Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons

We introduce a class of interatomic potential models that can be automatically generated from data consisting of the energies and forces experienced by atoms, derived from quantum mechanical calculations. The resulting model does not have a fixed functional form and hence is capable of modeling complex potential energy landscapes. It is systematically improvable with more data. We apply the method to bulk carbon, silicon and germanium and test it by calculating properties of the crystals at high temperatures. Using the interatomic potential to generate the long molecular dynamics trajectories required for such calculations saves orders of magnitude in computational cost.Comment: v3-4: added new material and reference

Adoption of total quality management in the educational sector: case study of Engineering Institutions

Abstract : Due to the aspirations of various institutional stakeholders clamoring for improvement in the quality of education in their various institutions, the concept of total quality management has gained so much attention to this regard. In the recent time, several emphases have been made on the need for quality improvement and efforts are been put in place on the possible ways of increasing the standard of education globally. The productivity of any tertiary institution, especially the Engineering colleges is centered on the quality culture of such institutions, also, the customer’s satisfaction is another thing to put into consideration, to achieve the desired productivity. Generally, there are some constructs which are the major critical success factors that enhances quality improvement in any organization, customer satisfaction has been identified as another important factor to put into consideration to achieve optimum quality of products as well as services. This paper gives an insight on how the implementation of Total Quality Management in an Engineering educational system can aid the Quality of Engineering Education

A Bayesian Nonparametric Approach to Modeling Motion Patterns

The most difficult—and often most essential— aspect of many interception and tracking tasks is constructing motion models of the targets to be found. Experts can often provide only partial information, and fitting parameters for complex motion patterns can require large amounts of training data. Specifying how to parameterize complex motion patterns is in itself a difficult task. In contrast, nonparametric models are very flexible and generalize well with relatively little training data. We propose modeling target motion patterns as a mixture of Gaussian processes (GP) with a Dirichlet process (DP) prior over mixture weights. The GP provides a flexible representation for each individual motion pattern, while the DP assigns observed trajectories to particular motion patterns. Both automatically adjust the complexity of the motion model based on the available data. Our approach outperforms several parametric models on a helicopter-based car-tracking task on data collected from the greater Boston area

Targeting Methylglyoxal in Diabetic Kidney Disease Using the Mitochondria-Targeted Compound MitoGamide.

Diabetic kidney disease (DKD) remains the number one cause of end-stage renal disease in the western world. In experimental diabetes, mitochondrial dysfunction in the kidney precedes the development of DKD. Reactive 1,2-dicarbonyl compounds, such as methylglyoxal, are generated from sugars both endogenously during diabetes and exogenously during food processing. Methylglyoxal is thought to impair the mitochondrial function and may contribute to the pathogenesis of DKD. Here, we sought to target methylglyoxal within the mitochondria using MitoGamide, a mitochondria-targeted dicarbonyl scavenger, in an experimental model of diabetes. Male 6-week-old heterozygous Akita mice (C57BL/6-Ins2-Akita/J) or wildtype littermates were randomized to receive MitoGamide (10 mg/kg/day) or a vehicle by oral gavage for 16 weeks. MitoGamide did not alter the blood glucose control or body composition. Akita mice exhibited hallmarks of DKD including albuminuria, hyperfiltration, glomerulosclerosis, and renal fibrosis, however, after 16 weeks of treatment, MitoGamide did not substantially improve the renal phenotype. Complex-I-linked mitochondrial respiration was increased in the kidney of Akita mice which was unaffected by MitoGamide. Exploratory studies using transcriptomics identified that MitoGamide induced changes to olfactory signaling, immune system, respiratory electron transport, and post-translational protein modification pathways. These findings indicate that targeting methylglyoxal within the mitochondria using MitoGamide is not a valid therapeutic approach for DKD and that other mitochondrial targets or processes upstream should be the focus of therapy

Multi-objective optimization using Deep Gaussian Processes: Application to Aerospace Vehicle Design

International audienceThis paper is focused on the problem of constrained multi-objective design optimization of aerospace vehicles. The design of such vehicles often involves disciplinary legacy models considered as black-box and computationally expensive simulations characterized by a possible non-stationary behavior (an abrupt change in the response or a different smoothness along the design space). The expensive cost of an exact function evaluation makes the use of classical evolutionary multi-objective algorithms not tractable. While Bayesian Optimization based on Gaussian Process regression can handle the expensive cost of the evaluations, the non-stationary behavior of the functions can make it inefficient. A recent approach consisting of coupling Bayesian Optimization with Deep Gaussian Processes showed promising results for single-objective non-stationary problems. This paper presents an extension of this approach to the multi-objective context. The efficiency of the proposed approach is assessed with respect to classical optimization methods on an analytical test-case and on an aerospace design problem

Towards Better Integration of Surrogate Models and Optimizers

Surrogate-Assisted Evolutionary Algorithms (SAEAs) have been proven to be very effective in solving (synthetic and real-world) computationally expensive optimization problems with a limited number of function evaluations. The two main components of SAEAs are: the surrogate model and the evolutionary optimizer, both of which use parameters to control their respective behavior. These parameters are likely to interact closely, and hence the exploitation of any such relationships may lead to the design of an enhanced SAEA. In this chapter, as a first step, we focus on Kriging and the Efficient Global Optimization (EGO) framework. We discuss potentially profitable ways of a better integration of model and optimizer. Furthermore, we investigate in depth how different parameters of the model and the optimizer impact optimization results. In particular, we determine whether there are any interactions between these parameters, and how the problem characteristics impact optimization results. In the experimental study, we use the popular Black-Box Optimization Benchmarking (BBOB) testbed. Interestingly, the analysis finds no evidence for significant interactions between model and optimizer parameters, but independently their performance has a significant interaction with the objective function. Based on our results, we make recommendations on how best to configure EGO

Population structure, connectivity, and demographic history of an apex marine predator, the bull shark <i>Carcharhinus leucas</i>

Knowledge of population structure, connectivity, and effective population size remains limited for many marine apex predators, including the bull shark Carcharhinus leucas. This large‐bodied coastal shark is distributed worldwide in warm temperate and tropical waters, and uses estuaries and rivers as nurseries. As an apex predator, the bull shark likely plays a vital ecological role within marine food webs, but is at risk due to inshore habitat degradation and various fishing pressures. We investigated the bull shark\u27s global population structure and demographic history by analyzing the genetic diversity of 370 individuals from 11 different locations using 25 microsatellite loci and three mitochondrial genes (CR, nd4, and cytb). Both types of markers revealed clustering between sharks from the Western Atlantic and those from the Western Pacific and the Western Indian Ocean, with no contemporary gene flow. Microsatellite data suggested low differentiation between the Western Indian Ocean and the Western Pacific, but substantial differentiation was found using mitochondrial DNA. Integrating information from both types of markers and using Bayesian computation with a random forest procedure (ABC‐RF), this discordance was found to be due to a complete lack of contemporary gene flow. High genetic connectivity was found both within the Western Indian Ocean and within the Western Pacific. In conclusion, these results suggest important structuring of bull shark populations globally with important gene flow occurring along coastlines, highlighting the need for management and conservation plans on regional scales rather than oceanic basin scale

Improving gravitational-wave parameter estimation using Gaussian process regression

Folding uncertainty in theoretical models into Bayesian parameter estimation is necessary in order to make reliable inferences. A general means of achieving this is by marginalizing over model uncertainty using a prior distribution constructed using Gaussian process regression (GPR). As an example, we apply this technique to the measurement of chirp mass using (simulated) gravitational-wave signals from binary black holes that could be observed using advanced-era gravitational-wave detectors. Unless properly accounted for, uncertainty in the gravitational-wave templates could be the dominant source of error in studies of these systems. We explain our approach in detail and provide proofs of various features of the method, including the limiting behavior for high signal-to-noise, where systematic model uncertainties dominate over noise errors. We find that the marginalized likelihood constructed via GPR offers a significant improvement in parameter estimation over the standard, uncorrected likelihood both in our simple one-dimensional study, and theoretically in general. We also examine the dependence of the method on the size of training set used in the GPR; on the form of covariance function adopted for the GPR, and on changes to the detector noise power spectral density.Comment: 25 pages, 11 figures, Published March 201