Miracle-dz/UniFAR-FTUs huggingface dataset:
GALARIO: a GPU accelerated library for analysing radio interferometer observations

We present GALARIO, a computational library that exploits the power of modern graphical processing units (GPUs) to accelerate the analysis of observations from radio interferometers like Atacama Large Millimeter and sub-millimeter Array or the Karl G. Jansky Very Large Array. GALARIO speeds up the computation of synthetic visibilities from a generic 2D model image or a radial brightness profile (for axisymmetric sources). On a GPU, GALARIO is 150 faster than standard PYTHON and 10 times faster than serial C++ code on a CPU. Highly modular, easy to use, and to adopt in existing code, GALARIO comes as two compiled libraries, one for Nvidia GPUs and one for multicore CPUs, where both have the same functions with identical interfaces. GALARIO comes with PYTHON bindings but can also be directly used in C or C++. The versatility and the speed of GALARIO open new analysis pathways that otherwise would be prohibitively time consuming, e.g. fitting high-resolution observations of large number of objects, or entire spectral cubes of molecular gas emission. It is a general tool that can be applied to any field that uses radio interferometer observations. The source code is available online at http://github.com/mtazzari/galario under the open source GNU Lesser General Public License v3.
Media multitasking and performance on attentionally demanding tasks

v Acknowledgements . vii Dedication . viii List of Figures . xiv List of Tables . xvii Chapter 1: General Introduction . 1 Individual Differences in Media Multitasking . 4 Media Multitasking in-the-Moment . 8 Modulating Media Multitasking Behaviour. 12 Overview of Chapters . 16 Chapter 2: Individual differences in media multitasking and performance on the n-back . 19 Chapter Introduction . 20 Method . 23 Participants . 23 Stimuli and Procedure . 24 Results . 27
A new approach for electronic image stabilization based on block matching

The paper introduces a new electronic image stabilization approach based on block matching. It chooses some fixed points which form a grid at the present frame and find out the corresponding points at the next frame. The better points are found out by the center-reservation algorithm. We can obtain the global motion estimation parameters from local motion parameters. Then the stable image sequence can be obtained by compensating image frames. Experimental results show that the algorithm can stabilize image sequence robustly and effectively.
Toward sub‐micron oxide thin‐film transistors for digital holography

High‐performance 2‐μm‐channel oxide thin‐film transistors (TFT) on glass substrate for a 7‐μm‐pixel‐pitch spatial light modulator panel for digital holography applications were fabricated using a two‐step source/drain etching process. It showed a μFE of 45.5 cm2/Vs, SS of 0.10 V/dec, and Von of near zero voltage. Furthermore, we succeeded in the demonstration of sub‐micron TFTs, which is an indispensable route to next‐generation spatial light modulation devices with near 1‐μm pixel pitch. The issue of short‐channel transistors for display applications is also introduced. Finally, the digital holographic demonstration results based on the fabricated backplane are presented.
“Only English Counts”: the impact of English hegemony on South-Korean athletes

Purpose While conceived to examine key factors affecting post-retirement career advancement of retired elite athletes in South Korea, the purpose of this paper is to report how English, as a de facto global lingua franca, functions as a powerful gatekeeper in the sports administration field. Design/methodology/approach Interpreted through the lens of Bourdieu’s linguistic capital and Gramsci’s hegemony of language, the present study draws on content analysis of semi-structured individual interviews, as well as focus group interviews, conducted with thirty former South Korean elite athletes. Findings Based on the data analysis, systematic bias toward athletes was uncovered, privileging English as the single determining factor for employment. Furthermore, the educational implications for adult learners of English as a Foreign or English an Additional Language reveal unrealistic expectations of top–down language policies. Originality/value Perspectives of athlete participants, an underrepresented group in educational research, within the South Korean globalization context shed critical light on the pervasive aspects of English hegemony and its unexamined dimensions.
Analysis of Criminal Profiling Utilizing Structured and Unstructured Data

In general, the structured data knows the meaning of the sentence and unstructured data refers to an unknown means. Although the quantity of structured information in the entire data and within organizations is increasing, the majority of information remains available only in unstructured data. While different in form, both unstructured and structured information sources provide information about entities in the world and their properties and relations. Due to the recent rapid changes in society and wide spread of information devices, diverse digital information is utilized in a variety of economic and social analysis. Information related to the crime statistics by type of crime has been used as a major factor in crime. However, statistical analysis using only the structured data has the difficulty in the investigation by providing limited information to investigators and users. In this paper, structured data and unstructured data are analyzed by applying Korean Natural Language Processing (Ko-NLP) and the Latent Semantic Analysis (LSA) technique. It will provide a crime profile optimum system that can be applied to the crime profiling system or statistical analysis [1].
Application of HRM’s Universal Model: an examination of people vs institutions as barriers of internationalization for SMEs in a small developing country

Although the importance of people and institutions has been highlighted in MNE studies, the association between people, institutions and the internationalization of SMEs is under explored from a small developing country context. We examine theories subsumed under the Universal Model of HRM onto SMEs to study the hypothesized people and institutionally-orientated barriers posed to SMEs’ internationalization. Using quantitative analysis, the impacts of the barriers on internationalization are highlighted with particular attention to employer attractiveness. Following extant literature review, this study has used primary data collected through questionnaires from 212 Bangladeshi SMEs. We developed and validated partial least square based structural equation model (PLS-SEM) to assess the impacts and successfully framed People versus Institutional barriers as a second order hierarchical reflective model which has not been done previously. Given their marginal significance over institutions, people-oriented cultural barriers should be included as an extension of the Universal Model to study SME internationalization
Improving Hardware Reusability: Software Defined Hardware

Modern software engineering techniques such as reusable libraries and retargetable compilers have allowed high productivity and fast design cycles, but hardware development practices lag far behind. We hypothesize that better hardware construction languages and compiler infrastructures will help close the gap, and evaluate this approach with several processor designs written in the Chisel hardware construction language and compiled through the FIRRTL compiler infrastructure.
Intégration Produit-Process appliquée à la sélection de procédés de Fabrication Additive

Cette recherche vise a proposer une approche integree permettant la prise en compte simultanee des parametres Produits / process dans le cadre d’une fabrication par ajout de matiere. Le developpement produit est en profonde mutation, prenant en compte les contraintes de personnalisation, de temps de mise sur le marche de plus en plus court, la volonte d’une approche eco-responsable etc. Ce changement de paradigme conduit a s’interesser au choix du couple materiau /process des la phase de conception afin de prendre en compte les contraintes liees au procede identifie. Cette approche multi critere s’interesse a la fois au couple materiau procede mais prend en compte les aspect fonctionnels de la piece. Ainsi ce travail de these presente une methodologie de decision generique, basee sur des outils de prise de decision multicriteres, qui peut non seulement proposer une solution satisfaisant les contraintes liees aux materiaux, processus et processus par addition de matiere, mais propose egalement de servir de guide aux concepteurs permettant un choix raisonne base sur des combinaisons materiau-machine orientees conception et obtenu a partir d’une base de donnees de 38 fournisseurs internationaux de machine de fabrication par ajout de matiere.
Research on Keyword Extraction and Sentiment Orientation Analysis of Educational Texts

Data acquisition, text keyword extraction and text sentiment orientation analysis are important technologies which can be applied in text content analysis. In this paper, a general crawler based on Scrapy framework is designed. It can be applied to many kinds of web sites and improved the efficiency and the versatility. A mixed method based on Textrank algorithm and TF-IDF algorithm is proposed. It can be applied to mobile client to extract the keywords. For the text tendency analysis, a new method based on SnowNLP is proposed. The experiment shows that crawling and extracting results and the tendency judge are more accurate for the long educational text.
Constructing Classroom Contexts that Engage Students in the Learning of Mathematics: a Teacher's Perspective

This paper explores the construction of classroom contexts facilitative of student engagement in Mathematics. Employing a form of discourse analysis framed within a participation approach to learning, the paper provides insights into the construction of such contexts. The affordances and constraints of constructing such a context are discussed in the light of the writings of one Year 7 teacher as she employed Collective Argumentation to re-construct her classroom context to better engage students in the learning of Mathematics.
Formalization and modeling of human values for recipient sentiment prediction

Sentiment analysis is viewed generally as a text classification problem involving the prediction of the semantic orientation of a text. Much of the analysis has focused on the sentiment expressed in the sentence or by the writer but not the sentiment of the recipient. For example, the sentence “Housing costs have dropped significantly” might be assigned a negative classification by a sentiment analysis model, however humans from different works of life might express different sentiments. A landlord will likely express a negative sentiment while a renter might express a positive sentiment. Therefore, traditional sentiment analysis methods fail to capture the human centric aspects that motivate diverse sentiments. Additionally, attempts at predicting recipient sentiment have involved considerable human effort in the form of content analysis and empirical surveys, making the process expensive and time-consuming. Thus, the aim of this research is to develop a method of recipient sentiment analysis that is devoid of human input in the form of annotations or empirical surveys. The approach taken in this research involves applying a model of human values towards recipient sentiment prediction. The justification for this approach is based on the well-established principle that values influence human behaviour of which sentiment is a form. Therefore, if a persons’ values can be modelled quantitatively, when presented with some text, in theory the sentiment of the recipient can be predicted. This research proposes that the application of values in developing sentences is a generative process, that can be represented as a language model. A mechanism called Feature Switching (FS) that enables the determination of recipient’s sentiment from the value language model is also discussed. The resulting sentiment prediction model has an accuracy in the range of 72.2%-72.5% which is in and about the range of performance of existing systems which make use of content analysis and human annotated data.
Insight in Information: from Abstract to Anomaly

As a result of cheap data storage, nowadays it is not the question if a company or institution collects data or not, but rather how much they collect. Transforming data into information and getting insight in this information is perhaps the most important problem in our data rich society. That is, only collecting data serves no goal, but data becomes valuable when insight can be gained from it. Data mining is the subfield of computer science that concerns itself with transforming large amounts of data into information in the form of patterns. The idea is that the identified patterns result in new insights by exposing interesting structure or behaviour in the data. It may be obvious that defining what exactly is interesting is one of the key challenges. One of the main applications of data mining on which we focus in this thesis is exploratory data analysis. In this analysis we make use of summaries and characterisations of a dataset to gain insight. That is, by inspecting and exploring the patterns that comprise these models we can extract important information from the data. In this thesis we employ the Minimum Description Length (MDL) principle to find such models which we call summaries. That is, we find the best summary as the set of patterns that give the best compression of the data. Additionally, these summaries can also be used for other data mining tasks, such as the identification of irregular or abnormal data points. All these deviations from what could be expected are called anomalies. We also focus on anomaly detection in this thesis, for which the goal is to gain more insight in the information we already have. Finally, we conclude that the MDL principle can be successfully employed in the domain of multivariate sequential data. Both for summarisation and anomaly detection successful algorithms have been introduced which are tested on a variety of synthetic and real world datasets.
Revisions about Government Subsidy Disclosures and Investors’ Response to Earnings Reports: Evidence from the 2017 Government Subsidy Disclosures

We examine whether the quality of information in earnings reports, as indicated by the earnings response coefficient (ERC) decreases when the measurement and disclosure of government subsidy has been changed by revising accounting standards. According to the old accounting standards in China, the gains from government subsidy should be reflected in “non-operating” income. However, the newly revised accounting standards regulates that a part of government subsidy can be reflected in the report item of “other income” in under the operating income item. We use 2017 revisions of accounting standards in China as an instrument to capture a change in the position of financial statements where the government subsidy is revealed. Employing a difference-in-differences design and exploiting the adoption of the new accounting standards, we find a statistically and economically significant decrease in ERC for treated firms relative to controlled firms. Our findings represent the first empirical evidence that the change of government disclosures can negatively affect the information content of earnings report.
Low-dimensional data embedding for scalable astronomical imaging in the SKA telescope era

Astronomy is one of the oldest sciences known to humanity. We have been studying celestial objects for millennia, and continue to peer deeper into space in our thirst for knowledge about our origins and the universe that surrounds us. Radio astronomy – observing celestial objects at radio frequencies – has helped push the boundaries on the kind of objects we can study. Indeed, some of the most important discoveries about the structure of our universe, like the cosmic microwave background, and entire classes of objects like quasars and pulsars, were made using radio astronomy. Radio interferometers are telescopes made of multiple antennas spread over a distance. Signals detected at different antennas are combined to provide images with much higher resolution and sensitivity than with a traditional singledish radio telescope. The Square Kilometre Array (SKA) is one such radio interferometer, with plans to have antennas separated by as much as 3000 km. In its quest for ever-higher resolution and ever-wider coverage of the sky, the SKA heralds a data explosion, with an expected acquisition rate of 5 terabits per second. The high data rate fed into the pipeline can be handled with a two-pronged approach – (i) scalable, parallel imaging algorithms that fully utilize the latest computing technologies like accelerators and distributed clusters, and (ii) dimensionality reduction methods that embed the high-dimensional telescope data to much smaller sizes without losing information and guaranteeing accurate recovery of the images, thereby enabling imaging methods to scale to big data sizes and alleviating heavy loads on pipeline buffers without compromising on the science goals of the SKA. In this thesis we propose fast and robust dimensionality reduction methods that embed data to very low sizes while preserving information present in the original data. These methods are presented in the context of compressed sensing theory and related signal recovery techniques. The effectiveness of the reduction methods is illustrated by coupling them with advanced convex optimization algorithms to solve a sparse recovery problem. Images thus reconstructed from extremely low-sized embedded data are shown to have quality comparable to those obtained from full data without any reduction. Comparisons with other standard ‘data compression’ techniques in radio interferometry (like averaging) show a clear advantage in using our methods which provide higher quality images from much lower data sizes. We confirm these claims on both synthetic data simulating SKA data patterns as well as actual telescope data from a state-of-the-art radio interferometer. Additionally, imaging with reduced data is shown to have a lighter computational load – smaller memory footprint owing to the size and faster iterative image recovery owing to the fast embedding.
Emerging Research in the Analysis and Modeling of Gene Regulatory Networks

While technological advancements have been critical in allowing researchers to obtain more and better quality data about cellular processes and signals, the design and practical application of computational models of genomic regulation continues to be a challenge. Emerging Research in the Analysis and Modeling of Gene Regulatory Networks presents a compilation of recent and emerging research topics addressing the design and use of technology in the study and simulation of genomic regulation. Exploring both theoretical and practical topics, this publication is an essential reference source for students, professionals, and researchers working in the fields of genomics, molecular biology, bioinformatics, and drug development.
Essays on Information in Financial Markets

In efficient markets, information, once public, should be instantaneously and correctly reflected in prices. But do investors consume information efficiently? Or do they allocate attention based on presentation, vary in their reading speed and sophistication, and hold overconfident beliefs? My dissertation addresses frictions in information processing and belief formation that may prevent even publicly available information from being correctly and immediately priced. I find that positioning of financial news plays a large role in determining how the information gets reflected in prices. When a piece of news is saliently highlighted to investors, the price response can be very efficient, taking under an hour. The incorporation of less saliently presented information takes much longer, however: although the price paths eventually converge, this process can take multiple days. I also address the puzzle of increased trading volume around news events. I find that differences in when investors see the news are just as instrumental in explaining trading volume as the diversity of who is reading the news, especially for more straightforward news. Lastly, I consider biased belief formation and overconfidence. Focusing specifically on the domain of present bias, I document that individuals are aware of this bias in others but remain overoptimistic specifically about themselves. This wedge in beliefs is relevant not only for trading in financial markets, but also in a variety of other settings including teams in the workplace.
Converting internal brand knowledge into employee performance

Purpose Internal branding efforts are essential in improving employee performance in services marketing. Drawing on reformulation of attitude theory, this paper aims to contribute to the internal branding literature by positing that while internal brand knowledge (IBK) is essential for transforming brand vision into brand reality, it is not brand knowledge per se but its integration with other brand- and customer-related aspects that drive superior employee performance. In particular, this paper develops a cognitive-affective-behaviour model of internal branding proposing that IBK results in higher levels of employee brand identification (EBI); this sense of identification then motivates employees to engage in both employee-related and brand- and customer-focussed behaviours (i.e. brand citizenship behaviour [BCB] and customer-oriented behaviour [COB]), which in turn foster employee performance. Design/methodology/approach The hypotheses were empirically tested using a sample of 697 from services industry in Vietnam. Findings The findings indicate a sequential mediation model in that employee brand knowledge affects employee performance (both objective and subjective measures) through EBI, BCB and COB. Employee brand knowledge results in higher levels of EBI; this sense of identification then motivates employees to engage in employee-related brand and customer-focussed behaviours (BCB and COB), which in turn foster employee performance. Practical implications Firms should understand that IBK may not directly result in high levels of service performance, and instead should embrace the culture of self-driven positive brand-connection attitudes that motivate employees to engage in BCB and COB that are consistent with their sense of self. Originality/value This study makes a unique contribution to the internal branding literature by unravelling a pathway that integrates employees’ self-related psychological mechanism (EBI) and employee-related brand and customer-focussed behaviours (BCB and COB) through which employee brand knowledge is converted into employee performance.
Regresión quantílica para la cuantificación del riesgo

La regresion cuantilica de Koenker y Bassett (1978) ha supuesto una herramienta muy util en los estudios economico-financieros. En este trabajo, nos enfocamos en su aplicacion en el ambito de la gestion y la cuantificacion de riesgos, que se caracteriza por datos con distribucion de cola pesada. Bajo un estudio de simulacion se constata que el modelo de Koenker y Bassett (1978) y las nuevas metodologias no parametricas basada en splines y estimacion nucleo fracasan en la prediccion de las observaciones del modelo generado para cuantiles extremos. Por lo tanto, se constata la necesidad de proporcionar metodos alternativos que nos permitan mejorar la estimacion de los cuantiles condicionales para niveles de confianza proximos a 1 y con distribucion de cola pesada. Finalmente, se analizan los principales factores causantes de los costes mas extremos asociados a una cartera de polizas de automovil y hogar.