PBMA Pause and Learn Video Nuggets Transcript

This document is a transcript for a video about a practice practiced at Goddard Space Flight Center called Pause and Learn (PaL). The PaL process is intended to, first of all, help the team learn. So, the team that was involved in the activity, the group that actually did the work, that handled the review, or ran the tests, or developed the piece of equipment, they sit down and actually say, "What did we learn from this exercise?" The idea is to create a learning environment at various key milestones in the execution of a process, rather than wait until the end of the given process, be it a launch or a mission

The ethics of pharmaceutical industry relationships with medical students

The document attached has been archived with permission from the editor of the Medical Journal of Australia. An external link to the publisher’s copy is included.Little research has been done on the extent of the relationship between the pharmaceutical industry and medical students, and the effect on students of receiving gifts. Potential harms to patients are documented elsewhere; we focus on potential harms to students. Students who receive gifts may believe that they are receiving something for nothing, contributing to a sense of entitlement that is not in the best interests of their moral development as doctors. Alternatively, students may be subject to recognised or unrecognised reciprocal obligations that potentially influence their decision making. Medical educators have a duty of care to protect students from influence by pharmaceutical companies.Wendy A Rogers, Peter R Mansfield, Annette J Braunack-Mayer and Jon N Jureidin

Threat Image Projection (TIP) into X-ray images of cargo containers for training humans and machines

We propose a framework for Threat Image Projection (TIP) in cargo transmission X-ray imagery. The method exploits the approximately multiplicative nature of X-ray imagery to extract a library of threat items. These items can then be projected into real cargo. We show using experimental data that there is no significant qualitative or quantitative difference between real threat images and TIP images. We also describe methods for adding realistic variation to TIP images in order to robustify Machine Learning (ML) based algorithms trained on TIP. These variations are derived from cargo X-ray image formation, and include: (i) translations; (ii) magnification; (iii) rotations; (iv) noise; (v) illumination; (vi) volume and density; and (vii) obscuration. These methods are particularly relevant for representation learning, since it allows the system to learn features that are invariant to these variations. The framework also allows efficient addition of new or emerging threats to a detection system, which is important if time is critical. We have applied the framework to training ML-based cargo algorithms for (i) detection of loads (empty verification), (ii) detection of concealed cars (ii) detection of Small Metallic Threats (SMTs). TIP also enables algorithm testing under controlled conditions, allowing one to gain a deeper understanding of performance. Whilst we have focused on robustifying ML-based threat detectors, our TIP method can also be used to train and robustify human threat detectors as is done in cabin baggage screening

Come Down McGinty

https://digitalcommons.library.umaine.edu/mmb-vp/3060/thumbnail.jp

The First Wench Done Turned White

https://digitalcommons.library.umaine.edu/mmb-vp/1417/thumbnail.jp

Queen Of The Bungalow

https://digitalcommons.library.umaine.edu/mmb-vp/6732/thumbnail.jp

A Self-Reference False Memory Effect in the DRM Paradigm: Evidence from Eastern and Western Samples

It is well established that processing information in relation to oneself (i.e., selfreferencing) leads to better memory for that information than processing that same information in relation to others (i.e., other-referencing). However, it is unknown whether self-referencing also leads to more false memories than other-referencing. In the current two experiments with European and East Asian samples, we presented participants the Deese-Roediger/McDermott (DRM) lists together with their own name or other people’s name (i.e., “Trump” in Experiment 1 and “Li Ming” in Experiment 2). We found consistent results across the two experiments; that is, in the self-reference condition, participants had higher true and false memory rates compared to those in the other-reference condition. Moreover, we found that selfreferencing did not exhibit superior mnemonic advantage in terms of net accuracy compared to other-referencing and neutral conditions. These findings are discussed in terms of theoretical frameworks such as spreading activation theories and the fuzzytrace theory. We propose that our results reflect the adaptive nature of memory in the sense that cognitive processes that increase mnemonic efficiency may also increase susceptibility to associative false memories

Dan Dan Danuel / music by Ed Rogers; words by Ed Rogers

Cover: drawing of an African American male wearing a tuxedo; description reads A Crazy Coon Concoction; Publisher: F. B. Haviland Pub. Co. (New York)https://egrove.olemiss.edu/sharris_b/1044/thumbnail.jp

Interactions between social learning and technological learning in electric vehicle futures

The transition to electric vehicles is an important strategy for reducing greenhouse gas emissions from passenger cars. Modelling transition pathways helps identify critical drivers and uncertainties. Global integrated assessment models (IAMs) have been used extensively to analyse climate mitigation policy. IAMs emphasise technological change processes but are largely silent on important social and behavioural dimensions to technological transitions. Here, we develop a novel conceptual framing and empirical evidence base on social learning processes relevant for vehicle adoption. We then implement this formulation of social learning in IMAGE, a widely-used global IAM. We apply this new modelling approach to analyse how technological learning and social learning interact to influence electric vehicle transition dynamics. We find that technological learning and social learning processes can be mutually reinforcing. Increased electric vehicle market shares can induce technological learning which reduces technology costs while social learning stimulates diffusion from early adopters to more risk-averse adopter groups. In this way, both types of learning process interact to stimulate each other. In the absence of social learning, however, the perceived risks of electric vehicle adoption among later adopting groups remains prohibitively high. In the absence of technological learning, electric vehicles remain relatively expensive and therefore only for early adopters an attractive choice. This first-of-its-kind model formulation of both social and technological learning is a significant contribution to improving the behavioural realism of global IAMs. Applying this new modelling approach emphasises the importance of market heterogeneity, real-world consumer decision-making, and social dynamics as well as technology parameters, to understand climate mitigation potentials