Solving kk-means on High-dimensional Big Data

In recent years, there have been major efforts to develop data stream algorithms that process inputs in one pass over the data with little memory requirement. For the $k$-means problem, this has led to the development of several $(1+\varepsilon)$-approximations (under the assumption that $k$ is a constant), but also to the design of algorithms that are extremely fast in practice and compute solutions of high accuracy. However, when not only the length of the stream is high but also the dimensionality of the input points, then current methods reach their limits. We propose two algorithms, piecy and piecy-mr that are based on the recently developed data stream algorithm BICO that can process high dimensional data in one pass and output a solution of high quality. While piecy is suited for high dimensional data with a medium number of points, piecy-mr is meant for high dimensional data that comes in a very long stream. We provide an extensive experimental study to evaluate piecy and piecy-mr that shows the strength of the new algorithms.Comment: 23 pages, 9 figures, published at the 14th International Symposium on Experimental Algorithms - SEA 201

Knaster's problem for (Z2)k(Z_2)^k-symmetric subsets of the sphere S2k−1S^{2^k-1}

We prove a Knaster-type result for orbits of the group $(Z_2)^k$ in $S^{2^k-1}$, calculating the Euler class obstruction. Among the consequences are: a result about inscribing skew crosspolytopes in hypersurfaces in $\mathbb R^{2^k}$, and a result about equipartition of a measures in $\mathbb R^{2^k}$ by $(Z_2)^{k+1}$-symmetric convex fans

Data Mining and Machine Learning in Astronomy

We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra figures, some minor additions to the tex

From the discrete to the continuous - towards a cylindrically consistent dynamics

Discrete models usually represent approximations to continuum physics. Cylindrical consistency provides a framework in which discretizations mirror exactly the continuum limit. Being a standard tool for the kinematics of loop quantum gravity we propose a coarse graining procedure that aims at constructing a cylindrically consistent dynamics in the form of transition amplitudes and Hamilton's principal functions. The coarse graining procedure, which is motivated by tensor network renormalization methods, provides a systematic approximation scheme towards this end. A crucial role in this coarse graining scheme is played by embedding maps that allow the interpretation of discrete boundary data as continuum configurations. These embedding maps should be selected according to the dynamics of the system, as a choice of embedding maps will determine a truncation of the renormalization flow.Comment: 22 page

Multi-criteria Resource Allocation in Modal Hard Real-Time Systems

In this paper, a novel resource allocation approach dedicated to hard real-time systems with distinctive operational modes is proposed. The aim of this approach is to reduce the energy dissipation of the computing cores by either powering them off or switching them into energy-saving states while still guaranteeing to meet all timing constraints. The approach is illustrated with two industrial applications, an engine control management and an engine control unit. Moreover, the amount of data to be migrated during the mode change is minimised. Since the number of processing cores and their energy dissipation are often negatively correlated with the amount of data to be migrated during the mode change, there is some trade-off between these values, which is also analysed in this paper

AI-powered transmitted light microscopy for functional analysis of live cells

Transmitted light microscopy can readily visualize the morphology of living cells. Here, we introduce artificial-intelligence-powered transmitted light microscopy (AIM) for subcellular structure identification and labeling-free functional analysis of live cells. AIM provides accurate images of subcellular organelles; allows identification of cellular and functional characteristics (cell type, viability, and maturation stage); and facilitates live cell tracking and multimodality analysis of immune cells in their native form without labeling

When Subterranean Termites Challenge the Rules of Fungal Epizootics

Over the past 50 years, repeated attempts have been made to develop biological control technologies for use against economically important species of subterranean termites, focusing primarily on the use of the entomopathogenic fungus Metarhizium anisopliae. However, no successful field implementation of biological control has been reported. Most previous work has been conducted under the assumption that environmental conditions within termite nests would favor the growth and dispersion of entomopathogenic agents, resulting in an epizootic. Epizootics rely on the ability of the pathogenic microorganism to self-replicate and disperse among the host population. However, our study shows that due to multilevel disease resistance mechanisms, the incidence of an epizootic within a group of termites is unlikely. By exposing groups of 50 termites in planar arenas containing sand particles treated with a range of densities of an entomopathogenic fungus, we were able to quantify behavioral patterns as a function of the death ratios resulting from the fungal exposure. The inability of the fungal pathogen M. anisopliae to complete its life cycle within a Coptotermes formosanus (Isoptera: Rhinotermitidae) group was mainly the result of cannibalism and the burial behavior of the nest mates, even when termite mortality reached up to 75%. Because a subterranean termite colony, as a superorganism, can prevent epizootics of M. anisopliae, the traditional concepts of epizootiology may not apply to this social insect when exposed to fungal pathogens, or other pathogen for which termites have evolved behavioral and physiological means of disrupting their life cycle

Augmented Two-Channel Arrhythmia Detection: An Efficient Diagnostic Method for Implantable Devices

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75624/1/j.1540-8159.1996.tb03164.x.pd

Surrogate Models and Mixtures of Experts in Aerodynamic Performance Prediction for Mission Analysis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140436/1/6.2014-2301.pd