Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196

Transmission of Fusarium boothii Mycovirus via Protoplast Fusion Causes Hypovirulence in Other Phytopathogenic Fungi

There is increasing concern regarding the use of fungicides to control plant diseases, whereby interest has increased in the biological control of phytopathogenic fungi by the application of hypovirulent mycoviruses as a possible alternative to fungicides. Transmission of hypovirulence-associated double-stranded RNA (dsRNA) viruses between mycelia, however, is prevented by the vegetative incompatibility barrier that often exists between different species or strains of filamentous fungi. We determined whether protoplast fusion could be used to transmit FgV1-DK21 virus, which is associated with hypovirulence on F. boothii (formerly F. graminearum strain DK21), to F. graminearum, F. asiaticum, F. oxysporum f. sp. lycopersici, and Cryphonectria parasitica. Relative to virus-free strains, the FgV1-DK21 recipient strains had reduced growth rates, altered pigmentation, and reduced virulence. These results indicate that protoplast fusion can be used to introduce FgV1-DK21 dsRNA into other Fusarium species and into C. parasitica and that FgV1-DK21 can be used as a hypovirulence factor and thus as a biological control agent

Mechanism of subunit interaction at ketosynthase-dehydratase junctions in trans-AT polyketide synthases

Modular polyketide synthases (PKSs) produce numerous structurally complex natural products with diverse applications in medicine and agriculture. They typically consist of several multienzyme subunits that utilize structurally-defined docking domains (DDs) at their N- and C-termini to ensure correct assembly into functional multi-protein complexes. Here we report a fundamentally different mechanism for subunit assembly in trans-AT modular PKSs at the junction between ketosynthase (KS) and dehydratase (DH) domains. This involves direct interaction of a largely unstructured docking domain (DD) at the C-terminus of the KS with the surface of the downstream DH. Acyl transfer assays and mechanism-based cross-linking established that the DD is required for the KS to communicate with the acyl carrier protein appended to the DH. Two distinct regions for binding of the DD to the DH were identified using NMR spectroscopy, carbene foot-printing and mutagenesis, providing a foundation for future elucidation of the molecular basis for interaction specificity

The LUX-ZEPLIN (LZ) Experiment

We describe the design and assembly of the LUX-ZEPLIN experiment, a direct detection search for cosmic WIMP dark matter particles. The centerpiece of the experiment is a large liquid xenon time projection chamber sensitive to low energy nuclear recoils. Rejection of backgrounds is enhanced by a Xe skin veto detector and by a liquid scintillator Outer Detector loaded with gadolinium for efficient neutron capture and tagging. LZ is located in the Davis Cavern at the 4850' level of the Sanford Underground Research Facility in Lead, South Dakota, USA. We describe the major subsystems of the experiment and its key design features and requirements

The LUX-ZEPLIN (LZ) radioactivity and cleanliness control programs

LUX-ZEPLIN (LZ) is a second-generation direct dark matter experiment with spin-independent WIMP-nucleon scattering sensitivity above 1.4×10−48cm2 for a WIMP mass of 40GeV/c2 and a 1000days exposure. LZ achieves this sensitivity through a combination of a large 5.6t fiducial volume, active inner and outer veto systems, and radio-pure construction using materials with inherently low radioactivity content. The LZ collaboration performed an extensive radioassay campaign over a period of six years to inform material selection for construction and provide an input to the experimental background model against which any possible signal excess may be evaluated. The campaign and its results are described in this paper. We present assays of dust and radon daughters depositing on the surface of components as well as cleanliness controls necessary to maintain background expectations through detector construction and assembly. Finally, examples from the campaign to highlight fixed contaminant radioassays for the LZ photomultiplier tubes, quality control and quality assurance procedures through fabrication, radon emanation measurements of major sub-systems, and bespoke detector systems to assay scintillator are presented

Analysis of Divergence in Loglinear Models When Expected Frequencies are Subject to Linear Constraints

Asymptotic distribution, Multinomial sampling, -divergence test statistics, Loglinear models,