1,338 research outputs found
Validation and Comparison of Non-Stationary Cognitive Models: A Diffusion Model Application
Cognitive processes undergo various fluctuations and transient states across
different temporal scales. Superstatistics are emerging as a flexible framework
for incorporating such non-stationary dynamics into existing cognitive model
classes. In this work, we provide the first experimental validation of
superstatistics and formal comparison of four non-stationary diffusion decision
models in a specifically designed perceptual decision-making task. Task
difficulty and speed-accuracy trade-off were systematically manipulated to
induce expected changes in model parameters. To validate our models, we assess
whether the inferred parameter trajectories align with the patterns and
sequences of the experimental manipulations. To address computational
challenges, we present novel deep learning techniques for amortized Bayesian
estimation and comparison of models with time-varying parameters. Our findings
indicate that transition models incorporating both gradual and abrupt parameter
shifts provide the best fit to the empirical data. Moreover, we find that the
inferred parameter trajectories closely mirror the sequence of experimental
manipulations. Posterior re-simulations further underscore the ability of the
models to faithfully reproduce critical data patterns. Accordingly, our results
suggest that the inferred non-stationary dynamics may reflect actual changes in
the targeted psychological constructs. We argue that our initial experimental
validation paves the way for the widespread application of superstatistics in
cognitive modeling and beyond
Integrated quantized electronics: a semiconductor quantized voltage source
The Josephson effect in superconductors links a quantized output voltage Vout
= f \cdot(h/2e) to the natural constants of the electron's charge e, Planck's
constant h, and to an excitation frequency f with important applications in
electrical quantum metrology. Also semiconductors are routinely applied in
electrical quantum metrology making use of the quantum Hall effect. However,
despite their broad range of further applications e.g. in integrated circuits,
quantized voltage generation by a semiconductor device has never been obtained.
Here we report a semiconductor quantized voltage source generating quantized
voltages Vout = f\cdot(h/e). It is based on an integrated quantized circuit of
a single electron pump operated at pumping frequency f and a quantum Hall
device monolithically integrated in series. The output voltages of several \muV
are expected to be scalable by orders of magnitude using present technology.
The device might open a new route towards the closure of the quantum
metrological triangle. Furthermore it represents a universal electrical quantum
reference allowing to generate quantized values of the three most relevant
electrical units of voltage, current, and resistance based on fundamental
constants using a single device.Comment: 15 pages, 3 figure
A quantized current source with mesoscopic feedback
We study a mesoscopic circuit of two quantized current sources, realized by
non-adiabatic single- electron pumps connected in series with a small
micron-sized island in between. We find that quantum transport through the
second pump can be locked onto the quantized current of the first one by a
feedback due to charging of the mesoscopic island. This is confirmed by a
measurement of the charge variation on the island using a nearby charge
detector. Finally, the charge feedback signal clearly evidences loading into
excited states of the dynamic quantum dot during single-electron pump
operation
Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive Model
Mathematical models of cognition are often memoryless and ignore potential
fluctuations of their parameters. However, human cognition is inherently
dynamic. Thus, we propose to augment mechanistic cognitive models with a
temporal dimension and estimate the resulting dynamics from a superstatistics
perspective. Such a model entails a hierarchy between a low-level observation
model and a high-level transition model. The observation model describes the
local behavior of a system, and the transition model specifies how the
parameters of the observation model evolve over time. To overcome the
estimation challenges resulting from the complexity of superstatistical models,
we develop and validate a simulation-based deep learning method for Bayesian
inference, which can recover both time-varying and time-invariant parameters.
We first benchmark our method against two existing frameworks capable of
estimating time-varying parameters. We then apply our method to fit a dynamic
version of the diffusion decision model to long time series of human response
times data. Our results show that the deep learning approach is very efficient
in capturing the temporal dynamics of the model. Furthermore, we show that the
erroneous assumption of static or homogeneous parameters will hide important
temporal information
Stadt.Geschichte.Basel: Gesamtkonzept fĂŒr eine neue Basler Stadtgeschichte
Die neue, fundierte Gesamtdarstellung ist ein Kompass fĂŒr alle, die Basel tagtĂ€glich beleben und mitgestalten â als Einwohnerin und Pendler, als Arbeitnehmer und Unternehmerin, als FasnĂ€chtler, Durchreisende oder Rheinschwimmerin. Die Stadt.Geschichte.Basel schliesst eine LĂŒcke fĂŒr die vielen Geschichtsinteressierten und Mitglieder der historischen Vereinigungen, fĂŒr Lehrerinnen, Kulturschaffende und Politiker. Und sie ist ein unentbehrliches Arbeitsinstrument fĂŒr alle, die sich genauer mit der Stadt und ihrer Vergangenheit befassen und ihr Wissen weitergeben wollen: fĂŒr Frauen und MĂ€nner in Archiven, Museen, Bibliotheken und UniversitĂ€ten
Bisâethynylphosphonamidates as an Modular Conjugation Platform to Generate MultiâFunctional Proteinâ and AntibodyâDrugâConjugates
Bis-ethynylphosphonamidates allow for a simple chemoselective addition of two thiol-containing modules in a row. We describe four such bis-electrophiles that carry different functional O-substituents with tunable hydrophilicity and enable further subsequent conjugations, thus facilitating a simple protocol for constructing protein-protein conjugates. An increased spacer length between the two ethynylphosphonamidates simplifies the formation of a conjugate from two bulky proteins. We apply these reagents to obtain homogeneous Antibody-Drug-Conjugates (ADCs) from DM1 and trastuzumab with excellent cytotoxicity and selectivity for the targeted cell line. Moreover, a bis-ethynylphosphonamidate, carrying an additional alkyne for a chemoselective triple conjugation, has been subjected to fluorescent labeling of an ADC specifically at the drug site give an Antibody-Drug-Fluorophore-Conjugate (ADFC), allowing for the observation of intracellular trafficking after ADC uptake into the targeted cell.LMU centerDeutsche Forschungsgemeinschaft (DFG)Einstein Foundation Berlin
http://dx.doi.org/10.13039/501100006188Boehringer-Ingelheim FoundationFonds der Chemischen IndustrieLeibniz Association
http://dx.doi.org/10.13039/501100001664German Federal Ministry for Economic Affairs and EnergyEuropean Social Fund
http://dx.doi.org/10.13039/501100004895Bavarian Ministry of Economic Affairs, Regional Development and Energy
http://dx.doi.org/10.13039/501100020639Peer Reviewe
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