Related papers: Adapting SAM for CDF
Federated data processing (FDP) offers a promising approach for enabling collaborative analysis of sensitive data without centralizing raw datasets. However, real-world adoption remains limited due to the complexity of managing…
Extremely high data rates at modern synchrotron and X-ray free-electron laser light source beamlines motivate the use of machine learning methods for data reduction, feature detection, and other purposes. Regardless of the application, the…
The next decade will feature a growing number of massive ground-based photometric, spectroscopic, and time-domain surveys, including those produced by DECam, DESI, and LSST. The NOAO Data Lab was launched in 2017 to enable efficient…
Carrier Aggregation (CA) is a technique used in 5G and previous cellular generations to temporarily increase the data rate of a specific user during peak demand periods or to reduce carrier congestion. CA is achieved by combining two or…
Federated Learning (FL) has emerged as a promising paradigm for collaborative model training across distributed edge devices while preserving data privacy especially with the huge increase amount of data due to the adoption of technologies…
Scientific collaborations require a strong computing infrastructure to successfully process and analyze data. While large-scale collaborations have access to resources such as Analysis Facilities, small-scale collaborations often lack the…
Complex applications implemented as Systems on Chip (SoCs) demand extensive use of system level modeling and validation. Their implementation gathers a large number of complex IP cores and advanced interconnection schemes, such as…
The increasing volumes of data produced by high-throughput instruments coupled with advanced computational infrastructures for scientific computing have enabled what is often called a {\em Fourth Paradigm} for scientific research based on…
During the past decades significant efforts have been made to propose data structures for answering connectivity queries on fully dynamic graphs, i.e., graphs with frequent insertions and deletions of edges. However, a comprehensive…
Federated and Continual Learning have emerged as potential paradigms for the robust and privacy-aware use of Deep Learning in dynamic environments. However, Client Drift and Catastrophic Forgetting are fundamental obstacles to guaranteeing…
Machine learning on large-scale genomic or transcriptomic data is important for many novel health applications. For example, precision medicine tailors medical treatments to patients on the basis of individual biomarkers, cellular and…
The escalating influx of data generated by networked edge devices, coupled with the growing awareness of data privacy, has restricted the traditional data analytics workflow, where the edge data are gathered by a centralized server to be…
Data centers require high-performance network equipment that consume low power and support high bandwidth requirements. In this context, a combinatorial approach was proposed to design data center network (DCN) equipment from a library of…
This study proposes a scalable Digital Twin framework for energy optimization in data centers.The framework integrates IoT-based data acquisition, cloud computing, and machine learning techniques to enable real-time monitoring, forecasting,…
In the recent years, generation of data have escalated to extensive dimensions and big data has emerged as a propelling force in the development of various machine learning advances and internet-of-things (IoT) devices. In this regard, the…
Data on transient events, like GRBs, are often contained in large databases of unstructured data from space experiments, merged with potentially large amount of background or simply undesired information. We present a computational formal…
Over the last decade we have witnessed an increasing use of data processing in embedded systems. Where in the past the data processing was limited (if present at all) to the handling of a small number of "on-off control signals", more…
Federated Learning (FL) has emerged as a transformative approach for distributed machine learning, particularly in edge computing environments where data privacy, low latency, and bandwidth efficiency are critical. This paper presents a…
Due to growing population and technological advances, global electricity consumption, and consequently also CO2 emissions are increasing. The residential sector makes up 25% of global electricity consumption and has great potential to…
Artificial Intelligence for scientific applications increasingly requires training large models on data that cannot be centralized due to privacy constraints, data sovereignty, or the sheer volume of data generated. Federated learning (FL)…