Miracle-dz/UniFAR-FTUs huggingface dataset:
UW Faculty of Management Working Paper Series No 2 / September 2016 D O MACROPRUDENTIAL POLICY INSTRUMENTS AFFECT THE LINK BETWEEN LENDING AND CAPITAL RATIO ? – CROSS-COUNTRY EVIDENCE

In this paper we ask about the capacity of macroprudential policies to reduce the positive association between loans growth and the capital ratio. We focus on aggregated macroprudential policy measures and on individual instruments and test whether their effect on the association between lending and capital depends on bank size, the economic development of a country as well as on the extent of capital account openness. Applying the GMM 2-step Blundell and Bond approach to a sample covering over 60 countries, we find that macroprudential policy instruments reduce the impact of capital on bank lending during both crisis and noncrisis times. This result is stronger in large banks than in other banks. Of individual macroprudential instruments, only borrower-targeted LTV caps and DTI ratio weaken the association between lending and capital. Our results also show that the effect of macroprudential policies on the association between lending and the capital ratio in non-crisis periods is stronger in advanced countries than in emerging countries. Additionally, differentiating by the level of capital account openness, we find that macroprudential policies are more effective in increasing the resilience of banks and thus weakening the association between loan supply and capital ratio for relatively closed economies but less effective for relatively open economies. Generally, with our study we are able to support the view that macroprudential policy has the potential to curb the procyclical impact of bank capital on lending and therefore, the introduction of more restrictive international capital standards included in Basel III and of macroprudential policies are fully justified.
Evaluate Impacts of Big Data on Organizational Performance : Using Intellectual Capital as a Proxy

Big data analytics has come out as a new important field of study for both researchers and practitioners, demonstrating the significant demand for solutions to business problems in a data-driven knowledge-based economy. Employing the emergent technology successfully is not easy, and assessing the roles of big data in improving firm performance is even much harder. Additionally, empirical studies examining the impacts of the nascent technology on organizational performance remain scarce. The present study aimed to fill the gap. This study suggested using firms’ intellectual capital as a proxy for the performance of big data implementation in assessing its influence on business performance. The present study employed the Value Added Intellectual Coefficient method to measure corporate intellectual capital, via its three main components: human capital efficiency, structural capital efficiency, and capital employed efficiency, and then used the structural equation modeling technique to model the data and test the models. The financial fundamental and market data of 100 randomly selected publicly listed firms in the sector of pharmaceutical, biotechnology, and life sciences were collected. The results of the tests showed that only human capital efficiency and capital employed efficiency had a significant positive impact on firm profitability, which highlighted the prominent roles of enterprise employees and financial capital in the impacts of big data technology.
Towards modeling Arabic lexicons compliant LMF in OWL-DL

Elaborating reusable lexical databases and especially making interoperability operational are crucial tasks effecting both Natural Language Processing (NLP) and Semantic Web. With this respect, we consider that modeling Lexical Markup Framework (LMF) in Web Ontology Language Description Logics (OWL-DL) can be a beneficial attempt to reach these aims. This proposal will have large repute since it concerns the reference standard LMF for modeling lexical structures. In this paper, we study the requirement for this suggestion. We first make a quick presentation of the LMF framework. Next, we define the three ontology definition sublanguages that may be easily used by specific users: OWL Lite, OWL-DL and OWL Full. After comparing of the three, we have chosen to work with OWL-DL. We then define the ontology language OWL and describe the steps needed to model LMF in OWL. Finally, we apply this model to develop an instance for an Arabic lexicon.
The Biophilic Index Predicts Healing Effects of the Built Environment

The “biophilic index B ”, a number from 0 to 20, predicts positive health effects that spaces and structures may have on users. This measure can be estimated before something is built, anticipating the eventual response of users to a new building or urban space. Nothing like this is performed in current practice, which makes no attempt to quantify assessments. In addition, the “biophilic index B ” is useful for repair, since it identifies which aspects of an existing building or space could be improved by renovation. Different portions, or different directions could have widely different biophilic indices. Ten separate factors that constitute the biophilic index identify different ways in which nature affects our body in an intrinsic yet subconscious manner. For a high value of the biophilic index, we would expect healing effects from long-term physical experience of that immediate region. Existing data support this quantitative approach to designing healing environments. Experiments are proposed to further explore the mechanisms responsible for the biophilic effect.
REFINING USER SEARCH GOALS WITH FEEDBACK SESSION AND CAP

— Now a day the users are generally using the internet for several information needs. Different users may have different information on dissimilar facets upon the submission of same query to search engine. So discovering different user search goals becomes complicated. The estimation and representation of user search goals can be very useful in improving search engine applicability and user knowledge. This paper proposes an approach for concluding user search goals by analyzing user query logs from several search engines. In proposed method is to determine dissimilar user search goals for a query by clustering the user feedback sessions. The Feedback sessions are built from click-through logs of various search engines. This method generates virtual-documents to better represent feedback sessions for clustering. Finally, clustering virtual documents to discover different user search goals and des cribe them with some keywords. Then these user search goals are used to restructure the web search results.
The Productivity of Cash and the Cross-Section of Expected Stock Returns

We first show, analytically, that the expected return on the stock, firm size, and the book-to-market ratio (B/M) are functionally dependent on the "productivity of cash," defined as the firm's economic rents scaled by its cash holdings. We then show that, empirically, the productivity of cash is a highly significant and robust negative predictor of stock returns. Our research suggests that the predictive power of size and B/M in earlier studies may stem from their role as proxies for the productivity of casha new and economically-rationalized factor that explains the cross-section of stock returns. Satyajit Chandrashekar Advanced Research Center State Street Global Advisors 1 Lincoln Street Boston, MA 02111 617-664-9603 satyajit_chandrashekar@ssga.com Ramesh K. S. Rao* McDermott Professor of Finance McCombs School of Business University of Texas at Austin Austin, TX 78712 512-475-8756 ramesh.rao@mccombs.utexas. edu
Heritability of some meat quality traits in ducks

1Poznan University of Life Sciences, Department of Genetics and Animal Breeding, ul. Wołyńska 33, 60-637 Poznań, Poland; #e-mail: tomasz@up.poznan. pl 2Koluda Wielka Experimental Unit Station of the National Institute of Animal Production, Waterfowl Genetic Resources Station in Dworzyska, 62-035 Kórnik, Poland 3National Poultry Council – Chamber of Commerce, Division of Poultry Performance Tests and Breeding, ul. Sarmacka 7, 60-975 Poznań, Poland 4Poznan University of Life Sciences, Department of Animal Breeding and Product Quality Assessment, ul. Słoneczna 1, Złotniki, 62-002 Suchy Las
Dynamic evaluation of a mini train powered by the hybrid fuel cell

Fuel cell system is widely used in the mobile devices and USA space program. This project studies the feasibility of powering a small train with a hybrid fuel cell system by employing electric power integration technology. Specifically, this study performs the dynamic tests on a mini train powered by Proton Exchange Membrane Fuel Cell (PEMFC). The original 12 V, 100 AH lead-acid battery, which can provide 12 V, 0.5 Hp for the locomotive and carry 9~12 people, is replaced by a hybrid Proton Exchange Membrane Fuel Cell. After an oval railroad and a low-pressure metal hydrogen storage canister recharging station are built, the mini train is tested on weekend and holidays at the outdoor park of National Science and Technology Museum, Taiwan. The dynamic performance of the PEMFC system is analyzed and the feasibility of applying this system to mini trains is evaluated. After one year of running, the mini train has been operated over 1,200 hours and has been transported over 30,000 passengers. The hybrid PEMFC system works perfectly and meets the original goal. Results also indicate that the temperature significantly affects system performance.
Cu 3 O 7 − δ thin films on MgO substrates : a platform to study strain effects on the local orders in cuprates

Riccardo Arpaia, 2, ∗ Eric Andersson, Alexei Kalaboukhov, Elsebeth Schröder, Edoardo Trabaldo, Regina Ciancio, Goran Dražić, Pasquale Orgiani, Thilo Bauch, and Floriana Lombardi † Quantum Device Physics Laboratory, Department of Microtechnology and Nanoscience, Chalmers University of Technology, SE-41296 Göteborg, Sweden Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133 Milano, Italy CNR-IOM, TASC Laboratory, Area Science Park, Basovizza S.S. 14 km 163.5, I-34149 Trieste, Italy Laboratory for Materials Chemistry, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia CNR-SPIN, University of Salerno, I-84084 Fisciano (SA), Italy (Dated: August 8, 2019)
Human-AI Collaboration : Towards Socially-Guided Machine Learning

As Machine Learning (ML) systems become increasingly ubiquitous, capable and autonomous, it has become essential to take a human-centered view to consider how people’s interactions with ML systems, including the effort to develop and evolve ML systems, impact their work practices, wellbeing and the social-organizational environment. Built on our work on human-agent collaboration, we suggest a change of perspective, by considering human(s) and the ML model(s) they interact with as a team engaging in collaborative work. With that, we can apply metaphoric thinking based on team collaboration to inform the design of human-model interactions and rethink the collective goals to be embedded in computational models. Based on pillars of Computer-Supported Cooperative Work (CSCW) research, we point to three areas for future research into technologies to support human-model interaction as collaborative work: (1) model training as knowledge sharing; (2) interactions as communication actions; and (3) coordination for better collaboration and construction of trust.
Multi-dimensional Reverse k NN Search

Given a multi-dimensional point q, a reversek nearest neighbor (RkNN) query retrieves all the data points that haveq as one of theirk nearest neighbors. Existing methods for processing such qu eries have at least one of the following deficiencies: they (i) do not sup port arbitrary values of k, (ii) cannot deal efficiently with database updates, (iii) are applicable onl y to 2D data but not to higher dimensionality, and (iv) retrieve only approximate results. Motivated by th ese shortcomings, we develop algorithms for exactRkNN processing witharbitrary values ofk ondynamic, multi-dimensional datasets. Our methods utilize a conventional data-partitioning index on the data se and do not require any pre-computation. As a second step, we extend the proposed techniques to continuousRkNN search, which returns the R kNN results for every point on a line segment. We evaluate the eff ectiveness of our algorithms with extensive experiments using both real and synthetic datasets.
NUMERICAL INVESTIGATION OF HEAT-INDUCED EFFECTS ON STEEL WORKPIECES DURING HARD TURNING MACHINING PROCESS

Hard turning is a machining process intended for significantly hard materials such as steel workpieces with hardness more than 45 HRC. Hard turning constitutes also a promising cost-efficient alternative of grinding regarding surface finishing operations. However, the large amount of heat produced during this process primarily by friction work in the tool-chip interface, affects significantly the workpiece surface and subsurface layers as well. In order to investigate thoroughly the heat-induced effects on machined workpiece, finite elements models are developed to simulate hard turning for various cases with variable depth of cut and feed values. In each case, temperature profiles are analyzed to determine heat-induced effects on subsurface layers such as heat affected zone and thermally induced stresses in the workpiece and desirable set of machining parameters for the reduction of heat-induced effects is determined.
Smoothing Parameters for Recursive Kernel Density Estimators under Censoring

In this paper, we are concerned with the nonparametric estimation of an unknown density under censoring. Firstly, we propose a recursive kernel density estimators under censoring, based on a stochastic approximation algorithm. Then, we showed that our recursive estimator is consistent and asymptotically normally distributed. Moreover, we describe and investigate a data-driven bandwidth selection procedure based on normal pilot bandwidth reference distributions. We showed that the proposed recursive estimators can be better than the non-recursive in terms of estimation error and much better in terms of computational costs. We corroborated these theoretical results through a simulation study and on Malaria in Senegalese children dataset.
Comparison of detrending methods

This thesis examines the difference between the extracted cyclical components of some macroeconomic time series using four popular detrending methods HP, BK, CF and FOD. We use different approaches to compare their differences. A standard examination of the cyclical component is applied. We also take a frequency domain approach and examine the sample spectra for each cycle. Moreover, impulse responses and the correlation between the cyclical components extracted by each detrending method are studied. We conclude that for quarterly data HP, BK and CF produce similar cycles. However, when considering annual data the HP diverges from the BK and CF. The FOD extracts cycles that are not similar to those of the other three examined filters.
On Visual Coreference Chains Resolution

We explore to what degree an existing textual coreference resolution tools can be applied to visual dialogue data. The analysis of error of the coreference system (i) demonstrates the extent to which such data differs from the written document texts that these tools are typically applied on; (ii) informs about the relation between information expressed in language and vision; and (iii) suggests further directions in which coreference tools should be adapted for visual dialogue.
MESENTERIC OXYGEN DESATURATION IN AN NEWBORN WITH INTRAUTERINE GROWTH RESTRICTION AND COMPLEX CONGENITAL HEART DISEASE WHO DEVELOPED NECROTISING ENTEROCOLITIS-CASE PRESENTATION

Introduction. Early diagnosis and aggressive treatment is fundamental in the management of patients with ventilator-associated pneumonia (VAP). Aim. The aim of this study was to determine the incidence of VAP among mechanically ventilated children and to identify the main risk factors and nosocomial germs for development of VAP in a critically ill PICU population. Material and methods. A retrospective, observational study was conducted over a period of 2 years (January 2011 – December 2012) in the Fist Pediatric Intensive Care Unit (PICU) of Emergency Hospital for Children "Louis Turcanu" Timisoara and included all mechanically ventilated children ≥ 48 hours aged 0-18 years. Results. Of all 51 mechanically ventilated patients, who met the inclusion criteria, 43.13% developed VAP. Patients with VAP needed a greater number of days of mechanical ventilation (mean 23.59 vs. 5.68 days) and a longer duration of hospitalization (mean 42.18 vs. 20.27 days) than those without VAP. Multiple regression analysis identified 4 factors associated with VAP (p < 0.05): previously use of an antibiotic (t-statistics (t-stat) = 2.33, p = 0.036), previously use of more than one antibiotic (t-stat = 2.89, p < 0.01), previously use of an antifungal drug (t-stat = 2.00, p = 0.05), and reintubation (t-stat = 2.71, p < 0.01). Organisms identified by culture, involved in the etiology of VAP were: gram-negative bacteria 88.8%, fungi 6.6%, and gram-positive bacteria 4.4%. Conclusions. The incidence of VAP was higher (43%) in our study. Children on previously use of antibiotics or antifungal drugs, or experienced reintubation, developed VAP and had a longer period of mechanical ventilation and hospitalization. Pseudomonas aeruginosa was the most common Gram-negative bacteria associated with VAP.
DEIM Forum 2019 G 4-1 Scope-aware Code Completion with Discriminative Modeling

Code completion is a traditional popular feature for API access in integrated development environments (IDEs). It not only frees programmers from remembering specific details about an API but also saves keystrokes and corrects typographical errors. Existing methods for code completion usually suggest APIs based on statistics in code bases described by language models. However, they neglect the fact that the user’s input is also very useful for ranking, as the underlying patterns can be used to improve the accuracy of predictions of intended APIs. In this paper, we propose a novel method to improve the quality of code completion by incorporating the users’ acronym– like input conventions and the APIs’ scope context into a discriminative model. The users’ input conventions are learned using a logistic regression model by extracting features from collected training data. The weights in the discriminative model are learned using a support vector machine (SVM). To improve the real-time efficiency of code completion, we employ a trie to index and store the scope context information. An efficient top-k algorithm is developed. Experiments show that our proposed method outperforms the baseline methods in terms of both effectiveness and efficiency.
On Bayes Factors for Success Rate A/B Testing

This paper discusses Bayes factors, an alternative to classical frequentionist hypothesis testing, within the standard A/B proportion testing setup - observing outcomes of independent trails (which finds applications in industrial conversion testing). It is shown that the Bayes factor is controlled by the Jensen-Shannon divergence of success ratios in two tested groups, and the latter one is bounded (under mild conditions) by Welch’s t-statistic. The result implies an optimal bound on the necessary sample size for Bayesian testing, and demonstrates the relation to its frequentionist counterpart (effectively bridging Bayes factors and p-values).
Graphene-based polymeric nano-composites: an introspection into functionalization, processing techniques and biomedical applications

Although, there have been numerous efforts in synthesis of polymers, their mechanical properties have limited their applications. Graphene has been investigated for excellent properties such as superior mechanical properties, high thermal conductivity that has attracted the attention of scientific community to employ graphene as a filler material in polymeric matrices to form composites with multi-functional capabilities. The excellent properties possessed by Graphene has motivated users to fabricate flexible nanocomposites that can be used for applications requiring superior mechanical, chemical and thermal performances. Characteristics of both the components if explored synergistically through proper structural and interfacial organization. The investigation in this direction has resulted into combination of graphene with variety of polymeric materials and hence the development of different graphene-based nanocomposites. The present work reviews the application of graphene-based nanocomposites in the biomedical domain. With this objective, the polymeric matrices suitable for biomedical applications as well as the techniques of producing graphene polymeric nanocomposites have been discussed. Finally the application particularly in biosensing, wound healing and drug delivery system has been discussed.
SpectraMosaic: An Exploratory Tool for the Interactive Visual Analysis of Magnetic Resonance Spectroscopy Data

Magnetic resonance spectroscopy (MRS) allows for assessment of tissue metabolite characteristics used often for early detection and treatment evaluation of brain-related pathologies. However, a steep learning curve for metabolite interpretation, paired with limited visualization tools, have constrained the more widespread adoption of MRS in clinical practice. In this design study, we collaborated with domain experts to design a novel visualization tool for the exploration of tissue metabolite concentration ratios in MRS clinical and research studies. We present a data and task analysis for this domain, with categorization of MRS data attributes into tiers of visual priority. We furthermore introduce a novel set of visual encodings for these attributes. Our result is SpectraMosaic (Figure 1), an interactive insight-generation tool for rapid exploration and comparison of metabolite ratios. We validate our approach with two case studies from MR spectroscopy experts, providing early qualitative evidence of the efﬁcacy of the system and affording deeper insights into these complex data.
CORPORATE CULTURE , STRUCTURE AND OPERATIONAL PROCESSES AS THE CONTEXT FOR THE INNOVATIVENESS OF AN ORGANIZATION Katarzyna

The paper refers to relationships between the innovativeness of an organization and its selected determinants such as strategy, corporate culture, structure and operational processes. Research was conducted among 200 managers at the top, middle and operational levels of management (200 companies operating in Poland implementing innovations  innovative companies). Conditions leading to innovations are described as the combination of processes, which result from the internal and external dynamics of a given entity. The analysis assumed that the assessment of company innovativeness would be based on an assessment of whether the company is a leader in the industry in terms of new products (the higher the rating, the more innovative the company is considered to be). The relationship between the dimensions of the company's functioning and the degree of its innovativeness was rated on the basis of two measures: the Spearman's rank correlation coefficient and the chi-square coefficient. It was assumed that the most innovative companies have structures similar to organic ones, with a weak influence of the elements of mechanistic structures. This assumption was proven in relation to features of organizations considered important for innovativeness.