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2,572 papers
This paper presents a labeling scheme that uses $O(\log^{2}n)$-bit labels that can be computed in deterministic polynomial time and is the first labeling scheme that produces an $\tilde{O}(1)$-size labeling which is simultaneously correct across all queries.
The first sublinear approximation algorithm for the minimum dilation tree in the Euclidean plane is given, whose approximation ratio is $\tilde{O}(n^{14/15})$ and the algorithm runs in polynomial time.
FlexSpIM, a digital CIM architecture supporting arbitrary operand resolution and shape within a unified storage for weights and neuron states, is introduced, enabling a layer-level hybrid weight- and output-stationary dataflow, maximizing operand reuse and reducing costly on- and off-chip data movement during SNN execu...
This survey consolidates the state of the art across eight thematic areas and presents a roadmap toward deployable 6G ISAC-V2X systems.
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Intentional degradation provides a simple, architecture-agnostic-by-design strategy for stabilizing residual learning in coarse-to-fine medical image refinement, validated within a single ConvLSTM-based refinement framework; extension to other architectures remains for future validation.
A reinforcement learning-based VEC task scheduling approach that integrates a fuzzy reward mechanism with multi-agent proximal policy optimization (FRMPPO) that satisfies the real-time processing demands of perception tasks in VaaS scenarios is proposed.
The query complexity of estimating scalar summaries of zigzag bar lifetimes from snapshot-adjacency bits is studied, finding quantum estimators are optimal in two regimes.
We develop a manifestly gauge and Lorentz invariant perturbative calculus to study the finite time and distance evolution of the electrically charged states created by Wilson-line-dressed scalar field operators in scalar quantum electrodynamics. By an Osterwalder-Schrader reflection and Euclidean gluing, the overlap of...
The tree-to-cycle transition scale is introduced, a single number given by the radius of the thickest edge outside the maximum spanning tree, which is interpreted as the highest cost a network accepts for redundancy and suggests a route to comparing observed networks by the trade-off they realize.
This paper provides a streamlined overview of Edge AI’s technological foundations highlighting how they enable low‑latency, privacy‑preserving, and context‑aware intelligence on resource‑constrained devices.
Machine vision enables artificial intelligence and edge computing, yet its efficiency is hindered by energy‐intensive data transfer between sensing and processing units. Two‐dimensional (2D) optoelectronic neuromorphic devices provide an appealing route toward in‐sensor vision, but existing approaches typically rel...
Structural connectivity partially mediates the seizure-survival association, suggesting preserved network architecture may contribute to why seizure patients survive longer, though the majority of the effect operates through other pathways.
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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