Fluid semantics for passive stochastic process algebra cooperation

Fluid modelling is a next-generation technique for analysing massive performance models. Passive cooperation is a popular cooperation mechanism frequently used by performance engineers. Therefore having an accurate translation of passive cooperation into a fluid model is of direct practical application. We compare different existing styles of fluid model translation of passive cooperation in a stochastic process algebra. We explain why the development of a fluid semantics for passive cooperation is not straightforward and we present an alternative definition which more closely matches the underlying discrete model. Finally, we present quantitative comparisons with a previous version of the fluid semantics in which numerical discrepancies can be observed. © 2008 ICST ISBN

Evaluating fluid semantics for passive stochastic process algebra cooperation

Fluid modelling is a next-generation technique for analysing massive performance models. Passive cooperation is a popular cooperation mechanism frequently used by performance engineers. Therefore having an accurate translation of passive cooperation into a fluid model is of direct practical application. We compare different existing styles of fluid model translation of passive cooperation in a stochastic process algebra and show how the previous model can be improved upon significantly. We evaluate the new passive cooperation fluid semantics and show that the first-order fluid model is a good approximation to the dynamics of the underlying continuous-time Markov chain. We show that in a family of possible translations to the fluid model, there is an optimal translation which can be expected to introduce least error. Finally, we use these new techniques to show how the scalability of a passively-cooperating distributed software architecture could be assessed

Framework For Improving Complex System Performance

This paper introduces a framework for improvement of complex system performance. Complex systems are besieged with conditions marked by increasing uncertainty, emergence, and ambiguity. Additionally, demands for increased productivity, resource efficiencies, and performance improvement make new approaches paramount for modern systems engineers. In response, a framework to improve complex system performance is developed. Following an introduction, the paper pursues four objectives: (1) introduction of Complex System Governance (CSG) as a foundation to describe essential system functions, (2) suggest system `pathologies\u27 as an explanation for deep system performance issues, (3) exploration of system performance improvement as a function of `requisite variety\u27 to compensate for deep system issues, and (4) introduce a framework for complex system performance improvement using system pathologies as `unab-sorbed variety\u27. The paper closes with some challenges for further development of the framework for deployment and application guidance for practitioners

A network-based dynamical ranking system for competitive sports

From the viewpoint of networks, a ranking system for players or teams in sports is equivalent to a centrality measure for sports networks, whereby a directed link represents the result of a single game. Previously proposed network-based ranking systems are derived from static networks, i.e., aggregation of the results of games over time. However, the score of a player (or team) fluctuates over time. Defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods. To account for this factor, we propose a dynamic variant of such a network-based ranking system and apply it to professional men's tennis data. We derive a set of linear online update equations for the score of each player. The proposed ranking system predicts the outcome of the future games with a higher accuracy than the static counterparts.Comment: 6 figure

Soccer Team Vectors

In this work we present STEVE - Soccer TEam VEctors, a principled approach for learning real valued vectors for soccer teams where similar teams are close to each other in the resulting vector space. STEVE only relies on freely available information about the matches teams played in the past. These vectors can serve as input to various machine learning tasks. Evaluating on the task of team market value estimation, STEVE outperforms all its competitors. Moreover, we use STEVE for similarity search and to rank soccer teams.Comment: 11 pages, 1 figure; This paper was presented at the 6th Workshop on Machine Learning and Data Mining for Sports Analytics at ECML/PKDD 2019, W\"urzburg, Germany, 201

Limit theorems for von Mises statistics of a measure preserving transformation

For a measure preserving transformation $T$ of a probability space $(X,\mathcal F,\mu)$ we investigate almost sure and distributional convergence of random variables of the form $$x \to \frac{1}{C_n} \sum_{i_1<n,...,i_d<n} f(T^{i_1}x,...,T^{i_d}x),\, n=1,2,...,$$ where $f$ (called the \emph{kernel}) is a function from $X^d$ to $\R$ and $C_1, C_2,...$ are appropriate normalizing constants. We observe that the above random variables are well defined and belong to $L_r(\mu)$ provided that the kernel is chosen from the projective tensor product $$L_p(X_1,\mathcal F_1, \mu_1) \otimes_{\pi}...\otimes_{\pi} L_p(X_d,\mathcal F_d, \mu_d)\subset L_p(\mu^d)$$ with $p=d\,r,\, r\ \in [1, \infty).$ We establish a form of the individual ergodic theorem for such sequences. Next, we give a martingale approximation argument to derive a central limit theorem in the non-degenerate case (in the sense of the classical Hoeffding's decomposition). Furthermore, for $d=2$ and a wide class of canonical kernels $f$ we also show that the convergence holds in distribution towards a quadratic form $\sum_{m=1}^{\infty} \lambda_m\eta^2_m$ in independent standard Gaussian variables $\eta_1, \eta_2,...$. Our results on the distributional convergence use a $T$--\,invariant filtration as a prerequisite and are derived from uni- and multivariate martingale approximations

Evolution in the Cluster Early-type Galaxy Size-Surface Brightness Relation at z =~ 1

We investigate the evolution in the distribution of surface brightness, as a function of size, for elliptical and S0 galaxies in the two clusters RDCS J1252.9-2927, z=1.237 and RX J0152.7-1357, z=0.837. We use multi-color imaging with the Advanced Camera for Surveys on the Hubble Space Telescope to determine these sizes and surface brightnesses. Using three different estimates of the surface brightnesses, we find that we reliably estimate the surface brightness for the galaxies in our sample with a scatter of < 0.2 mag and with systematic shifts of \lesssim 0.05 mag. We construct samples of galaxies with early-type morphologies in both clusters. For each cluster, we use a magnitude limit in a band which closely corresponds to the rest-frame B, to magnitude limit of M_B = -18.8 at z=0, and select only those galaxies within the color-magnitude sequence of the cluster or by using our spectroscopic redshifts. We measure evolution in the rest-frame B surface brightness, and find -1.41 \+/- 0.14 mag from the Coma cluster of galaxies for RDCS J1252.9-2927 and -0.90 \+/- 0.12 mag of evolution for RX J0152.7-1357, or an average evolution of (-1.13 \+/- 0.15) z mag. Our statistical errors are dominated by the observed scatter in the size-surface brightness relation, sigma = 0.42 \+/- 0.05 mag for RX J0152.7-1357 and sigma = 0.76 \+/- 0.10 mag for RDCS J1252.9-2927. We find no statistically significant evolution in this scatter, though an increase in the scatter could be expected. Overall, the pace of luminosity evolution we measure agrees with that of the Fundamental Plane of early-type galaxies, implying that the majority of massive early-type galaxies observed at z =~ 1 formed at high redshifts.Comment: Accepted in ApJ, 16 pages in emulateapj format with 15 eps figures, 6 in colo

Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

Accurate reconstruction of insertion-deletion histories by statistical phylogenetics

The Multiple Sequence Alignment (MSA) is a computational abstraction that represents a partial summary either of indel history, or of structural similarity. Taking the former view (indel history), it is possible to use formal automata theory to generalize the phylogenetic likelihood framework for finite substitution models (Dayhoff's probability matrices and Felsenstein's pruning algorithm) to arbitrary-length sequences. In this paper, we report results of a simulation-based benchmark of several methods for reconstruction of indel history. The methods tested include a relatively new algorithm for statistical marginalization of MSAs that sums over a stochastically-sampled ensemble of the most probable evolutionary histories. For mammalian evolutionary parameters on several different trees, the single most likely history sampled by our algorithm appears less biased than histories reconstructed by other MSA methods. The algorithm can also be used for alignment-free inference, where the MSA is explicitly summed out of the analysis. As an illustration of our method, we discuss reconstruction of the evolutionary histories of human protein-coding genes.Comment: 28 pages, 15 figures. arXiv admin note: text overlap with arXiv:1103.434