155 research outputs found
312. Storage Organization and Integrity
No full textThe two papers in Session 312 explore the issues and topics pertaining to the theme of Storage Organization and Integrity with recent examples of advances in practice.
Session chair: Andrea Goethals
312. Storage Organization and Integrity
No full textThe two papers in Session 312 explore the issues and topics pertaining to the theme of Storage Organization and Integrity with recent examples of advances in practice.
Session chair: Andrea Goethals
312. Storage Organization and Integrity
No full textThe two papers in Session 312 explore the issues and topics pertaining to the theme of Storage Organization and Integrity with recent examples of advances in practice.
Session chair: Andrea Goethals
Dr. Scott Allison and Dr. Al Goethals – Faculty Author Interview
No full textDr. Scott Allison, Professor, Department of Psychology and Dr. Al Goethals, Professor, Jepson School of Leadership Studies discuss their recent book, Heroes: What They Do and Why We Need Them. Published by Oxford University Press, the book offers a stimulating tour of the psychology of heroism, shedding light on what heroism and villainy mean to most people and why heroes — both real people and fictional characters — are so vital to our lives. For more information on the book and project, connect to the “Heroes” blog
Digital Preservation New Zealand
No full textSince 2004, the New Zealand government has invested approximately $50 million in the
digital preservation programmes of the National Library of New Zealand (NLNZ) and
Archives New Zealand. These funds have been used to develop the repository infrastructure
and staff expertise needed to operate, manage and support sustainable long-term digital
programmes to care for the nation’s cultural heritage and government records in digital
form.Recognising that data with long-term value, and therefore in need of digital preservation, is
being produced by many individuals and organisations across NZ, the NLNZ began a project
five years ago to explore a national approach to digital preservation. The idea is that NLNZ’s
digital preservation programme could be expanded to provide a digital preservation service
for other NZ organisations creating digital content with ‘high value’, i.e. that will contribute
to economic, social, cultural or economic outcomes, now or in the future. The NLNZ’s research has included surveys of targeted populations to understand for NZ the
value of data being created; the policies, strategies, practices and systems in place to
manage and maintain access to it; and the appetite to use a NZ digital preservation service.
CIO/CTOs of NZ state sector organisations were surveyed because of their responsibility to
maintain access to their organisation’s digital material, and eResearchers were surveyed
because they generate digital material of value. The surveys were first conducted in 2015,
and then repeated in 2019 to understand the extent to which changes in digital preservation
practice and needs in NZ had changed or remained the same. This presentation will share what has been learned through this research, and the
eResearch conference attendees will be invited to provide feedback on a potential nationallevel digital preservation service.ABOUT THE AUTHOR(S) Andrea Goethals manages the digital preservation team at the National Library of New
Zealand. She has primary responsibility for the overall day-to-day operations of the
National Digital Heritage Archive and contributes to the strategic direction of the
Library’s digital preservation programme. She champions digital preservation issues
and collaborates closely with others at the Library and around the world to advance
digital preservation standards and practices. </div
Inductive queries for a drug designing robot scientist
No full textIt is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments
Do I need a DOI?
No full textThis lightning talk will be a short introduction to DOIs (Digital Object Identifiers). John Kunze (2021) [1] describes ten persistent myths about persistent identifiers. Similarly, in this lightning talk I will describe some of the misunderstandings I have encountered in my discussions with people about DOIs in my role coordinating the NZ DOI Consortium. References: [1] John Kunze 2021, ARK Alliance website, ARK Alliance Key (ARK), accessed 27 October 2023, https://arks.org/blog/ten-persistent-myths-about-persistent-identifiers/. ABOUT THE AUTHORAndrea Goethals started her digital preservation career in 2003 as a computer scientist working on the technical challenges. Since then she has worked in many different roles focusing on the policies, strategies and people that make digital preservation programmes possible. She is now Manager of Digital Preservation and Data Capability at the National Library of New Zealand. She participates in many local, regional and international working groups including NZ DOI Consortium, Australasia Preserves, IIPC Steering Committee, iPres Steering Group, Digital Preservation Storage Criteria WG, DataCite CESG, NSLA DPN and DPC-Australasia.For more information about eResearch NZ / eRangahau Aotearoa, visit:https://eresearchnz.co.nz/</p
Model-Based Reinforcement Learning with State Abstraction: A Survey
No full textModel-based reinforcement learning methods are promising since they can increase sample efficiency while simultaneously improving generalizability. Learning can also be made more efficient through state abstraction, which delivers more compact models. Model-based reinforcement learning methods have been combined with learning abstract models to profit from both effects. We consider a wide range of state abstractions that have been covered in the literature, from straightforward state aggregation to deep learned representations, and sketch challenges that arise when combining model-based reinforcement learning with abstraction. We further show how various methods deal with these challenges and point to open questions and opportunities for further research.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Interactive IntelligencePattern Recognition and Bioinformatic
k-Point semidefinite programming bounds for equiangular lines
No full textWe propose a hierarchy of k-point bounds extending the Delsarte–Goethals–Seidel linear programming 2-point bound and the Bachoc–Vallentin semidefinite programming 3-point bound for spherical codes. An optimized implementation of this hierarchy allows us to compute 4, 5, and 6-point bounds for the maximum number of equiangular lines in Euclidean space with a fixed common angle.Discrete Mathematics and Optimizatio
A system and method for imaging body areas
No full textThe invention relates to a system for imaging one or more external human body areas comprising a photographic device configured to acquire, store and output an image or images of the one or more body areas. The invention also relates to a method for determining a probable disease state of an external human body area.Delft University of Technolog
- …
