Miracle-dz/UniFAR-FTUs huggingface dataset:
MMSE DFE for high-rate MIMO transmission over channels with ISI

Minimum mean-squared error decision-feedback equalisation (DFE) for joint spatial/temporal equalisation is derived. No finite length assumptions are made, but asymptotic results, i.e., ultimate limits, are studied. The performance of MMSE DFE for transmission over MIMO ISI channels is assessed by means of numerical simulations for typical high-rate wireless scenarios.
The effect of a constructivist learning environment for field-dependent/independent students on achievement in introductory computer programming

Research was conducted to determine the effect of a constructivist learning environment upon field dependent (FD) and field independent (FI) students' achievement in an introductory computer programming course. Prior research in traditional environments had established a correlation between field dependence / independence (FD/I) and the design stage of programming. A correlational design was followed, using introductory computer science students in their first programming course. A pretest of BASIC programming ability was given to assess baseline proficiency. The Group Embedded Figures Test (GEFT) was given to assess each subject's measure of FD/I. A constructivist model for learning programming was implemented. Achievement tests in two of the stages of programming, design and coding were administered. As additional dependent variables, student construction of programming elements — the design (semantic) elements, and the language-specific coding (syntax) elements - were collected in a portfolio and examined for both qualitative and quantitative differences. No significant correlations were found between FD/I and the achievement scores of design and coding. No correlation was expected between FD/I and coding. There was no interaction between FD/I and design versus coding. The correlation between FD/I and the quantity of reconstructed programming elements was not significant. The correlation between FD/I and the quality of reconstructed programming elements was not significant. No interaction was found between FD/I and the quantity or quality of semantic versus syntactic elements. Stepwise multiple regression identified two predictors, for design, the predictor was the pretest. Coding was predicted by the quantity of syntactic programming elements. The principal finding of this research, in contrast to findings in traditional environments is that within this constructivist environment, the cognitive style of FD/I was not found to influence programming achievement.
A Toolkit on Mobile Apps for Business Growth: Insights from Design and Development

This paper shares the experiences of creating a toolkit for women microentrepreneurs in India. The toolkit is designed as a self-learning resource that introduces mobile applications for use in micro-businesses owned by women. Porter's value chain framework was used to select the mobile apps. Each application was then explained in a form of a story that is derived from the context of potential users. The color and text of the content was selected to appeal to the women with low literacy rates. This final toolkit is an open source resource that can be used freely by anyone for non-commercial usage. The toolkit has potential to be adopted in training programmes for the women microentrepreneurs.
IAN: Interference-aware routing geometry on proximity for cognitive radio networks

In this paper, we propose the interference-aware routing geometry algorithm that can approximate the interference percentage of the multiple overlapping transmissions between primary network and secondary network in cognitive radio networks for balancing routing costs. This algorithm allows selected routes that are corresponding to the fair costs for routing performance in cognitive networks. We also utilize the cumulant of the aggregate interference as the interference model in order to analyze the impact of overlapping interference in terms of varying distances in the network. Simulation results demonstrate the efficiency of our proposed algorithm and distance-based analysis in CRNs.
Investigation of In Vivo Targeting Kinetics of αvβ3-Specific Superparamagnetic Nanoprobes by Time-Resolved MRI

Nanoparticulate imaging probes have become an increasingly important arsenal in the visualization of molecular markers for early diagnosis and post-therapy assessment of diseases. Surface functionalization of these nanoparticles has led to the development of a variety of targeted nanoprobes for various imaging modalities (e.g. PET, MRI, optical). Despite these advances, detailed understanding of the nanoparticle targeting kinetics, particularly at the early time points immediately after injection, is still lacking. In this study, we report the combination of a T2*-weighted time-resolved-MRI (TR-MRI) method with ultra-sensitive superparamagnetic polymeric micelle (SPPM) nanoprobes to quantify the targeting kinetics of cyclic (RGDfK) (cRGD)-encoded SPPM to angiogenic endothelium in subcutaneous human tumor xenograft models in mice. TR-MRI analyses of the αvβ3-targeted and non-targeted SPPMs allowed for the subtraction of blood volume and extravascular signal components from the cRGD-SPPM data, resulting in a specific measurement of the accumulation kinetics of nanoprobes in lung, breast and brain cancer preclinical models. In all three models, αvβ3-specific accumulation of SPPM nanoprobes was observed in the first 5 mins after intravenous injection (first order rate constants were in the range of 0.22-0.24 min-1). Similar αvβ3-targeting kinetics was observed for cRGD-SPPM nanoprobes in different tumor xenograft models, consistent with the targeting of mouse angiogenic endothelium despite tumor inoculation from different human cancer cell lines. Results from this study offer new opportunities in the quantitative characterization of the targeting kinetics of cancer-specific nanoparticles to their intended biological targets in an intact animal, which provides fundamental insights on molecular recognition processes in vivo for further development of these nanoprobes.
User experience evaluation with a Wizard of Oz approach: Technical and methodological considerations

User experience evaluation in human-robot interaction is most often an expensive and difficult task. To allow the evaluation of various factors and aspects of user experience, a fully functional (humanoid) robot is recommended. This work presents technical and methodological considerations on the applicability of the Wizard of Oz (WOz) approach to enable user experience evaluation in the field of Human-Robot Interaction. We briefly describe the technical aspects of the setup, the applicability of the method, and a first case study using this methodological approach to gain an early understanding of the user experience factors that are important for the development of a human-humanoid interaction scenario.
The impact of T‐TREC‐retrieved wind and radial velocity data assimilation using EnKF and effects of assimilation window on the analysis and prediction of Typhoon Jangmi (2008)

This study examines the relative impact of assimilating T‐TREC‐retrieved winds (VTREC) versus radial velocity (Vr) on the analysis and forecast of Typhoon Jangmi (2008) using an ensemble Kalman filter (EnKF). The VTREC and Vr data at 30 min intervals are assimilated into the ARPS model at 3 km grid spacing over four different assimilation windows that cover, respectively, 0000–0200, 0200–0400, 0400–0600, and 0000–0600 UTC, 28 September 2008. The assimilation of VTREC data produces better analyses of the typhoon structure and intensity than the assimilation of Vr data during the earlier assimilation windows, but during the later assimilation windows when the coverage of Vr data on the typhoon from four Doppler radars is much improved, the assimilation of Vr outperforms VTREC data. The combination of VTREC and Vr data, either by assimilating both VTREC and Vr data in all cycles or by assimilating VTREC in the first cycle and Vr in the remaining cycles (labeled VTFVR), further improves the analyses of the typhoon structure and intensity compared to assimilating VTREC or Vr data alone. Quantitative verifications of 24 h forecasts of the typhoon show that the VTFVR assimilation experiments produces forecasts that best match the best track data and also have the highest precipitation prediction skills. The track forecast errors in experiment that assimilate VTREC data through the later cycles are the largest. The behaviors are discussed based on the coverage, information content, and accuracy of the various forms of data.
A Bloom Filter Bank Based Hash Table for High Speed Packet Processing

The use of hash tables in high speed packet processing applications is widely adopted and many different variants of such structures have been proposed in the literature. Nonetheless, the extremely variegated scenarios of application call for new specialized solutions, to best satisfy their possibly peculiar requirements. In this paper, we propose a randomized approach to the design of compact hash tables for network applications that need to frequently store and access short valued data. The approach is based on the use of a bank of Bloom filters and proves to be very effective both in terms of space occupancy and complexity, at the obvious cost of a non zero error probability.
The Power of Political Image: Justin Trudeau, Instagram, and Celebrity Politics

This article explores dynamics of online image management and its impact on leadership in a context of digital permanent campaigning and celebrity politics in Canada. Recent studies have shown that images can play a critical role when members of the public are evaluating politicians. Specifically, voters are looking for specific qualities in political leaders, including honesty, intelligence, friendliness, sincerity, and trustworthiness, when making electoral decisions. Image management techniques can help create the impression that politicians possess these qualities. Heads of governments using social media to capture attention through impactful images or videos on an almost daily basis seems like a new norm. Specifically, this article takes interest in Justin Trudeau’s use of Instagram during the first year immediately following his election on October 19, 2015. Through a hybrid quantitative and qualitative approach, we examine how Trudeau and his party convey a specific image to voters in a context of permanent and increasingly personalized campaigning. We do so through an analysis of his Instagram feed focusing on different elements, including how he frames his governing style visually, how his personal life is used on his Instagram to support the Liberal Party of Canada’s values and ideas, and how celebrity culture codes are mobilized to discuss policy issues such as environment, youth, and technology. This analysis sheds light on the effects and implications of image management in Canada. More generally, it offers a much-needed look at image-based e-politicking and contributes to the academic literature on social media, permanent campaigning, as well as celebrity and politics in Canada.
Seasonal Characterization of Solar Radiation Estimates Obtained from a MSG-SEVIRI-Derived Dataset and a RAMS-Based Operational Forecasting System over the Western Mediterranean Coast

Solar radiation is a key factor in the Earth’s energy balance and it is used as a crucial input parameter in many disciplines such as ecology, agriculture, solar energy and hydrology. Thus, accurate information of the global downward surface shortwave flux integration into the grid is of significant importance. From the different strategies used for grid integration of the surface solar radiation estimates, satellite-derived and numerical weather prediction forecasts are two interesting alternatives. In the current work, we present a comprehensive evaluation of the global downward solar radiation forecasts provided by the Regional Atmospheric Modeling System (RAMS) and the Downwelling Surface Shortwave Flux (DSSF) product, derived from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Both solar radiation estimates are compared to thirteen ground-based weather station measurements for the winter 2010–2011 and the summer 2011 seasons. For these periods, the most recent versions of RAMS (4.4 and 6.0) were running in parallel within the real-time weather forecasting system implemented over the Valencia Region. The solar radiation performance and accuracy are evaluated for these datasets segmented into two atmospheric conditions (clear and cloudy skies) and two terrain classes (flat and hilly). DSSF shows a very good agreement over the study area. Statistical daily evaluations show that corresponding errors vary between seasons, with absolute bias ranging from −30 to 40 W·m−2, absolute root mean square errors (RMSE) from 25 to 60 W·m−2, relative bias ranging from −11% to 7% and relative RMSE from 7% to 22%, depending on the sky condition and the terrain location as well, thus reproducing the observations more faithfully than RAMS, which produces higher errors in comparison to the measurements. In this regard, statistical daily evaluations show absolute bias values varying from −50 to 160 W·m−2, absolute RMSE from 60 to 240 W·m−2, relative bias ranging from −30% to 40% and relative RMSE from 10% to 80%, also depending on the daily initialization and the forecast horizon. This bias variability demonstrates that there is a different trend in the deviation of the model results in relation to the observations, both for the DSSF product and RAMS forecasts, and considering the summer and the winter seasons independently. In this regard, although there is an overestimation of the observed solar radiation within the summer months, this magnitude is underestimated during the winter. Finally, comparing this solar radiation estimates for different atmospheric conditions and different terrain classes, the best results are found under clear skies over flat terrain. This result is achieved using both methodologies.
Mitigating the obsolescence of specification models of service-based systems

Service-based systems (SBS) must be able to adapt their architectural configurations during runtime in order to keep satisfied their specification models. These models are the result of design time derivation of requirements into precise and verifiable specifications by using the knowledge about the current service offerings. Unfortunately, the design time knowledge may be no longer valid during runtime. Then, nonfunctional constraints may have different numerical meanings at different time even for the same observers. Thus, specification models become obsolete affecting the SBS' capability of detecting requirement violations during runtime and therefore they trigger reconfigurations when appropriated. In order to mitigate the obsolescence of specification models, we propose to specify and verify them using the computing with words (CWW) methodology. First, non-functional properties (NFPs) of functionally-equivalent services are modeled as linguistic variables, whose domains are concepts or linguistic values instead of precise numbers. Second, architects specify at design time their requirements as linguistic decision models (LDMs) using these concepts. Third, during runtime, the CWW engine monitors the requirements satisfaction by the current chosen architectural configuration. And fourth, each time a global concept drift is detected in the NFPs of the services market, the numerical meanings are updated. Our initial results are encouraging, where our approach mitigates effectively and efficiently the obsolescence of the specification models used by SBS to drive their reconfigurations.
Apprentissage par renforcement développemental

L'apprentissage par renforcement permet a un agent d'apprendre un comportement qui n'a jamais ete prealablement defini par l'homme. L'agent decouvre l'environnement et les differentes consequences de ses actions a travers des interactions avec celui-ci : il apprend de sa propre experience, sans avoir de connaissances preetablies des buts ni des effets de ses actions. Cette these s'interesse a la facon dont l'apprentissage profond peut aider l'apprentissage par renforcement a gerer des espaces continus et des environnements ayant de nombreux degres de liberte dans l'optique de resoudre des problemes plus proches de la realite. En effet, les reseaux de neurones ont une bonne capacite de mise a l'echelle et un large pouvoir de representation. Ils rendent possible l'approximation de fonctions sur un espace continu et permettent de s'inscrire dans une approche developpementale necessitant peu de connaissances a priori sur le domaine. Nous cherchons comment reduire l'experience necessaire a l'agent pour atteindre un comportement acceptable. Pour ce faire, nous avons propose le cadre Neural Fitted Actor-Critic qui definit plusieurs algorithmes acteur-critique efficaces en donnees. Nous examinons par quels moyens l'agent peut exploiter pleinement les transitions generees par des comportements precedents en integrant des donnees off-policy dans le cadre propose. Finalement, nous etudions de quelle maniere l'agent peut apprendre plus rapidement en tirant parti du developpement de son corps, en particulier, en procedant par une augmentation progressive de la dimensionnalite de son espace sensorimoteur
An Inverted Saliency Model for Display Enhancement

Visual attention is crucial to a subject’s ability to retrieve information from complex visual stimuli. The order of saccades and fixations as a subject scans a scene determines the mental representation formed from it. It has been shown that image comprehension is significantly improved when a subject is provided with visual cues to guide the gaze path to semantically important regions (Bétrancourt & Tversky, In press). This problem is particularly important in image analysis scenarios involving medical, geological, or remote sensing data where important features might not appear to the untrained eye.
A model driven approach for context-aware services development

Nowadays, context-awareness constitutes an essential aspect for service oriented applications development. Indeed, applications are supposed to react to context changes in order to provide end-users with pertinent services. To deal with such requirement, Context-Aware Service (CAS) has emerged as an underlying software engineering paradigm for context-aware applications development. In this paper, we present a model driven approach that highlights the fundamental context-awareness challenges for CAS development. So, we first present our context metamodel based on a practical specification of the context. Then, we propose a CAS specification and metamodel and show how can Aspect Paradigm (AP) be used to fulfill service adaptation to its execution contexts. We illustrate our approach by an E-tourism motivating scenario.
An Information Theoretic Approach to Constructing Robust Boolean Gene Regulatory Networks

We introduce a class of finite systems models of gene regulatory networks exhibiting behavior of the cell cycle. The network is an extension of a Boolean network model. The system spontaneously cycles through a finite set of internal states, tracking the increase of an external factor such as cell mass, and also exhibits checkpoints in which errors in gene expression levels due to cellular noise are automatically corrected. We present a 7-gene network based on Projective Geometry codes, which can correct, at every given time, one gene expression error. The topology of a network is highly symmetric and requires using only simple Boolean functions that can be synthesized using genes of various organisms. The attractor structure of the Boolean network contains a single cycle attractor. It is the smallest nontrivial network with such high robustness. The methodology allows construction of artificial gene regulatory networks with the number of phases larger than in natural cell cycle.
Subject pool effects in price competition games: students versus professionals

ABSTRACT Questioning the external validity of experiments that rely on student participants is an evergreen theme in experimental economics. Yet, there is ambiguous evidence of potential subject-pool bias. We add to the subject-pool debate by enlarging the set of experiments for which subject-pool differences have been studied. In a duopolistic Bertrand market setup designed to test for collusive behaviour, we test two treatments. The first is a baseline treatment, where participants cannot communicate with each other, while the second is a communication treatment in which participants are allowed to communicate. Each treatment is first conducted with students and then replicated with professionals. Our results show that student subjects and professionals differ significantly. However, these differences manifest themselves in quantitative rather than qualitative terms. Professionals do collude more, but their behavioural difference between treatments is similar. Students are thus a valid surrogate, if the research question is qualitative, but results generated by student samples should be handled with caution, if quantitative differences matter.
2′-O Methylation of the Viral mRNA Cap by West Nile Virus Evades Ifit1-Dependent and -Independent Mechanisms of Host Restriction In Vivo

Prior studies have shown that 2′-O methyltransferase activity of flaviviruses, coronaviruses, and poxviruses promotes viral evasion of Ifit1, an interferon-stimulated innate immune effector protein. Viruses lacking 2′-O methyltransferase activity exhibited attenuation in primary macrophages that was rescued in cells lacking Ifit1 gene expression. Here, we examined the role of Ifit1 in restricting pathogenesis in vivo of wild type WNV (WNV-WT) and a mutant in the NS5 gene (WNV-E218A) lacking 2′-O methylation of the 5′ viral RNA cap. While deletion of Ifit1 had marginal effects on WNV-WT pathogenesis, WNV-E218A showed increased replication in peripheral tissues of Ifit1 −/− mice after subcutaneous infection, yet this failed to correlate with enhanced infection in the brain or lethality. In comparison, WNV-E218A was virulent after intracranial infection as judged by increased infection in different regions of the central nervous system (CNS) and a greater than 16,000-fold decrease in LD50 values in Ifit1 −/− compared to wild type mice. Ex vivo infection experiments revealed cell-type specific differences in the ability of an Ifit1 deficiency to complement the replication defect of WNV-E218A. In particular, WNV-E218A infection was impaired in both wild type and Ifit1 −/− brain microvascular endothelial cells, which are believed to participate in blood-brain barrier (BBB) regulation of virus entry into the CNS. A deficiency of Ifit1 also was associated with increased neuronal death in vivo, which was both cell-intrinsic and mediated by immunopathogenic CD8+ T cells. Our results suggest that virulent strains of WNV have largely evaded the antiviral effects of Ifit1, and viral mutants lacking 2′-O methylation are controlled in vivo by Ifit1-dependent and -independent mechanisms in different cell types.
Liquid crystal nanoparticle formulation as an oral drug delivery system for liver-specific distribution

Liquid crystal nanoparticles have been utilized as an efficient tool for drug delivery with enhanced bioavailability, drug stability, and targeted drug delivery. However, the high energy requirements and the high cost of the liquid crystal preparation have been obstacles to their widespread use in the pharmaceutical industry. In this study, we prepared liquid crystal nanoparticles using a phase-inversion temperature method, which is a uniquely low energy process. Particles prepared with the above method were estimated to be ~100 nm in size and exhibited a lamellar liquid crystal structure with orthorhombic lateral packing. Pharmacokinetic and tissue distribution studies of a hydrophobic peptide-based drug candidate formulated with the liquid crystal nanoparticles showed a five-fold enhancement of bioavailability, sustained release, and liver-specific drug delivery compared to a host–guest complex formulation.