Miracle-dz/UniFAR-FTUs huggingface dataset:
Surface-Base Approach Using a Multi-scale EM-ICP Registration for Statistical Population Analysis

The human cortex is a folded ribbon of neurons with a high inter-individual variability. It is a challenging structure to study especially when measuring small changes resulting from normal aging and neurodegenerative disorders such as Alzheimer's Disease (AD). Recent studies have proposed surface based approaches for statistical population comparison of cortical changes since such approaches better cope with the surfacic nature of the cortex. In this paper we present a new multi-scale EM-ICP registration that is embedded into a surface-based approach. We compare this new registration algorithm with the shape context in the context of statistical population analysis. When comparing the cortical thickness between healthy elderly subjects to Alzheimer's disease patients, the new pipeline reduces the intra class variability while increasing the statistical power of the T-tests between both groups.
Changing Conditions for Information Use and Learning in Swedish Schools : A Synthesis of Research

This article presents findings from a series of research studies conducted between 1998 and 2010 on the ways in which knowledge formation occurs through students’ own research and on the interaction between information seeking and use and learning. Our point of departure is that information seeking and learning are closely interwoven in constantly on-going human activity. Our studies have directed particular interest to the ways in which information and communication technologies (ICTs) and new digital media shape conditions for learning. The research design was inspired by ethnographical studies, including observations, field notes, video recordings, interviews, questionnaires and documents produced in the practices studied. In the present article we have chosen to focus on four main findings common to the series of research projects, which together indicate not only changes within schools but also on a structural level. Firstly, we claim that the new digital tools which mediate information seeking and learning change the conditions for learning itself. Secondly, information seeking via new technologies changes conditions for how students assume responsibility and construct knowledge. Thirdly, new conditions for the division of responsibility and meaning-making were identified throughout all six projects. The fourth main finding concerns how the communicative structures of school were changed. We suggest that these four main findings contribute to an understanding of a gradual shift in the school discourse, which took place during the first decade of this millennium.
Frequency Domain Stability Analysis of MMC-Based HVdc for Wind Farm Integration

This paper investigates the stability of offshore wind farms integration through a modular multilevel converter-based high-voltage dc (MMC-HVdc) transmission system. Resonances or instability phenomena have been reported in between wind farms and MMC-HVdc systems. They are arguably originated due to interactions between the MMC and the wind power inverters. However, the nature of these interactions is neither well understood nor reported in the literature. In this paper, the impedance-based analytical approach is applied to analyze the stability and to predict the phase margin of the interconnected system. For that, analytical impedance models of a three-phase MMC in a compensated modulation case and a direct modulation case are separately derived using the small-signal frequency domain method. Moreover, the impedance models of the MMC take the circulating current control into account. The derived impedance models are then verified by comparing the frequency responses of the analytical model with the impedance measured in a nonlinear time-domain simulation model developed in MATLAB. The results show that the potential resonances or instability of the interconnected system can be readily predicted through the Nyquist diagrams. In addition, the analysis indicates that the circulating current control of the MMC has a significant impact on the stability of the interconnected system. Finally, the time-domain simulations validate the theoretical analysis.
[Evaluation of diagnostic performance of alpha-fetoprotein (AFP) and des-gamma-carboxy prothrombin (DCP) for HCV related hepatocellular carcinoma developed after long-term follow up].

PURPOSE This study evaluated the diagnostic performance of two tumor markers, alpha-fetoprotein (AFP) and des-gamma-carboxy prothrombin (DCP), a combination of these tests and other common tests for patients developing hepatocellular carcinoma (HCC) during long-term follow-up for HCV-related liver disease. METHODS We reviewed the medical records of 144 patients who developed HCC after at least one-year follow up for HCV-related liver disease in Yamaguchi University Hospital or Tenri Hospital and demographic, clinical and laboratory data were collected retrospectively. To evaluate diagnostic test performance, we defined the oldest data collected from each patient at least one year before the diagnosis of HCC as negative control data. RESULTS Sensitivity and specificity were 0.52, 0.61 for AFP with a cut-off of 20 ng/ml compared to 0.47 and 1.00 for DCP with a cut-off of 40 mAU/ml, respectively. Combination tests for AFP at 100 ng/ml and DCP at 40 mAU/ml yielded the best sensitivity at 0.84 with a specificity of 0.50. Multivariate logistic regression model using AFP, AST, LD, hemoglobin, prothrombin time and male ratio as variables showed significantly better diagnostic performance, i.e. sensitivity 0.85, specificity 0.74, than single test or combination test using AFP and DCP alone. CONCLUSIONS AFP and DCP alone were not sufficient for detection of HCC developing during long-term follow-up of HCV-related cirrhosis. Logistic regression including AFP and commonly used laboratory tests showed superior diagnostic accuracy compared to that of tumor marker tests alone.
On Interest Rate Policy and Asset Bubbles

In a provocative paper, Gal (2014) showed that a policymaker who raises interest rates to rein in a potential bubble will only make a bubble bigger if one exists. This poses a challenge to advocates of lean-against-the-wind policies that call for raising interest rates to mitigate potential bubbles. In this paper, we argue there are situations in which the lean-against-the wind view is justified. First, we argue Gal? s framework abstracts from the possibility that a policymaker who raises rates will crowd out resources that would have otherwise been spent on the bubble. Once we modify Gal? s model to allow for this possibility, policymakers can intervene in ways that raise interest rates and dampen bubbles. However, there is no reason policymakers should intervene to dampen the bubble in this case, since the bubble that arises in Gal? s setup is not one that society would be better off without. We then further modify Gal? s model to generate the type of credit-driven bubbles that alarm policymakers, and argue there may be justification for intervention in that case.
Steun voor traditionele vrouwenrollen in 32 landen: De invloed van gezinsbeleid en verschillen tussen de seksen

Cross-national research on support of traditional female roles is scarce. This research fills this knowledge gap by studying 32 countries around the world, to provide insight into categories that support or reject traditional female roles. We test individual and contextual explanations for differences in the support of traditional female roles within and between countries as well as between men and women. We find that the differences between individuals are larger than between countries. We explain the differences with a combination of individual and contextual characteristics. Higher educated, employed people and those who do not adhere to a religion are the least supportive of traditional female roles. It turns out that people living under a dual earner family policy model support traditional female roles least strongly. Furthermore, we do not find the gender gap to be significantly different in countries with a dual-earner family policy model as compared to traditional countries, but we do find smaller gender differences in countries with other family policy models.
The Association of Health and Income in the Elderly: Experience from a Southern State of Brazil

Objectives In high income, developed countries, health status tends to improve as income increases, but primarily through the 50th-66th percentile of income. It is unclear whether the same limitation holds in middle income countries, and for both general assessments of health and specific conditions. Methods Data were obtained from Brazil, a middle income country. In-person interviews with a representative sample of community residents age ≥60 (N=6963), in the southern state of Rio Grande do Sul, obtained information on demographic characteristics including household income and number of persons supported, general health status (self-rated health, functional status), depression, and seven physician-diagnosed, self-reported health conditions. Analyses used household income (adjusted for number supported and economies of scale) together with higher order income terms, and controlled for demographics and comorbidities, to ascertain nonlinearity between income and general and specific health measures. Results In fully controlled analyses income was associated with general measures of health (linearly with self-rated health, nonlinearly with functional status). For specific health measures there was a consistent linear association with depression, pulmonary disorders, renal disorders, and sensory impairment. For musculoskeletal, cardiovascular (negative association), and gastrointestinal disorders this association no longer held when comorbidities were controlled. There was no association with diabetes. Conclusion Contrary to findings in high income countries, the association of household-size-adjusted income with health was generally linear, sometimes negative, and sometimes absent when comorbidities were controlled.
Clustering Methods for Collaborative Filtering

Grouping people into clusters based on the items they have purchased allows accurate recommendations of new items for purchase: if you and I have liked many of the same movies, then I will probably enjoy other movies that you like. Recommending items based on similarity of interest (a.k.a. collaborative filtering) is attractive for many domains: books, CDs, movies, etc., but does not always work well. Because data are always sparse - any given person has seen only a small fraction of all movies - much more accurate predictions can be made by grouping people into clusters with similar movies and grouping movies into clusters which tend to be liked by the same people. Finding optimal clusters is tricky because the movie groups should be used to help determine the people groups and visa versa. We present a formal statistical model of collaborative filtering, and compare different algorithms for estimating the model parameters including variations of K-means clustering and Gibbs Sampling. This formal model is easily extended to handle clustering of objects with multiple attributes.
The NPD Benchmark for OBDA Systems

In Ontology-Based Data Access (OBDA), queries are posed over a high-level conceptual view, and then translated into queries over a potentially very large (usually relational) data source. The ontology is connected to the data sources through a declarative specification given in terms of mappings. Although prototype OBDA systems providing the ability to answer SPARQL queries over the ontology are available, a significant challenge remains: performance. To properly evaluate OBDA systems, benchmarks tailored towards the requirements in this setting are needed. OWL benchmarks, which have been developed to test the performance of generic SPARQL query engines, however, fail to evaluate OBDA specific features. In this work, we propose a novel benchmark for OBDA systems based on the Norwegian Petroleum Directorate (NPD). Our benchmark comes with novel techniques to generate, from available data, datasets of increasing size, taking into account the requirements dictated by the OBDA setting. We validate our benchmark on significant OBDA systems, showing that it is more adequate than previous benchmarks not tailored for OBDA.
A bootstrapping framework for annotating and retrieving WWW images

Most current image retrieval systems and commercial search engines use mainly text annotations to index and retrieve WWW images. This research explores the use of machine learning approaches to automatically annotate WWW images based on a predefined list of concepts by fusing evidences from image contents and their associated HTML text. One major practical limitation of employing supervised machine learning approaches is that for effective learning, a large set of labeled training samples is needed. This is tedious and severely impedes the practical development of effective search techniques for WWW images, which are dynamic and fast-changing. As web-based images possess both intrinsic visual contents and text annotations, they provide a strong basis to bootstrap the learning process by adopting a co-training approach involving classifiers based on two orthogonal set of features -- visual and text. The idea of co-training is to start from a small set of labeled training samples, and successively annotate a larger set of unlabeled samples using the two orthogonal classifiers. We carry out experiments using a set of over 5,000 images acquired from the Web. We explore the use of different combinations of HTML text and visual representations. We find that our bootstrapping approach can achieve a performance comparable to that of the supervised learning approach with an F1 measure of over 54%. At the same time, it offers the added advantage of requiring only a small initial set of training samples.
Modelling the effect of combination antiretroviral treatments on HIV incidence

ObjectiveTo assess the competing effects on HIV incidence in homosexual men of the decreased infectiousness of men with HIV receiving effective combination antiretroviral treatments and homosexual men engaging in unprotected anal intercourse with increased numbers of partners (levels of unsafe sex). MethodsA mathematical model of HIV transmission in homosexual men was developed, based on the HIV epidemic in Australia in 1996, when effective antiretroviral treatments first became widely available. Uncertainties in parameters were modelled using 1000 simulations. The effect of treatments on decreasing infectiousness was randomly sampled with a median 10-fold decrease in infectiousness (range 100-fold to no decrease). Levels of unsafe sex were randomly sampled with a median 50% increase in unsafe sex (range 100% to no increase). The percentage change in HIV incidence after one year was obtained by comparison with a null model in which there was no decrease in infectiousness as a result of treatment and no change in unsafe sex. ResultsResults of the models suggested that whereas increased levels of unsafe sex were linearly associated with increases in HIV incidence, decreases in infectiousness because of treatments were non-linearly associated with decreases in HIV incidence. An assessment of the competing effects suggested that decreases in infectiousness of two-, five-, and 10-fold would be counterbalanced by increases in unsafe sex of approximately 40, 60 and 70%, respectively. ConclusionThese models suggest that apparently large decreases in infectiousness as a result of treatment could be counterbalanced in terms of new HIV infections by much more modest increases in unsafe sex.
Seeing the wood for the trees: data-oriented translation

Data-Oriented Translation (DOT), which is based on Data-Oriented Parsing (DOP), comprises an experience-based approach to translation, where new translations are derived with reference to grammatical analyses of previous translations. Previous DOT experiments [Poutsma, 1998, Poutsma, 2000a, Poutsma, 2000b] were small in scale because important advances in DOP technology were not incorporated into the translation model. Despite this, related work [Way, 1999, Way, 2003a, Way, 2003b] reports that DOT models are viable in that solutions to ‘hard’ translation cases are readily available. However, it has not been shown to date that DOT models scale to larger datasets. In this work, we describe a novel DOT system, inspired by recent advances in DOP parsing technology. We test our system on larger, more complex corpora than have been used heretofore, and present both automatic and human evaluations which show that high quality translations can be achieved at reasonable speeds.
Genetic diversity of alfalfa (Medicago spp.) estimated by molecular markers and morphological characters

Background and Purpose: Genetic diversity of initial selection materials is essential for successful breeding and creation of new cultivars. The objectives of this research were to study genetic diversity among alfalfa (Medicago spp.) cultivars/population with a different geographical origin by RAPD markers and morphological characters, to determine genetic variation among and within materials using RAPD markers, and to compare results based on molecular markers and morphological characters. Materials and Methods: Fourteen European, Australian, South and North American M. sativa and M. media cultivars and one French wild population M. falcate were studied. Analyses were conducted on 20 randomly chosen individual plants of each cultivars/population. Genetic distances were calculated on the basis of RAPD data and morphological characters. Cluster analysis and AMOVA were shown for molecular data. Statistical analyses were performed in SAS 8.0, Arlequin 2.0 and NTSYS-pc 2.20 software. Results and Conclusions: The six RAPD primers generated 90 polymorphic bands across the 300 individual plants analyzed. Genetic distance among cultivars/population ranged from 0.28 to 0.40. Cluster analysis clearly separated Maron (M. falcata) population from all M. sativa and M. media cultivars which were grouped into two main clusters. Most of the genetic variability estimated by AMOVA was attributed to variation among individuals within cultivars (91.86%). Eight morphological characters, used for morphological analysis, were not sufficient to determine the differences among cultivars. Significant correlations between morphological and molecular distances were not observed. The obtained data suggest that RAPD markers could be useful and powerful tools for estimation of genetic diversity among diverse alfalfa germplasm.
Author Attribution Evaluation with Novel Topic Cross-validation

The practice of using statistical models in predicting authorship (so-called author attribution models) is long established. Several recent authorship attribution studies have indicated that topic-specific cues impact author attribution machine learning models. The arrival of new topics should be anticipated rather than ignored in an author attribution evaluation methodology; a model that relies heavily on topic cues will be problematic in deployment settings where novel topics are common. We develop a protocol and test bed for measuring sensitivity to topic cues using a methodology called novel topic cross-validation. Our methodology performs a cross-validation where only topics unseen in training data are used in the test portion. Analysis of the testing framework suggests that corpora with large numbers of topics lead to more powerful hypothesis testing in novel topic evaluation studies. In order to implement the evaluation metric, we developed two subsets of the New York Times Annotated Corpus including one with 15 authors and 23 topics. We evaluated a maximum entropy classifier in standard and novel topic cross validation in order to compare the mechanics of the two procedures. Our novel topic evaluation framework supports automatic learning of stylometric cues that are topic neutral, and our test bed is reproducible using document identifiers available from the authors.
Making Geo-Replicated Systems Fast as Possible, Consistent when Necessary

Online services distribute and replicate state across geographically diverse data centers and direct user requests to the closest or least loaded site. While effectively ensuring low latency responses, this approach is at odds with maintaining cross-site consistency. We make three contributions to address this tension. First, we propose RedBlue consistency, which enables blue operations to be fast (and eventually consistent) while the remaining red operations are strongly consistent (and slow). Second, to make use of fast operation whenever possible and only resort to strong consistency when needed, we identify conditions delineating when operations can be blue and must be red. Third, we introduce a method that increases the space of potential blue operations by breaking them into separate generator and shadow phases. We built a coordination infrastructure called Gemini that offers RedBlue consistency, and we report on our experience modifying the TPC-W and RUBiS benchmarks and an online social network to use Gemini. Our experimental results show that RedBlue consistency provides substantial performance gains without sacrificing consistency.
Online working condition monitoring system integrated power saving and security using zigbee wireless sensor network

In this paper, an online working condition monitoring system is designed to early warn environmental quality in manufacturing industry. It is very crucial to provide real time and accurate data for staffs, stakeholders and control center to monitor factory air quality at working place. By uploading the all collected data on internet, the environmental condition can be monitored in real time by users. A feedback system is realized to detect sensor's status. An event filler is designed to process and extract only the useful data to manage the huge amount of transmitted data by a large number of sensors in system. By doing so, we can save up to 48% data sent to coordinator on average and save up to 30% energy saving for a sensor node. An advanced MD5 Hash algorithm is used to encrypt data before uploading secured data to server.
Probabilistic Equalization With a Smoothing Expectation Propagation Approach

In this paper, we face the soft equalization of channels with inter-symbol interference for large constellation sizes, <inline-formula> <tex-math notation="LaTeX">$ \mathtt {M}$ </tex-math></inline-formula>. In this scenario, the optimal BCJR solution and most of their approximations are intractable, as the number of states they track grows fast with <inline-formula> <tex-math notation="LaTeX">$ \mathtt {M}$ </tex-math></inline-formula>. We present a probabilistic equalizer to approximate the posterior distributions of the transmitted symbols using the expectation propagation (EP) algorithm. The solution is presented as a recursive sliding window approach to ensure that the computational complexity is linear with the length of the frame. The estimations can be further improved with a forward–backward approach. This novel soft equalizer, denoted as smoothing EP (SEP), is also tested as a turbo equalizer, with a low-density parity-check (LDPC) channel decoder. The extensive results reported reveal remarkably good behavior of the SEP. In low dimensional cases, the bit error rate (BER) curves after decoding are closer than 1 dB from those of the BJCR, robust to the channel response. For large <inline-formula> <tex-math notation="LaTeX">$ \mathtt {M}$ </tex-math></inline-formula>, the SEP exhibits gains in the range of 3–5 dB compared to the linear minimum mean square error algorithm.