151 research outputs found
Quinta Seon's Quick Files
No full textThe Quick Files feature was discontinued and it’s files were migrated into this Project on March 11, 2022. The file URL’s will still resolve properly, and the Quick Files logs are available in the Project’s Recent Activity
Quinta Seon's Quick Files
No full textThe Quick Files feature was discontinued and it’s files were migrated into this Project on March 11, 2022. The file URL’s will still resolve properly, and the Quick Files logs are available in the Project’s Recent Activity
A methodology for spatial and time series data mining and its applications
No full textIn this dissertation, we present several methodologies for mining spatial and time-sequence data obtained in diverse domains. We first propose a new spatial randomness test and classification method for binary spatial data with specific application to the detection and identification of spatial defect patterns on semiconductor wafer maps. We present the generalized join-count (JC)-based statistic as an alternative approach, and derive a procedure to determine the optimal weights of JC-based statistics. In the proposed methodology, a spatial correlogram, which transforms binary spatial data into time-sequence data, is used as a novel feature to detect spatial autocorrelation and classify spatial defect patterns on the wafer maps. Secondly, we propose a novel distance measure, denoted weighted dynamic time warping (WDTW), for time series classification and clustering problems. The dynamic time warping (DTW) algorithm has been extensively used as a distance measure in combination with the distance-based classifiers. However, the DTW algorithm ignores the relative importance of the phase distance between points in a time series, possibly leading to misclassification. Therefore, we propose a WDTW distance measure which does account for the relative importance of each point in terms of the phase distance between the time series points. Thirdly, we propose a wavelet-based anomaly detection procedure to detect any possible process fault with time-sequence data that have some local variations even under normal working conditions. To handle the large number of parameters in both the mean and variance models, we have developed the wavelet-based mean and variance thresholding procedure to extract a few important wavelet coefficients that may explain local variations in the time domain. Finally, we propose a kernel-based regression with lagged dependent variables. Kernel-based regression techniques are extensively used for exploring the nonlinearity of data in a relatively easy procedure involving the use of various kernel functions. However, the major drawback of current kernel-based regression techniques is their underlying assumption that there is no autocorrelation in the residuals of observations. To avoid this problem, we propose a kernel-based regression model with lagged dependent variables (LDVs), considering autocorrelations of both the response variables and the nonlinearity of data.Ph.D.Includes bibliographical referencesIncludes vitaby Young-Seon Jeon
Il Commentario della vita di Giannozzo Manetti di Vespasiano da Bisticci (con un’edizione critica e commentata del testo)
No full textThis work consists in a critical edition and a historical and cultural analysis of the Commentario della vita di Giannozzo Manetti (henceforth Commentario), a work written by Vespasiano da Bisticci (1422-1498). We propose a rereading of it, ascribing the Commentario to an environment of opposition to the Medici’s regime, and we suppose a date of composition in the afterwards of the Pazzi conspiracy.
The Commentario has already been published twice: a first time by Pietro Fanfani in 1862 and a second time by Aulo Greco in 1976. However, both publications are somewhat imprecise from the philological point of view, and limited insofar as they are only based on one version of the Commentario: in reality there are six witnesses with numerous authorial variants, the analysis of which enriches the interpretation of the work. In our dissertation, we highlight that the Commentario is not a mere literary exercise by the author but conveys in fact contents of protest towards the gradual establishment of a Medici lordship in Florence, in contrast to what has hitherto been assumed. Such contents are however cautiously expressed by the author through recourse to Giannozzo Manetti’s biography. Manetti was indeed an important character of Fifteenth Century Florentine public life who showed his reluctance to submit to the centralization of state power in Cosimo the Elder’s hands
Depth Psychological Elements in Seon Master Daehaeng’s Dharma Talks, with Special Reference to <i>Hanmaum Yeojeon</i>
No full textThis essay attempts to approach the dharma talks of Korean Seon Master Daehaeng (1927–2012) from a modern scientific perspective. In particular, it tries to articulate depth psychological elements which belong to or which are relevant in some way to her dharma talks. In so doing, it will attend to the content of her magnum opus, Hanmaum Yeojeon (The Principle of One Mind), which was compiled from her extensive dharma talks. This essay articulates that she could be regarded in contemporary Korean Buddhism as a pioneer, the author of the first works which can be only understood properly if one’s point of departure is the kind of meaning revealing depth psychological elements
Linking Chan/Seon/Zen Figures and Their Texts: Problems and Developments in the Construction of a Relational Database
No full textIssues related to the construction of a database on Buddhist historical figures and their written legacy are discussed in the paper, which deliberately takes the researcher\u27s point of view, reviewing concrete examples rather than elaborating on technical issues. One part of the IRIZ "Zen Knowledge Base" project initiated by Urs App is to establish a unique ID number for each Chan/Seon/Zen figure, thereby enabling each author to be linked with the extant documents. The primary stages of this project having now been completed, the paper presents some initial results and working hypotheses [see endnote], and reflects on wider issues related to the digitization of Buddhist research materials
Constraints on the solar Δm2 using Daya Bay and RENO data
No full textWe demonstrate that the currently running short baseline reactor experiments, especially Daya Bay, can put a significant upper bound on Δm212. This novel approach to determining Δm212 can be performed with the current data of both Daya Bay and RENO and provides additional information on Δm212 in a different L/E range (∼0.5 km/MeV) for an important consistency check on the 3 flavor massive neutrino paradigm. Upper limits by Daya Bay and RENO and a possible lower limit from Daya Bay, before the end of 2020, will be the only new information on this important quantity until the medium baseline reactor experiment, JUNO, gives a very precise measurement in the middle of the next decade. In this study θ12 value is fixed since its impact on the Δm212 measurement is relatively small as discussed in the Appendix. © 2019 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Funded by SCOAP 3. c.Published by the American Physical Society11sci
Social profiling through image understanding: Personality inference using convolutional neural networks
No full textThe role of images in the last ten years has changed radically due to the advent of social networks: from media objects mainly used to communicate visual information, images have become personal, associated with the people that create or interact with them (for example, giving a “like”). Therefore, in the same way that a post reveals something of its author, so now the images associated to a person may embed some of her individual characteristics, such as her personality traits. In this paper, we explore this new level of image understanding with the ultimate goal of relating a set of image preferences to personality traits by using a deep learning framework. In particular, our problem focuses on inferring both self-assessed (how the personality traits of a person can be guessed from her preferred image) and attributed traits (what impressions in terms of personality traits these images trigger in unacquainted people), learning a sort of wisdom of the crowds. Our characterization of each image is locked within the layers of a CNN, allowing us to discover more entangled attributes (aesthetic patterns and semantic information) and to better generalize the patterns that identify a trait. The experimental results show that the proposed method outperforms state-of-the-art results and captures what visually characterizes a certain trait: using a deconvolution strategy we found a clear distinction of features, patterns and content between low and high values in a given trait
Prototyping Business Models for IoT Service
No full textAbstractThe Internet of Things (IoT) generates new business opportunities by connecting physical objects with a multitude of sensors. IoT research mainly focused on technology and business models are relatively unexplored, although developing IoT business models is important for successful IoT service. The existing literature on IoT business models are industry or context-dependent. The aim of this research is to develop a generic business model framework for IoT business through literature analysis and interviews. To test the proposed business model framework, we undertake case studies of current IoT companies. The findings suggest that capability for data analytics is an essential element for IoT service. Also, open ecosystems help companies provide new integrated service and offer greater value for consumers. This research acts as a starting point for designing or developing business models for IoT services
Models of diffuse Hα in the interstellar medium : the relative contributions from in situ ionization and dust scattering
No full textJB acknowledges the support of an STFC studentship. LMH acknowledges support from the US National Science Foundation through award AST-1108911.Using three-dimensional Monte Carlo radiation transfer models of photoionization and dust scattering, we explore different components of the widespread diffuse Hα emission observed in the interstellar medium of the Milky Way and other galaxies. We investigate the relative contributions of Hα from recombination emission in ionized gas and Hα that originates in HII regions near the Galactic mid-plane and scatters off high-altitude dust in the diffuse interstellar medium. For the radiation transfer simulations, we consider two geometries for the interstellar medium: a three-dimensional fractal geometry that reproduces the average density structure inferred for hydrogen in the Milky Way, and a density structure from a magnetohydrodynamic simulation of a supernova-driven turbulent interstellar medium. Although some sight lines that are close to HII regions can be dominated by scattered light, overall we find that less than ~20 per cent of the total Hα intensity in our simulations can be attributed to dust scattering. Our findings on the relative contribution of scattered Hα are consistent with previous observational and theoretical analyses. We also investigate the relative contributions of dust scattering and in situ ionization of high-density dust clouds in the diffuse gas. Dust scattering in these partially ionized clouds contribute ~40 per cent to the total intensity of Hα.Peer reviewe
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