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Autonomous vehicles (AVs) rely on accurate and robust sensor observations for safety-critical decision making in a variety of conditions. The fundamental building blocks of such systems are sensors and classifiers that process ultrasound, radar, GPS, lidar ...
In order to evaluate the seismic vulnerability at large scale, it is necessary to gain awareness of the soil properties, types of materials used in the construction of buildings during different periods, the construction standards of each period, different ...
This paper proposes a real-time algorithm for processing and quality improvement of synchrophasor data (SD). The proposed algorithm first recovers the missing SD reported by phasor measurement units (PMUs), and performs low-rank approximation on data strea ...
Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. While many federated learning architectures process data in an online manner, and are hence adaptive by nature, most performance analyse ...
We consider a Gross-Pitaevskii equation which appears as a model in the description of dipolar Bose-Einstein condensates, without a confining external trapping potential. We describe the asymptotic dynamics of solutions to the corresponding Cauchy problem ...
To explore connections between rock strength and rock falls, we undertook a comprehensive rock mechanics testing program for six granitic rock types in Yosemite Valley (California, USA) where rock falls are a common geomorphic and sometimes hazardous proce ...
The origin and reactivity of dissolved organic matter (DOM) have received attention for decades due to the key role DOM plays in global carbon cycling and the ecology of aquatic systems. However, DOM dynamics in river networks remain unresolved, hampered b ...
This report summarizes the research results obtained by trying to improve an existing image segmentation deep learning model on photovoltaic panels (PV) [1], exploring mainly the impact of transfer learning. Many metrics and tools to assess performance in ...
We propose fingerprinting, a new technique that consists in constructing compact, fast-to-compute and privacy-preserving binary representations of datasets. We illustrate the effectiveness of our approach on the emblematic big data problem of K-Nearest-Nei ...
Trust in the use of sensitive data is not nearly widespread. Even though there is no shortage in tools and methods to address the digitisation of our society and industries, the acquisition and access to non-open data represent the greatest challenge. In t ...