Representation Of Segments Research Articles

Analyzing the Theme of The Everlasting Regret from the Perspective of Segmented Discourse Representation Theory

This research conducts a thematic analysis of The Everlasting Regret based on Segmented Discourse Representation Theory (SDRT), aiming to uncover its deeper meanings by exploring the text's discourse structure and rhetorical relations. Unlike traditional literary criticism approaches, this study adopts the SDRT framework, with a focus on analyzing the rhetorical relations between Compound Discourse Units (CDUs). The academic debate surrounding the theme of The Everlasting Regret is extensive, with major perspectives including allegorical, romantic, and dual-theme interpretations. Through detailed analysis, this research argues that The Everlasting Regret does not present a dual theme, but rather a single one. Moreover, this theme is not a celebration of love, but rather a subtle and nuanced form of satire. Additionally, the research offers an in-depth examination of key discourse units within the text, revealing that the rhetorical relations among them can be interpreted as Contrast, Result, and Narration. This not only highlights the complexity of discourse coherence and textual structure, but also suggests a potential intrinsic link among these three rhetorical relations.

Radial awareness with adaptive hybrid CNN-Transformer range-view representation for outdoor LiDAR point cloud semantic segmentation

Radial awareness with adaptive hybrid CNN-Transformer range-view representation for outdoor LiDAR point cloud semantic segmentation

Single-Cell Analysis in Cerebrovascular Research: Primed for Breakthroughs and Clinical Impact.

Data generated using single-cell RNA-sequencing has the potential to transform understanding of the cerebral circulation and advance clinical care. However, the high volume of data, sometimes generated and presented without proper pathophysiological context, can be difficult to interpret and integrate into current understanding of the cerebral circulation and its disorders. Furthermore, heterogeneity in the representation of brain regions and vascular segments makes it difficult to compare results across studies. There are currently no standards for tissue collection and processing that allow easy comparisons across studies and analytical platforms. There are no standards either for single-cell data analysis and presentation. This topical review introduces single-cell RNA-sequencing to physicians and scientists in the cerebrovascular field, with the goals of highlighting opportunities and challenges of applying this technology in the cerebrovascular field and discussing key concepts and knowledge gaps that can be addressed by single-cell RNA-sequencing.

Possibilistic C-means with novel image representation for image segmentation

Image segmentation is the process of automatically dividing an image into several parts and extracting the relevant data and information. Compared to the traditional Fuzzy C-Means algorithm, the Possibilistic C-Means (PCM) algorithm has advantages in reducing the influence of noise on cluster center estimation. However, the PCM algorithm still shows poor clustering performance under high-intensity noise, which may lead to overlapping cluster centers. Considering the impact of neighborhood information of image pixels on the image segmentation results, this paper proposes a Vector-Based Possibilistic C-Means (VBPCM) algorithm. The algorithm incorporates neighborhood information and uses a vector representation method to describe image pixels. Additionally, an adjustable distance based on an exponential function is proposed to describe the similarity between vectors. The proposed VBPCM algorithm outperforms the conventional PCM, obtaining uplifiting gains of 4%, 2%, and 9% in Pixel Accuracy, Mean Pixel Accuracy, and Mean Intersection over Union, respectively. The experimental outputs illustrate that VBPCM algorithm can achieve more satisfactory cluster effect with high-intensity noise, further perform better in image segmentation task.

Analyzing TripAdvisor Reviews: Investigating Customer Feedback to Enhance Guest Retention Strategies in Leading Ankara Hotels

This study investigates how premium hotels in Ankara can leverage data-driven strategies to enhance customer experiences, boost loyalty, and increase repeat guest expenditure. A qualitative content analysis was conducted on 186 TripAdvisor guest reviews from four leading luxury hotels in Ankara: Hilton Ankara Hotel, JW Marriott Ankara Hotel, Lugal, a Luxury Collection Hotel, and Sheraton Ankara Hotel & Convention Center. The reviews were analyzed focusing on key themes such as service quality, room cleanliness, breakfast offerings, and staff interactions. The study employed a thematic analysis approach to identify recurring patterns and themes within the guest feedback. The sample was selected based on the hotels' premium status and pricing, ensuring representation of the luxury segment in Ankara's hospitality market. Findings reveal high levels of overall satisfaction but also highlight areas for improvement, particularly in side services. The study emphasizes the importance of analyzing guest feedback to create personalized and targeted service offerings. Recommendations for hoteliers include optimizing service quality and tailoring experiences to meet guest preferences, thereby strengthening long-term customer loyalty.

The Coherence Relations of "Mollie" in Animal Farm under Segmented Discourse Representation Theory(SDRT)

This paper delves into the coherence relations of Mollie, a character from George Orwell's "Animal Farm," utilizing the Segmented Discourse Representation Theory (SDRT) to analyze her role within the narrative. The study reviews the current research on "Animal Farm," highlighting the scarcity of in-depth analysis of Mollie's character, which is often overlooked in favor of more prominent characters. The paper introduces SDRT, a linguistic theory that dissects discourse into smaller segments to examine logical connections and coherence, providing a fresh perspective to explore Mollie's actions and their implications in the novel. The application of the SDRT framework to Mollie's character reveals how her individual desires and actions contrast with the collective ethos of the farm, symbolizing the broader themes of individualism versus collectivism. It also underscores her reactions to the power structures, which are indicative of the farm's societal dynamics. The analysis contributes to a deeper understanding of Mollie's role and her interactions with other characters, offering insights into the underlying social and political commentary of "Animal Farm." The paper concludes that SDRT not only enriches our comprehension of Mollie's character but also enhances the interpretation of the novel's overall message, shedding light on the complexities of power, freedom, and loyalty within the context of a totalitarian regime.

Dilated dendritic learning of global–local feature representation for medical image segmentation

Medical image segmentation serves as an important tool in the treatment of various medical diseases. However, achieving precise and efficient segmentation remains challenging due to the intricate structures and variations. Although neural network methods based on U-shaped structures have shown impressive results, they often lack effective representation of global–local features, leading to insufficient extraction of multi-scale and contextual information in medical image segmentation tasks. To tackle these challenges, we propose a novel approach: a dilated dendritic module with deep supervision, namely 3DL-Net. It integrates the flexible dilated convolution mechanism into the segmentation architecture, aiming to expand the model’s receptive field and capture richer global features. Additionally, in contrast to other segmentation architectures, we innovatively introduce the processing of local feature of shallow structures through the dendritic neuron module in medical images into 3DL-Net. This is the first time dendritic learning has been employed at the channel level and represents a pioneering approach to the local feature process. During the training process, 3DL-Net incorporates a deep supervision mechanism that utilizes our designed loss function. Due to the intermediate supervision signals at various network stages, providing feedback at multiple levels, the model refines its predictions across various scales, contributing to further enhanced segmentation outcomes. To evaluate the effectiveness of our proposed method, we conducted extensive experiments on three medical image datasets to demonstrate significant improvements in segmentation accuracy compared to state-of-the-art models. Our mDice metrics on three datasets achieved 86.61%, 87.87%, and 85.06%, surpassing the second-best models by 3.85%, 1.54%, and 0.95%.

ERST: A Signaled Graph Theory of Discourse Relations and Organization

Abstract In this article we present Enhanced Rhetorical Structure Theory (eRST), a new theoretical framework for computational discourse analysis, based on an expansion of Rhetorical Structure Theory (RST). The framework encompasses discourse relation graphs with tree-breaking, non-projective and concurrent relations, as well as implicit and explicit signals which give explainable rationales to our analyses. We survey shortcomings of RST and other existing frameworks, such as Segmented Discourse Representation Theory, the Penn Discourse Treebank, and Discourse Dependencies, and address these using constructs in the proposed theory. We provide annotation, search, and visualization tools for data, and present and evaluate a freely available corpus of English annotated according to our framework, encompassing 12 spoken and written genres with over 200K tokens. Finally, we discuss automatic parsing, evaluation metrics, and applications for data in our framework.

In the Tunisian Opposition (Again): Ennahda's ‘Renaissance’ Through Failure?

AbstractTunisian President Kais Saied's coup in July 2021 has confronted the Renaissance Party, or Ennahda, with a dilemma: Reassume the comfortable position of defending freedoms and maintain party unity, or implement internal reforms, including an overhaul of its leadership and platform. But the party is trapped in a vicious circle. The political context requires unity and is therefore not conducive to a messy process of internal change. However, reform is needed to restore Ennahda's relevance. The repression of Ennahda and imprisonment of its key leaders—including its president, Rached Ghannouchi—make it unlikely that the party's 11th congress, which had been due to take place in 2020, will ever be scheduled. This article examines Ennahda's ascent after the 2011 uprisings and its role both in the transition to democracy and in the events that led to Saied's takeover. It then analyzes critiques by current and former party officials to understand the internal debate over renewing the platform and installing fresh leadership. It also evaluates alternatives to the party, as it is likely to fade gradually. At stake for Tunisia more broadly is the representation of the conservative segment of the population and the return of a pluralist and competitive political scene.

Organizational Diffusion: What India Tells Us about How the Far Right Wins

Presenting a world-first data set of 2,831 constitutive organizations of history's largest far-right interorganizational network, this article presents a new explanation for far-right normalization: organizational diffusion. Providing, for the first time, empirical evidence of the large network characteristics of the Indian far right, this article paints a picture of what this network actually looks like, how far it has spread, and what explains its success. In doing so, this explanation unearths a far-right strategy of covert civil society expansion that has largely evaded the party-focused extant study of global far-right electoral mobilization. Identifying this strategy of organizational diffusion, this article argues that it produces three effects that produce broad, flexible, and durable mobilizations: segmented representation, reputational control, and leadership accommodation. Organizational diffusion, present in far-right mobilizations as diverse as Ma Ba Tha, Nippon Kaigi, and the Thai military, presents an important far-right mobilizing tool that exhorts scholars to refocus on covert civil society expansion as a key mechanism of far-right normalization.

Production priming of stress in nonwords

In this study, we demonstrate production-to-production priming of stress in nonwords, when participants must assign a stress pattern without reference to lexical information. This priming effect is boosted when segmental material overlaps between prime and target, but is robust even when no segmental material overlaps. We argue that stress patterns are represented in the minds of speakers, independently from segmental representations. These independent stress representations must be active alongside segmental representations during production.

Bidirectional temporal and frame-segment attention for sparse action segmentation of figure skating

Temporal action segmentation is a task for understanding human activities in long-term videos. Most of the efforts have been focused on dense-frame action, which relies on strong correlations between frames. However, in the figure skating scene, technical actions are sparsely shown in the video. This brings new challenges: a large amount of redundant temporal information leads to weak frame correlation. To end this, we propose a Bidirectional Temporal and Frame-Segment Attention Module (FSAM). Specifically, we propose an additional reverse-temporal input stream to enhance frame correlation, learned by fusing bidirectional temporal features. In addition, the proposed FSAM contains a Multi-stage segment-aware GCN and decoder interaction module, aiming to learn the correlation between segment features across time domains and integrate embeddings between frame and segment representations. To evaluate our approach, we propose the Figure Skating Sparse Action Segmentation (FSSAS) dataset: The dataset comprises 100 samples of the Olympic figure skating final and semi-final competition, with more than 50 different men and women athletes. Extensive experiments show that our method achieves an accuracy of 87.75 and an edit score of 90.18 on the FSSAS dataset.

DAT-Net: Deep Aggregation Transformer Network for automatic nuclear segmentation

Detection and segmentation of cell nuclei in hematoxylin and eosin-stained tissue images pose critical clinical challenges, including intricate backgrounds, variable nuclear appearances, overlapping nuclei, and unclear boundaries. These complexities render automated instance segmentation a difficult research area. Thus, this paper proposes a deep aggregation transformer network for automatic nucleus instance segmentation. The method employs a robust Vision Transformer-based backbone to extract informative features from images. To fuse multi-level information, we propose the group aggregation multi-level feature fusion module, which merges low-level features for shape representation and high-level features for pixel edge segmentation, enhancing overall model performance. The depthwise integrated multilayer perceptron module enhances feature learning by combining the global context-dependent representation of MLP and the local representation of convolution through channel separation. This fosters feature learning across different modes, thereby improving network representation. The bidirectional path interaction feature processing module is introduced for multi-scale information extraction and fusion. This module enhances the entire feature pyramid hierarchy by transferring deep semantic information to shallow layers with robust spatial information via bidirectional paths. The Large kernel Attention module at each layer extensively improves feature extraction through a large receptive field. Our approach outperforms state-of-the-art models, including StarDIST, CPP-Net, TSFD-Net and TransNuSeg, by 7.8%, 6.3%, 1.5% and 3.5% in average panoptic quality for different cell nuclei segmentation categories across tissues of the Pannuke dataset. Furthermore, DAT-Net outperforms all other state-of-the-art models in terms of the F1 score for the five classes of cell nuclei in the PanNuke dataset.

A Coherent Analysis of The Lottery by Shirley Jackson Using Segmented Discourse Representation Theory

Shirley Jackson's The Lottery is a powerful narrative widely analyzed for its social, cultural, and literary dimensions. However, the linguistic intricacies of Jackson's work remain underexplored. This study addresses this gap by applying Segmented Discourse Representation Theory (SDRT) to analyze the discourse structure of The Lottery. SDRT, a formal theory of discourse that emphasizes coherence relations, provides a systematic framework for examining how different narrative segments interact to create a cohesive and impactful story. By employing SDRT, this research uncovers the intricate ways in which Jackson's linguistic choices contribute to the story's thematic depth and emotional resonance. This analysis not only enhances our understanding of Jackson's narrative techniques but also demonstrates the applicability of SDRT in literary discourse analysis, offering new insights into the dynamic development of discourse within literary texts.

Directional latent space representation for medical image segmentation

Directional latent space representation for medical image segmentation

Exploiting Low-Level Representations for Ultra-Fast Road Segmentation

Exploiting Low-Level Representations for Ultra-Fast Road Segmentation

NeighborNet: Learning Intra- and Inter-Image Pixel Neighbor Representation for Breast Lesion Segmentation.

Breast lesion segmentation from ultrasound images is essential in computer-aided breast cancer diagnosis. To alleviate the problems of blurry lesion boundaries and irregular morphologies, common practices combine CNN and attention to integrate global and local information. However, previous methods use two independent modules to extract global and local features separately, such feature-wise inflexible integration ignores the semantic gap between them, resulting in representation redundancy/insufficiency and undesirable restrictions in clinic practices. Moreover, medical images are highly similar to each other due to the imaging methods and human tissues, but the captured global information by transformer-based methods in the medical domain is limited within images, the semantic relations and common knowledge across images are largely ignored. To alleviate the above problems, in the neighbor view, this paper develops a pixel neighbor representation learning method (NeighborNet) to flexibly integrate global and local context within and across images for lesion morphology and boundary modeling. Concretely, we design two neighbor layers to investigate two properties (i.e., number and distribution) of neighbors. The neighbor number for each pixel is not fixed but determined by itself. The neighbor distribution is extended from one image to all images in the datasets. With the two properties, for each pixel at each feature level, the proposed NeighborNet can evolve into the transformer or degenerate into the CNN for adaptive context representation learning to cope with the irregular lesion morphologies and blurry boundaries. The state-of-the-art performances on three ultrasound datasets prove the effectiveness of the proposed NeighborNet.

Online trajectory anomaly detection model based on graph neural networks and variational autoencoder

Abstract To efficiently determine whether an entire trajectory exhibits abnormal behavior, we introduce an online trajectory anomaly detection model known as GeoGNFTOD, which employs graph neural networks for road segment representation, creating a directed graph by mapping trajectories onto the road network. The graph representation is constructed based on the road segments in this directed graph. By utilizing Transformer sequence encoding, the trajectory representation is derived and hierarchical geographic encoding captures the GPS mapping of the original trajectories. Merging these two representations produces the final trajectory representation, serving as input for an LSTM-based variational autoencoder to reconstruct the trajectory representation, facilitating rapid online anomaly detection. Experimental findings on a large-scale taxi trajectory dataset illustrate the superior performance of GeoGNFTOD compared to baseline algorithms.

FDiff-Fusion: Denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation

In recent years, the denoising diffusion model has achieved remarkable success in image segmentation modeling. With its powerful nonlinear modeling capabilities and superior generalization performance, denoising diffusion models have gradually been applied to medical image segmentation tasks, bringing new perspectives and methods to this field. However, existing methods overlook the uncertainty of segmentation boundaries and the fuzziness of regions, resulting in the instability and inaccuracy of the segmentation results. To solve this problem, a denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation (FDiff-Fusion) is proposed in this paper. By integrating the denoising diffusion model into the classical U-Net network, this model can effectively extract rich semantic information from input medical images, thus providing excellent pixel-level representation for medical image segmentation. In this paper, a fuzzy learning module is designed on the skip path of U-Net network because of the widespread boundary uncertainty and region blurring of medical image segmentation. The module sets several fuzzy membership functions for the input encoded features to describe the similarity degree between the feature points, and applies fuzzy rules to the fuzzy membership functions, thus enhancing the modeling ability of the model for uncertain boundaries and fuzzy regions. In addition, in order to improve the accuracy and robustness of the model segmentation results, we introduced an iterative attention feature fusion method in the test phase, which added local context information to the global context information in the attention module to fuse the prediction results of each denoising time step. Finally, to validate the effectiveness of FDiff-Fusion, we compare it with existing advanced segmentation networks on the BRATS 2020 brain tumor dataset and the BTCV abdominal multi-organ dataset. The results show that FDiff-Fusion significantly improves the Dice scores and HD95 distance on these two datasets, demonstrating its superiority in medical image segmentation tasks.

ST-CellSeg: Cell segmentation for imaging-based spatial transcriptomics using multi-scale manifold learning.

Spatial transcriptomics has gained popularity over the past decade due to its ability to evaluate transcriptome data while preserving spatial information. Cell segmentation is a crucial step in spatial transcriptomic analysis, as it enables the avoidance of unpredictable tissue disentanglement steps. Although high-quality cell segmentation algorithms can aid in the extraction of valuable data, traditional methods are frequently non-spatial, do not account for spatial information efficiently, and perform poorly when confronted with the problem of spatial transcriptome cell segmentation with varying shapes. In this study, we propose ST-CellSeg, an image-based machine learning method for spatial transcriptomics that uses manifold for cell segmentation and is novel in its consideration of multi-scale information. We first construct a fully connected graph which acts as a spatial transcriptomic manifold. Using multi-scale data, we then determine the low-dimensional spatial probability distribution representation for cell segmentation. Using the adjusted Rand index (ARI), normalized mutual information (NMI), and Silhouette coefficient (SC) as model performance measures, the proposed algorithm significantly outperforms baseline models in selected datasets and is efficient in computational complexity.