Reduction in chemical usage for crop management due to the environmental and health issues is a key area in achieving sustainable agricultural practices. One area in which this can be achieved is through the development of intelligent spraying systems which can identify the target for example crop disease or weeds allowing for precise spraying reducing chemical usage. Artificial intelligence and computer vision has the potential to be applied for the precise detection and classification of crops. In this paper, a study is presented that uses instance segmentation for the task of leaf and rust disease detection in apple orchards using Mask R-CNN. Three different Mask R-CNN network backbones (ResNet-50, MobileNetV3-Large and MobileNetV3-Large-Mobile) are trained and evaluated for the tasks of object detection, segmentation and disease detection. Segmentation masks on a subset of the Plant Pathology Challenge 2020 database are annotated by the authors, and these are used for the training and evaluation of the proposed Mask R-CNN based models. The study highlights that a Mask R-CNN model with a ResNet-50 backbone provides good accuracy for the task, particularly in the detection of very small rust disease objects on the leaves.

Autism spectrum condition (ASC) is primarily diagnosed by behavioural symptoms including social, sensory and motor aspects. Although stereotyped, repetitive motor movements are considered during diagnosis, quantitative measures that identify kinematic characteristics in the movement patterns of autistic individuals are poorly studied, preventing advances in understanding the aetiology of motor impairment, or whether a wider range of motor characteristics could be used for diagnosis. The aim of this study was to investigate whether data-driven machine learning based methods could be used to address some fundamental problems with regard to identifying discriminative test conditions and kinematic parameters to classify between ASC and neurotypical controls. Data was based on a previous task where 16 ASC participants and 14 age, IQ matched controls observed then imitated a series of hand movements. 40 kinematic parameters extracted from eight imitation conditions were analysed using machine learning based methods. Two optimal imitation conditions and nine most significant kinematic parameters were identified and compared with some standard attribute evaluators. To our knowledge, this is the first attempt to apply machine learning to kinematic movement parameters measured during imitation of hand movements to investigate the identification of ASC. Although based on a small sample, the work demonstrates the feasibility of applying machine learning methods to analyse high-dimensional data and suggest the potential of machine learning for identifying kinematic biomarkers that could contribute to the diagnostic classification of autism.

This paper proposes a novel Adaptive Region-based Edge Smoothing Model (ARESM) for automatic boundary detection of optic disc and cup to aid automatic glaucoma diagnosis. The novelty of our approach consists of two aspects: 1) automatic detection of initial optimum object boundary based on a Region Classification Model (RCM) in a pixel-level multidimensional feature space; 2) an Adaptive Edge Smoothing Update model (AESU) of contour points (e.g. misclassified or irregular points) based on iterative force field calculations with contours obtained from the RCM by minimising energy function (an approach that does not require predefined geometric templates to guide auto-segmentation). Such an approach provides robustness in capturing a range of variations and shapes. We have conducted a comprehensive comparison between our approach and the state-of-the-art existing deformable models and validated it with publicly available datasets. The experimental evaluation shows that the proposed approach significantly outperforms existing methods. The generality of the proposed approach will enable segmentation and detection of other object boundaries and provide added value in the field of medical image processing and analysis.

Accurate stock return forecasting remains a central challenge in quantitative finance, as it directly informs the construction of portfolios and the management of risk. Although traditional static factor models are widely used, they are limited by manual factor selection and fixed weight assignments, which makes them vulnerable to evolving market conditions and regime shifts. To overcome these limitations, we introduce Market Regime Aware-Augmented Attention GRU (MRA-AGRU), an automated dynamic factor gating framework that adaptively reweights factors in response to market regime signals. By integrating an attention-enhanced GRU network, MRA-AGRU effectively suppresses obsolete or noisy factors while amplifying those most relevant to the prevailing environment, thereby capturing nuanced temporal and cross-factor dependencies. Extensive experiments on the CSI 300 and NASDAQ 100 demonstrate the superior performance of MRA-AGRU, highlighting machine-driven factor modulation's role in improving robustness to structural breaks and reducing bias in factor engineering.

The performance of deep neural networks (DNNs) in accomplishing tasks heavily relies on feature selection and sparse representation of high-dimensional data. Previous work has treated feature selection and sparse representation as separate mechanisms for improving DNNs performance, focusing on identifying and leveraging informative features to enhance task-specific outcomes. However, few studies have established a connection between feature selection and sparse representation. To address this gap, this article proposes an optimization framework termed informative sparse transport (IST), which integrates feature selection and sparse coding into a unified multiobjective optimization framework. Using optimal transport as a bridge, the IST framework harmonizes the relationship between feature selection and sparse representation, offering an informational advantage. In the IST framework, feature selection aims to identify an optimal subset of features to maximize mutual information or minimize redundancy, while sparse representation seeks to approximate data with the fewest possible features. Although these objectives differ, they are fundamentally complementary, as both emphasize extracting task-relevant information while eliminating redundancy. By unifying feature selection and sparse representation, the IST framework effectively mitigates challenges posed by high-dimensional data, delivering a robust solution for enhanced feature extraction and representation. We validate the IST framework on generative and classification tasks, demonstrating IST framework improves model performance through the complementary synergy of feature selection and sparse representation.