Miracle-dz/UniFAR-FTUs huggingface dataset:
UNIVERSITA ’ CA ’ FOSCARI DI VENEZIA novembre 2005 Stochastic Optimisation for Allocation Problem with Shortfall Risk Constraints

One of the most important aspects in asset allocation problems is the assumption concerning the probability distribution of asset returns. Financial managers generally suppose normal distribution, even if extreme realizations usually have an higher frequency than in the Gaussian case. We propose a general Monte Carlo simulation approach in order to solve an asset allocation problem with shortfall constraint, and to evaluate the exact portfolio risk-level when managers assume a misspecified tails behaviour. In particular, in a stochastic optimisation problem, we assume that returns are generated by a multivariate Student-t distribution, while in reality returns come from a multivariate distribution where each marginal is a Student-t but with different degrees of freedom. Stochastic optimisation allows us to value the effective risk for managers. In the specific case analysed, it is also interesting to observe that a multivariate density resulting from different Student-t marginal distributions produces a shortfall probability and a shortfall return level that can be approximated adequately by assuming a multivariate Student-t with a common degree of freedom in the optimisation problem. The present simulation based approach could be an important instrument for investors who need a qualitative assessment of the reliability and sensitivity of their investment strategies when their models are potentially misspecified.
Comparison of radiation dose in Multidetector computed tomography and Flat-panel computed tomography for imaging of the wrist

Poster: "ECR 2015 / C-1457 / Comparison of radiation dose in Multidetector computed tomography and Flat-panel computed tomography for imaging of the wrist" by: "J. Neubauer1, C. Neubauer1, M. Langer1, E. Kotter1, J. M. Voigt2, M. Fiebich2; 1Freiburg/DE, 2Giesen/DE"
Choosing Feature Selection and Learning Algorithms in QSAR

Feature selection is an important part of contemporary QSAR analysis. In a recently published paper, we investigated the performance of different feature selection methods in a large number of in silico experiments conducted using real QSAR datasets. However, an interesting question that we did not address is whether certain feature selection methods are better than others in combination with certain learning methods, in terms of producing models with high prediction accuracy. In this report we extend our work from the previous investigation by using four different feature selection methods (wrapper, ReliefF, MARS, and elastic nets), together with eight learners (MARS, elastic net, random forest, SVM, neural networks, multiple linear regression, PLS, kNN) in an empirical investigation to address this question. The results indicate that state-of-the-art learners (random forest, SVM, and neural networks) do not gain prediction accuracy from feature selection, and we found no evidence that a certain feature selection is particularly well-suited for use in combination with a certain learner.
The Measles Virus Hemagglutinin β-Propeller Head β4-β5 Hydrophobic Groove Governs Functional Interactions with Nectin-4 and CD46 but Not Those with the Signaling Lymphocytic Activation Molecule

ABSTRACT Wild-type measles virus (MV) strains use the signaling lymphocytic activation molecule (SLAM; CD150) and the adherens junction protein nectin-4 (poliovirus receptor-like 4 [PVRL4]) as receptors. Vaccine MV strains have adapted to use ubiquitous membrane cofactor protein (MCP; CD46) in addition. Recently solved cocrystal structures of the MV attachment protein (hemagglutinin [H]) with each receptor indicate that all three bind close to a hydrophobic groove located between blades 4 and 5 (β4-β5 groove) of the H protein β-propeller head. We used this structural information to focus our analysis of the functional footprints of the three receptors on vaccine MV H. We mutagenized this protein and tested the ability of individual mutants to support cell fusion through each receptor. The results highlighted a strong overlap between the functional footprints of nectin-4 and CD46 but not those of SLAM. A soluble form of nectin-4 abolished vaccine MV entry in nectin-4- and CD46-expressing cells but only reduced entry through SLAM. Analyses of the binding kinetics of an H mutant with the three receptors revealed that a single substitution in the β4-β5 groove drastically reduced nectin-4 and CD46 binding while minimally altering SLAM binding. We also generated recombinant viruses and analyzed their infections in cells expressing individual receptors. Introduction of a single substitution into the hydrophobic pocket affected entry through both nectin-4 and CD46 but not through SLAM. Thus, while nectin-4 and CD46 interact functionally with the H protein β4-β5 hydrophobic groove, SLAM merely covers it. This has implications for vaccine and antiviral strategies.
Bias due to participant overlap in two‐sample Mendelian randomization

Mendelian randomization analyses are often performed using summarized data. The causal estimate from a one‐sample analysis (in which data are taken from a single data source) with weak instrumental variables is biased in the direction of the observational association between the risk factor and outcome, whereas the estimate from a two‐sample analysis (in which data on the risk factor and outcome are taken from non‐overlapping datasets) is less biased and any bias is in the direction of the null. When using genetic consortia that have partially overlapping sets of participants, the direction and extent of bias are uncertain. In this paper, we perform simulation studies to investigate the magnitude of bias and Type 1 error rate inflation arising from sample overlap. We consider both a continuous outcome and a case‐control setting with a binary outcome. For a continuous outcome, bias due to sample overlap is a linear function of the proportion of overlap between the samples. So, in the case of a null causal effect, if the relative bias of the one‐sample instrumental variable estimate is 10% (corresponding to an F parameter of 10), then the relative bias with 50% sample overlap is 5%, and with 30% sample overlap is 3%. In a case‐control setting, if risk factor measurements are only included for the control participants, unbiased estimates are obtained even in a one‐sample setting. However, if risk factor data on both control and case participants are used, then bias is similar with a binary outcome as with a continuous outcome. Consortia releasing publicly available data on the associations of genetic variants with continuous risk factors should provide estimates that exclude case participants from case‐control samples.
Multiagent Coordination in Cooperative Q-learning Systems

Many reinforcement learning architectures fail to learn optimal group behaviors in the multiagent domain. Although these coordination difficulties are often attributed to the non-Markovian environment created by the gradually-changing policies of concurrently learning agents, a careful analysis of the situation reveals an underlying problem structure which can cause suboptimal group policies even when the Markovian properties of the learning environment are preserved. This underlying structure is termed the multiagent coordination problem, and it can be viewed as a combination of two related but distinct limitations of cooperative multiagent learning systems: action shadowing and joint action prediction. This paper discusses the causes of each of these limitations and their effects on systems of cooperative Q-learning agents, including the conditions which must be met in order to guarantee the execution of optimal group policies. Multiagent coordination strategies presented by other researchers are considered from the perspective of this new problem framework, and an algorithm is presented which extends some of these coordination strategies to improve the tractability of large-scale multiagent learning.
Cloud Mining: Web usage mining and user behavior analysis using fuzzy C-means clustering

There is a rapid development of World Wide Web in its volume of traffic and the size and complexity of web sites. In this paper, a new approach is presented based on hybrid clustering methods for Web Usage Mining (WUM). The WUM process contains three steps: pre-processing, data mining and result analysis. First, it gives a brief description of the WUM process and Web data, then the presentation of the pre-processing step and the data warehouse that were employed. The hybrid clustering methods based on Fuzzy C-means clustering are used for analyzing and the Web logs taken from the real world Web servers. The results obtained after applying these methods and the corresponding interpretations are also presented. Furthermore, this paper also described web usage mining through cloud computing i.e. cloud mining. The Future work of web mining is to
Segmentation and semantic labelling of RGBD data with convolutional neural networks and surface fitting

We present an approach for segmentation and semantic labelling of RGBD data exploiting together geometrical cues and deep learning techniques. An initial over-segmentation is performed using spectral clustering and a set of non-uniform rational B-spline surfaces is fitted on the extracted segments. Then a convolutional neural network (CNN) receives in input colour and geometry data together with surface fitting parameters. The network is made of nine convolutional stages followed by a softmax classifier and produces a vector of descriptors for each sample. In the next step, an iterative merging algorithm recombines the output of the over-segmentation into larger regions matching the various elements of the scene. The couples of adjacent segments with higher similarity according to the CNN features are candidate to be merged and the surface fitting accuracy is used to detect which couples of segments belong to the same surface. Finally, a set of labelled segments is obtained by combining the segmentation output with the descriptors from the CNN. Experimental results show how the proposed approach outperforms state-of-the-art methods and provides an accurate segmentation and labelling.
How Has the Health of Software Ecosystems Been Evaluated?: A Systematic Review

The health of the software ecosystems concerns to the growing and continuity to exist remaining variable and productive over time. Research on this area is becoming more important. Even today, no studies have been available summarizing the research on evaluation approaches for the health of software ecosystems. The objective of this study is to structure and analyze the available literature on this field identifying the state-of-the-art of the research. We conducted a systematic literature review to obtain an overview of the existing studies in this area. 23 studies were selected as primary studies by applying inclusion, exclusion and quality criteria. The findings show that the research area is quite immature. There are few approaches and tools to support the evaluation work. In these studies, only 3 reported a complete evaluation of the health of ecosystems, 5 studies were considered as initial proposals, and the others evaluated the health partially.
A 3′-Transcribed Region of the HLA-A2 Gene Mediates Posttranscriptional Stimulation by IFN-γ1

The expression of several MHC class I genes is up-regulated at the transcriptional level by IFN-γ. Posttranscriptional mechanisms also have been implicated, but not well characterized. To investigate the mechanism of IFN-γ stimulation of the human MHC class I gene HLA-A2, several human tumor cell lines were transfected with reporter gene constructs driven by the HLA-A2 promoter. We have previously shown that the extended 525-bp HLA-A2 promoter alone, which includes a 5′ IFN-stimulated response element consensus sequence, is not sufficient for IFN-γ response in either K562 or Jurkat cells. In the current study, stable transfection of a genomic HLA-A2 gene construct, containing both 5′- and 3′-flanking sequences, resulted in stimulation of the gene by IFN-γ. Nuclear run-on assays revealed that, unlike other class I genes, IFN-γ stimulation of HLA-A mRNA accumulation occurs almost entirely through posttranscriptional mechanisms. RNA stability assays showed that the effect is not mediated by alteration of the half-life of the HLA-A2 mRNA. Formation of the 3′ end was unaffected by IFN-γ treatment. Sequences that mediate the majority of IFN-γ induction of HLA-A2 mRNA reside in a 127-bp 3′-transcribed region of the gene. This region contains the terminal splice site, the usage of which is not affected by IFN-γ treatment. These results demonstrate a novel posttranscriptional mechanism of regulation of MHC class I genes by IFN-γ.
Application of Geo-Informatics to Estimate Agricultural Water Use in Samut Songkhram Province, Thailand

Water use of human depends on daily life activities. Agriculture is the main activity of Thailand. In the present, there are the extension of agriculture, industry, residence, and community which increase water demand. It can cause a problem of water use among urban, industrial and agricultural. Most agricultural in Samut Songkhram Province, Thailand were planting crops such as pomelo, coconut and lychee, which are famous and generate a lot of income for farmers. The quality and quantity of water is an indicator for measure of the quality and yield of agricultural products. The aim of research is to estimate water use through geographic information system, and remote sensing from Landsat8 satellite. This research considers the main parameters which were; estimation of spatial rainfall runoff and quantity of evaporation, infiltration rate of the soil. To evaluate the amount of soil moisture in order to create soil moisture map for planning the utilization and management of water for agriculture. The classification land use was to investigate from Landsat8 which shown currently agricultural area. The result showed the soil moisture which distributes in each area especially the agricultural area. This information can be used by agriculturists and related organizations to plan and make the decision of growing appropriate plants according to the soil moisture for the sustainability of the agriculture.
The Effect of Virtual Community Culture and Group Cohesion on Knowledge Sharing: A Case Study of Professional Virtual Community

With the rapid development of internet, virtual community has become the most import knowledge sharing platform. And this study investigates the inter-relationships among virtual community culture, group cohesion and knowledge sharing. Based on 185 questionnaires from Chinese netizens in professional virtual community, this paper implements a structural equation model to test the research framework and hypotheses. The results show that virtual community culture has direct positive impact on group cohesion but no direct positive impact on knowledge sharing. In addition, group cohesion plays an intermediary role between virtual community culture and knowledge sharing, and has direct positive impact on knowledge sharing, which indicates that group cohesion is very important to Chinese netizens for knowledge sharing activity under the rapid changed environment of knowledge economy.
Measuring Social Value Orientation

Narrow self-interest is often used as a simplifying assumption when studying people making decisions in social contexts. Nonetheless, people exhibit a wide range of different motivations when choosing unilaterally among interdependent outcomes. Measuring the magnitude of the concern people have for others, sometimes called Social Value Orientation (SVO), has been an interest of many social scientists for decades and several different measurement methods have been developed so far. Here we introduce a new measure of SVO that has several advantages over existent methods. A detailed description of the new measurement method is presented, along with norming data that provide evidence of its solid psychometric properties. We conclude with a brief discussion of the research streams that would benefit from a more sensitive and higher resolution measure of SVO, and extend an invitation to others to use this new measure which is freely available.
ExperTime: tracking expertise over time

This paper presents ExperTime, a web-based system for tracking expertise over time. We visualize a person's expertise profile on a timeline, where we detect and characterize changes in the focus or topics of expertise. It is possible to zoom in on a given time period in order to examine the underlying data that is used as supporting evidence. It is also possible to perform visual and quantitative comparison of two arbitrarily selected time periods in a highly interactive environment. We invite profile owners to evaluate and fine-tune their profiles, and to leave feedback.
Coronary artery calcification is increased in patients with COPD and associated with increased morbidity and mortality

Background Coronary artery calcification is pathognomonic of coronary artery disease (CAD). Whether CAD in patients with COPD is linked to lung function, functional capacity and/or clinically relevant outcomes is unknown. The objective was to assess the association between CAD and disease severity, functional capacity and outcomes in patients with COPD. Methods Coronary artery calcium score (CACS; Agatston score) was measured using chest CT in patients with COPD, smokers with normal spirometry and non-smokers from the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) study. Results CACS was measured in 942 subjects: 672 with COPD (mean age±SD, 63±7 years; FEV1 49±16% predicted), 199 smokers with normal spirometry (54±9 years; FEV1 110±12% predicted) and 71 non-smokers (55±9 years; FEV1 114±14% predicted). CACS was higher in patients with COPD than smokers or non-smokers (median (IQR), 128 (492) vs 0 (75) vs 0 (3) Agatston units (AU), p<0.001). In patients with COPD, CACS correlated with age, pack-years, 6 min walking distance, modified Medical Research Council Dyspnoea score and circulating levels of interleukin (IL)-6, IL-8, Clara Cell protein 16, surfactant protein D and peripheral blood neutrophil count, but not with emphysema, exacerbation frequency, % predicted FEV1 or decline in FEV1. CACS was higher in patients with COPD who died than in those who survived until 3-year follow-up (CACS 406 vs 103 AU, p<0.001), and was associated with mortality in a Cox proportional hazards model (p=0.036). Conclusions Patients with COPD have more CAD than controls and this is associated with increased dyspnoea, reduced exercise capacity and increased mortality. These data indicate that the presence of CAD in patients with COPD is associated with poor clinical outcomes.
RHdb: the Radiation Hybrid database

Since July 1995, the European Bioinformatics Institute (EBI) has maintained RHdb (http://www.ebi.ac.uk/RHdb), a public database for radiation hybrid data. Radiation hybrid mapping is an important technique for determining high resolution maps. RHdb is also served by CORBA servers. The EBI is an Outstation of the European Molecular Biology Laboratory (EMBL).
Efficient Large Scale Clustering Based on Data Partitioning

Clustering techniques are very attractive for extracting and identifying patterns in datasets. However, their application to very large spatial datasets presents numerous challenges such as high-dimensionality data, heterogeneity, and high complexity of some algorithms. For instance, some algorithms may have linear complexity but they require the domain knowledge in order to determine their input parameters. Distributed clustering techniques constitute a very good alternative to the big data challenges (e.g.,Volume, Variety, Veracity, and Velocity). Usually these techniques consist of two phases. The first phase generates local models or patterns and the second one tends to aggregate the local results to obtain global models. While the first phase can be executed in parallel on each site and, therefore, efficient, the aggregation phase is complex, time consuming and may produce incorrect and ambiguous global clusters and therefore incorrect models. In this paper we propose a new distributed clustering approach to deal efficiently with both phases, generation of local results and generation of global models by aggregation. For the first phase, our approach is capable of analysing the datasets located in each site using different clustering techniques. The aggregation phase is designed in such a way that the final clusters are compact and accurate while the overall process is efficient in time and memory allocation. For the evaluation, we use two well-known clustering algorithms, K-Means and DBSCAN. One of the key outputs of this distributed clustering technique is that the number of global clusters is dynamic, no need to be fixed in advance. Experimental results show that the approach is scalable and produces high quality results.
Isolation of a sixth dynein subunit adenosine triphosphatase of Chlamydomonas axonemes

This study of the axoneme led to the identification of a previously unknown adenosine triphosphatase (ATPase), which is likely a major component of inner dynein arms. The ATPase was isolated from a soluble fraction of axonemes obtained from pf 28, a Chlamydomonas mutant lacking the outer dynein arms. The activity hydrolyzed up to 2.3 mumol of ATP.min-1.mg-1 of protein (at pH 7.2, in the presence of both Ca++ and Mg++), had a sedimentation coefficient of 11S in sucrose gradient, and cosedimented with four polypeptides of apparent molecular weight 325,000, 315,000 140,000, and 42,000. Several arguments indicate that the new ATPase is a component of the inner dynein arms. Three or four polypeptides cosedimenting with the activity belong to a group of axonemal components that are deficient in the axonemes of pf 23 and pf 30, two mutants that display different levels of inner dynein arm deficiency. The 42,000 component is axonemal actin, a subunit of two other inner dynein ATPases. The two polypeptides of molecular weight greater than 300,000 have electrophoretic mobility similar to that of high molecular weight components of outer and inner dynein arms. In spite of some similarities each ATPase isolated from inner or outer arms is composed of a different set of polypeptides. Different ATPases may be required for the modulation of localized sliding of adjacent outer double microtubules in the axoneme.
Language level and nonverbal social-communicative behaviors in autistic and language-delayed children.

OBJECTIVE To explore the relationship of general expressive language delay to nonverbal social-communicative deficits and repetitive behaviors associated with autism in preschool children. METHOD Interviews of the parents of 51 autistic and 43 nonautistic 3- to 5-year-olds with language impairments were compared. RESULTS Main effects of the children's language level occurred for the majority of social and nonverbal communication items but not for restricted, repetitive behaviors or showing and directing attention, socially directed gaze, or range of facial expression. CONCLUSIONS In addition to the specific impairments of autism, a broader conceptualization of communication handicap is urged for preschool children with developmental delays.
Clustering shapes using heat content invariants

In this paper, we investigate the use of invariants derived from the heat kernel as a means of clustering graphs. We turn to the heat-content, i.e. the sum of the elements of the heat kernel. The heat content can be expanded as a polynomial in time, and the coefficients of the polynomial are known to be permutation invariants. We demonstrate how the polynomial coefficients can be computed from the Laplacian eigen-system. Graph-clustering is performed by applying principal components analysis to vectors constructed from the polynomial coefficients. We experiment with the resulting algorithm on the COIL database, where it is demonstrated to outperform the use of Laplacian eigenvalues.