Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulatory Limits to Risk Management

Variable annuities, the largest liability of U.S. life insurers, are investment products containing long-dated minimum return guarantees. I show that guarantees with similar economic risks are treated differently by regulation and these differences impact insurers’ hedging behavior. When the regulatory regime recognizes certain risks, insurers start to hedge these risks in a substantial way. For some guarantees, this involves hedging both interest rate and equity market risks. However, for others, it involves hedging only equity market risk. As the regulatory regime still does not recognize the interest rate risk of all guarantees, insurers remain exposed to substantial interest rate risk.
Essential oils to control Botrytis cinerea in vitro and in vivo on grape fruits

Purpose: The effect of abusing chemical biocides in controlling pests and diseases has drawn the attention of policymakers to the development of methods potentially available in nature for this purpose. Research method: In the present study, the inhibitory effects of four different essential oils against Botrytis cinerea were tested at various concentrations (0, 200, 400, 600, and 800 μL L-1) in in vitro and in vivo. Main findings: The in vitro results showed that the growth of B. cinerea was completely inhibited by the application of anise oil at concentrations of 800 μL L-1. The in vivo results indicated that treated fruits with marjoram oil had more total soluble solids, and anthocyanin content in comparison to anise, chamomile, and black caraway oil. Furthermore, among essential oils, treated fruits with black caraway essential oils had the lowest pH, while anise, chamomile, and marjoram oil had the highest pH. The highest anthocyanin content and pH were obtained at 200 and 400 μL L-1 concentration and lowest values were found at a control treatment (respectively). The most total soluble solids were observed at control treatment and the lowest values were recorded at 600 μL L-1. The application of each essential oil decreased the percentage loss in fresh weight significantly and increased the storage-life of the fruit. Limitations: Higher cost of application was a limitation. Originality/Value: This research confirms the antifungal effects of anise, fennel, chamomile, and marjoram essential oils both in vitro and in vivo on grape fruits postharvest. Therefore, these essential oils could be an alternative to chemicals to control postharvest phytopathogenic fungi on grape fruits.
Promoting Health-Related Abilities and Behaviors via Health Apps: A Self-Regulation Perspective

Motivated by the gap between the use of health technologies and actual health-related outcomes, this study builds on self-regulation theory to construct a research model that aims to unveil how technological intervention of health app overall quality affects self-regulated abilities and behaviors about health app use, health information literacy, and physical activity, which together determine health-related outcomes pertaining to health status and satisfaction with life. Responding to the lack of exploration of the interaction among different apps, narcissistic theory is applied to explain how the usage of selfie apps reinforce the effects of health apps. To empirically test our proposed research model, we plan to administer a large-scale survey questionnaire on health app usage.
Heterogeneous Network-based Group Recommendation Method for Scientific Social Network

The overwhelming number of groups in Scientific Social Network (SSN) makes it a great burden for researchers to find their desirable groups. Therefore, it’s critical to automatically recommend groups to researchers according to their preferences. Moreover, recommending groups to researchers in SSN naturally belongs to a OneClass Collaborative Filtering (OCCF) problem. Therefore, in this paper, a Heterogeneous Network-based (HN) Group Recommendation (HNGR) method is proposed to recommend groups to researchers in SSN. Specifically, to consider both the direct and indirect relations between entities in SSN, HN is employed to conduct similarity calculation to support further negative instances extraction and Probabilistic Matrix Factorization (PMF) recommendation processes. Experiments were conducted on the real world CiteULike dataset. The experimental results demonstrate the effectiveness of the proposed HNGR method, and the superiority of HN for similarity calculation which indicates the necessity of considering the indirect connections between researchers and groups.
Interpolating accurate TerraSAR-X science orbit data

TerraSAR-X is a modern satellite SAR sensor offering various new possibilities for Earth Observation. The accurate direct georeferencing is among the key characteristics of the sensor. The TerraSAR-X data is accompanied by accurate science orbit data and other metadata which allow the computation of the 3D-2D projection transformation between the 3D object space and the 2D SAR image space without the need to employ ground control information. In order to project an arbitrary 3D point on the 2D SAR image, the position and velocity of the sensor at the time of the point acquisition must be computed. The orbit is provided as the position and the velocity vectors of the sensor for 12 time stamps. In order to compute the position and the velocity at arbitrary time (at point acquisitions) an interpolation must be used. The whole SAR image is acquired between 2 of the time stamps. Thus, the interpolation may be computed using 2 or more of the 12 time stamps. In this paper we study the performance of various interpolation methods: Linear interpolation, cubic interpolation, Lagrange polynomial interpolation, Trigonometric interpolation and Chebyshev interpolation. The interpolation schemes are tested with various TerraSAR-X data all over the world.
The Prediction of Sales Volume and WoM Effect through Network Structure and Topic Modeling on Social media

Social media has emerged as a diverse platform and its influence has gradually improved. The company recently carried out a strategy to build a network of communication between users through social media. In this study, we analyze the empirical analysis of social media communication structure and predicting the product sales volume based on the literature review of the existing media theory. Social media can be divided into two dimensions of the communication process (Conveyance vs. Convergence). The Conveyance means information transmission, and that purpose is to convey information broadly. The Convergence means gathering opinion and aiming to enhance knowledge by sharing of users. Using social media, users are gained by social capital through the structural, cognitive and relational structure. As a result of the study, we drawn three communication structure affecting the company’s sales volume. In addition, the factors can predict the sales volume.
How does Organisational Culture Influence the Relationship between Information Technology Governance and Organisational Performance in the Financial Services Industry?

The aim of this research is to examine how organisational culture moderates the relationships between IT governance performance measurement systems (ITG-PMS) usage and three organisational outcomes (i.e., organisational learning orientation, information systems (IS) strategic alignment and organisational performance). While the IS literature has been informative about various mechanisms of ITG, very few empirical insights are available on how various usage patterns of ITG-PMS influence organisational outcomes moderated by organisational culture in the financial services industry. Different organisational culture types lean towards different strategic outcomes and may lead to different usage patterns of ITG-PMS to enhance organisational learning orientation. This could in turn influence IS strategic alignment and overall organisational performance. This study will develop hypotheses that will be tested via surveys.
Grasp of Goals: Successful management of supply chain networks in the agribusiness of Central and Eastern Europe

Verticalization of the agri-food sector in Central and East-European countries is to a large extent the result of the foreign investors’ efforts to organize their supply chains. Wellbranded multinational retailers and food manufactur ers export their chain management concepts aiming to structure exchange interactions with local suppliers. This process leads to formation of supply chain networks involving long-t erm collaborative relationships among different stages of the food chain. To ensure that t ese relationships are mutually beneficial, chain management has to account for goals set in th e supply chain networks. This study attempts to contribute to a theoretical understandi ng of the supply chain network goals. Specifically, we argue that both, network-level and firm-level goals have to be achieved to maintain long-term and successful network relations hips. Furthermore, we discuss how to deal with network goals in chain management, and pr ovide some examples from the agribusiness in Central and Eastern Europe.
Apropos Replacement : Development of a full text search tool for man pages

Manual pages are a key component of Unix like operating systems, they have played a significant role in the success of Unix over the years. Man pages are the most authentic source of reference for a system administrator or a programmer. However, there has been a lack of good search tool to make man pages more accessible. Traditionally, apropos(1) has been there as a search interface but it was developed in the early days of Unix when computing resources were scarce and that is the primary reason for its simple design and limited search capabilities. This paper discusses a new implementation of apropos done by Abhinav Upadhyay and mentored by Jörg Sonnenberger as part of Google Summer of Code 2011, for The NetBSD Foundation. The goal of this project was to replace the conventional apropos in NetBSD with a modern version which supports full text searches.
Old Maps Georeferencing – Overview and a New Method for Map Series

Old maps are often the basic source of modern environmental studies focused on landscape reconstruction. Georeferencing of these maps is a key activity in the implementation of these studies. It is necessary to know the theory of the basic methods of georeferencing. This paper summarizes the current knowledge of methods, and further proposes a new method for the georeferencing of multiple sheet map series. It is based on the overall adjustment of all transformation coefficients with conditions of the map edges continuity.
DEVELOPMENT OF AN ACCELERATED DEGRADATION TESTBED FOR INTEGRATED VIBRATION MONITORING AND ONLINE OIL ANALYSIS OF VEHICLE DIFFERENTIAL BEARINGS

This project, sponsored by the Office of Naval Research, integrates oil and vibration analysis to develop a more complete diagnostic and prognostic model for monitoring bearing degradation and performance in the field. The results of this project will be used to optimize repair schedules and minimize the risk of catastrophic failure of machine components for military personnel during a mission through enhanced conditionbased maintenance. Each test bearing is first introduced with an outer race fault. The fault's degradation is accelerated by running the bearing under overloaded conditions in the boundary lubrication regime within a large test rig designed for these conditions. Accumulated damage to the bearing is characterized in another clean, low-noise test rig to collect vibration signatures. Finally, the bearing is implemented into the differential for insitu testing to emulate data in the field. Dynamic characterization of the designed test machines was performed by long runs spanning multiple hours under rated conditions to determine any wear-in effects and ramp-up tests to distinguish order-based bearing frequencies and structural resonances. Modal analyses were performed on the static system to provide additional evidence of structural resonances within the machines. This paper will discuss the design challenges and solutions for creating a test bed to monitor bearings in accelerated-degradation conditions and in realistic operating conditions.
Psychometric properties of Spanish version of QIDS-SR 16 in depressive patients

292 Introduction. Depression is a worldwide health problem. Thus, making the diagnosis with reliable and short tests is crucial. In this regard, the Quick Inventory of Depressive Symptomatology-Self Report (QIDS-SR16) has been validated in several countries. It was found that this instrument has a correct balance between time and reliability. This study has aimed to assess psychometric properties of QIDS-SR16 Spanish version, and to calculate several cutoffs to evaluate the depressive disorder severity.
No Fees for Higher Education can be costly

This paper estimates the causal effect of tuition fees for higher education on study duration and graduation in an entire country. In 2006/2007, fees for university studies were introduced by a large number of German universities and also applied to students already enrolled at the time. This unusual feature allows estimating causal effects of fees at the intensive margin, holding constant composition. We observe graduation for all students and all universities and find that even modest fees significantly shorten the duration of studies. This is driven by earlier and more graduation of students due to higher study effort and more time invested. We combine these findings with causal estimates at the extensive margin, which go in the opposite direction. We relate these opposing effects and conclude that zero tuition has unintended negative consequences that likely outweigh the costs of modest levels of fees, even in societies with high inequality aversion and when universities throw fee income away. ∗We thank Manuel Arellano, David Dorn, Josef Zweimüller, and numerous seminar and conference audiences for comments and feedback. We thank Frank Multrus for help with the German Student Survey data and Constantin Tielkes for research assistance. We gratefully acknowledge funding from the German Science Foundation (CRC TRR 190) and the Stiftelsen för fr ̈̈amjande av ekonomisk forskning vid Lunds Universitet. All remaining errors are our own. †Lund University, IZA and DIW Berlin ‡Universität Hamburg and DIW Berlin §DIW Berlin, CEP, IZA and CESifo
An Exploration of Sources of Variation in Teacher Evaluation Ratings across Classrooms, Schools, and Districts

We investigate sources of variation in teacher evaluation ratings across classrooms, schools, and districts. We show that assignment to high achieving classrooms increases teacher evaluation ratings. We also document significant variation in the sensitivity of performance ratings to value-added measures of teacher effectiveness across districts. Consequently, the probability that high or low performing teachers, as measured by value added, receive the highest or lowest evaluation ratings differs considerably across school districts. Our findings suggest that statewide policies that attach high stakes to performance evaluations are likely to have different consequences across schools and districts.
Real Time Eye Gaze Detection Using Machine Learning Techniques

Drowsiness and exhaustion of vehicle drivers reduce the driver's abilities to control the vehicle, his natural reflex, and perception. Such diminished vigilance level of drivers is observed at night driving or prolonged driving, causing an accident and pose a severe threat to commuters. Therefore, it is an absolute requisite in the automobile industry to assimilate the driver assistance system that can detect drowsiness and fatigue of the drivers. This paper proposes a prototype for monitoring the driver's vigilance using a computer vision system in real-time. Eye-tracking is one of the critical parts in future driver assistance systems since human eyes contain much-needed information about the driver's condition, such as attention level, gaze, and fatigue level. One common problem faced by many eyetracking methods proposed so far is their sensitivity to change in lighting conditions. This tends to limit their scope for automotive applications significantly. This paper illustrates an eye-gaze detection and tracking method that works under realistic and variable lighting conditions in real-time.
THE PRIMACY OF FRACTION COMPONENTS IN ADULTS' NUMERICAL JUDGMENTS

A fundamental question in numerical cognition concerns how people make judgments about the magnitude of fractions. There is much debate around the issue of whether fraction representations are holistic or component-based. In the present study, we measured hand movements as people mentally compared fractions to 1⁄2. We found that participants’ hands tended to move according to the size of components rather than the overall magnitude of the fraction. This indicates that people form an initial automatic representation that is tied to surface format (i.e., component-based), but later refine this representation according to task demands.
Speech Processing for Digital Home Assistants

Once a popular theme of futuristic science fiction or far-fetched technology forecasts, digital home assistants with a spoken language interface have become a ubiquitous commodity today. This success has been made possible by major advancements in signal processing and machine learning for so-called far-field speech recognition, where the commands are spoken at a distance from the sound capturing device. The challenges encountered are quite unique and different from many other use cases of automatic speech recognition. The purpose of this tutorial article is to describe, in a way amenable to the non-specialist, the key speech processing algorithms that enable reliable fully hands-free speech interaction with digital home assistants. These technologies include multi-channel acoustic echo cancellation, microphone array processing and dereverberation techniques for signal enhancement, reliable wake-up word and end-of-interaction detection, high-quality speech synthesis, as well as sophisticated statistical models for speech and language, learned from large amounts of heterogeneous training data. In all these fields, deep learning has occupied a critical role.
1 AERIAL MANIPULATION FOR REMOTE ROBOTIC MACHINERY DIAGNOSTICS

There is a growing trend of using unmanned aerial vehicles (UAVs, or drones) for inspection of industrial facilities and infrastructure. The types of inspections currently done entail remote sensing using cameras: qualitative assessment and photogrammetry using standard cameras, as well as temperature monitoring using thermal imaging cameras. The benefits of using drones are to reduce risk to inspection personnel near operating equipment, reduced cost, and improved auditability of archived data. Machine condition monitoring can benefit from this enabling technology provided that vibration monitoring and lubricant analysis can also be done remotely. Laser vibrometers can be used for remote monitoring of equipment that does not have permanently mounted, dedicated vibration monitoring sensors; but this sensing method is capital intensive, requires a laser head and controller with a typical mass of over 5 kg, and must be pointed from an appropriate direction, which may not be accessible. Lubricant analysis requires access to a lubricant sampling port and a means for collecting and analyzing the sample. A system is described for deploying a drone with a manipulator that engages the equipment of interest for vibration data collection and employs a more versatile payload for drop-tube vacuum sampling of lubricant by inserting a tube through a fill port or dip stick port and withdrawing an oil sample from the sump cavity. The key technical challenges are collision-free navigation and hovering, followed by control of dynamic deployment of the payload that connects to the machinery. A proof-of-concept system is described with very preliminary experimental results.
A Three-Arm Randomized Controlled Trial

and Introduction Abstract Psychosocial interventions for serious mental illness are resource intensive and poorly accessible. Brief interventions (eg, single session) that are augmented by follow-on automated mobile health intervention may expand treatment access. This was a randomized single-blind controlled trial with 255 individuals diagnosed with schizophrenia or bipolar disorder. Participants were randomized to one of three conditions: CBT2go, which combined one individual session of cognitive behavioral therapy with automated thought challenging/adaptive behavior delivered through mobile devices; Self-Monitoring (SM), which combined single-session illness psychoeducation with self-monitoring of symptoms; and treatment-as-usual (TAU). Participants were assessed at baseline, 6 weeks (midpoint), 12 weeks (posttreatment), and 24 weeks (follow-up) with our primary outcome global psychopathology (Brief Psychiatric Rating Scale–expanded version [BPRS-24]), and secondary outcomes community functioning (Specific Level of Function; SLOF) and defeatist performance beliefs (DPBs). We also collected data on adverse events. Outcome analyses on the primary outcome, BPRS Total score, indicated a significant time (0–24 wk) by group interaction with significant but modest improvement comparing two active conditions (CBT2go and SM) relative to TAU. Effects of CBT2go were not different from SM. There was a significant time × group interaction with better SLOF scores in CBT2go across 24 weeks, but not in SM. There were no time-by-group effects on DPBs. DPBs decreased in the CBT2go condition but not in SM. These results indicated that single intervention augmented by mobile intervention was feasible and associated with small yet sustained effects on global psychopathology and, when inclusive of CBT, community function compared with usual care.
Avian as a model for human ovarian cancer: advantages reviewed

Ovarian cancer (OC) is the most lethal gynaecological cancer. Early detection of OC is crucial for providing efficient treatment, whereas high mortality rates correlate with late detection of OC, when the tumor has already metastasized to other organs. The most prevalent type of OC is epithelial OC (EOC). Models that have been used to study EOC include the fruit fly, mouse and laying hen, in addition to human EOC cells in 3D culture in vitro. These models have helped in the elucidation of the genetic component of this disease and the development of drug therapies. However, the histological origin of EOC and early markers of the disease remain largely unknown. In this study, we aimed to review the relative value of each of the different models in EOC and their contributions to understanding this disease. It was concluded that the spontaneous occurrence of EOC in the adult hen, the prolific ovulation, the similarity of metastatic progression with that in humans and the advantages of using the chicken embryo for modelling the development of the reproductive system, renders the hen particularly suitable for studying the early development of EOC. Further investigation of this avian model may contribute to a better understanding of EOC, improve clinical insight and ultimately contribute to decreasing its mortality rates among humans.