Category
2,612 papers
This paper investigates the application of quantum walks to network security analysis, specifically focusing on graph traversal algorithms for anomaly detection and vulnerability assessment. Traditional graph traversal methods often struggle with the complexity of large and dynamic network topologies. Quantum walks, le...
Artificial intelligence is dismantling the long‑standing computational bottlenecks of Earth system modeling. Traditional numerical weather prediction (NWP)—rooted in Navier‑Stokes discretization and dependent on exascale supercomputers—faces scaling limits that make sub‑kilometer global resolution prohibitively expensi...
This record freezes the analytical architecture for CP-H1-SRC-XSC-001, the next load-bearing source-sector programme of the theory-neutral 6D-to-3D Cut-and-Project / Incidence-Spectral research programme. CP-H1-SRC-XSC-001 follows the completed CP-H1-CONT-BRIDGE-001 common-space comparison programme. The authoritative...
This paper proposes a novel cryptographic key exchange protocol leveraging concepts inspired by quantum mechanics, specifically superposition and probabilistic exploration, through a graph-based routing mechanism. Traditional key exchange protocols often rely on computationally intensive mathematical problems, making t...
Reach audiences
Advertise in front of researchers, engineers, and readers.
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.
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...
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.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
