Speech comprehension under dynamic cocktail party conditions requires auditory search for relevant speech content and focusing spatial attention on the target talker. Here, we investigated the development of these cognitive processes in a population of 329 participants aged 20-70 years. We used a multi-talker speech detection and perception task in which pairs of words (each consisting of a cue and a target word) were simultaneously presented from lateralized positions. Participants attended to predefined cue words and responded to the corresponding target. Task difficulty was varied by presenting cue and target stimuli at different intensity levels. Decline in performance was observed only in the oldest group (age range 53-70 years) and only in the most difficult condition. The EEG analysis of neurocognitive correlates of lateralized auditory attention and stimulus evaluation (N2ac, LPCpc, alpha power lateralization) revealed age-associated changes in focussing on and processing of task-relevant information, while no such deficits were found on early auditory search and target segregation. Irrespective of age, more challenging listening conditions were associated with an increased allocation of attentional resources.
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http://dx.doi.org/10.1016/j.neurobiolaging.2023.02.003 | DOI Listing |
Sensors (Basel)
January 2025
Department of Computer Science, King AbdulAziz University, Jeddah 21589, Saudi Arabia.
Traffic flow prediction is a pivotal element in Intelligent Transportation Systems (ITSs) that provides significant opportunities for real-world applications. Capturing complex and dynamic spatio-temporal patterns within traffic data remains a significant challenge for traffic flow prediction. Different approaches to effectively modeling complex spatio-temporal correlations within traffic data have been proposed.
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January 2025
Department of AI & Big Data, Honam University, Gwangju 62399, Republic of Korea.
This study proposes an advanced plant disease classification framework leveraging the Attention Score-Based Multi-Vision Transformer (Multi-ViT) model. The framework introduces a novel attention mechanism to dynamically prioritize relevant features from multiple leaf images, overcoming the limitations of single-leaf-based diagnoses. Building on the Vision Transformer (ViT) architecture, the Multi-ViT model aggregates diverse feature representations by combining outputs from multiple ViTs, each capturing unique visual patterns.
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January 2025
School of Information Engineering, China University of Geosciences, Beijing 100083, China.
Extracting fragmented cropland is essential for effective cropland management and sustainable agricultural development. However, extracting fragmented cropland presents significant challenges due to its irregular and blurred boundaries, as well as the diversity in crop types and distribution. Deep learning methods are widely used for land cover classification.
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January 2025
Department of Information Technology, Quaid e Awam University, Nawabshah 67450, Pakistan.
Detection of anomalies in video surveillance plays a key role in ensuring the safety and security of public spaces. The number of surveillance cameras is growing, making it harder to monitor them manually. So, automated systems are needed.
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December 2024
School of Physics and Electronics, Nanning Normal University, Nanning 530100, China.
Remote sensing change detection (RSCD), which utilizes dual-temporal images to predict change locations, plays an essential role in long-term Earth observation missions. Although many deep learning based RSCD models perform well, challenges remain in effectively extracting change information between dual-temporal images and fully leveraging interactions between their feature maps. To address these challenges, a constraint- and interaction-based network (CINet) for RSCD is proposed.
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