graph neural networks

When External Numerical Guards Detect Injected Faults in Neural-Network Inference

External guards can withhold an accelerator's output and trigger recovery, but their usefulness depends on which faults their numerical tests detect. We examine software fault-injection campaigns in convolutional networks, a vision transformer, and a decoder language model, distinguishing reported results from independ...

Deep Ensemble Learning with Graph Attention Mechanisms for Molecular Classification of HIV Inhibitors

NeuroSymbolic-RLNet: a neuro-symbolic reinforcement learning framework for transparent sequential decision-making

The integration of symbolic reasoning with deep reinforcement learning presents a promising paradigm for achieving transparent and interpretable sequential decision-making in complex environments. This work introduces NeuroSymbolic-RLNet, a novel hybrid framework that combines symbolic state transition graphs with neur...

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

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 extraction...

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

Abstract 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 qu...