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Existing news recommendation methods often rely on static user–news interactions that fail to account for the evolving nature of users’ preferences over time. To address the prevalent challenges, which frequently struggle to accurately capture ...

Word order, a linguistic concept essential for conveying accurate meaning, is seemingly not that necessary in language models based on the existing works. Contrary to this prevailing notion, our paper delves into the impacts of word order by ...

• Word order is reexamined by 4 tasks, 3 strategies, 3 languages and 5 models.
• The tested datasets includes TruthfulQA, MGSM, XWinoGrande and WiQueen.
• The word order perturbation strategies include Random, Rotate and Adjacent.
• ...

We study sentiment analysis task where the outcomes are mainly contributed by a few key elements of the inputs. Motivated by the two-streams hypothesis, we explore processing input items and their weights separately by developing a neural ...

• We apply the two-stream hypothesis to SA, and process items and weights separately.
• We propose a neural network, namely TraceNet, consisting of encoders and locators.
• Smoothness regularization, sparsity constraint, and proactive ...

Several studies have focused on incorporating language models with entity descriptions to facilitate the model with a better understanding of knowledge. Existing methods usually either integrate descriptions in the pre-training stage by designing ...

With the popularization of DNA microarray technology, gene data classification has become one of the major frontiers in bioinformatics. The complexity of mi-croarray experiments leads to gene expression profiling data with small samples and high ...

Multi-task Learning (MTL) makes a positive difference in many fields by improving the prediction effects of correlated tasks among multiple related data sets. Some financial Multivariate Time Series (MTS) also have a high correlation, but ...

As more and more netizens participate in financial market transactions, online discussions on asset price movements are becoming more comprehensive and timely. Online text, especially from social media, has the potential to be an important data ...

Subtle changes in fine motor control and quantitative electroencephalography (qEEG) in patients with mild cognitive impairment (MCI) are important in screening for early dementia in primary care populations. In this study, an automated,...

• An efficient measure based on handwriting and EEG data is presented for detection of MCI.

Smoothing the background and highlighting the foreground text of document images not only improve the reading experience but also benefits the subsequent document analysis and recognition algorithms. However, most of the past low-light image enhancement ...

To address the problems of sparse data, low recommendation accuracy and poor recommendation effect in recommendation systems. In this paper, we propose a recommendation algorithm that fuses the self-attention mechanism and knowledge graph. The algorithm ...

For self-driving cars and advanced driver assistance systems, lane detection is imperative. On the one hand, numerous current lane line detection algorithms perform dense pixel-by-pixel prediction followed by complex post-processing. On the other ...

Most existing regression-based classification methods cope with pixelwise noise via ${\ell }_1$-norm or ${\ell }_2$-norm, but neglect the structural information between pixels. To the best of our knowledge, nuclear norm-based matrix regression approaches have ...

Community detection is a critical issue in the field of complex networks. Capable of extracting inherent patterns and structures in high dimensional data, the non-negative matrix factorization (NMF) method has become one of the hottest research ...

Functional connectivity network, which as a simplified representation of functional interactions, it has been widely used for diseases diagnosis and classification, especially for Alzheimer’s disease (AD). Although, many methods for functional ...

Investigating functional brain networks and activities using sparse representation of fMRI data has received significant interests in the neuroimaging field. It has been reported that sparse representation is effective in reconstructing concurrent ...

A variety of studies in the brain mapping field have reported that the dictionary learning and sparse representation framework is efficient and effective in reconstructing concurrent functional brain networks based on the functional magnetic resonance ...

To solve the problem that the single-peak and sensitivity of conventional image sharpness functions come down which results in slowing auto-focusing speed or even falling to focus because of noise, an anti-noise auto-focusing algorithm is proposed in ...

A novel method, multilayer electroplating, was proposed to prepare alloy bumps. It consists to electroplating different structural elements of alloys sequentially and then forming uniform alloy through reflowing. The formation of eutectic SnAg alloy ...

In order to study the CO/HC change status of the exhaust pollutant concentration after using ethanol gasoline in vehicle, the chosen 30 automobiles are tested as the samples in Zhengzhou City. The results show that the exhaust of CO/HC of the vehicles ...

New blind estimation of MIMO channels using HGA was proposed for the frequency selective channel. By exploiting the output statistics, an objective function that coming from blind estimation of MIMO channels based on the subspace could be obtained. Then ...