EFormer: Enhanced Transformer towards Semantic-Contour Features of Foreground for Portraits Matting

The portrait matting task aims to extract an alpha matte with complete semantics and finely-detailed contours. In comparison to CNN-based approaches, transformers with self-attention allow a larger receptive field, enabling it to better capture long-range dependencies and low-frequency semantic information of a portrait. However, the recent research shows that self-attention mechanism struggle with modeling high-frequency information and capturing fine contour details, which can lead to bias while predicting the portrait's contours. To address the problem, we propose EFormer to enhance the model's attention towards semantic and contour features. Especially the latter, which is surrounded by a large amount of high-frequency details. We build a semantic and contour detector (SCD) to accurately capture the distribution of semantic and contour features. And we further design contour-edge extraction branch and semantic extraction branch for refining contour features and complete semantic information. Finally, we fuse the two kinds of features and leverage the segmentation head to generate the predicted portrait matte. Remarkably, EFormer is an end-to-end trimap-free method and boasts a simple structure. Experiments conducted on VideoMatte240K-JPEGSD and AIM datasets demonstrate that EFormer outperforms previous portrait matte methods.Comment: 17 pages, 6 figure

Allelic Polymorphism of GIGANTEA is Responsible for Naturally Occurring Variation in Circadian Period in Brassica Rapa

GIGANTEA (GI) was originally identified by a late-flowering mutant in Arabidopsis, but subsequently has been shown to act in circadian period determination, light inhibition of hypocotyl elongation, and responses to multiple abiotic stresses, including tolerance to high salt and cold (freezing) temperature. Genetic mapping and analysis of families of heterogeneous inbred lines showed that natural variation in GI is responsible for a major quantitative trait locus in circadian period in Brassica rapa. We confirmed this conclusion by transgenic rescue of an Arabidopsis gi-201 loss of function mutant. The two B. rapa GI alleles each fully rescued the delayed flowering of Arabidopsis gi-201 but showed differential rescue of perturbations in red light inhibition of hypocotyl elongation and altered cold and salt tolerance. The B. rapa R500 GI allele, which failed to rescue the hypocotyl and abiotic stress phenotypes, disrupted circadian period determination in Arabidopsis. Analysis of chimeric B. rapa GI alleles identified the causal nucleotide polymorphism, which results in an amino acid substitution (S264A) between the two GI proteins. This polymorphism underlies variation in circadian period, cold and salt tolerance, and red light inhibition of hypocotyl elongation. Loss-of-function mutations of B. rapa GI confer delayed flowering, perturbed circadian rhythms in leaf movement, and increased freezing and increased salt tolerance, consistent with effects of similar mutations in Arabidopsis. Collectively, these data suggest that allelic variation of GI—and possibly of clock genes in general—offers an attractive target for molecular breeding for enhanced stress tolerance and potentially for improved crop yield

Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages

Chain-of-thought (CoT) is capable of eliciting models to explicitly generate reasoning paths, thus promoting reasoning accuracy and attracting increasing attention. Specifically, zero-shot CoT achieves remarkable improvements in a wide range of reasoning tasks by simply instructing the LLM with the prompt "Let's think step by step!". Despite the success of zero-shot CoT, the existing zero-shot prompting techniques remain limited to a single language, making it challenging to generalize to other languages and hindering global development. In this work, we introduce cross-lingual prompting (CLP), aiming to improve zero-shot CoT reasoning across languages. Specifically, CLP consists of two main components: (1) cross-lingual alignment prompting and (2) task-specific solver prompting. The cross-lingual alignment prompting is responsible for aligning representations across different languages, whereas the task-specific solver prompting is used to generate the final chain of thoughts and results for the reasoning task. In addition, we further introduce cross-lingual self-consistent prompting (CLSP) to ensemble different reasoning paths across languages. Our experimental evaluations on several benchmarks demonstrate that CLP and CLSP significantly outperform the existing prompting methods and achieve state-of-the-art performance. We hope this work will inspire further breakthroughs in cross-lingual CoT.Comment: Accepted at EMNLP2023 Main Conferenc

A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding

Zero-shot dialogue understanding aims to enable dialogue to track the user's needs without any training data, which has gained increasing attention. In this work, we investigate the understanding ability of ChatGPT for zero-shot dialogue understanding tasks including spoken language understanding (SLU) and dialogue state tracking (DST). Experimental results on four popular benchmarks reveal the great potential of ChatGPT for zero-shot dialogue understanding. In addition, extensive analysis shows that ChatGPT benefits from the multi-turn interactive prompt in the DST task but struggles to perform slot filling for SLU. Finally, we summarize several unexpected behaviors of ChatGPT in dialogue understanding tasks, hoping to provide some insights for future research on building zero-shot dialogue understanding systems with Large Language Models (LLMs).Comment: Technical Repor

Training and Validation of the Fast PCRTM_Solar Model

In this work, we extended PCRTM to including the contribution from solar radiation, including the nonlocal thermal equilibrium (NLTE) effect

Detection-driven exposure-correction network for nighttime drone-view object detection.

Drone-view object detection (DroneDet) models typically suffer a significant performance drop when applied to nighttime scenes. Existing solutions attempt to employ an exposure-adjustment module to reveal objects hidden in dark regions before detection. However, most exposure-adjustment models are only optimized for human perception, where the exposure-adjusted images may not necessarily enhance recognition. To tackle this issue, we propose a novel Detection-driven Exposure-Correction network for nighttime DroneDet, called DEDet. The DEDet conducts adaptive, non-linear adjustment of pixel values in a spatially fine-grained manner to generate DroneDet-friendly images. Specifically, we develop a Fine-grained Parameter Predictor (FPP) to estimate pixel-wise parameter maps of the image filters. These filters, along with the estimated parameters, are used to adjust pixel values of the low-light image based on non-uniform illuminations in drone-captured images. In order to learn the non-linear transformation from the original nighttime images to their DroneDet-friendly counterparts, we propose a Progressive Filtering module that applies recursive filters to iteratively refine the exposed image. Furthermore, to evaluate the performance of the proposed DEDet, we have built a dataset NightDrone to address the scarcity of the datasets specifically tailored for this purpose. Extensive experiments conducted on four nighttime datasets show that DEDet achieves a superior accuracy compared with the state-of-the-art methods. Furthermore, ablation studies and visualizations demonstrate the validity and interpretability of our approach. Our NightDrone dataset can be downloaded from https://github.com/yuexiemail/NightDrone-Dataset

X-Ray crystal structure study of zerumbone

599-60

株式買取請求権の理論と実践

早大学位記番号:新7234早稲田大

The experience of long-stay patients in a forensic psychiatric hospital in China: a qualitative study

open access articleBackground Long stay in forensic psychiatric hospitals is common in patients who are defined as “not criminally responsible on account of mental disorder”. However, little is known about how these patients experience and perceive the long stay within these settings. The aim of this study is to explore the perception and needs of long-stay patients in forensic psychiatric hospitals in China. Methods In-depth semi-structured interviews were conducted with 21 participants who had lived in the forensic psychiatry hospital for more than 8 years. We used thematic analysis strategies to analyse the qualitative data. Results Participants’ perceptions clustered seven themes: hopelessness, loneliness, worthlessness, low mood, sleep disturbances, lack of freedom, and lack of mental health intervention. Conclusions The views and opinions expressed by long-stay patients showed that psychological distress is prevailing in forensic psychiatric hospitals. Adequate and effective care and mental health interventions are recommended to be tailored for their special needs