Spin-orbit controlled quantum capacitance of a polar heterostructure

Oxide heterostructures with polar films display special electronic properties, such as the electronic reconstruction at their internal interfaces with the formation of two-dimensional metallic states. Moreover, the electrical field from the polar layers is inversion-symmetry breaking and generates a Rashba spin-orbit coupling (RSOC) in the interfacial electronic system. We investigate the quantum capacitance of a heterostructure in which a sizeable RSOC at a metallic interface is controlled by the electric field of a surface electrode. Such a structure is, for example, given by a LaAlO_3 film on a SrTiO_3 substrate which is gated by a top electrode. Such heterostructures can exhibit a strong enhancement of their capacitance [Li et al., Science 332, 825 (2011)]. The capacitance is related to the electronic compressibility of the heterostructure, but the two quantities are not equivalent. In fact, the transfer of charge to the interface controls the relation between capacitance and compressibility. We find that due to a strong RSOC, the quantum capacitance can be larger than the classical geometric value. However, in contrast to the results of recent investigations [Caprara et al., Phys. Rev. Lett. 109, 196401 (2012); Bucheli et al., Phys. Rev. B 89, 195448 (2014); Seibold et al., Europhys. Lett. 109, 17006 (2015)] the compressibility does not become negative for realistic parameter values for LaAlO_3/SrTiO_3 and, therefore, we find that no phase-separated state is induced by the strong RSOC at these interfaces

Capacitance and compressibility of heterostructures with strong electronic correlations

Strong electronic correlations related to a repulsive local interaction suppress the electronic compressibility in a single-band model, and the capacitance of a corresponding metallic film is directly related to its electronic compressibility. Both statements may be altered significantly when two extensions to the system are implemented which we investigate here: (i) we introduce an attractive nearest-neighbor interaction $V$ as antagonist to the repulsive on-site repulsion $U$, and (ii) we consider nano-structured multilayers (heterostructures) assembled from two-dimensional layers of these systems. We determine the respective total compressibility $\kappa$ and capacitance $C$ of the heterostructures within a strong coupling evaluation, which builds on a Kotliar-Ruckenstein slave-boson technique. Whereas the capacitance $C(n)$ for electronic densities $n$ close to half-filling is suppressed---illustrated by a correlation induced dip in $C(n)$---it may be appreciably enhanced close to a van Hove singularity. Moreover, we show that the capacitance may be a non-monotonic function of $U$ close to half-filling for both attractive and repulsive $V$. The compressibility $\kappa$ can differ from $C$ substantially, as $\kappa$ is very sensitive to internal electrostatic energies which in turn depend on the specific set-up of the heterostructure. In particular, we show that a capacitor with a polar dielectric has a smaller electronic compressibility and is more stable against phase separation than a standard non-polar capacitor with the same capacitance

Unions and Managerial Pay

Unions compress the wage distribution among workers covered by union contracts. We ask whether unions also have an effect on the managers of unionized firms. To this end we collected and assembled data on unionization and managerial pay within firms and industries in the U.S. and across countries. Generally, we find a negative correlation between executive compensation and unionization in our cross-section data, but no relationship of changes in unionization on the growth of compensation of executives over time. Using NLRB elections data, we find that a loss of union members due to decertification elections is associated with higher CEO pay, although our estimates are imprecise. With CPS data we consistently find that where unions are stronger, fewer managers are employed

Artificial intelligence in steam cracking modeling : a deep learning algorithm for detailed effluent prediction

Chemical processes can benefit tremendously from fast and accurate effluent composition prediction for plant design, control, and optimization. The Industry 4.0 revolution claims that by introducing machine learning into these fields, substantial economic and environmental gains can be achieved. The bottleneck for high-frequency optimization and process control is often the time necessary to perform the required detailed analyses of, for example, feed and product. To resolve these issues, a framework of four deep learning artificial neural networks (DL ANNs) has been developed for the largest chemicals production process-steam cracking. The proposed methodology allows both a detailed characterization of a naphtha feedstock and a detailed composition of the steam cracker effluent to be determined, based on a limited number of commercial naphtha indices and rapidly accessible process characteristics. The detailed characterization of a naphtha is predicted from three points on the boiling curve and paraffins, iso-paraffins, olefins, naphthenes, and aronatics (PIONA) characterization. If unavailable, the boiling points are also estimated. Even with estimated boiling points, the developed DL ANN outperforms several established methods such as maximization of Shannon entropy and traditional ANNs. For feedstock reconstruction, a mean absolute error (MAE) of 0.3 wt% is achieved on the test set, while the MAE of the effluent prediction is 0.1 wt%. When combining all networks-using the output of the previous as input to the next-the effluent MAE increases to 0.19 wt%. In addition to the high accuracy of the networks, a major benefit is the negligible computational cost required to obtain the predictions. On a standard Intel i7 processor, predictions are made in the order of milliseconds. Commercial software such as COILSIM1D performs slightly better in terms of accuracy, but the required central processing unit time per reaction is in the order of seconds. This tremendous speed-up and minimal accuracy loss make the presented framework highly suitable for the continuous monitoring of difficult-to-access process parameters and for the envisioned, high-frequency real-time optimization (RTO) strategy or process control. Nevertheless, the lack of a fundamental basis implies that fundamental understanding is almost completely lost, which is not always well-accepted by the engineering community. In addition, the performance of the developed networks drops significantly for naphthas that are highly dissimilar to those in the training set. (C) 2019 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company

Investigation of cryogenic mixed-refrigerant cooled current leads in combination with Peltier elements

Current leads supply electrical energy from a room-temperature power supply to a superconducting application, representing thus a major thermal load. State-of-the-art cooling solutions use either open (vapor cooled) or multi-stage closed cycle systems. The multi-stage concept can be integrated in one cryogenic mixed refrigerant cycle (CMRC), where a wide-boiling fluid mixture absorbs the heat load continuously along the current lead. In this paper, we study the combination of CMRC cooling with Peltier elements at the warm end of DC current leads. The Peltier cooling may cause a temperature drop on the order of 80 K. This allows an optimization of the CMRC mixture composition towards lower temperatures, avoiding the use of high-boilers that risk to freeze out at low temperatures. Our studies suggest that Peltier and CMRC cooling can reduce the thermal load at the cold end by 30 to 45% compared to conventional conduction-cooled current leads

Bad metal and negative compressibility transitions in a two-band Hubbard model

We analyze the paramagnetic state of a two-band Hubbard model with finite Hund's coupling close to integer filling at n=2 in two spacial dimensions. Previously, a Mott metal-insulator transition was established at $n=2$ with a coexistence region of a metallic and a bad metal state in the vicinity of that integer filling. The coexistence region ends at a critical point beyond which a charge instability persists. Here we investigate the transition into negative electronic compressibility states for an extended filling range close to n=2 within a slave boson setup. We analyze the separate contributions from the (fermionic) quasiparticles and the (bosonic) multiparticle incoherent background and find that the total compressibility depends on a subtle interplay between the quasiparticle excitations and collective fields. Implementing a Blume-Emery-Griffiths model approach for the slave bosons, which mimics the bosonic fields by Ising-like pseudospins, we suggest a feedback mechanism between these fields and the fermionic degrees of freedom. We argue that the negative compressibility can be sustained for heterostructures of such strongly correlated planes and results in a large capacitance of these structures. The strong density dependence of these capacitances allows to tune them through small electronic density variations. Moreover, by resistive switching from a Mott insulating state to a metallic state through short electric pulses, transitions between fairly different capacitances are within reach.Comment: 22 pages, 18 figure

Type-based cost analysis for lazy functional languages

We present a static analysis for determining the execution costs of lazily evaluated functional languages, such as Haskell. Time- and space-behaviour of lazy functional languages can be hard to predict, creating a significant barrier to their broader acceptance. This paper applies a type-based analysis employing amortisation and cost effects to statically determine upper bounds on evaluation costs. While amortisation performs well with finite recursive data, we significantly improve the precision of our analysis for co-recursive programs (i.e. dealing with potentially infinite data structures) by tracking self-references. Combining these two approaches gives a fully automatic static analysis for both recursive and co-recursive definitions. The analysis is formally proven correct against an operational semantic that features an exchangeable parametric cost-model. An arbitrary measure can be assigned to all syntactic constructs, allowing to bound, for example, evaluation steps, applications, allocations, etc. Moreover, automatic inference only relies on first-order unification and standard linear programming solving. Our publicly available implementation demonstrates the practicability of our technique on editable non-trivial examples.PostprintPeer reviewe

Visual Odometry using Convolutional Neural Networks

Visual odometry is the process of tracking an agent\u27s motion over time using a visual sensor. The visual odometry problem has only been recently solved using traditional, non-machine learning techniques. Despite the success of neural networks at many related problems such as object recognition, feature detection, and optical flow, visual odometry still has not been solved with a deep learning technique. This paper attempts to implement several Convolutional Neural Networks to solve the visual odometry problem and compare slight variations in data preprocessing. The work presented is a step toward reaching a legitimate neural network solution