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The use of stone hammers to produce sharp stone flakes—knapping—is thought to represent a significant stage in hominin technological evolution because it facilitated the exploitation of novel resources, including meat obtained from medium‐to‐large‐sized vertebrates. The invention of knapping may have occurred via an additive (i.e., cumulative) process that combined several innovative stages. Here, we propose that one of these stages was the hominin use of ‘naturaliths,’ which we define as naturally produced sharp stone fragments that could be used as cutting tools. Based on a review of the literature and our own research, we first suggest that the ‘typical’ view, namely that sharp‐edged stones are seldom produced by nonprimate processes, is likely incorrect. Instead, naturaliths can be, and are being, endlessly produced in a wide range of settings and thus may occur on the landscape in far greater numbers than archaeologists currently understand or acknowledge. We then explore the potential role this ‘naturalith prevalence’ may have played in the origin of hominin stone knapping. Our hypothesis suggests that the origin of knapping was not a ‘Eureka!’ moment whereby hominins first made a sharp flake by intention or by accident and then sought something to cut, but instead was an emulative process by hominins aiming to reproduce the sharp tools furnished by mother nature and already in demand. We conclude with a discussion of several corollaries our proposal prompts, and several avenues of future research that can support or question our proposal. 

We present two graph quantities Psi(G, S) and Psi_2(G) which give constant factor estimates to the Dirichlet and Neumann eigenvalues, Lambda(G, S) and Lambda_2(G), respectively. Our techniques make use of a discrete Hardy-type inequality due to Muckenhoupt. 

Data-sensitive metrics adapt distances locally based the density of data points with the goal of aligning distances and some notion of similarity. In this paper, we give the first exact algorithm for computing a data-sensitive metric called the nearest neighbor metric. In fact, we prove the surprising result that a previously published 3-approximation is an exact algorithm. The nearest neighbor metric can be viewed as a special case of a density-based distance used in machine learning, or it can be seen as an example of a manifold metric. Previous computational research on such metrics despaired of computing exact distances on account of the apparent difficulty of minimizing over all continuous paths between a pair of points. We leverage the exact computation of the nearest neighbor metric to compute sparse spanners and persistent homology. We also explore the behavior of the metric built from point sets drawn from an underlying distribution and consider the more general case of inputs that are finite collections of path-connected compact sets. The main results connect several classical theories such as the conformal change of Riemannian metrics, the theory of positive definite functions of Schoenberg, and screw function theory of Schoenberg and Von Neumann. We also develop some novel proof techniques based on the combination of screw functions and Lipschitz extensions that may be of independent interest. 

We present the measurement of ${\pi }^{+}$ -argon inelastic cross sections using the ProtoDUNE single-phase liquid argon time projection chamber in the incident ${\pi }^{+}$ kinetic energy range of 500–800 MeV in multiple exclusive channels (absorption, charge exchange, and the remaining inelastic interactions). The results of this analysis are important inputs to simulations of liquid argon neutrino experiments such as the Deep Underground Neutrino Experiment and the Short Baseline Neutrino program at Fermi National Accelerator Laboratory. They will be employed to improve the modeling of final state interactions within neutrino event generators used by these experiments, as well as the modeling of ${\pi }^{+}$ -argon secondary interactions within the liquid argon. This is the first measurement of ${\pi }^{+}$ -argon absorption at this kinetic energy range as well as the first ever measurement of ${\pi }^{+}$ -argon charge exchange. 

The 2x2 Demonstrator, a prototype for the Deep Underground Neutrino Experiment (DUNE) liquid argon (LAr) Near Detector, was exposed to the Neutrinos from the Main Injector (NuMI) neutrino beam at Fermi National Accelerator Laboratory (Fermilab). This detector is a prototype of a new modular design for a liquid argon time-projection chamber (LArTPC), comprising a two-by-two array of four modules, each further segmented into two optically isolated LArTPCs. The 2x2 Demonstrator features a number of pioneering technologies, including a low-profile resistive field shell to establish drift fields, native 3D ionization pixelated imaging, and a high-coverage dielectric light readout system. The 2.4-tonne active mass detector is flanked upstream and downstream by supplemental solid-scintillator tracking planes, repurposed from the MINERvA experiment, which track ionizing particles exiting the argon volume. The antineutrino beam data collected by the detector over a 4.5 day period in 2024 include over 30,000 neutrino interactions in the LAr active volume—the first neutrino interactions reported by a DUNE detector prototype. During its physics-quality run, the 2x2 Demonstrator operated at a nominal drift field of 500 V/cm and maintained good LAr purity, with a stable electron lifetime of approximately 1.25 ms. This paper describes the detector and supporting systems, summarizes the installation and commissioning, and presents the initial validation of collected NuMI beam and off-beam self-triggers. In addition, it highlights observed interactions in the detector volume, including candidate muon antineutrino events.