Augmentation Strategies Research Articles

Capacity augmentation strategy in α-Fe2O3 nanoparticles through multifaceted structural and electrochemical analyses

Hematite (α-Fe₂O₃) has garnered significant research interest for supercapacitor applications, but capacity fading and limited cyclic life remain serious challenges. In this context, the present research provides a comprehensive study of the factors that significantly impact charge kinetics within the electrode and at the solid/electrolyte interface. By integrating structural and electrochemical analyses, this study offers an effective strategy to enhance the capacity of α-Fe₂O₃ nanoparticles through rigorous scientific analysis and identification of various qualitative and quantitative factors that influence charge storage capacity. A simple one-step co-precipitation method was adopted to synthesize porous α-Fe₂O₃ nanoparticles. The quantitative analysis of charge kinetics demonstrated the domination of diffusion-controlled charge storage as the main contributor to the total charge storage. The α-Fe₂O₃ nanoparticles-based electrode delivered a high energy density of 24.6 Wh kg⁻1 at an impressive power density of 478.7 W kg⁻1. Additionally, it displayed a capacity retention of 79 % over 10,000 cycle operations. The choice of electrode material and the superior porous nanoarchitecture have proven to be advantageous for high-performance supercapacitor applications, offering fast electron/ion transport.

Strategic data augmentation with CTGAN for smart manufacturing: Enhancing ML predictions of paper breaks in pulp-and-paper production

This article addresses the significant challenge posed by the rare occurrence of paper breaks in the pulp-and-paper industry, which, despite their infrequency during production, have considerable economic consequences. We analyze operational data from a paper manufacturing machine, focusing on the rare instances of paper breaks (124 out of 18,398 cases) identified through a quality assurance protocol. This scarcity presents a challenge for machine learning-based predictive models. To overcome this, we introduce a novel data augmentation strategy using Conditional Generative Adversarial Networks (CTGAN) and Synthetic Minority Oversampling Technique (SMOTE), aimed at generating synthetic data that not only reflects the real operational data’s distribution but also improves the predictive models’ performance metrics. We assess the impact of this data augmentation on three machine learning algorithms—Decision Trees (DT), Random Forest (RF), and Logistic Regression (LR)—both before and after augmentation. The use of a CTGAN-enhanced dataset led to notable enhancements in predictive maintenance metrics. Specifically, the ability of the models to detect machine breaks (Class 1) increased significantly: over 30% for Decision Trees, 20% for Random Forest, and nearly 90% for Logistic Regression. This methodological progress offers valuable contributions to the fields of industrial quality control and maintenance scheduling by improving the prediction of rare events in manufacturing processes.

Generating graph perturbations to enhance the generalization of GNNs

Graph neural networks (GNNs) have become the standard approach for performing machine learning on graphs. Such models need large amounts of training data, however, in several graph classification and regression tasks, only limited training data is available. Unfortunately, due to the complex nature of graphs, common augmentation strategies employed in other settings, such as computer vision, do not apply to graphs. This work aims to improve the generalization ability of GNNs by increasing the size of the training set of a given problem. The new samples are generated using an iterative contrastive learning procedure that augments the dataset during the training, in a task-relevant approach, by manipulating the graph topology. The proposed approach is general, assumes no knowledge about the underlying architecture, and can thus be applied to any GNN. We provided a theoretical analysis regarding the equivalence of the proposed approach to a regularization technique. We demonstrate instances of our framework on popular GNNs, and evaluate them on several real-world benchmark graph classification datasets. The experimental results show that the proposed approach, in several cases, enhances the generalization of the underlying prediction models reaching in some datasets state-of-the-art performance.

COMPARATIVE ANALYSIS OF MULTILINGUAL QA MODELS AND THEIR ADAPTATION TO THE KAZAKH LANGUAGE

This paper presents a comparative analysis of large pretrained multilingual models for question-answering (QA) systems, with a specific focus on their adaptation to the Kazakh language. The study evaluates models including mBERT, XLM-R, mT5, AYA, and GPT, which were tested on QA tasks using the Kazakh sKQuAD dataset. To enhance model performance, fine-tuning strategies such as adapter modules, data augmentation techniques (back-translation, paraphrasing), and hyperparameter optimization were applied. Specific adjustments to learning rates, batch sizes, and training epochs were made to boost accuracy and stability. Among the models tested, mT5 achieved the highest F1 score of 75.72%, showcasing robust generalization across diverse QA tasks. GPT-4-turbo closely followed with an F1 score of 73.33%, effectively managing complex Kazakh QA scenarios. In contrast, native Kazakh models like Kaz-RoBERTa showed improvements through fine-tuning but continued to lag behind larger multilingual models, underlining the need for additional Kazakh-specific training data and further architectural enhancements. Kazakh’s agglutinative morphology and the scarcity of high-quality training data present significant challenges for model adaptation. Adapter modules helped mitigate computational costs, allowing efficient fine-tuning in resource-constrained environments without significant performance loss. Data augmentation techniques, such as back-translation and paraphrasing, were instrumental in enriching the dataset, thereby enhancing model adaptability and robustness. This study underscores the importance of advanced fine-tuning and data augmentation strategies for QA systems tailored to low-resource languages like Kazakh. By addressing these challenges, this research aims to make AI technologies more inclusive and accessible, offering practical insights for improving natural language processing (NLP) capabilities in underrepresented languages. Ultimately, these findings contribute to bridging the gap between high-resource and low-resource language models, fostering a more equitable distribution of AI solutions across diverse linguistic contexts.

Improving Localization in Wireless Sensor Networks for the Internet of Things Using Data Replication-Based Deep Neural Networks.

Localization is one of the most challenging problems in wireless sensor networks (WSNs), primarily driven by the need to develop an accurate and cost-effective localization system for Internet of Things (IoT) applications. While machine learning (ML) algorithms have been widely applied in various WSN-based tasks, their effectiveness is often compromised by limited training data, leading to issues such as overfitting and reduced accuracy, especially when the number of sensor nodes is low. A key strategy to mitigate overfitting involves increasing both the quantity and diversity of the training data. To address the limitations posed by small datasets, this paper proposes an intelligent data augmentation strategy (DAS)-based deep neural network (DNN) that enhances the localization accuracy of WSNs. The proposed DAS replicates the estimated positions of unknown nodes generated by the Dv-hop algorithm and introduces Gaussian noise to these replicated positions, creating multiple modified datasets. By combining the modified datasets with the original training data, we significantly increase the dataset size, which leads to a substantial reduction in normalized root mean square error (NRMSE). The experimental results demonstrate that this data augmentation technique significantly improves the performance of DNNs compared to the traditional Dv-hop algorithm at a low number of nodes while maintaining an efficient computational cost for data augmentation. Therefore, the proposed method provides a scalable and effective solution for enhancing the localization accuracy of WSNs.

Augmentation with prazosin for patients with depression and a history of trauma: A randomised, double-blind, placebo-controlled study.

Depression with a history of trauma often responds poorly to conventional antidepressants and has a poor prognosis. Prazosin, an α1-adrenoceptor blocker, has shown promise in treating post-traumatic stress disorder symptoms, particularly nightmares. Its potential in treating depression with trauma history warrants investigation. This randomised, double-blind, placebo-controlled study aimed to investigate the efficacy and tolerability of low-dose prazosin (0.5-1 mg/day) as an augmentation strategy in patients with depression and a history of trauma. We sought to determine if prazosin could provide rapid symptom improvement and enhance overall treatment response compared to placebo in this difficult-to-treat patient population. This randomised, double-blind, placebo-controlled clinical study included 59 patients with first-episode or recurrent unipolar or bipolar depression. After basic antidepressant treatment, they were randomly assigned to a prazosin (0.5-1 mg/day) or placebo group for a 6-week double-blind controlled study. The Montgomery-Åsberg Depression Rating Scale, 17-item Hamilton Depression Scale (HAMD-17), and Hamilton Anxiety Scale (HAMA) were used to evaluate efficacy. There were no significant differences in the results of the demographic and clinical symptom assessment between the two groups (p > 0.05). The difference between the HAMD-17 and HAMA scores was statistically significant after 3 days of treatment (p < 0.05). The difference in response rate between the two groups was statistically significant after week 4 of treatment (end of week 4, 56.7% vs. 24.1%, p = 0.011; end of week 6, 80.0% vs. 48.3%, p = 0.011). The incidence of adverse reactions in the prazosin and placebo groups was 20.0% and 24.1%, respectively, with no statistically significant differences (p > 0.05); however, the prazosin group had a lower incidence of sleeplessness or nightmares (3.3% vs. 20.7%, p = 0.039) but a higher incidence of orthostatic hypotension (16.7% vs. 0%, p = 0.007). The severity of orthostatic hypotension was mild to moderate. Low-dose prazosin can effectively improve the emotional symptoms of patients with depression and a history of trauma, and the common adverse reaction is mild-to-moderate orthostatic hypotension. ChiCTR2200063642.

Cardiovascular care with digital twin technology in the era of generative artificial intelligence.

Digital twins, which are in silico replications of an individual and its environment, have advanced clinical decision-making and prognostication in cardiovascular medicine. The technology enables personalized simulations of clinical scenarios, prediction of disease risk, and strategies for clinical trial augmentation. Current applications of cardiovascular digital twins have integrated multi-modal data into mechanistic and statistical models to build physiologically accurate cardiac replicas to enhance disease phenotyping, enrich diagnostic workflows, and optimize procedural planning. Digital twin technology is rapidly evolving in the setting of newly available data modalities and advances in generative artificial intelligence, enabling dynamic and comprehensive simulations unique to an individual. These twins fuse physiologic, environmental, and healthcare data into machine learning and generative models to build real-time patient predictions that can model interactions with the clinical environment to accelerate personalized patient care. This review summarizes digital twins in cardiovascular medicine and their potential future applications by incorporating new personalized data modalities. It examines the technical advances in deep learning and generative artificial intelligence that broaden the scope and predictive power of digital twins. Finally, it highlights the individual and societal challenges as well as ethical considerations that are essential to realizing the future vision of incorporating cardiology digital twins into personalized cardiovascular care.

Impact of augmentation strategy variations on the mechanical characteristics of patients with osteoporotic proximal humerus fractures with medial column instability.

Low bone density and lack of medial support are the two most important factors affecting the stability of locking plate fixation for osteoporotic proximal humeral fractures (PHFs). This study aimed to compare the biomechanical characteristics of PHILOS locking plates combined with calcar screws, bone cement, fibular allografts, and medial locking plate support strategies for treating osteoporotic PHFs with medial column instability. A three-part osteoporotic PHF (AO 11-B3.2) model with metaphyseal loss was generated using 40 synthetic humeri and fixed via four distinct medial support strategies. All models were mechanically tested to quantify the mechanical characteristics. Subsequently, finite element models were created for each biomechanical test case. The stress distribution and displacement of the four different fixation structures were analyzed using finite element analysis. The results demonstrated that the PHILOS locking plate combined with the medial locking plate, exhibited the greatest stability when subjected to axial, shear, and torsional loading. Furthermore, the PHILOS locking plate combined with bone cement showed structural stability similar to that of the PHILOS locking plate combined with fibular allograft but with lower stress levels on the fracture surface. In conclusion, the PLP-MLP fixation structure showed superior biomechanical properties under axial, shear, and torsional loading compared to other medial support methods. Repairing the medial support when treating osteoporotic PHFs with medial column instability can enhance the mechanical stability of the fracture end in both the short and long term.

ML-GAP: machine learning-enhanced genomic analysis pipeline using autoencoders and data augmentation.

The advent of RNA sequencing (RNA-Seq) has significantly advanced our understanding of the transcriptomic landscape, revealing intricate gene expression patterns across biological states and conditions. However, the complexity and volume of RNA-Seq data pose challenges in identifying differentially expressed genes (DEGs), critical for understanding the molecular basis of diseases like cancer. We introduce a novel Machine Learning-Enhanced Genomic Data Analysis Pipeline (ML-GAP) that incorporates autoencoders and innovative data augmentation strategies, notably the MixUp method, to overcome these challenges. By creating synthetic training examples through a linear combination of input pairs and their labels, MixUp significantly enhances the model's ability to generalize from the training data to unseen examples. Our results demonstrate the ML-GAP's superiority in accuracy, efficiency, and insights, particularly crediting the MixUp method for its substantial contribution to the pipeline's effectiveness, advancing greatly genomic data analysis and setting a new standard in the field. This, in turn, suggests that ML-GAP has the potential to perform more accurate detection of DEGs but also offers new avenues for therapeutic intervention and research. By integrating explainable artificial intelligence (XAI) techniques, ML-GAP ensures a transparent and interpretable analysis, highlighting the significance of identified genetic markers.

Perbandingan Kinerja Model Pre-Trained CNN (VGG16, RESNET, dan INCEPTIONV3) untuk Aplikasi Pengenalan Wajah pada Sistem Absensi Karyawan

Face recognition has become a key technology in improving the efficiency and security of modern employee attendance systems. This study compares the performance of three pre-trained Convolutional Neural Network (CNN) models - VGG16, ResNet50, and InceptionV3 - in the context of face recognition for employee attendance systems. The study evaluated the accuracy, consistency, and generalization of the models on a dataset of employee faces, using prediction accuracy, confusion matrix, and classification report measurement methods. Results showed InceptionV3 performed best overall, with high consistency and confidence, achieving up to 99% accuracy on the test data. ResNet50 showed consistent performance in some cases but required further fine-tuning, while VGG16 showed the worst performance. These findings have significant practical implications for the industry, recommending the use of InceptionV3 for the implementation of reliable face recognition-based attendance systems, with consideration of the use of confidence thresholds to optimize accuracy. This research also highlights the importance of further optimization, including hyperparameter fine-tuning and more sophisticated data augmentation strategies, to improve system performance under various work environment conditions.

Automated Deep Learning-Based Finger Joint Segmentation in 3-D Ultrasound Images With Limited Dataset.

Ultrasound imaging has shown promise in assessing synovium inflammation associated early stages of rheumatoid arthritis (RA). The precise identification of the synovium and the quantification of inflammation-specific imaging biomarkers is a crucial aspect of accurately quantifying and grading RA. In this study, a deep learning-based approach is presented that automates the segmentation of the synovium in ultrasound images of finger joints affected by RA. Two convolutional neural network architectures for image segmentation were trained and validated in a limited number of 2-D images, extracted from N = 18 3-D ultrasound volumes acquired from N = 9 RA patients, with sparse ground truth annotations of the synovium. Various augmentation strategies were employed to enhance the diversity and size of the training dataset. The utilization of geometric and noise augmentation transforms resulted in the highest dice score (0.768 0.040, N = 6), as determined via six-fold cross-validation. In addition, the segmentation model is used to generate dense 3-D segmentation maps in the ultrasound volumes, based on the available sparse annotations. The developed technique shows promise in facilitating more efficient and standardized workflow for RA screening using ultrasound imaging.

A deep learning-powered intelligent microdroplet analysis workflow for in-situ monitoring and evaluation of a dynamic emulsion

Accurately determining microdroplet size and uniformity in dispersed phase systems is key for assessing emulsion stability, and essential for quality monitoring and control of emulsion products. Microscopic imaging probes have provided intuitive and advanced insights for characterizing particle microscopic morphology and dispersion in multiphase flows. However, wide size distributions, edge-cutting, overlapping, and high-density target droplets can compromise the image processing accuracy, thereby affecting the precise assessment of emulsion status. In this study, we present a deep learning-based intelligent workflow for microdroplet characterization using an in-situ high-speed particle imaging probe combined with advanced image processing techniques. With the assistance of progressive automatic annotation, a droplet image database was constructed for training the Mask R-CNN model. By incorporating image patching and supervised data augmentation strategies, our well-trained model with accuracy of 95.5% can precisely detect, localize, and segment microscale droplets with various patterns. Additionally, the shape reconstruction module visualizes true shapes of overlapping and edge-cutting droplets, facilitating precise quantification of droplet size information. To validate the feasibility and generalizability of the proposed workflow, we obtained sufficient droplet images through offline sampling during emulsion processes while employing back and front illumination for in-situ monitoring. The automated method achieves precise droplet detection and size measurement, demonstrating its immense potential in online monitoring, evaluation, and control of emulsion quality.

Exploring Communication Needs and Challenges in the Intensive Care Unit: A Survey Study From Providers' and Patients' Perspectives.

Effective communication between nonspeaking patients and providers is critical for the quality of care in intensive care units (ICUs). This study aims to evaluate perspectives of health care providers and nonspeaking patients on effective communication and communication barriers in the ICU. Qualitative and quantitative survey methodologies were employed to evaluate providers' and patients' perspectives on effective communication. Rating scales were utilized to measure patients' frustration levels and communication effectiveness. Open-ended questions were employed to reflect on barriers to communication in the ICU, instances of ineffective communication, and recommendations for enhancing effective communication. The results of the study suggest that nonspeaking patients experienced high levels of frustration due to ineffective communication. However, the data reveal that access to appropriate augmentative and alternative communication (AAC) strategies and materials could help mitigate patients' frustration. Providers mainly communicated via asking yes/no questions, which largely limited the information patients conveyed, leading to frequent communication breakdowns. Providers expressed a desire to participate in training programs to utilize appropriate AAC strategies and promote effective communication. This study provides preliminary survey results on perspectives of patients and providers on effective communication in the ICU. Both providers and patients reported experiencing challenges and frustration during their communication, due to barriers such as providers' insufficient training experience and lack of access to AAC materials. Skill training is warranted to promote effective patient-provider communication in intensive care settings. https://doi.org/10.23641/asha.26339623.

FlexibleCP: A data augmentation strategy for traffic sign detection

AbstractIn the field of traffic sign detection, effective data augmentation can improve the model's detection capacity, enabling the model to distinguish and locate traffic signs more precisely and enhancing driving safety. However, due to the small size and low representation of traffic signs in the dataset, standard common data augmentation techniques are not suitable for traffic sign detection. To address this issue, a novel data augmentation strategy called flexible cut and paste (FlexibleCP) is proposed. The overall enhancement approach is shifted from multi‐image fusion to target cropping and pasting. By introducing parameters to control the target pasting ratio and scaling ratio, the diversity of small target data and their size variations are enriched. Additionally, target size and type filters are added to enable targeted enhancement for different sizes and types of targets. This study, evaluates the proposed strategy using two representative traffic sign detection datasets, namely CTSD and GTSDB. The experimental results demonstrate a significant improvement in both detection and recognition performance of the model: on the CTSD dataset, the models trained with FlexibleCP data enhancement achieve 88.9% and 64.5% mAP0.5 and mAP0.5:0.95, respectively, which are 3.5% and 2.5% better than those trained with mosaic data enhancement; on the GTSDB dataset mAP0.5 and mAP0.5:0.95 reached 89.2% and 56.0%, respectively, an improvement of 4.0% and 3.9% over mosaic.

YOLOv8-Based Drone Detection: Performance Analysis and Optimization

The extensive utilization of drones has led to numerous scenarios that encompass both advantageous and perilous outcomes. By using deep learning techniques, this study aimed to reduce the dangerous effects of drone use through early detection of drones. The purpose of this study is the evaluation of deep learning approaches such as pre-trained YOLOv8 drone detection for security issues. This study focuses on the YOLOv8 model to achieve optimal performance in object detection tasks using a publicly available dataset collected by Mehdi Özel for a UAV competition that is sourced from GitHub. These images are labeled using Roboflow, and the model is trained on Google Colab. YOLOv8, known for its advanced architecture, was selected due to its suitability for real-time detection applications and its ability to process complex visual data. Hyperparameter tuning and data augmentation techniques were applied to maximize the performance of the model. Basic hyperparameters such as learning rate, batch size, and optimization settings were optimized through iterative experiments to provide the best performance. In addition to hyperparameter tuning, various data augmentation strategies were used to increase the robustness and generalization ability of the model. Techniques such as rotation, scaling, flipping, and color adjustments were applied to the dataset to simulate different conditions and variations. Among the augmentation techniques applied to the specific dataset in this study, rotation was found to deliver the highest performance. Blurring and cropping methods were observed to follow closely behind. The combination of optimized hyperparameters and strategic data augmentation allowed YOLOv8 to achieve high detection accuracy and reliable performance on the publicly available dataset. This method demonstrates the effectiveness of YOLOv8 in real-world scenarios, while also highlighting the importance of hyperparameter tuning and data augmentation in increasing model capabilities. To enhance model performance, dataset augmentation techniques including rotation and blurring are implemented. Following these steps, a significant precision value of 0.946, a notable recall value of 0.9605, and a considerable precision–recall curve value of 0.978 are achieved, surpassing many popular models such as Mask CNN, CNN, and YOLOv5.

A versatile framework for attributed network clustering via K-nearest neighbor augmentation

AbstractAttributed networks containing entity-specific information in node attributes are ubiquitous in modeling social networks, e-commerce, bioinformatics, etc. Their inherent network topology ranges from simple graphs to hypergraphs with high-order interactions and multiplex graphs with separate layers. An important graph mining task is node clustering, aiming to partition the nodes of an attributed network into k disjoint clusters such that intra-cluster nodes are closely connected and share similar attributes, while inter-cluster nodes are far apart and dissimilar. It is highly challenging to capture multi-hop connections via nodes or attributes for effective clustering on multiple types of attributed networks. In this paper, we first present as an efficient approach to attributed hypergraph clustering (AHC). includes a carefully-crafted K-nearest neighbor augmentation strategy for the optimized exploitation of attribute information on hypergraphs, a joint hypergraph random walk model to devise an effective AHC objective, and an efficient solver with speedup techniques for the objective optimization. The proposed techniques are extensible to various types of attributed networks, and thus, we develop as a versatile attributed network clustering framework, capable of attributed graph clustering, attributed multiplex graph clustering, and AHC. Moreover, we devise with algorithmic designs tailored for GPU acceleration to boost efficiency. We have conducted extensive experiments to compare our methods with 19 competitors on 8 attributed hypergraphs, 16 competitors on 6 attributed graphs, and 16 competitors on 3 attributed multiplex graphs, all demonstrating the superb clustering quality and efficiency of our methods.

Leukocyte classification using relative-relationship-guided contrastive learning

Hematologic diseases and blood disorders can be studied through microscopic examination of blood smear images or chemical assays. Many researchers are focused on utilizing deep learning (DL) to identify, quantify, and classify various types of blood cells, which is crucial for addressing theoretical and practical challenges in disease diagnosis and treatment planning. However, there is a significant deficiency in annotations for leukocyte classification, limiting the effectiveness of current DL methods. Contrastive learning (CL) facilitates the extraction of prior knowledge from unlabeled data, reducing the dependency on manual annotations. While CL demonstrates remarkable achievements in classifying natural images, the direct application to leukocyte classification presents certain defects. We observe that standard data augmentation techniques in CL exhibit varying sensitivity to different categories of white blood cell images. Employing a uniform augmentation strategy may lead the network into an optimization trap, acquiring inadequate representations from erroneous sample relationships. To address this issue, we propose a Relative-Relationship-Guided Contrastive Learning Representation (ReCLR) framework for the leukocyte classification. ReCLR mines positive and negative samples based on relative distance knowledge. Specifically, we provide adversarial guidance for the positive sample, restricting the distance between the positive sample and the original sample less than but as close as possible to the distance between the original sample and the farthest negative sample. Then, we utilize the entropy constraint to regulate the relative distance relationship between the negative and original samples. Finally, the guided positive and negative samples are employed for contrastive learning in leukocyte classification. The extensive experiments, including linear evaluation, domain transfer, and fine-tuning, demonstrate the effectiveness of the proposed method. Our ReCLR achieves accuracies of 92.07%, 65.36%, and 92.49% on three real-world leukocyte datasets, respectively, outperforming several state-of-the-art methods. The source code is released at https://github.com/AlchemyEmperor/ReCLR.

Knowledge-driven hierarchical intents modeling for recommendation

Previous studies on user–item interaction graphs have typically concentrated on simple interactions, often overlooking the significant role of user intent in shaping these interactions. While some recent research has explored intent relationships to enhance modeling, these approaches mainly focus on user preferences derived from interactions, ignoring the knowledge information with good interpretation in knowledge graphs. In addition, some recent work usually utilize undenoised graph structure information to learn the node representations, which introduces plenty of noise and impedes the well learning of users’ preference. In this paper, we utilize the rich interpretable knowledge information in the knowledge graph to design a novel knowledge-driven hierarchical intent modeling framework called KHIM. The focus is on designing a hierarchical user intent modeling process and an intent-based multi-view contrastive learning mechanism. The former extracts both the popular and personalized preferences of users from attribute tuples within the knowledge graph at the global-level and local-level, respectively. For global level, we capture the user’s true intents from positive view and augment the positive intent representation with negative view. While the latter generates high-quality user and item representations through multi-level cross-view contrastive learning. Two data augmentation strategies are designed during the contrastive process to mitigate the effects of noise in the learning process. Additionally, we also designed a neighbor filtering strategy based on semantic view to obtain more neighbor semantic information of user and item nodes, so as to further improve the recommendation performance. Experimental results on three benchmark datasets demonstrate that KHIM significantly outperforms various state-of-the-art approaches, highlighting its effectiveness in leveraging knowledge graph information for better recommendations.

Collaborative graph neural networks for augmented graphs: A local-to-global perspective

In the field of graph neural networks (GNNs) for representation learning, a noteworthy highlight is the potential of embedding fusion architectures for augmented graphs. However, prevalent GNN embedding fusion architectures mainly focus on handling graph combinations from a global perspective, often ignoring their collaboration with the information of local graph combinations. This inherent limitation constrains the ability of the constructed models to handle multiple input graphs, particularly when dealing with noisy input graphs collected from error-prone sources or those resulting from deficiencies in graph augmentation methods. In this paper, we propose an effective and robust embedding fusion architecture from a local-to-global perspective termed collaborative graph neural networks for augmented graphs (LoGo-GNN). Essentially, LoGo-GNN leverages a pairwise graph combination scheme to generate local perspective inputs. Together with the global graph combination, this serves as the basis to generate a local-to-global perspective. Specifically, LoGo-GNN employs a perturbation augmentation strategy to generate multiple augmentation graphs, thereby facilitating collaboration and embedding fusion from a local-to-global perspective through the use of graph combinations. In addition, LoGo-GNN incorporates a novel loss function for learning complementary information between different perspectives. We also conduct theoretical analysis to assess its expressive power under ideal conditions, demonstrating the effectiveness of LoGo-GNN. Our experiments, focusing on node classification and clustering tasks, highlight the superior performance of LoGo-GNN compared to state-of-the-art methods. Additionally, robustness analysis further confirms its effectiveness in addressing uncertainty challenges.

Study Assessing Use of Mood Stabilizers (Sodium Valproate and Oxcarbazepine) as an Augmentation Strategy in Patients of Treatment-resistant Depression

Study Assessing Use of Mood Stabilizers (Sodium Valproate and Oxcarbazepine) as an Augmentation Strategy in Patients of Treatment-resistant Depression