Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning

Computational study of molecules and materials from first principles is a cornerstone of physics, chemistry and materials science, but limited by the cost of accurate and precise simulations. In settings involving many simulations, machine learning can reduce these costs, sometimes by orders of magnitude, by interpolating between reference simulations. This requires representations that describe any molecule or material and support interpolation. We review, discuss and benchmark state-of-the-art representations and relations between them, including smooth overlap of atomic positions, many-body tensor representation, and symmetry functions. For this, we use a unified mathematical framework based on many-body functions, group averaging and tensor products, and compare energy predictions for organic molecules, binary alloys and Al-Ga-In sesquioxides in numerical experiments controlled for data distribution, regression method and hyper-parameter optimization

Connectivity-Based Self-Localization in WSNs

Efficient localization methods are among the major challenges in wireless sensor networks today. In this paper, we present our so-called connectivity based approach i.e, based on local connectivity information, to tackle this problem. At first the method fragments the network into larger groups labeled as packs. Based on the mutual connectivity relations with their surrounding packs, we identify border nodes as well as the central node. As this first approach requires some a-priori knowledge on the network topology, we also present a novel segment-based fragmentation method to estimate the central pack of the network as well as detecting so-called corner packs without any a-priori knowledge. Based on these detected points, the network is fragmented into a set of even larger elements, so-called segments built on top of the packs, supporting even more localization information as they all reach the central node

Constituent and current quark masses at low chiral energies

Light constituent quark masses and the corresponding dynamical quark masses are determined by data, the Quark-Level Linear $\sigma$ Model, and infrared QCD. This allows to define effective nonstrange and strange current quark masses which reproduce the experimental pion and kaon masses very accurately, by simple additivity. Moreover, the masses of the light scalar mesons $\sigma(600)$ and $\kappa(800)$ can be obtained straightforwardly from the constituent quark masses. In contrast, the usual nonstrange and strange current quark masses employed by Chiral Perturbation Theory do not allow a simple quantitative explanation of the pion and kaon masses.Comment: 5 pages, EPL style, accepted for publication in Europhys. Let

A performance comparison of fullband and different subband adaptive equalisers

We present two different fractionally spaced (FS) equalisers based on subband methods, with the aim of reducing the computational complexity and increasing the convergence rate of a standard fullband FS equaliser. This is achieved by operating in decimated subbands; at a considerably lower update rate and by exploiting the prewhitening effect that a filter bank has on the considerable spectral dynamics of a signal received through a severely distorting channel. The two presented subband structures differ in their level of realising the feedforward and feedback part of the equaliser in the subband domain, with distinct impacts on the updating. Simulation results pinpoint the faster convergence at lower cost for the proposed subband equalisers

Airline Schedule Recovery after Airport Closures: Empirical Evidence Since September 11th

Since the September 11, 2001 terrorist attacks, repeated airport closures due to potential security breaches have imposed substantial costs on travelers, airlines, and government agencies in terms of flight delays and cancellations. Using data from the year following September 11th, this study examines how airlines recover flight schedules upon reopening of airports that have been closed for security reasons. As such, this is the first study to examine service quality during irregular operations. Our results indicate that while outcomes of flights scheduled during airport closures are difficult to explain, a variety of factors, including potential revenue per flight and logistical variables such as flight distance, seating capacity and shutdown severity, significantly predict outcomes of flights scheduled after airports reopen. Given the likelihood of continued security-related airport closings, understanding the factors that determine schedule recovery is potentially important.

Hospital Cost and Efficiency Under Per Service and Per Case Payment in Maryland: A Tale of the Carrot and the Stick

The simultaneous operation of per case and per servicepayment systems in Maryland, and the varying levels of stringency used in setting per case rates allows comparison of effects of differing incentive structures on hospital costs. This paper presents such a comparison with 1977-1981 data. Cost per case and total cost regressions show evidence of lower costs only when per case payment limits are very stringent. Positive net revenue incentives appear insufficient to induce reductions in length of stay and in ancillary services use. Our results suggest these changes in medical practice patterns are more likely under the threat of financial losses.

Does School Choice Increase School Quality?

Federal No Child Left Behind' legislation, which enables students of low-performing schools to exercise public school choice, exemplies a widespread belief that competing for students will spur public schools to higher achievement. We investigate how the introduction of school choice in North Carolina, via a dramatic increase in the number of charter schools across the state, affects the performance of traditional public schools on statewide tests. We find test score gains from competition that are robust to a variety of specifications. The introduction of charter school competition causes an approximate one percent increase in the score, which constitutes about one quarter of the average yearly growth.

Likelihood Consensus and Its Application to Distributed Particle Filtering

We consider distributed state estimation in a wireless sensor network without a fusion center. Each sensor performs a global estimation task---based on the past and current measurements of all sensors---using only local processing and local communications with its neighbors. In this estimation task, the joint (all-sensors) likelihood function (JLF) plays a central role as it epitomizes the measurements of all sensors. We propose a distributed method for computing, at each sensor, an approximation of the JLF by means of consensus algorithms. This "likelihood consensus" method is applicable if the local likelihood functions of the various sensors (viewed as conditional probability density functions of the local measurements) belong to the exponential family of distributions. We then use the likelihood consensus method to implement a distributed particle filter and a distributed Gaussian particle filter. Each sensor runs a local particle filter, or a local Gaussian particle filter, that computes a global state estimate. The weight update in each local (Gaussian) particle filter employs the JLF, which is obtained through the likelihood consensus scheme. For the distributed Gaussian particle filter, the number of particles can be significantly reduced by means of an additional consensus scheme. Simulation results are presented to assess the performance of the proposed distributed particle filters for a multiple target tracking problem

Adaptive Positive Position Feedback Control of Flexible Aircraft Structures Using Piezoelectric Actuators

Buffet Adaptively Managed Fin (BAMF) focused on vibrations due to the interaction of aerodynamic forces with aircraft structure. Past failures of F-16 ventral fins due to vibrations provided grounds for control research. The fin used had piezoelectric patches as collocated sensors/actuators. A custom amplifier and transformer were restructured into a system that could safely and reliably run, and adaptive software was created to address issues of system plant changes. Generating a PSD from the fin sensors, the highest peaks were assumed to represent the low damped vibration modes. A PPF controller for each mode was designed and control signals were sent to the fin actuators. Limited data were collected in a wind tunnel behind a custom system that caused buffet by varying vortex strength/shedding frequencies from pods upstream of the fin. While minimal testing was accomplished to optimize gains, the system showed significant PSD peak reductions for the first three modes of the fin up to -14.9, -15.3, and -16.4 dB, respectively. The system maintained stability and effective control even when both sensor input and controller output were saturated