Under leakage-controlled inter-patient evaluation, causal physiological representations enable an interpretable linear model to achieve efficient and competitive arrhythmia detection potentially suitable for resource-constrained wearable and bedside decision-support systems, matching the discriminative evidence exploit...
Moirangthem Tiken Singh, Manibhushan Yaikhom, R. K. Prasad· Computers in Biology and Med...· 0 citations
Elastic clouds must continuously decide how much compute, memory and storage capacity to grant to workloads whose intensity and mix change from minute to minute, and classical threshold, queueing and forecasting controllers struggle to hold that balance without either over-provisioning or violating service level agreem...
Elastic clouds must continuously decide how much compute, memory and storage capacity to grant to workloads whose intensity and mix change from minute to minute, and classical threshold, queueing and forecasting controllers struggle to hold that balance without either over-provisioning or violating service level agreem...
This white paper presents Project Amanda (Version 2.2), a novel decentralized neuromorphic hardware architecture designed to break through the constraints of conventional deterministic computing paradigms. Conventional neuromorphic computing relies excessively on cost-prohibitive pre-hardware perceptual training and cu...
tao Luo· Zenodo (CERN European Organi...· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
# LeMARLA **Large Language Model-Enhanced Multi-Agent Reinforcement Learning for Autonomous Enterprise Workflow Orchestration via Graph Semantic Routing.** LeMARLA couples an LLM-driven task decomposition module, a heterogeneous graph attention routing module over tasks, employees and skills, and a CTDE multi-agent rei...
Peizhi He· Zenodo (CERN European Organi...· 0 citations
An optimised hybrid ensemble learning strategy integrating threshold-based detection, Isolation Forest, Random Forest, Support Vector Machine (SVM), and Logistic Regression (LR) classifiers is proposed to improve fault detection accuracy in IoT-enabled 3D printers.
Chitranjanjit Kaur, S. Chopra, C. R. Tripathy· Discover Computing· 0 citations
It is suggested that edge based controllability captures circuit level properties relevant to treatment response and may inform personalized neuromodulation strategies.
This work shows that production‐grade, privacy‐preserving intrusion detection is feasible in IoT networks at the edge, and addresses the concerns of data privacy and non‐IID data distribution inherent to distributed IoT networks.
Vaibhav Joshi, Abishi Chowdhury, Amrit Pal et al.· Software, Practice & Experie...· 0 citations
MineSSM is a efficient design that routes each band to the operator it needs: a linear-complexity state-space model (Mamba) that homogenizes the global illumination and a lightweight convolutional branch that performs dust suppression on the high-frequency bands, so that no heavy operator ever runs at full resolution.
An open-source Learning Management System architecture supporting hybrid pre-laboratory instruction in a resource-constrained university setting is reported, together with a quantified account of its capacity envelope and its limits.
S. Aimara, M. Radid, G. Chemsi et al.· international journal of eng...· 0 citations
Across the corpus, blockchain is used most often for device authentication, data integrity, access control, traceability, secure data exchange, and distributed trust management.
E. L. Huamaní, Erasmo Montufar-Barrientos, Carlos Ponce-Sanchez· international journal of eng...· 0 citations
This paper proposes a three-layer security-by-design architecture that reaches 94.3% accuracy, the highest of the models tested, while cutting attack success rate from over 60–84% down to 38.9% and raising the query cost for model extraction more than five times over the weakest baseline.
Haifa A. Alanazi, Khaznah Alshammari· Italian National Conference...· 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
