EXPLORING GENERIC STRUCTURE POTENTIAL OF SELECTED EDITORIALS IN THE MYANMAR TIMES NEWSPAPER

Systemic Functional Linguists introduced Generic Structural Potential (GSP) that can be used to distinguish different social activities of a text. A variety of researches have been conducted on Generic Structural Potential of the editorials of other countries. However, no research has been conducted about the editorials of Myanmar newspaper. Therefore, The Myanmar Times newspaper, written in English, is chosen to carry out a research. The aim of the research paper is to identify the schematic structural elements of the editorials in The Myanmar Times newspaper. The materials are measured using Generic Structure Potential proposed by Halliday and Hason (1985). The result of the study shows that there are two obligatory elements and five optional elements. Heading (H), and Contributing the Writer’s Opinion (O) are obligatory while Picture (P), Caption(C ), Addressing the issue (AI) , Background Information (BI), and Discussing the issue Raised (D) are optional elements. The sequence of the elements follows the procedure H^ (P)^{ *(BI) *(AI) *(D) }^O

Attitude and Graduation: Appraisal Resources in a Decision of the African Court on Human and Peoples’ Rights

Court Judgments are classified within the legal genre of case-law, which is intended to be objective and impartial. However, despite efforts to conceal speakers’ presence and subjectivity in this context, stance must be taken in pronouncing judgment. This study seeks to understand how linguistic attitude and graduation resources are expressed in legal texts and to examine the mechanisms used for this purpose. The text chosen for analysis is a 2017 judgment of the African Court on Human and Peoples’ Rights (ACHPR) titled: the “African Commission on Human and Peoples’ Rights V. Republic of Kenya”. The applicant, in respect of the Ogiek community of the Greater Mau Forest in the Republic of Kenya, submits to the ACHPR, denouncing violation of Articles 1, 2, 4, and 17 (2) and (3) of the African Charter on Human and Peoples’ Rights by the Republic of Kenya. In order to determine the semantic nature of the linguistic elements set up in this judgement, the Attitude and Graduation systems of the Appraisal theory (Martin and White 2005) in Systemic Functional Linguistics, as well as some conceptual instruments of Raccah’s (2005) Semantic Structure of Points of View (SSPV) are applied to the selected corpus. Keywords: Attitude system, Graduation system, Appraisal framework, points of view, ACHPR, Court judgement DOI: 10.7176/JLLL/77-04 Publication date:March 31st 202

A COMPARISON OF MOOD STRUCTURES IN TWO TV TALK SHOWS WITH GUESTS OF DIFFERENT SOCIAL STATUS

Language cannot be separated from the social context and culture. In everyday communication, people act and react differently depending on interactants, topics involved, and means of communication. This study, therefore, analyses two Talk Shows with guests of different power status- Ellen with Vice President Joe Biden and Ellen with Special Education teacher Jenna Albi. The analysis is carried out using Halliday’s Mood analysis framework, as revised by Matthiessen (2014) and Eggin (2004). The present study compares Ellen’s use of Mood structures and their functions in the interaction with different social beings. It reveals that in the talk with the Vice President, Ellen uses questions most (28.37%), followed by statements (20%)  while in the talk with the Special Education teacher, Ellen prioritizes statements (43%) over questions (14%). On the other hand, she does not enjoy any Commands (0%) in the talk with the Vice President but does so with the teacher (18%). It turns out that, unlike in Fairclough’s (2001) finding, participants with high power status tend to answer questions rather than asking questions

Innovative Cyanine-Based Fluorescent Dye for Targeted Mitochondrial Imaging and Its Utility in Whole-Brain Visualization

Conducting in vivo brain imaging can be a challenging task due to the complexity of brain tissue and the strict requirements for safe and effective imaging agents. However, a new fluorescent dye called Cy5-PEG2 has been developed that selectively accumulates in mitochondria, enabling the visualization of these essential organelles in various cell lines. This dye is versatile and can be used for the real-time monitoring of mitochondrial dynamics in living cells. Moreover, it can cross the blood-brain barrier, making it a promising tool for noninvasive in vivo brain imaging. Based on the assessment of glial cell responses in the hippocampus and neocortex regions using GFAP and Iba1 biomarkers, Cy5-PEG2 seems to have minimal adverse effects on brain immune response or neuronal health. Therefore, this mitochondria-targeting fluorescent dye has the potential to advance our understanding of mitochondrial dynamics and function within the broader context of whole-brain physiology and disease progression. However, further research is needed to evaluate the safety and efficacy of Cy5-PEG2

Breaking Modality Disparity: Harmonized Representation for Infrared and Visible Image Registration

Since the differences in viewing range, resolution and relative position, the multi-modality sensing module composed of infrared and visible cameras needs to be registered so as to have more accurate scene perception. In practice, manual calibration-based registration is the most widely used process, and it is regularly calibrated to maintain accuracy, which is time-consuming and labor-intensive. To cope with these problems, we propose a scene-adaptive infrared and visible image registration. Specifically, in regard of the discrepancy between multi-modality images, an invertible translation process is developed to establish a modality-invariant domain, which comprehensively embraces the feature intensity and distribution of both infrared and visible modalities. We employ homography to simulate the deformation between different planes and develop a hierarchical framework to rectify the deformation inferred from the proposed latent representation in a coarse-to-fine manner. For that, the advanced perception ability coupled with the residual estimation conducive to the regression of sparse offsets, and the alternate correlation search facilitates a more accurate correspondence matching. Moreover, we propose the first ground truth available misaligned infrared and visible image dataset, involving three synthetic sets and one real-world set. Extensive experiments validate the effectiveness of the proposed method against the state-of-the-arts, advancing the subsequent applications.Comment: 10 pages, 11 figure

Few-Shot Non-Parametric Learning with Deep Latent Variable Model

Most real-world problems that machine learning algorithms are expected to solve face the situation with 1) unknown data distribution; 2) little domain-specific knowledge; and 3) datasets with limited annotation. We propose Non-Parametric learning by Compression with Latent Variables (NPC-LV), a learning framework for any dataset with abundant unlabeled data but very few labeled ones. By only training a generative model in an unsupervised way, the framework utilizes the data distribution to build a compressor. Using a compressor-based distance metric derived from Kolmogorov complexity, together with few labeled data, NPC-LV classifies without further training. We show that NPC-LV outperforms supervised methods on all three datasets on image classification in low data regime and even outperform semi-supervised learning methods on CIFAR-10. We demonstrate how and when negative evidence lowerbound (nELBO) can be used as an approximate compressed length for classification. By revealing the correlation between compression rate and classification accuracy, we illustrate that under NPC-LV, the improvement of generative models can enhance downstream classification accuracy.Comment: Accepted to NeurIPS202

WaterFlow: Heuristic Normalizing Flow for Underwater Image Enhancement and Beyond

Underwater images suffer from light refraction and absorption, which impairs visibility and interferes the subsequent applications. Existing underwater image enhancement methods mainly focus on image quality improvement, ignoring the effect on practice. To balance the visual quality and application, we propose a heuristic normalizing flow for detection-driven underwater image enhancement, dubbed WaterFlow. Specifically, we first develop an invertible mapping to achieve the translation between the degraded image and its clear counterpart. Considering the differentiability and interpretability, we incorporate the heuristic prior into the data-driven mapping procedure, where the ambient light and medium transmission coefficient benefit credible generation. Furthermore, we introduce a detection perception module to transmit the implicit semantic guidance into the enhancement procedure, where the enhanced images hold more detection-favorable features and are able to promote the detection performance. Extensive experiments prove the superiority of our WaterFlow, against state-of-the-art methods quantitatively and qualitatively.Comment: 10 pages, 13 figure

Contrastive Learning Based Recursive Dynamic Multi-Scale Network for Image Deraining

Rain streaks significantly decrease the visibility of captured images and are also a stumbling block that restricts the performance of subsequent computer vision applications. The existing deep learning-based image deraining methods employ manually crafted networks and learn a straightforward projection from rainy images to clear images. In pursuit of better deraining performance, they focus on elaborating a more complicated architecture rather than exploiting the intrinsic properties of the positive and negative information. In this paper, we propose a contrastive learning-based image deraining method that investigates the correlation between rainy and clear images and leverages a contrastive prior to optimize the mutual information of the rainy and restored counterparts. Given the complex and varied real-world rain patterns, we develop a recursive mechanism. It involves multi-scale feature extraction and dynamic cross-level information recruitment modules. The former advances the portrayal of diverse rain patterns more precisely, while the latter can selectively compensate high-level features for shallow-level information. We term the proposed recursive dynamic multi-scale network with a contrastive prior, RDMC. Extensive experiments on synthetic benchmarks and real-world images demonstrate that the proposed RDMC delivers strong performance on the depiction of rain streaks and outperforms the state-of-the-art methods. Moreover, a practical evaluation of object detection and semantic segmentation shows the effectiveness of the proposed method.Comment: 13 pages, 16 figure