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2,751 papers
Automatic dubbing systems now produce intelligible speech, but professional subtitling and dubbing remain constrained by three problems that are seldom addressed together. Expressive delivery has to be directed by people who read the picture; profane or censorable content has to obey rules that differ by country and de...
User-side electricity consumption data form the basis for various AI-based applications in active distribution networks and are with significant commercial value. However, as user-owned digital assets, these diverse data often exhibit heterogeneous quality and raise privacy concerns, making their acquisition and tradin...
Multimodal applications such as educational assistants and medical diagnosis demand high concurrency and low latency, and are increasingly served by edge inference enhanced with Retrieval-Augmented Generation (RAG). However, heterogeneous multimodal models and fragmented edge resources couple three problems that prior...
Quantum computing (QC) emulation is critical for exploring and advancing near-term quantum algorithms, given the ongoing scalability and reliability challenges of quantum hardware. While FPGA-based QC emulators have emerged as efficient alternatives to bridge the gap toward practical quantum computers, most existing so...
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As a key 5G enabler, edge computing locates resources near users to enable low-latency services, and microservices architecture (MSA) decomposes monolithic services into microservices, easing deployment and scaling of mission-critical applications with high reliability requirements. Existing studies on microservice bac...
The convergence of the Internet of Medical Things (IoMT), advanced communication technologies, and biomedical engineering has advanced intelligent, connected healthcare. This comprehensive survey investigates the evolution of IoMT architectures and communication protocols, along with their application across a range of...
Dermoscopy is a widely adopted, non-invasive imaging technique that plays a crucial role in the early detection and clinical assessment of skin lesions (1). Its effectiveness, however, depends on the quality of the acquired images. In clinical practice, dermoscopic images often contain visual artifacts, such as hair, r...
Abstract We present a comprehensive observational study of seven eclipsing binaries composed of massive and intermediate-mass components. Combining ground-based spectroscopy and space photometry of target stars, we compute precise physical parameters of these systems and investigate pulsational properties of the compon...
Modern Industrial Internet of Things (IIoT) edge deployments face a fundamental operational trade-off: localized execution on micro-edge architectures is restricted by hardware compute and memory bounds, whereas continuous cloud telemetry offloading incurs prohibitive API token expenditures, network transmission latenc...
Neuromorphic computing is a computer-system design approach inspired by the structure and information-processing principles of biological neural systems. Unlike conventional von Neumann architectures, neuromorphic systems integrate computation and memory more closely and exploit event-driven communication, sparse activ...
Version 4. Adds a screening-level plant power balance (net electric power from assumed thermal, heating and overhead efficiencies, with a Sobol study of the assumptions) and a relative cost proxy, as post-processing of the existing results (code/plant.py). Under the central assumptions the reference device at 40 MW hea...
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.
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.
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