AQuaMaM: An Autoregressive, Quaternion Manifold Model for Rapidly Estimating Complex SO(3) Distributions

Accurately modeling complex, multimodal distributions is necessary for optimal decision-making, but doing so for rotations in three-dimensions, i.e., the SO(3) group, is challenging due to the curvature of the rotation manifold. The recently described implicit-PDF (IPDF) is a simple, elegant, and effective approach for learning arbitrary distributions on SO(3) up to a given precision. However, inference with IPDF requires $N$ forward passes through the network's final multilayer perceptron (where $N$ places an upper bound on the likelihood that can be calculated by the model), which is prohibitively slow for those without the computational resources necessary to parallelize the queries. In this paper, I introduce AQuaMaM, a neural network capable of both learning complex distributions on the rotation manifold and calculating exact likelihoods for query rotations in a single forward pass. Specifically, AQuaMaM autoregressively models the projected components of unit quaternions as mixtures of uniform distributions that partition their geometrically-restricted domain of values. When trained on an "infinite" toy dataset with ambiguous viewpoints, AQuaMaM rapidly converges to a sampling distribution closely matching the true data distribution. In contrast, the sampling distribution for IPDF dramatically diverges from the true data distribution, despite IPDF approaching its theoretical minimum evaluation loss during training. When trained on a constructed dataset of 500,000 renders of a die in different rotations, AQuaMaM reaches a test log-likelihood 14% higher than IPDF. Further, compared to IPDF, AQuaMaM uses 24% fewer parameters, has a prediction throughput 52$\times$ faster on a single GPU, and converges in a similar amount of time during training

Imaging X-ray spectrometer

An X-ray spectrometer for providing imaging and energy resolution of an X-ray source is described. This spectrometer is comprised of a thick silicon wafer having an embedded matrix or grid of aluminum completely through the wafer fabricated, for example, by thermal migration. The aluminum matrix defines the walls of a rectangular array of silicon X-ray detector cells or pixels. A thermally diffused aluminum electrode is also formed centrally through each of the silicon cells with biasing means being connected to the aluminum cell walls and causes lateral charge carrier depletion between the cell walls so that incident X-ray energy causes a photoelectric reaction within the silicon producing collectible charge carriers in the form of electrons which are collected and used for imaging

SEAGRID: A New Dynamic Modelling Tool for Power System Analysis of Ocean Energy Devices

International audienceAs the ocean energy industry approaches commercial readiness, there will be a greater focus on integration of ocean energy devices (OEDs) into the electrical power system network. Device developers will be required to provide dynamic models of their device for grid connection, and ensure their device operates within the limits laid out in the grid code. Project developers will need to assess the impact of different wavefarm configurations, ratings for the electrical equipment, power losses, and performance during a fault. Grid operators will require dynamic models to investigate the impact an OED will have on the grid and also for future grid planning studies. The SEAGRID dynamic modelling tool attempts to address each of these issues using its generic modelling approach. The SEAGRID model is capable of producing a scalable time domain power system dynamic model using empirical test data and component specifications, bypassing the need for a full hydrodynamic study of the device

Get it from the Source: Identifying Library Resources and Software Used in Faculty Research

Libraries and Information Technology departments aim to support the educational and research needs of students, researchers, and faculty members. Close matches between the resources those departments provide and the resources the institution’s community members actually use highlight the value of the departments, demonstrate fiscally responsibility, and show attentiveness to the community’s needs. Traditionally, libraries rely on usage statistics to guide collection development decisions, but usage statistics can only imply value. Identifying a resource by name in a publication demonstrates the value of that resource more clearly. This pilot project examined the full-text of articles published in 2016-2017 by faculty members at a mid-sized, special-focus institution to answer the questions “Do faculty members have university-provided access to the research tools they need to publish?” and “If not, where are they getting them?” Using a custom database, the presenters indexed every publication by author, publication, resources used, availability of the identified resources, and more. This pilot study can be adapted to projects at other institutions, allowing them to gain a better understanding of the strengths and weaknesses of their own institution’s offerings. In addition, they will be able to identify ways to use that data to negotiate for additional resources, inform strategic partnerships, and facilitate open discussions with the institution’s community