Iván G. Torre (7122416), Łukasz Dębowski (11307384), Antoni Hernández-Fernández (7122419) · Figshare · 2021
<p>The memoryless source has only one state and emits in an infinite loop symbols <i>C</i>, <i>V</i> and <i>S</i> with probabilities <i>p</i><sub><i>c</i></sub>, <i>p</i><sub><i>v</i></sub>, and 1 − <i>p</i><sub><i>c</i></sub> − <i>p</i><sub><i>v</i></sub>, respectively.</p>
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Fallon Nicholas, Xiaoyun Li, Stancak Andrej · Figshare · 2015
<p><b>A.</b> The grand average waveforms of four equivalent source dipoles and their isopotential line maps. The isopotential maps were plotted at the temporal maxima, highlighted with an arrow and labelled with the latency value. The source dipoles are numbered from 1 to 4. <b>B.</b> CLARA source activation maps and source dipole locations of four cortical sources. The peak latency of each source corresponds to that in panel A. A = anterior, P = posterior, L = left, R = right. The numbering of dipoles corresponds to that in A. 1 = blue dipole, 2 = green dipole, 3 = ice blue dipole, 4 = magenta dipole. <b>C.</b> The grand average waveforms of four equivalent source dipoles, numbered from 1 to 4, in high and low pain catastrophising groups during viewing pain and non-pain scenes. Red line = pain photographs in High-Cat group, blue line = non-pain photographs in High-Cat group, black line = pain photographs in Low-Cat group, green line = non-pain photographs in Low-Cat group. The grey-filled rectangles indicate epochs used in statistical analyses. <b>D</b>. Bar chart of mean source activations and standard error bars for each condition during time windows of interest identified by permutation analysis (grey rectangles).</p>
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YANG, KAI CHEN · Zenodo (CERN European Organization for Nuclear Research) · 2025
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Sam Gijsen (10074341), Miro Grundei (10074344), Robert T. Lange (10074347) et al. · Figshare · 2021
<p>(A) Statistical results of distributed source reconstruction. Red: 18-25ms, Green: 35-45ms, Blue: 110-160ms. Below: Table with corresponding detailed data of the clusters. (B) Location and orientation of fitted equivalent current dipoles. Red: S1 (N20), Green: S1 (P50), Blue: bilateral S2. Below: Table with their corresponding values.</p>
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YANG, KAI CHEN · Zenodo (CERN European Organization for Nuclear Research) · 2025
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Thomas Heisser (9137307), Korbinian Weigl (9137310), Michael Hoffmeister (108162) et al. · Figshare · 2020
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Helmut Schmitz (322417), Herbert Bousack (322418) · Figshare · 2015
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David F Little · OSF Preprints (OSF Preprints) · 2019
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David F Little · OSF Preprints (OSF Preprints) · 2019
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Little, David F · Open Science Framework · 2019
All source code used to implement the model, as described in paper Materials and methods section.
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John Koupal, Mitch Cumberworth, Megan Beardsley · 2015
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Andrej Stancak (351847), Jamaan Alghamdi (351848), Turo J. Nurmikko (351849) · Figshare · 2015
<p>Grand average evoked fields were modelled by five equivalent source dipoles. The isopotential flux maps for each source, numbered from 1 to 5, are shown in the left column. The source waveforms are in the middle column. Tha locations of all sources are shown in four slices in the right column. L = left, R = right.</p>
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Jin Lu (428513), Ji Li (207201) · Figshare · 2025
Open source model and closed source model in this study.
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Roger Seiler, Gunther Kucza · Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences) · 2017
Customers find themselves in a flood of information and testimonials are one way of gaining the customer’s attention. Previous studies have shown the positive effects of testimonials on purchase intentions. In this present study, different types of testimonials (celebrity and expert) are tested, and the fit between the product (ring, motorcycle and in/out-trays) congruency of the testimonial with the customer’s image is introduced and analysed. Furthermore, not only is source credibility measured, but source attractiveness and expertise are also measured and combined in a new model integrating these three theories. The model shows direct and indirect effects of credibility and congruency on attitudes and purchase intentions. Product fit, in contrast to congruency, has a positive effect on attitude towards the ad and in turn influences attitude towards the brand, affecting purchase intention. Credible testimonials strongly influence this effectiveness, with trustworthiness being a main driver.
Cited by 61Open access
Nicolas Dütsch, Sebastian Graf, Javier García-frías et al. · 2006
The integration of a source model into lossless source coding based on punctured turbo codes is considered. We use the turbo principle to iteratively estimate the source statistics and to compensate the errors due to compression. In order to cope with the critical part of joint estimation and decoding, the start-up, we propose the use of an asymmetric turbo code with one recursive Quick-Look-In component code. Moreover, we present an alternative puncturing method, which ensures the compression of the data in a predetermined number of reconstruction attempts. Simulation results show that the proposed scheme outperforms traditional source codes when compressing data with memory. 1
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Jack Resnik · 2012
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Gail C. Murphy, David Notkin · ACM SIGSOFT Software Engineering Notes · 1995
article Free Access Share on Lightweight source model extraction Authors: Gail C. Murphy Department of Computer Science & Engineering, University of Washington, Box 352350, Seattle, WA Department of Computer Science & Engineering, University of Washington, Box 352350, Seattle, WAView Profile , David Notkin Department of Computer Science & Engineering, University of Washington, Box 352350, Seattle, WA Department of Computer Science & Engineering, University of Washington, Box 352350, Seattle, WAView Profile Authors Info & Claims ACM SIGSOFT Software Engineering NotesVolume 20Issue 4Oct. 1995 pp 116–127https://doi.org/10.1145/222132.222147Online:01 October 1995Publication History 26citation364DownloadsMetricsTotal Citations26Total Downloads364Last 12 Months4Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
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Haley, Jagger · Zenodo (CERN European Organization for Nuclear Research) · 2025
After over 100 rounds of academic remediation I am proud to present the single Source vibration model as it exists in its current form (SSVM 10.4)
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Gourevitch, Jesse, Diehl, Rebecca · Figshare · 2021
It is critical to effectively communicate current and future flood risks as the intensity and frequency of storms shift with a changing climate, yet existing flood risk maps are insufficient. Modifying, extending, and/or updating flood inundation extents is difficult, especially over large scales, because traditional floodplain mapping approaches are data and resource intensive. Low-complexity floodplain mapping techniques are promising alternatives, but their simplistic representation of process falls short of capturing inundation patterns in all situations and/or settings. To address these needs and deficiencies, we formalize and extend the functionality of the HAND floodplain mapping approach into the probHAND model by making the code available and incorporating an uncertainty analysis. With publicly available datasets, the probHAND model can produce probability floodplain maps for large areas relatively rapibdly. We describe the modeling approach and then provide an example application in the Lake Champlain Basin, Vermont, USA. Uncertainties translate to on-the-ground changes to inundated areas, or floodplain widths, in the study area by an average of 40%. We found that the spatial extent of probable inundation captured the distribution of observed and modeled flood extents well, suggesting that low-complexity models may be sufficient for representing inundation extents in support of flood risk and conservation mapping applications, especially when uncertainties in parameter inputs and process simplifications are accounted for. To improve the accuracy of flood risk datasets, we recommend investing limited resources in accurate topographic datasets and improved flood frequency-magnitude relationships will have the greatest impact on decreasing model output variability, therefore increasing the certainty of flood inundation extents.
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Andrew R. Spanjer (8795999), Andrew S. Gendaszek (5240750), Elyse J. Wulfkuhle (12575258) et al. · Figshare · 2022
<p>Hourly predicted versus observed temperature values for the final Heat Source model at all monitored temperature locations in 2019 (n = 6) and 2018 (n = 5).</p>
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