graph neural networks

Architectural Exploration of Reinforcement Learning and Graph Neural Networks for Gate-Level Logic Synthesis in Modern EDA

This review-style study examines how reinforcement learning agents and graph neural networks are being integrated into gate-level logic synthesis for electronic design automation. It discusses how GNN-derived structural embeddings support pre-layout estimation of signal probability and switching activity, how RL agents...

Architectural Exploration of Reinforcement Learning and Graph Neural Networks for Gate-Level Logic Synthesis in Modern EDA

This review-style study examines how reinforcement learning agents and graph neural networks are being integrated into gate-level logic synthesis for electronic design automation. It discusses how GNN-derived structural embeddings support pre-layout estimation of signal probability and switching activity, how RL agents...

Sensor to pixels: swarm gathering via image-based reinforcement learning

Abstract This study highlights the potential of image-based reinforcement learning methods for addressing swarm-related tasks. In multi-agent reinforcement learning, effective policy learning depends on how agents sense, interpret, and process local inputs. Traditional approaches often rely on handcrafted feature extra...

LLM-Driven Verilog Generation and Verification for RISC-V Processor Design

Large language models (LLMs) are increasingly used in Electronic Design Automation (EDA) to write hardware description code. This paper reviews how LLMs generate and verify Verilog for a RISC-V processor datapath, a core topic in Computer Architecture and Organization. The review is built around the AI-driven logic syn...

AI-Driven Encryption and Resource Optimization in Cloud Security Frameworks

Abstract Cloud-based systems demand secure yet efficient file access mechanisms. Traditional encryption frameworks often introduce latency and resource overhead, limiting scalability. Building on Astillero's (2026) AI-driven logic gate synthesis, this paper explores how Artificial Intelligence (AI) can optimize encrypt...

AI-Driven Encryption and Resource Optimization in Cloud Security Frameworks

Abstract Cloud-based systems demand secure yet efficient file access mechanisms. Traditional encryption frameworks often introduce latency and resource overhead, limiting scalability. Building on Astillero's (2026) AI-driven logic gate synthesis, this paper explores how Artificial Intelligence (AI) can optimize encrypt...

LLM-Driven Verilog Generation and Verification for RISC-V Processor Design

Large language models (LLMs) are increasingly used in Electronic Design Automation (EDA) to write hardware description code. This paper reviews how LLMs generate and verify Verilog for a RISC-V processor datapath, a core topic in Computer Architecture and Organization. The review is built around the AI-driven logic syn...

Author Response to Referee #1

Abstract. Glacierized high-mountain basins supply water to approximately two billion people yet remain among the most data-scarce hydrologic regions globally, making truly ungauged streamflow prediction a critical challenge. Deep learning (DL) offers a promising alternative to traditional regionalization, but fundament...

Discovery of dietary polyphenols targeting the autoinducer-2 quorum sensing system of pathogenic bacteria.

Targeting autoinducer-2 (AI-2) quorum sensing (QS) systems with dietary compounds represents a promising strategy to combat pathogens, yet mechanisms remain elusive. Here, we develop a machine learning-driven framework combining computational screening with multi-level experimental analysis to identify AI-2 quorum sens...

Every Locale Has Its Rhythm: Endow Time-Series Transformers with Spatial Insights for Long-term Spatio-Temporal Forecasting

Transformers have demonstrated promise in time-series forecasting, attributed to their superior capability of capturing temporal dependencies. Nevertheless, prevailing transformer models predominantly concentrate on the temporal dependencies within single-/multi-variate time series. This focus results in insufficient c...

Physics-Informed Graph Neural Networks for N-Body Dark Matter Halo Dynamics and Symplectic Orbital Integration Technical Monograph & Computational Astrophysics Architecture

GraphLLM4CTR: Large Language Model-Enhanced Graph Neural Networks for Click-through Rate Prediction in Recommender Systems

Click-through rate (CTR) prediction is a crucial task to estimate the probability that users will click on items in online platforms. Recently, researchers have incorporated semantics derived from large language models (LLMs) into representations of users and items in graph neural networks (GNNs) framework to facilitat...