Miracle-dz/UniFAR-FTUs huggingface dataset:
Erosion of State Power, Corruption Control, and Political Stability

How do corruption and the state apparatus interact, and how are they connected to the political and economic dimensions of state capacity? Motivated by historians' analysis of powerful empires, we build a model that emphasizes the corrosive effect of corruption on state power. Under general assumptions about fat-tailed risk, we show that, if fiscal capacity is strong, then the optimal response for the head of the state apparatus will be an endogenous lexicographic rule whereby local corruption is maintained at such a level that no erosion of state power is tolerated. Comparative statics shows the impacts of additional risk of crisis on corruption tolerance as well as the complementarity between personalistic rule and corruption. Implications of corruption at the head of the state apparatus are also analyzed. If fiscal capacity is not sufficiently strong, however, the state will have to over-tolerate corruption to retain its affiliates, risking its control in crises. Our model predicts that the correlation between state's political stability and corruption is non-monotonic across different levels of fiscal capacity, and this prediction is robustly consistent with recent cross-country panel-data.
ADOLESCENT IMMIGRANTS’ HOST SATISFACTION IN A RECENT IMMIGRATION RECEIVING COUNTRY

Host country satisfaction, as an index of immigrants’ psychological adaptation, has particular importance for immigrant communities in the destination country. We explain immigrant adolescents’ satisfaction with their level of life in Spain by examining the contribution of sociodemographic variables, ethnic and mainstream identification, the perception of ethnic group discrimination, and the length of time lived in the host country. We examine a group of 347 Moroccans and Ecuadorians adolescent immigrants, two of the biggest immigrant communities in Spain that differ in their cultural distance (e.g., language, religion) to the mainstream culture. We provide empirical evidences that; first, Spanish language proficiency, mainstream (and not ethnic) identification, and time lived in Spain are positively related to host country satisfaction; second, immigrants’ perception of ethnic discrimination is negatively associated with host country satisfaction; and third, cultural distance to the mainstream does not significantly moderate these associations (i.e. the relationships are similar for the two ethnic groups). Considering these results, social policies aimed at promoting positive attitudes towards cultural diversity and supporting immigrants’ participation in the host society should be encouraged.
Adenosine Receptors and Stimulates Mast Cell Degranulation

We investigated the mechanism by which inosine, a metabolite of adenosine that accumulates to . 1 mM levels in ischemic tissues, triggers mast cell degranulation. Inosine was found to do the following: ( a ) compete for [ 125 I] N 6 -aminobenzyladenosine binding to recombinant rat A 3 adenosine receptors (A 3 AR) with an IC 50 of 25 6 6 m M; ( b ) not bind to A 1 or A 2A ARs; ( c ) bind to newly identified A 3 ARs in guinea pig lung (IC 50 5 15 6 4 m M); ( d ) lower cyclic AMP in HEK293 cells expressing rat A 3 ARs (ED 50 5 12 6 5 m M); ( e ) stimulate RBL-2H3 rat mast-like cell degranulation (ED 50 5 2.3 6 0.9 m M); and ( f ) cause mast cell–dependent constriction of hamster cheek pouch arterioles that is attenuated by A 3 AR blockade. Inosine differs from adenosine in not activating A 2A ARs that dilate vascular smooth muscle and inhibit mast cell degranulation. The A 3 selectivity of inosine may explain why it elicits a monophasic arteriolar constrictor response distinct from the multiphasic dilator/constrictor response to adenosine. Nucleoside accumulation and an increase in the ratio of inosine to adenosine may provide a physiologic stimulus for mast cell degranulation in ischemic or inflamed tissues. ( J. Clin Invest. 1997. 100:2849–2857.)
Restaurant’s Multidimensional Evaluation Concerning Food Quality, Service, and Sustainable Practices: A Cross-National Case Study of Poland and Lithuania

The purpose of this study was to identify and analyze consumer choices and evaluate the restaurant service quality, including quality of meals and services, and sustainability practices in restaurants in Warsaw and Kaunas. Our research was conducted using a sample of 1200 adult Poles and Lithuanians. Polish and Lithuanian consumers used catering services with varying frequencies. Different elements influenced their choice of restaurant. However, the common feature was the quality of meals, which in Lithuania was compared only with the price of meals, and with other elements in Poland. In the context of restaurant’s sustainable practices, it has been revealed that surveyed consumers had only partially fit into the contemporary consumption trends. In both countries, consumers have appreciated the use of reusable cutlery and crockery, as well as local and seasonal ingredients, while they did not pay attention to sustainable restaurant practices, such as the use of alternative sources of protein, environmentally friendly forms of energy, and reducing waste and minimization of food losses. The use of cluster analysis and principal component analysis (PCA) allowed a comprehensive assessment of consumer opinions on restaurants in terms of meal quality and service as well as sustainable practices. Restaurateurs should monitor the satisfaction of their customers and recognize the changing needs and habits of consumers.
A Deep Model for Predicting Online Course Performance

Online learning has attracted a large number of participants because it has no limit to enrollment and regardless of personal background and location. One of main goals of education is improving students’ learning gain. However, the completion rates for online learning are notoriously low. We focus on predicting students’ learning performance early and help instructors to provide intervention in-time. We propose a deep online learning performance prediction model incorporate clickstream and demographic data of students. The experiments on the Open University Learning Analytics Dataset (OULAD) show that fusion of learner demographic information can make up for inadequate online learning behavior data early and improve prediction performance. And our model can achieve reliable performance both in intra-course and inter-course outcome prediction.
Accelerated Reader:

Program description The Accelerated Reader program is a guided reading intervention in which teachers are closely involved with student reading of text. It involves two components, the Accelerated Reader software and Accelerated Reader Best Classroom Practices (formerly called Reading Renaissance). The Accelerated Reader software is a computerized supplementary reading program. Accelerated Reader relies on independent reading practice as a way of managing student performance by providing students and teachers feedback from quizzes based on books the students read. Accelerated Reader Best Classroom Practices are a set of recommended principles on guided independent reading (or teachers’ direction of students’ interactions with text) that ensure Accelerated Reader is implemented with integrity.2
Microbiota dependent production of butyrate is required for the bone anabolic activity of PTH.

PTH is a critical regulator of skeletal development that promotes both bone formation and bone resorption. Using microbiota depletion by wide-spectrum antibiotics and germ-free (GF) female mice we showed that the microbiota was required for PTH to stimulate bone formation and increase bone mass. Microbiota depletion lowered butyrate levels, a metabolite responsible for gut-bone communication, while reestablishment of physiologic levels of butyrate restored PTH-induced anabolism. The permissive activity of butyrate was mediated by GPR43 signaling in dendritic cells (DCs) and by GPR43-independent signaling in T cells. Butyrate was required for PTH to increase the number of bone marrow (BM) regulatory T cells (Tregs). Tregs stimulated production of the osteogenic Wnt ligand Wnt10b by BM CD8+ T cells, which activated Wnt dependent bone formation. Together, these data highlight the role that butyrate produced by gut luminal microbiota plays in triggering regulatory pathways which are critical for the anabolic action of PTH in bone.
OTT_A_192572 2981..2988

1Division of Medical Oncology, Department of Medical and Surgical Sciences for Children and Adults, University Hospital of Modena, Modena, Italy; 2Department of Medical Oncology, Cagliari University Hospital, SS 554 Monserrato, CA, Italy; 3Department of Oncology, Azienda Sanitaria locale di Biella, Ponderano, Italy; 4Biosciences Laboratory, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, 47014 Meldola, Italy; 5Department of Medical Oncology, Card. G. Panico Hospital of Tricase, 73039 Tricase, Italy; 6Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna 40126, Italy; 7General and Oncologic Surgery, Morgagni-Pierantoni Hospital, Forlì, Italy; 8Department of Internal Medicine, Degli Infermi Hospital, Faenza 48018, Italy
External Command Study of the Phase Shifter for Applications to Array Antenna with Nematic Liquid Crystal

This article is devoted to characterization by finite difference method in simulation software HFSS of Coplanar microwave phase shifter based on liquid crystal for array antenna printed electronic scanning. In this paper we propose a new external Phase Shifter command. The structure consists of three parts of liquid crystal placed between the ground plane and transmission line. The simulation results concerning the losses in reflection of phase shifter and the influence of the static magnetic bias field of liquid crystal on these parameters are shown in detail.
A Virtual Workplace: Connecting and challenging classroom, university and preservice teachers

This paper reports on the continuance of sustainable learning outcomes from a project (2001-2002) which created a Virtual Workplace (VW) for preservice teacher education and in-service professional development. Through the use of videoconferencing and web-based technologies, the project created a ‘virtual workplace’ for both synchronous and asynchronous interaction between classroom teachers, preservice (student) teachers and staff of Queensland University of Technology’s (QUT) Kelvin Grove Campus. Basically, preservice teachers watch via videoconference in the large lecture theatres, live lessons being taught from school classrooms, interact with the teachers before and after the lessons, and participate in discussions in tutorials and on-line on specific teaching/learning topics. This is still a particularly innovative approach providing QUT preservice teachers with classroom observation and an alternative practice teaching experience of classrooms. In turn, the university teachers and the partner teachers and their schools benefit by extending their knowledge and use of the technical facilities for curriculum delivery and participating in professional development programs. The paper presents how the teaching/learning experience of the medium of videoconference has also shown that it can influence the development of university and teacher pedagogies and teacher identities in both experienced and neophyte teachers.
SWAT LANDSCAPE MODEL

Accurate simulation of landscape processes in natural resource models requires spatial distribution of basin hydrology and transport processes. To better represent these processes, a landscape version of the SWAT model has been developed to simulate the runoff, run‐on, and infiltration processes that typically occur in different parts of the landscape. The model addresses flow and transport across hydrologic response units prior to concentration in streams, and is capable of simulating flow and transport from higher landscape positions to lower positions. The SWAT landscape model was tested using data collected from a heavily vegetated riparian buffer system in the Atlantic Coastal Plain near Tifton, Georgia. Simulations of surface runoff, lateral subsurface runoff, and groundwater flow for an upland field, a grass buffer, and a sub‐divided forested buffer floodplain were generated. Model results and field data indicate that surface runoff was dominant in the upland field, while groundwater flow was dominant in the grass buffer and the floodplain. While average annual surface runoff agreed satisfactorily with observations from the site, annual and monthly simulated values varied considerably from observed values. Simulated surface runoff tracked general trends in the observed data, but winter months and extreme events were overestimated while summer months were underestimated. Annual surface runoff predictions at the edge of the upland field varied from the observed data by 11% to 44%. The Nash‐Sutcliffe efficiency for annual estimates of surface runoff at the field edge was 0.83 for the three‐year calibration period. The results demonstrate the ability of the model to simulate the surface runoff and enhanced infiltration typically associated with riparian buffer systems. Additional revision of the model will likely be necessary to adequately represent redistribution of water between surface, lateral subsurface flow, and
A study on behaviors of purchasing life insurance in Vietnam

Article history: Received: October 14, 2019 Received in revised format: November 29 2019 Accepted: January 15, 2020 Available online: January 15, 2020 This research is conducted to examine the process from the intention to purchase life insurance among people in two major cities of Hanoi and Ho Chi Minh City in Vietnam, based on the theoretical framework of theory of planned behavior. The study is conducted in Hanoi and Ho Chi Minh City in Vietnam through an online questionnaire of 358 people. Based on the data collected, the study used Cronbach’s alpha, exploratory factor analysis, KMO and Bartlett’s test and regression analysis by using SPSS software. The results show that life insurance buying behavior was influenced by purchase intention, attitudes, financial insight and product accessibility. Moreover, it was recognized higher weight of financial literacy and product accessibility in life insurance buying behavior. The research determined that financial knowledge could promote buying intention to actual purchase behavior in the life insurance area. © 2020 by the authors; licensee Growing Science, Canada
Deep-AIR: A Hybrid CNN-LSTM Framework forFine-Grained Air Pollution Forecast

Poor air quality has become an increasingly critical challenge for many metropolitan cities, which carries many catastrophicphysical and mental consequences on human health and quality of life. However, accurately monitoring and forecasting air qualityremains a highly challenging endeavour. Limited by geographically sparse data, traditional statistical models and newly emergingdata-driven methods of air quality forecasting mainly focused on the temporal correlation between the historical temporal datasets of airpollutants. However, in reality, both distribution and dispersion of air pollutants are highly location-dependant. In this paper, we proposea novel hybrid deep learning model that combines Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM)together to forecast air quality at high-resolution. Our model can utilize the spatial correlation characteristic of our air pollutant datasetsto achieve higher forecasting accuracy than existing deep learning models of air pollution forecast.
Analysis of quasar magnitudes

Since their discovery the analysis of quasar magnitudes has generally required some form of evolution. Assuming that quasars do not have evolution this paper shows that they have a well-defined intrinsic magnitude distribution that is independent of cosmological models. However the average apparent magnitudes are essentially constant which means that the only cosmological information they contain is that the absolute flux density has a power law distribution. Thus quasar magnitudes, by themselves, are essentially useless for cosmological investigations.
Learning How to Walk: Warm-starting Optimal Control Solver with Memory of Motion

In this paper, we propose a framework to build a memory of motion for warm-starting an optimal control solver for the locomotion task of a humanoid robot. We use HPP Loco3D, a versatile locomotion planner, to generate offline a set of dynamically consistent whole-body trajectory to be stored as the memory of motion. The learning problem is formulated as a regression problem to predict a single-step motion given the desired contact locations, which is used as a building block for producing multi-step motions. The predicted motion is then used as a warm-start for the fast optimal control solver Crocoddyl. We have shown that the approach manages to reduce the required number of iterations to reach the convergence from ∼9.5 to only ∼3.0 iterations for the single-step motion and from ∼6.2 to ∼4.5 iterations for the multi-step motion, while maintaining the solution’s quality.
Routing-Led Placement of VNFs in Arbitrary Networks

The ever increasing demand for computing resources has led to the creation of hyperscale datacentres with tens of thousands of servers. As demand continues to rise, new technologies must be incorporated to ensure high quality services can be provided without the damaging environmental impact of high energy consumption. Virtualisation technology such as network function virtualisation (NFV) allows for the creation of services by connecting component parts known as virtual network functions (VNFs). By optimising the placement and routing of VNFs this technique can be used to maximally utilise available datacentre resources, to maintain a high quality of service whilst minimising energy consumption. Current research on this problem has focussed on placing VNFs and considered routing as a secondary concern. In this work we argue that the opposite approach, a routing-led approach is preferable. We propose a novel routing-led algorithm and analyse each of the component parts over a range of different topologies on problems with up to 16000 variables and compare its performance against a traditional placement based algorithm. Empirical results show that our routing-led algorithm can produce significantly better solutions to large problem instances on a range of datacentre topologies.
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization

Bayesian optimization (BO) is a popular approach to optimize expensive-to-evaluate black-box functions. A significant challenge in BO is to scale to high-dimensional parameter spaces while retaining sample efficiency. A solution considered in existing literature is to embed the high-dimensional space in a lower-dimensional manifold, often via a random linear embedding. In this paper, we identify several crucial issues and misconceptions about the use of linear embeddings for BO. We study the properties of linear embeddings from the literature and show that some of the design choices in current approaches adversely impact their performance. We show empirically that properly addressing these issues significantly improves the efficacy of linear embeddings for BO on a range of problems, including learning a gait policy for robot locomotion.
Tracing the anemic stellar halo of M 101

Models of galaxy formation in a cosmological context predict that massive disk galaxies should have structured extended stellar halos. Recent studies in integrated light, however, report that a few galaxies, including the nearby disk galaxy M 101, have no measurable stellar halos to the detection limit. We aim to quantify the stellar content and structure of M 101’s outskirts by resolving its stars. We present the photometry of its stars based on deepF606WandF814Wimages taken withHubbleSpace Telescope (HST) as part of the GHOSTS survey. The HST fields are placed along the east and west sides of M 101 out to galactocentric distance (R) of ∼70 kpc. The constructed color-magnitude diagrams of stars reach down to two magnitudes below the tip of the red giant branch. We derived radial number density profiles of the bright red giant branch (RGB) stars. The mean color of the RGB stars atR ∼ 40−60 kpc is similar to those of metal-poor globular clusters in the Milky Way. We also derived radial surface brightness profiles using the public image data provided by the Dragonfly team. Both the radial number density and surface brightness profiles were converted to radial mass density profiles and combined. We find that the mass density profiles show a weak upturn at the very outer region, where surface brightness is as faint asμg ≈ 33 mag arcsec−2. An exponential disk + power-law halo model on the mass density profiles finds the total stellar halo mass ofMhalo= 8.2−2.2+3.5× 107M⊙. The total stellar halo mass does not exceedMhalo = 3.2 × 108 M⊙when strongly truncated disk models are considered. In combining the halo mass with the total stellar mass of M 101, we obtain the stellar halo mass fraction ofMhalo/Mgal= 0.20−0.08+0.10% with an upper limit of 0.78%. We compare the halo properties of M 101 with those of six GHOSTS survey galaxies as well as the Milky Way and M 31 and find that M 101 has an anemic stellar halo similar to the Milky Way.
Break It Down: A Question Understanding Benchmark

Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decomposition Meaning Representation (QDMR) for questions. QDMR constitutes the ordered list of steps, expressed through natural language, that are necessary for answering a question. We develop a crowdsourcing pipeline, showing that quality QDMRs can be annotated at scale, and release the Break dataset, containing over 83K pairs of questions and their QDMRs. We demonstrate the utility of QDMR by showing that (a) it can be used to improve open-domain question answering on the HotpotQA dataset, (b) it can be deterministically converted to a pseudo-SQL formal language, which can alleviate annotation in semantic parsing applications. Last, we use Break to train a sequence-to-sequence model with copying that parses questions into QDMR structures, and show that it substantially outperforms several natural baselines.
AGN jets versus accretion as reionization sources

Context. Cosmic reionization put an end to the dark ages that came after the recombination era. Observations seem to favor the scenario of massive-star photons generated in low-mass galaxies being responsible for the bulk of reionization and, whereas a possible contribution from AGN accretion disks has been widely considered, they are currently thought to have had a minor role in reionization. Aims. We aim to study the possibility of AGN having contributed to reionization not only through their accretion disks, but also through ionizing photons coming from the AGN jets interacting with the intergalactic medium. Methods. We adopt an empirically derived AGN luminosity function at z ≃ 6, use X-ray observations to correct it for the presence of obscured sources, and estimate the density of jetted AGN. We then use analytical calculations to derive the fraction of jet energy that goes into ionizing photons. Finally, we compute the contribution of AGN jets to the H II volume filling factor at redshifts z ≃ 15−5. Results. We show that the contribution of the AGN jet lobes to the reionization of the Universe at z ∼ 6 might have been as high as ≳10% of that of star-forming galaxies, under the most favorable conditions of jetted and obscuration fraction. Conclusions. The contribution of AGN to the reionization, while most likely not dominant, could have been higher than previously assumed, thanks to the radiation originated in the jet lobes.