Miracle-dz/UniFAR-FTUs huggingface dataset:
Examining Changes in Filter Question (FQ) Reporting in the Consumer Expenditure Quarterly Interview Survey

The survey design involving a screener or filter question followed by a series of more detailed questions is used in many surveys. A concern in these surveys is that respondents may learn that reporting a certain answer to a filter question will extend the interview through a series of follow-up questions and thus will alter their responses in a way that avoids their exposure to follow-up questions. Alternatively, the cumulative cognitive burden experienced by respondents after answering many survey questions may be a principal factor in respondent satisficing, and thus measurement error, toward the end of surveys. The exploratory research described in this paper identifies changes in response patterns to filter questions in the Consumer Expenditure Quarterly Interview Survey (CEQ). A model is used to examine the role of respondent characteristics and interview burden measures on declining filter question endorsement rates. Aside from interview content, this research found that respondent characteristics impacting cognitive burden had effects on a declining trend in filter question endorsement rates.
VegaMC: a R/bioconductor package for fast downstream analysis of large array comparative genomic hybridization datasets

SUMMARY Identification of genetic alterations of tumor cells has become a common method to detect the genes involved in development and progression of cancer. In order to detect driver genes, several samples need to be simultaneously analyzed. The Cancer Genome Atlas (TCGA) project provides access to a large amount of data for several cancer types. TGCA is an invaluable source of information, but analysis of this huge dataset possess important computational problems in terms of memory and execution times. Here, we present a R/package, called VegaMC (Vega multi-channel), that enables fast and efficient detection of significant recurrent copy number alterations in very large datasets. VegaMC is integrated with the output of the common tools that convert allele signal intensities in log R ratio and B allele frequency. It also enables the detection of loss of heterozigosity and provides in output two web pages allowing a rapid and easy navigation of the aberrant genes. Synthetic data and real datasets are used for quantitative and qualitative evaluation purposes. In particular, we demonstrate the ability of VegaMC on two large TGCA datasets: colon adenocarcinoma and glioblastoma multiforme. For both the datasets, we provide the list of aberrant genes which contain previously validated genes and can be used as basis for further investigations. AVAILABILITY VegaMC is a R/Bioconductor Package, available at http://bioconductor.org/packages/release/bioc/html/VegaMC.html. CONTACT morganella@unisannio.it SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
High-resolution spectroscopy of the low-mass X-ray binary EXO 0748-67

We present initial results from observations of the low-mass X-ray binary EXO 0748-67 with the Reflection Grating Spectrometer on board the XMM-Newton Observatory. The spectra exhibit discrete structure due to absorption and emission from ionized neon, oxygen, and nitrogen. We use the quantitative constraints imposed by the spectral features to develop an empirical model of the circumsource material. This consists of a thickened accretion disk with emission and absorption in the plasma orbiting high above the binary plane. This model presents challenges to current theories of accretion in X-ray binary systems.
On the vulnerability of a Group Key Transfer protocol based on secret sharing

Group Key Transfer (GKT) protocols allow multiple parties to share a common secret key: a trusted entity selects a private key and securely distributes it to the qualified participants. Hsu et al. introduced a GKT protocol based on secret sharing, which they claimed to be secure. Unlike their affirmation, we report a vulnerability: an insider can cancel key consistency such that at the end of the protocol distinct users own different keys. This leads to the futility of the protocol. Even more, the attacker is able to choose the values of the injected keys on his own wish. Finally, we propose a simple and efficient countermeasure that stands against the revealed attack.
Activity recognition from accelerometer signals based on Wavelet-AR model

In this paper, a new Wavelet-AR feature for activity recognition from a tri-axial acceleration signals has been proposed. We use Wavelet Transform to decompose the raw accelerometer signals and obtain the decomposed signals that can efficiently discriminate the different activities. After that we build autoregressive (AR) model for the decomposed signals and extract the AR coefficients as features for activity recognition. As a consequence, Multi-class Support Vector Machines is used to distinguish different human activities. The average recognition results for four activities using the proposed Wavelet-AR features are 95.45%, which are better than traditional features. The results show that Wavelet-AR feature obvious discriminates different human activities and it can be extract as an effective feature for the recognition of accelerometer data.
Association Between Helicobacter pylori cagA, babA2 Virulence Factors and Gastric Mucosal Interleukin-33 mRNA Expression and Clinical Outcomes in Dyspeptic Patients

Helicobacter pylori (H. pylori) infection has been reported in more than half of the world human population. It is associated with gastric inflammation and noticeable infiltration of the immune cells to the stomach mucosa by several cytokines secretion. IL-1β, IL-18 have been shown to contribute to H. pylori induced gastritis, but the details of inflammation and association of virulence factors remain unclear. IL-1 cytokine family has a new additional cytokine, Interleukin-33 (IL-33), which is contemplated to have an important role for host defense against microorganisms. H. pylori virulence factors important in gastritis risk are the cag pathogenicity island (cag-PAI) and babA. This study evaluated IL-33 mucosal mRNA expression levels in infected and uninfected patients and its relationship with bacterial virulence factors cagA, babA2 and type of gastritis. Total RNA was extracted from gastric biopsies of 79 H. pylori-infected patients and 51 H. pylori-negative patients. Mucosal IL-33 mRNA expression levels in gastric biopsies were assessed using real-time PCR. Existence of virulence factors were detected by PCR. IL-33 mRNA expression was significantly higher in biopsies of H. pylori-infected patients compared to H. pylori-uninfected patients (P<0.0001). Also there was a direct relationship between virulence factor bab-A2 and enhancement in IL-33 mRNA expression. Furthermore, IL-33 mRNA expression level was significantly lower in chronic gastritis patients compared with patients with active gastritis (P<0.001). IL-33 may play a crucial role in the inflammatory response and induction of the chronic gastritis and severity of inflammatory changes in the gastric mucosa.
Dark energy as a modification of the Friedmann equation

Dark energy could actually be the manifestation of a modification to the Friendmann equation arising from new physics (e.g., extra dimensions). Writing the correction as (1 - {Omega}{sub M})H{sup {alpha}}/H{sub 0}{sup {alpha}-2}, they explore the phenomenology and detectability of such. They show that: (1) {alpha} must be {approx} z >> 1) and w = -1 in the distant future and can mimic w < -1 without violating the weak-energy condition; (3) w{sub eff} changes, dz/dw|{sub z {approx} 0.5} {approx} {Omicron}(0.2), which is likely detectable; and (4) a future supernova experiment like SNAP that can determine w with precision {sigma}{sub w}, could determine {alpha} to precision {sigma}{sub {alpha}} {approx} 2{sigma}{sub w}.
Adaptive Kalman filter for orientation estimation in micro-sensor motion capture

One of the biggest challenges in micro-sensor motion capture is the drift problem caused by integration of angular rates to obtain orientation estimation. To reduce the drift, existing algorithms make use of gravity and earth magnetic field measured by accelerometers and magnetometers. Unfortunately, the gravity measurement can be strongly interfered by human motion acceleration. This paper presents a quaternion-based adaptive Kalman filter for drift-free orientation estimation. In this filter, the motion acceleration associated with the quaternion is included in the state vector, to compensate the effects which human body motion may have on the reliability of gravity measurement. Moreover, the process noise covariance is adapted based on the variation of sensor signals, to optimize the performance under human motion existence. The final experiments show that the proposed algorithm has least error compared with the existing methods, and the use of motion acceleration compensation together with the adaptive mechanism can improve the accuracy of human motion capture.
A large field of view two-photon mesoscope with subcellular resolution for in vivo imaging

Imaging is used to map activity across populations of neurons. Microscopes with cellular resolution have small (< 1 millimeter) fields of view and cannot simultaneously image activity distributed across multiple brain areas. Typical large field of view microscopes do not resolve single cells, especially in the axial dimension. We developed a 2-photon random access mesoscope (2p-RAM) that allows high-resolution imaging anywhere within a volume spanning multiple brain areas (Ø 5 mm × 1 mm cylinder). 2p-RAM resolution is near diffraction limited (lateral, 0.66 μm, axial 4.09 μm at the center; excitation wavelength = 970 nm; numerical aperture = 0.6) over a large range of excitation wavelengths. A fast threedimensional scanning system allows efficient sampling of neural activity in arbitrary regions of interest across the entire imaging volume. We illustrate the use of the 2p-RAM by imaging neural activity in multiple, non-contiguous brain areas in transgenic mice expressing protein calcium sensors.
The extremal process of two-speed branching Brownian motion

We construct and describe the extremal process for variable speed branching Brownian motion, studied recently by Fang and Zeitouni, for the case of piecewise constant speeds; in fact for simplicity we concentrate on the case when the speed is $\sigma_1$ for $s\leq bt$ and $\sigma_2$ when $bt\leq s\leq t$. In the case $\sigma_1>\sigma_2$, the process is the concatenation of two BBM extremal processes, as expected. In the case $\sigma_1<\sigma_2$, a new family of cluster point processes arises, that are similar, but distinctively different from the BBM process. Our proofs follow the strategy of Arguin, Bovier, and Kistler.
Towards holistic chromatic intelligent monitoring of complex systems

Purpose – To introduce chromatic methods and their application to monitoring complex systems.Design/methodology/approach – Complexity is introduced and the need for holistic monitoring is suggested. The chromatic methodologies are introduced and their broad‐ranging applicability is illustrated with reference to a number of diverse applications.Findings – The generic chromatic monitoring technology has much to offer as a way of visualising, understanding, monitoring and controlling complex processes and system behaviour.Practical implications – The technology is leading to new ways of characterising and evaluating aspects of system behaviour, in a holistic and non‐intrusive manner, that are difficult to measure – e.g. walking behaviour of the elderly, tiredness of drivers.Originality/value – Draws out the generic basis of the chromatic methodology and how it may be applied to a wide range of complex systems and situations to predict when and where human intervention is required.
Switch from αvβ5 to αvβ6 integrin expression protects squamous cell carcinomas from anoikis

Stratified squamous epithelia express the αvβ5 integrin, but in squamous cell carcinomas (SCCs) there is down-regulation of αvβ5 and up-regulation of αvβ6. To investigate the significance of this finding, we transduced an αv-negative human SCC line with retroviral vectors encoding αv integrins. αvβ5-expressing cells underwent suspension-induced apoptosis (anoikis), whereas αv-negative cells and cells expressing αvβ6 did not. Resistance to anoikis correlated with PKB/Akt activation in suspension, but not with changes in PTEN or p110α PI3 kinase levels. Anoikis was induced in parental and αvβ6-expressing cells by inhibiting PI3 kinase. Conversely, activation of Akt or inhibition of caspases in αvβ5-expressing cells suppressed anoikis. Caspase inhibition resulted in increased phosphoAkt, placing caspase activation upstream of decreased Akt activation. Anoikis required the cytoplasmic domain of β5 and was independent of the death receptor pathway. These results suggest that down-regulation of αvβ5 through up-regulation of αvβ6 may protect SCCs from anoikis by activating an Akt survival signal.
Analysing Emotions in Social Media Coverage on Paris Terror Attacks: a Pilot Study

—Social media provide an increasingly used platform for crisis communication. Governments need to understand how publics consume and react to crisis information via social media. One option to do this is by applying emotion analysis. In this pilot study, we target the November 2015 terrorist attacks in Paris as a case study for emotion analysis and detection. We constructed a Dutch Facebook corpus manually annotated with i) Ekman’s basic emotions and ii) irony use. The annotations reveal that anger is the most recurrent emotion, however the basic emotions do not cover all emotions in the dataset. The corpus also exhibits a fair number of ironic utterances, mostly expressing emotions like disgust and anger . The experimental results show that the detection of some emotions (e.g., fear ) is challenging compared to others and that the classiﬁer suffers from data sparseness.
Map aided SLAM in neighbourhood environments

Robust and accurate localization is a very important issue for the application of smart vehicles in neighbourhood environments such as theme parks, industrial estates, university campuses, etc. Conventional and classical approaches based on global positioning system (GPS) when used in closed spaces like neighbourhood environments pose problems due to signal blockages and multiple path effects. Feature based localization techniques suffer from feature detection failures, especially when features are sparse or not recognisable. Dead reckoning and inertial methods have to deal with the problem of drift in the sensors to be able to localize reliably over long periods of operation. To localize a vehicle reliably, robustly and accurately, a framework that enables the fusion of the different localization techniques is thus required, for this purpose, a road network topology constrained unified localization scheme is proposed based on the general Bayesian probabilistic estimation theoretic framework. The experimental results obtained from a vehicle driven in a large neighbourhood environment are presented to demonstrate the effectiveness of the proposed methodology.
First Structure Formation: A Simulation of Small-Scale Structure at High Redshift

We describe the results of a simulation of collisionless cold dark matter in a ΛCDM universe to examine the properties of objects collapsing at high redshift (z = 10). We analyze the halos that form at these early times in this simulation and find that the results are similar to those of simulations of large-scale structure formation at low redshift. In particular, we consider halo properties such as the mass function, density profile, halo shape, spin parameter, and angular momentum alignment with the minor axis. By understanding the properties of small-scale structure formation at high redshift, we can better understand the nature of the first structures in the universe, such as Population III stars.
Re-inventing invention: new tendencies in capitalist commodification

Abstract This paper argues that a shift is taking place in the fabric of capitalism as a result of a change in how the business of invention is understood. Using theoretical approaches that rely on the notion that capitalism increasingly tries to draw in the whole intellect, in the first part of the paper I argue that the new understanding of innovation currently shows up as three associated developments: as the mobilization of forethought, as the deepening of the lure of the commodity through the co-creation of commodities with consumers, and as the construction of different kinds of apparently more innovative space suffused with information technology. The second part of the paper then argues that these disclosures are leading to new forms of value, based on generating moments of rightness. There is a brief conclusion.
Using Non-Photorealistic Rendering to Communicate Shape

The advent of photography and computer graphics has not replaced artists. Imagery generated by artists provides information about objects that may not be readily apparent in photographs or real life. The same goal should apply to computer-generated images. This is the driving force behind non-photorealistic rendering. The term non-photorealistic rendering (NPR) is applied to imagery that looks as though it was made by artists, such as pen-and-ink or watercolor. Many computer graphics researchers are exploring NPR techniques as an alternative to photorealistic rendering. More importantly, non-photorealistic rendering is now being acknowledged for its ability to communicate the shape and structure of complex models. Techniques which have long been used by artists can emphasize specific features, expose subtle shape attributes, omit extraneous information, and convey material properties. These artistic techniques are the result of an evolutionary process, refined over centuries. Therefore, imitating some of these artistic methods and exploring the perceptual psychology behind the techniques of artists are good first steps in going beyond photorealistic rendering. In these notes, we have gathered the ideas and methods from our previous work [11, 12, 13] to demonstrate how non-photorealistic rendering methods can be used to convey a more accurate representation of the shape and material properties of objects than traditional computer graphics methods. In order to demonstrate how non-photorealistic rendering can be used to communicate shape, we have explored computer-generated technical illustrations.
Low SNR Speech Recognition using SMKL

While traditional speech recognition methods have achieved great success in a number of real word applications, their further applications to some difficult situations, such as Signal-to-Noise Ratio (SNR) signal and local languages, are still limited by their shortcomings in adaption ability. In particular, their robustness to pronunciation level noise is not satisfied enough. To overcome these limitations, in this paper, we propose a novel speech recognition approach for low signal-to-noise ratio signal. The general steps for our speech recognition approach are composed of signal preprocessing, feature extraction and recognition with simple multiple kernel learning (SMKL) method. Then the application of SMKL in speech recognition with low SNR is presented. We evaluate the proposed approach over a standard data set. The experimental results show that the performance of SMKL method for low SNR speech recognition is significantly higher than that of the method based on other popular approaches. Further, SMKL based method can be straightforwardly applied to recognition problem of large scale dataset, high dimension data, and a large amount of isomerism information.
Object Localization Based on Global Structure Constraint Model and Optimal Algorithm

We present a novel object localization approach based on the global structure constraint model (GSC) and optimal algorithm. In GSC, Objects are described as constellations of points satisfied with their specific global structure constraints. The spatial relations among all the patches having stable color information and their representative color information around patches are encoded. Then, the searching algorithm, i.e. genetic algorithm, is used to locate target objects in images by finding out the exactly matched position. In the experiment, we tested the approach on a collection of human face images and the results demonstrated the approach is simple, effective and efficient.
BCI competition 2003-data set IV:An algorithm based on CSSD and FDA for classifying single-trial EEG

This paper presents an algorithm for classifying single-trial electroencephalogram (EEG) during the preparation of self-paced tapping. It combines common spatial subspace decomposition with Fisher discriminant analysis to extract features from multichannel EEG. Three features are obtained based on Bereitschaftspotential and event-related desynchronization. Finally, a perceptron neural network is trained as the classifier. This algorithm was applied to the data set of "BCI Competition 2003" with a classification accuracy of 84% on the test set.
Investigating "Locality" of Intra-Urban Spatial Interactions in New York City Using Foursquare Data

Thanks to the increasing popularity of location-based social networks, a large amount of user-generated geo-referenced check-in data is now available, and such check-in data is becoming a new data source in the study of mobility and travel. Conventionally, spatial interactions between places were measured based on the trips made between them. This paper empirically investigates the use of social media data (i.e., Foursquare data) to study the “locality” of such intra-urban spatial interactions in New York City, and specifically: (i) the level of “locality” of spatial interactions; (ii) the impacts of personal characteristics on “locality” of spatial interaction and finally; (iii) the heterogeneity in spatial distribution of “local” interactions. The results of this study indicate that: (1) spatial interactions show a high degree of locality; (2) gender does not have a considerable impact on the locality of spatial interactions and finally; (3) “local” interactions likely cluster in some places within the research city.
Involving generator capability curves in optimal power flow

The results of optimal power flow that can be directly applied into the system are the desire of every power system operator, especially in operating the competitive electric power system. But traditionally, optimization problem in the electric power does not involve generator capability curves so the results are very possible able to violate those curves. If the results of the optimization violate the constraints of the system, operators need to adjust these results again until all constraints have been met. It should be noted that the workings to adjust those results are something very difficult to be done by the operators. This paper sought to involve the generator capability curves into the optimization problem on Interior Point Method with Predictor-Corrector and Reduction Technique. This is a perfect optimization method, so the results are more efficiently and effectively, and can directly be applied into system. Numerical example for the three buses show that the economic dispatch results has the lowest total cost but 4 constraints were violated. The optimization results without the generator capability curves do not cause problems to the network but the capability curve of generator 1 is violated. Both these results need to be adjusted again before they are applied into the system. While the optimization results with capability curves have met all the constraints and can be directly applied into the system.