Miracle-dz/UniFAR-FTUs huggingface dataset:
Growing up in Africa Age and Pro-social attitudes in Primary Schoolchildren in Goma (DRC)

While an extensive literature based on analysis conducted in developed countries shows that primary school children develop prosocial attitudes as they grow older, with school acting as the main driver of the socialisation process, there is little evidence of what may happen in very different socio-cultural and economic context. The paper aims at testing the relation between age and prosocial attitudes and behaviours by focusing on a sample of about 400 children attending 10 primary schools located in pheripheral areas of Goma, capital city of the North Kivu province in the northeast region of Democratic Republic of Congo. The evidence of behavioural experiments shows that schoolchildren attitude to truthfully report their choices tends to decrease with age (i.e. cheating increases); we also explore the relationship between other prosocial attitudes and age, finding mixed and weak evidence.
Intrafirm Conflicts and Interfirm Price Competition by Werner Güth Kerstin Pull

We study interfirm price competition in the presence of horizontal and vertical intrafirm conflicts in each firm. Intrafirm conflicts are captured by a principal-agent framework with firms employing more than one agent and implementing a tournament incentive scheme. The principals offer premium incentives in the sense of revenue shares to which agents react by proposing a sales price. Introducing such intrafirm conflicts results in higher prices and lower effort levels. Increasing the number of agents lowers the optimal surplus share of the agents as well as the individual effort and the sales prices. Firm profits first increase and then decrease when employing more and more agents suggesting that principals should employ an intermediate number of agents.
Presymptomatic cerebral blood flow changes in CHMP2B mutation carriers of familial frontotemporal dementia (FTD-3), measured with MRI

Objectives To assess functional changes measured by cerebral blood flow (CBF) in the presymptomatic stage of frontotemporal dementia linked to chromosome 3 (FTD-3) caused by a truncating mutation in CHMP2B. Design Case–control study. Setting A memory clinic and tertiary referrals centre for dementia and inherited neurodegenerative disorders. Participants The authors included 11 presymptomatic CHMP2B mutation carriers and seven first-degree-related family non-carriers. Participants were MRI scanned twice with an interval of 15 months. Primary and secondary outcome measures Local functional changes in brain tissue perfusion were measured as CBF with two different MR techniques, gradient echo (GRE) and spin echo (SE), focusing on CBF in all cerebral vessels (GRE) and cerebral capillaries (SE), respectively. As planned, data analysis included co-registration of perfusion images to structural T1 images. Perfusion data were then extracted from seven regions-of-interest, normalised to white matter and statistically compared between carriers and non-carriers. Results For SE, contrasts between carriers and non-carriers showed significant differences in temporal, occipital and parietal lobes and in hippocampus. There was no evidence of changes from baseline to follow-up. For GRE, there were no significant differences between carriers and non-carriers. Conclusions Significantly decreased CBF was found in presymptomatic CHMP2B mutation carriers in occipital-and parietal lobes. Comparing SE with GRE, data indicate that FTD-3 vascular pathology might primarily affect brain capillaries.
Multifocal suppurative granuloma caused by Actinobacillus lignieresii in the peritoneum of a beef steer

An imported crossbred Angus beef steer aged eight to twelve months died suddenly on the eighth day of a quarantine period in Japan. Gross examination showed the peritoneum and mesentery consisted of numerous nodules of various sizes. Histological examination revealed chronic suppurative granulomatous peritonitis with eosinophilic rosettes surrounding colonies of Gram-negative bacilli. The bacteria isolated from the nodules were confirmed to be Actinobacillus lignieresii based on the results of 16S rRNA gene sequencing and immunohistochemistry. Antibiotic sensitivity testing showed that the isolate was resistant to penicillin. Thus, a diagnosis of atypical actinobacillosis caused by A. lignieresii was made.
Simultaneous inference of linear models with time varying coefficients

Summary. The paper considers construction of simultaneous confidence tubes for time varying regression coefficients in functional linear models. Using a Gaussian approximation result for non‐stationary multiple time series, we show that the constructed simultaneous confidence tubes have asymptotically correct nominal coverage probabilities. Our results are applied to the problem of testing whether the regression coefficients are of certain parametric forms, which is a fundamental problem in the inference of functional linear models. As an application, we analyse an environmental data set and study the association between levels of pollutants and hospital admissions.
Towards New Scholarly Communication: A Case Study of the 4A Framework

This paper discusses the use of semantic web technology to realize the vision of future scientific publishing and scholarly communication. It introduces a novel knowledge system that builds on the popularity of social tagging and collaboration systems as well as on the idea of “Annotations Anywhere, Annotations Anytime” (hence 4A). Key technical characteristics of the realized components are presented. User experience observations and the results of a preliminary experiment are also reported.
ON-LINE DETERMINATION OF THE MEASUREMENT UNCERTAINTY OF THE STOCHASTIC MEASUREMENT METHOD

− The paper presents a stochastic sampling method applied to a measurement of the mean value of the product of two arbitrary signals. This method is especially convenient for measuring electric power, electric energy consumption, as well as root-mean-square of voltage and/or current. The method features a very simple hardware and the possibility of selecting the measurement accuracy by choosing a suitable measurement interval. When measuring electric energy, accuracy better than 0.01 % is achieved. The measurement uncertainty of the stochastic sampling method is analysed and the influence of dominant factors is quantitatively derived. Special consideration is given to the influence of dispersion of A/D converter outputs and the waveforms of the two input signals as contributors to the measurement uncertainty. As the exact shapes of the waveforms are not known in advance, it is not possible to determine their influence on the measurement uncertainty. To overcome this issue, a novel additional hardware is designed, which provides the estimate of measurement uncertainty during the experiment simultaneously with the measurement result, its measurement uncertainty is available on-line.
Fairness study on two-hop selection cooperation in wireless network

The traditional selection cooperation scheme where the "best" relay is selected to forward the message achieves good outage performance, but may cause poor fairness among relays. In this paper, we propose a practical scheme for the Decode-and-Forward (DF) protocol to solve the unfairness problem in the selection cooperation. Compared to previous work which obtained fairness but introduced performance loss, the proposed scheme aimed at improving fairness performance without outage performance deterioration. Numerical simulations show that the fairness among all relays is greatly improved by the proposed scheme without any loss of outage performance. It is also validated that the proposed scheme has lower complexities than other schemes and can be used in all signal-to-noise ratio (SNR) regime, making it more practical for real networks.
Building a Morphogen Gradient without Diffusion in a Growing Tissue

In many developmental systems, spatial pattern arises from morphogen gradients, which provide positional information for cells to determine their fate. Typically, diffusion is thought to be the mechanism responsible for building a morphogen gradient. An alternative mechanism is investigated here. Using mathematical modeling, we demonstrate how a non-diffusive morphogen concentration gradient can develop in axially growing tissue systems, where growth is due to cell proliferation only. Two distinct cases are considered: in the first, all cell proliferation occurs in a localized zone where active transcription of a morphogen-producing gene occurs, and in the second, cell proliferation is uniformly distributed throughout the tissue, occurring in both the active transcription zone and beyond. A cell containing morphogen mRNA produces the morphogen protein, hence any gradient in mRNA transcripts translates into a corresponding morphogen protein gradient. Proliferation-driven growth gives rise to both advection (the transport term) and dilution (a reaction term). These two key mechanisms determine the resultant mRNA transcript distribution. Using the full range of uniform initial conditions, we show that advection and dilution due to cell proliferation are, in general, sufficient for morphogen gradient formation for both types of axially growing systems. In particular, mRNA transcript degradation is not necessary for gradient formation; it is only necessary with localized proliferation for one special value of the initial concentration. Furthermore, the morphogen concentration decreases with distance away from the transcription zone, except in the case of localized proliferation with the initial concentration sufficiently large, when the concentration can either increase with distance from the transcription zone or sustain a local minimum. In both localized and uniformly distributed proliferation, in order for a concentration gradient to form across the whole domain, transcription must occur in a zone equal to the initial domain size; otherwise, it will only form across part of the tissue.
Just noticeable difference survey of computer generated imagery using normal maps

Hoerter, Michael E. M.S., Purdue University, May, 2014. Just Noticeable Difference Survey of Computer Generated Imagery Using Normal Maps. Major Professor: David Whittinghill. Normal maps are widely used as a resource-efficient means of simulating detailed topology on 3D surfaces in the gaming, simulation, and film industries. However, as surface mesh density increases, it is unknown at what level of density these increases become no longer perceivable, and whether normal maps significantly affect this threshold. This study examined at what point participants were unable to discern differences between one level of mesh density and another using an adapted staircase model. Participants identified this threshold for five different organic character models. The averages of each of these thresholds were taken and compared against the results of a control group, which observed the same models without normal maps. The study found that the average threshold for discerning differences in level of detail occurred in the 3,000 to 14,000 polygon range for normal mapped models, and the 240,000 to 950,000 range for the control group. This analysis suggested that normal maps have a significant impact on the viewer's ability to discern differences in detail, and that developing graphics beyond the range of 3,000 to 14,000 polygons is unnecessary for organic character models when normal maps are used.
Measurement of protein carbonyls in plasma of smokers and in oxidized LDL by an ELISA.

Protein carbonyls are considered as markers of oxidative stress (1). The conventional colorimetric assay (2) that measures reactive protein carbonyls using 2,4-dinitrophenylhydrazine (DNPH) is labor-intensive, time-consuming, and uses a large quantity of solvents. Recently, a new ELISA method for protein carbonyls was developed (3). However, the validity of this assay in smokers, who clearly have increased lipid peroxidation (4), and as a measure of LDL oxidation has not been studied. Hence, in this study we evaluated a modification of this ELISA method for protein carbonyls and compared it with the conventional colorimetric assay. We also validated the ELISA as an oxidative stress measurement in the plasma of smokers and as a measure of LDL oxidation. Native bovine serum albumin (BSA; 50 g/L) was oxidized with 0.42 mmol/L Fe2+ and 0.73 mol/L H2O2 in phosphate-buffered saline (PBS) containing EDTA (0.1 g/L) for 2 h at 37 °C as reported previously (5). Oxidation was stopped with 40 μmol/L butylated hydroxytoluene. The carbonyl content of the oxidized BSA was measured spectrophotometrically (2). Aliquots of oxidized BSA were stored at −70 °C. Before use, an aliquot was thawed and diluted to a carbonyl content of 2 nmol/L in PBS. The ELISA method used was a modification of the original assay described by Buss …
Academic Relevance: College Students' Perspective.

This study examined academic relevance from the perspective of college students. A qualitative focus group method was used to explore how students perceived the applicability and usefulness of their academic courses and coursework. Two focus groups of college students (N = 22) with varied class rank and academic majors were conducted. Data suggests that academic relevance is a complex construct; six distinct domains of academic relevance were identified. Students perceived their course work as relevant to their current and future academic endeavors, vocational preparation, and personal growth and development. Results provide a preliminary factorial structure to an underdeveloped yet important learning construct. Implications are discussed, and suggestions are offered to bolster students’ perceptions of academic relevance.
HMM based Offline Handwritten Writer Independent English Character Recognition using Global and Local Feature Extraction

rate of handwritten character is still limited around 90 percent due to the presence of large variation of shape, scale and format in hand written characters. A sophisticated hand written character recognition system demands a better feature extraction technique that would take care of such variation of hand writing. In this paper, we propose a recognition model based on multiple Hidden Markov Models (HMMs) followed by few novel feature extraction techniques for a single character to tackle its different writing formats. We also propose a post-processing block at the final stage to enhance the recognition rate further. We have created a data-base of 13000 samples collected from 100 writers written five times for each character. 2600 samples have been used to train HMM and the rest are used to test recognition model. Using our proposed recognition system we have achieved a good average recognition rate of 98.26 percent.
The blizzard challenge - 2005: evaluating corpus-based speech synthesis on common datasets

In order to better understand different speech synthesis techniques on a common dataset, we devised a challenge that will help us better compare research techniques in building corpusbased speech synthesizers. In 2004, we released the first two 1200-utterance single-speaker databases from the CMU ARCTIC speech databases, and challenged current groups working in speech synthesis around the world to build their best voices from these databases. In January of 2005, we released two further databases and a set of 50 utterance texts from each of five genres and asked the participants to synthesize these utterances. Their resulting synthesized utterances were then presented to three groups of listeners: speech experts, volunteers, and US English-speaking undergraduates. This paper summarizes the purpose, design, and whole process of the challenge. 1. Background With a view to allowing closer comparison of corpus-based techniques, from labeling, pruning, join costs, signal processing techniques, and others, we devised a challenge for participants to use the same databases to synthesize utterances from a small number of genres. An organized evaluation, based on listening tests, was then carried out to try to rank the systems and help identify the effectiveness of the techniques. The sister field of speech recognition has clearly benefited from the availability of common datasets in order to provide valid comparisons between systems [1]. These evaluations concentrate efforts in the speech recognition fields, particularly through the 1990s with DARPA workshops where NIST (and others) devised standardized tests for speech recognition. It is clear that these standardized tests and widely available datasets allowed speech recognition results to be more easily compared and more importantly cause the core technology to improve. Although today many may criticize a naive word error metric as a sole accuracy measure for speech recognition systems, few would complain that it has not contributed to drastic improvement in the utility of speech recognition as a viable technology. Speech synthesis has not been as lucky in having a welldefined evaluation metric, nor has it had a well-funded centralized community that could be targeted to the same task. With the rise of general corpus-based speech synthesis over the last ten years, we have moved from a domain where new synthetic voices could only be built with many man-years of effort from highly skilled researchers. Such systems were tuned to the particular data sets being used, thus comparisons of techniques such as labeling and signal processing could only be done within the research group that originally developed the dataset. Such tying of databases to particular systems made it hard to genuinely compare techniques since the quality of the original recorded voice itself contributed greatly to the resulting synthetic voice quality.
Examining the Durability of PEERS for Adolescents with ASD: Maintenance of Neurological and Behavioral Effects

EXAMINING THE DURABILITY OF PEERS FOR ADOLESCENTS WITH ASD: MAINTENANCE OF NEUROLOGICAL AND BEHAVIORAL EFFECTS Bridget K. Dolan, M.S. Marquette University, 2016 To date, there are no known published studies that have assessed the maintenance of treatment effects in the context of neurological changes and their relationship to behavioral outcomes following a social skills intervention for adolescents with Autism Spectrum Disorder (ASD). The few studies that have incorporated long-term assessment into their design have focused exclusively on sustained behavioral responses to treatment. Individuals with ASD across the lifespan exhibit aberrant neural activity, which is thought to underlie social skill deficits noted in persons on the spectrum. Thus, this study sought to examine the impact of a social skills intervention, the Program for the Education and Enrichment of Relational Skills (PEERS: Laugeson, Frankel, Mogil, & Dillon, 2009), on the maintenance of neural plasticity and treatment gains in social functioning. Neural activity was assessed via electroencephalography (EEG) in terms of spectral power and asymmetry, which also was compared to a cohort of typically developing adolescents. Additionally, behavioral outcomes, examining a variety of social domains, at pre, post, and 6-month follow-up, were investigated for their relationship to changes in EEG activity. Results revealed that adolescents with ASD demonstrated a decrease in gamma activity in the right temporal region following PEERS, which was maintained at 6-month follow-up. This sustained neural change related to fewer problem behaviors and improved social cognition, which highlights the role of neural plasticity as a mechanism for maintaining improvements in behavioral presentation following intervention.
Map building of indoor environment using laser range finder and geometrics

This paper proposes map building method. Sensors such as a laser range finder, a gyroscope, encoders multiply compose the system. Generally, mobile robot can measure its relative position using a gyroscope and encoders in an environment. However, in this case, a large number of errors occur due to accumulative errors of sensor over time. Therefore, a map based on feature points is used. And an absolute position is measured by the feature points and geometrics. As combining the relative position and the absolute position, mobile robot can recognize its position. According to compositional data of a map, in case of a laser range finder, it takes a long time when adding a map or calculating a position of mobile robot. Therefore, it is necessary to arrange the feature points for computational time and map building. In this paper, a map was formed by laser range finder and features of geometrics. As a result of proposed method, the map was built efficiently in an aspect of time.
Salsa: Scalable Asynchronous Replica Exchange for Parallel Molecular Dynamics Applications

This paper presents Salsa, a novel, decentralized and asynchronous realization of the "replica exchange" algorithm for simulating the structure, function, folding, and dynamics of proteins. Salsa provides a scalable communication and interaction substrate that presents a virtual shared space abstraction and enables the dynamic and asynchronous interactions required by the simulations to be simply and efficiently implemented. The design, implementation and experimental evaluation of Salsa and replica exchange-based simulations using Salsa are presented
A High Coherent Association Rule Mining Algorithm

The goal of data mining is to help market managers find relationships among items from large data sets to increase sales volume. The Apriori algorithm is a method for association rule mining, a data mining technique. Although a lot of mining approaches have been proposed based on the Apriori algorithm, most focus on positive association rules, such as "If milk is bought, then bread is bought". However, such a rule may be misleading since customers that buy milk may not buy bread. In this paper, an algorithm for mining highly coherent rules that takes the properties of propositional logic into consideration is proposed. The derived association rules may thus be more thoughtful and reliable. Experiments are conducted on simulation data sets to demonstrate the performance of the proposed approach.
A Controlled Experiment: Evolution for Learning Diicult Image Classiication

The signal-to-symbol problem is the task of converting raw sensor data into a set of symbols that Artiicial Intelligence systems can reason about. We have developed a method for directly learning and combining algorithms that map signals into symbols. This new method is based on evolutionary computation and imposes little burden on or bias from the humans involved. Previous papers of ours have focused on PADO, our learning architecture. We showed how it applies to the general signal-to-symbol task and in particular the impressive results it brings to natural image object recognition. The most exciting challenge this work has received is the idea that PADO's success in natural image object recognition may be due to the underlying simplicity of the problems we posed it. This challenge implicitly assumes that our approach suuers from many of the same aaictions that traditional computer vision approaches suuer in natural image object recognition. This paper responds to this challenge by designing and executing a controlled experiment speciically designed to solidify PADO's claim to success.
The Forecast Ability of Risk-Neutral Densities of Foreign Exchange

We estimate the process underlying the pricing of American options by using higher-order lattices combined with a multigrid method. This paper also tests whether the risk-neutral densities given from American options provide a good forecasting tool. We use a nonparametric test of the densities that is based on the inverse probability functions and is modified to account for correlation across time between our random variables, which are uniform under the null hypothesis. We find that the densities based on the American option markets for foreign exchange do quite well for the forecasting period over which the options are thickly traded. Further, simple models that fit the densities do about as well as more sophisticated models.