Miracle-dz/UniFAR-FTUs huggingface dataset:
MultiView High Precision VLBI Astrometry at Low Frequencies

The arrival of the Square Kilometer Array (SKA) will revitalize all aspects of Very Long Baseline Interferometry (VLBI) astronomy at lower frequencies. In the last decade, there have been huge strides toward routinely achieving high precision VLBI astrometry at frequencies dominated by tropospheric contributions, most notably at 22 GHz, using advanced phase-referencing techniques. Nevertheless, to increase the capability for high precision astrometric measurements at low radio frequencies (<8 GHz), an effective calibration strategy of the systematic ionospheric propagation effects that is widely applicable is required. Observations at low frequencies are dominated by distinct direction-dependent ionospheric propagation errors, which place a very tight limit on the angular separation of a suitable phase-referencing calibrator. The MultiView technique holds the key to compensating for atmospheric spatial-structure errors, by using observations of multiple calibrators and two-dimensional interpolation in the visibility domain. In this paper we present the first demonstration of the power of MultiView using three calibrators, several degrees from the target, along with a comparative study of the astrometric accuracy between MultiView and phase-referencing techniques. MultiView calibration provides an order of magnitude improvement in astrometry with respect to conventional phase referencing, achieving ∼100 μas astrometry errors in a single epoch of observations, effectively reaching the thermal noise limit. MultiView will achieve its full potential with the enhanced sensitivity and multibeam capabilities of SKA and the pathfinders, which will enable simultaneous observations of the target and calibrators. Our demonstration indicates that the 10 μas goal of astrometry at ∼1.6 GHz using VLBI with SKA is feasible using the MultiView technique.
The Economic Consequences of Financial Restatements: Evidence from the Market for Corporate Control

This paper investigates whether and how financial restatements affect the market for corporate control. We show that firms that recently filed financial restatements are significantly less likely to become takeover targets than a propensity score-matched sample of non-restating firms. For those restating firms that do receive takeover bids, the bids are more likely to be withdrawn or take longer to complete than those made to non-restating firms. Finally, there is some evidence that deal value multiples are significantly lower for restating targets than for non-restating targets. Our analyses suggest that the information risk associated with restating firms is the main driver of these results. Overall, this study finds that financial restatements have profound consequences for the allocation of economic resources in the market for corporate control.
Using Firm Level Leverage as an Investment Strategy

We use an investment strategy based on firm level capital structures. Investing in low leverage firms yields abnormal returns of 4.43 percent per annum. If an investor holds a portfolio of low leverage and low market to book ratio firms abnormal returns increase to 16.18 percent per annum. A portfolio of low leverage and low market risk yields abnormal returns of 6.67 percent and a portfolio of small firms with low leverage earns 5.37 percent per annum. We use the Fama-Macbeth (1973) methodology with modifications. We confirm that portfolios based on low leverage earn higher returns in longer investment horizons. Our results are robust to other risk factors and the risk class of the firm. JEL: G11, G17, G32 Keywords: Leverage; Capital Structure; Stock Returns; Industry; Investment Strategy; Risk; Size
Novel approach to tensile testing of micro- and nanoscale fibers

Due to the strength and size of the micro- and nanoscale fibers, larger conventional universal testing machines are not suitable in performing stretch test of such fibers. Existing microtensile testing machines are custom-made and are complex and expensive to construct. Here, a novel method of using an existing atomic force microscope (AFM)-based nanoindenation system for the tensile testing of microscale or bundled nanoscale fibers is proposed. The microscale poly (L-lactic-co-glycolic acid) fiber (∼25 μm diameter) was used as an example to illustrate this technique. The microfiber was first attached to a nanoindenter tip and the base via a custom-made holder to ensure that the microfiber was taut and vertically aligned. The force transducer of the nanoindenter was used to measure the tensile force required to stretch the microfiber. The microfiber was stretched using the stepper motor of the AFM system. The elongation of the microfiber was measured by subtracting the elongation of the transducer spring .
Using a Space Filling Curve for the Management of Dynamic Point Cloud Data in a Relational DBMS

The rapid developments in the field of point cloud acquisition technologies have allowed point clouds to become an important source of information for many applications. One of the newest applications of point clouds concerns the monitoring of the coast. Many countries, among which the Netherlands, use this source of data in order to determine the changes in coastal elevations. This means that point clouds are collected every hour, day, month, year; ultimately talking about dynamic point clouds. To be able to efficiently use this plethora of data, the management of those point clouds, dynamic or not, is proven to be crucial. Point clouds, like the majority of geodata, have been traditionally managed using file-based solutions. Nevertheless, the last years database solutions have emerged. Typical examples are the point cloud extensions for PostgreSQL and the Oracle Database. Both options use a similar block-based organisation. In addition to the block based organisations, point clouds can also be managed using a flat table where each point is stored in a separate row. While the first approach is very scalable and efficient, the second is easier to implement and to update. To make the flat model scalable, a Space Filling Curve (SFC) can be used to cluster the data. Nonetheless, both approaches in their current forms, are not suited for the management of dynamic points. The reason for this is the fact that they do not consider the time dimension as part of the organisation and further insertions for the block-based approaches are not straightforward. Within this thesis a SFC approach for managing dynamic point clouds is investigated. For this, the flat model approach using an Index Organised Table (IOT) within a Relational Database Management System (RDBMS) is used. Two variants coming from two extremes of the space - time continuum are then taken into account. In the first approach, space and time are both used within the SFC (integrated approach), while in the second one, time dominates over space (non-integrated approach). Along these two approaches, two treatments of the z dimension are, also, studied: as attribute or as part of the SFC. In addition to that, building on the coastal monitoring applications, the most important queries are identified: space - time, only time, only space. The efficiency of the implemented methodology is tested through the execution of a benchmark. Using two use cases coming from coastal applications, the benchmark is executed once for daily and once for yearly data. The results show that the SFC approach is an appropriate method for managing dynamic point clouds. Furthermore, the integrated approach is the most suitable way to proceed. Achieving scalability, time efficiency and dynamic insertions can be achieved for various use cases.
Cloud Computing as a Catalyst for Change in STEM Education

Cloud computing drives value to profit-centric businesses by establishing a utility computing model, allowing companies to focus on their core business function rather than concern themselves with the “plumbing” associated with technology infrastructure. How then, can this profit-centric model be applied to improve the delivery of Science, Technology, Engineering, and Math (STEM) education? Studies indicate that students perform far better in the workplace if their education in technical fields is relevant, current, and based on real-world scenarios (Pryor, 2014). If businesses are operating in the cloud, STEM education must follow suit. While cloud computing is traditionally associated with profit-centric organizations, this model has demonstrated benefits to non-profit and government organizations as well in terms of reduction of enterprise costs and time to delivery of new products and services. Even when a profit motive is non-existent (or at least not central to the mission of the organization), cloud computing can be a catalyst to transformative improvements in the academic community. As organizations adopt cloud computing, they must face changes in core business practices in order to take advantage of the on-demand service, rapid elasticity, and broad network access associated with cloud computing (Mell & Grance, 2011). Businesses must transform their business architecture in order to adopt this new technology architecture effectively. In the same vein, educators must consider how their business processes (i.e., pedagogy) must change in order to adopt this technology. This chapter considers cloud computing as a technology enabler for STEM education, and how it requires dramatic changes in pedagogy in order to ensure that STEM education is relevant, useful, and effective in the digital world.
Proactive Crisis-management Strategies and the Archaeological Heritage

Heritage organisations depend on public support, which presupposes a reputation for responsible custodianship, technical expertise, sound financial management and sensitivity to conflicting public expectations (e.g. conservation and public access). Public support, however, can be fickle; a reputation can be severely damaged in a few days as a result of an unexpected crisis. Heritage managers have traditionally adopted a reactive approach to crisis handling. Such approaches frequently give rise to panic-driven responses, which amplify the scale of the original crisis and lead to a breakdown of trust among stakeholders. Having explored some examples of crises in the management of the archaeological heritage, this paper concludes with a case for managers to develop proactive crisis-management strategies. The proposed framework recognises its symbiotic relationship with the disciplines of strategy and reputation management. It incorporates three key elements: the identification and evaluation of risks; procedural arrangements for operating in a disaster situation; and effective communications to ensure the continuing support of stakeholders.
Product Life‐Cycle Management of Packaged Software

A software product becomes less valuable for its consumers over time due to technological and economic obsolescence. As a result, firms have an opportunity to introduce and sell upgrades that provide higher utility to consumers compared to an older and out‐of‐date software product. In a market that is growing and consists of homogeneous customers, we prove that the optimal upgrade intervals are monotonically increasing throughout the product's life cycle solely because of demand and cost considerations. This finding is in conformity with empirical evidence, thus validating our theoretical model. We then present comparative statics results to show that increase in the rate of obsolescence or network externalities may sometimes increase upgrade intervals for early upgrades and decrease these for later upgrades in the product's life cycle, but increase in market growth rate always decreases these intervals. Further, when successive software upgrades are forward compatible, upgrade intervals are longer than when they are not. Finally, we present three separate extensions of our model to showcase the robustness of our results. Since upgrade development costs depend on upgrade intervals, these insights help managers understand how costing for upgrades changes over the product's life cycle.
Witnessing Your Own Cognitive Bias: A Compendium of Classroom Exercises

ABSTRACT: Accounting and auditing professors continually stress the importance of effective judgment and decision making (JDM), yet few accounting programs or textbooks discuss the biases that may impact an individual's ability to exercise high-quality professional judgment. In recent years, KPMG (Ranzilla, Chevalier, Herrmann, Glover, and Prawitt 2011) and the Committee of Sponsoring Organizations of the Treadway Commission (KPMG, Glover, and Prawitt 2012) addressed this gap at the corporate level by publishing guidance for accounting professionals and board members on how to identify and mitigate common judgment biases, yet there remain few resources designed for accounting students. This collection of exercises enables instructors to introduce the topic of effective JDM in the classroom. It provides students with the opportunity to identify bias in their own judgments by highlighting five frequently occurring biases that adversely impact business judgments (i.e., availability, anchoring, overconfidence.
Analysis of Classifier Ensembles for Network Intrusion Detection Systems

In this paper an ensemble approach to classify network security data have been presented. The experiments are carried out with Decision trees and Naïve Bayes classifiers and ensemble them with methods like Bagging, Adaboost.M1, Random Forests, MultiBoosting, Rotation Forest and Random Sub Space on NSL KDD dataset which is a modified KDD anomaly detection dataset [1]. Results with different performance measures suggest that no single classification method is the best for all types of datasets on all type of performance measures. The results based on the experiments have been tabulated and their comparative performance suggests that the decision trees ensembles performed better than the Naïve Bayes ensembles. Results also suggest that single decision tree is a good classifier for this data as it has reasonable classification accuracy and less training and testing time.
Bimodal Cross-Validation Approach for Recommender Systems Diagnostics

Recommender systems are becoming an inseparable part of many modern Internet web sites and web shops. The quality of recommendations made may significantly influence the browsing experience of the user and revenues made by web site owners. Developers can choose between a variety of recommender algorithms; unfortunately no general scheme exists for evaluation of their recall and precision. In this chapter, the authors propose a method based on cross-validation for diagnosing the strengths and weaknesses of recommender algorithms. The method not only splits initial data into a training and test subsets, but also splits the attribute set into a hidden and visible part. Experiments were performed on a userbased and item-based recommender algorithm. These algorithms were applied to the MovieLens dataset, and the authors found classical user-based methods perform better in terms of recall and precision.
The Power of Culturally Relevant Texts: What Teachers Learn about their Emergent Bilingual Students

Abstract This chapter details a cultural relevance of text project in-service K-12 teachers engage in during their graduate literacy methods class. Through this project, teachers, who work with emergent bilinguals, learn how to select culturally relevant texts using a rubric. They read their selection with a student and reflect on the experience. Four important conclusions came from an analysis of the projects teachers carried out. Teachers found that their students made connections and were more engaged when reading culturally relevant books. Participants found the rubric helpful in identifying what they should look for in a culturally relevant text. In addition, through this project, the teachers discovered that their libraries tended to lack culturally relevant literature. Finally, participant teachers learned more about their emergent bilingual students through the process of finding and reading culturally relevant stories. While this project was carried out by a limited number of teachers in urban environments, it has implications for teachers of emergent bilingual students in other contexts.
Global photometric analysis of Galactic HII regions

Total infrared fluxes are estimated for 99 HII regions around massive stars. The following wavebands have been used for the analysis: 8 and 24 μm, based on data from the Spitzer Space Telescope (IRAC and MIPS, respectively); 70, 160, 250, 350 and 500 μm, based on data from the Herschel Space Observatory (PACS and SPIRE). The estimated fluxes are used to evaluate the polycyclic aromatic hydrocarbon (PAH) mass fraction (qPAH) and the intensity of ultraviolet emission in the studied objects. It is shown that the PAH mass fraction, qPAH, is much lower in these objects than the average Galactic value, implying effective destruction of aromatic particles in HII regions. Estimated radiation field intensities (U) are close to those derived for extragalactic HII complexes. Color indices [F24/F8], [F70/F24], [F160/F24] and [F160/F70] are compared to criteria proposed to distinguish between regions of ionized hydrogen and planetary nebulae. Also, we relate our results to analogous color indices for extragalactic complexes of ionized hydrogen.
Classroom Audio Distribution in the Postsecondary Setting: A Story of Universal Design for Learning.

Classroom Audio Distribution Systems (CADS) consist of amplification technology that enhances the teacher’s, or sometimes the student’s, vocal signal above the background noise in a classroom. Much research has supported the benefits of CADS for student learning, but most of it has focused on elementary school classrooms. This study investigated the effects of CADS in the postsecondary setting. Surveys and focus groups were used to elicit the perspectives of both students and professors toward CADS in university classrooms, revealing many themes and multidimensional attitudes. Teachers’ and students’ perspectives are considered within the principles of Universal Design for Learning (UDL), which encourages a flexible approach toward teaching in order to include as many different types of students as possible in the learning process. CADS is seen as one way to support UDL in higher education settings.
Exploring Tabletops as an Interaction Medium in the Context of Reservoir Engineering

Digital tabletops are powerful interaction mediums. As a virtual medium their computational capabilities allow the user to digitally explore, transform and embellish the input content to gain further insights about the pertinent information. On the other hand, as a physical medium, their form factor inherently supports collaboration and presents opportunities to place other physical objects atop to assist and enhance the exploration experience. In this thesis, we propose the use of tabletops as an interaction medium to explore reservoir post-processing flow simulation models using virtual content visualizations and a physical agent Spidey : a tabletop robotic assistant. We discuss results that emerged from evaluating each of the prototypes, presenting the potential of each of these concepts and their applicability to the domain of reservoir engineering. With the Spidey testbed we explored the notion of proxemics between a user and a robotic tabletop assistant and performed a user study in which participants interacted with Spidey. Thus in the results, we also discuss the proxemics results reflecting on the interaction between people and tabletop robots.
New Sensorless Sliding Mode Control of a Five-Phase Permanent Magnet Synchronous Motor Drive Based on Sliding Mode Observer

This paper proposes a sensorless sliding mode control (SMC) for a five phase permanent magnet synchronous motor (PMSM) based on a sliding mode observer (SMO). The stability of the proposed strategy is proved in the sense of the Lyapunov theory. The sliding mode controller is designed with an integral switching surface and the sliding mode observer is developed for the estimation of rotor position and rotor speed. The proposed sensorless control strategy exhibits good dynamic response to disturbances. Simulation results are provided to prove the effectiveness of the proposed strategy.
A Literature analysis on Privacy PreservingData Mining

Privacy Preserving Data Mining (PPDM) is a research area concerned with the privacy driven from personally identifiable information when considered for data mining. Therefore, PPDM has become an increasingly important field of research. PPDM is a novel research direction in data mining. A number of methods and techniques have been developed for privacy preserving data mining. This paper provides a complete review on PPDM and different techniques such as data partition, data modification, data restriction technique which could be used to prevent the data access from unauthorized users. Privacy preserving data mining has become increasingly popular because it allows sharing of privacy Sensitive data for analysis purposes. Several data mining algorithms, incorporating privacy preserving mechanisms, have been developed that allow one to extract relevant knowledge from large amount of data, while hide sensitive data or information from disclosure or inference. We provide a review of the state-of-the-art methods for privacy and analyze the representative technique for privacy preserving data mining.
Tactile Dematerialization, Sensory Politics: Hélio Oiticica's Parangolés

Like the word parangolé, a slang term from Rio de Janeiro that refers to a range of events or states including idleness, a sudden agitation, an unexpected situation, or a dance party, the more than thirty objects so titled by Brazilian artist Hélio Oiticica have an indeterminate status. Produced mainly between 1964 and 1968, these flags, tents, and capes made out of jute and plastic bags, painted or printed fabrics, and sometimes including painted or stenciled texts, are meant to be used by the viewer. A Parangolé cape on a hanger is not a Parangolé: its complex textures can only be revealed through the gestures and movements of the person who wears it. As the artist explained in a 1965 text, the spectator of these works becomes a participant or “participator” (participador).
Obstacle Avoidance for Redundant Manipulators Utilizing a Backward Quadratic Search Algorithm

Obstacle avoidance can be achieved as a secondary task by appropriate inverse kinematics (IK) resolution of redundant manipulators. Most prior literature requires the time-consuming determination of the closest point to the obstacle for every calculation step. Aiming at the relief of computational burden, this paper develops what is termed a backward quadratic search algorithm (BQSA) as another option for solving IK problems in obstacle avoidance. The BQSA detects possible collisions based on the root property of a category of quadratic functions, which are derived from ellipse-enveloped obstacles and the positions of each link's end-points. The algorithm executes a backward search for possible obstacle collisions, from the end-effector to the base, and avoids obstacles by utilizing a hybrid IK scheme, incorporating the damped least-squares method, the weighted least-norm method and the gradient projection method. Some details of the hybrid IK scheme, such as values of the damped factor, weights and the clamping velocity, are discussed, along with a comparison of computational load between previous methods and BQSA. Simulations of a planar seven-link manipulator and a PUMA 560 robot verify the effectiveness of BQSA.