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1,721 papers
Source code, benchmark suite, and generated client files for the paper Schema-Augmented LLM Prompting for Converting ML Training Scripts to Federated Learning Clients (ACM TOSEM, resubmission 2026). v2.0 corrects FedAvg to sample-count weighting, partitions the IID simulation, moves generated-module import to preflight...
This study evaluates federated continual learning for heart failure risk prediction under changing clinical data conditions. Using a simulated 12-month, five-site dataset, it compares attention-based continual learning with FedAvg, FedProx, linear, and replay-based approaches. The findings show that apparent benefits o...
This version (2026-09-26) corrects a citation error found by an automated check and confirmed by hand against the arXiv abstracts. Version 2 cited the identifier 2607.07314, an unrelated federated-learning paper posted after this synthesis was drafted, for the quantum software debugging paper that notes quantum bugs "o...
Pharmacovigilance plays a crucial role in detecting, assessing, understanding, and preventing adverse drug reactions throughout the life cycle of medicines. However, the increasing volume, complexity, and diversity of safety data have created significant limitations for conventional, mainly manual and reactive pharmaco...
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The Mostafa El-Rakhawi Doctrine in Cybernetic Law redefines legislation as a dynamic, self-evolving control system. Moving beyond static statutes, it applies feedback loops, information theory, and real-time monitoring to ensure laws continuously adapt to societal, economic, and technological changes while maintaining...
The Unified Computational Jurisprudential System (UCJS) establishes the definitive global standard for Computational Legal Science, bridging normative theory with empirical application through mathematically rigorous, culturally adaptive architecture. The system functions as a Justice Accelerator and Objective Calibrat...
This paper addresses vulnerabilities in standard federated learning aggregation against gradient inversion attacks by evaluating three layered privacy-enhancing mechanisms: classical differential privacy (DP) noise injection, homomorphic encryption, and a novel quantum-inspired random unitary rotation of embedding vect...
The Mostafa El-Rakhawi Doctrine in Cybernetic Law redefines legislation as a dynamic, self-evolving control system. Moving beyond static statutes, it applies feedback loops, information theory, and real-time monitoring to ensure laws continuously adapt to societal, economic, and technological changes while maintaining...
Pharmacovigilance plays a crucial role in detecting, assessing, understanding, and preventing adverse drug reactions throughout the life cycle of medicines. However, the increasing volume, complexity, and diversity of safety data have created significant limitations for conventional, mainly manual and reactive pharmaco...
The Unified Computational Jurisprudential System (UCJS) establishes the definitive global standard for Computational Legal Science, bridging normative theory with empirical application through mathematically rigorous, culturally adaptive architecture. The system functions as a Justice Accelerator and Objective Calibrat...
A Systematic Review Protocol “Beyond Diagnostic Accuracy: Calibration and Predictive Uncertainty of Artificial Intelligence Models for Oral and Dental Disease Diagnosis” Title of the ReviewBeyond Diagnostic Accuracy: Calibration and Predictive Uncertainty of Artificial Intelligence Models for Oral and Dental Disease Di...
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
The effort aims to help U.S. learners from kindergarten to community college, with an emphasis on math, making, and the constructive use of AI.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
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