Post-disciplinary education with groups: In-person vs. online

At the University of Manitoba, several librarians are educators in Post-Discipline Education; a program where students involved in academic misconduct can learn about tools, techniques and services to encourage future academic success. Although consultations are typically one-on-one, when an allegation involves a group assignment where multiple students are involved, is this the best approach? In this session, presenters will share their experiences in developing sessions for small groups. Participants will learn about the advantages and challenges in providing this type of support, the differences between in-person and online delivery, and have the opportunity to consider how similar practices could be applied within their own work environments. &nbsp

Nurses Alumni Association Bulletin, Fall 2011

2011 - 2012 Meeting Date Calendar Cover 2012 Annual Luncheon & Meeting Notice Officers, Committee Chairs, Satellite and Volunteers Bulletin Publication Committee The President\u27s Message Treasurer\u27s Report Resume of Minutes Office News Committee Reports Social Scholarship Relief Trust Fund Nominating Satellite - Harrisburg Satellite Area Bulletin By Laws Annual Giving Janet C. Hindson Award Janet C. Hindson Award Criteria Janet C. Hindson Award Recipient and Nominees Memories Era Ending (Part Two) Mary Schaal, EdD, RN Medical Clinic Psychology and Nursing Happy Birthday -To Be 80 or More 50th Anniversary Class Lists for 1961 Luncheon Attendees Center Page 1962 - Anniversary Class List for 2012 Annual Luncheon Comments In Memoriam, Names of Deceased Graduates Class News Additional Information, Pins, Transcripts & Address Info Scholarship Fund Application Certification Reimbursement Application Relief Fund Application List of Hotels Campus Ma

Potential of natural language processing for metadata extraction fromenvironmental scientific publications

Summarizing information from large bodies of scientific literature is anessential but work-intensive task. This is especially true in environmentalstudies where multiple factors (e.g., soil, climate, vegetation) cancontribute to the effects observed. Meta-analyses, studies thatquantitatively summarize findings of a large body of literature, rely onmanually curated databases built upon primary publications. However, giventhe increasing amount of literature, this manual work is likely to requiremore and more effort in the future. Natural language processing (NLP)facilitates this task, but it is not clear yet to which extent theextraction process is reliable or complete. In this work, we explore threeNLP techniques that can help support this task: topic modeling, tailoredregular expressions and the shortest dependency path method. We apply thesetechniques in a practical and reproducible workflow on two corpora ofdocuments: the Open Tension-diskInfiltrometer Meta-database (OTIM) and the Meta corpus. The OTIM corpus contains the sourcepublications of the entries of the OTIM database of near-saturated hydraulicconductivity from tension-disk infiltrometer measurements(https://github.com/climasoma/otim-db, last access: 1 March 2023). The Meta corpus is constituted ofall primary studies from 36 selected meta-analyses on the impact ofagricultural practices on sustainable water management in Europe. As a firststep of our practical workflow, we identified different topics from theindividual source publications of the Meta corpus using topic modeling.This enabled us to distinguish well-researched topics (e.g., conventionaltillage, cover crops), where meta-analysis would be useful, from neglectedtopics (e.g., effect of irrigation on soil properties), showing potentialknowledge gaps. Then, we used tailored regular expressions to extractcoordinates, soil texture, soil type, rainfall, disk diameter and tensionsfrom the OTIM corpus to build a quantitative database. We were able toretrieve the respective information with 56 % up to 100 % of allrelevant information (recall) and with a precision between 83 % and100 %. Finally, we extracted relationships between a set of driverscorresponding to different soil management practices or amendments (e.g.,"biochar", "zero tillage") and target variables (e.g., "soilaggregate", "hydraulic conductivity", "crop yield") from thesource publications' abstracts of the Meta corpus using the shortestdependency path between them. These relationships were further classifiedaccording to positive, negative or absent correlations between the driverand the target variable. This quickly provided an overview of the differentdriver-variable relationships and their abundance for an entire body ofliterature. Overall, we found that all three tested NLP techniques were ableto support evidence synthesis tasks. While human supervision remainsessential, NLP methods have the potential to support automated evidencesynthesis which can be continuously updated as new publications becomeavailable

The Design and Synthesis of Farnesyl Tail Analogues Incorporating Aromatic Rings: A Comparison of Wittig and Grignard Reaction Sequences

Mutant RAS proteins have been linked to over 30 of all human cancers. It has been shown that mutant RAS proteins that cannot be farnesylated do not induce malignant transformation. Therefore, farnesyl protein transferase (FPTase) inhibitors have become attractive targets as potential chemotherapeutic agents. Two farnesyl tail analogues have been prepared that incorporate aromatic rings. One of the compounds, trans-9-phenyl-8-nonen-1-ol, could only be prepared pure using a Grignard reaction sequence. This sequence is compared to the initially attempted Wittig reaction sequence that results in an inseparable mixture of cis/trans isomers. It is anticipated thatwhen coupled with poal diphosphate head mimetics, the tails prepared in this pper will help illuminate the importance of nonbonding interactions in the binding of farnesyl pyrophosphate analogues to the FPTase enzyme active site

Nurses Alumni Association Bulletin, Fall 2010

2010 - 2011 Meeting Date Calendar 2011 Annual Luncheon & Meeting Notice Officers, Committee Chairs, Satellite and Volunteers Bulletin Publication Committee The President\u27s Message Treasurer\u27s Report Resume of Minutes Office News Committee Reports Social Relief Trust Fund Satellite - Harrisburg Satellite Area Scholarship Nominating Bulletin Development Annual Giving Janet C. Hindson Award Janet C. Hindson Award Criteria Janet C. Hindson Award Recipient and Nominees Janet C. Hindson Lifetime Achievement Award Assisting in the HIV I Aids Epidemic in Lesotho, Africa News About and From our Graduates Memories Era Ending Happy Birthday To Be 80 or More 50th Anniversary Class Lists for 1960 Luncheon Attendees 1961 - Anniversary Class List for 2011 Annual Luncheon Center page Why Not? It\u27s Our Money! In Memoriam, Names of Deceased Graduates Class News Additional Information, Pins, Transcript & Address Info Scholarship Fund Application Certification Reimbursement Application Relief Fund Application List of Hotels Campus Map Constitution & By-Laws Revision

Contextual Pre-Planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning

Recent studies show that deep reinforcement learning (DRL) agents tend to overfit to the task on which they were trained and fail to adapt to minor environment changes. To expedite learning when transferring to unseen tasks, we propose a novel approach to representing the current task using reward machines (RM), state machine abstractions that induce subtasks based on the current task's rewards and dynamics. Our method provides agents with symbolic representations of optimal transitions from their current abstract state and rewards them for achieving these transitions. These representations are shared across tasks, allowing agents to exploit knowledge of previously encountered symbols and transitions, thus enhancing transfer. Our empirical evaluation shows that our representations improve sample efficiency and few-shot transfer in a variety of domains.Comment: IJCAI Workshop on Planning and Reinforcement Learning, 202

Security and Privacy Implications of Pervasive Memory Augmentation

Pervasive computing is beginning to offer the potential to rethink and redefine how technology can support human memory augmentation. For example, the emergence of widespread pervasive sensing, personal recording technologies, and systems for the quantified self are creating an environment in which it's possible to capture fine-grained traces of many aspects of human activity. Contemporary psychology theories suggest that these traces can then be used to manipulate our ability to recall - to both reinforce and attenuate human memories. Here, the authors consider the privacy and security implications of using pervasive computing to augment human memory. They describe a number of scenarios, outline the key architectural building blocks, and identify entirely new types of security and privacy threats-namely, those related to data security (experience provenance), data management (establishing new paradigms for digital memory ownership), data integrity (memory attenuation and recall-induced forgetting), and bystander privacy. Together, these threats present compelling research challenges for the pervasive computing research community. This article is part of a special issue on privacy and security