Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering

Collaborative filtering (CF) is a widely employed technique that predicts user preferences based on past interactions. Negative sampling plays a vital role in training CF-based models with implicit feedback. In this paper, we propose a novel perspective based on the sampling area to revisit existing sampling methods. We point out that current sampling methods mainly focus on Point-wise or Line-wise sampling, lacking flexibility and leaving a significant portion of the hard sampling area un-explored. To address this limitation, we propose Dimension Independent Mixup for Hard Negative Sampling (DINS), which is the first Area-wise sampling method for training CF-based models. DINS comprises three modules: Hard Boundary Definition, Dimension Independent Mixup, and Multi-hop Pooling. Experiments with real-world datasets on both matrix factorization and graph-based models demonstrate that DINS outperforms other negative sampling methods, establishing its effectiveness and superiority. Our work contributes a new perspective, introduces Area-wise sampling, and presents DINS as a novel approach that achieves state-of-the-art performance for negative sampling. Our implementations are available in PyTorch

Enriched environment improves working memory impairment of mice with traumatic brain injury by enhancing histone acetylation in the prefrontal cortex

Working memory impairment is a common cognitive dysfunction after traumatic brain injury (TBI), which severely affects the quality of life of patients. Acetylcholine is a neurotransmitter which is closely related to cognitive functions. In addition, epigenetic modifications are also related to cognitive functions. A neurorehabilitation strategy, enriched environment (EE) intervention, has been widely used to improve cognitive impairment. However, studies of the mechanism of EE on cholinergic system and epigenetic modifications in mouse with TBI have not been reported yet. In this paper, a mouse model with traumatic frontal lobe injury was established, and the mechanism on EE for the mice with TBI was explored. It was found that EE could improve Y-maze performance of mice with TBI, the function of cholinergic system, and the imbalance of acetylation homeostasis in the prefrontal cortex of contralateral side of TBI. In addition, EE also could increase the level of CREB binding protein and histones H3 acetylation at ChAT gene promoter region in the prefrontal cortex of contralateral side of TBI. These indicate that EE has an important effect on the improvement of working memory impairment and the underlying mechanism may involve in histones H3 acetylation at ChAT gene promoter regions in the prefrontal cortex

Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic

Chinese News Text Classification Method via Key Feature Enhancement

(1) Background: Chinese news text is a popular form of media communication, which can be seen everywhere in China. Chinese news text classification is an important direction in natural language processing (NLP). How to use high-quality text classification technology to help humans to efficiently organize and manage the massive amount of web news is an urgent problem to be solved. It is noted that the existing deep learning methods rely on a large-scale tagged corpus for news text classification tasks and this model is poorly interpretable because the size is large. (2) Methods: To solve the above problems, this paper proposes a Chinese news text classification method based on key feature enhancement named KFE-CNN. It can effectively expand the semantic information of key features to enhance sample data and then combine the zero–one binary vector representation to transform text features into binary vectors and input them into CNN model for training and implementation, thus improving the interpretability of the model and effectively compressing the size of the model. (3) Results: The experimental results show that our method can significantly improve the overall performance of the model and the average accuracy and F1-score of the THUCNews subset of the public dataset reached 97.84% and 98%. (4) Conclusions: this fully proved the effectiveness of the KFE-CNN method for the Chinese news text classification task and it also fully demonstrates that key feature enhancement can improve classification performance

A Modified 2D Multiresolution Hybrid Algorithm for Ultrasound Strain Imaging

Ultrasound elastography is an imaging modality to evaluate elastic properties of soft tissue. Recently, 1D quasi-static elastography method has been commercialized by some companies. However, its performance is still limited on high strain level. In order to improve the precision of estimation during high compression, some algorithms have been proposed to expand the 1D window to a 2D window for avoiding the side-slipping. But they are usually more computationally expensive. In this paper, we proposed a modified 2D multiresolution hybrid method for displacement estimation, which can offer an efficient strain imaging with stable and accurate results. A FEM phantom with a stiffer circular inclusion is simulated for testing the algorithm. The elastographic contrast-to-noise rate (CNRe) is calculated for quantitatively comparing the performance of the proposed algorithm with conventional 1D elastography using phase zero estimation and the 1D elastography using downsampled (d-s) baseband signals. Results show that the proposed method is robust and performs similarly as other algorithms in low strain but is superior when high level strain is applied. Particularly, the CNRe of our algorithm is 15 times higher than original method under 4% strain level. Furthermore, the execution time of our algorithm is five times faster than other algorithms

Robustness Analysis on Graph Neural Networks Model for Event Detection

Event Detection (ED), which aims to identify trigger words from the given text and classify them into corresponding event types, is an important task in Natural Language Processing (NLP); it contributes to several downstream tasks and is beneficial for many real-world applications. Most of the current SOTA (state-of-the-art) models for ED are based on Graph Neural Networks (GNN). However, a few studies focus on the issue of GNN-based ED models’ robustness towards text adversarial attacks, which is a challenge in practical applications of EDs that needs to be solved urgently. In this paper, we first propose a robustness analysis framework for an ED model. Using this framework, we can evaluate the robustness of the ED model with various adversarial data. To improve the robustness of the GNN-based ED model, we propose a new multi-order distance representation method and an edge representation update method based on attention weights, then design an innovative model named A-MDL-EEGCN. Extensive experiments illustrate that the proposed model can achieve better performance than other models both on original data and various adversarial data. The comprehensive robustness analysis according to experimental results in this paper brings new insights into the evaluation and design of a robust ED model