Understanding the underlying argumentative flow in analytic argumentative writing is essential for discourse comprehension, especially in complex argumentative discourse such as think-tank commentary.However, existing structure modeling approaches often rely on surface-level topic segmentation, failing to capture the author's rhetorical intent and reasoning process.To address this limitation, we propose a Question-Focus discourse structuring framework that explicitly models the underlying argumentative flow by anchoring each argumentative unit to a guiding question (reflecting the author's intent) and a set of attentional foci (highlighting analytical pathways).To assess its effectiveness, we introduce an argument reconstruction task in which the modeled discourse structure guides both evidence retrieval and argument generation.We construct a high-quality dataset comprising 600 authoritative Chinese think-tank articles for experimental analysis.To quantitatively evaluate performance, we propose two novel metrics: (1) Claim Coverage, measuring the proportion of original claims preserved or similarly expressed in reconstructions, and (2) Evidence Coverage, assessing the completeness of retrieved supporting evidence.Experimental results show that our framework uncovers the author's argumentative logic more effectively and offers better structural guidance for reconstruction, yielding up to a 10% gain in claim coverage and outperforming strong baselines across both curated and LLM-based metrics.

Abstract Due to the powerful ability in capturing the global information, transformer has become an alternative architecture of CNNs for hyperspectral image classification. However, general transformer mainly considers the global spectral information while ignores the multiscale spatial information of the hyperspectral image. In this paper, we propose a multiscale spectral–spatial convolutional transformer (MultiFormer) for hyperspectral image classification. First, the developed method utilizes multiscale spatial patches as tokens to formulate the spatial transformer and generates multiscale spatial representation of each band in each pixel. Second, the spatial representation of all the bands in a given pixel are utilized as tokens to formulate the spectral transformer and generate the multiscale spectral–spatial representation of each pixel. Besides, a modified spectral–spatial CAF module is constructed in the MultiFormer to fuse cross‐layer spectral and spatial information. Therefore, the proposed MultiFormer can capture the multiscale spectral–spatial information and provide better performance than most of other architectures for hyperspectral image classification. Experiments are conducted over commonly used real‐world datasets and the comparison results show the superiority of the proposed method.

Convolutional neural networks (CNNs) have shown their potential ability to extract discriminative features for hyperspectral image classification. However, traditional deep learning methods using CNNs tend to overlook the influence of complex environmental factors. These factors contribute to an increase in intraclass variance and a decrease in interclass variance, making it considerably more challenging to extract meaningful features. To overcome this problem, this work develops a novel deep intrinsic decomposition with adversarial learning, namely AdverDecom, for hyperspectral image classification to mitigate the negative impact of environmental factors on classification performance. First, we develop a generative network for hyperspectral images (HyperNet) to extract the environment-related features and category-related features from the image. Then, a discriminative network is constructed to distinguish different environmental categories. Finally, an environment-category joint learning loss is developed for adversarial learning to make the deep model learn discriminative features. Experiments are conducted over four commonly used real-world datasets and the comparison results show the superiority of the proposed method. The implementation of the proposed method could be accessed athttps://github.com/shendu-sw/Adversarial_Learning_Intrinsic_Decompositionfor the sake of reproducibility.

The dissection of hyperspectral images into intrinsic components through hyperspectral intrinsic image decomposition (HIID) enhances the interpretability of hyperspectral data, providing a foundation for more accurate classification outcomes. However, the classification performance of HIID is constrained by the model’s representational ability. To address this limitation, this study rethinks hyperspectral intrinsic image decomposition for classification tasks by introducing deep feature embedding. The proposed framework, HyperDID, incorporates the Environmental Feature Module (EFM) and Categorical Feature Module (CFM) to extract intrinsic features. Additionally, a Feature Discrimination Module (FDM) is introduced to separate environment-related and category-related features. Experimental results across three commonly used datasets validate the effectiveness of HyperDID in improving hyperspectral image classification performance. This novel approach holds promise for advancing the capabilities of hyperspectral image analysis by leveraging deep feature embedding principles. The implementation of the proposed method could be accessed soon at https://github.com/shendu-sw/HyperDID for the sake of reproducibility.

A scientific claim typically begins with the formulation of a research question or hypothesis, which is a tentative statement or proposition about a phenomenon or relationship between variables.Within the realm of scientific claim verification, considerable research efforts have been dedicated to attention architectures and leveraging the text comprehension capabilities of Pre-trained Language Models (PLMs), yielding promising performances.However, these models overlook the causal structure information inherent in scientific claims, thereby failing to establish a comprehensive chain of causal inference.This paper delves into the exploration to highlight the crucial role of qualitative causal structure in characterizing and verifying scientific claims based on evidence.We organize the qualitative causal structure into a heterogeneous graph and propose a novel attentionbased graph neural network model to facilitate causal reasoning across relevant causallypotent factors.Our experiments demonstrate that by solely utilizing the qualitative causal structure, the proposed model achieves comparable performance to PLM-based models.Furthermore, by incorporating semantic features, our model outperforms state-of-the-art approaches comprehensively. 1

For the emerging mobility-on-demand services, it is of great significance to predict passenger demands based on historical mobility trips towards better vehicle distribution. Prior works have focused on predicting next-step passenger demands at selected locations or hotspots. However, we argue that multi-step citywide passenger demands encapsulate both time-varying demand trends and global statuses, and hence are more beneficial to avoiding demand-service mismatching and developing effective vehicle distribution/scheduling strategies. Furthermore, we find that adaptations of single-step methods are unable to achieve robust prediction with high accuracy for further steps. In this paper, we propose an end-to-end deep neural network model to the prediction task. We employ an encoder-decoder framework based on convolutional and ConvLSTM units to identify complex features that capture spatiotemporal influence and pickup-dropoff interactions on citywide passenger demands. We introduce a multi-level attention model (global attention and temporal attention) to emphasize the effects of latent citywide mobility regularities and capture relevant temporal dependencies. We evaluate our proposed method using real-world mobility trips (taxis and bikes) and the experimental results show that our method achieves higher prediction accuracy than the state-of-the-art approaches.

The goal of traffic prediction is to predict future traffic values such as speeds or flows in a transportation network. Existing studies mainly focus on exploring spatiotemporal dependencies in time and space domains. However, they generally neglect the frequency information encoded in the traffic data, which sheds light on the true spatiotemporal correlations among any vertices in the transportation network. In this paper, we aim to incorporate two kinds of frequency information, time-frequency and space-frequency, into the modeling of spatiotemporal dependencies towards more accurate traffic prediction. We investigate the importance of two frequencies using real traffic data and develop an end-to-end neural approach to utilizing both frequencies for traffic prediction effectively. Specifically, we propose an attention module to aggregate features from semantically related vertices according to their time-frequency similarities, and derive a semantic representation. A novel space-frequency-based gating mechanism is designed to control how much local information is propagated to the state updating, enforced with the graph convolution to generate an effective local representation. Finally, our model combines the semantic and local representations with gated recurrent units to predict future traffic values. Extensive experiments on two real-world traffic datasets demonstrate that our proposed approach yields better prediction performance than the state-of-the-art methods.

Predicting passenger pickup/dropoff demands based on historical mobility trips has been of great importance towards better vehicle distribution for the emerging mobility-on-demand (MOD) services. Prior works focused on predicting next-step passenger demands at selected locations or hotspots. However, we argue that multi-step citywide passenger demands encapsulate both time-varying demand trends and global statuses, and hence are more beneficial to avoiding demand-service mismatching and developing effective vehicle distribution/scheduling strategies. In this paper, we propose an end-to-end deep neural network solution to the prediction task. We employ the encoder-decoder framework based on convolutional and ConvLSTM units to identify complex features that capture spatiotemporal influences and pickup-dropoff interactions on citywide passenger demands. A novel attention model is incorporated to emphasize the effects of latent citywide mobility regularities. We evaluate our proposed method using real-word mobility trips (taxis and bikes) and the experimental results show that our method achieves higher prediction accuracy than the adaptations of the state-of-the-art approaches.