Deep Learning for Short-Term Precipitation Prediction in Four Major Indian Cities: A ConvLSTM Approach with Explainable AI

The article discusses the limitations of saliency maps used in deep learning for visual explanations. It highlights the lack of consensus on their purpose and alignment with user queries, which affects their evaluation and utility. To address this, the authors propose the Reference-Frame × Granularity (RFxG) taxonomy, which categorizes saliency explanations based on two axes: Reference-Frame, distinguishing between pointwise and contrastive explanations, and Granularity, ranging from fine-grained to coarse-grained interpretations. This framework aims to improve the evaluation metrics for salie…

NervePool is a newly proposed pooling layer designed for deep learning applications involving graph-structured data. It focuses on simplicial complexes, which extend traditional graphs by incorporating higher-dimensional simplices. This innovation aims to enhance the modeling of complex relationships while addressing issues such as computational efficiency and overfitting. The pooling layer operates through a learned hierarchical representation, allowing for effective down sampling and information compression.

The study addresses the challenge of blood coagulation after drawing, which can lead to inaccurate hematological analyses. It presents a deep learning-enhanced framework utilizing Digital Holographic Microscopy (DHM) to evaluate anticoagulant efficacy ex vivo. The method allows for label-free, non-invasive analysis of human blood samples, focusing on cell counting and morphology. The research compares the effects of conventional EDTA and potassium ferric oxalate nanoparticles (KFeOx-NPs) on blood coagulation rates, revealing significant findings.

SimuFreeMark is a proposed watermarking framework designed to enhance image security against editing attacks, particularly in the context of artificial intelligence-generated content (AIGC). Unlike existing methods that depend on noise simulation, SimuFreeMark directly embeds watermarks into the low-frequency components of images, which have shown significant robustness against various attacks. This innovation aims to address the growing need for reliable watermarking solutions in an era of advanced image manipulation techniques.

An AI-assisted Augmented Reality (AR) assembly workflow has been developed, utilizing deep learning-based object recognition to identify assembly components and provide step-by-step instructions. The system displays bounding boxes around components in real-time, indicating their placement, thus eliminating the need for manual searching or sorting. A case study involving the assembly of LEGO sculptures demonstrates the system's feasibility and effectiveness in enhancing the assembly process.

The paper titled 'SPOT: Single-Shot Positioning via Trainable Near-Field Rainbow Beamforming' introduces a novel deep learning-based approach for generating frequency-dependent rainbow beams and estimating user positions. Utilizing phase-time arrays that combine phase shifters and true-time delays, the method allows for the optimization of localization accuracy by treating coefficients as trainable variables. The proposed scheme significantly reduces overhead and consistently achieves lower two-dimensional positioning errors compared to existing methods.

The article titled 'Bayesian ICA with super-Gaussian Source Priors' discusses advancements in Independent Component Analysis (ICA), a key method in machine learning for feature extraction. The authors introduce a horseshoe-type prior with a latent Polya-Gamma scale mixture representation, enabling scalable algorithms for point estimation and full posterior inference. The study establishes theoretical guarantees for hierarchical Bayesian ICA, including results for the unmixing matrix. Simulation studies indicate that the proposed methods are competitive with existing ICA tools.

Diabetic Retinopathy (DR) is a major cause of preventable blindness, making early detection essential for reducing global vision loss. Recent advancements in deep learning have significantly improved DR screening, evolving from basic convolutional neural networks to sophisticated methodologies that tackle issues like class imbalance and label scarcity. This survey synthesizes findings from over 50 studies and 20 datasets, highlighting methodological innovations and ongoing challenges in validation and reproducibility.