Micropatterned Electrostatic Traps for Indirect Excitons in Coupled GaAs Quantum Wells

We demonstrate an electrostatic trap for indirect excitons in a field-effect structure based on coupled GaAs quantum wells. Within the plane of a double quantum well indirect excitons are trapped at the perimeter of a SiO2 area sandwiched between the surface of the GaAs heterostructure and a semitransparent metallic top gate. The trapping mechanism is well explained by a combination of the quantum confined Stark effect and local field enhancement. We find the one-dimensional trapping potentials in the quantum well plane to be nearly harmonic with high spring constants exceeding 10 keV/cm^2.Comment: 21 pages, 6 figures, submitted to Phys. Rev.

Making Sense of Non-Binding Retail-Price Recommendations

This paper provides a theoretical rationale for non-binding retail price recommendations (RPRs) in vertical supply relations. Analyzing a bilateral manufacturer-retailer relationship with repeated trade, we show that linear relational contracts can implement the surplusmaximizing outcome. If the manufacturer has private information about production costs or consumer demand, RPRs may serve as a communication device from manufacturer to retailer. We characterize the properties of efficient bilateral relational contracts with RPRs and discuss extensions to settings where consumer demand is affected by RPRs, and where there are multiple retailers or competing supply chains.vertical relationships, relational contracts, asymmetric information, price recommendations

Making Sense of Non-Binding Retail-Price Recommendations

We model non-binding retail-price recommendations (RPRs) as a communication device facilitating coordination in vertical supply relations. Assuming both repeated vertical trade and asymmetric information about production costs, we show that RPRs may be part of a relational contract, communicating private information from manufacturer to retailer that is indispensable for maximizing joint surplus. We show that this contract is self-enforcing if the retailer’s profit is independent of production costs and punishment strategies are chosen appropriately. We also extend our analysis to settings where consumer demand is variable or depends directly on the manufacturer’s RPRs.vertical relationships, relational contracts, asymmetric information, price recommendations

Variable Metric Random Pursuit

We consider unconstrained randomized optimization of smooth convex objective functions in the gradient-free setting. We analyze Random Pursuit (RP) algorithms with fixed (F-RP) and variable metric (V-RP). The algorithms only use zeroth-order information about the objective function and compute an approximate solution by repeated optimization over randomly chosen one-dimensional subspaces. The distribution of search directions is dictated by the chosen metric. Variable Metric RP uses novel variants of a randomized zeroth-order Hessian approximation scheme recently introduced by Leventhal and Lewis (D. Leventhal and A. S. Lewis., Optimization 60(3), 329--245, 2011). We here present (i) a refined analysis of the expected single step progress of RP algorithms and their global convergence on (strictly) convex functions and (ii) novel convergence bounds for V-RP on strongly convex functions. We also quantify how well the employed metric needs to match the local geometry of the function in order for the RP algorithms to converge with the best possible rate. Our theoretical results are accompanied by numerical experiments, comparing V-RP with the derivative-free schemes CMA-ES, Implicit Filtering, Nelder-Mead, NEWUOA, Pattern-Search and Nesterov's gradient-free algorithms.Comment: 42 pages, 6 figures, 15 tables, submitted to journal, Version 3: majorly revised second part, i.e. Section 5 and Appendi

Two New Bounds on the Random-Edge Simplex Algorithm

We prove that the Random-Edge simplex algorithm requires an expected number of at most 13n/sqrt(d) pivot steps on any simple d-polytope with n vertices. This is the first nontrivial upper bound for general polytopes. We also describe a refined analysis that potentially yields much better bounds for specific classes of polytopes. As one application, we show that for combinatorial d-cubes, the trivial upper bound of 2^d on the performance of Random-Edge can asymptotically be improved by any desired polynomial factor in d.Comment: 10 page

Drift mobility of long-living excitons in coupled GaAs quantum wells

We observe high-mobility transport of indirect excitons in coupled GaAs quantum wells. A voltage-tunable in-plane potential gradient is defined for excitons by exploiting the quantum confined Stark effect in combination with a lithographically designed resistive top gate. Excitonic photoluminescence resolved in space, energy, and time provides insight into the in-plane drift dynamics. Across several hundreds of microns an excitonic mobility of >10^5 cm2/eVs is observed for temperatures below 10 K. With increasing temperature the excitonic mobility decreases due to exciton-phonon scattering.Comment: 3 pages, 3 figure

Inductive queries for a drug designing robot scientist

It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments

The long noncoding RNA neuroLNC regulates presynaptic activity by interacting with the neurodegeneration-associated protein TDP-43

The cellular and the molecular mechanisms by which long noncoding RNAs (lncRNAs) may regulate presynaptic function and neuronal activity are largely unexplored. Here, we established an integrated screening strategy to discover lncRNAs implicated in neurotransmitter and synaptic vesicle release. With this approach, we identified neuroLNC, a neuron-specific nuclear lncRNA conserved from rodents to humans. NeuroLNC is tuned by synaptic activity and influences several other essential aspects of neuronal development including calcium influx, neuritogenesis, and neuronal migration in vivo. We defined the molecular interactors of neuroLNC in detail using chromatin isolation by RNA purification, RNA interactome analysis, and protein mass spectrometry. We found that the effects of neuroLNC on synaptic vesicle release require interaction with the RNA-binding protein TDP-43 (TAR DNA binding protein-43) and the selective stabilization of mRNAs encoding for presynaptic proteins. These results provide the first proof of an lncRNA that orchestrates neuronal excitability by influencing presynaptic function