Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal monetary policy revisited: does considering US real-time data change things?

ABSTRACT The primary goal of this article is to investigate whether properly modelling real-time data and optimal real-time decision-making of a monetary planner provides new insights into monetary policy behaviour and outcomes. This article extends a variant of the asymmetric preference model suggested by Ruge-Murcia to investigate the use of real-time data available to policymakers when making their decisions and revised data which more accurately measure economic performance, but is only available much later. In our extended model, the central banker targets a weighted average of revised and real-time inflation together with a weighted average of revised and real-time output. Moreover, we allow for an asymmetric central bank response to real-time data depending on whether the unemployment rate is high or low. Our model identifies several new potential sources of inflation bias due to data revisions. Our empirical results suggest that the Federal Reserve Bank focuses on targeting revised inflation during low unemployment periods, but it weighs heavily real-time inflation during high unemployment periods. The inflation bias due to data revisions is comparable in magnitude to the bias from asymmetric central banker preferences with the bias being somewhat larger during high unemployment.
Structure of Motor Abilities of Pre-School Children before and after Kinesiology Treatment

The research included the sample of 114 pre-school children aged 6 and 7, with average values of height (TV=123.23 cm ± 5.43) and weight (TT=24.53 kg ± 2.71). The children were divided into two groups: (E) experimental group (n=55), which had an additional programme of kinesiology activities lasting for 12 weeks, and (K) control group (n=59), which, apart from regular activities in physical education classes in the kindergarten did not have a programme of kinesiology activities. The aim of the research was to determine the latent structure of motor abilities before and after the application of kinesiology activities, and to determine the compatibility of factor saturation. Correlation was performed by Pearson correlation coefficient, and determination of the structure of motor activities was performed by factor analysis and by the method of principal components with bent promax rotation of isolated principal components. The Tucker index of saturation factor was used for determining the compatibility of isolated factors. Research results indicate that both in the initial and the final measurements of both tested groups, a two-dimensional structure of motor abilities was determined, consisting of coordination and strength, which could most appropriately be defined as one, general motor factor. The congruence between the isolated factors in E and K groups gained in the initial and final measurements shows that there are no differences in the structure of isolated factors. It can be concluded that motor functioning of children is still under the mechanism of structuring movement, so after the application of kinesiology activities the same factors remained, and quality changes in the structure of motor abilities have not been detected in the tested age.
Title Exosomes released by granulocytic myeloid-derived suppressorcells attenuate DSS-induced colitis in mice

Myeloid-derived suppressor cells (MDSC) have been described in inflammatory bowel disease (IBD), but their role in the disease remains controversial. We sought to define the effect of granulocytic MDSC-derived exosomes (G-MDSC exo) in dextran sulphate sodium (DSS)-induced murine colitis. G-MDSC exo-treated mice showed greater resistance to colitis, as reflected by lower disease activity index, decreased inflammatory cell infiltration damage. There was a decrease in the proportion of Th1 cells and an increase in the proportion of regulatory T cells (Tregs) in mesenteric lymph nodes (MLNs) from G-MDSC exo-treated colitis mice. Moreover, lower serum levels of interferon (IFN)-γ and tumor necrosis factor (TNF)-α were detected in G-MDSC exotreated colitis mice. Interestingly, inhibition of arginase (Arg)-1 activity in G-MDSC exo partially abrogated the spontaneous improvement of colitis. In addition, G-MDSC exo could suppress CD4+ T cell proliferation and IFN-γ secretion in vitro and inhibit the delayed-type hypersensitivity (DTH) response, and these abilities were associated with Arg-1 activity. Moreover, G-MDSC exo promoted the expansion of Tregs in vitro. Taken together, these results suggest that G-MDSC exo attenuate DSS-induced colitis through inhibiting Th1 cells proliferation and promoting Tregs expansion.
Deformation analysis of terrestrial monitoring observations on Turtle Mountain, Alberta

Abstract Deformation monitoring has been carried out in two epochs on Turtle Mountain, Alberta, using a high-precision total station and a terrestrial laser scanner. From the total station observations, coordinates have been computed for seven signalized target points in a least-squares network adjustment. Then, a deformation analysis using a Multi-Parameter Transformation has been performed to derive movements between epochs. The two point clouds obtained with the laser scanner were registered using the iterative closest point algorithm. Differences in elevation between the two point clouds were then derived for the entire scene. Results indicate a downward movement of South Peak, and no significant horizontal deformations were found.
Social media analytics dashboard for academics and the decision-making process: A systematic literature review

Our worlds have changed since the social media exploded, and it affects not only our social, everyday lives but also our academic endeavours. Now, academics can disseminate knowledge through social media platforms, created specifically for academics and for the public. Uses of social media are now analyzed for providing an overview of the impact of academic dissemination, might be termed as social media analytics for academics — a non-traditional statistical dashboard that include both citation impact metrics and webometrics of scientific publications. The analytics have potential to change the way researchers disseminate, choose study focus, research fields, and much more. Readers also could rely on the analytics in the selection process. However, along with the social media analytics, comes a need for new terminology and use of metrics to evaluate the impact of research articles. Online interaction metrics have evolved to become alternative bibliometric matrices, that view downloads, likes, shares, comments, and other similar online engagements as the indicators of impact. The impact evaluation no longer solely depends on citations, but on the various forms of engagement and activity surrounding an article. This systematic literature review attempts to uncover whether literature about dashboards on social media for academics exists. Also, whether any study has been conducted on the decision-making process that comes with the recent social media dashboards for academics. The literature review uncovered 11 texts of relevance to the topic, along with five pre-determined texts. In order to create a legible overview of the literature, a qualitative content analysis was conducted, coded with 21 themes, and merged into three categories: (1) Bibliometrics, social media analytics and alternative metrics for the reputation of academics, (2) Academics’ strategy for-and impact of dissemination and (3) Dashboard for Academics’ knowledge dissemination analytics. The study shows that no study exists about dashboards for social media for academics, nor is there a focus on the decision-making process. Thereby, a need to study dashboards on social media exist, because, not only will altmetrics on the dashboards provide authors with critical numerical information, but also create an opportunity for the readers to make decisions regarding academic work and academics. Authors will, at the same time, be able to make decisions on what to further investigate/study and how to make greater impact in the broader society than just readers of the bibliographic databases.
Loughborough University Institutional Repository Novel metrics and methodology for the characterisation of 3 D imaging systems

The modelling, benchmarking and selection process for non-contact 3D imaging systems relies on the ability to characterise their performance. Characterisation methods that require optically compliant artefacts such as matt white spheres or planes, fail to reveal the performance limitations of a 3D sensor as would be encountered when measuring a real world object with problematic surface finish. This paper reports a method of evaluating the performance of 3D imaging systems on surfaces of arbitrary isotropic surface finish, position and orientation. The method involves capturing point clouds from a set of samples in a range of surface orientations and distances from the sensor. Point clouds are processed to create a single performance chart per surface finish, which shows both if a point is likely to be recovered, and the expected point noise as a function of surface orientation and distance from the sensor. In this paper, the method is demonstrated by utilising a low cost pantilt table and an active stereo 3D camera. Its performance is characterised by the fraction and quality of recovered data points on aluminium isotropic surfaces ranging in roughness average (Ra) from 0.09 to 0.46 μm at angles of up to 55° relative to the sensor over a distances from 400 to 800 mm to the scanner. Results from a matt white surface similar to those used in previous characterisation methods contrast drastically with results from even the dullest aluminium sample tested, demonstrating the need to characterise sensors by their limitations, not just best case performance.
Transcription Networks , Microarray Chips and Sparse Linear Methods

This paper is an introduction to some basic concepts on trans c iption networks, microarray chips and sparse linear methods. We describe the application of sparse linear methods to the modeling of gene expression data. Firs t, we review the gene expression process and how transcription networks regulat e protein production rates in cells. After this, DNA microarray chips are describ ed alongside with the specific features of the gene expresion data generated by thi s technology. Finally, we also introduce different sparse linear methods that are f requently used in the modeling of gene expression data.
The New User Problem in Collaborative Filtering

The new user problem is a problem that all collaborative filtering systems will face. How do we make recommendations when we do not know what the user likes? This thesis provides an overview of collaborative filtering and the new user problem. We develop new metrics to measure the performance of a system when dealing with a new user. We also show a progression of methods for dealing with the new user problem the last of which is a substantial improvement over the best current method.
A simple heuristic for perishable item inventory control under non-stationary stochastic demand

In this paper, we study the single-item single-stocking location non-stationary stochastic lot sizing problem for a perishable product. We consider fixed and proportional ordering cost, holding cost and penalty cost. The item features a limited shelf life, therefore we also take into account a variable cost of disposal. We derive exact analytical expressions to determine the expected value of the inventory of different ages. We also discuss a good approximation for the case in which the shelf-life is limited. To tackle this problem, we introduce two new heuristics that extend Silver’s heuristic and compare them to an optimal Stochastic Dynamic Programming policy in the context of a numerical study. Our results demonstrate the effectiveness of our approach.
A new solution algorithm for skip-free processes to the left

This paper proposes a new solution algorithm for steady state models describing skip-free processes to the left where each level has one phase. The computational complexity of the algorithm is independent of the number of levels of the system. If the skip parameter of the skip-free process is significantly smaller than the number of levels of the system, our algorithm numerically outperforms existing algorithms for skip-free processes. The proposed algorithm is based on a novel method for applying generalized Fibonacci series to the solution of steady state models.
Fusing Two Kinds of Virtual Samples for Small Sample Face Recognition

Face recognition has become a very active field of biometrics. Different pictures of the same face might include various changes of expressions, poses, and illumination. However, a face recognition system usually suffers from the problem that nonsufficient training samples cannot convey these possible changes effectively. The main reason is that a system has only limited storage space and limited time to capture training samples. Many previous literatures ignored the problem of nonsufficient training samples. In this paper, we overcome the insufficiency of training sample size problem by fusing two kinds of virtual samples and the original samples to perform small sample face recognition. The two used kinds of virtual samples are mirror faces and symmetrical faces. Firstly, we transform the original face image to obtain mirror faces and symmetrical faces. Secondly, we fuse these two kinds of virtual samples to achieve the matching scores between the test sample and each class. Finally, we integrate the matching scores to get the final classification results. We compare the proposed method with the single virtual sample augment methods and the original representation-based classification. The experiments on various face databases show that the proposed scheme achieves the best accuracy among the representation-based classification methods.
PROFESSIONAL QUALITY ASSESSMENT OF THE CROATIAN STATE WRITTEN EXAM IN BIOLOGY

The aim of the tool presented in this study is to enable teachers’ qualitative analysis of the questions within the Croatian state written exam in biology, and the eventual correction of the questions before their application in the student assessment. We have identified the two basic categories that determine the question quality: 1) the importance of questions (regarding the profession, life, curriculum, critical thinking), and 2) the influence of questions on students’ answers (i.e., type, shape and intelligibility of the questions as well as logical reasoning and further learning path in relation to the students’ answers). The tool we have developed was tested for its effectiveness on a sample exam designed for students aged 13. A correlation between logical reasoning and the “importance-of-questions” categories, and the success rate of the exam was observed. This simple tool has proven to be effective for both teachers’ self-assessment and peer evaluation.
Financing Renewable Energy Through Household Adoption of Green Electricity Tariffs: A Diffusion Model of an Induced Environmental Market

Green electricity tariffs are a means by which, green consumers, can contribute to investment in renewable energy. In order to conceptualize factors constraining the adoption of green electricity tariffs this paper develops a model that links the willingness-to-pay (WTP) literature with the established innovation diffusion literature. This concern arises from a need to reconcile the large disparities that have been empirically observed between the proportion of households actually adopting green electricity tariffs and the proportion in WTP surveys that claim they would (Stated-Willingness-to-Adopt or SWA). Using the Bass Model as the point of departure our model depicts how increasing consumer environmental concern, driven by word-of-mouth and mass media communication channels, results in an increasing proportion of households with a SWA. The presence of response bias and the free rider problem result in, feasible adoption, being below the SWA. Feasible adoption is, in turn, differentiated from actual adoption by the extent of market imperfections, such as the supply side problems and regulatory failures often discussed in the empirical literature.
Probabilistic Matrix Factorization for Automated Machine Learning

In order to achieve state-of-the-art performance, modern machine learning techniques require careful data pre-processing and hyperparameter tuning. Moreover, given the ever increasing number of machine learning models being developed, model selection is becoming increasingly important. Automating the selection and tuning of machine learning pipelines consisting of data pre-processing methods and machine learning models, has long been one of the goals of the machine learning community. In this paper, we tackle this meta-learning task by combining ideas from collaborative filtering and Bayesian optimization. Using probabilistic matrix factorization techniques and acquisition functions from Bayesian optimization, we exploit experiments performed in hundreds of different datasets to guide the exploration of the space of possible pipelines. In our experiments, we show that our approach quickly identifies high-performing pipelines across a wide range of datasets, significantly outperforming the current state-of-the-art.
SELENIUM, TELLURIUM, ARSENIC AND ANTIMONY CONTENTS OF PRIMARY MANTLE SULFIDES

Sulfide phases in peridotitic mantle xenoliths from Ichinomegata in Japan, Nunivak Island in Alaska, and southern Africa occur as globular grains within silicate minerals, and along grain boundaries of silicate and oxide minerals. The morphology of the sulfide grains suggests that the sulfide liquid was not interconnected in the mantle, even within single samples. This is supported by compositional variations of sulfides. Proton-induced X-ray excitation (PIXE) micro-analyses of 53 sulfide grains larger than 20 µm in diameter reveal a wide range of compositions: Se 21‐280 ppm, S/Se 1120‐18800; Te up to 103 ppm, S/Te 2,600‐31,000; As up to 670 ppm, As/S up to 208 � 10 ‐5 , and Sb, up to 146 ppm, Sb/S up to 37 � 10 ‐5 . S/Se and Ni show an inverse correlation in the sub-arc xenoliths, suggesting preferential retention of Se in the mantle during partial melting. Sul fur is the most readily removed element, whereas Te is the most likely retained element in the residual mantle. This interpretation is consistent with low S/Se values for sulfides from refractory mantle wedges compared to values for the primitive mantle, general ly high S/Se and Se/Te values in basalts, and low S/Se and Se/Te in boninites compared to mid-ocean-ridge basalts. The evidence implies that S/Se values of mantle-derived magmas may vary depending on the degree of partial melting and previous melting history in the source mantle. High concentrations of As and Sb in arc magmas are considered to be supplied from subducting slabs. Our data showing low levels of As and Sb in sulfides from sub-arc mantle suggest their fast removal from mantle wedges compared to the rate of supply of these elements from subducting slabs. Alternatively, these elements may be transported by a fluid phase from slabs to the site of partial melting without residing in the mantle.
Fundamental Aspects of H2S Adsorption on CPO-27-Ni

Adsorption of H2S on the Ni2(dhtp)(H2O)2·8H2O metal–organic framework (known as CPO-27-Ni or MOF-74-Ni) is characterized by in situ powder X-ray diffraction (PXRD), Fourier transform infrared (FTIR), Raman, and UV–visible spectroscopy) and by first-principles periodic boundary conditions calculations. PXRD results show very high stability of CPO-27-Ni framework in the presence of H2S. Nevertheless, as evidenced by change in color of the sample from pale yellow to dark green, the adsorption of H2S strongly affects the coordination of Ni sites. FTIR results show the reversible molecular adsorption of H2S. Experimental and computed energies of interaction reveal good agreement. Quantitative data considering energetic aspects (calorimetric measurements) are also included. This work highlights the fundamentals of H2S adsorption onto the CPO-27-Ni framework.
Local asymptotics for the first intersection of two independent renewals

We study the intersection of two independent renewal processes, $\rho=\tau\cap\sigma$. Assuming that $\mathbf{P}(\tau_1 = n ) = \varphi(n)\, n^{-(1+\alpha)}$ and $\mathbf{P}(\sigma_1 = n ) = \tilde\varphi(n)\, n^{-(1+ \tilde\alpha)} $ for some $\alpha,\tilde \alpha \geq 0$ and some slowly varying $\varphi,\tilde\varphi$, we give the asymptotic behavior first of $\mathbf{P}(\rho_1>n)$ (which is straightforward except in the case of $\min(\alpha,\tilde\alpha)=1$) and then of $\mathbf{P}(\rho_1=n)$. The result may be viewed as a kind of reverse renewal theorem, as we determine probabilities $\mathbf{P}(\rho_1=n)$ while knowing asymptotically the renewal mass function $\mathbf{P}(n\in\rho)=\mathbf{P}(n\in\tau)\mathbf{P}(n\in\sigma)$. Our results can be used to bound coupling-related quantities, specifically the increments $|\mathbf{P}(n\in\tau)-\mathbf{P}(n-1\in\tau)|$ of the renewal mass function.
The Usefulness of Comprehensive Income and Other Comprehensive Income

Author: Kishan T. Royer (429278) Subject: Master’s Thesis Supervisor: prof. dr. E.A. de Groot Co-reader: drs. T.P.M. Welten Date: June 22, 2017 Abstract This master’s thesis examines the usefulness of comprehensive income and other comprehensive income compared to net income, for investors, among European firms. The usefulness of these performance measures is measured by assessing the value relevance and incremental value relevance, which are the associations with share prices and share returns. The evidence indicates that overall net income is more useful to investors, however, comprehensive income is also useful to investors of European firms. Furthermore, other comprehensive income is incrementally value relevant, once added to net income. Additional analyses show that net income is more persistent and a better predictor of future cash flows, than comprehensive income. The sample consists of European listed firms, available in Datastream and Worldscope in the period 2005-2016. The results provide strong evidence that comprehensive income and other comprehensive income are useful to investors. Finally, the results also indicate that comprehensive income is more useful to investors of financial firms.