Global approximation to arbitrary cost functions: A Bayesian approach with application to US banking

This paper proposes and estimates a globally flexible functional form for the cost function, which we call Neural Cost Function (NCF). The proposed specification imposes a priori and satisfies globally all the properties that economic theory dictates. The functional form can be estimated easily using Markov Chain Monte Carlo (MCMC) techniques or standard iterative SURE. We use a large panel of U.S. banks to illustrate our approach. The results are consistent with previous knowledge about the sector and in accordance with mathematical production theory.     

95 GHz millimeter wave signal generation using an arrayed waveguide grating dual wavelength semiconductor laser

We report the generation of a 95?GHz carrier frequency by optical heterodyning of two wavelengths from adjacent channels from an arrayed waveguide grating-based multiwavelength laser. The extended cavity structure of the device provides low phase noise and narrow optical linewidth, further enhanced by the intracavity filter effect of the arrayed waveguide grating. We demonstrate that the generated RF beat note, at 95?GHz, has a -3 dB linewidth of 250?kHz. To the best of our knowledge, this is the narrowest RF linewidth generated from a free-running dual-wavelength semiconductor laser. The device is realized as a photonic integrated circuit using active-passive integration technology, and fabricated on a multiproject wafer run, constituting a novel approach for a compact, low-cost dual-wavelength heterodyne source.     

Development and Application of Instrumental Methods for Strain Analysisof Semiconductor Layers and Devices

 Strain effects on semiconductor layers were studied by means of optical spectroscopic techniques with a device developed especially for the study of layered structures and microstructures. Raman, modulated photoreflectance and reflectance anisotropy spectroscopy (RAS) were applied. Measurements were performed on elemental semiconductors (Si), semiconductor alloys (Si–Ge) and III–V semiconductor compounds (GaAs). By application of RAS, strains lower than 10⁻⁴ could be resolved, which is at least one order of magnitude lower than those observable with Raman and modulated reflectance techniques. The RAS spectra of layers strained along either the [010] or [011] direction showed a derivative-like structure at E₁-gap energies, which increased linearly and very quickly with increasing strain. The dependence of this spectral feature on applied strain was used to evaluate strain-dependent effects. This behaviour strongly suggests that RAS can be applied for the optical characterisation of strain in semiconductor microstructures and devices, with a higher efficiency and accuracy than that achieved by previously established optical methods such as Raman and modulation spectroscopy. In addition, the compactness and ease of operation of the instrumentation of RAS provides considerable potential for in situ monitoring/control of semiconductor fabrication conditions.     

Realization of a Framework for Simulation-Based Large-Scale Shape Optimization Using Vertex Morphing

Journal of Optimization Theory and Applications - There is a significant tendency in the industry for automation of the engineering design process. This requires the capability of analyzing an...     

molSimplify: A toolkit for automating discovery in inorganic chemistry

We present an automated, open source toolkit for the first‐principles screening and discovery of new inorganic molecules and intermolecular complexes. Challenges remain in the automatic generation of candidate inorganic molecule structures due to the high variability in coordination and bonding, which we overcome through a divide‐and‐conquer tactic that flexibly combines force‐field preoptimization of organic fragments with alignment to first‐principles‐trained metal‐ligand distances. Exploration of chemical space is enabled through random generation of ligands and intermolecular complexes from large chemical databases. We validate the generated structures with the root mean squared (RMS) gradients evaluated from density functional theory (DFT), which are around 0.02 Ha/au across a large 150 molecule test set. Comparison of molSimplify results to full optimization with the universal force field reveals that RMS DFT gradients are improved by 40%. Seamless generation of input files, preparation and execution of electronic structure calculations, and post‐processing for each generated structure aids interpretation of underlying chemical and energetic trends. © 2016 Wiley Periodicals, Inc.     

An improved multi-staged algorithmic process for the solution of the examination timetabling problem

The efficient creation of examination timetables is a recurring and important problem for universities worldwide. Good timetables typically are characterized by balanced distances between consecutive exams for all students. In this contribution an approach for the examination timetabling problem as defined in the second International Timetabling Competition () is presented. The solution approach is managed on the top level by GRASP (Greedy Randomized Adaptive Search Procedure) and it involves several optimization algorithms, heuristics and metaheuristics. A construction phase is executed first producing a relatively high quality feasible solution and an improvement phase follows that further ameliorates the produced timetable. Each phase consists of stages that are consumed in a circular fashion. The procedure produces feasible solutions for each dataset provided under the runtime limit imposed by the rules of the ITC07 competition. Results are presented and analyzed.     

Globally flexible functional forms: The neural distance function

The output distance function is a key concept in economics. However, its empirical estimation often violates properties dictated by neoclassical production theory. In this paper, we introduce the neural distance function (NDF) which constitutes a global approximation to any arbitrary production technology with multiple outputs given by a neural network (NN) specification. The NDF imposes all theoretical properties such as monotonicity, curvature and homogeneity, for all economically admissible values of outputs and inputs. Fitted to a large data set for all US commercial banks (1989–2000), the NDF explains a very high proportion of the variance of output while keeping the number of parameters to a minimum and satisfying the relevant theoretical properties. All measures such as total factor productivity (TFP) and technical efficiency (TE) are computed routinely. Next, the NDF is compared with the Translog popular specification and is found to provide very satisfactory results as it possesses the properties thought as desirable in neoclassical production theory in a way not matched by its competing specification.     

Estimation of production risk and risk preference function: a nonparametric approach

While estimating parametric production models with risk, one faces two main problems. The first problem is associated with the choice of functional forms on the mean production function and the risk (variance) function. The second problem is associated with the specification of the risk preference function. In a parametric model the researcher chooses some ad hoc functional form on all these. It is obvious that the estimated (i) technology (mean production function), (ii) risk and (iii) risk preference functions are affected by the choice of functional form. In this paper we consider an estimation framework that avoids assuming parametric functions on all three. In particular, this paper deals with nonparametric estimation of the technology, risk and risk preferences of producers when they face uncertainty in production. Uncertainty is modeled in the context of production theory where producers’ maximize expected utility of anticipated profit. A multi-stage nonparametric estimation procedure is used to estimate the production function, the output risk function and the risk preference function. No distributional assumption is made on the random term representing production uncertainty. No functional form is assumed on the underlying utility function. Rice farming data from Philippines are used for an empirical application of the proposed model. Rice farmers are, in general, found to be risk averse; labor is risk decreasing while fertilizer, land and materials are risk increasing. The mean risk premium is about 3% of mean profit.     

High-gradient plasma-wakefield acceleration with two subpicosecond electron bunches

A plasma-wakefield experiment is presented where two 60 MeV subpicosecond electron bunches are sent into a plasma produced by a capillary discharge. Both bunches are shorter than the plasma wavelength, and the phase of the second bunch relative to the plasma wave is adjusted by tuning the plasma density. It is shown that the second bunch experiences a 150 MeV/m loaded accelerating gradient in the wakefield driven by the first bunch. This is the first experiment to directly demonstrate high-gradient, controlled acceleration of a short-pulse trailing electron bunch in a high-density plasma.