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The ever-growing number of edge devices (e.g., smartphones) and the exploding volume of sensitive data they produce, call for distributed machine learning techniques that are privacy-preserving. Given the increasing computing capabilities of modern edge de ...
A graph is a versatile data structure facilitating representation of interactions among objects in various complex systems. Very often these objects have attributes whose measurements change over time, reflecting the dynamics of the system. This general da ...
Data produced by an electron cyclotron interferometer diagnostic are now available to the real-time control systems of of the joint European torus (JET) tokamak. The data consist of absolutely calibrated electron temperature profiles, covering the plasma l ...
Photoswitching of a charged azobenzene-stilbene dye is investigated through laser excitation in a tandem ion mobility mass spectrometer. Action spectra associated with E -> Z and Z -> E photoisomerisation of the stilbene group exhibit bands at 685 and 440 ...
In this paper, a new data-driven distributed control structure for frequency/voltage regulation and active/reactive power sharing of islanded microgrids is proposed. By using a droop-free concept, the proposed method avoids the inherent timescale separatio ...
Kernel methods are fundamental tools in machine learning that allow detection of non-linear dependencies between data without explicitly constructing feature vectors in high dimensional spaces. A major disadvantage of kernel methods is their poor scalabili ...
Phasor data concentrators (PDCs) are essential functions in synchrophasor networks that collect and aggregate phasor measurement unit (PMU) data from across the electric grid. PDC buffers synchrophasor datasets during the “wait time” period, then communica ...
It is now clear that some cysteines on some proteins are highly tuned to react with electrophiles. Based on numerous studies, it is also established that electrophile sensing underpins rewiring of several critical signaling processes. These electrophile-se ...
We argue that frequent sampling of the fraction ofa priorinon-symptomatic but infectious humans (either by random or cohort testing) significantly improves the management of the COVID-19 pandemic, when compared to intervention strategies relying on data fr ...
Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building supervised or unsupervised machine learning models. Principal covariates regression (PC ...
