Strea MRAK a streaming multi-resolution adaptive kernel algorithm

Kernel ridge regression (KRR) is a popular scheme for non-linear non-parametric learning. However, existing implementations of KRR require that all the data is stored in the main memory, which severely limits the use of KRR in contexts where data size far exceeds the memory size. Such applications are increasingly common in data mining, bioinformatics, and control. A powerful paradigm for computing on data sets that are too large for memory is the streaming model of computation, where we process one data sample at a time, discarding each sample before moving on to the next one. In this paper, we propose StreaMRAK - a streaming version of KRR. StreaMRAK improves on existing KRR schemes by dividing the problem into several levels of resolution, which allows continual refinement to the predictions. The algorithm reduces the memory requirement by continuously and efficiently integrating new samples into the training model. With a novel sub-sampling scheme, StreaMRAK reduces memory and computational complexities by creating a sketch of the original data, where the sub-sampling density is adapted to the bandwidth of the kernel and the local dimensionality of the data. We present a showcase study on two synthetic problems and the prediction of the trajectory of a double pendulum. The results show that the proposed algorithm is fast and accurate

Impact of 440 GeV Proton beams on Superconductors in a Cryogenic Environment

The superconducting magnets used in high energy particle accelerators such as CERN\u27s LHC can be impacted by the circulating beam in case of specific failures. This leads to interaction of the beam particles with the magnet components, like the superconducting coils, through direct beam impact or via secondary particle showers. The interaction causes energy deposition in the timescale of microseconds and induces large thermal gradients within the superconductors, which are in the order of 100 K/mm. To investigate the effect on the superconductors, an experiment at CERN\u27s HiRadMat facility was designed and executed, exposing short samples of Nb-Ti and Nb$_{3}$Sn strands in a cryogenic environment to microsecond 440 GeV proton beams. The irradiated samples were extracted and analyzed for their critical transport current I$_{c}$. This paper describes the results and analysis of the measurements of I$_{c}$ as well as thermo-mechanical simulations of the Nb$_{3}$Sn strands to evaluate the degradation of I$_{c}$ as a function of the mechanical strain present during and after the beam impact

Impact of 440 GeV Proton beams on Superconductors in a Cryogenic Environment

The superconducting magnets used in high energy particle accelerators such as CERN’s LHC can be impacted by the circulating beam in case of specific failures. This leads to interaction of the beam particles with the magnet components, like the superconducting coils, through direct beam impact or via secondary particle showers. The interaction causes energy deposition in the timescale of microseconds and induces large thermal gradients within the superconductors, which are in the order of 100 K/mm. To investigate the effect on the superconductors, an experiment at CERN’s HiRadMat facility was designed and executed, exposing short samples of Nb-Ti and Nb$_3$Sn strands in a cryogenic environment to microsecond 440 GeV proton beams. The irradiated samples were extracted and analyzed for their critical transport current $I_c$. This paper describes the results and analysis of the measurements of $I_c$ as well as thermo-mechanical simulations of the Nb3Sn strands to evaluate the degradation of $I_c$ as a function of the mechanical strain present during and after the beam impact