Attentional Encoder Network for Targeted Sentiment Classification

Targeted sentiment classification aims at determining the sentimental tendency towards specific targets. Most of the previous approaches model context and target words with RNN and attention. However, RNNs are difficult to parallelize and truncated backpropagation through time brings difficulty in remembering long-term patterns. To address this issue, this paper proposes an Attentional Encoder Network (AEN) which eschews recurrence and employs attention based encoders for the modeling between context and target. We raise the label unreliability issue and introduce label smoothing regularization. We also apply pre-trained BERT to this task and obtain new state-of-the-art results. Experiments and analysis demonstrate the effectiveness and lightweight of our model.Comment: 7 page

Transversus abdominis plane block reduces remifentanil and propofol consumption, evaluated by closed-loop titration guided by bispectral index.

The present prospective, randomized, double-blind study aimed to determine the impact of transversus abdominis plane (TAP) block on propofol and remifentanil consumption, when administered by closed-loop titration guided by processed electroencephalography, i.e., bispectral index (BIS) values. Following institutional review board approval, 60 patients were scheduled for laparoscopic colectomy under general anesthesia. Patients were randomly assigned to receive bilateral TAP block with 20 ml 0.375% ropivacaine (TAP group) or 20 ml 0.9% saline [control (CON) group]. General anesthesia was maintained with propofol and remifentanil administration using closed-loop titration guided by BIS values. The primary outcome was perioperative propofol and remifentanil consumption. The secondary outcomes were hypertensive or hypotensive events requiring treatment, recovery time in PACU and time to first rescue analgesia following surgery. A total of 58 patients participated in the present study. At similar depths of anesthesia, as measured by BIS during the maintenance phase (45-55), patients who received TAP blocks required less propofol (4.2±1.3 vs. 5.5±1.6 mg/kg/h; P<0.001) and remifentanil (0.16±0.05 vs. 0.21±0.05 µg/kg/min; P<0.001). Time to extubation was significantly shorter in the TAP group (9.8±3.2 min) than in the CON group (14.2±4.9 min) (P<0.05). The requirement to treat hemodynamic change was also significantly lower (P<0.05). Pain score at 2 h after surgery was also significantly reduced in the TAP group compared with the CON group (P<0.05), whereas the time to first rescue analgesia was delayed in patients who received TAP block (P<0.05). Postoperative nausea and vomiting occurred at comparable rates in each group (P>0.05). In conclusion, TAP block combined with general anesthesia reduced propofol and remifentanil consumption, shortened time to tracheal extubation and promoted hemodynamic stability in laparoscopic colectomy

Localization and discrimination of GG mismatch in duplex DNA by synthetic ligand-enhanced protein nanopore analysis

: Mismatched base pairs in DNA are the basis of single-nucleotide polymorphism, one of the major issues in genetic diseases. However, the changes of physical and chemical properties of DNA caused by single-site mismatches are often influenced by the sequence and the structural flexibility of the whole duplex, resulting in a challenge of direct detection of the types and location of mismatches sensitively. In this work, we proposed a synthetic ligand-enhanced protein nanopore analysis of GG mismatch on DNA fragment, inspired by in silico investigation of the specific binding of naphthyridine dimer (ND) on GG mismatch. We demonstrated that both the importing and unzipping processes of the ligand-bound DNA duplex can be efficiently slowed down in α-hemolysin nanopore. This ligand-binding induced slow-down effect of DNA in nanopore is also sensitive to the relative location of the mismatch on DNA duplex. Especially, the GG mismatch close to the end of a DNA fragment, which is hard to be detected by either routine nanopore analysis or tedious nanopore sequencing, can be well differentiated by our ND-enhanced nanopore experiment. These findings provide a promising strategy to localize and discriminate base mismatches in duplex form directly at the single-molecule level

Remote Sensing Road Extraction by Road Segmentation Network

Road extraction from remote sensing images has attracted much attention in geospatial applications. However, the existing methods do not accurately identify the connectivity of the road. The identification of the road pixels may be interfered with by the abundant ground such as buildings, trees, and shadows. The objective of this paper is to enhance context and strip features of the road by designing UNet-like architecture. The overall method first enhances the context characteristics in the segmentation step and then maintains the stripe characteristics in a refinement step. The segmentation step exploits an attention mechanism to enhance the context information between the adjacent layers. To obtain the strip features of the road, the refinement step introduces the strip pooling in a refinement network to restore the long distance dependent information of the road. Extensive comparative experiments demonstrate that the proposed method outperforms other methods, achieving an overall accuracy of 98.25% on the DeepGlobe dataset, and 97.68% on the Massachusetts dataset

A Deep Joint Network for Monocular Depth Estimation Based on Pseudo-Depth Supervision

Depth estimation from a single image is a significant task. Although deep learning methods hold great promise in this area, they still face a number of challenges, including the limited modeling of nonlocal dependencies, lack of effective loss function joint optimization models, and difficulty in accurately estimating object edges. In order to further increase the network’s prediction accuracy, a new structure and training method are proposed for single-image depth estimation in this research. A pseudo-depth network is first deployed for generating a single-image depth prior, and by constructing connecting paths between multi-scale local features using the proposed up-mapping and jumping modules, the network can integrate representations and recover fine details. A deep network is also designed to capture and convey global context by utilizing the Transformer Conv module and Unet Depth net to extract and refine global features. The two networks jointly provide meaningful coarse and fine features to predict high-quality depth images from single RGB images. In addition, multiple joint losses are utilized to enhance the training model. A series of experiments are carried out to confirm and demonstrate the efficacy of our method. The proposed method exceeds the advanced method DPT by 10% and 3.3% in terms of root mean square error (RMSE(log)) and 1.7% and 1.6% in terms of squared relative difference (SRD), respectively, according to experimental results on the NYU Depth V2 and KITTI depth estimation benchmarks

Regional anesthesia did not improve postoperative long-term survival of tumor patients: a systematic review and meta-analysis of randomized controlled trials

Experimental research and clinical trials have reported a positive effect of regional anesthesia (RA) on prognosis of cancers. We systematically reviewed the efficacy of RA on recurrence-free survival (RFS) and overall survival (OS) after oncology surgeries

Low-Light Image Enhancement by Combining Transformer and Convolutional Neural Network

Within low-light imaging environment, the insufficient reflected light from objects often results in unsatisfactory images with degradations of low contrast, noise artifacts, or color distortion. The captured low-light images usually lead to poor visual perception quality for color deficient or normal observers. To address the above problems, we propose an end-to-end low-light image enhancement network by combining transformer and CNN (convolutional neural network) to restore the normal light images. Specifically, the proposed enhancement network is designed into a U-shape structure with several functional fusion blocks. Each fusion block includes a transformer stem and a CNN stem, and those two stems collaborate to accurately extract the local and global features. In this way, the transformer stem is responsible for efficiently learning global semantic information and capturing long-term dependencies, while the CNN stem is good at learning local features and focusing on detailed features. Thus, the proposed enhancement network can accurately capture the comprehensive semantic information of low-light images, which significantly contribute to recover normal light images. The proposed method is compared with the current popular algorithms quantitatively and qualitatively. Subjectively, our method significantly improves the image brightness, suppresses the image noise, and maintains the texture details and color information. For objective metrics such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), image perceptual similarity (LPIPS), DeltaE, and NIQE, our method improves the optimal values by 1.73 dB, 0.05, 0.043, 0.7939, and 0.6906, respectively, compared with other methods. The experimental results show that our proposed method can effectively solve the problems of underexposure, noise interference, and color inconsistency in micro-optical images, and has certain application value

Regional anesthesia did not improve postoperative long-term survival of tumor patients: a systematic review and meta-analysis of randomized controlled trials

Experimental research and clinical trials have reported a positive effect of regional anesthesia (RA) on prognosis of cancers. We systematically reviewed the efficacy of RA on recurrence-free survival (RFS) and overall survival (OS) after oncology surgeries