7,126 research outputs found
Humans of AI3SD: Professor Tim Albrecht
No full textThis interview forms part of our Humans of AI3SD Series. Professor Tim Albrecht, a Professor of Physical Chemistry at the University of Birmingham was interviewed by Michelle Pauli at our AI3SD Network+ Conference in 2019. Tim is a PI on one of the funding projects from AI3SD-FundingCall1
AI3SD Video: Event detection in single-molecule data – how to find molecular signatures without (too many) prior assumptions
No full textData from single-molecule experiments, such as from current-time or conductance-distance spectroscopy or sensors, are often “noisy” and characterised by complex molecular behaviour. In some cases, extracting the physically relevant information may be based on supervised approaches, i.e. where labelled data are available for training. In other cases, such data are either not available or it may simply be undesirable to make a priori assumptions about the molecular characteristics, for example to prevent loss of information and expectation bias.[1,2] This may require unsupervised methods or alternative approaches that put an emphasis on “what is not background?”, rather than “what does an event look like?”. In my talk, I will discuss some of the approaches we have taken, including some based on image recognition networks (AlexNet, VGG16),[3,4] and show those can be used to extract not only physically meaningful characteristics, but also previously unknown molecular behaviour.[1] M. Lemmer et al., “Unsupervised vector-based classification of single-molecule charge transport data”, Nat. Commun. 2016, 7, art. no. 12922[2] T. Albrecht et al., “Deep learning for single-molecule science”, Nanotechnol. 2017, 28, 423001.[3] A. Vladyka, T. Albrecht, “Unsupervised classification of single-molecule data with autoencoders and transfer learning”, Machin. Learn.: Sci. Technol. 2020, 1, 035013.[4] C. Weaver et al., “Unsupervised Classification of Voltammetric Data with Image Recognition and Dimensionality Reduction” (in preparation
AI3SD Video: When charge transport data are a worm – a transfer learning approach for unsupervised data classification
No full textAdvanced data analysis methodologies, and in particular dimensionality reduction techniques, are now used more and more widely in the single-molecule charge transport community. They allow for comprehensive exploration of large datasets, where data display significant variance and sometimes contain (unknown) sub-populations. To this end, unsupervised approaches, which do not rely on class labels or pre-defined expectations can be advantageous. Multi-Parameter Vector Classification (MPVC) is one example and PCA-based methods have also been employed in this context [1,2,3]. We have recently shown how Transfer Learning may be employed to identify and quantify hidden features in single-molecule charge transport data [3]. Using open-access neural networks such as AlexNet, trained on millions of seemingly unrelated image data, feature recognition then does not require network training with application-specific data. Instead, the network recognises features in the input that it had learned in other contexts and, for example, identifies different shapes in conductance-distance traces as images of different worm species. Thus, our results show how Deep Learning methodologies can readily be employed for unsupervised data classification, even if the amount of problem-specific, ‘own’ data is limited.[1] M Lemmer, MS Inkpen, K Kornysheva, NJ Long, T Albrecht, “Unsupervised vector-based classification of single-molecule charge transport data”, Nat. Comm. 2016, 7, 12922.[2] T Albrecht, G Slabaugh, E Alonso, SMMR Al-Arif, “Deep learning for single-molecule science”, Nanotechnology 2017, 28 (42), 423001.[3] A Vladyka, T Albrecht, “Unsupervised classification of single-molecule data with autoencoders and transfer learning”, Mach. Learn.: Sci. Technol. 2020, 1, 035013
AI3SD Video: How to detect unexpected features & physical processes in single-molecule data
No full textThis talk forms part of the ML4MC (Machine Learning for Materials and Chemicals Series which has been organised as a joint venture between the Artificial Intelligence for Scientific Discovery Network+ (AI3SD) and the Directed Assembly Network. This series ran over summer 2021 and covers topics that encompass our overlapping Network interests of AI, Machine Learning, Artificial Photosynthesis, Biomimetic Materials, Crystal Design & Engineering, Materials, Molecules, Photochemistry, Photocatalysis and Supramolecular Chemistry. This video was the sixth talk in the ML4MC series and formed part of the session "Research Talks"
Do dolphins benefit from nonlinear mathematics when processing their sonar returns?
No full textAn interview with author Tim Leighton about the paper
Opportunities for linking young surveyors across professional surveying member organisations and FIG
No full textTim Di Muzio on 'Sabotage'
No full textIn a series of essays published in 2013 and 2014 on capitaspower.com, political economist Tim Di Muzio explored the concept of ‘sabotage’ as it applies to capitalist power. I recently rediscovered these essays and was so impressed by them that I have reposted them here as a single piece.
About the author: Tim Di Muzio is a researcher at the University of Wollongong. He is the author of numerous books, including Debt as power, Carbon capitalism, and The 1% and the Rest of us
1996-1997 Tim Gautreaux
No full textTim Gautreaux is the author of three novels and two earlier short story collections. His work has appeared in The New Yorker, The Best American Short Stories, The Atlantic, Harper’s, and GQ. After teaching for thirty years at Southeastern Louisiana University, he now lives, with his wife, in Chattanooga, Tennessee. (Photo credit: Randy Bergeron)https://egrove.olemiss.edu/grisham_res/1023/thumbnail.jp
Grande Ronde Basin spring chinook salmon captive broodstock program: F₁ generation performance
No full textTim Hoffnagle, Rich Carmichael, Joseph Feldhaus, Deb Eddy, Nick Albrecht, Sally Gee.This archived document is maintained by the State Library of Oregon as part of the Oregon Documents Depository Program. It is for informational purposes and may not be suitable for legal purposes.Mode of access: Internet from the Oregon Government Publications Collection.Text in English
First person - Tim Petzold
Full text linkFirst Person is a series of interviews with the first authors of a selection of papers published in Biology Open, helping researchers promote themselves alongside their papers. Tim Petzold is first author on ‘ Connexin 41.8 governs timely haematopoietic stem and progenitor cell specification’, published in BiO. Tim conducted the research described in this article while a PhD student in Julien Bertrand's lab at the Department of Pathology and Immunology, Faculty of Medicine, University of Geneva, Switzerland. He is now a postdoc in the lab of Holger Gerhardt at the Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany, investigating developmental biology – previously his focus was on how blood stem cells develop and now it has shifted to how the vascular system develops
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