16,136 research outputs found
Albrecht, T, 785006
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/368039Surname: ALBRECHT
Given Name(s) or Initials: T
Military Service Number or Last Known Location: 785006
Missing, Wounded and Prisoner of War Enquiry Card Index Number: 14718178086
Item: [2016.0049.00371] "Albrecht, T, 785006
The Role of Attention in Processing of Visual Stimuli in Metacontrast Masking
No full textBy analyzing individual data, Albrecht and colleagues found qualitative inter-individual differences in studies with metacontrast masking, appearing in phenomenological perception as well as in discrimination performance (Albrecht & Mattler, 2012a, 2012b). They used the metacontrast paradigm, where two stimuli are presented sequentially and the visibility of the first stimulus (target) is reduced due to the appearance of the second stimulus (mask). The visibility is a function of the stimulus-onset-asynchrony (SOA). Participants differ in that respect whether the visibility of the target increases with increasing SOA (type A) or whether it is U-shaped (type B). These differences in the objective performance correlate with differences in the phenomenological experience (apparent motion vs. negative afterimage) as well as in the response criteria. A first ERP study also indicated differences in the sensory neural processing. This study aims to clarify whether these neural differences reflect either a different intentional attention on experimental stimuli (top-down) or a different bottom-up processing. For this, participants attended two sessions. In the first session metacontrast stimuli were presented but they had to focus the fixation point and detect an occasionally appearing color change (condition “without attention”). The experimental design in the second session was identical to the first, but participants had to focus their attention on the metacontrast stimuli and to discriminate the shape of the target (condition “with attention”). We expect to find group differences between type A and type B participants in the condition “with attention” and replicate data of the first ERP study. Finding these differences in the condition “without attention” as well would indicate a bottom-up processing; no differences would indicate a different top-down processing
The Role of Attention in Processing of Visual Stimuli in Metacontrast Masking
No full textBy analyzing individual data, Albrecht and colleagues found qualitative inter-individual differences in studies with metacontrast masking, appearing in phenomenological perception as well as in discrimination performance (Albrecht & Mattler, 2012a, 2012b). They used the metacontrast paradigm, where two stimuli are presented sequentially and the visibility of the first stimulus (target) is reduced due to the appearance of the second stimulus (mask). The visibility is a function of the stimulus-onset-asynchrony (SOA). Participants differ in that respect whether the visibility of the target increases with increasing SOA (type A) or whether it is U-shaped (type B). These differences in the objective performance correlate with differences in the phenomenological experience (apparent motion vs. negative afterimage) as well as in the response criteria. A first ERP study also indicated differences in the sensory neural processing. This study aims to clarify whether these neural differences reflect either a different intentional attention on experimental stimuli (top-down) or a different bottom-up processing. For this, participants attended two sessions. In the first session metacontrast stimuli were presented but they had to focus the fixation point and detect an occasionally appearing color change (condition “without attention”). The experimental design in the second session was identical to the first, but participants had to focus their attention on the metacontrast stimuli and to discriminate the shape of the target (condition “with attention”). We expect to find group differences between type A and type B participants in the condition “with attention” and replicate data of the first ERP study. Finding these differences in the condition “without attention” as well would indicate a bottom-up processing; no differences would indicate a different top-down processing
Albrecht Fitness Studio Business Plan
No full textThis business outlines what it would take to develop a designer fitness studio in Charlotte, NC. The plan contains all key aspects of a business plan, from an executive summary to a cash flow statement. The author is also the acting CEO of the company Albrecht Fitness and plans to follow through with the plan in the next 5 years. This business plan proposes an $80,000 loan needed from potential investors. The components of this business plan outline the company's potential profitability, key strategies, and overall business model
pism/pik/paleo_07dev: PISM version as used in Kingslake, Scherer, Albrecht et al. Nature publication
No full text<p>This is a code release of the Parallel Ice Sheet Model (PISM) used for paleo simulations of the Antarctic Ice Sheet as discussed in</p>
<p><a href="https://doi.org/10.1038/s41586-018-0208-x">Kingslake, J., Scherer, R.P., Albrecht, T., Coenen, J., Powell, R.D., Reese, R., Stansell, N.D., Tulaczyk, S., Wearing, M.G. and Whitehouse, P.L., 2018. Extensive retreat and re-advance of the West Antarctic Ice Sheet during the Holocene. <em>Nature</em>, <em>558</em>(7710), p.430</a>.</p>
<p>For input data and plotting scripts please contact the author ([email protected]).</p>
What Ever happened to Francis Glisson? Albrecht Haller and the Fate of Eighteenth-Century Irritability
No full textThis article investigates the reasons behind the disappearance of Francis Glisson’s theory of irritability during the eighteenth century. At a time when natural investigations were becoming increasingly polarized between mind and matter in the attempt to save both man’s consciousness and the inert nature of the res extensa, Glisson’s notion of a natural perception embedded in matter did not satisfy the new science’s basic injunction not to superimpose perceptions and appetites on nature. Knowledge of nature could not be based on knowledge within nature, i.e., on the very knowledge that nature has of itself; or – to look at the same question from the point of view of the human mind – man’s consciousness could not be seen as participating in forms of natural selfhood. Albrecht Haller played a key role in this story. Through his experiments, Haller thought he had conclusively demonstrated that the response given by nature when irritated did not betray any natural perceptivity, any inner life, any sentiment interi´eur. In doing so, he provided a less bewildering theory of irritability for the rising communities of experimental physiology
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: 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
[coin] Schelling, Doornik, Albrecht en Isabella (derde emissie), Zuidelijke Nederlanden. /
No full textRecto: Een pauw staat met gespreide vleugels en opengesperde staart in vooraanzicht ; op zijn borst is een Spaans schild bevestigd met een gedeeld wapen (1, Oostenrijk ; 2, Bourgondië) ; boven zijn naar links gewende kop, een kroon ; rondom, een cirkel, ALB[ERTV]S·ET·ELISABET·D[E]I.GRAT[IA·](toren)]en een parelcirkel.Verso: Een gekroond Spaans schild, met het volledig wapen van de aartshertogen, is geplaatst op een schuin stokkenkruis dat het omschrift snijdt ; rondom, een cirkel, [•]ARC – HI•AVST•D – V[CES·B] – VRG•D[OM•T] – ORN[•Z] en een parelcirkel.Enno Van Gelder, H. & Hoc, M., Les monnaies des Pays-Bas bourguignons et Espagnols 1434-1713. Amsterdam : J. Schulman, 1960, nr. 314-7a.De Mey, J., Les monnaies du Tournaisis, Bruxelles : De Mey, 1975, nr. 229.Van Keymeulen, A., De munten van de Zuidelijke Nederlanden van Albrecht en Isabella tot Willem I. Brussel : Koninklijke bibliotheek Albert I, 1981, nr. 46 T.Bastiaens, A, De muntslag der aartshertogen Albrecht en Isabella 1598-1621, s.l, 1981, nr. 33.Vanhoudt, H. Atlas der munten van België : van de Kelten tot heden. Herent : H. Vanhoudt, 1996, nr. I 414.Vanhoudt, H. De munten van de Bourgondische, Spaanse en Oostenrijkse Nederlanden en van de Franse en Hollandse periode 1434-1830. Heverlee : Peeters, 2015, nr. 623.TO.Standard Catalog of World Coins 1601-1700, 7th Edition, Iola : Krause Publications, 2018, p. 1543, nr. 40
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