Bibliography of Sources on Dena’ina and Cook Inlet Anthropology Through 2016

This version 4.3 will be the final version for this bibliography, a project that was begun in 1993 by Greg Dixon. We have intentionally excluded all potential references for the year 2017. This version is about 29 pages longer and has about 211 entries added since the previous version 3.1 of 2012. Aaron Leggett has added over fifty sources many being rare items from newpapers and magazines. Also many corrections and additions were made to entries in earlier versions.I wish to thank Kenaitze Indian Tribe and the “Dena’ina Language Revitalization Project” for their support for several projects during 2017-2018, including this Vers. 4.3. Previous versions have had partial support from "Dena'ina Archiving, Training and Access" project (NSF-OPP 0326805, 2004) and from Lake Clark National Park. I thank Katherine Arndt of Alaska & Polar Regions at UAF for her careful proofreading

Non-d0d^0 Mn-driven ferroelectricity in antiferromagnetic BaMnO3_3

Using first-principles density functional theory we predict a ferroelectric ground state -- driven by off-centering of the magnetic Mn$^{4+}$ ion -- in perovskite-structure BaMnO$_3$. Our finding is surprising, since the competition between energy-lowering covalent bond formation, and energy-raising Coulombic repulsions usually only favors off-centering on the perovskite $B$-site for non-magnetic $d^0$ ions. We explain this tendency for ferroelectric off-centering by analyzing the changes in electronic structure between the centrosymmetric and polar states, and by calculating the Born effective charges; we find anomalously large values for Mn and O consistent with our calculated polarization of 12.8 $\mu$C/cm$^2$. Finally, we suggest possible routes by which the perovskite phase may be stabilized over the usual hexagonal phase, to enable a practical realization of a single-phase multiferroic.Comment: 6 pages, 3 figure

Prototype for SONTRAC: a scintillating plastic fiber detector for solar neutron spectroscopy

We report the scientific motivation for and performance measurements of a prototype detector system for SONTRAC, a solar neutron tracking experiment designed to study high- energy solar flare processes. The full SONTRAC instrument will measure the energy and direction of 20 to 200 MeV neutrons by imaging the ionization tracks of the recoil protons in a densely packed bundle of scintillating plastic fibers. The prototype detector consists of a 12.7 mm square bundle of 250 micrometer scintillating plastic fibers, 10 cm long. A photomultiplier detects scintillation light from one end of the fiber bundle and provides a detection trigger to an image intensifier/CCD camera system at the opposite end. The image of the scintillation light is recorded. By tracking the recoil protons from individual neutrons the kinematics of the scattering are determined, providing a high signal to noise measurement. The predicted energy resolution is 10% at 20 MeV, improving with energy. This energy resolution translates into an uncertainty in the production time of the neutron at the Sun of 30 s for a 20 MeV neutron, also improving with energy. A SONTRAC instrument will also be capable of detecting and measuring high-energy gamma rays greater than 20 MeV as a \u27solid-state spark chamber.\u27 The self-triggering and track imaging features of the prototype are demonstrated with cosmic ray muons and 14 MeV neutrons. Design considerations for a space flight instrument are presented

A prototype for SONTRAC, a scintillating plastic fiber tracking detector for neutron imaging and spectroscopy

We report on tests of a prototype detector system designed to perform imaging and spectroscopy on 20 to 250 MeV neutrons. Although developed for the study of high-energy solar flare processes, the detection techniques employed for SONTRAC, the SOlar Neutron TRACking experiment, can be applied to measurements in a variety of disciplines including atmospheric physics, radiation therapy and nuclear materials monitoring. The SONTRAC instrument measures the energy and direction ofneutrons by detecting double neutron-proton scatters and recording images of the ionization tracks of the recoil protons in a densely packed bundle of scintillating plastic fibers stacked in orthogonal layers. By tracking the recoil protons from individual neutrons, the kinematics of the scatter are determined. This directional information results in a high signal to noise measurement. SONTRAC is also capable of detecting and measuring high-energy gamma rays \u3e20 MeV as a “solid-state spark chamber”. The self-triggering and track imaging features of a prototype for tracking in two dimensions are demonstrated in calibrations with cosmic-ray muons, 14 to ~65 MeV neutrons and ~20 MeV protons

A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines

Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural network weights. Conventional processing architectures are not well suited for simulating neural networks, often requiring large amounts of energy and time. Additionally, synapses in biological neural networks are not binary connections, but exhibit a nonlinear response function as neurotransmitters are emitted and diffuse between neurons. Inspired by neuroscience principles, we present a digital neuromorphic architecture, the Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex synaptic response functions without requiring additional hardware components. We consider the paradigm of spiking neurons with temporally coded information as opposed to non-spiking rate coded neurons used in most neural networks. In this paradigm we examine liquid state machines applied to speech recognition and show how a liquid state machine with temporal dynamics maps onto the STPU-demonstrating the flexibility and efficiency of the STPU for instantiating neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN

Sparsifying generalized linear models

We consider the sparsification of sums $F : \mathbb{R}^n \to \mathbb{R}$ where $F(x) = f_1(\langle a_1,x\rangle) + \cdots + f_m(\langle a_m,x\rangle)$ for vectors $a_1,\ldots,a_m \in \mathbb{R}^n$ and functions $f_1,\ldots,f_m : \mathbb{R} \to \mathbb{R}_+$. We show that $(1+\varepsilon)$-approximate sparsifiers of $F$ with support size $\frac{n}{\varepsilon^2} (\log \frac{n}{\varepsilon})^{O(1)}$ exist whenever the functions $f_1,\ldots,f_m$ are symmetric, monotone, and satisfy natural growth bounds. Additionally, we give efficient algorithms to compute such a sparsifier assuming each $f_i$ can be evaluated efficiently. Our results generalize the classic case of $\ell_p$ sparsification, where $f_i(z) = |z|^p$, for $p \in (0, 2]$, and give the first near-linear size sparsifiers in the well-studied setting of the Huber loss function and its generalizations, e.g., $f_i(z) = \min\{|z|^p, |z|^2\}$ for $0 < p \leq 2$. Our sparsification algorithm can be applied to give near-optimal reductions for optimizing a variety of generalized linear models including $\ell_p$ regression for $p \in (1, 2]$ to high accuracy, via solving $(\log n)^{O(1)}$ sparse regression instances with $m \le n(\log n)^{O(1)}$, plus runtime proportional to the number of nonzero entries in the vectors $a_1, \dots, a_m$