This paper reviews the current state of autonomous driving, identifies limitations in scalability and adaptability, and proposes a data-driven, two-stage fine-tuning process and a "service-oriented modular end-to-end (SO-M-E2E)" architecture to achieve fully autonomous driving while integrating technological and socio-political aspects.

This paper introduces ePAI, an AI-powered system designed for the early and prediagnostic detection of pancreatic ductal adenocarcinoma (PDAC) from routine computed tomography (CT) scans. The system demonstrates high accuracy in detecting small lesions and significantly outperforms radiologists, offering a median lead time of 347 days before clinical diagnosis.

This paper analyzes how the current wave of Artificial Intelligence (AI) impacts the US economy through national accounts, using a macro-accounting framework and describing the AI production process. It highlights data centers' pivotal role, finding that while AI investment significantly boosts demand, its net contribution to GDP growth is tempered by high import content, with future AI service production expected to yield substantial GDP increases.

This study develops a stakeholder-informed AI system to improve course articulation and credit transfer between colleges. By addressing concerns about superficial matching and institutional biases, their supervised alignment method achieves a significant accuracy improvement and projects a substantial increase in valid credit mobility opportunities for students.

This paper comprehensively reviews methods for assessing and enhancing the resilience of community-centric power systems, examining engineering-based and data-driven metrics, the interdependence of power and community resilience, and the integration of AI/ML strategies, alongside relevant techno-legal frameworks. It highlights critical research gaps and future directions for robust energy infrastructure.

This paper investigates how fair voting methods can revitalize democracies by addressing long-standing challenges and new threats from AI. It presents a trilogy of findings showing these methods enhance legitimacy, accelerate novel impacts, and safeguard against AI biases, using real-world evidence from participatory budgeting.