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The advent of diverse frequency bands in 5G networks has promoted measurement studies focused on 5G signal propagation, aiming to understand its pathloss, coverage, and channel quality characteristics. Nonetheless, conducting a thorough 5G measurement campaign is markedly laborious given the large number of 5G measurement samples that must be collected. To alleviate this burden, the present contribution leverages principled active learning (AL) methods to prudently select only a few, yet most informative locations to collect 5G measurements. The core idea is to rely on a Gaussian Process (GP) model to efficiently extrapolate 5G measurements throughout the coverage area. Specifically, an ensemble (E) of GP models is adopted that not only provides a rich learning function space, but also quantifies uncertainty, and can offer accurate predictions. Building on this EGP model, a suite of acquisition functions (AFs) are advocated to query new locations on-the-fly. To account for realistic 5G measurement campaigns, the proposed AFs are augmented with a novel distance-based AL rule that selects informative samples, while penalizing queries at long distances. Numerical tests on 5G data generated by the Sionna simulator and on real urban and suburban datasets, showcase the merits of the novel EGP-AL approaches. 

The diversity of SLAM benchmarks affords extensive testing of SLAM algorithms to understand their performance, individually or in relative terms. The ad-hoc creation of these benchmarks does not necessarily illuminate the particular weak points of a SLAM algorithm when performance is evaluated. In this paper, we propose to use a decision tree to identify challenging benchmark properties for state-of-the-art SLAM algorithms and important components within the SLAM pipeline regarding their ability to handle these challenges. Establishing what factors of a particular sequence lead to track failure or degradation relative to these characteristics is important if we are to arrive at a strong understanding for the core computational needs of a robust SLAM algorithm. Likewise, we argue that it is important to profile the computational performance of the individual SLAM components for use when benchmarking. In particular, we advocate the use of time-dilation during ROS bag playback, or what we refer to as slo-mo playback. Using slo-mo to benchmark SLAM instantiations can provide clues to how SLAM implementations should be improved at the computational component level. Three prevalent VO/SLAM algorithms and two low-latency algorithms of our own are tested on selected typical sequences, which are generated from benchmark characterization, to further demonstrate the benefits achieved from computationally efficient components. 

We report an improved measurement of the valence $u$and $d$quark distributions from the forward-backward asymmetry in the Drell-Yan process using $8.6{\mathrm{fb}}^{-1}$of data collected with the D0 detector in $p\overline{p}$collisions at $\sqrt{s}=1.96$. This analysis provides the values of new structure parameters that are directly related to the valence up and down quark distributions in the proton. In other experimental results measuring the quark content of the proton, $d$quark contributions are mixed with those from other quark flavors. In this measurement, the $u$and $d$quark contributions are separately extracted by applying a factorization of the QCD and electroweak portions of the forward-backward asymmetry. Published by the American Physical Society2024