Robust Structured Low-Rank Approximation on the Grassmannian

Over the past years Robust PCA has been established as a standard tool for reliable low-rank approximation of matrices in the presence of outliers. Recently, the Robust PCA approach via nuclear norm minimization has been extended to matrices with linear structures which appear in applications such as system identification and data series analysis. At the same time it has been shown how to control the rank of a structured approximation via matrix factorization approaches. The drawbacks of these methods either lie in the lack of robustness against outliers or in their static nature of repeated batch-processing. We present a Robust Structured Low-Rank Approximation method on the Grassmannian that on the one hand allows for fast re-initialization in an online setting due to subspace identification with manifolds, and that is robust against outliers due to a smooth approximation of the $\ell_p$-norm cost function on the other hand. The method is evaluated in online time series forecasting tasks on simulated and real-world data

Collective Animal Behavior from Bayesian Estimation and Probability Matching

Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is based on empirical fits to observations and we lack first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching.
In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability given by the Bayesian estimation that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior

Ideological Labels in America

This paper extends Ellis and Stimson’s (Ideology in America. New York: Cambridge UniversityPress, 2012) study of the operational-symbolic paradox using issue-level measures of ideological incongruence based on respondent positions and symbolic labels for these positions across 14 issues. Like Ellis and Stimson, we find that substantial numbers—over 30 %—of Americans experience conflicted conservatism. Our issue-level data reveal, furthermore, that conflicted conservatism is most common on the issues of education and welfare spending. In addition, we also find that 20 % of Americans exhibit conflicted liberalism. We then replicate Ellis and Stimson’s finding that conflicted conservatism is associated with low sophistication and religiosity, but also find that it is associated with being socialized in a post-1960s generation and using Fox News as a main news source. Finally, we show the important role played by identities, with both conflicted conservatism and conflicted liberalism linked with partisan and ideological identities, and conflicted liberalism additionally associated with ethnic identities

Radiation chemistry of solid-state carbohydrates using EMR

We review our research of the past decade towards identification of radiation-induced radicals in solid state sugars and sugar phosphates. Detailed models of the radical structures are obtained by combining EPR and ENDOR experiments with DFT calculations of g and proton HF tensors, with agreement in their anisotropy serving as most important criterion. Symmetry-related and Schonland ambiguities, which may hamper such identification, are reviewed. Thermally induced transformations of initial radiation damage into more stable radicals can also be monitored in the EPR (and ENDOR) experiments and in principle provide information on stable radical formation mechanisms. Thermal annealing experi-ments reveal, however, that radical recombination and/or diamagnetic radiation damage is also quite important. Analysis strategies are illustrated with research on sucrose. Results on dipotassium glucose-1-phosphate and trehalose dihydrate, fructose and sorbose are also briefly discussed. Our study demonstrates that radiation damage is strongly regio-selective and that certain general principles govern the stable radical formation

Greenland ice sheet surface mass loss: recent developments in observation and modeling

Surface processes currently dominate Greenland ice sheet (GrIS) mass loss. We review recent developments in the observation and modelling of GrIS surface mass balance (SMB), published after the July 2012 deadline for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Since IPCC AR5 our understanding of GrIS SMB has further improved, but new observational and model studies have also revealed that temporal and spatial variability of many processes are still poorly quantified and understood, e.g. bio-albedo, the formation of ice lenses and their impact on lateral meltwater transport, heterogeneous vertical meltwater transport (‘piping’), the impact of atmospheric circulation changes and mixed-phase clouds on the surface energy balance and the magnitude of turbulent heat exchange over rough ice surfaces. As a result, these processes are only schematically or not at all included in models that are currently used to assess and predict future GrIS surface mass loss

Parameter estimation of the kinetic α-Pinene isomerization model using the MCSfilter algorithm

This paper aims to illustrate the application of a derivative-free multistart algorithm with coordinate search filter, designated as the MCSFilter algorithm. The problem used in this study is the parameter estimation problem of the kinetic α -pinene isomerization model. This is a well known nonlinear optimization problem (NLP) that has been investigated as a case study for performance testing of most derivative based methods proposed in the literature. Since the MCSFilter algorithm features a stochastic component, it was run ten times to solve the NLP problem. The optimization problem was successfully solved in all the runs and the optimal solution demonstrates that the MCSFilter provides a good quality solution.(undefined)info:eu-repo/semantics/publishedVersio

Recent Advances in Our Understanding of the Role of Meltwater in the Greenland Ice Sheet System

Nienow, Sole and Cowton’s Greenland research has been supported by a number of UK NERC research grants (NER/O/S/2003/00620; NE/F021399/1; NE/H024964/1; NE/K015249/1; NE/K014609/1) and Slater has been supported by a NERC PhD studentshipPurpose of the review:  This review discusses the role that meltwater plays within the Greenland ice sheet system. The ice sheet’s hydrology is important because it affects mass balance through its impact on meltwater runoff processes and ice dynamics. The review considers recent advances in our understanding of the storage and routing of water through the supraglacial, englacial, and subglacial components of the system and their implications for the ice sheet Recent findings:   There have been dramatic increases in surface meltwater generation and runoff since the early 1990s, both due to increased air temperatures and decreasing surface albedo. Processes in the subglacial drainage system have similarities to valley glaciers and in a warming climate, the efficiency of meltwater routing to the ice sheet margin is likely to increase. The behaviour of the subglacial drainage system appears to limit the impact of increased surface melt on annual rates of ice motion, in sections of the ice sheet that terminate on land, while the large volumes of meltwater routed subglacially deliver significant volumes of sediment and nutrients to downstream ecosystems. Summary:  Considerable advances have been made recently in our understanding of Greenland ice sheet hydrology and its wider influences. Nevertheless, critical gaps persist both in our understanding of hydrology-dynamics coupling, notably at tidewater glaciers, and in runoff processes which ensure that projecting Greenland’s future mass balance remains challenging.Publisher PDFPeer reviewe

Pharmacokinetic-Pharmacodynamic Modeling in Pediatric Drug Development, and the Importance of Standardized Scaling of Clearance.

Pharmacokinetic/pharmacodynamic (PKPD) modeling is important in the design and conduct of clinical pharmacology research in children. During drug development, PKPD modeling and simulation should underpin rational trial design and facilitate extrapolation to investigate efficacy and safety. The application of PKPD modeling to optimize dosing recommendations and therapeutic drug monitoring is also increasing, and PKPD model-based dose individualization will become a core feature of personalized medicine. Following extensive progress on pediatric PK modeling, a greater emphasis now needs to be placed on PD modeling to understand age-related changes in drug effects. This paper discusses the principles of PKPD modeling in the context of pediatric drug development, summarizing how important PK parameters, such as clearance (CL), are scaled with size and age, and highlights a standardized method for CL scaling in children. One standard scaling method would facilitate comparison of PK parameters across multiple studies, thus increasing the utility of existing PK models and facilitating optimal design of new studies

Biochemical systems identification by a random drift particle swarm optimization approach

BACKGROUND: Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for signalling pathways. The problem is stated as a data-driven nonlinear regression problem, which is converted into a nonlinear programming problem with many nonlinear differential and algebraic constraints. Due to the typical ill conditioning and multimodality nature of the problem, it is in general difficult for gradient-based local optimization methods to obtain satisfactory solutions. To surmount this limitation, many stochastic optimization methods have been employed to find the global solution of the problem. RESULTS: This paper presents an effective search strategy for a particle swarm optimization (PSO) algorithm that enhances the ability of the algorithm for estimating the parameters of complex dynamic biochemical pathways. The proposed algorithm is a new variant of random drift particle swarm optimization (RDPSO), which is used to solve the above mentioned inverse problem and compared with other well known stochastic optimization methods. Two case studies on estimating the parameters of two nonlinear biochemical dynamic models have been taken as benchmarks, under both the noise-free and noisy simulation data scenarios. CONCLUSIONS: The experimental results show that the novel variant of RDPSO algorithm is able to successfully solve the problem and obtain solutions of better quality than other global optimization methods used for finding the solution to the inverse problems in this study

Sucrose in the concentrated solution or the supercooled “state” : a review of caramelisation reactions and physical behaviour

Sucrose is probably one of the most studied molecules by food scientists, since it plays an important role as an ingredient or preserving agent in many formulations and technological processes. When sucrose is present in a product with a concentration near or greater than the saturation point—i.e. in the supercooled state—it possesses high potentialities for the food industry in areas as different as pastry industry, dairy and frozen desserts or films and coatings production. This paper presents a review on critical issues and research on highly concentrated sucrose solutions—mainly, on sucrose thermal degradation and relaxation behaviour in such solutions. The reviewed works allow identifying several issues with great potential for contributing to significant advances in Food Science and Technology.Authors are grateful for the valuable discussions with Teresa S. Brandao and Rosiane Lopes da Cunha during this research. Author M. A. C. Quintas acknowledges the financial support of her research by FCT grant SFRH/BPD/41715/2007