pre-trained Language Model to Pre-trained Machine Reader

We present Pre-trained Machine Reader (PMR), a novel method to retrofit Pre-trained Language Models (PLMs) into Machine Reading Comprehension (MRC) models without acquiring labeled data. PMR is capable of resolving the discrepancy between model pre-training and downstream fine-tuning of existing PLMs, and provides a unified solver for tackling various extraction tasks. To achieve this, we construct a large volume of general-purpose and high-quality MRC-style training data with the help of Wikipedia hyperlinks and design a Wiki Anchor Extraction task to guide the MRC-style pre-training process. Although conceptually simple, PMR is particularly effective in solving extraction tasks including Extractive Question Answering and Named Entity Recognition, where it shows tremendous improvements over previous approaches especially under low-resource settings. Moreover, viewing sequence classification task as a special case of extraction task in our MRC formulation, PMR is even capable to extract high-quality rationales to explain the classification process, providing more explainability of the predictions

One-step Method to Fabricate Poly(ethylene terephthalate)/Gd(OH)3 Magnetic Nanofibers tTowards MRI-active Materials with High T1 Relaxivity and Long-term Visibility

Magnetic resonance imaging (MRI)-active polymers exhibit unique advantages for in vivo diagnosis. Here, in order to endow electrospun fibers with long-term T1 positive MRI visibility, MRI contrast agent (CA), Gd(OH)3, is introduced in a new, extremely convenient method. Crucially, GdCl3 is reacted with NaOH in situ during electrospinning, with flexibility to deliver both well-dispersed and aggregated Gd(OH)3 clusters within a poly(ethylene terephthalate) (PET) matrix. T1 and T2 relaxivities of Gd(OH)3 in PET nanofibers are studied. Well-dispersed Gd(OH)3 (sub-nanometer in size) exhibits 34 times higher T1 relaxivity than aggregated nanoparticles when embedded within the fibers. The morphology, structure, magnetic properties, tensile properties, imaging performance and biosafety of the PET/Gd(OH)3 composite fibers are evaluated to identify the optimum conditions to produce new materials with balanced properties, excellent in vivo positive contrast and approximately 139 days imaging lifetime. Comparing this sample with a commercial CA, only 0.32 wt.% Gd loading is needed to attain similar MRI signal intensity. In summary, PET/Gd(OH)3 long-term MRI-active fibers show great potential for future biomedical applications and the study also provides a promising new general strategy to enhance the MRI T1 positive contrast of electrospun fibers of a whole host of other systems

Pendelluft as a predictor of weaning in critically ill patients: An observational cohort study

Objective: Weaning failure is associated with adverse clinical outcomes. This study aimed to evaluate the accuracy of pendelluft during the spontaneous breathing trials (SBT) as a predictor of weaning outcome of patients with mechanical ventilation.Methods: An observational cohort study included 60 critically ill patients who were eligible for extubation. Pendelluft and electrical activity of the diaphragm (Edi) were monitored at baseline and every 10 minutes for the first 30 min of SBT denoted as T0, T1, T2, and T3. The pendelluft was measured using electrical impedance tomography (EIT), and Edi parameters were collected by Edi catheter. Patients were followed up after extubation and were divided into success group and failure group. Pendelluft, Edi parameters, respiratory parameters, and clinical outcomes such as intensive care units (ICU) stay, mortality, and 28-day ventilator-free days were compared between the two groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the ability of pendelluft to predict weaning outcome.Results: Fifty patients (50/60) were successfully weaned from the machine and 10 (10/60) failed, with weaning failure rate of 16.7%. Respiratory parameters such as rapid shallow breathing index (RSBI), respiratory rate (RR) and Edi parameters such as maximum value of Edi (Edimax), Edi variation between a maximum and minimum(ΔEdi) in the failure group were higher than those in the success group. The ICU stay and the 28-day ventilator-free days in the failure group were significantly longer than those in the success group. The 28-day mortality rate was higher in the failure group. The pendelluft mainly occurred in the early stage of SBT. Ventral pendelluft and total pendelluft in the failure group were higher than those in the success group at T1. Edimax and ΔEdi were positively correlated with pendelluft. The area under ROC curve (AUC) showed moderate predictive ability for ventral pendelluft in predicting weaning failure at T1 (AUC 0.76, 95% CI 0.58–0.94, cut-off value > 3% global tidal variation).Conclusion: Pendelluft is one of the factors leading to weaning failure, which may be related to diaphragm function. Measuring pendelluft volume maybe helpful to predict weaning