Miracle-dz/UniFAR-FTUs huggingface dataset:
Demonstration of femtosecond laser micromachining for figure correction of thin silicon optics for x-ray telescopes

Future space X-ray telescopes, for example, the Lynx X-ray Observatory under study in the 2020 Astrophysics Decadal Survey, require high angular resolution, large field of view and large effective area X-ray mirrors. Various scientific, engineering and economic considerations make the manufacturing of the telescope optics challenging. In spite of many major improvements in current methods, including slumping (glass shaping), silicon pore optics, and monocrystalline silicon polishing, etc., the required resolution and stability in thin optics have not yet been demonstrated. Furthermore, the high reflective coating films on the mirrors can stress and distort the mirror figure. Therefore, additional steps to correct the mirrors are needed to achieve the stringent requirements for the next generation high-performance X-ray mirrors. In this paper, we demonstrate a novel X-ray mirror figure correction method with the use of femtosecond lasers. Over the last two decades, rapid developments of ultrafast laser technologies have triggered wide applications in the processing of both transparent and opaque materials, from material micromachining to nano-surgeries. We apply this technology in a novel stress-based figure correction technique for X-ray telescope mirrors. We use femtosecond laser beams to micromachine thermal oxide layers on the back side of silicon mirrors, from which regions of intrinsic compressive stress are removed. We pattern laser micromachined spots over the full mirror to compensate the undesired stress introduced from mirror manufacturing processes and reflective coatings. We built a new optics setup using an infrared laser of 220 fs pulse duration and 1 kHz repetition rate, and we designed a procedure for imaging, correcting and measuring mirror substrates with this setup. In this paper, we present the experimental results on the stress manipulation in flat silicon substrates, showing the laser induced integrated stress increases almost linearly with the fraction of area removal in the micromachining. This indicates great potential for correcting thin silicon optics by using appropriate machining parameters for future X-ray telescopes.
Identification of Grafted Grapevines by RFID

The unmistakeable recognition of grafted grapevines is of essential significance for breeders. Different clones of a variety or plants from various origins have identical appearance and cannot be distinguished by traditional ampelographic methods. Valuable propagation material is under constant threat of being mixed up with other batches and becoming lost. A marking system to tag vines permanently during the grafting process could overcome this problem and provide breeders with the required safety in their motherblocks. In addition a permanent tagging system for grafted vines may also be used to back-track propagation material and e.g. identify time and place of a virus infection and take measures to protect other valuable breeding stock from the same origin. Such a tagging system may provide a high safety level in the production of elite, base or pre-base material and protect breeders and nurseries from liability charges.
Falcon : A Graph Manipulation Language for Distributed Heterogeneous Systems

Graphs model relationships across real-world entities in web graphs, social network graphs, and road network graphs. Graph algorithms analyze and transform a graph to discover graph properties or to apply a computation. For instance, a pagerank algorithm computes a rank for each page in a webgraph, and a community detection algorithm discovers likely communities in a social network, while a shortest path algorithm computes the quickest way to reach a place from another, in a road network. In Domains such as social information systems, the number of edges can be in billions or trillions. Such large graphs are processed on distributed computer systems or clusters. Graph algorithms can be executed on multi-core CPUs, GPUs with thousands of cores, multi-GPU devices, and CPU+GPU clusters, depending on the size of the graph object. While programming such algorithms on heterogeneous targets, a programmer is required to deal with parallelism and and also manage explicit data communication between distributed devices. This implies that a programmer is required to learn CUDA, OpenMP, MPI, etc., and also the details of the hardware architecture. Such codes are error prone and difficult to debug. A Domain Specific Language (DSL) which hides all the hardware details and lets the programmer concentrate only the algorithmic logic will be very useful. With this as the research goal, Falcon, graph DSL and its compiler have been developed. Falcon programs are explicitly parallel and Falcon hides all the hardware details from the programmer. Large graphs that do not fit into the memory of a single device are automatically partitioned by the Falcon compiler. Another feature of Falcon is that it supports mutation of graph objects and thus enables programming dynamic graph algorithms. The Falcon compiler converts a single DSL code to heterogeneous targets such as multi-core CPUs, GPUs, multi-GPU devices, and CPU+GPU clusters. Compiled codes of Falcon match or outperform state-of-theart graph frameworks for different target platforms and benchmarks.
Does Aid Promote Electoral Integrity?

ABSTRACT Since the late 1990s, aid spending for elections has witnessed a dramatic increase. Yet, we lack a comprehensive evaluation of aid effectiveness in this particular programme area. Here, we investigate the impact of aid on electoral integrity using panel data on purpose-disaggregated aid disbursements and a multi-dimensional index of electoral quality from the Varieties of Democracy project. Based on 502 elections in 126 aid-receiving countries during 2002–2015, we estimate a statistically significant effect of election-support ODA on the integrity of elections. The estimated effect is, however, economically small and not very persistent. In the long run, a permanent increase in aid spending by one million US$ leads to an improvement in electoral quality of 1.4 per cent of a standard deviation on the integrity index. We also find that different dimensions of electoral integrity are variably responsive to donor interventions. Additionally, aid spending for elections is subject to diminishing marginal returns, and is less effective at higher levels of development. These findings underline the difficulty of promoting democratic change in countries with adverse structural conditions. Still, donors may improve the cost-effectiveness of electoral assistance programmes by targeting specific countries and prioritising certain types of intervention.
X-ray Absorption Spectroscopy Calculations From First Principles

This work deals with improving the numerical models describing the X-ray absorption process. As far as X-ray absorption phenomena are concerned, experimental data (X-ray absorption spectroscopy, resonant scattering and magnetic dichroism) are often not fully understood or ill interpreted. The current Ph.D. work aims to reduce the gap between the experiments and their theoretical description. All numerical developments were integrated to the FDMNES code. A self-consistent procedure for the calculation of the electronic structure was implemented on top of the already existing framework. We equally feature a novel, behind state of the art procedure to describe many body effects based on the time dependent density functional theory (TDDFT).
Emotion‐aware polarity lexicons for Twitter sentiment analysis

Theoretical frameworks in psychology map the relationships between emotions and sentiments. In this paper, we study the role of such mapping for computational emotion detection from text (e.g., social media) with an aim to understand the usefulness of an emotion‐rich corpus of documents (e.g., tweets) to learn polarity lexicons for sentiment analysis. We propose two different methods that leverage a corpus of emotion‐labelled tweets to learn word‐polarity lexicons. The proposed methods model the emotion corpus using a generative unigram mixture model, combined with the emotion‐sentiment mapping proposed in psychology for automated generation of word‐polarity lexicons that capture emotion‐rich vocabulary. We comparatively evaluate the quality of the proposed mixture model in learning emotion‐aware sentiment lexicons with those generated using supervised latent dirichlet allocation (sLDA) and word‐document‐frequency (WDF) statistics. Sentiment analysis experiments on benchmark Twitter data sets confirm the quality of our proposed lexicons. Further, a comparative analysis with sLDA, WDF‐based emotion‐aware lexicons, and standard sentiment lexicons that are agnostic to emotion knowledge suggests that the proposed lexicons lead to a significantly better performance in both sentiment classification and sentiment intensity prediction tasks.
A fast and efficient method for solving the multiple line detection problem

In this paper, we consider the multiple line detection problem on the basis of a data points set coming from a number of lines not known in advance. A new and eﬃcient method is proposed, which is based upon center-based clustering, and it solves this problem quickly and precisely. The method has been tested on 100 randomly generated data sets. In comparison to the incremental algorithm, the method gives signiﬁcantly better results. Also, in order to identify a partition with the most appropriate number of clusters, a new index has been proposed for the case of a cluster whose lines are cluster-centers. The index can also be generalized for other geometrical objects.
Mining information interaction behavior : Academic papers and enterprise emails

In the first part of the thesis, we uncover how academic searchers interact with information objects. We start by characterizing academic search queries, and find that they are different from general web search queries. We also find that it is possible to suggest good query recommendations to help users that encounter query failures, using query characteristics and session information. Next, we examine user behavior over a longer period, in particular, query reformulation and topic shift. We identify multiple query reformulation strategies, and find that revisiting queries is especially common. We look for correlations between query reformulation and topic shift. Surprisingly, we find little correlation in the long term. Topic shift in the short term is correlated with certain reformulation types such as submitting new queries. We also study how users download papers. We characterize their download behavior, and observe different patterns among disciplines. Based on the observations, we propose to predict user downloads, using LSTM-based models in combination with user segmentations. In another scenario, we study a recommender that sends out paper recommendations through newsletters and propose the task of reranking the recommendations, using a hybrid reranking model that considers both content and behavior. In the second part of the thesis, we focus on how users read their enterprise emails, and characterize user reading time. We find that reading time is correlated with many contextual factors. The results improve our understanding of user behavior on email platforms, and also shed light on system improvements to make email reading more efficient.
Temporal and spectral cues in Mandarin tone recognition

This study evaluates the relative contributions of envelope and fine structure cues in both temporal and spectral domains to Mandarin tone recognition in quiet and in noise. Four sets of stimuli were created. Noise-excited vocoder speech was used to evaluate the temporal envelope. Frequency modulation was then added to evaluate the temporal fine structure. Whispered speech was used to evaluate the spectral envelope. Finally, equal-amplitude harmonics were used to evaluate the spectral fine structure. Results showed that normal-hearing listeners achieved nearly perfect tone recognition with either spectral or temporal fine structure in quiet, but only 70%–80% correct with the envelope cues. With the temporal envelope, 32 spectral bands were needed to achieve performance similar to that obtained with the original stimuli, but only four bands were necessary with the additional temporal fine structure. Envelope cues were more susceptible to noise than fine structure cues, with the envelope cues producing significantly lower performance in noise. These findings suggest that tonal pattern recognition is a robust process that can make use of both spectral and temporal cues. Unlike speech recognition, the fine structure is more important than the envelope for tone recognition in both temporal and spectral domains, particularly in noise.
Investment Analysis by Using Tax Assessment Database on Land Lots in Gwangjin District, Seoul, Korea

This paper addresses the issues regarding the performance evaluation of generic investment strategies in land lots in a retail market trade area in a district in Seoul, Korea. To this end, this paper investigates 1993–2016 data on 6,478 parcels of commercially used land parcels in Gwangjin District, one of 25 districts in Seoul, the capital city of South Korea. Gwangjin District extends over 17 km2, with33,307 land lots and a population of 366,939 as of 2015. Because tax assessments in this district are determined by both certified property appraisers (appraisal) and government staff (mass appraisal) in a pegged order, it is generally accepted that the resulting assessed numbers are biased to certain level. Thus, we use the gradient measure to eliminate the most likely biases. Our innovative term of gradient is calculated by subtracting the assessed value of one land lot from that of another in the neighborhood; simply put, the difference of assessed values of two land parcels within certain distance bands. We analyze whole land lots in this district currently used for commercial purposes (retail shops and small offices such as FIRE and clinics), along two dimensions: assessed values and their gradients. What we call the HVHG group consists of land lots with higher assessed values and higher gradients. The HVLG group consists of land lots with higher assessed values but lower gradients. We also consider LVHG (lower assessed values and higher gradients) and LVLG (lower assessed values and lower gradients) groups in our analysis. This research finds that the differences in mean returns between HVLG and other groups except HVHG group are statistically significant based on a paired t-test.We test a couple of investment analyses. Our investment analyses control for the impact of both the value of land lots and distance from subway station on the realized rate of returns. We find that both the smaller valued and remote from the station groups beat the other groups in a statistically significant manner. In addition, a simple one-year momentum land investment strategy is again tested and the result is statistically not significant. Finally, we also visualize the retail market area and intertemporal changes in the market area.
Using Online Job Vacancies to Understand the UK Labour Market from the Bottom-Up

What type of disaggregation should be used to analyse heterogeneous labour markets? How granular should that disaggregation be? Economic theory does not currently tell us; perhaps data can. Analyses typically split labour markets according to top-down classification schema such as sector or occupation. But these may be slow-moving or inaccurate relative to the structure of the labour market as perceived by firms and workers. Using a dataset of 15 million job adverts posted online between 2008 and 2016, we create an empirically driven, ‘bottom-up’ segmentation of the labour market which cuts across wage, sector, and occupation. Our segmentation is based upon applying machine learning techniques to the demand expressed in the text of job descriptions. This segmentation automatically identifies traditional job roles but also surfaces sub-markets not apparent in current classifications. We show that the segmentation has explanatory power for offered wages. The methodology developed could be deployed to create data-driven taxonomies in conditions of rapidly changing labour markets and demonstrates the potential of unsupervised machine learning in economics.
Does Hedge Fund Activism Create Value

Using a data set of 139 events from 2006 to 2019, we demonstrate that announcements of hedge fund activism increases shareholder value in the short run. The abnormal return for the announcement of activism is approximately 6% over a [-10,+10] window. We find statistically significant abnormal returns both when the hedge fund purchases shares in the target firm, and when the hedge fund engages with the firm it owns a stake in. Further, we show that abnormal returns following hedge fund activism are higher in Britain and Ireland than in Continental Europe.
The problem of materials in CLIL

Globalisierung und die damit einhergehende Vernetzung von Gesellschaften macht die Forderung von Sprachkompetenzen unerlasslich. Die Bildungspolitik versucht daher stets, den Ausbau von Sprachkenntnissen zu forcieren. CLIL ist eine Unterrichtsmethode, die genau dieses Ziel erreicht, und deren Einfuhrung in Europa deshalb in den letzten Jahrzehnten stark gefordert wird. Wenig Aufmerksamkeit wurde jedoch seit Einfuhrung der Unterrichtsmethode auf die Bereitstellung von qualitativen Unterrichtsmaterialien gelegt, was zu einem betrachtlichen Materialienmangel gefuhrt hat. Dieses Fehlen von fertig anwendbaren CLIL Materialien geht besonders in Osterreich mit begrenzter Forschung zu Materialienbeschaffung in CLIL einher. Da Geschichte eines der haufigsten CLIL Facher in osterreichischen Schulen ist, mussen vor allem mehr Studien uber CLIL Geschichtsmaterialien durchgefuhrt werden. Die vorliegende Arbeit gibt deshalb uber den Status Quo von CLIL Geschichtsmaterialien und die Materialienbeschaffung durch LehrerInnen in Osterreich Aufschluss. Die vorliegende Studie zeigt funf Interviews mit CLIL Geschichte LehrerInnen an osterreichischen Gymnasien (AHS) und Handelsakademien (HAK). Als Ergebnis dieser Diplomarbeit konnen Moglichkeiten der Materialienbeschaffung sowie Prinzipien und Herausforderungen dabei festgehalten werden. Die Studie beschaftigt sich ebenfalls mit der Zufriedenheit der LehrerInnen mit dem aktuellen Materialienbestand und deren Wunsche fur die Zukunft. Die Ergebnisse zeigen eine weitgehende Unzufriedenheit mit den verfugbaren Materialien und eine Tendenz, Materialien zu adaptieren oder selbst zu erstellen. Feststellen lassen sich auch ahnliche Adaptionsstrategien und Charakteristika bei der Erstellung von Materialien als auch das Ausweisen ahnlicher Qualitatsprinzipien wie kritisches Denken, Aufgabenstellung, interkulturelle Dimension und Authentizitat in Zusammenhang mit Materialentwicklung. Der enorme Zeitaufwand, das Finden des richtigen Sprachniveaus und die Beschaffung von osterreichischen Geschichtsthemen bilden die am haufigsten genannten Herausforderungen bei der Materialbeschaffung. Die Anliegen der LehrerInnen spiegeln gleichzeitig die Schlussfolgerungen der Arbeit wider: Die Materialienbeschaffung kann durch ein speziell fur CLIL Geschichte angefertigtes Schulbuch sowie durch eine online Sammelbank erleichtert werden. Des Weiteren mussen Fortbildungen fur CLIL mehr gefordert und ein groserer Fokus auf Materialienbeschaffung und -entwicklung gelegt werden.
Predicting the NBA Playoff Using SVM

In this paper, we examine the use of Support Vector machine as a tool for predicting the success of basketball teams in the National Basketball Association (NBA). Statistics for 2012–2015 NBA games were collected and used to examine and predict the outcome of NBA playoffs. The best results were able to correctly predict the winning team is 88 percent. Further, we investigate which subset of features input to the SVM model are the most salient features for prediction.
Análisis espacial de las unidades educativas del milenio en el Ecuador y su cobertura en zonas de pobreza.

Uno de los parámetros que más influyen en la pobreza es la mala calidad en la educación. El estudio sistemático de la pobreza es fundamental para mejorar la aplicación de planes y proyectos. En el Ecuador, a partir del año del 2005 inicia el “Proyecto para mejorar las condiciones de escolaridad, el acceso y la cobertura de la educación” en zonas de alto índice de pobreza a través del Gobierno Nacional (Ministerio de Educación, 2016). Este estudio realiza un análisis espacial de dicho proyecto gubernamental del Ecuador mediante el uso del software libre. Dicho análisis se fundamenta en la existencia de las instituciones educativas públicas denominadas “Unidades Educativas del Milenio” (UEM), cuyo fin es mejorar la calidad académica, satisfacer la demanda estudiantil rural y atender a sectores históricamente relegados, partiendo de 57 unidades educativas operativas en el año 2016 y utilizando técnicas de análisis espacial estadístico, apoyados en una base de datos relacional robusta como es el caso de PostgreSQL, con el fin de determinar cuál es su área de afectación a la población, creando varios tipos de coberturas para identificar las parroquias y el porcentaje de pobreza que es atendido por este proyecto educacional, logrando determinar que existe un 77% y el 96% de UEM, en zonas de extrema pobreza.
Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements

Abstract. The environments in which we live, work, and play are subject to enormous variability in air pollutant concentrations. To adequately characterize air quality (AQ), measurements must be fast (real time), scalable, and reliable (with known accuracy, precision, and stability over time). Lower-cost air-quality-sensor technologies offer new opportunities for fast and distributed measurements, but a persistent characterization gap remains when it comes to evaluating sensor performance under realistic environmental sampling conditions. This limits our ability to inform the public about pollution sources and inspire policy makers to address environmental justice issues related to air quality. In this paper, initial results obtained with a recently developed lower-cost air-quality-sensor system are reported. In this project, data were acquired with the ARISense integrated sensor package over a 4.5-month time interval during which the sensor system was co-located with a state-operated (Massachusetts, USA) air quality monitoring station equipped with reference instrumentation measuring the same pollutant species. This paper focuses on validating electrochemical (EC) sensor measurements of CO, NO, NO2, and O3 at an urban neighborhood site with pollutant concentration ranges (parts per billion by volume, ppb; 5 min averages, ±1σ): [CO] = 231 ± 116 ppb (spanning 84–1706 ppb), [NO] = 6.1 ± 11.5 ppb (spanning 0–209 ppb), [NO2] = 11.7 ± 8.3 ppb (spanning 0–71 ppb), and [O3] = 23.2 ± 12.5 ppb (spanning 0–99 ppb). Through the use of high-dimensional model representation (HDMR), we show that interference effects derived from the variable ambient gas concentration mix and changing environmental conditions over three seasons (sensor flow-cell temperature = 23.4 ± 8.5 °C, spanning 4.1 to 45.2 °C; and relative humidity = 50.1 ± 15.3 %, spanning 9.8–79.9 %) can be effectively modeled for the Alphasense CO-B4, NO-B4, NO2-B43F, and Ox-B421 sensors, yielding (5 min average) root mean square errors (RMSE) of 39.2, 4.52, 4.56, and 9.71 ppb, respectively. Our results substantiate the potential for distributed air pollution measurements that could be enabled with these sensors.