Miracle-dz/UniFAR-FTUs huggingface dataset:
CD 40 L Deficiency Protects Against AngII-Induced Dissecting Aneurysm Formation in Apoe

An abdominal aortic aneurysm (AAA) is defined as a permanent dilation of the abdominal aorta by >50% or >3 cm and occurs in up to 8.00% of men over the age of 65 years and between 0.37% and 1.53% of women over the age of 60 years. Complications of AAAs, such as rupture or dissection, have an associated mortality of >80%. Inflammation is of major importance in aneurysm formation and progression. Patients with an AAA display increased serum concentrations of interleukin-1β, TNF (tumor necrosis factor) α, CCL2 (chemokine [C-C motif] ligand 2), interleukin-6, and interferon γ (IFNγ) while several inflammatory mediators affect AAA development in mice. Similarly, expression levels of matrix metalloproteinase (MMP) 9 are increased in human AAA tissue, and levels of tissue inhibitor of metalloproteinase-2 are decreased. A TNF-related signaling pathway implicated in inflammation and matrix turnover is the CD40-CD40L dyad. In atherosclerosis, inhibition of CD40-CD40L signaling decreases plaque size and results in a plaque phenotype that contains high amounts of fibrosis and low amounts of inflammation, reminiscent of a stable plaque. Here, we investigate the role of CD40L in AAA development in humans and mice.
Syllable-a promising recognition unit for LVCSR

We present an attempt to model syllable level acoustic information as a viable alternative to the conventional phone level acoustic unit for large vocabulary continuous speech recognition. The motivation for this work were the inherent limitations in the phone based approach, primarily the decompositional nature and lack of larger scale temporal dependencies. We present preliminary but encouraging results on a syllable based recognition system which exceeds the performance of a comparable triphone system both in terms of word error rate (WER) and complexity. The WER of the best syllable system reported here was 49.1% on a standard SWITCHBOARD evaluation.
Convolutional Recurrent Network for Road Boundary Extraction

Creating high definition maps that contain precise information of static elements of the scene is of utmost importance for enabling self driving cars to drive safely. In this paper, we tackle the problem of drivable road boundary extraction from LiDAR and camera imagery. Towards this goal, we design a structured model where a fully convolutional network obtains deep features encoding the location and direction of road boundaries and then, a convolutional recurrent network outputs a polyline representation for each one of them. Importantly, our method is fully automatic and does not require a user in the loop. We showcase the effectiveness of our method on a large North American city where we obtain perfect topology of road boundaries 99.3% of the time at a high precision and recall.
RDF Browser for Data Discovery and Visual Query Building

The free-form nature of triplestores offers a lot of flexibility for constructing databases, but that freedom can also make it less obvious how to find arbitrary data for retrieval, errorchecking, or general browsing. Gruff is a graphical triplestore browser that attempts to make data retrieval more pleasant and powerful with a variety of tools for laying out cyclical graphs, displaying tables of properties, managing queries, and building SPARQL and queries as visual diagrams. Author Keywords
Development and efficacy of a compensatory skill building program: Parkinson’s disease cognitive rehabilitation for executive functioning (PD-CoRE)

Objective: Cognitive dysfunction is a major clinical feature of PD that contributes to disability, caregiver strain, and diminished quality of life over the disease course. Cognitive rehabilitation has mounting evidence as an intervention relevant for improving quality of life for people living with PD. The Parkinson’s Disease Cognitive Rehabilitation of Executive functioning (PD-CoRE) program is a new cognitive rehabilitation program designed to teach compensatory skills that address daily struggles secondary to executive dysfunction and to break the cycle of cognitive impairment, depression, apathy, poor self-efficacy, reduced quality of life, and increased caregiver burden. The aim of the current study was to evaluate the efficacy of the PD-CoRE program in improving executive functions of individuals with PD and mild cognitive impairment.
Digital Ecosystems, Collective Intelligence, ontology and the 2nd law of thermodynamics

Digital ecosystems are interconnected environments which enable the proliferation and harnessing of collective intelligence. Ontologies built in support of information systems that constitute the fabric of digital environments need to take into account dynamic factors which contribute to dasiaongoing changepsila in the underlying reality. In this paper we put forward the hypothesis that dasiaentropypsila (as defined in the second law of thermodynamics) as well as similar metrics must be used as a measurement to evaluate dynamic factors, and lay down the foundation for further research.
Improved SURF Algorithm Based On ACO

SURF (Speeded-Up Robust Features) is a scale- and rotation-invariant algorithm, which has a better repeatability, distinctiveness, robustness, and a faster computing and comparing speed. In this paper, we propose an improved SURF algorithm based on ACO (Ant Colony Optimization). First of all, the algorithm uses SURF to find all interest points. Secondly, each pixel of the original image is seen as an ant, imitates the process of ants search food to get the image edge. Finally, selects the interest points from the area around the image edge. The experimental results show that the interest points extracted by the improved SURF algorithm based on ACO are more robust, and the number of them is effectively reduced. In this way, we can reduce the amount of calculation for the subsequent image registration.
Implementation of Parallel Matrix Diagonalization for Ab-Initio Molecular Dynamics Program using ScaLAPACK

A matrix diagonalization is one of the most time-consuming parts of the ab-initio molecular dynamics programs. The bigger system to be simulated, the bigger matrix needs to be diagonalized to obtain the eigenvalues and eigenvectors. To raise this matter, we have applied matrix diagonalization provided by Scalable Linear Algebra PACKage (ScaLAPACK) to the ab-initio molecular dynamics program. We analyzed the eﬃciency of RS routine provided by EISPACK and PDSYEVX and PZHEEVX by ScaLAPACK
THE APPARENT TOTAL TRACT DIGESTIBILITY , APPARENT ILEAL DIGESTIBILITY AND FECAL NOXIOUS GAS CONTENT OF GROWING PIGS FED PROBIOTICS SUPPLEMENTED DIETS

The objective of this study was to determine the effects of probiotics ( Bacillus subtilis endorspores and Clostridium butyricum endorspores complex) supplementation on the apparent ileal digestibility (AID), apparent total tract digestibility (ATTD) and fecal noxious gas content in growing pigs. Three barrows [(Landrace × Yorkshire) × Duroc] with an average initial BW of 26.48 ± 1.47 kg followed a 3 × 3 factorial arrangement of treatments (CON, P0.1, P0.2) with 3 levels of probiotics (0, 0.1, and 0.2%), respectively. In this study, the inclusion of probiotics increased (P<0.05) AID of dry matter (DM) and nitrogen (N) compared with CON group. Pigs fed P0.2 diet led to a higher (P<0.05) AID of energy than those fed CON diet. No difference was observed (P>0.05) on ATTD in the current study. Dietary probiotics decreased (P<0.05) the NH3, propionic acid and butyric acid concentrations compared with CON diet. In conclusion, dietary added probiotics supplementation increased AID and reduced fecal noxious gas content in growing pigs.
A verification technique for self-adaptive software by using model-checking

Self-adaptive software refers to software that can change its behavior by itself to perform an intended objective according to changes in the surrounding environment. In this study, a technique is proposed to detect the possibility of problem occurrence in advance, before the self-adaptive software carries out self-adaptation according to changes in the surrounding environment. For the proposed technique, a model-checking technique is applied, which is a software modeling verification technique. The proposed technique was applied to ZNN.com, which provides a self-adaptive software scenario. Through this, the possibility was shown that an adaptive-strategy can be prepared in advance by self-adaptive software through the modelchecking technique in the stage prior to the execution of selfadaptation. Keywords— Self-adaptive software, Model-Checking, Software verification
Seasonal Adjustment with the R Packages x12 and x12GUI

The X-12-ARIMA seasonal adjustment program of the US Census Bureau extracts the different components (mainly: seasonal component, trend component, outlier component and irregular component) of a monthly or quarterly time series. It is the state-of-the- art technology for seasonal adjustment used in many statistical offices. It is possible to include a moving holiday effect, a trading day effect and user-defined regressors, and additionally incorporates automatic outlier detection. The procedure makes additive or multiplicative adjustments and creates an output data set containing the adjusted time series and intermediate calculations. The original output from X-12-ARIMA is somehow static and it is not always an easy task for users to extract the required information for further processing. The R package x12 provides wrapper functions and an abstraction layer for batch processing of X-12-ARIMA. It allows summarizing, modifying and storing the output from X-12-ARIMA within a well-defined class-oriented implementation. On top of the class-oriented (command line) implementation the graphical user interface allows access to the R package x12 without requiring too much R knowledge. Users can interactively select additive outliers, level shifts and temporary changes and see the impact immediately. The provision of the powerful X-12-ARIMA seasonal adjustment program available directly from within R, as well as of the new facilities for marking outliers, batch processing and change tracking, makes the package a potent and functional tool.
Case study of an open source application for 3 D acquisition of archaeological structures at the archaeological site

In 2009, the National Museum of the Czech Republic launched its archaeological activities at the Meroitic site of Wad Ben Naga, located some 130 kilometers north of Khartoum (Fig. 1). So far, the National Museum’s Archaeological Expedition to Wad Ben Naga has completed six excavation seasons. The first three seasons2 were focused on a general survey of the site and re-excavation of structures unearthed by the expedition of the Sudanese Antiquities Services headed by Said Thabit Hassan Thabit between 1958 and 1960.3 By the third season (Autumn 2011), the Expedition started exploration of the socalled Typhonium (WBN 200), a temple complex remains of which are known from the accounts of early European and American travelers who visited the Middle Nile Region between 1821 and the mid 1880s. This period marks the time from the military campaign the Egyptian viceroy Muhammad Ali sent to the present-day Sudan to the outbreak of the Mahdist revolution. The exploration of the temple complex still remains the main focus of the Expedition’s archaeological activities at the site. (Fig. 2)4
Artificial Neural Network in Prognosticating Human Personality from Social Networks

The analysis of text in the form of tweets, chat or posts can be an interesting as well as challenging area of research. In this paper, such an analysis provides information about the human behavior as positive, negative or neutral. For simplicity, tweets from social networking site, Twitter, are extracted for analyzing human personality. Various concepts from natural language processing, text mining and neural networks are used to establish the final outcome of the application. For analyzing text, Neural Networks are implemented which are so modeled that they predict the Human behavior as positive, negative or neutral based on extracted and preprocessed data. Using Neural Networks, the particular pattern is identified and weights are provided to words based on the extracted pattern.Neural networks have an added advantage of adaptive learning. This application can be immensely useful for politics, medical science, sports, matrimonial purposes etc.The results so obtained are quite promising.
Enhance the Performance of Associative Memory by Using New Methods

Data or instructions that are regularly used are saved in cache so that it is very easy to retrieve for the purpose of increase the cache performance. Evaluating the execution of multi-core systems the part of the cache memory is very important. A multicore processor is shared circuit in which two or more processors are joined to enhance the performance and perform multiple tasks. This paper describes the performance of cache memory based on cache access time, miss rate and miss penalty. Cache mapping methods are defined to increase the performance of cache but it face many difficulties. Some methods and algorithms are used to decrease these difficulties. In this paper describes the study of recent competing processors to evaluate the cache memory performance.
GENERALIZED GAUSSIANS FOR CONTINUOUS OBSERVATION DISTRIBUTIONS IN SPEECH RECOGNITION

One of the most successful models for speech recognition has been the HMM with mixture of Gaussians in the states to generate/capture observations. In this work we show how the addition of a parameter to model higher order moment statistics, such us the kurtosis, can provide improvements to the system. The distributions in which this degree of freedom is integrated are the generalized Gaussians. It is shown a method to estimate the parameters of these distributions even if they are embedded in a HMM or mixture of distributions. Some experimental results are obtained with this method compared to baseline systems of full and diagonal covariance matrices.
Properties of double porosity clayfills and suitable constitutive models

In the Czech Republic, double porosity soils exhibiting large total porosity are abundant in the clayfills of open-pit coal mines. Numerical analyses of such soils require unconventional procedures. The paper presents an approach to the constitutive modelling of double porosity materials. Application of the framework is demonstrated by an enhancement of the Modified Cam clay model. The results of laboratory experiments that have been carried out in the triaxial apparatus are used in calibrating the reference MCC model. The performance of the enhanced model is demonstrated by a parametric study. RÉSUMÉ En République Tchèque, les sols à double porosité présentant une porosité totale importante sont fréquents dans les argiles de remplissage de mines de charbon à ciel ouvert. La modélisation numérique du comportement mécanique de tels sols nécessite des procédures non conventionnelles. Cet article présente une approche à la modélisation constitutive des matériaux a double porosité. Cet approche est ici appliqué afin d’améliorer le modèle Modified Cam clay (MCC). Le modèle MCC de référence a été calé à partir de résultats expérimentaux de laboratoire obtenus lors d’essais triaxiaux. On présente les résultats d’une étude paramétrique qui montrent l’amélioration obtenue grâce à l’approche proposée.
Object Description and Finding of Geometric Structures on the Base of Extracted Straight Edge Segments in Digital Images

– We present a novel method of grouping straight line segments to describe objects in digital images after straight edges extraction by oriented filtering edge-based algorithm. This method uses crossings in the ordered segments as the main property and it also takes into consideration some specific properties of grouped lines such as anti-parallelism, proximity and adjacency. Structures obtained by this method present an intermediate-level description of interesting objects which have polygonal view (buildings, parts of roads, bridges and some natural places of landscape). At the first step simple structures are obtained by lines grouping taking into consideration all crossing lines or only part of them. At the second step simple structures are joined allowing for restrictions. Application with real aerial and satellite images shows a good ability to separate and extract specific objects like buildings and other line-segment-rich structures.
Genetically divergent cytotypes of Vaccinium uliginosum co-occurring in the central Eastern Alps can be distinguished based on the morphology of their flowers

The cloudberries (Vaccinium uliginosum s. lat.) are among the most frequent constituents of dwarf shrub communities in the Alps. Recently, we have shown that (i) both diploid and tetraploid cytotypes are widespread in the Eastern Alps and sometimes occur in mixed “populations” and that (ii) these two cytotypes correspond to two non-overlapping AFLP groups, which are in almost perfect congruence with two plastid lineages. Further (iii), we revealed that diploids and tetraploids show some degree of niche separation, but co-occur in low-alpine heath vegetation. Finally, (iv), in spite of the clear separation of diploids and tetraploids in the molecular data sets, we failed to detect consistent morphological differences. Here, we focused on patterns of genetic, ecological and morphological divergence between cytotypes at mixed-ploidy sites as well as on the underlying processes. Our study is based on a sampling of a hundred individuals each at three sites with previously determined co-occurrence of diploids and tetraploids in the Central Alps of Tirol, Austria. We employed an integrative approach combining flow cytometry, plastid DNA sequencing, AFLP fingerprinting, morphometry of leaf and flower characters as well as a characterization of ecological niches based on the vegetation surrounding the individual Vaccinium plants, in order to address the following questions. What is the small-scale distribution of diploid and tetraploid V. uliginosum s. lat. at the three sites investigated? Are the molecular genetic (AFLP and plastid DNA) relationships reflecting the previously detected divergence governed by differences in ploidy level or is there evidence of across-cytotype gene flow in areas of immediate co-occurrence of diploid and tetraploid V. uliginosum s. lat. Is there evidence for ecological or morphological separation between cytotypes? More specifically, do flower characters allow for a better discrimination between cytotypes than the previously used leaf characters? We show that mixed-ploidy sites are clearly secondary contact zones as based on AFLP markers individuals cluster by cytotype and not by locality. We found no evidence for recent hybridization across the ploidy boundary but the intersection of biparentally inherited recombining AFLPs with maternally inherited plastid DNA sequences provided strong evidence for past inter-ploidy gene flow as one fifth of the tetraploids exhibited a haplotype otherwise restricted to diploids. Further, small-scale ecological segregation between cytotypes was weak, but statistically significant. Leaf characters indicated little differentiation between the cytotypes, whereas flower characters had relatively strong discriminative power. Overall, from a taxonomic point of view, we suggest that clear genome-wide differentiation combined with consistent morphological differences in flower size do not preclude the differentiation of two distinct species (diploid V. gaultherioides and tetraploid V. uliginosum s. str.).
A Collective Model of Female Labour Supply: Evidence for East and West German Women

Using data from the German Socio-Economic Panel for the years 1999–2004, I estimate a collective discrete choice model of female labour supply that takes into account non-participation and that uses a detailed microsimulation model of the German tax and transfer system. My goal is to infer the share of household net income that women in couples receive for their individual consumption. In particular, I study whether the allocation within the household is more egalitarian for women who grew up in the former East Germany. The key identifying assumption is that some (but not all) parameters of the utility function are identical for single women and women in couples. I find that women receive about 67 percent of household net income for their individual consumption. There is no statistically significant difference with respect to the woman’s origin.
Laptops in the classroom

This paper focuses on a rapidly growing population on many college and university campuses today: students equipped with laptop computers capable of wireless connection to the Internet. More and more departments are requiring that students enrolling in certain courses bring laptop computers to class. The course instructor is sometimes overwhelmed by the prospect, asking "How should I change my lesson plan in order to integrate the laptop in my daily lecture? " This paper first describes the effort to bring the laptop to campus at a number of colleges and universities around the country. It then offers concrete suggestions based on the experiences of several professors who either have taught, or are now teaching, laptop-required courses. The paper concludes with a summary and a look toward the future.
THERMAL AND OPTICAL CHARACTERIZATION OF A GNSS RETROREFLECTOR ARRAY AT THE SCF_LAB

SCF_LAB. A. Boni, S. Dell’Agnello, C. Cantone, E. Ciocci, S. Contessa, G. O. Delle Monache, C. Lops, M. Martini, C. Mondaini, G. Patrizi, L. Porcelli, M. Tibuzzi, P. Tuscano, L. Salvatori, R. Vittori, G. Bianco, Laboratori Nazionali di Frascati (LNF) dell’INFN, Frascati (Rome), Italy, alessandro.boni@lnf.infn. it Aeronautica Militare Italiana (AMI) and Italian Ministry of Foreign Affairs, Embassy of Italy, 3000 Whiteheaven St. NW, Washington, DC 20008, roberto.vittori@esteri.it, ASI, Centro di Geodesia Spaziale “G. Colombo” (CGS), Matera, Italy, giuseppe.bianco@asi.it.
Online identification and control of a DC motor using learning adaptation of neural networks

This paper tackles the problem of the speed control of a DC motor in a very general sense. Use is made of the power of feedforward artificial neural networks to capture and emulate detailed nonlinear mappings, in order to implement a full nonlinear control law. The random training for the neural networks is accomplished online, which enables better absorption of system uncertainties into the neural controller. An adaptive learning algorithm, which attempts to keep the learning rate as large as possible while maintaining the stability of the learning process is proposed. This simplifies the learning algorithm in terms of computation time, which is of special importance in real-time implementation. The effectiveness of the control topologies with the proposed adaptive learning algorithm is demonstrated. It is found that the proposed adaptive leaning mechanism accelerates training speed. Promising results have also been observed when the neural controller is trained in an environment contaminated with noise.