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The simulation of quantum systems on classical computers faces significant challenges due to the exponential scaling of computational resources required to accurately represent quantum states and their evolution. This work proposes a novel approach to quantum computing simulation based on graph representation and graph...
This paper proposes a novel approach to distributed edge computing resource scheduling based on consensus mechanisms. Traditional edge computing resource management often suffers from inefficiencies due to manual allocation, lack of real-time adaptability, and limited scalability. This research addresses these limitati...
Climate tipping points—abrupt, potentially irreversible transitions in Earth subsystems such as the Atlantic Meridional Overturning Circulation (AMOC)— represent one of the most consequential risks in climate science. Existing early-warning indicators based on critical slowing down (rising variance and autocorrelation)...
The integration of intelligent multiple access technologies, edge intelligence and advanced antenna technologies would be critical for reliable and scalable wireless networks that would be able to support next generation applications such as extended reality, autonomous systems and large-scale IoT systems.
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This paper proposes a novel approach to edge computing resource sharing leveraging blockchain technology. Traditional edge computing platforms often suffer from centralized control, leading to vulnerabilities and a suboptimal utilization of resources. This research introduces a decentralized system where edge computing...
This paper proposes a novel approach to distributed edge computing resource scheduling based on consensus mechanisms. Traditional edge computing resource management often suffers from inefficiencies due to manual allocation, lack of real-time adaptability, and limited scalability. This research addresses these limitati...
The proliferation of Internet of Things (IoT) devices has generated vast amounts of data, presenting significant challenges for centralized processing. Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing complex, interconnected data, particularly in applications like smart cities, industrial moni...
The energy sector is undergoing a profound transition from conventional, centrally managed electricity systems toward intelligent, connected and increasingly decentralized smart energy ecosystems. This transformation is being accelerated by Artificial Intelligence (AI), the Internet of Things (IoT), advanced data analy...
FINDING: Topological insulators are bulk-insulating but surface/edge-conducting quantum phases, with disorder-driven metal-TI transitions showing critical exponent ν≈2.7. | MATH: Critical exponent ν≈2.7 (from arXiv:1211.5026v2); topological invariant (Z₂ index) classifying band structure; bulk-boundary correspondence l...
FINDING: Topological insulators are bulk-insulating but surface/edge-conducting quantum phases, with disorder-driven metal-TI transitions showing critical exponent ν≈2.7. | MATH: Critical exponent ν≈2.7 (from arXiv:1211.5026v2); topological invariant (Z₂ index) classifying band structure; bulk-boundary correspondence l...
The energy sector is undergoing a profound transition from conventional, centrally managed electricity systems toward intelligent, connected and increasingly decentralized smart energy ecosystems. This transformation is being accelerated by Artificial Intelligence (AI), the Internet of Things (IoT), advanced data analy...
Graph partitioning is a fundamental problem in various fields, including computer graphics, high-performance computing, and network design. Traditional graph partitioning algorithms often suffer from high computational complexity, particularly when dealing with large and complex graphs. This research investigates the p...
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
Announcing Gemini 4 Argon, our frontier model for real-world coding, enterprise knowledge work and cyber defense, rolling out soon.
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