Gaussian process hyper-parameter estimation using parallel asymptotically independent Markov sampling

Gaussian process emulators of computationally expensive computer codes provide fast statistical approximations to model physical processes. The training of these surrogates depends on the set of design points chosen to run the simulator. Due to computational cost, such training set is bound to be limited and quantifying the resulting uncertainty in the hyper-parameters of the emulator by uni-modal distributions is likely to induce bias. In order to quantify this uncertainty, this paper proposes a computationally efficient sampler based on an extension of Asymptotically Independent Markov Sampling, a recently developed algorithm for Bayesian inference. Structural uncertainty of the emulator is obtained as a by-product of the Bayesian treatment of the hyper-parameters. Additionally, the user can choose to perform stochastic optimisation to sample from a neighbourhood of the Maximum a Posteriori estimate, even in the presence of multimodality. Model uncertainty is also acknowledged through numerical stabilisation measures by including a nugget term in the formulation of the probability model. The efficiency of the proposed sampler is illustrated in examples where multi-modal distributions are encountered. For the purpose of reproducibility, further development, and use in other applications the code used to generate the examples is freely available for download at https://github.com/agarbuno/paims_codesComment: Computational Statistics \& Data Analysis, Volume 103, November 201

Transitional annealed adaptive slice sampling for Gaussian process hyper-parameter estimation

Surrogate models have become ubiquitous in science and engineering for their capability of emulating expensive computer codes, necessary to model and investigate complex phenomena. Bayesian emulators based on Gaussian processes adequately quantify the uncertainty that results from the cost of the original simulator, and thus the inability to evaluate it on the whole input space. However, it is common in the literature that only a partial Bayesian analysis is carried out, whereby the underlying hyper-parameters are estimated via gradient-free optimization or genetic algorithms, to name a few methods. On the other hand, maximum a posteriori (MAP) estimation could discard important regions of the hyper-parameter space. In this paper, we carry out a more complete Bayesian inference, that combines Slice Sampling with some recently developed sequential Monte Carlo samplers. The resulting algorithm improves the mixing in the sampling through the delayed-rejection nature of Slice Sampling, the inclusion of an annealing scheme akin to Asymptotically Independent Markov Sampling and parallelization via transitional Markov chain Monte Carlo. Examples related to the estimation of Gaussian process hyper-parameters are presented. For the purpose of reproducibility, further development, and use in other applications, the code to generate the examples in this paper is freely available for download at http://github.com/agarbuno/ta2s2_codes

Performance characterization of a multiplexed space-to-ground optical network

Advances in phased array systems for multi-beam free space optical communications are a key enabler for a new space-to-ground network architecture, namely a multiplexed optical architecture. The fundamental idea of a multiplexed space-to-ground optical network is the utilization of a multi-beam optical payload that allows each spacecraft to establish links with multiple ground stations within its line of sight. Information is then downlinked in parallel, from the satellite to the ground, through the subset of links not disrupted by clouds. In this paper we evaluate the performance of a multiplexed optical space-to-ground architecture from a systems perspective, with particular emphasis on the effect of cloud correlation in the network throughput. In particular, we first derive the expected data volume returned in a multiplexed architecture as a function of the optical network availability and the system total capacity. Then, we compare the performance of the proposed multiplexed architecture against a traditional single-beam downlink system that utilizes site diversity to mitigate cloud coverage effects. This comparison is based on two canonical scenarios, a global highly uncorrelated network representative of a geosynchronous satellite; and local, highly correlated, network representative of a low Earth orbit spacecraft. Through this analysis, we demonstrate that multiplexed architectures can improve the throughput of a space-to-ground optical network as compared to that of a single ground telescope without requiring a beam switching mechanism

Architecting space communication networks under mission demand uncertainty

NASAs Space Network has been a successful program that has provided reliable communication and navigation services for three decades. As the third generation of satellites is being launched, alternatives to the current architecture of the system are being studied in order to improve the performance of the system, reduce its costs and facilitate its integration with the Near Earth Network and the Deep Space Network. Within this context, past research has proven the feasibility of efficiently exploring a large space of alternative network architectures using a tradespace search framework. Architecting a space communication network is a complex task that requires consideration of uncertainty, namely (1) factoring in customer demand variability, (2) predicting technology improvements and (3) considering possible budgetary constraints. This paper focuses on adding uncertainty associated with (1) to the existing communications network architecture tool by describing a heuristic-based model to derive mission concept of operations (conops) as a function of communication requirements. The accuracy of the model is assessed by comparing real conops from current TDRSS-supported missions with the predicted concept of operations. The model is used to analyze how customer forecast uncertainty affects the choice of the future network architecture. In particular, four customer scenarios are generated and compared with the current TDRSS capabilities.United States. National Aeronautics and Space Administration (NNX11AR70G

ATLAS Infrastructure

This document describes the civil engineering and infrastructure work done on the surface and underground for the ATLAS experiment at point 1 of the LHC ring

Transitional annealed adaptive slice sampling for Gaussian process hyper-parameter estimation

Surrogate models have become ubiquitous in science and engineering for their capability of emulating expensive computer codes, necessary to model and investigate complex phenomena. Bayesian emulators based on Gaussian processes adequately quantify the uncertainty that results from the cost of the original simulator, and thus the inability to evaluate it on the whole input space. However, it is common in the literature that only a partial Bayesian analysis is carried out, whereby the underlying hyper-parameters are estimated via gradient-free optimization or genetic algorithms, to name a few methods. On the other hand, maximum a posteriori (MAP) estimation could discard important regions of the hyper-parameter space. In this paper, we carry out a more complete Bayesian inference, that combines Slice Sampling with some recently developed sequential Monte Carlo samplers. The resulting algorithm improves the mixing in the sampling through the delayed-rejection nature of Slice Sampling, the inclusion of an annealing scheme akin to Asymptotically Independent Markov Sampling and parallelization via transitional Markov chain Monte Carlo. Examples related to the estimation of Gaussian process hyper-parameters are presented. For the purpose of reproducibility, further development, and use in other applications, the code to generate the examples in this paper is freely available for download at http://github.com/agarbuno/ta2s2_codes

Uncertainty quantification of network availability for networks of optical ground stations

This paper analyzes differences in the availability of networks of optical ground stations computed using different methods and datasets, and quantifies the uncertainty of the results. For that purpose, we first review existing methods proposed in the literature, and then existing cloud coverage datasets, and we compare the results obtained using different methods and datasets for several scenarios. Finally, we propose a new probabilistic global cloud coverage model that aggregates values from existing datasets and quantifies the uncertainty in measuring cloud probability, and present a method to compute the availability of a network of multiple optical ground stations, along with the corresponding uncertainty.Fundación Obra Social de La Caix

SAVASA project @ TRECVid 2013: semantic indexing and interactive surveillance event detection

In this paper we describe our participation in the semantic indexing (SIN) and interactive surveillance event detection (SED) tasks at TRECVid 2013 [11]. Our work was motivated by the goals of the EU SAVASA project (Standards-based Approach to Video Archive Search and Analysis) which supports search over multiple video archives. Our aims were: to assess a standard object detection methodology (SIN); evaluate contrasting runs in automatic event detection (SED) and deploy a distributed, cloud-based search interface for the interactive component of the SED task. Results from the SIN task, underlying retrospective classifiers for the surveillance event detection and a discussion of the contrasting aims of the SAVASA user interface compared with the TRECVid task requirements are presented

An air-stable DPP-thieno-TTF copolymer for single-material solar cell devices and field effect transistors

Following an approach developed in our group to incorporate tetrathiafulvalene (TTF) units into conjugated polymeric systems, we have studied a low band gap polymer incorporating TTF as a donor component. This polymer is based on a fused thieno-TTF unit that enables the direct incorporation of the TTF unit into the polymer, and a second comonomer based on the diketopyrrolopyrrole (DPP) molecule. These units represent a donor–acceptor copolymer system, p(DPP-TTF), showing strong absorption in the UV–visible region of the spectrum. An optimized p(DPP-TTF) polymer organic field effect transistor and a single material organic solar cell device showed excellent performance with a hole mobility of up to 5.3 × 10–2 cm2/(V s) and a power conversion efficiency (PCE) of 0.3%, respectively. Bulk heterojunction organic photovoltaic devices of p(DPP-TTF) blended with phenyl-C71-butyric acid methyl ester (PC71BM) exhibited a PCE of 1.8%