Miracle-dz/UniFAR-FTUs huggingface dataset:
Recommendations for optimizing the collective user experience

Traditional recommender systems aim to satisfy individual users by providing them with recommendations that match their preferences. Such recommender systems don’t take into consideration how the number of users recommended to use a particular item affects the users’ experience. For example, a highly-recommended restaurant may match the preferences of many users. However, increasing its popularity via recommendations may make the experience unsatisfactory due to high volume of customers, long lines and inevitably slow service. In this paper, we develop a new recommendationsystem paradigm that we call collective recommendations. Collective recommendations take into consideration not only the user preferences, but also the effect of the popularity of a venue to the overall user experience. We formally define the algorithmic problems motivated by collective recommendations and develop an algorithmic framework for solving them effectively. Our experiments with real data demonstrate the effectiveness of our methods in practice. Nobody goes there any more. It’s too crowded
Title A Test for Autocorrelation in Dynamic Panel Data Models

This paper presents an autocorrelation test that is applicable to dynamic panel data models with serially correlated errors. Our residual-based GMM t-test (hereafter: t-test) differs from the m2 and Sargan’s over-identifying restriction(hereafter: Sargan test) in Arellano and Bond (1991), both of which are based on residuals from the first-difference equation. It is a significance test which is applied after estimating a dynamic model by the instrumental variable (IV) method and is directly applicable to any other consistently estimated residual. Two interesting points are found: the test depends only on the consistency of the first-step estimation, not on its efficiency; and the test is applicable to both forms of serial correlation (i.e., AR(1) or MA(1)). Monte Carlo simulations are also performed to study the practical performance of these three tests, the m2, the Sargan and the t-test for models with first-order auto-regressive AR(1) and first-order moving-average MA(1) serial correlation. The m2 and Sargan test statistics appear to accept too often in small samples even when the autocorrelation coefficient approaches unity in the AR(1) disturbance. Overall, our residual based t-test has considerably more power than the m2 test or the Sargan test.
Development of a Reciprocal Health Care Model for Determination of Safety Level in the Nursing Homes in Estonia

Abstract The aim of the current paper was to assess the care workers’ psychosocial and physical health; patient’s safety and examine the variations of care workers’ working conditions in the national nursing homes. The NOSACQ- 50 questionnaire was used as a research method. The majority of the care workers in nursing homes complain about physical pain, especially low back pain, and work-related stress. The study results show, that several specific features, such as management safety priority, commitment and ability, are found to influence the six dimensions of safety climate. Based on these results, the importance of good communication practices, management commitment and effective safety training to ensure a strong safety climate and safe behaviour among health care workers is highlighted. Mutual support from the employers to the care workers is needed to create safety as an organizational value. Thus, an effective assessment tool for the evaluation of safety level in nursing homes could be proposed based on the results of this study. The current paper presents a Reciprocal Health Care Model for determination the levers of safety improvement in nursing homes. The model refers to the importance of management safety priority and abilities as well as peer safety communication and trust in the safety ability.
Supervised Link Prediction Developed For Bipartite Social Networks

Link prediction is a prominent issue that involves predicting the occurrence of future relationships between nodes in a social network. Our work proposes similarity metrics that are extended for a weighted bipartite social network to predict prospective links in the social network by applying a supervised machine learning scheme. Link (edge) weights in the network could provide valuable information for prediction as they express the strength of relationships between nodes (person, item etc.). The target attribute of prediction is a label that shows the existence or absence of a link between two nodes in the network. The feature attributes of the machine learning model are similarity/centrality metrics calculated from the current social network. Particularly, a weighted bipartite graph was built from the MovieLens dataset by connecting users to movies via the users' movie ratings; then new links were attempted to predict for a later time. Several types of machine learning algorithms for link prediction on this bipartite graph were applied by using network similarity metrics and a binary supervised classifier. The combination of four network centrality metrics provided higher prediction performance compared their individual performances on the bipartite movie ratings network. Our preliminary experiments led satisfactory results when link weights were considered, which encourages us for further analysis on bipartite and weighted social networks.
Identification of Generalized Dyna ic Factor odels fro ixed-frequency data

Modeling of high dimensional time series by linear time series models such as vector autoregressive models is often marred by the so-called “curse of dimensionality”. In order to overcome this problem generalized linear dynamic factor models (GDFM’s) maybe used. In high-dimensional time series the single univariate time series are often sampled at different frequencies. This is the so-called mixed-frequency situation. We consider identifiability of the underlying high-frequency GDFM (i.e. the GDFM generating the data at the highest sampling frequency occurring) in the case of mixed frequency data and we shortly describe two estimation procedures in this situation based on the EM algorithm.
Pristup liječenju Alzheimerove bolesti / Treatment Approach to Alzheimer’s Disease

Aida Križaj Grden1, Ninoslav Mimica1,2 1Klinika za psihijatriju Vrapče, Referentni centar Ministarstva zdravstva Republike Hrvatske za Alzheimerovu bolest i psihijatriju starije životne dobi, Zagreb, 2 Sveučilište u Zagrebu, Medicinski fakultet, Zagreb, Hrvatska / 1University Psychiatric Hospital Vrapče, Referral Centre for Alzheimer’s disease and Old Age Psychiatry of the Ministry of Health, Zagreb, 2University of Zagreb, School of Medicine, Zagreb, Croatia
Association of Parity and Breastfeeding With Risk of Early Natural Menopause

Key Points Question What is the association of parity and breastfeeding with early natural menopause? Findings In this cohort study that included 108 887 premenopausal women, parity and breastfeeding were each associated with a significantly lower risk of early natural menopause. Findings for breastfeeding suggest that some of the lower risk attributed to parity could be attributable to breastfeeding. Meaning The findings suggest that breastfeeding at levels consistent with current recommendations may confer an additional benefit of lower risk of early menopause.
Long Run Technical Change in an Energy-Environment-Economy ( E 3 ) Model for an IA System : A Model of Kondratiev Waves

A world macroeconomic model E3MG is being developed to investigate policies for climate change and sustainable development, as a module of a new IAM structure. As climate change is a long term phenomenon, a model of long term economic changes is required. There is no suitable and generally accepted theory of long term technical change, but Freeman and Louç~a [1] have developed a good descriptive theory. This paper interprets this theory in quantitative terms, in the context of the macroeconomic analysis. It will outline a model of the changes in economic structure via Input-Output coefficients and the growth of new technology industries, incorporating endogenous technical change from R&D and investment, with learning-by-doing.
Star formation history of barred disc galaxies

We present the first results of a pilot study aimed at understanding the influence of bars on the evolution of galaxy discs through the study of their stellar content. We examine here the kinematics, star formation history, mass-weighted, luminosity-weighted and single stellar population (SSP) equivalent ages and metallicities for four galaxies ranging from lenticulars to late-type spirals. The data employed extend to 2-3 disc scalelengths, with S/N(angstrom) > 50, where S/N stands for the signal-to-noise ratio. Several techniques are explored to derive star formation histories and SSP-equivalent parameters, each of which is shown to be compatible. We demonstrate that the age-metallicity degeneracy is highly reduced by using spectral fitting techniques - instead of indices - to derive these parameters. Our results are robust to the choices of stellar population models. We found that the majority of the stellar mass in our sample is composed of old (similar to 10 Gyr) stars. This is true in the bulge and disc regions, even beyond 2 disc scalelengths. In the bulge region, we find that the young, dynamically cold, structures produced by the presence of the bar (e. g. nuclear discs or rings) are responsible for shaping the bulges' age and metallicity gradients, as suggested by Peletier et al. In the disc region, a larger fraction of young stars are present in the external parts of the disc compared with the inner parts. The disc growth is therefore compatible with a moderate inside-out formation scenario, where the luminosity-weighted age changes from similar to 10 Gyr in the centre to similar to 4 Gyr at 2 disc scalelengths, depending upon the galaxy. However, the presence of substructures, like star-forming rings, can produce stellar population trends that are not directly related to the growing of the disc but to the bar potential. For two galaxies, we compare the metallicity and age gradients of the disc major-axis with that of the bar, finding very important differences. In particular, the stellar population of the bar is more similar to that of the bulge than to that of the disc, indicating that, at least in those two galaxies, bars formed long ago and have survived to the present day.
Everything-as-a-hack: claims-making for access to digital and social resources

In media and public discourse, cyber incidents are typically covered in terms of cybercriminals or other external threat agents managing to gain access to sensitive data and systems through weaknesses in technology and/or human factors. Such a framing of incidents foregrounds the (problematic) access claims of "hackers" and the protection against those as the key issue in security. However, other access claims play a role in the same incidents, such as those of the data owners, service providers, advertising companies, intelligence agencies, etc. These access claims are made via different means, and they are backgrounded when the problem is framed in terms of unauthorised access through hacks. In this contribution, I investigate the activity of claiming access as a key analytical concept in a more symmetrical treatment of cybersecurity and associated incidents. Rather than implicit, normalised, and technologically congealed notions of threats and associated access claims, this analytical framework aims at highlighting all access claims within the scope of a cybersecurity phenomenon, in order to uncover the politics behind cybersecurity and associated discourses and infrastructures, and thereby increase transparency. By covering different types of resources and different means of access, the approach also has the potential to connect the rather separated discourses on cybersecurity, privacy, and social manipulation through technology.
Accounting Measurement of Assets and Earnings and the Market Valuation of Firm Assets

We empirically examine the association between market valuations of firm assets and the amount of information about firms’asset productivity provided by the accounting measurements of assets and earnings. We quantify this information by the R-squared from a firm-specific regression of future earnings on past assets. We document statistically and economically significant positive relations between the R-squared and both the marginal and average values of firm assets. The relations are robust to a variety of variables controlling for both profitability and the underlying volatility of firms’operations, to alternative estimation methods, and to the inclusion of alternative measures of accounting quality (e.g., accrual quality, earnings persistence). We interpret these findings as consistent with the ideas that the mapping between accounting measurement of assets and earnings provides information about the underlying productivity of firm assets, and this information is reflected in investors’valuation of firm assets.
Understanding the Spatial Structure of Urban Commuting Using Mobile Phone Location Data: A Case Study of Shenzhen, China

Understanding commuting patterns has been a classic research topic in the fields of geography, transportation and urban planning, and it is significant for handling the increasingly serious urban traffic congestion and air pollution and their impacts on the quality of life. Traditional studies have used travel survey data to investigate commuting from the aspects of commuting mode, efficiency and influence factors. Due to the limited sample size of these data, it is difficult to examine the large-scale commuting patterns of urban citizens, especially when exploring the spatial structure of commuting. This study attempts to understand the spatial structure characteristics generated by human commutes to work by using massive mobile phone datasets. A three-step workflow was proposed to accomplish this goal, which includes extracting the home and work locations of phone users, detecting the communities from the commuting network, and identifying the commuting convergence and divergence areas for each community. A case study of Shenzhen, China was implemented to determine the commuting structure. We found that there are thirteen communities detected from the commuting network and that some of the communities are in accordance with urban planning; moreover, spatial polycentric polygons exist in each community. These findings can be referenced by urban planners or policy-makers to optimize the spatial layout of the urban functional zones.
Chemical Identification of Single Ultrafine Particles Using Surface-Enhanced Infrared Absorption

Due to their large impact on health and environmental safety, fine and ultrafine particles are of special importance. While infrared (IR) spectroscopy can yield important material-specific information, direct investigation of small samples or single nano-objects is impossible, due to the great mismatch between particle size and IR wavelengths. Fortunately, surface-enhanced infrared absorption spectroscopy allows detection of $i\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}d\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}v\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}d\phantom{\rule{0}{0ex}}u\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}l$ ultrafine dust particles a few tens of nanometers in diameter. The extraordinary near-field enhancement of the IR vibrational signal is achieved using plasmonically resonant Au ``bowtie'' apertures, in which the ultrafine specks are easily trapped.
The Role of Invitational Education and Intelligence Beliefs in Academic Performance

The purpose of the present study is to examine the role of Invitational Education and intelligence beliefs in the academicperformance of high school students. The research population comprised all male and female students studying at high schoolsin the academic year of 2009-2010 in Kashmar, a city in Iran. Selected through multi-stage random sampling The researchsample included 540 students (270 females and 270 males). The research instruments were the Invitational Teaching Survey(Amos, Purkey, & Tobias, 1984), and Intelligence Beliefs Questionnaire (Zabihi, 2005). Students’ grade point average wasused as an indicator of academic performance during high school. Data were analyzed using path analysis of direct andindirect effects of Invitational Education on intelligence beliefs and performance of high school. The results showed that of thesub-components of Invitational Education, as described in the Invitational Teaching Survey (Amos, Purkey & Tobias, 1984),consideration has positive and significant effect on incremental intelligence and performance. Coordination has positive andsignificant effect on inherent intelligence beliefs. Skill has positive and significant effect on incremental intelligence andperformance. Incremental intelligence beliefs have positive and significant effect on performance. Consideration has negativeand significant effect on inherent intelligence beliefs. Coordination has negative and significant effect on inherent intelligencebeliefs. Inherent intelligence beliefs have negative and significant effect on performance. Skill has negative and significanteffect on inherent intelligence beliefs. Consideration, coordination and skill components of Invitational Education haveindirect and significant effect on performance through inherent and incremental intelligence beliefs. These findings show thatit is necessary to take the role of Invitational Education and intelligence beliefs into account when studying academicperformance.
Water Area Extraction Using Geocoded High Resolution Imagery of TerraSAR-X Radar Satellite in Cloud Prone Brahmaputra River Valley

In this study, supervised pixel based maximum likelyhood classifier (MLC) was evaluated for differentiating water area from non-water area in a cloud prone river valley region using images obtained from German TerraSAR-X radar satellite. TerraSAR-X satellite microwave data acquired on 8th May 2008 was used for a test site in Brahmputra river valley, India. The performance of traditional supervised (MLC) classification method was evaluated on TerraSAR's X band strip geo coded high resolution (6 meter) images in VV polarization channel. The 42 training sites for water area and 40 training sites for non-water areas were carefully selected over the entire image where optical Resourcesat - 1 LISS IV multi-spectral image and pre- classification ground truth were used as apriori knowledge. The calculated backscatter coefficient ranges from approximately - 24.1 to 6.5 dB where water areas usually have low dB value around -20 dB. Overall classification accuracy was 94.92 %. This study shows that high resolution TerraSAR-X radar satellite image have advantage of weather independence over optical data. Furthermore, the supervised (MLC) classification can be used for extraction of water area from single band high resolution radar images where traditional water area extraction methods i.e., NDVI, NDWI etc., can not be used on radar images.
Name Lookup in Named Data Networking: A Review

Named data networking (NDN) is an alternative model to the current traditional IP-based Internet by improving content distribution in a network with content name. In NDN, content is sent/received based on its name rather than its address. Forwarding is still considered the bottleneck of NDN despite the effort in naming, routing, mobility, and security. NDN requires per-packet update that is preceded by a one-name lookup operation, which results in heavy time processing. The content names consume a large amount of memory, which causes the fast growth of NDN tables. To review the current solutions for NDN name lookup, this paper explores the existing NDN approaches and architectures by using a novel classification method. Furthermore, name lookup approaches were analyzed and compared in a novel manner. This survey highlighted the issues that affect name lookup performance in NDN, such as scalability, memory consumption, time efficiency, latency, and validity. We pointed out directions for future work and open issues to improve NDN name lookup in minimizing implementation cost and enhancing performance.
Maternal obesogenic diet combined with postnatal exposure to high‐fat diet induces metabolic alterations in offspring

Maternal obesity has been shown to impact the offspring health during childhood and adult life. This study aimed to evaluate whether maternal obesity combined with postnatal exposure to an obesogenic diet could induce metabolic alterations in offspring. Female CD1 mice were fed a control diet (CD, 11.1% of energy from fat) or with a high‐fat diet (HFD, 44.3% of energy from fat) for 3 months. After weaning, pups born from control and obese mothers were fed with CD or HFD for 3 months. Both mothers and offspring were weighted weekly and several blood metabolic parameters levels were evaluated. Here, we present evidence that the offspring from mothers exposed to a HFD showed increased acetylation levels of histone 3 on lysine 9 (H3K9) in the liver at postnatal Day 1, whereas the levels of acetylation of H4K16, dimethylation of H3K27, and trimethylation of H3K9 showed no change. We also observed a higher perinatal weight and increased blood cholesterol levels when compared to the offspring on postnatal Day 1 born from CD‐fed mothers. When mice born from obese mothers were fed with HFD, we observed that they gained more weight, presented higher blood cholesterol levels, and abdominal adipose tissue than mice born to the same mothers but fed with CD. Collectively, our results point toward maternal obesity and HFD consumption as a risk factor for epigenetic changes in the liver of the offspring, higher perinatal weight, increased weight gain, and altered blood cholesterol levels.
Sex Effect on Presurgical Language Mapping in Patients With a Brain Tumor

Differences between males and females in brain development and in the organization and hemispheric lateralization of brain functions have been described, including in language. Sex differences in language organization may have important implications for language mapping performed to assess, and minimize neurosurgical risk to, language function. This study examined the effect of sex on the activation and functional connectivity of the brain, measured with presurgical functional magnetic resonance imaging (fMRI) language mapping in patients with a brain tumor. We carried out a retrospective analysis of data from neurosurgical patients treated at our institution who met the criteria of pathological diagnosis (malignant brain tumor), tumor location (left hemisphere), and fMRI paradigms [sentence completion (SC); antonym generation (AG); and resting-state fMRI (rs-fMRI)]. Forty-seven patients (22 females, mean age = 56.0 years) were included in the study. Across the SC and AG tasks, females relative to males showed greater activation in limited areas, including the left inferior frontal gyrus classically associated with language. In contrast, males relative to females showed greater activation in extended areas beyond the classic language network, including the supplementary motor area (SMA) and precentral gyrus. The rs-fMRI functional connectivity of the left SMA in the females was stronger with inferior temporal pole (TP) areas, and in the males with several midline areas. The findings are overall consistent with theories of greater reliance on specialized language areas in females relative to males, and generalized brain areas in males relative to females, for language function. Importantly, the findings suggest that sex could affect fMRI language mapping. Thus, considering sex as a variable in presurgical language mapping merits further investigation.