Model Agnostic Meta-Learning(MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating demand.The presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease management.With advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a challenge.This challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease pathogens.In this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data scarcity.This study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural Networks.This paper also proposes a Model AgnosticMeta Learning(MAML)based ensemble classifier for accurate classification.The proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens detection.In future,more diseases can be included for detection along with some other modalities like pests and weed.

Gambling on God： A Qualified Defense of Pascal＇s Wager

The paper critically examines and refutes some of the standard arguments against Pascal＇s Wager, particularly, the ＂Many Gods Objection＂. The paper argues that Pascal＇s philosophical and theological opponents proceed far too quickly in dismissing his argument as implausible. Their determined attempts to tear down Pascal＇s Wager have caused them to miss its power and force. While the Wager may not be sound for today＇s multi culturally sophisticated audience, the Wager is quite cogent relative to Pascal＇s time, when theism and agnosticism were the only genuine possibilities. Thus, the paper concludes that Pascal＇s Wager is less vulnerable than most detractors seem to think.

BING: Binarized normed gradients for objectness estimation at 300fps

Training a generic objectness measure to produce object proposals has recently become of significant interest. We observe that generic objects with well-defined closed boundaries can be detected by looking at the norm of gradients, with a suitable resizing of their corresponding image windows to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64 D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed gradients(BING), can be used for efficient objectness estimation, which requires only a few atomic operations(e.g., add, bitwise shift, etc.). To improve localization quality of the proposals while maintaining efficiency, we propose a novel fast segmentation method and demonstrate its effectiveness for improving BING's localization performance, when used in multithresholding straddling expansion(MTSE) postprocessing. On the challenging PASCAL VOC2007 dataset, using 1000 proposals per image and intersectionover-union threshold of 0.5, our proposal method achieves a 95.6% object detection rate and 78.6% mean average best overlap in less than 0.005 second per image.

Opportunities and challenges in developing tissue-agnostic anti-cancer drugs

The rapid advances in the understanding of oncogenic process and the maturation of affordable precision diagnostic tools have enabled the development of targeted therapeutic agents,such as those targeting BCR-ABL,epithelial growth factor receptor L858R,EML4-anaplastic lymphoma kinase,and BRAF V600E,to treat cancers that harbor specific molecular alterations.Traditionally,each targeted drug has been developed for a particular tumor type where such alteration is most frequently found.Recently,the widespread adoption of next generation sequencing has led to an increase in the identification of rare and ultra-rare alterations,and,in some cases,the same rare alterations are found across multiple tumor types.The rarity of these alterations makes clinical trials traditionally designed for specific tumor types infeasible.As a result,tissue-agnostic trials have been developed to study the efficacy of these treatments and increase patient access.This review summarizes current successful cases of tissue-agnostic development,such as drugs targeting tropomyosin receptor kinase fusions,and proposes the next wave of potential tissue-agnostic targets,including fusions of ROS1,anaplastic lymphoma kinase,fibroblast growth factor receptor,and rearranged during transfection.In addition,the advantages and the challenges of such approach are discussed in the context of clinical development and approval.