Modeling Retinal Ganglion Cells with Neural Differential Equations

A new dataset named MultiBanAbs has been developed to facilitate Bangla abstractive summarization, comprising over 54,000 articles and summaries from diverse sources including blogs and newspapers. This initiative addresses the limitations of existing summarization systems that primarily focus on news articles, which often do not reflect the varied nature of real-world Bangla texts.

A recent study has introduced a bidirectional Long Short-Term Memory (LSTM) neural network designed to classify light curves of transient astronomical objects using data from the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC). The study reorganized the original fourteen object classes into five categories to mitigate class imbalance, achieving high performance in certain classifications while facing challenges with others.

A new paper presents a hybrid framework for portfolio optimization that combines Long Short-Term Memory (LSTM) forecasting with Proximal Policy Optimization (PPO) reinforcement learning. This innovative approach aims to enhance portfolio management by leveraging deep learning to predict market trends and dynamically adjust asset allocations across various financial instruments, including U.S. and Indonesian equities, U.S. Treasuries, and cryptocurrencies.

A new framework for Electric Vehicle (EV) charging forecasting has been proposed, addressing cybersecurity threats that compromise operational efficiency and grid stability. This framework employs a federated learning approach that integrates LSTM autoencoder-based anomaly detection, interpolation for data mitigation, and collaborative learning without centralized data aggregation.

A recent study compared the performance of Kolmogorov-Arnold Networks (KAN) and Long Short-Term Memory (LSTM) networks in forecasting non-deterministic stock price data. The findings revealed that LSTM outperformed KAN across all tested prediction horizons, demonstrating its effectiveness in sequential data modeling while KAN showed higher error rates despite its theoretical interpretability.