Development of the relationship between visual selective attention and auditory change detection by Yuanjun Kong, Xuye Yuan, Yiqing Hu, Bingkun Li, Dongwei Li, Jialiang Guo, Meirong Sun, Yan Song

“…Our one recent study has shown a positive correlation between the event-related potential (ERP) amplitudes associated with visual selective attention (posterior contralateral N2) and auditory change detection (mismatch negativity) in adults, suggesting an intimate relationship and potential shared mechanism between visual selective attention and auditory change detection. …”
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Adaptive Video Anomaly Detection by Attention-Based Relational Knowledge Distillation by Burcak Asal, Ahmet Burak Can

“…Detecting anomaly patterns in videos is a challenging task due to complex scenes, huge diversity of anomalies, and fuzzy nature of the task. …”
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Graph-Attention Diffusion for Enhanced Multivariate Time-Series Anomaly Detection by Vadim Lanko, Ilya Makarov

“…In this article, we propose a novel reconstruction-based approach that enhances normal pattern learning through data masking and leverages diffusion models to capture both temporal and spatial interrelations via graph-attention layers. …”
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Attention dual transformer with adaptive temporal convolutional for diabetic retinopathy detection by Mishmala Sushith, Ajanthaa Lakkshmanan, M. Saravanan, S. Castro

“…Abstract An Attention Dual Transformer with Adaptive Temporal Convolutional (ADT-ATC) model is proposed in this research work for enhanced detection of Diabetic Retinopathy (DR) from retinal fundus images. …”
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Multi-Scale Venation Pattern Analysis for Medicinal Plant Species Recognition by Arnav Sanjay Karnik, Nikhil Nair, Yashas Sagili, P. B. Pb

“…To validate the effectiveness of our approach, we developed and evaluated three distinct model architectures: 1) a modified ResNet-50 model utilizing transfer learning with an adapted input pipeline for venation-aware channels; 2) a custom-built convolutional neural network, VenationNet, explicitly designed for multi-scale venation analysis; and 3) a Dual-Stream CNN architecture that processes leaf texture and venation maps independently before merging via attention-based fusion. Preprocessing involves contrast enhancement, Frangi filtering for venation extraction, and edge detection to create a three-channel input comprising RGB, venation, and edge maps. …”
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Challenges of early renal cancer detection: symptom patterns and incidental diagnosis rate in a multicentre prospective UK cohort of patients presenting with suspected renal cancer by Michelle Wilson, Naveen S Vasudev, Grant D Stewart, Adebanji Adeyoju, Jon Cartledge, Michael Kimuli, Shibendra Datta, Damian Hanbury, David Hrouda, Grenville Oades, Poulam Patel, Naeem Soomro, Mark Sullivan, Jeff Webster, Peter J Selby, Rosamonde E Banks

“…Symptomatic presentation was associated with poorer outcomes, likely reflecting the presence of higher stage disease. Symptom patterns among the 54 patients subsequently found to have a benign renal mass were similar to those with a confirmed RCC.Conclusions Raising public awareness of RCC-related symptoms as a strategy to improve early detection rates is limited by the fact that related symptoms are relatively uncommon and often associated with advanced disease. …”
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INTRACELLULAR SYSTEMS FOR DETECTION OF EXOGENOUS NUCLEIC ACIDS AND TRIGGERING MECHANISMS OF IMMUNE RESPONSE TO DNA INTERNALIZATION by E. A. Alyamkina, E. V. Dolgova, A. S. Proskurina, V. A. Rogachev, A. A. Ostanin, E. R. Chernykh, S. S. Bogachev, M. A. Shurdov

“…There are some specific mechanisms in cell that detect the invasion into the cell of any foreign molecules, so-called pathogen-associated molecular patterns (PAMPs). …”
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Temporal Logical Attention Network for Log-Based Anomaly Detection in Distributed Systems by Yang Liu, Shaochen Ren, Xuran Wang, Mengjie Zhou

“…Our approach makes three key contributions: (1) a temporal logical attention mechanism that explicitly models both time-series patterns and logical dependencies between log events across distributed components, (2) a multi-scale feature extraction module that captures system behaviors at different temporal granularities while preserving causal relationships, and (3) an adaptive threshold strategy that dynamically adjusts detection sensitivity based on system load and component interactions. …”
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Detecting Lameness in Dairy Cows Based on Gait Feature Mapping and Attention Mechanisms by Xi Kang, Junjie Liang, Qian Li, Gang Liu

“…This limitation is exacerbated by the distinct kinematic patterns exhibited across lameness severity grades, ultimately reducing detection accuracy. …”
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Harnessing self-supervised learning to boost malicious traffic detection with enhanced attention by SUN Jianwen, ZHANG Bin, LI Hongyu, CHANG Heyu

“…The existing deep learning-based malicious traffic detection methods generally suffered from three main problems: labeled sample scarcity, inadequate representation of malicious behavior traffic features, and a high false positive rate due to ineffective integration of behavioral association patterns during detection. …”
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ADA-NAF: Semi-Supervised Anomaly Detection Based on the Neural Attention Forest by Andrey Ageev, Andrei Konstantinov, Lev Utkin

“…In this study, we present a novel model called ADA-NAF (Anomaly Detection Autoencoder with the Neural Attention Forest) for semi-supervised anomaly detection that uniquely integrates the Neural Attention Forest (NAF) architecture which has been developed to combine a random forest classifier with a neural network computing attention weights to aggregate decision tree predictions. …”
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Vision transformer embedded video anomaly detection using attention driven recurrence by Ummay Maria Muna, Shanta Biswas, Syed Abu Ammar Muhammad Zarif, Philip Jefferson Deori, Tauseef Tajwar, Swakkhar Shatabda

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Attention-based multi-scale convolution and conformer for EEG-based depression detection by Ze Yan, Ze Yan, Ze Yan, Yumei Wan, Xin Pu, Xiaolin Han, Mingming Zhao, Haiyan Wu, Wentao Li, Xueying He, Yunshao Zheng

“…Depression is a common mental health issue, and early detection is crucial for timely intervention. This study proposes an end-to-end EEG-based depression recognition model, AMCCBDep, which combines Attention-based Multi-scale Parallel Convolution (AMPC), Conformer, and Bidirectional Gated Recurrent Unit (BiGRU). …”
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Pattern transition recognition based on transfer learning for exoskeleton across different terrains by Yifan Gao, Jianbin Zheng, Yang Gao, Ziyao Chen, Jing Tang, Liping Huang

“…Human motion intention detection is a growing trend in wearable robots. In the study, a novel transfer learning method based on temporal convolutional network spatial attention (TCN-SA) is applied for pattern transition recognition under triple physical loads on different terrains. …”
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Taxonomy as a Genre and Cycle-Forming Pattern of “Fandorin corpus” by B. Akunin by A. B. Borunov, O. V. Afanasyeva

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The Same Ol’ Story…or Not? Patterns of (Dis)continuity in David Cameron’s European Policy by Monika Brusenbauch Meislová

“…As such, it explores how Cameron’s EU policy evolved under his Conservative Party leadership and investigates patterns of continuity and discontinuity in his EU discourse. …”
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Video anomaly detection via cross-modal fusion and hyperbolic graph attention mechanism by JIANG Di, LAI Huicheng, WANG Liejun

“…Finally, a hyperbolic graph attention mechanism was incorporated to effectively capture the hierarchical relationships between normal and abnormal representations through the pattern separation property of hyperbolic space, thereby improving detection accuracy. …”
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Deep Learning-Based Video Anomaly Detection Using Optimised Attention-Enhanced Autoencoders by Anjali S, Don S

“…Through the reconstruction of normal patterns and the computation of reconstruction error in relation to ground truth, convolutional autoencoders detect anomalies. …”
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EEG FREQUENCIES PATTERN AS BRAIN SIGNATURE OF EMOTIONAL BURNOUT. ANXIETY TENSION STAGE FORMATION by Semen A. Mushta, Svitlana P. Palamar, Dmytro V. Harmatiuk, Anton O. Popov, Joao M. Alves Ferreira, Mykola Yu. Makarchuk, Sergii V. Tukaiev

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Rhythm-Based Attention Analysis: A Comprehensive Model for Music Hierarchy by Fangzhen Zhu, Changhao Wu, Qike Huang, Na Zhu, Tuo Leng

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