Heterogeneous dual-decoder network for road extraction in remote sensing images.

Accurate road extraction from remote sensing images is crucial for autonomous driving, urban planning, and route planning. However, existing methods struggle to address the challenges of scale variation, occlusion, and blurred boundaries. To tackle these challenges, this paper proposes a heterogeneous dual-decoder network (HDDNet), which aims to simultaneously solve the multiple problems in remote sensing road extraction by designing two decoders with complementary functions.

Optimizing adaptive modulation technique using standard propagation model for enhanced wireless communication channels.

Recent progress in the domain of wireless communication systems has brought to light emerging trends that underscore the evolution of modern communication paradigms. Nonetheless, the performance of such systems remains constrained by persistent technical limitations, including packet loss, bandwidth scarcity, and suboptimal spectral efficiency, all of which necessitate rigorous analytical investigations. To address these constraints, the present study leverages an adaptive modulation technique tailored to enhance communication efficiency.

An Efficient Evolutionary Neural Architecture Search Algorithm Without Training.

Neural Architecture Search (NAS) has made significant advancements in autonomously constructing high-performance network architectures, capturing extensive attention. However, a key challenge of existing NAS approaches is the intensive performance evaluation, leading to significant time and computational resource consumption. In this paper, we propose an efficient Evolutionary Neural Architecture Search (ENAS) method to address this issue.

A Comprehensive Review of Multimodal Emotion Recognition: Techniques, Challenges, and Future Directions.

This paper presents a comprehensive review of multimodal emotion recognition (MER), a process that integrates multiple data modalities such as speech, visual, and text to identify human emotions. Grounded in biomimetics, the survey frames MER as a bio-inspired sensing paradigm that emulates the way humans seamlessly fuse multisensory cues to communicate affect, thereby transferring principles from living systems to engineered solutions. By leveraging various modalities, MER systems offer a richer and more robust analysis of emotional states compared to unimodal approaches.

Semi-supervised medical image segmentation based on multi-stage iterative training and high-confidence pseudo-labeling.

Due to the scarcity and high cost of pixel-level annotations for training data, semi-supervised learning has gradually become a key solution. Most existing methods rely on consistency regularization and pseudo-label generation, often adopting multi-branch structures to generate pseudo-labels for co-training. Such approaches, however, commonly yield low-confidence pseudo-labels from perturbed inputs, which can degrade model performance.

Enhancing buckwheat maturity classification with generative adversarial networks for spectroscopy data augmentation.

Introduction: The optimal harvest period for buckwheat is challenging to determine due to its short growth cycle. Harvesting too early or too late can negatively affect the quality of the crop. Traditional harvest methods are labor-intensive and fail to account for the spatial variability in buckwheat quality within a field.

Transfer Learning for Predicting ncRNA-Protein Interactions.

Noncoding RNAs (ncRNAs) interact with proteins, playing a crucial role in regulating gene expression and cellular functions. Accurate prediction of these interactions is essential for understanding biological processes and developing novel therapeutic agents. However, identifying ncRNA-protein interactions (ncRPI) through experimental methods is often costly and time-consuming.

Utilizing shallow features and spatial context for weakly supervised intracerebral hemorrhage segmentation.

Background: In deep learning-assisted intracerebral hemorrhage (ICH) diagnosis, because weak image-level labels cannot provide supervisory information about the target location, weakly-supervised semantic segmentation (WSSS) methods are relatively limited. Therefore, we developed a novel method for improving the ICH segmentation results from weak image-level labels.

Methods: This paper proposes the Shallow-Feature class activation map (CAM) module, which utilizes fine-grained information from the shallow feature maps of convolutional neural networks (CNNs) to generate CAM for accurate target localization and contour.

Small pre-trained model for background understanding in multi-round question answering.

Multi-round Q&A based on background text needs to infer the answer to the question through the current question, historical Q&A pairs, and background text. The pre-trained model has proved its effectiveness in this task; however, the existing model has many problems such as too many parameters and high resource consumption. We propose a knowledge transfer method that combines knowledge distillation, co-learning of similar datasets, and fine-tuning of similar tasks.

An optimized deterministic concurrency control approach for geo-distributed transaction processing on permissioned blockchains.

Concurrency control is crucial for ensuring consistency and isolation in distributed transaction processing. Traditional concurrency control algorithms, such as locking-based protocols, usually suffer from performance degradation due to heavy transaction coordination overheads. To overcome this problem, deterministic concurrency control approaches are widely adopted in many systems since they can avoid coordination overhead by eliminating uncertainty.

Stereo Endoscopic Camera Pose Optimal Estimation by Structure Similarity Index Measure Integration.

Background: Accurate endoscopic camera pose estimation is crucial for real-time AR navigation systems. While current methods primarily use depth and optical flow, they often ignore structural inconsistencies between images.

Methods: Leveraging the RAFT framework, we process sequential stereo RGB pairs to extract optical flow and depth features for pose estimation.

COVID-19CT+: A public dataset of CT images for COVID-19 retrospective analysis.

Background and objectiveCOVID-19 is considered as the biggest global health disaster in the 21st century, and it has a huge impact on the world.MethodsThis paper publishes a publicly available dataset of CT images of multiple types of pneumonia (COVID-19CT+). Specifically, the dataset contains 409,619 CT images of 1333 patients, with subset-A containing 312 community-acquired pneumonia cases and subset-B containing 1021 COVID-19 cases.

Low-cost video-based air quality estimation system using structured deep learning with selective state space modeling.

Air quality is crucial for both public health and environmental sustainability. An efficient and cost-effective model is essential for accurate air quality predictions and proactive pollution control. However, existing research primarily focuses on single static image analysis, which does not account for the dynamic and temporal nature of air pollution.

A Survey of the State of the Art in Monocular 3D Human Pose Estimation: Methods, Benchmarks, and Challenges.

Three-dimensional human pose estimation (3D HPE) from monocular RGB cameras is a fundamental yet challenging task in computer vision, forming the basis of a wide range of applications such as action recognition, metaverse, self-driving, and healthcare. Recent advances in deep learning have significantly propelled the field, particularly with the incorporation of state-space models (SSMs) and diffusion models. However, systematic reviews that comprehensively cover these emerging techniques remain limited.

The Modified Sparrow Search Algorithm with Brown Motion and Levy Flight Strategy for the Class Integration Test Order Generation Problem.

Software testing identifies potential errors and defects in software. A crucial component of software testing is integration testing, and the generation of class integration test orders (CITOs) is a critical topic in integration testing. The research shows that search-based algorithms can solve this problem effectively.

Multitask Swin Transformer for classification and characterization of pulmonary nodules in CT images.

Background: Early diagnosis of pulmonary nodules is essential for effective prevention and treatment of pulmonary cancer. However, the heterogeneous and complex characteristics of pulmonary nodules, such as shape, size, speculation, and texture, present significant challenges in clinical diagnosis, which computer-aided diagnosis (CAD) can help address. Moreover, the varied performance of deep learning methods in CAD and limited model interpretability often hinder clinicians' understanding of CAD results.

DMCM: Dwo-branch multilevel feature fusion with cross-attention mechanism for infrared and visible image fusion.

In response to the limitations of current infrared and visible light image fusion algorithms-namely insufficient feature extraction, loss of detailed texture information, underutilization of differential and shared information, and the high number of model parameters-this paper proposes a novel multi-scale infrared and visible image fusion method with two-branch feature interaction. The proposed method introduces a lightweight multi-scale group convolution, based on GS convolution, which enhances multi-scale information interaction while reducing network parameters by incorporating group convolution and stacking multiple small convolutional kernels. Furthermore, the multi-level attention module is improved by integrating edge-enhanced branches and depthwise separable convolutions to preserve detailed texture information.

Quality of Experience (QoE) in Cloud Gaming: A Comparative Analysis of Deep Learning Techniques via Facial Emotions in a Virtual Reality Environment.

Cloud gaming has rapidly transformed the gaming industry, allowing users to play games on demand from anywhere without the need for powerful hardware. Cloud service providers are striving to enhance user Quality of Experience (QoE) using traditional assessment methods. However, these traditional methods often fail to capture the actual user QoE because some users are not serious about providing feedback regarding cloud services.

HWA-ResMamba: automatic segmentation of coronary arteries based on residual Mamba with high-order wavelet-enhanced convolution and attention feature aggregation.

Automatic segmentation of coronary arteries is a crucial prerequisite in assisting in the diagnosis of coronary artery disease. However, due to the fuzzy boundaries, small-slender branches, and significant individual variations, automatic segmentation of coronary arteries is extremely challenging.This study proposes a residual Mamba with high-order wavelet-enhanced convolution and attention feature aggregation (HWA-ResMamba) for coronary arteries segmentation.

Brain CT image classification based on mask RCNN and attention mechanism.

Along with the computer application technology progress, machine learning, and block-chain techniques have been applied comprehensively in various fields. The application of machine learning, and block-chain techniques into medical image retrieval, classification and auxiliary diagnosis has become one of the research hotspots at present. Brain tumor is one of the major diseases threatening human life.

Unravelling patterns of food tolerance to pesticide residues via non-negative matrix factorization.

Gaining knowledge about the maximum residue limits (MALs) of pesticides on fresh or processed foods is critical to the process of pre-harvest cultivation, post-harvest processing and storage, and the downstream safety surveillance of food commodities. In this study, we explore the available MALs of 643 pesticides on 128 foods via non-negative matrix factorization (NMF) and hierarchical clustering to gain insights into the patterns of how similar pesticides exhibit similar MALs profiles on foods. Meanwhile, NMF predicts the MALs for untested foods via the implicitly-learnt patterns without conducting in vivo testing that potentially violates ethic regulations.

Single-task regression naturally adapts to multi-species (eco)toxicological modelling: a case study on animals.

In silico (eco)toxicological modelling has gained increasing popularity with chemical environmentalists in accelerating toxicity assessment of hazardous chemicals on environments, animal well-being and human health. Existing local and multi-task models commonly exhibit restricted extensibility in multi-species modelling scenarios. In this work, we propose a strategy of single-task regression to naturally adapt modelling to (eco)toxicological measurements on multiple species without requiring a certain number of common pesticides among tested species as multi-task regression does.

Enhanced Photocatalytic Oxidative Coupling of Methane over Metal-Loaded TiO Nanowires.

The photocatalytic oxidative coupling of methane (OCM) on metal-loaded one-dimensional TiO nanowires (TiO NWs) was performed. With metal loading, the electric and optical properties of TiO NWs were adjusted, contributing to the improvement of the activity and selectivity of the OCM reaction. In the photocatalytic OCM reaction, the 1.

A Decomposition-Based Evolutionary Algorithm with Neighborhood Region Domination.

The decomposition-based multi-objective optimization algorithm MOEA/D (multi-objective evolutionary algorithm based on decomposition) introduces the concept of neighborhood, where each sub-problem requires optimization through solutions within its neighborhood. Due to the comparison being only with solutions in the neighborhood, the obtained set of solutions is not sufficiently diverse, leading to poorer convergence properties. In order to adequately acquire a high-quality set of solutions, this algorithm requires a large number of population iterations, which in turn results in relatively low computational efficiency.

Cells Grouping Detection and Confusing Labels Correction on Cervical Pathology Images.

Cervical cancer is one of the most prevalent cancers among women, posing a significant threat to their health. Early screening can detect cervical precancerous lesions in a timely manner, thereby enabling the prevention or treatment of the disease. The use of pathological image analysis technology to automatically interpret cells in pathological slices is a hot topic in digital medicine research, as it can reduce the substantial effort required from pathologists to identify cells and can improve diagnostic efficiency and accuracy.