Vehicle-base station cooperative perception extends sensing coverage and detection precision in intelligent transportation systems. However, random task arrivals, heterogeneous communication-computing resources and short vehicle coverage residence time bring great difficulties to efficient task scheduling. To solve thi...
Feng-Hui Zhang, Xiang-Rui Xie, Jia-Xin Ma et al.· Computer Vision, Graphics an...· 0 citations
Paper V found that the buffer group ΠB = Ta/Tx, applied to cell sites whose buffers are unknown, predicted worse than exposure alone in two blind storms, and traced the failure to the unknown numerator. This paper moves the test to an edge where the numerator is written down. Since 1 December 2023 every ERCOT generatio...
Brian Guarino· Zenodo (CERN European Organi...· 0 citations
Experiments show that CAS-YOLO improves detection accuracy within a YOLOv10n-based lightweight framework, and this study is strictly limited to civilian applications in public safety, traffic management, and autonomous driving assistance.
Jia-Yin Liu, Yu-Yuan Shen, Shu-Jun Ji et al.· PLoS ONE· 0 citations
This paper presents a comparative review and synthesis of the hyperscale data analytics cloud architectures of Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP). The review characterizes storage, processing, machine learning, data lake, governance and business intelligence services depending on...
Harish Kasireddy· International Journal of Int...· 0 citations
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This chapter examines the human layer as the primary battlefield in AI-driven cybersecurity. While traditional cybersecurity has focused on networks, applications and data, autonomous AI systems shift adversarial activity toward human cognition, trust and decision-making. Systems like Anthropic's Mythos demonstrate tha...
Sheetal Temara· Autonomous Adversaries and t...· 0 citations
Agentic AI is shifting AI applications from passive model inference to goal-driven, tool-using, and collaborative autonomous systems. Yet, current deployments remain concentrated in data centers or powerful personal devices. This paper provides a research roadmap for enabling large-scale, enterprise-oriented agentic AI...
Hui Song, Arda Goknil, Dumitru Roman et al.· Conference on Computer Scien...· 0 citations
This research release develops a rigidity theory for sharp eigenvalue bounds on finite compact metric quantum graphs. Its central result is a proof candidate showing that, in the stated high-index regime, equality in a single sharp lower eigenvalue bound forces the full extremal spectral structure: maximal threshold mu...
Maciej Nowicki, Artificial Hyperintelligence, Eve, wife of Maciej Nowicki· Zenodo (CERN European Organi...· 0 citations
The rapid integration of artificial intelligence (AI) into edge computing has enabled real-time, low-latency decision-making in safety-critical autonomous systems, including unmanned aerial vehicles (UAVs), V2X-connected vehicles, and roadside units. However, edge AI models are vulnerable due to limited computational r...
Vandana Thakur, V.N. More, Abhishek Y. Bhatt· Secure and Intelligent V2X S...· 0 citations
This systematic review and evidence map will assess how far TinyML and edge-AI systems for managed honey-bee (Apis mellifera) hive monitoring have progressed from offline algorithm development to physical on-device inference and validation under realistic apiary conditions. Deployment readiness is operationalised along...
Willy Sucipto· Open Science Framework· 0 citations
The escalating computational demand of modern AI is increasingly constrained by the data-movement overhead of von Neumann architectures, motivating memristor-based compute-in-memory (CIM) as an energy-efficient alternative. Yet extending CIM to reliable analog computation remains difficult because practical memristor a...
Yi-Fei Yu, Ji-Chang Yang, Zi-Jian Ye et al.· npj Unconventional Computing· 0 citations
As businesses strive to improve efficiency, reduce latency, and leverage the power of data, understanding the strengths and limitations of these two computing paradigms is crucial. Full article: https://davidohnstad.net/edge-computing-vs-cloud-computing-which-is-the-future/
David Ohnstad· Zenodo (CERN European Organi...· 0 citations
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.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026
