The Nature of the Interlayer Interaction in Bulk and Few-Layer Phosphorus

An outstanding challenge of theoretical electronic structure is the description of van der Waals (vdW) interactions in molecules and solids. Renewed interest in resolving this is in part motivated by the technological promise of layered systems including graphite, transition metal dichalcogenides, and more recently, black phosphorus, in which the interlayer interaction is widely believed to be dominated by these types of forces. We report a series of quantum Monte Carlo (QMC) calculations for bulk black phosphorus and related few-layer phosphorene, which elucidate the nature of the forces that bind these systems and provide benchmark data for the energetics of these systems. We find a significant charge redistribution due to the interaction between electrons on adjacent layers. Comparison to density functional theory (DFT) calculations indicate not only wide variability even among different vdW corrected functionals, but the failure of these functionals to capture the trend of reorganization predicted by QMC. The delicate interplay of steric and dispersive forces between layers indicate that few-layer phosphorene presents an unexpected challenge for the development of vdW corrected DFT.Comment: 8 pages, 6 figure

Scope and Arbitration in Machine Learning Clinical EEG Classification

A key task in clinical EEG interpretation is to classify a recording or session as normal or abnormal. In machine learning approaches to this task, recordings are typically divided into shorter windows for practical reasons, and these windows inherit the label of their parent recording. We hypothesised that window labels derived in this manner can be misleading for example, windows without evident abnormalities can be labelled `abnormal' disrupting the learning process and degrading performance. We explored two separable approaches to mitigate this problem: increasing the window length and introducing a second-stage model to arbitrate between the window-specific predictions within a recording. Evaluating these methods on the Temple University Hospital Abnormal EEG Corpus, we significantly improved state-of-the-art average accuracy from 89.8 percent to 93.3 percent. This result defies previous estimates of the upper limit for performance on this dataset and represents a major step towards clinical translation of machine learning approaches to this problem.Comment: 10 pages, 6 figure

Written evidence submitted to ‘The Future of Banking Commission’ relating to three of their areas of investigation: appropriate structure of the banking system, competition and provision of suitable products for consumers

Written evidence submitted to ‘The Future of Banking Commission’ relating to three of their areas of investigation: appropriate structure of the banking system, competition and provision of suitable products for consumer

Simulate Less, Expect More: Bringing Robot Swarms to Life via Low-Fidelity Simulations

This paper proposes a novel methodology for addressing the simulation-reality gap for multi-robot swarm systems. Rather than immediately try to shrink or `bridge the gap' anytime a real-world experiment failed that worked in simulation, we characterize conditions under which this is actually necessary. When these conditions are not satisfied, we show how very simple simulators can still be used to both (i) design new multi-robot systems, and (ii) guide real-world swarming experiments towards certain emergent behaviors when the gap is very large. The key ideas are an iterative simulator-in-the-design-loop in which real-world experiments, simulator modifications, and simulated experiments are intimately coupled in a way that minds the gap without needing to shrink it, as well as the use of minimally viable phase diagrams to guide real world experiments. We demonstrate the usefulness of our methods on deploying a real multi-robot swarm system to successfully exhibit an emergent milling behavior.Comment: 9 pages, 9 figure

Window Stacking Meta-Models for Clinical EEG Classification

Windowing is a common technique in EEG machine learning classification and other time series tasks. However, a challenge arises when employing this technique: computational expense inhibits learning global relationships across an entire recording or set of recordings. Furthermore, the labels inherited by windows from their parent recordings may not accurately reflect the content of that window in isolation. To resolve these issues, we introduce a multi-stage model architecture, incorporating meta-learning principles tailored to time-windowed data aggregation. We further tested two distinct strategies to alleviate these issues: lengthening the window and utilizing overlapping to augment data. Our methods, when tested on the Temple University Hospital Abnormal EEG Corpus (TUAB), dramatically boosted the benchmark accuracy from 89.8 percent to 99.0 percent. This breakthrough performance surpasses prior performance projections for this dataset and paves the way for clinical applications of machine learning solutions to EEG interpretation challenges. On a broader and more varied dataset from the Temple University Hospital EEG Corpus (TUEG), we attained an accuracy of 86.7%, nearing the assumed performance ceiling set by variable inter-rater agreement on such datasets.Comment: 17 pages, 10 figure

Overview of AMALGUM – Large Silver Quality Annotations across English Genres

Corpus resources for Linguistics and NLP research on discourse phenomena, such as coreference and discourse trees, are limited by a lack of large scale, well-understood, annotated datasets: corpora are either very large (100M-10G tokens) but shallowly annotated and with unknown composition, or richly annotated, but smaller. Here, we present a resource that takes a middle path, combining some of the best features of scraped corpora - size, open licenses, lexical diversity - and high quality curated data for more interpretable inferences with complex annotations

Project POKE: Developing a STEM Community to Offset Learning Loss Amidst COVID Pandemic Through Aerospace Technologies Project-Based Learning in Hawaii\u27s K-12 Classrooms

Project POKE (Providing an Opportunity for the Keiki in Engineering) is a unique opportunity for middle and high school keiki (‘children’ in Hawaiian) in Hawaii to gain hands-on aerospace experience through interaction with a 1U CubeSat kit. The focus of the project is to build a STEM community based on collaboration and project-based learning to offset learning loss amidst the COVID-19 pandemic. The Project POKE program includes an educator course, CubeSat kit, collaborative digital space, open-ended design challenge, and symposium. Middle and high school teachers concurrently participate in the program’s educator course and meet with their students to teach the provided content. Project POKE builds off of the Artemis CubeSat Kit while adapting the educator course materials to K-12 education. Students are challenged to develop a mission concept to study a problem affecting their community using a CubeSat and present their design concept to STEM professionals at the culminating symposium. The first iteration of the program was completed in the 2021-2022 school year with 14 teachers and over 100 students. Surveyed participants indicated positive sentiment and several learning outcomes upon completing the program. Project POKE creates a diversified STEM community in Hawaii, while demystifying space science and aerospace engineering