Enhancing basal cell carcinoma classification in preoperative biopsies via transfer learning with weakly supervised graph transformers by Johan Björkman, Sigrid Lagerroth, Jan Siarov, Filmon Yacob, Noora Neittaanmäki

“…Features were formed into graphs for spatial information and the processed by a Vision Transformer. …”
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Low Noise Programmable DC Amplifier with Remote Control by Viktor E. Ivanov, Chye En Un

“…Experimental studies were based on a system consisting of a low-noise amplifying path and spectroanalyser using the data acquisition module E14-440. …”
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The Power of Data Journalism: The Effects of Data-Driven News Reports in Correcting Climate Change Misinformation by Mahmoud-Mohamed-Abdel Haleem, Hagar Talaat-Alnajjar

“…The findings indicate that data-driven journalism utilising interactive graphs is effective in altering the public’s existing beliefs and knowledge while also demonstrating its ability to persuade and counter misinformation. …”
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A graph attention network-based multi-agent reinforcement learning framework for robust detection of smart contract vulnerabilities by Philip Kwaku Adjei, Qin Zhiguang, Isaac Amankona Obiri, Ansu Badjie, Christian Nii Aflah Cobblah, Ali Alqahtani, Yeong Hyeon Gu, Mugahed A. Al-antari

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Self-Supervised Knowledge-Aware Recommendation Model Integrating Adaptive Hypergraph by ZHOU Jiaxuan, LIU Xianhui, ZHAO Xiaodong, HOU Wenlong, ZHAO Weidong

“…The model first utilizes a hybrid graph convolutional network to jointly learn the low-order interaction embeddings in the interaction graph and the higher-order interaction embeddings in the adaptive hypergraph. …”
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A novel framework for inferring dynamic infectious disease transmission with graph attention: a COVID-19 case study in Korea by Minji Lee, Heejin Choi, Chang Hyeong Lee

“…Conclusion MPUGAT offers a novel approach for effectively integrating easily accessible, low-dimensional, non-epidemic-related data into epidemic modeling frameworks. …”
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A Hybrid Model for Soybean Yield Prediction Integrating Convolutional Neural Networks, Recurrent Neural Networks, and Graph Convolutional Networks by Vikram S. Ingole, Ujwala A. Kshirsagar, Vikash Singh, Manish Varun Yadav, Bipin Krishna, Roshan Kumar

“…This increases the prediction accuracy by 10% and boosts the F1 score for low-yield area identification by 5%. Additionally, we introduce other improved model architectures: a custom UNet with attention mechanisms, Heterogeneous Graph Neural Networks (HGNNs), and Variational Auto-encoders. …”
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ContrastLOS: A Graph-Based Deep Learning Model With Contrastive Pre-Training for Improved ICU Length-of-Stay Prediction by Guangrui Fan, Aixiang Liu, Chao Zhang

“…Notably, it maintains an AUROC of 76.8% with only 10% labeled data, highlighting its effectiveness in low-resource settings. …”
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LightRoseTTA: High‐Efficient and Accurate Protein Structure Prediction Using a Light‐Weight Deep Graph Model by Xudong Wang, Tong Zhang, Guangbu Liu, Zhen Cui, Zhiyong Zeng, Cheng Long, Wenming Zheng, Jian Yang

“…Here, “a light‐weight deep graph network, named LightRoseTTA,” is reported to achieve accurate and highly efficient prediction for proteins. …”
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Overcoming System Complexity using Models and Knowledge Structures. Editorial Introduction to Issue 36 of CSIMQ by Erika Nazaruka, Tarmo Robal

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Manifold Adaptive Kernelized Low-Rank Representation for Semisupervised Image Classification by Yong Peng, Wanzeng Kong, Feiwei Qin, Feiping Nie

“…Among popular graph construction algorithms, low-rank representation (LRR) is a very competitive one that can simultaneously explore the global structure of data and recover the data from noisy environments. …”
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Embedding b-Metric Spaces of Reducible Fuzzy Digraphs into Normed Spaces by Umilkeram Qasim Obaid

“…This structure enhances the embedding of massive data entities or nodes into low-dimensional realm depicted by a normed space. …”
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Multiview clustering method for view-unaligned data by Ao LI, Cong FENG, Yutong NIU, Shibiao XU, Yingtao ZHANG, Guanglu SUN

“…A new challenge for multi-view learning was posed by corrupted view-correspondences.To address this issue， an effective multi-view learning method for view-unaligned data was proposed.First，to capture cross-view latent affinity in multi-view heterogenous feature spaces，representation learning was employed based on multi-view non-negative matrix factorization to embed original features into a measurable low-dimensional subspace.Second, view-alignment relationships were modeled as optimal matching of a bipartite graph, which could be generalized to multiple-views situations via the proposed concept reference view.Representation learning and data alignment were further integrated into a unified Bi-level optimization framework to mutually boost the two learning processes, effectively enhancing the ability to learn from view-unaligned data.Extensive experimental results of view-unaligned clustering on three public datasets demonstrate that the proposed method outperforms eight advanced multiview clustering methods on multiple evaluation metrics.…”
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Semi-automated data provenance tracking for transparent data production and linkage to enhance auditing and quality assurance in Trusted Research Environments by Katherine O'Sullivan, Milan Markovic, Jaroslaw Dymiter, Bernhard Scheliga, Chinasa Odo, Katie Wilde

“…Methods Using a participatory design process with Data Analysts, researchers and information governance teams, we undertook a contextual inquiry, user requirements interviews, co-design workshops, low-fidelity prototype evaluations. …”
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Fault diagnosis of power transformers based on dissolved gas analysis and multi-kernel graph convolution network integrated with dual-channel classifiers by Xuebin Lv, Fuzheng Liu, Mingshun Jiang, Faye Zhang, Lei Jia

“…A power transformer fault diagnosis method based on dissolved gas analysis and multi-kernel graph convolution network integrated with dual-channel classifiers (DM-DC) is proposed to address the problems of insufficient accuracy and large deviation in recognition when dealing with imbalanced data. …”
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An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontology-Enhanced Large Language Models: Development Study by Lang Cao, Jimeng Sun, Adam Cross

“…MethodsAutoRD is a pipeline system that involves data preprocessing, entity extraction, relation extraction, entity calibration, and knowledge graph construction. …”
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Low pressure PEM electrolyzer system modeling with heat loss representation by Asmaa A. Ghany, Mohamed Mahmoud Samy

“…Low-pressure proton exchange membrane (PEM) electrolyzers are increasingly recognized for their effectiveness in hydrogen production, especially when combined with renewable energy sources. …”
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Very low-frequency electrical spectroscopy analysis of an ovine renal tissue by Faezeh Azimi Pirsoltan, Mohammad Reza Karafi

“…The primary objective of this study is to address the gaps in low-frequency dielectric property data of kidney tissue, specifically focusing on the energy absorption characteristics of different regions of sheep’s kidney. …”
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THE EFFECT OF LIGHTNING ON HIGH VOLTAGE ELECTRICAL SUBSTATIONS’ LOW VOLTAGE SYSTEMS by M. I. Fursanov, P. V. Kriksin

“…The article presents the results of studies of the effects of lightning on low voltage systems of high voltage electrical substations with outdoor switchgears of 110 kV. …”
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Impact of low testing numbers on chronic wasting disease apparent prevalence by Jameson J. Mori, Nelda A. Rivera, William M. Brown, Daniel J. Skinner, Peter E. Schlichting, Jan E. Novakofski, Nohra E. Mateus-Pinilla

“…We hypothesized that when CWD testing is limited, AP is negatively driven by testing – rather than cases – with more tests corresponding to lower APs. Graphed CWD surveillance data from townships in Illinois and Wisconsin, USA, indicate that CWD AP values ≥50% were only observed when <23 deer were tested. …”
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