Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics

Song translation requires both translation of lyrics and alignment of music notes so that the resulting verse can be sung to the accompanying melody, which is a challenging problem that has attracted some interests in different aspects of the translation process. In this paper, we propose Lyrics-Melody Translation with Adaptive Grouping (LTAG), a holistic solution to automatic song translation by jointly modeling lyrics translation and lyrics-melody alignment. It is a novel encoder-decoder framework that can simultaneously translate the source lyrics and determine the number of aligned notes at each decoding step through an adaptive note grouping module. To address data scarcity, we commissioned a small amount of training data annotated specifically for this task and used large amounts of augmented data through back-translation. Experiments conducted on an English-Chinese song translation data set show the effectiveness of our model in both automatic and human evaluation.Comment: 13 page

SSR-2D: Semantic 3D Scene Reconstruction from 2D Images

Most deep learning approaches to comprehensive semantic modeling of 3D indoor spaces require costly dense annotations in the 3D domain. In this work, we explore a central 3D scene modeling task, namely, semantic scene reconstruction without using any 3D annotations. The key idea of our approach is to design a trainable model that employs both incomplete 3D reconstructions and their corresponding source RGB-D images, fusing cross-domain features into volumetric embeddings to predict complete 3D geometry, color, and semantics with only 2D labeling which can be either manual or machine-generated. Our key technical innovation is to leverage differentiable rendering of color and semantics to bridge 2D observations and unknown 3D space, using the observed RGB images and 2D semantics as supervision, respectively. We additionally develop a learning pipeline and corresponding method to enable learning from imperfect predicted 2D labels, which could be additionally acquired by synthesizing in an augmented set of virtual training views complementing the original real captures, enabling more efficient self-supervision loop for semantics. In this work, we propose an end-to-end trainable solution jointly addressing geometry completion, colorization, and semantic mapping from limited RGB-D images, without relying on any 3D ground-truth information. Our method achieves state-of-the-art performance of semantic scene reconstruction on two large-scale benchmark datasets MatterPort3D and ScanNet, surpasses baselines even with costly 3D annotations. To our knowledge, our method is also the first 2D-driven method addressing completion and semantic segmentation of real-world 3D scans

Peripheral Leukocytapheresis Attenuates Acute Lung Injury Induced by Lipopolysaccharide In Vivo

The mortality of acute lung injury and acute respiratory distress syndrome (ALI/ARDS) remains high and efforts for prevention and treatments have shown little improvement over the past decades. The present study investigated the efficacy and mechanism of leukocytapheresis (LCAP) to partially eliminate peripheral neutrophils and attenuate lipopolysaccharide (LPS)-induced lung injury in dogs. A total of 24 healthy male mongrel dogs were enrolled and randomly divided into LPS, LCAP and LCAP-sham groups. All animals were injected with LPS to induce endotoxemia. The serum levels of leucocytes, neutrophil elastase, arterial blood gas, nuclear factor-kappa B (NF-κB) subunit p65 in lung tissues were measured. The histopathology and parenchyma apoptosis of lung tissues were examined. We found that 7, 3, and 7 animals in the LPS, LCAP, and sham-LCAP groups, respectively, developed ALI 36 h after LPS infusion. The levels of NF-κB p65 in lung tissue, neutrophils and elastase in blood, decreased significantly following LCAP. LCAP also alleviated apoptosis, and NF-κB p65 in lung tissues. Collectively, our results show that partial removal of leucocytes from peripheral blood decreases elastase level in serum. This, in turn, attenuates lung injuries and may potentially decrease the incidence of ALI

Clinical research on liver reserve function by 13C-phenylalanine breath test in aged patients with chronic liver diseases

<p>Abstract</p> <p>Background</p> <p>The objective of this study was to investigate whether the ¹³C-phenylalanine breath test could be useful for the evaluation of hepatic function in elderly volunteers and patients with chronic hepatitis B and liver cirrhosis.</p> <p>Methods</p> <p>L-[1-¹³C] phenylalanine was administered orally at a dose of 100 mg to 55 elderly patients with liver cirrhosis, 30 patients with chronic hepatitis B and 38 elderly healthy subjects. The breath test was performed at 8 different time points (0, 10, 20, 30, 45, 60, 90, 120 min) to obtain the values of Delta over baseline, percentage ¹³CO₂exhalation rate and cumulative excretion (Cum). The relationships of the cumulative excretion with the ¹³C-%dose/h and blood biochemical parameters were investigated.</p> <p>Results</p> <p>The ¹³C-%dose/h at 20 min and 30 min combined with the cumulative excretion at 60 min and 120 min correlated with hepatic function tests, serum albumin, hemoglobin, platelet and Child-Pugh score. Prothrombin time, total and direct bilirubin were significantly increased, while serum albumin, hemoglobin and platelet, the cumulative excretion at 60 min and 120 min values decreased by degrees of intensity of the disease in Child-Pugh A, B, and C patients (P < 0.01).</p> <p>Conclusions</p> <p>The ¹³C-phenylalanine breath test can be used as a non-invasive assay to evaluate hepatic function in elderly patients with liver cirrhosis. The ¹³C-%dose/h at 20 min, at 30 min and cumulative excretion at 60 min may be the key value for determination at a single time-point. ¹³C-phenylalanine breath test is safe and helpful in distinguishing different stages of hepatic dysfunction for elderly cirrhosis patients.</p