A Study on Self-Translation of Eileen Chang’s Little Finger Up From Perspective of Translator’s Subjectivity

Based on both English and Chinese texts, this paper, with the help of corpus software, attempts to make a detailed analysis of translator’s subjectivity as revealed in Eileen Chang’s self-translation of Little Finger Up in terms of passivity, subjective initiative and purposefulness (self-benefiting) as well. Thereupon, the paper comes to the following conclusions. First, as the self-translator, Eileen Chang brings her subjective initiate into play in the self-translation in regard to sentence structure, proper nouns, culture-specific items, manifestation of the theme and way of expressing feelings. Second, privileged as she is, Eileen Chang is affected by both ideology and poetics. She deliberately eschews the sensitively political and warlike topic by way of omission and retains the heterogeneous elements of the source culture in the process of translation, reflecting her translator’s subjectivity in the self-translation while suffering the passivity imposed by ideology and poetics. Third, Eileen Chang usually adopts various strategies in the self-translation so as to fulfill her translation purposes, in which she deliberately deletes the plots and rewrites the title so as to highlight the problem of Chinese marriage and reveal her own pessimistic attitude towards marriage, indicating her self-benefiting in self-translation. In a nutshell, the self-translation seems to be concise and comprehensive as well as natural and unrestrained, indicating that the translator’s subjectivity is much more involved in self-translation, compared with that in conventional translation

ST-Producing E. coli Oppose Carcinogen-Induced Colorectal Tumorigenesis in Mice.

There is a geographic inequality in the incidence of colorectal cancer, lowest in developing countries, and greatest in developed countries. This disparity suggests an environmental contribution to cancer resistance in endemic populations. Enterotoxigenic bacteria associated with diarrheal disease are prevalent in developing countries, including enterotoxigenic E. coli (ETEC) producing heat-stable enterotoxins (STs). STs are peptides that are structurally homologous to paracrine hormones that regulate the intestinal guanylyl cyclase C (GUCY2C) receptor. Beyond secretion, GUCY2C is a tumor suppressor universally silenced by loss of expression of its paracrine hormone during carcinogenesis. Thus, the geographic imbalance in colorectal cancer, in part, may reflect chronic exposure to ST-producing organisms that restore GUCY2C signaling silenced by hormone loss during transformation. Here, mice colonized for 18 weeks with control E. coli or those engineered to secrete ST exhibited normal growth, with comparable weight gain and normal stool water content, without evidence of secretory diarrhea. Enterotoxin-producing, but not control, E. coli, generated ST that activated colonic GUCY2C signaling, cyclic guanosine monophosphate (cGMP) production, and cGMP-dependent protein phosphorylation in colonized mice. Moreover, mice colonized with ST-producing E. coli exhibited a 50% reduction in carcinogen-induced colorectal tumor burden. Thus, chronic colonization with ETEC producing ST could contribute to endemic cancer resistance in developing countries, reinforcing a novel paradigm of colorectal cancer chemoprevention with oral GUCY2C-targeted agents

Reconstruction of compressed spectral imaging based on global structure and spectral correlation

In this paper, a convolution sparse coding method based on global structure characteristics and spectral correlation is proposed for the reconstruction of compressive spectral images. The proposed method uses the convolution kernel to operate the global image, which can better preserve image structure information in the spatial dimension. To take full exploration of the constraints between spectra, the coefficients corresponding to the convolution kernel are constrained by the norm to improve spectral accuracy. And, to solve the problem that convolutional sparse coding is insensitive to low frequency, the global total-variation (TV) constraint is added to estimate the low-frequency components. It not only ensures the effective estimation of the low-frequency but also transforms the convolutional sparse coding into a de-noising process, which makes the reconstructing process simpler. Simulations show that compared with the current mainstream optimization methods (DeSCI and Gap-TV), the proposed method improves the reconstruction quality by up to 7 dB in PSNR and 10% in SSIM, and has a great improvement in the details of the reconstructed image

In Defense of Image Pre-Training for Spatiotemporal Recognition

Image pre-training, the current de-facto paradigm for a wide range of visual tasks, is generally less favored in the field of video recognition. By contrast, a common strategy is to directly train with spatiotemporal convolutional neural networks (CNNs) from scratch. Nonetheless, interestingly, by taking a closer look at these from-scratch learned CNNs, we note there exist certain 3D kernels that exhibit much stronger appearance modeling ability than others, arguably suggesting appearance information is already well disentangled in learning. Inspired by this observation, we hypothesize that the key to effectively leveraging image pre-training lies in the decomposition of learning spatial and temporal features, and revisiting image pre-training as the appearance prior to initializing 3D kernels. In addition, we propose Spatial-Temporal Separable (STS) convolution, which explicitly splits the feature channels into spatial and temporal groups, to further enable a more thorough decomposition of spatiotemporal features for fine-tuning 3D CNNs. Our experiments show that simply replacing 3D convolution with STS notably improves a wide range of 3D CNNs without increasing parameters and computation on both Kinetics-400 and Something-Something V2. Moreover, this new training pipeline consistently achieves better results on video recognition with significant speedup. For instance, we achieve +0.6% top-1 of Slowfast on Kinetics-400 over the strong 256-epoch 128-GPU baseline while fine-tuning for only 50 epochs with 4 GPUs. The code and models are available at https://github.com/UCSC-VLAA/Image-Pretraining-for-Video.Comment: Published as a conference paper at ECCV 202

Bronchoscopic ethanol injection combined with cryotherapy is an effective treatment for benign airway stenosis caused by endotracheal intubation or tracheotomyc

The benign tracheal stenosis is a challenge in interventional pulmonary disease. Bronchoscopic ethanol injection (BEI) is always used in airway stenosis caused by malignant tracheal tumor. The efficacy and safety of BEI in benign airway stenosis has not been studied before. To compare the safety and efficacy between bronchoscopic icryotherapy and BEI combined with bronchoscopic cryotherapy in the treatment of benign tracheal stenosis. A retrospective study included 61 patients with tracheal stenosis caused by endotracheal intubation and tracheotomy from July 2010 to June 2015 was made. 33 patients received repeated bronchoscopic cryotherapy alone were in Group A, 29 patients underwent repeated cryotherapy combined with BEI were in Group B. Dyspnea index, tracheal diameter were collected before and after treatment. Efficacy and complications were compared in two groups. The changes of tracheal diameter, dyspnea index were significant before and after treatment in both groups (P < 0.05). The long-term cure rate was higher in group B than that in group A (100% vs 84.8%). The average duration for dilated airway stable was much shorter in group B than group A (166±28 days vs 278±32 days, P < 0.05). The average cryotherapy session performed in group B was significantly less than that in group A (22.1±4.7 vs 34.9±6.5, P < 0.05). Meanwhile the complications in group A were seldom, the incidence of complications related to BEI were low in group B (mild chest pain 7.1%, bleeding 3.6% and cough 10.7%). BEI combined with bronchoscopic cryotherapy is an effective minimally invasive choice for releasing the airway obstructive symptoms

SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation

Recent advancements in large-scale Vision Transformers have made significant strides in improving pre-trained models for medical image segmentation. However, these methods face a notable challenge in acquiring a substantial amount of pre-training data, particularly within the medical field. To address this limitation, we present Masked Multi-view with Swin Transformers (SwinMM), a novel multi-view pipeline for enabling accurate and data-efficient self-supervised medical image analysis. Our strategy harnesses the potential of multi-view information by incorporating two principal components. In the pre-training phase, we deploy a masked multi-view encoder devised to concurrently train masked multi-view observations through a range of diverse proxy tasks. These tasks span image reconstruction, rotation, contrastive learning, and a novel task that employs a mutual learning paradigm. This new task capitalizes on the consistency between predictions from various perspectives, enabling the extraction of hidden multi-view information from 3D medical data. In the fine-tuning stage, a cross-view decoder is developed to aggregate the multi-view information through a cross-attention block. Compared with the previous state-of-the-art self-supervised learning method Swin UNETR, SwinMM demonstrates a notable advantage on several medical image segmentation tasks. It allows for a smooth integration of multi-view information, significantly boosting both the accuracy and data-efficiency of the model. Code and models are available at https://github.com/UCSC-VLAA/SwinMM/.Comment: MICCAI 2023; project page: https://github.com/UCSC-VLAA/SwinMM