The Analysis of 2007 APEC News Coverage on the ABC, CNN and Xinhua Websites

This essay examines major issues of Australia, the United States and China concerned about in the 2007 APEC Summit held in Australia, and discusses the attitudes of these three countries towards to the 2007 APEC, by examining all the articles about 2007 APEC Summit from three major websites from these countries, ABC, CNN and Xinhua websites. Key words:  2007 APEC Summit; ABC; CNN; Xinhua Résumé:  Cet article examine les grands événements de l'Australie, des États-Unis et de la Chine au Sommet de l'APEC 2007, qui s'est tenue en Australie, et examine les attitudes de ces trois pays vis-à-vis de l'APEC 2007 en examinant tous les articles sur le Sommet de l'APEC 2007 dans les trois grands sites Web de ces pays, ABC, CNN et Xinhua.Mots-clés: sommet APEC 2007; ABC; CNN; Xinhu

VS-TransGRU: A Novel Transformer-GRU-based Framework Enhanced by Visual-Semantic Fusion for Egocentric Action Anticipation

Egocentric action anticipation is a challenging task that aims to make advanced predictions of future actions from current and historical observations in the first-person view. Most existing methods focus on improving the model architecture and loss function based on the visual input and recurrent neural network to boost the anticipation performance. However, these methods, which merely consider visual information and rely on a single network architecture, gradually reach a performance plateau. In order to fully understand what has been observed and capture the dependencies between current observations and future actions well enough, we propose a novel visual-semantic fusion enhanced and Transformer GRU-based action anticipation framework in this paper. Firstly, high-level semantic information is introduced to improve the performance of action anticipation for the first time. We propose to use the semantic features generated based on the class labels or directly from the visual observations to augment the original visual features. Secondly, an effective visual-semantic fusion module is proposed to make up for the semantic gap and fully utilize the complementarity of different modalities. Thirdly, to take advantage of both the parallel and autoregressive models, we design a Transformer based encoder for long-term sequential modeling and a GRU-based decoder for flexible iteration decoding. Extensive experiments on two large-scale first-person view datasets, i.e., EPIC-Kitchens and EGTEA Gaze+, validate the effectiveness of our proposed method, which achieves new state-of-the-art performance, outperforming previous approaches by a large margin.Comment: 12 pages, 7 figure

InDL: A New Datasets and Benchmark for In-Diagram Logic Interpreting based on Visual Illusion

This paper introduces a novel approach to evaluating deep learning models' capacity for in-diagram logic interpretation. Leveraging the intriguing realm of visual illusions, we establish a unique dataset, InDL, designed to rigorously test and benchmark these models. Deep learning has witnessed remarkable progress in domains such as computer vision and natural language processing. However, models often stumble in tasks requiring logical reasoning due to their inherent 'black box' characteristics, which obscure the decision-making process. Our work presents a new lens to understand these models better by focusing on their handling of visual illusions -- a complex interplay of perception and logic. We utilize six classic geometric optical illusions to create a comparative framework between human and machine visual perception. This methodology offers a quantifiable measure to rank models, elucidating potential weaknesses and providing actionable insights for model improvements. Our experimental results affirm the efficacy of our benchmarking strategy, demonstrating its ability to effectively rank models based on their logic interpretation ability. As part of our commitment to reproducible research, the source code and datasets will be made publicly available here: \href{https://github.com/rabbit-magic-wh/InDL}{https://github.com/rabbit-magic-wh/InDL}

Hyperon polarization and its correlation with directed flow in high-energy nuclear collisions

We investigate the hyperon polarization and its correlation with the directed flow of the quark-gluon plasma (QGP) in non-central Au+Au collisions at $\sqrt{s_{\textrm{NN}}} = 27$ GeV. A modified 3-dimensional (3D) Glauber model is developed and coupled to a (3+1)-D viscous hydrodynamic evolution of the QGP. Within this framework, we obtain a satisfactory simultaneous description of the directed flow of identified particles and $\Lambda$ polarization, and show sensitivity of polarization to both the tilted geometry and the longitudinal flow profile of the QGP. A non-monotonic transverse momentum dependence of the $\Lambda$ polarization is found in our calculation, which is absent from hydrodynamic simulation using other initialization methods and can be tested by future experimental data with higher precision. A strong correlation (or anti-correlation) is found between the global polarization and directed flow of $\Lambda$ when the longitudinal flow field (or medium deformation) varies, indicating the common origin of these two quantities. Therefore, a combination of these observables may provide a more stringent constraint on the initial condition of the QGP.Comment: 15 pages, 12 figures, any comments are welcom