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  1. Free, publicly-accessible full text available December 8, 2022
  2. Using pulsed femtosecond laser irradiation, we demonstrate the creation of an array of microgrooves within a single laser spot on metals. The orientation of these grooves is not limited to being parallel to the plane of the laser beam’s propagation but can orient at any angle up to 30 degree from parallel. We control the orientation of the microgrooves by proportionally varying the laser’s polarization. Polarization, angle of incidence, and structural evolution dynamics have been thoroughly studied to help us understand this phenomenon. Our studies suggest that the formation of angled microgroove arrays is due to a feedback effect occurring between defect-focused ablation and polarization-dependent laser-induced periodic surface structures.
  3. Evaluating generative adversarial networks (GANs) is inherently challenging. In this paper, we revisit several representative sample-based evaluation metrics for GANs, and address the problem of how to evaluate the evaluation metrics. We start with a few necessary conditions for metrics to produce meaningful scores, such as distinguishing real from generated samples, identifying mode dropping and mode collapsing, and detecting overfitting. With a series of carefully designed experiments, we comprehensively investigate existing sample-based metrics and identify their strengths and limitations in practical settings. Based on these results, we observe that kernel Maximum Mean Discrepancy (MMD) and the 1-Nearest- Neighbor (1-NN) two-sample test seem to satisfy most of the desirable properties, provided that the distances between samples are computed in a suitable feature space. Our experiments also unveil interesting properties about the behavior of several popular GAN models, such as whether they are memorizing training samples, and how far they are from learning the target distribution.
  4. Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-of-the-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions.
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  6. Abstract

    Aria is a plant hosting a$${350}\,\hbox {m}$$350mcryogenic isotopic distillation column, the tallest ever built, which is being installed in a mine shaft at Carbosulcis S.p.A., Nuraxi-Figus (SU), Italy. Aria is one of the pillars of the argon dark-matter search experimental program, lead by the Global Argon Dark Matter Collaboration. It was designed to reduce the isotopic abundance of$${^{39}\hbox {Ar}}$$39Arin argon extracted from underground sources, called Underground Argon (UAr), which is used for dark-matter searches. Indeed,$${^{39}\hbox {Ar}}$$39Aris a$$\beta $$β-emitter of cosmogenic origin, whose activity poses background and pile-up concerns in the detectors. In this paper, we discuss the requirements, design, construction, tests, and projected performance of the plant for the isotopic cryogenic distillation of argon. We also present the successful results of the isotopic cryogenic distillation of nitrogen with a prototype plant.