Miracle-dz/UniFAR-FTUs huggingface dataset:
Tomb of a Sultan: A VR Digital Heritage Approach

This project presents the development of a room-scale Virtual Reality (VR) cultural heritage experience, a transmedia storytelling approach for showing museology VR content in a public setting. "Tomb of a Sultan: A VR Digital Heritage Approach" features the 19th century tomb of Sultan Hussein Shah as a three dimensional model reproduced using photogrammetry from the actual heritage site in Malacca. Room-scale VR experience has been made possible with the introduction of sensor-based head mount displays (HMD). However, the user experience and criteria for hyper-realistic VR in the cultural heritage context requires further study in terms of method and apparatus. In this paper we describe a prototype system, a user evaluation study and directions for future work.
LINEAR AND NONLINEAR STABILITY PREDICTIONS FOR A DOMESTIC BOILER WITH A HEAT EXCHANGER FOR PASSIVE INSTABILITY CONTROL

In this study, we investigate the stability behaviour of a domestic boiler system, when the heat absorption rate at the heat exchanger (hex) varies with the mean flow velocity and with the amplitude of velocity fluctuations upstream of the hex. The domestic boiler system is modelled as a 1D quarter-wave resonator (open at one end and closed at the other). The hex is placed near the closed end, and the flame is upstream of the hex. The flame is modelled as a compact heat source, assumed to follow a basic n − τ law. The heat absorption rates at the hex are treated as amplitude-dependent and are calculated in two ways: (1) computed from numerical simulations and (2) from an empirical correlation based on King’s law for the heat release rate of a hot wire. The stability analysis is done in the frequency domain, and the stability predictions are carried out using the classical eigenvalue method.
Long-term prognosis related to fasting total-cholesterol and triglyceride levels after TIA/stroke

Results: Men had an almost linear correlation between increasing TC levels (< 5 mmol/L; 5-6.4 mmol/L; and ≥ 6.5 mmol/L), and increasing risk of MI during life, P = 0.016, and cardiovascular death (CVD) (not index stroke), P = 0.002; women had a U-shaped correlation. Men had an inverse correlation between TC levels and death due to cancer, P=0.008. Comparisons pairwise between sexes: TC 5-6.4 mmol/L, increased survival in women, P=0.038. According to Cox regression analyses, TC ≥ 6.5 mmol/L was a predictor of MI, and TG > 2.2 mmol/L had an unfavourable effect on survival.
EUSMT: incorporating linguistic information to SMT for a morphologically rich language. Its use in SMT-RBMT-EBMT hybridation

This thesis is defined in the framework of machine translation for Basque. Having developed a Rule-Based Machine Translation (RBMT) system for Basque in the IXA group (Mayor, 2007), we decided to tackle the Statistical Machine Translation (SMT) approach and experiment on how we could adapt it to the peculiarities of the Basque language. First, we analyzed the impact of the agglutinative nature of Basque and the best way to deal with it. In order to deal with the problems presented above, we have split up Basque words into the lemma and some tags which represent the morphological information expressed by the inflection. By dividing each Basque word in this way, we aim to reduce the sparseness produced by the agglutinative nature of Basque and the small amount of training data. Similarly, we also studied the differences in word order between Spanish and Basque, examining different techniques for dealing with them. we confirm the weakness of the basic SMT in dealing with great word order differences in the source and target languages. Distance-based reordering, which is the technique used by the baseline system, does not have enough information to properly handle great word order differences, so any of the techniques tested in this work (based on both statistics and manually generated rules) outperforms the baseline. Once we had obtained a more accurate SMT system, we started the first attempts to combine different MT systems into a hybrid one that would allow us to get the best of the different paradigms. The hybridization attempts carried out in this PhD dissertation are preliminaries, but, even so, this work can help us to determine the ongoing steps.
Deep Neural Networks for Selected Natural Language Processing Tasks

This report presents research in several tasks of the natural language processing, namely optical character recognition, text categorization and dialogue act recognition. The report is focused on modern deep neural network classifiers, which are first introduced theoretically with the support of relevant publications and then they are experimentally verified on suitable datasets. For text categorization and dialogue act recognition tasks, multi- and cross-lingual approaches are presented and evaluated. Since the importance of multi-lingual text processing is significantly growing, it is crucial to be able to process information in multiple languages. Unfortunately, there is often a lack of annotated data in some not very widespread languages, so the cross-lingual approaches are requested. Methods for optical character recognition in historical documents are presented with the main emphasis on classifiers which achieve excellent results in natural language processing tasks. Finally, the aims of the future doctoral thesis are set with regard to the recent research.
Rules Versus Discretion: Inference Gleaned from Greenbook Forecasts and FOMC Decisions

From 1987 through 2013, the Federal Open Market Committee appears to have set its federal funds rate target with reference to Greenbook forecasts of the output gap and inflation and, at times, also to have made further adjustments to the funds rate as those forecasts were revised. If viewed in the context of the Taylor (1993) Rule, discretionary departures from the settings prescribed by Greenbook forecasts consistently presage business cycle turning points. Similarly, estimates from an interest rate rule with time‐varying parameters imply that, around such turning points, the FOMC responds less vigorously to information contained in Greenbook forecasts about the changing state of the economy. These results suggest possible gains from closer adherence to a rule with constant parameters.
Day-OfThe-Week in different stock markets : new evidence on model-dependency in testing seasonalities in stock returns

This paper investigates the day-of-the-week effects in the stock indexes of both developed and models. The results show many daily effects, occurring from Monday to Friday, which are different from the weekend effect. No consistent daily effects were found for either returns or volatility in any market by any of the tested models, and the presence of effects seems to be model-dependent. Surprisingly, the MSCI world index exhibits a strong positive return on Monday and Wednesday. The leverage effect on the arrival of new information is reliably found in three developed markets and the MSCI world index.
jmbReview Micronized and Heat-Treated Lactobacillus plantarum LM 1004 Stimulates Host Immune Responses Via the TLR-2 / MAPK / NF-κ B Signalling Pathway In Vitro and In Vivo

Probiotics are generally defined as live microorganisms, such as Lactobacillus and Bifidobacteria, which confer a beneficial health effect to the host when administered in adequate amounts [1]. It has been reported that probiotics have a wide range of biological activities including immune regulation, improvement in the intestinal environment, and cancer prevention [2, 3]. Most studies on the beneficial effects of probiotics have focused on viable microorganisms. However, it has been reported that there are several problems in the practical applications such as heterogeneity of live probiotics and low survival rate in the gastrointestinal tract due to low pH conditions and rich proteolytic enzymes [2]. Interestingly, emerging evidence from various studies has revealed that even heat-treated probiotics might confer apparent health benefits to the host even though the FAO/ WHO still emphasize a limited use of live microorganisms for probiotics [4, 5]. It was reported that the thermal treatments increase the cell coarseness and roughness, which influence their immune-modulating properties [6, 7]. In addition, heat-treated probiotics not only possess biological activities in communication with the host but also provide advantages for industrial production and consumer usage, including a longer product shelf life, easier storage, and more convenient transportation [4, 6, 8]. Received: December 27, 2018 Revised: March 28, 2019 Accepted: April 9, 2019
Microbial regulation of soil carbon properties under nitrogen addition and plant inputs removal

Background Soil microbial communities and their associated enzyme activities play key roles in carbon cycling in terrestrial ecosystems. Soil microbial communities are sensitive to resource availability, but the mechanisms of microbial regulation have not been thoroughly investigated. Here, we tested the mechanistic relationships between microbial responses and multiple interacting resources. Methods We examined soil carbon properties, soil microbial community structure and carbon-related functions under nitrogen addition and plant inputs removal (litter removal (NL), root trench and litter removal (NRL)) in a pure Larix principis-rupprechtii plantation in northern China. Results We found that nitrogen addition affected the soil microbial community structure, and that microbial biomass increased significantly once 100 kg ha−1 a−1 of nitrogen was added. The interactions between nitrogen addition and plant inputs removal significantly affected soil bacteria and their enzymatic activities (oxidases). The NL treatment enhanced soil microbial biomass under nitrogen addition. We also found that the biomass of gram-negative bacteria and saprotrophic fungi directly affected the soil microbial functions related to carbon turnover. The biomass of gram-negative bacteria and peroxidase activity were key factors controlling soil carbon dynamics. The interactions between nitrogen addition and plant inputs removal strengthened the correlation between the hydrolases and soil carbon. Conclusions This study showed that nitrogen addition and plant inputs removal could alter soil enzyme activities and further affect soil carbon turnover via microbial regulation. The increase in soil microbial biomass and the microbial regulation of soil carbon both need to be considered when developing effective sustainable forest management practices for northern China. Moreover, further studies are also needed to exactly understand how the complex interaction between the plant and below-ground processes affects the soil microbial community structure.
Deriving an optimality criterion for estimating VC dimension

We derive an objective function that can be optimized to give an estimator for the VapnikChervonenkis dimension for use in model selection in regression problems. We verify our estimator is consistent. Then, we verify it performs well compared to seven other model selection techniques. We do this for a variety of types of data sets.
Chapter 3 NNLO Analysis of Singlet and Non-singlet Structure Functions in the DGLAP Approach 3

Structure functions in lepton-nucleon DIS are the entrenched observables exploring QCD. They are defined as convolution of the universal parton momentum distributions and coefficient functions, which contain information about the boson-parton interaction. Therefore the structure functions provide exclusive information about the deep structure of hadrons and most importantly, they form the backbone of our knowledge of the parton densities, which are indispensable for analyses of hard scattering processes. Thus the measurements of the structure functions allow perturbative QCD to be precisely tested. The standard and the basic tool for theoretical investigation of DIS structure functions are the DGLAP evolution equations [1-4]. Therefore the solutions of DGLAP evolution equations give quark and gluon distribution functions which ultimately produce proton, neutron and deuteron structure functions. The solutions of the unpolarized DGLAP equation for the QCD evolution of structure functions have been discussed considerably over the past years. The standard and the most extensively used procedure of studying the hadron structure functions is via the numerical solution of these equations [5-10], with excellent agreement with the DIS data over a wide kinematic region in x and Q. However, apart from the
Comparative study of large cell neuroendocrine carcinoma and small cell lung carcinoma in high-grade neuroendocrine tumors of the lung: a large population-based study

Background: In 2015, large cell neuroendocrine carcinoma (LCNEC) was removed from the large cell carcinoma group and classified with small cell lung carcinoma (SCLC) constituting two members of the high-grade neuroendocrine tumors (NETs) of the lung. However, the difference between high-grade LCNEC and SCLC in terms of clinicopathological characteristics and prognosis has not been fully understood owing to the rarity of LCNEC. Patients and methods: Patients with high-grade LCNEC and SCLC at initial diagnosis between 2001 and 2014 were identified using the Surveillance, Epidemiology, and End Results (SEER) program database. Clinicopathological characteristics between high-grade LCNEC and SCLC were compared using the Pearson's chi-squared test or Fisher's exact test. Differences in overall survival (OS) and cancer-specific survival (CSS) were compared using the log-rank test, Cox models and propensity score matching (PSM) analysis. Results: A total of 1223 patients with high-grade LCNEC and 18182 patients with high-grade SCLC were enrolled. To the best of our knowledge, this study involved the largest number of high-grade LCNEC patients to date, with respect to a comparison between high-grade LCNEC and high-grade SCLC patients. There were significant differences in age, sex, race, laterality, SEER stage, nodal status, surgery, radiation and chemotherapy, but not marital status, between high-grade LCNEC and SCLC patients. High-grade LCNEC patients had a better OS and CSS than high-grade SCLC patients. Subgroup analysis also confirmed the better prognosis of the high-grade LCNEC patients in the regional stage, distant stage and surgery subgroups. However, no significant difference in prognosis was observed between the two non-surgery subgroups, which was confirmed using PSM analysis. Furthermore, high-grade LCNEC patients showed different metastatic patterns to high-grade SCLC patients. Conclusion: These results suggested that high-grade LCNEC and high-grade SCLC were different histological types, and that a detailed classification for high-grade NETs of the lung was needed.
Aggregation of Diverse Information with Double Auction Trading among Minimally-Intelligent Algorithmic Agents

Information dissemination and aggregation are key economic functions of financial markets. How intelligent do traders have to be for the complex task of aggregating diverse information (i.e., approximate the predictions of the rational expectations equilibrium) in a competitive double auction market? An apparent ex-ante answer is: intelligent enough to perform the bootstrap operation necessary for the task—to somehow arrive at prices that are needed to generate those very prices. Constructing a path to such equilibrium through rational behavior has remained beyond what we know of human cognitive abilities. Yet, laboratory experiments report that profit motivated human traders are able to aggregate information in some, but not all, market environments (Plott and Sunder 1988, Forsythe and Lundholm 1990). Algorithmic agents have the potential to yield insights into how simple individual behavior may perform this complex market function as an emergent phenomenon. We report on a computational experiment with markets populated by algorithmic traders who follow cognitively simple heuristics humans are known to use. These markets, too, converge to rational expectations equilibria in environments in which human markets converge, albeit slowly and noisily. The results suggest that high level of individual intelligence or rationality is not necessary for efficient outcomes to emerge at the market level; the structure of the market itself is a source of rationality observed in the outcomes.
Expression of thioredoxin reductase-1 and its effect in non-small cell lung cancer

Objective: We detected the expression of thioredoxin reductase-1 (TrxR1) in non-small cell lung cancer (NSCLC) and the effect of TrxR1 silencing in NSCLC. Method: TrxR1 expression was detected in 118 cases with NSCLC and 27 normal subjects using immunohistochemistry technique and the correlation with pathological parameters of NSCLC was analyzed. Semi-quantitative RT-PCR was employed to detect the TrxR1 mRNA level in 13 cases with NSCLC and 4 normal subjects; Western Blot was used to detect the TrxR1 protein level in normal lung epithelium and lung cancer cells; semi-quantitative RT-PCR was used to detect TrxR1 mRNA level in normal lung epithelium and lung cancer cells. After silencing of TrxR1 in A549 cells, the proliferation of cells was compared with that before silencing. Result: Immunohistochemistry and semi-quantitative PCR indicated overexpression of TrxR1 in NSCLC (P<0.05). After silencing of TrxR1 using shRNA, the cell proliferation was obviously reduced. Conclusion: TrxR1 mRNA and protein were overexpressed in NSCLC, indicating that TrxR1 may be a target molecule in NSCLC.
From SOV towards SVO: Explaining the word order distribution in terms of changing preferences

An explanation for the observation that most languages nowadays exhibit an SOV or SVO word order is provided by suggesting that SOV and SVO can both be seen as advantageous, but from different perspectives and under different circumstances. There seems to be an asymmetry in the preference for SOV and SVO word orders in the sense that SOV word orders are mostly adopted in newly emerging languages, while there also has been a general shift from SOV towards SVO in fully developed languages. The main point of this paper is therefore that the change into SVO can be explained as driven by functional preferences that become more prominent when more complexity arises in languages that evolve over time, while the emergence of an SOV proto-language can be explained because SOV is more preferred when languages are still in a rudimentary state and no stable lexicon is available.
A Comparative Study of Self-aerated Stepped Spillway and Smooth Invert Chute Flow : The effect of Step-induced Macro-roughness

Due to the rapid development of roller-compacted concrete technique in dam construction research interest in stepped spillway hydraulics increased in recent years. Because of the strong turbulence enhanced by the step-induced macro-roughness the flow velocity is lower than on comparable smooth spillways. By consequence, a higher flow resistance and hence, a higher energy dissipation must be noted as a main characteristic. Earlier investigations indicate that the energy dissipation potential is only slightly influenced by a change of slope on embankment stepped spillways with moderate slopes between 18.4° and 26.6° [2]. In order to quantify the general effect of the macroroughness a comparative study of self-aerated flow on stepped spillways and smooth invert chutes, respectively, with a slope of 26.6° has been carried out.
Optimal Operator preconditioning for hypersingular operator over 3 D screens

We propose a new Calderón-type preconditioner for the hypersingular integral operator for −∆ on screens in R. We introduce a modified weakly singular operator, which is the exact inverse of the hypersingular operator on the unit disk. It forms the foundation for dual-meshbased operator preconditioning. Applied to low-order boundary element Galerkin discretizations, it achieves h-uniformly bounded condition numbers. Heuristic extensions to general screens even with non-smooth boundaries are discussed. Their good performance is confirmed by numerical tests.
Detection and classification of Web robots with honeypots

Abstract : Web robots are automated programs that systematically browse the Web, collecting information. Although Web robots are valuable tools for indexing content on the Web, they can also be maliciousthrough phishing, spamming, or performing targeted attacks. In this thesis, we study an approach to Web-robot detection that uses honeypots in the form of hidden resources on Web pages. Our detection model isbased upon the observation that malicious Web robots do not consider a resources visibility whengathering information. We performed a test on an academic website and analyzed the honeypots performance using Web logs from the sites server. Our results did detect Web robots, but did not adequately detect the more sophisticated robots, such as those using deep-crawling algorithms with query generation.
The Turker Blues: Hidden Factors behind Increased Depression Rates in Amazon's Mechanical Turk

Data collection from online platforms, such as Mechanical Turk (MTurk), has become popular in clinical research. However, there are also concerns about the representativeness and the quality of this data for clinical studies. The present work explores these issues in the specific case of major depression. Analyses of two large data sets gathered from MTurk (N1 = 2,692 and N2 = 2,354) revealed two major findings: First, failing to screen for inattentive and fake respondents inflates the rates of major depression artificially and significantly (to 18.5% to 27.5%). Second, after cleaning the data sets, depression in MTurk is still 1.6 to 3.6 times higher than general population estimates. Approximately half of this difference can be attributed to differences in the composition of MTurk samples and the general population (i.e., sociodemographics, health and physical activity lifestyle). Several explanations for the other half are proposed and practical data-quality tools are provided.
Adherence to Antidepressant Therapy : Sociodemographic Factor Wise Distribution

Medication compliance is a well-established issue in the care of depression. There is evidence that more than 50% of depressed patients withdraw treatment prematurely. Some factors reported to have an important effect on adherence are: poor socioeconomic status, poverty, illiteracy, low level of education, unemployment, lack of effective social support networks, unstable living conditions, long distance from treatment centre, high cost of transport, high cost of medication, changing environmental situations, culture and lay beliefs about illness and treatment and family dysfunction. To study the socio demographic factors that influence compliance to the treatment of depression. The present Cross sectional study was carried out in the department of General Medicine (IP) of Rajiv Gandhi Institute of Medical Sciences (RIMS), Kadapa, AP, A total of 103 subjects were participated in the study during 6 months) & the patients were enrolled to the study according to the inclusion & exclusion criteria, after obtaining Informed Consent Form (ICF). Among the 103 psychiatric patients 31 patients were adherent and 72 patients were non adherent. In our study non-adherence was more unmarried, higher level of education occupy a lower socioeconomic strata and have a history of non-adherence. Our study revealed significant effect was observed only in level of education, and overall various barriers had less significant on nonadherence. Clinical pharmacist’s role still remains in psychiatry department for the improvement of patient medication adherence by creating awareness regarding disease and treatment and providing patient counselling services.