Slow magnetic dynamics and hysteresis loops of a bulk ferromagnet

Magnetic dynamics of a bulk ferromagnet, a new single crystalline compound Co7(TeO3)4Br6, was studied by ac susceptibility and the related techniques. Very large Arrhenius activation energy of 17.2 meV (201 K) and long attempt time (2x10^(-4)s) span the full spectrum of magnetic dynamics inside a convenient frequency window, offering a rare opportunity for general studies of magnetic dynamics. Within the experimental window the ac susceptibility data build almost ideally semicircular Cole-Cole plots. Comprehensive study of experimental dynamic hysteresis loops of the compound is presented and interpreted within a simple thermal-activation-assisted spin lattice relaxation model for spin reversal. Quantitative agreement between the experimental results and the model's prediction for dynamic coercive field is achieved by assuming the central physical quantity, the Debye relaxation rate, to depend on frequency, as well as on the applied field strength and sample temperature. Cross-over between minor- to major hysteresis loops is carefully analyzed. Low-frequency limitations of the model, relying on domain wall pinning effects, are experimentally detected and appropriately discussed.Comment: A paragraph on dynamical-hysteresis assymetry added, text partially revised; Accepted in Physical Review

Algorithmic and Statistical Perspectives on Large-Scale Data Analysis

In recent years, ideas from statistics and scientific computing have begun to interact in increasingly sophisticated and fruitful ways with ideas from computer science and the theory of algorithms to aid in the development of improved worst-case algorithms that are useful for large-scale scientific and Internet data analysis problems. In this chapter, I will describe two recent examples---one having to do with selecting good columns or features from a (DNA Single Nucleotide Polymorphism) data matrix, and the other having to do with selecting good clusters or communities from a data graph (representing a social or information network)---that drew on ideas from both areas and that may serve as a model for exploiting complementary algorithmic and statistical perspectives in order to solve applied large-scale data analysis problems.Comment: 33 pages. To appear in Uwe Naumann and Olaf Schenk, editors, "Combinatorial Scientific Computing," Chapman and Hall/CRC Press, 201

Strategic Planning, Hypercompetition, and Knowledge Management

When both product andresource markets are turbulentand disruptive, competitiveadvantage has to be continuallyrenewed. But a firm can do this onlyby managing its knowledge. Using twoelements of strategic planning—scenariobuilding and internal situation analysis—managers can map and integrate knowledge togain a sustainable competitive edge in today’shypercompetitive markets

Word Adjacency Graph Modeling: Separating Signal From Noise in Big Data

There is a need to develop methods to analyze Big Data to inform patient-centered interventions for better health outcomes. The purpose of this study was to develop and test a method to explore Big Data to describe salient health concerns of people with epilepsy. Specifically, we used Word Adjacency Graph modeling to explore a data set containing 1.9 billion anonymous text queries submitted to the ChaCha question and answer service to (a) detect clusters of epilepsy-related topics, and (b) visualize the range of epilepsy-related topics and their mutual proximity to uncover the breadth and depth of particular topics and groups of users. Applied to a large, complex data set, this method successfully identified clusters of epilepsy-related topics while allowing for separation of potentially non-relevant topics. The method can be used to identify patient-driven research questions from large social media data sets and results can inform the development of patient-centered interventions