Miracle-dz/UniFAR-FTUs huggingface dataset:
TAS WITH QAM-M (M=2, 3, 4, 5, 6) FOR MIMO COMMUNICATION SYSTEM

In this paper we study Orthogonal-Space-Ttime Block-Coded (OSTBC) Multiple-Input Multiple Output (MIMO) systems employing Transmit Antenna Selection (TAS) with frobenius norm as a selection criterion and compares the BER performance for QAM with order 2, 3, 4, 5 and 6. The minimum BER for the proposed MIMO system is of about 7.50x10 -05 at 14dB SNR, 1.67x10 05 at 20dB SNR, 0.225x10 -03 at 20dB SNR, 0.166x10 -02 at 20dB SNR, 0.84x10 -02 at 20dB SNR, for 4QAM, 8QAM, 16QAM, 32QAM and 64QAM respectively.
Text Mining in Survey Data

Free text responses in surveys contain important information and should be analyzed by researchers. However, human coding of survey text is not only expensive, but also vulnerable to subjectivity. An automated text mining approach can solve these problems. Therefore, we demonstrate using the supervised latent Dirichlet allocation (sLDA) to jointly analyze text and numerical data in an employee satisfaction survey. For each rating, the algorithm outputs selected words as the “topic” and estimates the credible interval. Finally, we discuss future applications and advantages of utilizing survey text.
STUDIES IN THE MORPHOGENESIS OF LEAVES

In Part I of this paper the results of experiments are reported which show that ageing in fronds of Lemna minor is accompanied by diminution (due to a decrease in cell number) of the size of their successive daughter fronds; this is true, notwithstanding the fact that successive daughter fronds all come from the same meristem and develop under the same external conditions. By an examination of the growth curves of successive daughter fronds it was shown that the decrease in area from frond to frond is due either to a decrease in the duration of the logarithmic phase of cell division, or to a decrease in the size of frond initials. In this second part of the paper some of the possible causes for such decreases are examined. Preliminary experiments were carried out to decide between two obvious alternative explanations to account for the decrease in cell number from frond to frond in successive fronds, namely: (i) Late-formed fronds might be small because division of their cells depends on a supply of some growth-promoting substance from the mother frond which diminishes as the mother frond becomes older. Alternatively, (ii) late-formed fronds might be small because their growth is suppressed by some inhibitor from the mother frond which increases as the mother frond becomes older. These alternatives were examined by the simple procedure of removing immature daughter fronds from their mother fronds, and postulating that these excised daughter fronds will on the one hand fail to reach normal size if they depend on the mother frond for a growth-promoting substance, and on the other hand will become abnormally large if it is a growth inhibitor which a daughter frond derives from a mother frond. It was found that fronds with areas as small as I-2 mm.2 could be severed without injury by a very small cut with a cataract knife into the ventral side of the mother frond. Such severed fronds live independently in the culture solution for several weeks. They contain stored starch both before being severed from the mother frond and after several days of independent existence, and they develop roots and daughter fronds. In these preliminary experiments it was found that neither severed first-daughter fronds nor severed fourth-daughter fronds reached areas as large as those of controls left on the mother frond. Therefore the effect of the mother frond is not to inhibit but to promote growth, and it is reasonable to assume that a mother frond produces some substance which promotes the growth of its daughter fronds and which
Validation of the Probiotic Concept: Lactobacillus reuteri Confers Broad-spectrum Protection against Disease in Humans and Animals

Nobel Laureate Elie Metchnikoff formulated the Probiotic Concept approximately 100 years ago. He proposed that consumption of certain 'lactic bacilli' would enhance one's health and well being by maximizing health-promoting activities of the gastrointestinal microbiota and minimizing their potentially harmful effects. It has taken almost all these intervening years to discover specific strains of 'lactic bacilli' able to accomplish these 'probiotic' tasks. And only very recently has an entire species, Lactobacillus reuteri, been shown to possess probiotic efficacy. L. reuteri is the only Lactobacillus species reported to inhabit the gastrointestinal tract of all vertebrates and mammals, ranging from birds to humans, and with whom it is believed to have established a symbiotic relationship. In this review the authors have attempted to compile all available information reported to date concerning L. reuteri and the ability of host-specific strains to protect their respective hosts from an assortment of diseases induced by biological agents (bacteria, viruses, fungi, and protozoans), certain chemical agents (methotrexate, acetic acid), or environmental stressors (cold-stress). This information is based on laboratory experiments, field trials with animals, and clinical trials with human subjects. It has been concluded that discovery of L. reuteri's broad-spectrum probiotic efficacy in a broad-spectrum of hosts has (a) fully validated Metchnikoff's Probiotic Concept, and (b) resulted in development of new bioprotective and biotherapeutic applications for improving human and animal health.
Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.
TEACHING IN A TWO-WAY VIDEO AND AUDIO DISTANCE LEARNING CLASSROOM- PERSPECTIVES FROM THE FIRST YEAR

In January, 1993, the University of North Texas activated a two-way, fullideo and audio distance learning system. The "SCHOOLinc" system,a research initiative of UNT's Center for Professional Development and Technology,links UNT with two schools in the Dallas Independent School District (Dallas,Texas, USA) via a TI telecommunications line. In addition to the video/audiolink, computers in the classrooms are fully networked.
Behind the Eight Ball: The Effects of Race and Number of Infractions on the Severity of Exclusionary Discipline Sanctions Issued in Secondary School

African American and Hispanic students receive more punitive school discipline than White students even when students of color commit similar infractions as Whites. Similarly, students with a disability status are more likely to experience harsher discipline in schools compared to their counterparts without a disability label. This study examines whether these discrepancies are a result of a difference in the number of infractions students of different racial/ethnic groups and disability categories commit. Using secondary educational data from a state educational agency in the United States, we demonstrate that African American and Hispanic students and students with an emotional behavioral disorder status receive more severe sanctions than White students and students without a disability label at their first discipline encounter. This racial disparity in discipline severity continues through six sanctions and is eliminated at the 13th sanction. The disability disparity in discipline severity dissipates after 10 sanctions for students with emotional behavioral disorder and intellectual disability. Implications for school personnel and future directions are discussed.
Mapping quantitative trait loci for seedling vigour and development in sunflower (Helianthus annuus L.) using recombinant inbred line population

The ability of seeds to germinate and establish seedlings in a predictable manner under a range of conditions has a direct contribution to the economic success of commercial crops, and should therefore be considered in plant breeding programs. Quantitative trait loci (QTLs) implicated in seedling vigour and development of sunflower were investigated using a population of recombinant inbred lines (RILs) developed through single-seed descent from the cross ‘PAC2 × RHA266’. The experiments were conducted in completely randomized design with three replications. Different traits associated with germination, seedling vigour, early growth and development were studied. Analysis of variance for all studied traits revealed significant differences ( P<0.01) among RILs and their parents. A large genetic variation and transgressive segregation was observed for all the studied traits. QTL-mapping was performed using a high-density simple sequence repeat/ amplified fragment length polymorphism (SSR/AFLP) linkage map and several QTLs associated with the studied traits were identified. The percentage of phenotypic variation explained by individual QTLs ranged from 5.4% to 35.7%. Based on overlapping support intervals, the co-location of QTLs for all ten traits was determined. QTLs controlling most of the traits were overlapped on different linkage groups, which was in accordance with the phenotypic correlation results among the traits. Although the detected regions need to be fine-mapped to dissect the genes underlying QTLs, the information obtained could help sunflower breeders in marker-assisted breeding programs for developmental traits.
A Decision Making Trial and Evaluation Laboratory method - key criteria in the selection of bricks.

Depending on the place of residence, social status, education or preferences, investors make various decisions regarding the construction material. As far as the functional characteristics of individual materials are known, the factors that govern decision-makers in the choice of construction material for building construction are not fully known. In the article, the authors have therefore attempted to find these criteria and to investigate causal relationships between compressive strength, acoustic insulation, economy, ecology, thermal conductivity and fire safety. The main aim of the article is also to find answers to the following questions: which criterion is decisive? What impact do the individual criteria have on each other? Whether and to what extent ecology influences on the decisions that are made? Unlike the traditional multiple criteria decision-making techniques which typically assume the criteria are mutually independent. By applying DEMATEL method, the importance of six criteria can be determined and the causal relations among the criteria can be constructed. The most important factor in the choice of construction material is price, and in the second place is ecology. The compressive strength (K1) and thermal conductivity (K5) have a strong impact on the other criteria. K1, K2, K5 and K6 have a strong impact on ecology (K4) and economy (K3). However, it should be emphasized that the least affect by fire safety (K6) and sound insulation (K2).
Database Meets AI: A Survey

—Database and Artiﬁcial Intelligence (AI) can beneﬁt from each other. On one hand, AI can make database more intelligent (AI4DB). For example, traditional empirical database optimization techniques (e.g., cost estimation, join order selection, knob tuning, index and view selection) cannot meet the high-performance requirement for large-scale database instances, various applications and diversiﬁed users, especially on the cloud. Fortunately, learning-based techniques can alleviate this problem. On the other hand, database techniques can optimize AI models (DB4AI). For example, AI is hard to deploy in real applications, because it requires developers to write complex codes and train complicated models. Database techniques can be used to reduce the complexity of using AI models, accelerate AI algorithms and provide AI capability inside databases. Thus both DB4AI and AI4DB have been extensively studied recently. In this paper, we review existing studies on AI4DB and DB4AI. For AI4DB, we review the techniques on learning-based conﬁguration tuning, optimizer, index/view advisor, and security. For DB4AI, we review AI-oriented declarative language, AI-oriented data governance, training acceleration, and inference acceleration. Finally, we provide research challenges and future directions.
A novel approach for TSK fuzzy modeling with outliers

The TSK type of fuzzy models has attracted a great attention of the fuzzy modeling community due to their good performance in various applications. In the literature, some approaches for modeling TSK fuzzy rules have been proposed. Most of them define their fuzzy subspaces based on the idea of training data being close enough instead of having similar functions. Recently, a new approach, fuzzy c-regression model (FCRM) clustering algorithm, is proposed to construct TSK fuzzy models. However, this approach does not take into account the training data with outliers. In order to overcome the effects of outliers, a robust TSK fuzzy modeling with outliers has been proposed. It is worth noting that this approach may need more computation time due to complicated formulas. Hence, a novel TSK fuzzy modeling approach with outliers is presented in this paper. In this approach, robust fuzzy regression (RFR) clustering algorithm is proposed to simultaneously define fuzzy subspaces and find the parameters in the consequent parts of TSK rules. In the clustering process, the similarity measure is used to reduce the redundant rules. To obtain a more precision model that is not affected by outliers, an annealing robust BP learning algorithm is employed. From the simulation results, the proposed TSK fuzzy model approach indeed showed superior performance.
Changes in soil cryptogamic communities in tropical Ecuadorean páramos.

Altitudinal gradients involve macroclimatic changes that can affect the diversity of several organisms. We tested the effects of elevation and small-scale variables on the diversity and composition of terricolous communities (lichens and bryophytes) in five paramos in southern Ecuador. The altitudinal range considered (from 2700 to 4000 m a.s.l.) is associated with changes in rainfall, temperature and irradiance. At each paramo, forty 40 x 40 cm2 sample plots were randomly selected in similar areas of vegetation (grass paramo) and conservation status. The presence/absence and cover of lichens and bryophytes were recorded in 200 sample plots. A total of 90 species (46 lichens and 44 bryophytes) were identified. Our results showed that total species richness, lichen and bryophyte richness, Simpson’s inverse and Shannon’s index were related to elevation and slope. Nevertheless, the response to elevation was dependent on the organism considered. Thus, meanwhile lichens had their maximum richness at the highest elevation (3930 m a.s.l.), bryophytes had a maximum peak at middle elevation (3300 m a.s.l.). Species composition also differed significantly among the five paramos, especially in bryophytes. We conclude that differences in the elevation of these paramos and small-scale variables such as slope significantly affect the diversity and composition of terricolous communities.
A variational image reconstruction algorithm reveals distortion and uncertainty in mental imagery

Subjective reports on mental images may not be consistent with the appearance of seen images (distortion), and may not be consistent with each other (uncertainty). We introduce a method for estimating the distortions and uncertainty associated with mental images from subjective reports. Application of the method to a small pre-liminary dataset suggests that distortion and uncertainty in mental imagery arise from overscaling and undersam-pling imagined objects.
Analysis of the setter’s tactical action in high-level women’s volleyball

Performance analysis is of fundamental importance to understand the factors underlying participation in top-level sports. Recently, an increased importance has been given to the preservation of the situation’s ecology, thus attempting the comprehension of game patterns through the establishment of relationships between variables of several orders. The purpose of this study was to analyse some match variables that could constraint the setter’s tactical action in high-performance women’s volleyball, as well as its outcomes. Six matches of the Women’s World Champhionships 2006 were analysed following a category system, recurring to observational methodology, namely the technique of sequential lag analysis. Attack tempo has emerged as the crucial variable of the setter’s tactical action. Patterns with closed blocks from the opponent, setting in the ideal zone with a jump set, attack simulation by the middle attacker and block anticipation, through committing strategies, tended to culminate in quick attacks. These, in turn, stimulate a debilitated block opposition. Open block formations, receptions in zones 4 and 1, setting in non-ideal zones, no simulation by the middle attacker and a non-anticipative block (read-and-react strategy) stimulated slow attacks, which, in turn, stimulated cohesive block opposition. Attack efficacy emerged as independent of the setter’s action, although the latter largely influenced the type of block opposition. Hence, match analysis provides a window into aspects involving the tactical actions in high-level athletes.
Effect of perfluorocarbon partial liquid ventilation-induced hypothermia on dogs with acute lung injury.

BACKGROUND Acute lung injury (ALI) is the damage of alveolar epithelial cells and capillary endothelial cells caused by various direct and indirect injury factors, resulting in diffuse pulmonary interstitial and alveolar edema, resulting in acute hypoxic respiratory insufficiency. This study aimed to investigate the effects of hypothermia induced by partial fluid ventilation on dogs with ALI. METHODS The experimental dogs were randomly divided into a conventional mechanical ventilation group (CMV) group, a normal temperature perfluorocarbon liquid ventilation group (NPLV) group, and a hypothermic perfluorocarbon liquid ventilation group (HPLV) group. After induction of ALI, the dogs of the CMV group was treated with CMV for respiratory support, the HPLV group was given a 15 °C low-temperature perfluorocarbon partial liquid ventilation (PLV), and the NPLV group was given partial fluid permeation of perfluorocarbon (PFC) at a room temperature of 37 °C. Anesthesia was stable at 0.5 h (T0), and successful modeling (T1), at 1 h (T2), 2 h (T3), 3 h (T4) and 4 h (T5) was completed. Blood gas analysis was performed, and rectum temperature, peak airway pressure (PIP), and lung compliance were measured. We performed enzyme-linked immunosorbent assay (ELISA) for peripheral blood and postoperative bronchoalveolar lavage fluid (BALF), calculation of lung tissue wet weight/dry weight ratio, and Western blot detection of NF-κB p65. RESULTS In the HPLV group, the blood gas index of dogs with ALI was close to normal. In T2¬-T5, the rectal temperature of the HPLV group was significantly lower than that of the NPLV group and the CMV group the lung compliance in the HPLV group and the NPLV group was lower than that in the CMV group at the T2-T5 time point, while the CLst value was significantly increased. The detection of peripheral blood and BALF in dogs showed that interleukin-10 (IL-10) was significantly increased and TNF-α was significantly decreased in the HPLV group compared with the CMV group and NPLV group. Compared with CMV group, the wet/dry ratio of lung tissue in the BALF of HPLV group was decreased. CONCLUSIONS The results indicate that mild hypothermia caused by partial fluid ventilation can increase oxygenation capacity, oxygen partial pressure, the expression of anti-inflammatory factor IL-10 and improve lung compliance in dogs with ALI.
Bayesian hyperparameter optimization : overfitting, ensembles and conditional spaces

In this thesis, we consider the analysis and extension of Bayesian hyperparameter optimization methodology to various problems related to supervised machine learning. The contributions of the thesis are attached to 1) the overestimation of the generalization accuracy of hyperparameters and models resulting from Bayesian optimization, 2) an application of Bayesian optimization to ensemble learning, and 3) the optimization of spaces with a conditional structure such as found in automatic machine learning (AutoML) problems. Generally, machine learning algorithms have some free parameters, called hyperparameters, allowing to regulate or modify these algorithms’ behaviour. For the longest time, hyperparameters were tuned by hand or with exhaustive search algorithms. Recent work highlighted the conceptual advantages in optimizing hyperparameters with more rational methods, such as Bayesian optimization. Bayesian optimization is a very versatile framework for the optimization of unknown and non-derivable functions, grounded strongly in probabilistic modelling and uncertainty estimation, and we adopt it for the work in this thesis. We first briefly introduce Bayesian optimization with Gaussian processes (GP) and describe its application to hyperparameter optimization. Next, original contributions are presented on the dangers of overfitting during hyperparameter optimization, where the optimization ends up learning the validation folds. We show that there is indeed overfitting during the optimization of hyperparameters, even with cross-validation strategies, and that it can be reduced by methods such as a reshuffling of the training and validation splits at every iteration of the optimization. Another promising method is demonstrated in the use of a GP’s posterior mean for the selection of final hyperparameters, rather than directly returning the model with the minimal crossvalidation error. Both suggested approaches are demonstrated to deliver significant improvements in the generalization accuracy of the final selected model on a benchmark