Renewable energy and economic growth hypothesis: Evidence from N-11 countries

In the recent years, the trend of environmental sustainability is rapidly increasing by adopting renewable energy resources. However, the main concern is that whether renewable energy consumption contributes to economic growth. To investigate the issue, this study analyzes renewable energy led economic growth hypothesis in the Next-11 economies over the period 1990–2020. Also, this study aims to examine the influence of industry value added, gross national expenditure, and trade openness on economic growth of these economies. Along with the second-generation panel unit root test, this study employed the nonparametric panel data approach, i.e., quantile method of moments regression. The estimated results reveal the slopes coefficients are heterogeneous and cross-sectional dependency is present in the panel. The non-parametric approach reveals that validity of renewable energy led growth hypothesis. Also, the industry value added, gross national expenditure, and trade openness are found positively affecting economic growth of these economies. The panel causality test gives indication of the two way causal association between the variables. Based on the obtained results, policy implications are also provided for governors and researchers

in KKAy mice

and mechanisms of resveratrol on the amelioration of oxidative stress and hepatic steatosi

Beyond the Obvious: Evaluating the Reasoning Ability In Real-life Scenarios of Language Models on Life Scapes Reasoning Benchmark~(LSR-Benchmark)

This paper introduces the Life Scapes Reasoning Benchmark (LSR-Benchmark), a novel dataset targeting real-life scenario reasoning, aiming to close the gap in artificial neural networks' ability to reason in everyday contexts. In contrast to domain knowledge reasoning datasets, LSR-Benchmark comprises free-text formatted questions with rich information on real-life scenarios, human behaviors, and character roles. The dataset consists of 2,162 questions collected from open-source online sources and is manually annotated to improve its quality. Experiments are conducted using state-of-the-art language models, such as gpt3.5-turbo and instruction fine-tuned llama models, to test the performance in LSR-Benchmark. The results reveal that humans outperform these models significantly, indicating a persisting challenge for machine learning models in comprehending daily human life

DeepFacePencil: Creating Face Images from Freehand Sketches

In this paper, we explore the task of generating photo-realistic face images from hand-drawn sketches. Existing image-to-image translation methods require a large-scale dataset of paired sketches and images for supervision. They typically utilize synthesized edge maps of face images as training data. However, these synthesized edge maps strictly align with the edges of the corresponding face images, which limit their generalization ability to real hand-drawn sketches with vast stroke diversity. To address this problem, we propose DeepFacePencil, an effective tool that is able to generate photo-realistic face images from hand-drawn sketches, based on a novel dual generator image translation network during training. A novel spatial attention pooling (SAP) is designed to adaptively handle stroke distortions which are spatially varying to support various stroke styles and different levels of details. We conduct extensive experiments and the results demonstrate the superiority of our model over existing methods on both image quality and model generalization to hand-drawn sketches.Comment: ACM MM 2020 (oral