Qing Yu (337275), Chunyue Teng (9040150), Bradley R. Postle (9040153) · Figshare · 2020
<p>Temporal generalization of orientation reconstructions, in early visual and IPS ROIs, for PMIs and UMIs using PMI-trained IEMs. Strength of reconstructions are indicated by the slope of reconstructions. The x- and y-axes show the tested and training time points, respectively. Data are available at osf.io/G4C3N. IEM, inverted encoding model; IPS, intraparietal sulcus; PMI, prioritized memory item; ROI, region of interest; UMI, unprioritized memory item.</p> <p>(TIF)</p>
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Qing Yu (337275), Chunyue Teng (9040150), Bradley R. Postle (9040153) · Figshare · 2020
<p>Temporal generalization of location reconstructions, in early visual and IPS ROIs, for PMIs and UMIs using PMI-trained IEMs. Strength of reconstructions are indicated by the slope of reconstructions. The x- and y-axes show the tested and training time points, respectively. Data are available at osf.io/G4C3N. IEM, inverted encoding model; IPS, intraparietal sulcus; PMI, prioritized memory item; ROI, region of interest; UMI, unprioritized memory item.</p> <p>(TIF)</p>
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National Cancer Institute · Definitions · 2020
Ope n Pe e r Re v ie w on Qe ios
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J.F.A.K. van Benthem · UvA-DARE (University of Amsterdam) · 1998
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2025
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van Benthem, J.F.A.K. · Data Archiving and Networked Services (DANS) · 1998
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National Cancer Institute · Definitions · 2020
Ope n Pe e r v ie w on Qe ios Ope
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2025
These data are from time point 2 of the project. The data are in wide format. Data contain constructs of work characteristics, income, housing, economic hardship, food insecurity, personal stress, personal self-efficacy, depression, anxiety, and hair cortisol confounders.
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J.F.A.K. van Benthem · UvA-DARE (University of Amsterdam) · 1998
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2014
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National Cancer Institute · Definitions · 2020
Ope n Pe e r Re v ie w on Qe ios
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National Cancer Institute · Definitions · 2020
Ope n Pe e r Re v ie w on Qe ios
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Aerobot Mission, Julia M. B. Braman, Richard M. Murray · 2016
The success of complex autonomous robotic systems depends on the quality and correct-ness of their fault tolerant control systems. A goal-based control approach is used to design an example control program for a proposed mission to Titan, a moon of Saturn. The state space of the example exceeds the capability of existing symbolic model checkers for hybrid automata, so a novel verification method is developed. An original design for verification software tool called SBT Checker that aids in the design of modular, state-based goal trees for the control of separate tasks in the overall mission, is discussed. The combination of the individual goal trees constitute a goal network control program that can be verified against unsafe conditions using the novel InVeriant software. InVeriant converts goal networks to hybrid systems whose special structure is exploited to quickly find the reachable unsafe states. The Titan aerobot mission example is then verified using this new method. Nomenclature Γ State space of all passive state variables Λ Set of all discrete states of passive state variables D Set of passive state variables G Set of goals in a goal network Lr,k Set of executable branches of root goal g0,0r Sr,k Set containing root goal g0,0r and descendants Θk Set of executable sets of goals in group Gk ζ Set of unsafe constraints gin,jnn Goal Rk Set of root goals with descendants in Gk
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2025
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2025
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Fabian M. Stuby (715075), Stefan Döbele (549839), Susanne-Dorothea Schäffer (715076) et al. · Figshare · 2015
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National Cancer Institute · Definitions · 2020
Ope n Pe e r v ie w on Qe ios
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Jacob Gray · Bulletin of Miscellaneous Information (Royal Gardens Kew) · 2013
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National Cancer Institute · Definitions · 2020
Ope n Pe e r v ie w on Qe ios
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Jacob Gray · Bulletin of Miscellaneous Information (Royal Gardens Kew) · 2013
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