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Pair bond endurance promotes cooperative food defense and inhibits conflict in coral reef butterflyfishes(Cold Spring Harbor Laboratory, 20171114)Pair bonding is generally linked to monogamous mating systems, where the reproductive benefits of extended mate guarding and/or of biparental care are considered key adaptive functions. However, in some species, including coral reef butterflyfishes (f. Chaetodonitidae), pair bonding occurs in sexually immature and homosexual partners, and in the absence of parental care, suggesting there must be nonreproductive adaptive benefits of pair bonding. Here, we examined whether pair bonding butterflyfishes cooperate in defense of food, conferring direct benefits to one or both partners. Pairs of Chaetodon lunulatus and C. baronessa use contrasting cooperative strategies. In C. lunulatus, both partners mutually defend their territory, while in C. baronessa, males prioritize territory defence; conferring improvements in feeding and energy reserves in both sexes relative to solitary counterparts. We further demonstrate that partner fidelity contributes to this function by showing that repairing invokes intrapair conflict and inhibits cooperativelyderived feeding benefits, and that partner endurance is required for these costs to abate. Overall, our results suggest that in butterflyfishes, pair bonding enhances cooperative defense of prey resources, ultimately benefiting both partners by improving food resource acquisition and energy reserves.

Parameterfree Network Sparsification and Data Reduction by Minimal Algorithmic Information Loss(arXiv, 20180216)The study of large and complex datasets, or big data, organized as networks has emerged as one of the central challenges in most areas of science and technology. Cellular and molecular networks in biology is one of the prime examples. Henceforth, a number of techniques for data dimensionality reduction, especially in the context of networks, have been developed. Yet, current techniques require a predefined metric upon which to minimize the data size. Here we introduce a family of parameterfree algorithms based on (algorithmic) information theory that are designed to minimize the loss of any (enumerable computable) property contributing to the object's algorithmic content and thus important to preserve in a process of data dimension reduction when forcing the algorithm to delete first the least important features. Being independent of any particular criterion, they are universal in a fundamental mathematical sense. Using suboptimal approximations of efficient (polynomial) estimations we demonstrate how to preserve network properties outperforming other (leading) algorithms for network dimension reduction. Our method preserves all graphtheoretic indices measured, ranging from degree distribution, clusteringcoefficient, edge betweenness, and degree and eigenvector centralities. We conclude and demonstrate numerically that our parameterfree, Minimal Information Loss Sparsification (MILS) method is robust, has the potential to maximize the preservation of all recursively enumerable features in data and networks, and achieves equal to significantly better results than other data reduction and network sparsification methods.

Parameters and Fractional Differentiation Orders Estimation for Linear ContinuousTime NonCommensurate Fractional Order Systems(Submitted to Elsevier, 20170531)This paper proposes a twostage estimation algorithm to solve the problem of joint estimation of the parameters and the fractional differentiation orders of a linear continuoustime fractional system with noncommensurate orders. The proposed algorithm combines the modulating functions and the firstorder Newton methods. Sufficient conditions ensuring the convergence of the method are provided. An error analysis in the discrete case is performed. Moreover, the method is extended to the joint estimation of smooth unknown input and fractional differentiation orders. The performance of the proposed approach is illustrated with different numerical examples. Furthermore, a potential application of the algorithm is proposed which consists in the estimation of the differentiation orders of a fractional neurovascular model along with the neural activity considered as input for this model.

Particle Simulation of Fractional Diffusion Equations(arXiv, 20170712)This work explores different particlebased approaches to the simulation of onedimensional fractional subdiffusion equations in unbounded domains. We rely on smooth particle approximations, and consider four methods for estimating the fractional diffusion term. The first method is based on direct differentiation of the particle representation, it follows the Riesz definition of the fractional derivative and results in a nonconservative scheme. The other three methods follow the particle strength exchange (PSE) methodology and are by construction conservative, in the sense that the total particle strength is time invariant. The first PSE algorithm is based on using direct differentiation to estimate the fractional diffusion flux, and exploiting the resulting estimates in an integral representation of the divergence operator. Meanwhile, the second one relies on the regularized Riesz representation of the fractional diffusion term to derive a suitable interaction formula acting directly on the particle representation of the diffusing field. A third PSE construction is considered that exploits the Green's function of the fractional diffusion equation. The performance of all four approaches is assessed for the case of a onedimensional diffusion equation with constant diffusivity. This enables us to take advantage of known analytical solutions, and consequently conduct a detailed analysis of the performance of the methods. This includes a quantitative study of the various sources of error, namely filtering, quadrature, domain truncation, and time integration, as well as a space and time selfconvergence analysis. These analyses are conducted for different values of the order of the fractional derivatives, and computational experiences are used to gain insight that can be used for generalization of the present constructions.

Passivity and Evolutionary Game Dynamics(arXiv, 20180321)This paper investigates an energy conservation and dissipation  passivity  aspect of dynamic models in evolutionary game theory. We define a notion of passivity using the statespace representation of the models, and we devise systematic methods to examine passivity and to identify properties of passive dynamic models. Based on the methods, we describe how passivity is connected to stability in population games and illustrate stability of passive dynamic models using numerical simulations.

Path to Stochastic Stability: Comparative Analysis of Stochastic Learning Dynamics in Games(arXiv, 20180408)Stochastic stability is a popular solution concept for stochastic learning dynamics in games. However, a critical limitation of this solution concept is its inability to distinguish between different learning rules that lead to the same steadystate behavior. We address this limitation for the first time and develop a framework for the comparative analysis of stochastic learning dynamics with different update rules but same steadystate behavior. We present the framework in the context of two learning dynamics: LogLinear Learning (LLL) and Metropolis Learning (ML). Although both of these dynamics have the same stochastically stable states, LLL and ML correspond to different behavioral models for decision making. Moreover, we demonstrate through an example setup of sensor coverage game that for each of these dynamics, the paths to stochastically stable states exhibit distinctive behaviors. Therefore, we propose multiple criteria to analyze and quantify the differences in the short and medium run behavior of stochastic learning dynamics. We derive and compare upper bounds on the expected hitting time to the set of Nash equilibria for both LLL and ML. For the medium to longrun behavior, we identify a set of tools from the theory of perturbed Markov chains that result in a hierarchical decomposition of the state space into collections of states called cycles. We compare LLL and ML based on the proposed criteria and develop invaluable insights into the comparative behavior of the two dynamics.

Penultimate modeling of spatial extremes: statistical inference for maxinfinitely divisible processes(arXiv, 20180109)Extremevalue theory for stochastic processes has motivated the statistical use of maxstable models for spatial extremes. However, fitting such asymptotic models to maxima observed over finite blocks is problematic when the asymptotic stability of the dependence does not prevail in finite samples. This issue is particularly serious when data are asymptotically independent, such that the dependence strength weakens and eventually vanishes as events become more extreme. We here aim to provide flexible subasymptotic models for spatially indexed block maxima, which more realistically account for discrepancies between data and asymptotic theory. We develop models pertaining to the wider class of maxinfinitely divisible processes, extending the class of maxstable processes while retaining dependence properties that are natural for maxima: maxid models are positively associated, and they yield a selfconsistent family of models for block maxima defined over any time unit. We propose two parametric construction principles for maxid models, emphasizing a point processbased generalized spectral representation, that allows for asymptotic independence while keeping the maxstable extremal$t$ model as a special case. Parameter estimation is efficiently performed by pairwise likelihood, and we illustrate our new modeling framework with an application to Dutch wind gust maxima calculated over different time units.

PerturbationBased Regularization for Signal Estimation in Linear Discrete Illposed Problems(arXiv, 20161129)Estimating the values of unknown parameters from corrupted measured data faces a lot of challenges in illposed problems. In such problems, many fundamental estimation methods fail to provide a meaningful stabilized solution. In this work, we propose a new regularization approach and a new regularization parameter selection approach for linear leastsquares discrete illposed problems. The proposed approach is based on enhancing the singularvalue structure of the illposed model matrix to acquire a better solution. Unlike many other regularization algorithms that seek to minimize the estimated data error, the proposed approach is developed to minimize the meansquared error of the estimator which is the objective in many typical estimation scenarios. The performance of the proposed approach is demonstrated by applying it to a large set of realworld discrete illposed problems. Simulation results demonstrate that the proposed approach outperforms a set of benchmark regularization methods in most cases. In addition, the approach also enjoys the lowest runtime and offers the highest level of robustness amongst all the tested benchmark regularization methods.

Possible evidence for spintransfer torque induced by spintriplet supercurrent(arXiv, 20171004)Cooper pairs in superconductors are normally spin singlet. Nevertheless, recent studies suggest that spintriplet Cooper pairs can be created at carefully engineered superconductorferromagnet interfaces. If Cooper pairs are spinpolarized they would transport not only charge but also a net spin component, but without dissipation, and therefore minimize the heating effects associated with spintronic devices. Although it is now established that triplet supercurrents exist, their most interesting property  spin  is only inferred indirectly from transport measurements. In conventional spintronics, it is well known that spin currents generate spintransfer torques that alter magnetization dynamics and switch magnetic moments. The observation of similar effects due to spintriplet supercurrents would not only confirm the net spin of triplet pairs but also pave the way for applications of superconducting spintronics. Here, we present a possible evidence for spintransfer torques induced by triplet supercurrents in superconductor/ferromagnet/superconductor (S/F/S) Josephson junctions. Below the superconducting transition temperature T_c, the ferromagnetic resonance (FMR) field at Xband (~ 9.0 GHz) shifts rapidly to a lower field with decreasing temperature due to the spintransfer torques induced by triplet supercurrents. In contrast, this phenomenon is absent in ferromagnet/superconductor (F/S) bilayers and superconductor/insulator/ferromagnet/superconductor (S/I/F/S) multilayers where no supercurrents pass through the ferromagnetic layer. These experimental observations are discussed with theoretical predictions for ferromagnetic Josephson junctions with precessing magnetization.

Predictive Systems Toxicology(arXiv, 20180115)In this review we address to what extent computational techniques can augment our ability to predict toxicity. The first section provides a brief history of empirical observations on toxicity dating back to the dawn of Sumerian civilization. Interestingly, the concept of dose emerged very early on, leading up to the modern emphasis on kinetic properties, which in turn encodes the insight that toxicity is not solely a property of a compound but instead depends on the interaction with the host organism. The next logical step is the current conception of evaluating drugs from a personalized medicine pointofview. We review recent work on integrating what could be referred to as classical pharmacokinetic analysis with emerging systems biology approaches incorporating multiple omics data. These systems approaches employ advanced statistical analytical data processing complemented with machine learning techniques and use both pharmacokinetic and omics data. We find that such integrated approaches not only provide improved predictions of toxicity but also enable mechanistic interpretations of the molecular mechanisms underpinning toxicity and drug resistance. We conclude the chapter by discussing some of the main challenges, such as how to balance the inherent tension between the predictive capacity of models, which in practice amounts to constraining the number of features in the models versus allowing for rich mechanistic interpretability, i.e. equipping models with numerous molecular features. This challenge also requires patientspecific predictions on toxicity, which in turn requires proper stratification of patients as regards how they respond, with or without adverse toxic effects. In summary, the transformation of the ancient concept of dose is currently successfully operationalized using rich integrative data encoded in patientspecific models.

Privacy preserving randomized gossip algorithms(arXiv, 20170623)In this work we present three different randomized gossip algorithms for solving the average consensus problem while at the same time protecting the information about the initial private values stored at the nodes. We give iteration complexity bounds for all methods, and perform extensive numerical experiments.

Proteomelevel assessment of origin, prevalence and function of LeucineAspartic Acid (LD) motifs(Cold Spring Harbor Laboratory, 20180311)Short Linear Motifs (SLiMs) contribute to almost every cellular function by connecting appropriate protein partners. Accurate prediction of SLiMs is difficult due to their shortness and sequence degeneracy. Leucineaspartic acid (LD) motifs are SLiMs that link paxillin family proteins to factors controlling (cancer) cell adhesion, motility and survival. The existence and importance of LD motifs beyond the paxillin family is poorly understood. To enable a proteomewide assessment of these motifs, we developed an activelearning based framework that iteratively integrates computational predictions with experimental validation. Our analysis of the human proteome identified a dozen proteins that contain LD motifs, all being involved in cell adhesion and migration, and revealed a new type of inverse LD motif consensus. Our evolutionary analysis suggested that LD motif signalling originated in the common unicellular ancestor of opisthokonts and amoebozoa by coopting nuclear export sequences. Interspecies comparison revealed a conserved LD signalling core, and reveals the emergence of speciesspecific adaptive connections, while maintaining a strong functional focus of the LD motif interactome. Collectively, our data elucidate the mechanisms underlying the origin and adaptation of an ancestral SLiM.

Quantitative SeqLGS: GenomeWide Identification of Genetic Drivers of Multiple Phenotypes in Malaria Parasites(Cold Spring Harbor Laboratory Press, 20161001)Identifying the genetic determinants of phenotypes that impact on disease severity is of fundamental importance for the design of new interventions against malaria. Traditionally, such discovery has relied on laborintensive approaches that require significant investments of time and resources. By combining Linkage Group Selection (LGS), quantitative whole genome population sequencing and a novel mathematical modeling approach (qSeqLGS), we simultaneously identified multiple genes underlying two distinct phenotypes, identifying novel alleles for growth rate and strain specific immunity (SSI), while removing the need for traditionally required steps such as cloning, individual progeny phenotyping and marker generation. The detection of novel variants, verified by experimental phenotyping methods, demonstrates the remarkable potential of this approach for the identification of genes controlling selectable phenotypes in malaria and other apicomplexan parasites for which experimental genetic crosses are amenable.

Randomized Block Cubic Newton Method(arXiv, 20180212)We study the problem of minimizing the sum of three convex functions: a differentiable, twicedifferentiable and a nonsmooth term in a high dimensional setting. To this effect we propose and analyze a randomized block cubic Newton (RBCN) method, which in each iteration builds a model of the objective function formed as the sum of the natural models of its three components: a linear model with a quadratic regularizer for the differentiable term, a quadratic model with a cubic regularizer for the twice differentiable term, and perfect (proximal) model for the nonsmooth term. Our method in each iteration minimizes the model over a random subset of blocks of the search variable. RBCN is the first algorithm with these properties, generalizing several existing methods, matching the best known bounds in all special cases. We establish ${\cal O}(1/\epsilon)$, ${\cal O}(1/\sqrt{\epsilon})$ and ${\cal O}(\log (1/\epsilon))$ rates under different assumptions on the component functions. Lastly, we show numerically that our method outperforms the stateoftheart on a variety of machine learning problems, including cubically regularized leastsquares, logistic regression with constraints, and Poisson regression.

A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments(arXiv, 20180117)We propose a class of subspace ascent methods for computing optimal approximate designs that covers both existing as well as new and more efficient algorithms. Within this class of methods, we construct a simple, randomized exchange algorithm (REX). Numerical comparisons suggest that the performance of REX is comparable or superior to the performance of stateoftheart methods across a broad range of problem structures and sizes. We focus on the most commonly used criterion of Doptimality that also has applications beyond experimental design, such as the construction of the minimum volume ellipsoid containing a given set of datapoints. For Doptimality, we prove that the proposed algorithm converges to the optimum. We also provide formulas for the optimal exchange of weights in the case of the criterion of Aoptimality. These formulas enable one to use REX for computing Aoptimal and Ioptimal designs.

Robustness Analysis of Visual QA Models by Basic Questions(arXiv, 20170914)Visual Question Answering (VQA) models should have both high robustness and accuracy. Unfortunately, most of the current VQA research only focuses on accuracy because there is a lack of proper methods to measure the robustness of VQA models. There are two main modules in our algorithm. Given a natural language question about an image, the first module takes the question as input and then outputs the ranked basic questions, with similarity scores, of the main given question. The second module takes the main question, image and these basic questions as input and then outputs the textbased answer of the main question about the given image. We claim that a robust VQA model is one, whose performance is not changed much when related basic questions as also made available to it as input. We formulate the basic questions generation problem as a LASSO optimization, and also propose a large scale Basic Question Dataset (BQD) and Rscore (novel robustness measure), for analyzing the robustness of VQA models. We hope our BQD will be used as a benchmark for to evaluate the robustness of VQA models, so as to help the community build more robust and accurate VQA models.

A salt water battery with high stability and charging rates made from solution processed conjugated polymers with polar side chains(arXiv, 20171128)We report a neutral salt water based battery which uses ptype and ntype solution processed polymer films as the cathode and the anode of the cell. The specific capacity of the electrodes (approximately 30 mAh cm3) is achieved via formation of bipolarons in both the ptype and ntype polymers. By engineering ethylene glycol and zwitterion based side chains attached to the polymer backbone we facilitate rapid ion transport through the nonporous polymer films. This, combined with efficient transport of electronic charge via the conjugated polymer backbones, allowed the films to maintain constant capacity at high charge and discharge rates (>1000 Crate). The electrodes also show good stability during electrochemical cycling (less than 30% decrease in capacity over >1000 cycles) and an output voltage up to 1.4 V. The performance of these semiconducting polymers with polar sidechains demonstrates the potential of this material class for fastcharging, water based electrochemical energy storage devices.

SBPSAT finite difference discretization of acoustic wave equations on staggered blockwise uniform grids(arXiv, 20180216)We consider the numerical simulation of the acoustic wave equations arising from seismic applications, for which staggered grid finite difference methods are popular choices due to their simplicity and efficiency. We relax the uniform grid restriction on finite difference methods and allow the grids to be blockwise uniform with nonconforming interfaces. In doing so, variations in the wave speeds of the subterranean media can be accounted for more efficiently. Staggered grid finite difference operators satisfying the summationbyparts (SBP) property are devised to approximate the spatial derivatives appearing in the acoustic wave equation. These operators are applied within each block independently. The coupling between blocks is achieved through simultaneous approximation terms (SATs), which impose the interface condition weakly, i.e., by penalty. Ratio of the grid spacing of neighboring blocks is allowed to be rational number, for which specially designed interpolation formulas are presented. These interpolation formulas constitute key pieces of the simultaneous approximation terms. The overall discretization is shown to be energyconserving and examined on test cases of both theoretical and practical interests, delivering accurate and stable simulation results.

SecretKeyAided Scheme for Securing Untrusted DF Relaying Networks(arXiv, 20170612)This paper proposes a new scheme to secure the transmissions in an untrusted decodeandforward (DF) relaying network. A legitimate source node, Alice, sends her data to a legitimate destination node, Bob, with the aid of an untrusted DF relay node, Charlie. To secure the transmissions from Charlie during relaying time slots, each data codeword is secured using a secretkey codeword that has been previously shared between Alice and Bob during the perfectly secured time slots (i.e., when the channel secrecy rate is positive). The secretkey bits exchanged between Alice and Bob are stored in a finitelength buffer and are used to secure data transmission whenever needed. We model the secretkey buffer as a queueing system and analyze its Markov chain. Our numerical results show the gains of our proposed scheme relative to benchmarks. Moreover, the proposed scheme achieves an upper bound on the secure throughput.