Miracle-dz/UniFAR-FTUs huggingface dataset:
The Influence of (Im)perfect Data Privacy on the Acquisition of Personal Health Data

We investigate the consequences of imperfect data privacy on information acquisition of personal health data. In a game of persuasion with ex-ante symmetric information players decide on whether or not to acquire and reveal information about their personal health status to convince a decision maker to interact. We contrast three institutional settings: automatic dissemination of acquired information, perfect data privacy and imperfect data privacy. Assuming that the ex-ante expected payoff of an interaction with an unknown type for the decision maker is positive, equilibria with complete information acquisition and complete information revelation exist only under perfect and imperfect data privacy. Equilibria without any information acquisition exist under all institutional settings. We test our predictions in a laboratory experiment. Automatic dissemination leads to incomplete information acquisition. Both imperfect and perfect data privacy yield almost complete information acquisition and thus imperfect data privacy does not reduce the amount of acquired information.
Reading between the lines: The activation of background knowledge during text comprehension

This paper presents an overview of the activation of background knowledge during text comprehension. We first review the cognitive processes involved in the activation of inferences during text comprehension, stressing the interaction between text and reader in the construction of situation models. Second, we review evidence for embodied theories of cognition and discuss how this new framework can inform our understanding of the nature and role of background knowledge. We then review the neuropsychological data on the activation of background knowledge during text comprehension. Finally, the paper presents existing and future challenges in researching the role of background knowledge both at a conceptual and a methodological level.
Interactive Knowledge Asset Management: Acquiring and Disseminating Tacit Knowledge

This paper defines and validates a solution to support organizational learning through the interactive evolution of organizational knowledge. The approach includes mechanisms to acquire the experiences of individuals working in different teams and environments, combine them with the existing company's knowledge assets, and disseminate the evolved knowledge to all the project teams. This solution was validated in a controlled experiment involving junior engineers in a training course over a two-year period. The main objective of this experiment was to assess whether the proposed solution provides an effective learning environment to develop quality products. Results show that the approach helps engineers to improve their ability to learn and adopt new practices for software development, obtaining also high levels of quality in the products developed. These improvements are not immediate, however, so the benefits only become evident with time.
Conformal field theory and Loewner-Kufarev evolution

One of the important aspects in recent trends in complex analysis has been the increasing degree of cross-fertilization between the latter and mathematical physics with great benefits to both subjects. Contour dynamics in the complex plane turned to be a meeting point for complex analysts, specialists in stochastic processes, and mathematical physicists. This was stimulated, first of all, by recent progress in understanding structures in the classical and stochastic L\"owner evolutions, and in the Laplacian growth. The Virasoro algebra provides a basic algebraic object in conformal field theory (CFT) so it was not surprising that it turned to play an important role of a structural skeleton for contour dynamics. The present paper is a survey of recent progress in the study of the CFT viewpoint on contour dynamics, in particular, we show how the Witt and Virasoro algebras are related with the stochastic L\"owner and classical L\"owner-Kufarev equations.
Design of embedded system for tracking and locating the patient suffering from Alzheimer's disease

The proposed project is to design a system to track and locate mobile Alzheimer's disease patients who suffer from lapses of memory and concentration. Alzheimer's disease is a type of dementia that causes problems with memory, thinking and behavior and is the most common form of dementia. It is a progressive disease, where dementia symptoms gradually make over a number of years. In these circumstances, tracking and locating a patient becomes very essential. In this paper the patient carries a tracking module that when queried from the caregiver, automatically locates itself by a GPS receiver, and then relays this information to the caregiver via GSM. The system is highly useful in tracking the patient's and informing the caregivers regarding their position.
TCP/IP over IEEE 802.11b WLAN: the Challenge of Harnessing Known-Corrupt Data

The two transport protocols DCCP and UDP-Lite can make use of data that are known to be erroneous, provided that the link layer hands over such data. A similar functionality has been suggested for TCP. In order to investigate the potential of these mechanisms in WiFi networks, we carried out a measurement study where we examined how often information about corrupt data reaches the transport layer when the corruption control at the link layer is disabled. Our results suggest that this may be a rare occurrence in certain scenarios.
NOMAD A Mobile Ad Hoc and Distruption Tolerant Routing Protocol for Tactical Military Networks

There has been a great deal of research in recent years within the general field of mobile ad hoc networks (MANET) with many proposals submitted to the IETF for consideration. Delay or Disruption Tolerant Networking (DTN) is a relatively new field for routing, concerned with networks that experience long transmission delay or periods of disruption. Military forces around the globe have applied one or the other networking paradigm with varying degrees of success to their own problems of mobility at the lower tactical level (Brigade and below). The fundamental reason for this limited success is that many of the desired tactical scenarios at this level require a network that is not exclusively ad hoc nor exclusively disrupted, but rather a network that dynamically adapts to a variety of mobility situations ranging from relatively stable, almost enterprise like, to completely disrupted. Clearly, a routing protocol that is capable of seamlessly transitioning between synchronous and asynchronous modes of operation is needed for tactical (and other) networks. This paper will outline such a protocol, called NOMAD – Native OLSR for Mobile Ad hoc and Disrupted networks.
Language Attitudes in CLIL and Traditional EFL Classes

Schoolchildren are starting to learn a foreign language sooner than ever as education authorities introduce early-start compulsory foreign language (FL) policies. As a result of this global trend, the learning of FLs is playing a major role in many educational systems (Coleman, 2006). This is the context in which CLIL (Content and Language Integrated Learning) programmes have been implemented during the last few years in many different contexts, in the belief that this will help to improve students’ language proficiency and to “nurture a feel good and can do attitude towards language learning in general” (Marsh, 2000: 10). The aim of this paper is to analyze the effect of CLIL on students’ attitudes towards English as a FL and the two official languages (Basque and Spanish) in the curriculum of a bilingual context, namely the Basque Country in Spain. The participants in the study were 287 secondary education students from four different schools and the results obtained seem to confirm that CLIL programmes help to foster positive attitudes towards language learning in general.
INFERENCE AND ERGODICITY IN THE AUTOREGRESSIVE CONDITIONAL ROOT MODEL

In this paper we develop a vector time series model which allows long-term disequilibriums to have epochs of non-stationarity, giving the impression that long term relationships between economic variables have temporarily broken down, before they endogenously collapse back towards their long term relationship. The autoregressive conditional root (ACR) process is shown to be geometrically ergodic, stationary and posess all moments under simple conditions. Furthermore, we establish consistency and asymptotic normality of the maximum likelihood estimators in the ACR model.
Miniaturized slot loaded proximity-coupled microstrip antenna for WLAN

The design of a miniaturized proximity-coupled microstrip antenna (PCMA) is presented here. The antenna has very small size, wide bandwidth and moderate gain and may be used as small, compact antenna for 5GHz band wireless local area network (WLAN). Simulated results using IE3D software are verified by measurement.
Mining Association Rules to Evade Network Intrusion in Network Audit Data

With the growth of hacking and exploiting tools and invention of new ways of intrusion, intrusion detection and prevention is becoming the major challenge in the world of network security. The increasing network traffic and data on Internet is making this task more demanding. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. The false positive rates make it extremely hard to analyse and react to attacks. Intrusion detection systems using data mining approaches make it possible to search patterns and rules in large amount of audit data. In this paper, we represent a model to integrate association rules to intrusion detection to design and implement a network intrusion detection system. Our technique is used to generate attack rules that will detect the attacks in network audit data using anomaly detection. This shows that the modified association rules algorithm is capable of detecting network intrusions. The KDD dataset which is freely available online is used for our experimentation and results are compared. Our intrusion detection system using association rule mining is able to generate attack rules that will detect the attacks in network audit data using anomaly detection, while maintaining a low false positive rate.
Pollen tube growth: where does the energy come from?

This review focuses on the energy metabolism during pollen maturation and tube growth and updates current knowledge. Pollen tube growth is essential for male reproductive success and extremely fast. Therefore, pollen development and tube growth are high energy-demanding processes. During the last years, various publications (including research papers and reviews) emphasize the importance of mitochondrial respiration and fermentation during male gametogenesis and pollen tube elongation. These pathways obviously contribute to satisfy the high energy demand, and there are many studies which suggest that respiration and fermentation are the only pathways to generate the needed energy. Here, we review data which show for the first time that in addition plastidial glycolysis and the balancing of the ATP/NAD(P)H ratio (by malate valves and NAD+ biosynthesis) contribute to satisfy the energy demand during pollen development. Although the importance of energy generation by plastids was discounted during the last years (possibly due to the controversial opinion about their existence in pollen grains and pollen tubes), the available data underline their prime role during pollen maturation and tube growth.
Using the High-Resolution X-Ray Spectrum of PSR B0656+14 to Constrain the Chemical Composition of the Neutron Star Atmosphere

Observations of PSR B0656+14 using the Chandra Low Energy Transmission Grating Spectrometer are presented. The zeroth-order events are pulsed at an amplitude of 10% ± 2%, and the image may be slightly extended. The extended emission is modeled as a Gaussian with a FWHM of about 0. ″75, for a linear size (at a distance of 760 pc) of 8.5 × 1015 cm. In the absence of systematic errors in the detector point-spread function, the extended emission comprises ≲50% of the observed flux in the 0.2-2.0 keV band, for a luminosity of ≲3 × 1032 ergs s-1. The spectrum is well modeled by a dominant blackbody with T = 8.0 ± 0.3 × 105 K and a size of 22.5 ± 2.1 km in addition to a harder component that is modeled as a hotter and much smaller blackbody. No significant absorption features are found in the spectrum that might be expected from ionization edges of H or He or bound-bound transitions of Fe in magnetized atmospheres. Such features are expected to be deep but could vary in position or strength with rotation phase. There are no strong absorption features in the pulse-phased spectra, however, so we conclude that the atmosphere is not dominated by Fe or other heavy elements that would be partially ionized at a temperature of 106 K.
Adaptive 4D MR imaging using navigator‐based respiratory signal for MRI‐guided therapy

For real‐time 3D visualization of respiratory organ motion for MRI‐guided therapy, a new adaptive 4D MR imaging method based on navigator echo and multiple gating windows was developed. This method was designed to acquire a time series of volumetric 3D images of a cyclically moving organ, enabling therapy to be guided by synchronizing the 4D image with the actual organ motion in real time. The proposed method was implemented in an open‐configuration 0.5T clinical MR scanner. To evaluate the feasibility and determine optimal imaging conditions, studies were conducted with a phantom, volunteers, and a patient. In the phantom study the root mean square (RMS) position error in the 4D image of the cyclically moving phantom was 1.9 mm and the imaging time was ≈10 min when the 4D image had six frames. In the patient study, 4D images were successfully acquired under clinical conditions and a liver tumor was discriminated in the series of frames. The image quality was affected by the relations among the encoding direction, the slice orientation, and the direction of motion of the target organ. In conclusion, this study has shown that the proposed method is feasible and capable of providing a real‐time dynamic 3D atlas for surgical navigation with sufficient accuracy and image quality. Magn Reson Med 59:1051–1061, 2008. © 2008 Wiley‐Liss, Inc.
Normal Perceptual Sensitivity Arising From Weakly Reflective Cone Photoreceptors.

PURPOSE To determine the light sensitivity of poorly reflective cones observed in retinas of normal subjects, and to establish a relationship between cone reflectivity and perceptual threshold. METHODS Five subjects (four male, one female) with normal vision were imaged longitudinally (7-26 imaging sessions, representing 82-896 days) using adaptive optics scanning laser ophthalmoscopy (AOSLO) to monitor cone reflectance. Ten cones with unusually low reflectivity, as well as 10 normally reflective cones serving as controls, were targeted for perceptual testing. Cone-sized stimuli were delivered to the targeted cones and luminance increment thresholds were quantified. Thresholds were measured three to five times per session for each cone in the 10 pairs, all located 2.2 to 3.3° from the center of gaze. RESULTS Compared with other cones in the same retinal area, three of 10 monitored dark cones were persistently poorly reflective, while seven occasionally manifested normal reflectance. Tested psychophysically, all 10 dark cones had thresholds comparable with those from normally reflecting cones measured concurrently (P = 0.49). The variation observed in dark cone thresholds also matched the wide variation seen in a large population (n = 56 cone pairs, six subjects) of normal cones; in the latter, no correlation was found between cone reflectivity and threshold (P = 0.0502). CONCLUSIONS Low cone reflectance cannot be used as a reliable indicator of cone sensitivity to light in normal retinas. To improve assessment of early retinal pathology, other diagnostic criteria should be employed along with imaging and cone-based microperimetry.
Production and Propagation of Cosmic-Ray Positrons and Electrons

We have made a new calculation of the cosmic-ray secondary positron spectrum using a diffusive halo model for Galactic cosmic-ray propagation. The code computes self-consistently the spectra of primary and secondary nucleons, primary electrons, and secondary positrons and electrons. The models are first adjusted to agree with the observed cosmic-ray boron/carbon ratio, and the interstellar proton and helium spectra are then computed; these spectra are used to obtain the source function for the secondary positrons/electrons that are finally propagated with the same model parameters. The primary electron spectrum is evaluated, again using the same model. Fragmentation and energy losses are computed using realistic distributions for the interstellar gas and radiation fields, and diffusive reacceleration is also incorporated. Our study includes a critical reevaluation of the secondary decay calculation for positrons. The predicted positron fraction is in good agreement with the measurements up to 10 GeV, beyond which the observed flux is higher than that calculated. Since the positron fraction is now measured accurately in the 1-10 GeV range, our primary electron spectrum should be a good estimate of the true interstellar spectrum in this range, of interest for gamma-ray and solar modulation studies. We further show that a harder interstellar nucleon spectrum, similar to that suggested to explain EGRET diffuse Galactic gamma-ray observations above 1 GeV, can reproduce the positron observations above 10 GeV without requiring a primary positron component.
Dodecamer Structure of Severe Acute Respiratory Syndrome Coronavirus Nonstructural Protein nsp10

ABSTRACT The severe acute respiratory syndrome coronavirus (SARS-CoV) nonstructural proteins nsp1 to nsp16 have been implicated by genetic analysis in the assembly of a functional replication/transcription complex. We report the crystal structure of nsp10 from SARS-CoV at 2.1-Å resolution. The nsp10 structure has a novel fold, and 12 identical subunits assemble to form a unique spherical dodecameric architecture. Two zinc fingers have been identified from the nsp10 monomer structure with the sequence motifs C-(X)2-C-(X)5-H-(X)6-C and C-(X)2-C-(X)7-C-(X)-C. The nsp10 crystal structure is the first of a new class of zinc finger protein three-dimensional structures to be revealed experimentally. The zinc finger sequence motifs are conserved among all three coronavirus antigenic groups, implicating an essential function for nsp10 in all coronaviruses. Based on the structure, we propose that nsp10 is a transcription factor for coronavirus replication/transcription.
Improving translation initiation site and stop codon recognition by using more than two classes

MOTIVATION The recognition of translation initiation sites and stop codons is a fundamental part of any gene recognition program. Currently, the most successful methods use powerful classifiers, such as support vector machines with various string kernels. These methods all use two classes, one of positive instances and another one of negative instances that are constructed using sequences from the whole genome. However, the features of the negative sequences differ depending on the position of the negative samples in the gene. There are differences depending on whether they are from exons, introns, intergenic regions or any other functional part of the genome. Thus, the positive class is fairly homogeneous, as all its sequences come from the same part of the gene, but the negative class is composed of different instances. The classifier suffers from this problem. In this article, we propose the training of different classifiers with different negative, more homogeneous, classes and the combination of these classifiers for improved accuracy. RESULTS The proposed method achieves better accuracy than the best state-of-the-art method, both in terms of the geometric mean of the specificity and sensitivity and the area under the receiver operating characteristic and precision recall curves. The method is tested on the whole human genome. The results for recognizing both translation initiation sites and stop codons indicated improvements in the rates of both false-negative results (FN) and false-positive results (FP). On an average, for translation initiation site recognition, the false-negative ratio was reduced by 30.2% and the FP ratio decreased by 10.9%. For stop codon prediction, FP were reduced by 41.4% and FN by 31.7%. AVAILABILITY AND IMPLEMENTATION The source code is licensed under the General Public License and is thus freely available. The datasets and source code can be obtained from http://cib.uco.es/site-recognition. CONTACT npedrajas@uco.es.
A NEW COMPUTER SOFTWARE FOR SIMULATION OF NEURONS

In this paper, a new computer software package ‘Simulron’ for simulation of neurons is introduced. Excitable membranes with voltage-gated ionic channels can be modeled by using the software, and current clamp and voltage clamp experiments can be simulated. The program allows user to determine the ionic channel count and set the rate functions of the channels. If the rate functions are not known, the program enables the user to set steady-state and time constant functions. First-order differential equations used to define dynamics of the gate and membrane potential are solved using forward Euler method of integration with variable time steps. Outputs of the simulations are shown on spreadsheet template allowing flexible data manipulation and can be graphically displayed.
Hybrid constant modulus array

The hybrid implementation of the multistage constant modulus CM array is designed for separating correlated signals. The canceller weights in the cascade structure provide estimates of the direction vectors of the captured signals. These estimates are then used in a parallel implementation of the linearly constraint CM (LCCM) array leading to the hybrid structure. In this paper, the hybrid implementation is extended to a three stage case for separating three correlated signals. The three correlated signals are of equal power and randomly generated. Mean Square Error (MSE) learning curves indicate that the proposed hybrid LCCM algorithm converges faster and has lower MSE than the conventional cascade implementation.
Multisensor data fusion for obstacle detection in automated factory logistics

This paper describes data fusion methodologies for obstacle detection in an automation system based on advanced Automatic Guided Vehicles (AGV), used for automated logistics in modern factories. We present the background of the problem, introducing generic aspects of the system architecture designed to cope with the obstacle detection in automated factory logistics; then, we focus on the system specification for the module responsible of integrating data from different sources and providing a global representation of the environment. Finally, we present a comparative analysis among different strategies of multisensor data fusion compliant with the requirements of the described system, highlighting their advantages and drawbacks.