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We propose a procedure for supervised classification that is based on potential functions. The potential of a class is defined as a kernel density estimate multiplied by the classs prior probability. The method transforms the data to a potential-potential (pot-pot) plot, where each data point is mapped to a vector of potentials. Separation of the classes, as well as classification of new data points, is performed on this plot. For this, either the $alpha$-procedure ($alpha$-P) or $k$-nearest neighbors ($k$-NN) are employed. For data that are generated from continuous distributions, these classifiers prove to be strongly Bayes-consistent. The potentials depend on the kernel and its bandwidth used in the density estimate. We investigate several variants of bandwidth selection, including joint and separate pre-scaling and a bandwidth regression approach. The new method is applied to benchmark data from the literature, including simulated data sets as well as 50 sets of real data. It compares favorably to known classification methods such as LDA, QDA, max kernel density estimates, $k$-NN, and $DD$-plot classification using depth functions.
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The ATLAS Roman Pot system is designed to determine the total proton-proton cross-section as well as the luminosity at the Large Hadron Collider (LHC) by measuring elastic proton scattering at very small angles. The system is made of four Roman Pot stations, located in the LHC tunnel in a distance of about 240~m at both sides of the ATLAS interaction point. Each station is equipped with tracking detectors, inserted in Roman Pots which approach the LHC beams vertically. The tracking detectors consist of multi-layer scintillating fibre structures readout by Multi-Anode-Photo-Multipliers.
Existing evaluation suites for multi-agent reinforcement learning (MARL) do not assess generalization to novel situations as their primary objective (unlike supervised-learning benchmarks). Our contribution, Melting Pot, is a MARL evaluation suite that fills this gap, and uses reinforcement learning to reduce the human labor required to create novel test scenarios. This works because one agents behavior constitutes (part of) another agents environment. To demonstrate scalability, we have created over 80 unique test scenarios covering a broad range of research topics such as social dilemmas, reciprocity, resource sharing, and task partitioning. We apply these test scenarios to standard MARL training algorithms, and demonstrate how Melting Pot reveals weaknesses not apparent from training performance alone.
The longitudinal and transverse beam coupling impedance of the first final TOTEM Roman Pot unit has been measured in the laboratory with the wire method. For the evaluation of transverse impedance the wire position has been kept constant, and the insertions of the RP were moved asymmetrically. With the original configuration of the RP, resonances with fairly high Q values were observed. In order to mitigate this problem, RF-absorbing ferrite plates were mounted in appropriate locations. As a result, all resonances were sufficiently damped to meet the stringent LHC beam coupling impedance requirements.
Recent measurements by the T2K neutrino oscillation experiment indicate that CP violation in neutrino mixing may be observed in the future by long-baseline neutrino oscillation experiments. We propose an extension to the currently approved T2K running from $7.8times 10^{21}~mbox{POT}$ to $20times 10^{21}~mbox{POT}$, aiming at initial observation of CP violation with 3$,sigma$ or higher significance for the case of maximum CP violation. The program also contains a measurement of mixing parameters, $theta_{23}$ and $Delta m^2_{32}$, with a precision of 1.7$^circ$ or better and 1%, respectively. With accelerator and beamline upgrades, as well as analysis improvements, this program would occur before the next generation of long-baseline neutrino oscillation experiments that are expected to start operation in 2026.
Since the LHC running season 2010, the TOTEM Roman Pots (RPs) are fully operational and serve for collecting elastic and diffractive proton-proton scattering data. Like for other moveable devices approaching the high intensity LHC beams, a reliable and precise control of the RP position is critical to machine protection. After a review of the RP movement control and position interlock system, the crucial task of alignment will be discussed.
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