Learning Markov networks with context-specific independences

Learning the Markov network structure from data is a problem that has received considerable attention in machine learning, and in many other application fields. This work focuses on a particular approach for this purpose called independence-based learning. Such approach guarantees the learning of the correct structure efficiently, whenever data is sufficient for representing the underlying distribution. However, an important issue of such approach is that the learned structures are encoded in an undirected graph. The problem with graphs is that they cannot encode some types of independence relations, such as the context-specific independences. They are a particular case of conditional independences that is true only for a certain assignment of its conditioning set, in contrast to conditional independences that must hold for all its assignments. In this work we present CSPC, an independence-based algorithm for learning structures that encode context-specific independences, and encoding them in a log-linear model, instead of a graph. The central idea of CSPC is combining the theoretical guarantees provided by the independence-based approach with the benefits of representing complex structures by using features in a log-linear model. We present experiments in a synthetic case, showing that CSPC is more accurate than the state-of-the-art IB algorithms when the underlying distribution contains CSIs.Comment: 8 pages, 6 figure

Correcting artifacts from finite image size in Differential Dynamic Microscopy

Differential Dynamic Microscopy (DDM) analyzes traditional real-space microscope images to extract information on sample dynamics in a way akin to light scattering, by decomposing each image in a sequence into Fourier modes, and evaluating their time correlation properties. DDM has been applied in a number of soft-matter and colloidal systems. However, objects observed to move out of the microscope's captured field of view, intersecting the edges of the acquired images, can introduce spurious but significant errors in the subsequent analysis. Here we show that application of a spatial windowing filter to images in a sequence before they enter the standard DDM analysis can reduce these artifacts substantially. Moreover, windowing can increase significantly the accessible range of wave vectors probed by DDM, and may further yield unexpected information, such as the size polydispersity of a colloidal suspension

Differential dynamic microscopy microrheology of soft materials: A tracking-free determination of the frequency-dependent loss and storage moduli

Particle-tracking microrheology (PT-μr) exploits the thermal motion of embedded particles to probe the local mechanical properties of soft materials. Despite its appealing conceptual simplicity, PT-μr requires calibration procedures and operating assumptions that constitute a practical barrier to its wider application. Here we demonstrate differential dynamic microscopy microrheology (DDM-μr), a tracking-free approach based on the multiscale, temporal correlation study of the image intensity fluctuations that are observed in microscopy experiments as a consequence of the translational and rotational motion of the tracers. We show that the mechanical moduli of an arbitrary sample are determined correctly over a wide frequency range provided that the standard DDM analysis is reinforced with an iterative, self-consistent procedure that fully exploits the multiscale information made available by DDM. Our approach to DDM-μr does not require any prior calibration, is in agreement with both traditional rheology and diffusing wave spectroscopy microrheology, and works in conditions where PT-μr fails, providing thus an operationally simple, calibration-free probe of soft materials

The IBMAP approach for Markov networks structure learning

In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC algorithm, designed for avoiding important limitations of existing independence-based algorithms. These algorithms proceed by performing statistical independence tests on data, trusting completely the outcome of each test. In practice tests may be incorrect, resulting in potential cascading errors and the consequent reduction in the quality of the structures learned. IBMAP contemplates this uncertainty in the outcome of the tests through a probabilistic maximum-a-posteriori approach. The approach is instantiated in the IBMAP-HC algorithm, a structure selection strategy that performs a polynomial heuristic local search in the space of possible structures. We present an extensive empirical evaluation on synthetic and real data, showing that our algorithm outperforms significantly the current independence-based algorithms, in terms of data efficiency and quality of learned structures, with equivalent computational complexities. We also show the performance of IBMAP-HC in a real-world application of knowledge discovery: EDAs, which are evolutionary algorithms that use structure learning on each generation for modeling the distribution of populations. The experiments show that when IBMAP-HC is used to learn the structure, EDAs improve the convergence to the optimum

An empirical examination of knowledge and community within the higher education matrix

This study is a conceptual and empirical investigation into the knowledge base of university administrators and thus their position as an emerging profession, which has been a hitherto neglected area of research. The aim is to examine what this knowledge consists of and how it is configured across academic and administrative staff working together on academic activities. I begin by discussing the changing nature of academic work in response to evolving external influences and show that it is now process-based. It involves academic staff and administrative staff working together, with the latter providing a different type of expertise in response to external sector needs. This knowledge base has been neglected up to this point by researchers. I go on to use the principles arising from Bernstein’s concepts of knowledge structures (Bernstein, 2000) and Adler’s concept of the collaborative community (Adler et al, 2006; 20008) to formulate a theoretical framework which I use as a lens to explore the knowledge held by university administrators working with academic staff in two different academic departments within a multi-faculty university. My research finds an increasingly identifiable knowledge base alongside a more generic type of expertise which is born of experience and tacit learning, but this group’s professionalisation and development is currently limited. I elaborate on how this knowledge has been acquired by the administrators interviewed and identify that it is situated on the axis of the organisation, as opposed to being part of academic subject expertise; academic activities such as delivering a degree programme are a product of these different type of knowledge. I conclude the study by defining the knowledge base utilised by university administrators, clarify the organisational relationship between this group and academic staff and thus the contribution of the former and then make suggestions for their professional development

A Study of D0 --> K0(S) K0(S) X Decay Channels

Using data from the FOCUS experiment (FNAL-E831), we report on the decay of $D^0$ mesons into final states containing more than one $K^0_S$. We present evidence for two Cabibbo favored decay modes, $D^0\to K^0_SK^0_S K^- \pi^+$ and $D^0\to K^0_SK^0_S K^+ \pi^-$, and measure their combined branching fraction relative to $D^0\to \bar{K} ^0\pi^+\pi^-$ to be $\frac{\Gamma(D^0\to K^0_SK^0_SK^{\pm}\pi^{\mp})}{\Gamma(D^0\to \bar{K} ^0\pi^+\pi^-)}$ = 0.0106 $\pm$ 0.0019 $\pm$ 0.0010. Further, we report new measurements of $\frac{\Gamma(D^0\to K^0_SK^0_SK^0_S)}{\Gamma(D^0\to \bar{K} ^0\pi^+\pi^-)}$ = 0.0179 $\pm$ 0.0027 $\pm$ 0.0026, $\frac{\Gamma(D^0\to K^0\bar{K} ^0)}{\Gamma(D^0\to \bar{K} ^0\pi^+\pi^-)}$ = 0.0144 $\pm$ 0.0032 $\pm$ 0.0016, and $\frac{\Gamma(D^0\to K^0_SK^0_S\pi^+\pi^-)}{\Gamma(D^0\to \bar{K} ^0\pi^+\pi^-)}$ = 0.0208 $\pm$ 0.0035 $\pm$ 0.0021 where the first error is statistical and the second is systematic.Comment: 11 pages, 3 figures, typos correcte

The Target Silicon Detector for the FOCUS Spectrometer

We describe a silicon microstrip detector interleaved with segments of a beryllium oxide target which was used in the FOCUS photoproduction experiment at Fermilab. The detector was designed to improve the vertex resolution and to enhance the reconstruction efficiency of short-lived charm particles.Comment: 18 pages, 14 figure

Study of Cabibbo Suppressed Decays of the Ds Charmed-Strange Meson involving a KS

We study the decay of Ds meson into final states involving a Ks and report the discovery of Cabibbo suppressed decay modes Ds -> Kspi-pi+pi+ (179 +/- 36 events) and Ds -> Kspi+ (113 +/-26 events). The branching ratios for the new modes are Gamma(Ds -> Kspi-pi+pi+)/Gamma(Ds -> KsK-pi+pi+) = 0.18 +/- 0.04 +/- 0.05 and Gamma(Ds -> Kspi+)/Gamma(Ds -> KsK+) = 0.104 +/- 0.024 +/- 0.013.Comment: 11 pages, 6 figure