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2,919 papers
The study finds that the technical leader must be prepared to constantly learn, adapt, and understand the technological and human factors to be effective in the AI era.
This study delves into the notion of AI agentic architectures for autonomous data engineering pipelines and investigates the potential benefits of intelligent agents in enhancing automation, resilience, and decision-making processes in contemporary data ecosystems.
A new scientific object – the Autonomous Recovery Efficiency Score (ARES) – is introduced – a quantitative measure of autonomous resilience, as well as a supporting foundation for future autonomous self-healing AI agentic infrastructure.
The design, deployment, and performance of cloud-based AI systems, especially scalability, system intelligence, and efficiency, and the challenges, such as latency problems in distributed computing systems, data privacy and security, and resource allocation, are indicated.
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This study offers new means to design a resilient multi-agent system that is able to resist emergent threats by formalizing trust as a dynamic security vector and introduces an Agentic Trust Dynamics Theory as a conceptual theory to model the evolution, loss, and impact of trust on emerging security vulnerabilities in...
This study develops a rule-based parts of speech tagging tool specifically for those people who want to learn or research on Bengali grammar and proposes and endeavor to implement this tool with an intent to achieve higher accuracy by optimizing memory overhead.
A serious behavioural failure may be uncommon within a person’s history yet become the principal basis for judgments of character and future risk. This conceptual article develops the conjunctive misfortune problem: adverse conditions, culpable action, stochastic harm, categorical institutional responses, and persisten...
A serious behavioural failure may be uncommon within a person’s history yet become the principal basis for judgments of character and future risk. This conceptual article develops the conjunctive misfortune problem: adverse conditions, culpable action, stochastic harm, categorical institutional responses, and persisten...
Reliability Beyond Accuracy in Crop Classification Benchmarks (Supplementary Materials)Introduction: Near-perfect crop-label accuracy can conceal uncertainty, perturbation sensitivity, and weak explanations. This study evaluates these reliability dimensions without treating benchmark classification as agronomic recomme...
Reliability Beyond Accuracy in Crop Classification Benchmarks (Supplementary Materials)Introduction: Near-perfect crop-label accuracy can conceal uncertainty, perturbation sensitivity, and weak explanations. This study evaluates these reliability dimensions without treating benchmark classification as agronomic recomme...
This thesis investigates the complex and often contentious question of whether predictive models are epistemically superior to causal models. It challenges the simplistic dichotomy that often frames this debate, arguing that the epistemic superiority of a model is not an intrinsic property but is contingent upon the sp...
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
Education Innovation
For decades, a fundamental question about the difficulty of computing in high-dimensional spaces has resisted proof. New research resolves this question, removing a major theoretical obstacle to understanding the efficiency of randomized algorithms used across science and computing. The Kannan–Lovász–Simonovits (KLS) conjecture was proposed in 1995. It asks whether, for a broad class of high-dimensional […] The post Solving a 31-year-old grand challenge in mathematics – the KLS conjecture appear…
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
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