Miracle-dz/UniFAR-FTUs huggingface dataset:
Kara, Alper, Marques-Ibanez, David and Ongena, Steven Securitization and Credit Quality in the European Market Original Citation Kara, Alper, Marques-Ibanez, David and Ongena, Steven (2018) Securitization and Credit Quality in the European Market. European Financial Management. ISSN 1468-036X

We assess the effect of securitization activity on relative credit quality employing a uniquely detailed dataset from the euro-denominated syndicated loan market. We find that at issuance, based on observable characteristics, banks do not seem to select and securitize loans of lower credit quality. Following securitization, the credit quality of borrowers whose loans are securitized deteriorates more than those in the control group. We find that poorer performance by borrowers of securitized loans seems to be connected to banks’ reduced monitoring incentives. Our results are supported by two additional methodologies and robust to controlling for predetermined borrower-lender matching. JEL Classification Numbers: G21; G28
OP-ESRJ190031 738..755

The German Socio-Economic Panel (SOEP) study is a rich resource for sociologists, mainly because it offers direct measures of respondents’ contexts. The SOEP data provide (i) information retrieved from individuals themselves, (ii) direct information retrieved from their parents, partners, and organizations, (iii) prospectively collected information on past characteristics, and (iv) regional and spatial identifiers allowing researchers to link the data with regional-level characteristics. As the study has been in the field since 1984, the data also reflect variation in institutional and structural settings over time. Regular refreshment samples provide options to identify cohort effects. Together, these features allow multi-layered contextual designs that offer substantive insights into the effects of formal and informal institutions on individual behaviour and living conditions. This article introduces the main types of SOEP-based sociological research designs and discusses their survey methodological origins. It also points to underexplored potentials as well as limitations of the SOEP. Finally, it offers basic suggestions for approaching the data in each of the research designs presented.
ORB-SLAM with Near-infrared images and Optical Flow data

The algorithms designed to solve the Simultaneous Localization And Mapping (SLAM) problem have to be often executed on embedded platforms in order to become part of complex robotics systems. Despite the continuous growth of their computational capabilities, the embedded devices still have considerable limitations, especially in terms of memory. This paper presents a modified version of the well known ORB-SLAM algorithm which improves its performance thanks to the use of Hardware-generated Optical Flow (HW-OF). The ORB-SLAM has been modified in order to run into the Stereo-cam embedded system by STMi-croelectronics. The Stereo-cam includes the VD56G3 sensor, able to provide Near Infrared (NIR) images and OF data computed by a hardware accelerator. The experiments showed an improvement of the ORB-SLAM performances in terms of memory consumption and frame rate.
Fault Diagnosis with Multi-State Alarms in a Nuclear Power Control Simulator

This research addresses how alarm systems can increase operator performance within nuclear power plant operations. The experiment examined the effect of two types of alarm systems (two-state and three-state alarms) on alarm compliance and diagnosis for two types of faults differing in complexity. We hypothesized three-state alarms would improve performance in alarm recognition and fault diagnoses over that of two-state alarms. We used sensitivity and criterion based on Signal Detection Theory to measure performance. We further hypothesized that operator trust would be highest when using three-state alarms. The findings from this research showed participants performed better and had more trust in three-state alarms compared to two-state alarms. Furthermore, these findings have significant theoretical implications and practical applications as they apply to improving the efficiency and effectiveness of nuclear power plant operations.
3D Building Reconstruction from Monocular Remote Sensing Images

3D building reconstruction from monocular remote sensing imagery is an important research problem and an economic solution to large-scale city modeling, compared with reconstruction from LiDAR data and multi-view imagery. However, several challenges such as the partial invisibility of building footprints and facades, the serious shadow effect, and the extreme variance of building height in large-scale areas, have restricted the existing monocular image based building reconstruction studies to certain application scenes, i.e., modeling simple low-rise buildings from near-nadir images. In this study, we propose a novel 3D building reconstruction method for monocular remote sensing images, which tackles the above difficulties, thus providing an appealing solution for more complicated scenarios. We design a multi-task building reconstruction network, named MTBR-Net, to learn the geometric property of oblique images, the key components of a 3D building model and their relations via four semantic-related and three offset-related tasks. The network outputs are further integrated by a prior knowledge based 3D model optimization method to produce the the final 3D building models. Results on a public 3D reconstruction dataset and a novel released dataset demonstrate that our method improves the height estimation performance by over 40% and the segmentation F1-score by 2% - 4% compared with current state-of-the-art.
ITAI: Adaptive Neural Machine Translation Platform

: We describe an adaptive neural machine translation platform which integrates continuous learning and supports multiple use-cases in the translation industry. The application is being developed and evaluated within the applied research project ITAI. Research within the project has shown the potential of the platform to cover the main identiﬁed use cases and provide rapid adaptation via continuous learning.
Examining Users' E-Satisfaction in the Usage of Social Networking Sites; Contribution from Utilitarian and Hedonic Information Systems

E-satisfaction (eSAT) is an important success factor of online service providers such as social networking sites (SNSs). The utilitarian and hedonic information systems are crucial in determining users' eSAT of SNSs, especially among young users. The utilitarian aspect of an information system is productivity-oriented which aims to enhance the users' task performance, and it is important in measuring eSAT of SNSs. In this study, the original constructs of Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of TAM of utilitarian information system was first developed in this research framework. The use of SNSs, such as Facebook, is pleasure-oriented, in which self-fulfilling values to the users are important in determining users' satisfaction towards the SNSs. Therefore, Perceived Enjoyment (PE) of hedonic information system is added to the framework. Thus, the research framework of this study includes both utilitarian (PEOU and PU) and hedonic (PE) aspects of information systems to determine Malaysian young users' eSAT in the usage of Facebook, a social networking site. In this framework, the effects of PEOU, PU and PE on eSAT in the usage of Facebook are examined among Facebook's users in the age of 18 – 24 years old. The effects of PEOU on PU and PE are also examined. Online questionnaire survey was employed and a total of 384 sets of questionnaires were gathered from users of Facebook. The results indicated that PEOU has positive effects on PU and PE in the context of Facebook. In addition, PEOU, PU and PE are also found to have positive effects on eSAT. PE of hedonic information system exerted higher effect on eSAT, compared to PEOU and PU of utilitarian information system, highlighting the importance of pleasure orientation in the usage of Facebook of SNSs. Managerial and theoretical implications of the study are discussed in term of measuring and enhancing users' eSAT in the usage of SNSs, particularly Facebook.
Tripping at the Finish Line: Experimental Evidence on the Role of Misperceptions on Secondary School Completion

Even in contexts where access to education is not the main barrier for educational achievement, completion rates can be low. In Argentina, more than 90 percent of teenagers are enrolled in upper secondary school, but only 50 percent graduate on time. I conducted a field experiment in Salta, Argentina, to test if lack of information about how inputs translate into outputs may prevent students who attend classes until the last day of high school from getting their diploma. To measure the relative importance of this treatment, I conducted a returns-to-education information intervention in a separate treatment arm. Providing information about the probability of graduation conditional on current standing and discussing intermediate steps to translate effort during the senior year of high school into graduation raises timely high school graduation by 5 percentage points, a 10 percent increase relative to the control group. Poor-performing students at baseline respond most to the treatment. The returns-toeducation arm increases graduation rates by 10 percentage points. Both treatments increase the probability of university enrollment by 5 percentage points, or more than 30 percent relative to the control group). Together, these findings indicate that inaccurate beliefs about own future performance explain a significant share of the “graduation gap”. ∗Brown University, Department of Economics. Email: carolina_lopez@brown.edu. I am incredibly thankful to Bryce Steinberg, Andrew Foster, Pedro Dal Bó, Anja Sautmann, and Neil Thakral for their support and encouragement. I am grateful to Alex Eble, Simone Schaner, Jesse Shapiro, Alejandro Ganimian, Natalie Bau, seminar participants at Brown Applied Micro Lunch, and the Development Tea group members at Brown University for helpful discussions and feedback. I am specially grateful to Asim Khwaja for valuable comments. Also, I would like to thank to Angélica Astorga, Felipe Brugués, Amanda Loyola Heufemann, Juan Ignacio Lopez, Ana Pacheco, Santiago Hermo, Diego Ramos-Toro, and Natalia Sández Pernas for their invaluable support. The field experiment would not have been possible without the authorization and support of the Directorate of Secondary Education of the Ministry of Education of Salta and the authorities of each participant school. I acknowledge the financial support to digitized academic records from the Orlando Bravo Center for Economic Research at Brown University and CAF–Development Bank of Latin America. The experiment was approved by Brown University Institutional Review Board and it was registered with the pre-analysis plan at AEA RCT registry (RCT ID: AEARCTR-0001299). All errors are my own.
Irina Roncaglia

A recent editorial on Psychreg Journal of Psychology (Lane, 2018) makes an argument for the importance of accessibility distribution and impact of scientific and psychological knowledge. Ultimately, academic and scientific knowledge ought to advance our practices and aims to make a meaningful difference in people’s lives, improving their overall well-being as well as developing an ability to recover, if illnesses have been experienced. This paper aims to explore and infiltrate reflections around the issue which necessitates the promotion of building bridges between these two areas: theoretical knowledge and practice, where all too often silos of academic and scientific knowledge have little or no effect at all in the real world. As highlighted by Lane, the rise of open access journals and social media platforms ought to be seen as a way to facilitate this process of dissemination and impact, helping to address some of the barriers that have historically prevented a vision of organic and interlocking systems working really together – rather than often seen working alongside in parallel – for and towards a shared aim. The thematic analysis identified four main themes: (1) promoting physical and psychological strengths; (2) promoting integrative educational services; (3) encouraging active engagement; and, (4) promoting relevant accessibility. Barriers and enablers are presented and discussed and how these main themes can be a starting point in guiding future discussions, research topics and ultimately more effective and affordable positive practices.
Information support of nature management and environmental protection in Ukraine

This research develops the problem of effectiveness of the management system and protection of the natural environment. In this research, the various factors of management activity are proposed. To achieve this goal we conducted research on information and analytical systems, which are implemented in the management of nature and environmental protection of Ukraine at three levels: regional (oblast), district and urban. The article presents the results of the analysis of classifiers for systematization of circulating documents, functions of computer document management systems, including those implemented in the management of nature and environmental protection, as well as the level of satisfaction with the quality of software products used to management. It is established that for information support of management activities the most necessary are databases on financial and economic activities, regulatory, personnel, resource, logistics and environmental statistics. The importance of information flow system analysis and information technologies are constantly developing and adapting to user awareness. In the investigation underlined the practical use of different methods to solve similar problems taking into account the peculiarities of the organization.
Modelling and Decentralized Adaptive Model Predictive Control for Alumina Trihydrate Precipitation Process

Abstract This paper presents a decentralized adaptive model predictive control scheme for alumina trihydrate precipitation process that is a large-scale nonlinear complex system with strong disturbances and a large time constant. The overall precipitation system is decomposed into several small-scale systems expressed as several Hammerstein structures with an adaptive disturbance model. The local controller takes the upstream state into account as a measured disturbance. The proposed method preserves the small computation and communication properties of decentralized MPC while ensuring a good performance. A simulation based on real data shows the model is true and the control method is effect.
Modeling of Artificial Muscles Made of Nylon Wire

Artificial muscles are an area where the bio-mechanical and bio-medical fields can merge their expertise with robotics. However, despite the effort to create new technologies or improve the existing ones, the majority of biological muscles’ properties are hard to duplicate. Therefore, prosthetic devices lack optimization in weight, reliability and do not feel natural and are expensive. This is a problem for amputees in developing countries, notably children, who quickly outgrow their prostheses. Recently, a work produced supercoiled polymer actuators with fishing line, which revealed attractive properties to manufacture artificial muscles to apply in prostheses. The present work studied strategies to improve the contraction and expansion of these actuators, and built an agonist and antagonist association, with the selected solution. Grey-box modeling, based on the linear mass-spring-damper model and on the Newton’s law of cooling, covered the association dynamics, together with a PID controller for the angle. Single actuators’ results were in line with other works findings and revealed some degree of similarity to biological muscles’ properties. Simulations and tests upon the association validated and verified the model and control scheme. It is possible to improve the results and explore new paths; still, they were satisfactory and in accordance with the expected behavior. The work also described the actuators’ manufacturing process.
Non-Stationary Representation Learning in Sequential Multi-Armed Bandits

Most of the existing theoretical studies on representation learning are focused on batch tasks. However, in practical decision-making scenarios, the learner often observes tasks in a sequential fashion. In such sequential problems, learning good representations becomes more challenging as the underlying task representation may change over time. In this paper, we address non-stationary representation learning in sequential multi-armed linear bandits. We introduce an online algorithm that is able to detect task switches and learn and transfer a non-stationary representation in an adaptive fashion. We derive a regret upper bound for our algorithm, which signiﬁcantly outperforms the existing ones that do not learn the representation. Our bound provides theoretical insights into problem-dependent quantities and reveals the excess regret incurred by representation learning, non-stationarity, and task switch detection.
Bandwidth-optimal Relational Joins on FPGAs

The relational equality-join is one of the most performance-critical operators in database management systems. For this reason, there have been many attempts to implement this operator on FPGAs in various sort-merge and hash join variants. However, most achieve suboptimal performance because they ineffectively use the limited memory bandwidths of current FPGA platforms. In this paper, we present an FPGA-based implementation of the partitioned hash join (PHJ), where both PHJ phases are executed on the FPGA. Contrary to prior work, we consider a commonly used PCIe-attached FPGA card with dedicated on-board memory. We discuss how to utilize this on-board memory effectively and propose a solution that uses this memory to store partitioned tuples, minimizing data transfers to system memory and thus optimally using the available bandwidth. In our experimental evaluation we demonstrate up to 2x faster end-to-end join times than state-of-the-art 32-threaded hash join implementations on the CPU.
Contribution of Asian emissions to upper tropospheric CO over the remote Pacific

Abstract. We use CO data from the MOPITT satellite instrument from 2000–2019 to compose a climatology of severe pollution events in the mid- and upper troposphere over the northern-hemispheric (NH-) Pacific. To link each individual pollution event detected by MOPITT with a CO source region, we performed trajectory calculations using MPTRAC, a lagrangian transport model. To analyse transport pathways and uplift mechanisms we combine MOPITT data, the trajectory calculations and ERA-Interim reanalysis data. Events of elevated CO which we detect at level between 500 hPa and 300 hPa over the NH-Pacific throughout the year, occur with a surprisingly high regularity and frequency (70 % of all days during winter, 80 % respectively during spring). Our study further indicates, that the spatial coverage of individual upper tropospheric pollution cluster increased in spring time during the 20 years we analysed. The position of upper tropospheric pollution plumes show a strong seasonal cycle. During winter, most pollution events are detected over the north-eastern and central NH-Pacific, during spring over the central NH-Pacific and during summer over the western NH-Pacific. We detect most pollution episodes during spring. Trajectory simulations reveal China as the major CO-source region throughout the year. The contribution of other source regions shows a strong seasonal cycle: NE-Asia is a significant CO-source region during winter and summer while India and SE-Asia are important source regions mainly during spring.
sEMG-Based Hand Posture Recognition Considering Electrode Shift, Feature Vectors, and Posture Groups

Surface electromyography (sEMG)-based gesture recognition systems provide the intuitive and accurate recognition of various gestures in human-computer interaction. In this study, an sEMG-based hand posture recognition algorithm was developed, considering three main problems: electrode shift, feature vectors, and posture groups. The sEMG signal was measured using an armband sensor with the electrode shift. An artificial neural network classifier was trained using 21 feature vectors for seven different posture groups. The inter-session and inter-feature Pearson correlation coefficients (PCCs) were calculated. The results indicate that the classification performance improved with the number of training sessions of the electrode shift. The number of sessions necessary for efficient training was four, and the feature vectors with a high inter-session PCC (r > 0.7) exhibited high classification accuracy. Similarities between postures in a posture group decreased the classification accuracy. Our results indicate that the classification accuracy could be improved with the addition of more electrode shift training sessions and that the PCC is useful for selecting the feature vector. Furthermore, hand posture selection was as important as feature vector selection. These findings will help in optimizing the sEMG-based pattern recognition algorithm more easily and quickly.
Grasp-based Object Identiﬁcation

The new advancements in technology and robotics allow for new and ever-improving methods for object classiﬁcation. However, many existing models either require preconceived visual notions of the object’s shape or require very detailed features of the object to be classiﬁed. In this work, we propose two cost efﬁcient methods for object identiﬁcation using a multi-ﬁngered robotic hand equipped with proprioceptive and tactile sensing. Both methods are trained on known objects and rely on a limited set of features, obtained by grasping the object. These features are produced using positions and orientations of contact locations and do not require detailed reconstructions of the object. In addition, contrary to most methods in the literature, our methods do not rely on the knowledge of the relative pose between the object and the robotic hand. This addition greatly expands the domain of application since the objects are determined from tactile information alone, without using the relative pose of the objects for the calculation of the features. However, if that knowledge is available, we propose an additional active exploration approach that decides the next best grasp, thereby reducing the overall number of grasps required for a good recognition of the objects. On top of this, pose estimation is added to the active exploration approach. This implementation, opposed to the simple active exploration approach, no longer requires a static object whilst testing, thereby maintaining the pose-free particularity of the random exploration. We test the proposed methods in the GraspIt! Simulator and show that haptic-based object classiﬁcation is possible in pose-free conditions. We evaluate the parameters that produce the most accurate results and the least number of grasps for classiﬁcation.