We consider distributed optimization over several devices, each sending incremental model updates to a central server. This setting is considered, for instance, in federated learning. Various schemes have been designed to compress the model updates in orde ...
Thermal motion of a room-temperature mechanical resonator typically dominates the quantum backaction of its position measurement. This is a longstanding barrier for exploring cavity optomechanics at room temperature. In order to enter the quantum regime of ...
We report the experimental nondemolition measurement of coherence, predictability and concurrence on a system of two qubits. The quantum circuits proposed by De Melo et al. (Phys Rev Lett 98(25):250501, 2007) are implemented on IBM Q (superconducting circu ...
State-of-the-art training algorithms for deep learning models are based on stochastic gradient descent (SGD). Recently, many variations have been explored: perturbing parameters for better accuracy (such as in Extra-gradient), limiting SGD updates to a sub ...
Due to conservative approaches in construction design and practice, infrastructure often has hidden reserve capacity. When quantified, this reserve has potential to improve decisions related to asset management. Field measurements, collected through load t ...
The proliferation of phasor measurement units (PMUs) presents new challenges in archiving and processing large amounts of synchrophasor data which necessitates advanced data compression methods. This paper proposes a singular value decomposition (SVD)-base ...
Meaningful energy analysis of industrial processes requires the installation of energy monitoring systems. However, a lack of systematic methods for identifying the required measurement points, joint to scarce information on the related benefits, results i ...
