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 für eine neue Basler Stadtgeschichte

Die neue, fundierte Gesamtdarstellung ist ein Kompass für alle, die Basel tagtäglich beleben und mitgestalten – als Einwohnerin und Pendler, als Arbeitnehmer und Unternehmerin, als Fasnächtler, Durchreisende oder Rheinschwimmerin. Die Stadt.Geschichte.Basel schliesst eine Lücke für die vielen Geschichtsinteressierten und Mitglieder der historischen Vereinigungen, für Lehrerinnen, Kulturschaffende und Politiker. Und sie ist ein unentbehrliches Arbeitsinstrument für alle, die sich genauer mit der Stadt und ihrer Vergangenheit befassen und ihr Wissen weitergeben wollen: für Frauen und Männer in Archiven, Museen, Bibliotheken und Universitäten

Bis‐ethynylphosphonamidates as an Modular Conjugation Platform to Generate Multi‐Functional Protein‐ and Antibody‐Drug‐Conjugates

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