Miracle-dz/UniFAR-FTUs huggingface dataset:
AIS Electronic Library (AISeL)

Users’ tourism activities are often influenced by the visiting time of Point-of-interests (POIs). Considering the prevalence of location-based social networks(LBSNs) and user-generated-content (UGC), this study tries to propose a novel method to deduce the temporal characteristics for city POIs. To that end, first, we introduce the NLP method to extract tourism-related features for POIs from massive online documents. Then, we analyze the distribution of the tourists’ visiting time for a specific POI by taking each check-in made by an individual visitor as a preference voting. Finally, we propose a learning model based on matrix factorization to derive the temporal characteristics of each tourism feature, which is the fundamental for the model proposed in this work for predicting the feasible visiting time for a POI. Our experimental results on two real-world datasets show that the methods proposed in this work are efficient for discovering the feasible visiting time for city POIs.
Predictive Autoscaling Orchestration for Cloud-native Telecom Microservices

Mobile traffic is dramatically increasing in recent several years with the evolution of mobile network toward 5G era. Network function virtualization (NFV) and cloud computing provide more flexibility and elasticity for mobile networks. The autoscaling orchestrator allows adjusting the number of virtual network functions (VNFs) in a telecom cloud platform corresponding the devices demand (smartphone, IoT, robots,.). However, the adjusting process produces the delay resulting on instance creating to make services available with the enough resource. The predictive mechanisms with workload forecasting enable the improvement in performance of the autoscaling orchestration system. In this paper, we investigate predictive autoscaling in the orchestration system for virtualized mobile networks. We propose a cloud-native approach for stateless telecom services. We also consider and investigate realtime prediction to detect the peak load or burst request of resource. We develop longterm forecasting for periodical resource that detect seasonal workload demand and provide the resource plan to cloud provider. Finally, we evaluate the accuracy of these predictive mechanisms with a growing workload with several accuracy criteria. We develop our telecom testbed using containerization technology and these approaches are integrated with Kubernetes orchestrator.
Improving Maps from CNNs Trained with Sparse, Scribbled Ground Truths Using Fully Connected CRFs

Convolutional Neural Networks (CNNs) have become the new standard for semantic segmentation of very high resolution images. But as for other methods, the map accuracy depends on the quantity and quality of ground truth used to train them. Having densely annotated data, i.e. a detailed, pixel-level ground truth (GT), allows obtaining effective models, but requires high efforts in annotation. For this reason, it is more common and efficient to work with point or scribbled annotations rather than with dense ones. A CNN model trained with such incomplete ground truths tends to mischaracterize the shapes of the objects and to be inaccurate near their boundaries. We propose to use an approximation of a fully connected Conditional Random Field (CRF) to solve these issues, in which long range connections are accounted for through auxiliary nodes based on clustering of CNN activation features. Experiments on the ISPRS Vaihingen benchmark, where a CNN is trained only with a non-dense, scribbled ground truth, show that the proposed method can fill part of the performance gap with respect to models trained on the densely annotated, but unrealistic, ground truth.
Feature Enhanced Sar Tomography Reconstruction Through Adaptive Nonparametric Array Processing

Synthetic aperture radar (SAR) tomography (TomoSAR) employs array signal processing techniques, in order to estimate the location of the vertical structures that compose the backscattering field, in the direction perpendicular to the line-of-sight (PLOS). Due to the limited number of tracks for practical TomoSAR sensing scenarios, it becomes challenging to accurately estimate the source parameters. Additionally, irregular sampling and non-uniform acquisition constellations introduce artifacts and increase ambiguity. The usage of super-resolved parametric methods and compressed sensing (CS) based approaches, improve the vertical resolution and mitigate the effect of sidelobes. However, parametric approaches have the main drawback related to the assumption that the scene is composed by a known finite number of point-type backscattering sources. Also, the CS-based techniques regularly imply a considerable computational burden. Overcoming the disadvantages of the above mentioned TomoSAR-adapted methods, this paper presents a novel non-parametric iterative approach for feature enhanced SAR tomography, in the context of maximum likelihood (ML) estimation theory. The feature enhancing capabilities of the proposed technique are corroborated via processing L-band airborne TomoSAR data of the German Aerospace Center (DLR), acquired by the F-SAR system over the forested test site of Froschham, Germany, in 2017.
Measuring fault indicators in electric machines - learning experience

The authors read with keen interest an article in Instrumentation and Measurement Magazine titled, "Life after graduation: Africa- Where Metrology Began" [1]. Therein some research works from Cape to Cairo which employed measurement techniques were spotlighted. In this short article, more insight and the learning experience in one of the papers reviewed, namely: "Development of a Test Rig for Eccentricity Fault Studies on an Axial-Flux Permanent Magnet (AFPM) Wind Generator," [2] is presented. The testrig serves as a tool and platform for analyzing fault signatures associated with the AFPM machine. Its effectiveness which hinges on reliable instrumentation and measurement system is revealed in the experimental results in [3]-[5].
An Adaptive Multilooking Scheme for Multi-Temporal Insar Data

Multilooking is an effective speckle reduction tool for InSAR images. However, it has some limitations. Based on the De-specKS and NL-SAR algorithms, an adaptive multilooking scheme for multi-temporal InSAR data has been proposed in this paper. Simulated and real SAR data sets have been employed to evaluate the performance of this scheme. The results show that, by taking advantages of DespecKS and NL-SAR, the proposed scheme presents better detail preservation and speckle noise reduction performances comparing with these two advanced and classical multilooking algorithms.
Simulation of the performances of multijunction solar cells with improved voltage by transfer and scattering matrix methods

So far the transfer matrix method (TMM) has been extensively used for the calculation of the propagation of an electromagnetic wave through planar stratified media and in particular for the calculation of the reflection coefficient and generation function in III-V solar cells. TMM however can present numerical instability when applied to MJ solar cells whose structures allow improving the cell photovoltage. It is shown that for such structures, in order to overcome the numerical instability of TMM, a simplified scattering matrix method can be successfully applied.
A predictive epilepsy index based on probabilistic classification of interictal spike waveforms

Quantification of interictal spikes in EEG may provide insight on epilepsy disease burden, but manual quantification of spikes is time-consuming and subject to bias. We present a probability-based, automated method for the classification and quantification of interictal events, using EEG data from kainate- and saline-injected mice (C57BL/6J background) several weeks post-treatment. We first detected high-amplitude events, then projected event waveforms into Principal Components space and identified clusters of spike morphologies using a Gaussian Mixture Model. We calculated the odds-ratio of events from kainate-versus saline-treated mice within each cluster, converted these values to probability scores, P(kainate), and calculated an Hourly Epilepsy Index for each animal by summing the probabilities for events where the cluster P(kainate) > 0.5 and dividing the resultant sum by the record duration. This Index is predictive of whether an animal received an epileptogenic treatment (i.e., kainate), even if a seizure was never observed. We applied this method to an out-of-sample dataset to assess epileptiform spike morphologies in five kainate mice monitored for ~1 month. The magnitude of the Index increased over time in a subset of animals and revealed changes in the prevalence of epileptiform (P(kainate) > 0.5) spike morphologies. Importantly, in both data sets, animals that had electrographic seizures also had a high Index. This analysis is fast, unbiased, and provides information regarding the salience of spike morphologies for disease progression. Future refinement will allow a better understanding of the definition of interictal spikes in quantitative and unambiguous terms.
Effect of Displayed Ratings on Provision of Emotional Online Reviews

Consumers are frequently sharing digitalized opinions such as online reviews to aid purchase decision of others. While each online review is supposed to portray an individual’s unique and genuine experience, previous consumers’ reviews may to some extent influence others’ subsequent reviews when the next consumers perceive the previous ones to be misleading. This study aims to examine the causal impact of previous average rating on provision of emotional text in a subsequent online review, especially when displayed rating is off from its true rating. Using online review data collected from Yelp, we apply natural language processing to measure the amount of emotions in each review and, then, use a regression discontinuity design by taking advantage of the Yelp’s rounded rating display. Our preliminary results suggest that, when the displayed rating is different from the true rating, subsequent reviews tend to include more emotional text. Specifically, a higher displayed rating leads to a sharp increase in provision of negative emotions such as sadness, fear, disgust, and anger.
Plasma Galectin-9 Concentrations in Normal and Diseased Condition

Background/Aims: Galectin-9 is a soluble immune modulator with versatile functions, including a role as an immune checkpoint molecule. Therefore, the amount of galectin-9 in the blood may reflect an individual’s immunological balance. Many studies have conducted galectin-9 measurements; however, the reported galectin-9 concentration in the blood varies greatly, even within healthy controls. This study investigates the variation between the reported and actual concentrations of galectin-9. Methods: A GalPharma ELISA and an R&D Systems ELISA kit were directly compared using the same set of plasma and a series of recombinant galectins, including degraded galectin-9. Furthermore, galectin-9 in plasma was concentrated using anti-galectin-9 antibody-conjugated beads, and subjected to western blotting to estimate the quantity and integrity of galectin-9 and assess the consistency of ELISA measurements. Results: The R&D Systems’ ELISA indicated a 50-fold higher median concentration of plasma galectin-9 than that indicated by the GalPharma ELISA. This variation is due to aberrantly enhanced reactivity of the R&D Systems’ ELISA to degraded galectin-9 present in small quantities in the plasma. The GalPharma ELISA could detect only intact galectin-9 and its results correlated well with the plasma galectin-9 level obtained by western blotting. Conclusion: ELISA kits from R&D Systems reacts aberrantly higher against degraded galectin-9 than the intact galectin-9. Therefore, the existence of a small amount of degraded galectin-9 in a test sample hinders the quantification. As galectin-9 is a fragile protein, this is a serious concern when using this kit. Based on quantifications from the GalPharma ELISA, the median (25th-75th percentiles) galectin-9 concentration in healthy subjects in the current study cohort was calculated as 110 pg/mL (67 -154 pg/mL).
Maximum Likelihood Phase Estimation Method Based on Split-Spectrum for Multi-Frequency InSAR System

Multi - frequency interferometric synthetic aperture radar (InSAR) has drawn more attention in reconstructing height profile. In this paper, a novel unwrapped phase estimation method based on split-spectrum strategy and maximum likelihood (ML) criterion is proposed for multi-frequency InSAR. Firstly, multiple range subband interferograms are generated with the split-spectrum strategy to form a multifrequency configuration. Then, unwrapped phase of the reference frequency is estimated via ML method modeled by the proposed normalized probability density function (pdf) which is able to remove the phase ambiguity. Finally, simulation experiments are carried out to evaluate algorithm performance. Smaller unwrapped phase error standard deviation verifies the effectiveness and exactness of the proposed method.
The Enhancement of Upper Ocean Nutrients Concentration in the Peripheries of Two Anti-Cyclonic Eddies

The upper ocean nutrients concentration is found to be enhanced in the peripheries of two anti-cyclonic mesoscale eddies in the Western North Pacific (WNP) through using AVISO Sea Level Anomaly (SLA), a Bio-Geo-Chemical Argo (BGC-Argo) float, and merged, multi-sensor Sea Surface Temperature (SST) data. In the first eddy, which is near the coast, nutrients concentration in the upper layer is low at the core but high along the periphery. In the second eddy, which is near the extension of Kuroshio, nutrients concentration along the periphery is higher than the surrounding areas. The enhancement of nutrients concentration along the peripheries is perhaps mainly due to strong vertical mixing. The distributions of dissolved oxygen, temperature, and salinity show similar patterns as the nutrients. In the peripheries, dissolved oxygen concentration is low, indicating high production induced by the high concentration of nutrients.
The Feasibility and Acceptability of Two Methods of Snack Portion Control in United Kingdom (UK) Preschool Children: Reduction and Replacement

Large portions of high energy dense (HED) snacks are offered to children from a young age and are pervasive in our food environment. This study aimed to explore the feasibility, acceptability, and preliminary efficacy of two strategies of snack portion control: reduction and replacement. Forty-six mother-child dyads aged 22–56 months (36.6 ± 9.5 m, 48% female) completed a three-week intervention. In week 1 (baseline) no changes were made to the child’s diet; week 2 (acclimation) children received a standardised selection of HED snacks, and in week 3 (intervention) participants were randomly assigned to snack replacement (n = 24) or snack reduction (n = 22). Snack replacement involved swapping HED snacks for fruits and vegetables, whilst snack reduction involved reducing the size of HED snacks by 50%. Food and energy intake were measured using a weighed food diary for four consecutive days. Snack replacement resulted in more positive changes to children’s diets; vegetable intake increased (p < 0.01), and total daily energy intake decreased when compared to snack reduction (p < 0.05). Mothers expressed a more favourable attitude to snack replacement, although snack reduction was also well received by mothers. Despite increased preliminary efficacy of snack replacement on dietary intake, both strategies were feasible and acceptable. The current pilot study provides the necessary information to inform the design of future interventions.
Hyperspectral Image Restoration Based on Salient Edges

Hyperspectral images acquired by remote sensing systems are generally degraded by noise and can be sometimes more severely degraded by blur. In this study, we address the semiblind restoration of the degraded images component-wise, according to a sequential scheme. We propose a new component-wise semi-blind method for estimating effectively and accurately both the blur and the corresponding latent image. To prove applicability and higher efficiency of the proposed method, we compare it against the method it originates from. Our attention is mainly paid to the objective analysis (via $l_{1}$-norm) of the estimation error accuracy. The tests are performed on a synthetic hyperspectral image. This image has been successively degraded with eight real blurs taken from the literature, each of a different support size. Conclusions, practical recommendations and perspectives are drawn from the results experimentally obtained.
Pyret: A Python package for analysis of neurophysiology data

The pyret package contains tools for analyzing neural electrophysiology data. It focuses on applications in sensory neuroscience, broadly construed as any experiment in which one would like to characterize neural responses to a sensory stimulus. Pyret contains methods for manipulating spike trains (e.g. binning and smoothing), pre-processing experimental stimuli (e.g. resampling), computing spike-triggered averages and ensembles (Schwartz et al. 2006), estimating linear-nonlinear cascade models to predict neural responses to different stimuli (Chichilnisky 2001), part of which follows the scikit-learn API (Pedregosa et al. 2011), as well as a suite of visualization tools for all the above. We designed pyret to be simple, robust, and efficient with broad applicability across a range of sensory neuroscience analyses.
Theoretical performance of a metamaterial-inspired antenna for MRI

Metamaterial-inspired anttenas offer the possibility to improve the RF coil performance to acquire magnetic resonance images at high field. Transmission line (TL) theory used to study composite right/left hand structures, together with Mansfield’s formalism for the high frequency cavity resonator, were used to derive and expression for the quality factor, Q of a metamaterial-inspired cyclic antenna. A simple analytical formula was computed as a function of the number of resonator and the permittivity and permeability. These results offer theoretical guidelines to develop metamaterial-inspired antennas with an specific performance. The experimental evidence will determine the validity of this theory.
High-performance carbon nanotube paste emitters by using the optimal size distribution of SiC nanometer-fillers aggregates

We improved carbon nanotube (CNT) paste field emitter by optimizing the size distribution of SiC aggregates as inorganic fillers using a novel milling process. The average size of SiC aggregates was reduced to sub-micrometer with a relatively narrow distribution. The CNT paste emitter with the optimized small SiC fillers of 1 m in D50, by the milling process, exhibited better adhesive property and more uniform surface morphology than that with large fillers from the as-purchased SiC. Furthermore, the optimized small fillers shows superior field emission performance even under a high current density, making the CNT paste technology very useful for a variety of field emission devices.
Enablers and Inhibitors of Discontinuous Use in Social Networking Sites: A Study on Weibo

In recent years, many Social networking sites (SNSs) have experienced a serious loss of users. In addressing this issue, this study investigates both enabling and inhibiting mechanisms of discontinuous intention based on cognition-affection-conation framework. Using data collected from 401 users of Weibo, we empirically test our theoretical model. The results indicated that overload (i
Fusion Scheme for Automatic and Large-Scaled Built-up Mapping

As more and more geospatial data are produced, Big Earth data is becoming a new key to the understanding of the Earth. Such opportunity also comes with new issues and challenges related to the massive and heteregenous amount of data to process and to analyse. The present work explores the use of three types of Earth Observation (EO) data in order to automatically classify built and non-built areas in Africa using a machine learning classifier: SAR (Sentinel) and optical (Landsat) imagery, and the OpenStreetMap (OSM) database as training data. Experimental results in ten african cities show that the use of satellite data from multiple sensors improves the performance of the classifiers in these areas. They also show that using crowd-sourced geospatial databases such as OSM as training data leads to similar accuracies than when relying on field surveys or hand-digitalized datasets.
Polypyrimidine tract-binding protein blocks miRNA-124 biogenesis to enforce its neuronal-specific expression in the mouse

Significance MicroRNAs repress genes controlling important decisions in animal development, but miRNA production and their interaction with larger genetic programs of development are not well understood. We found that the initial transcript of an important neuronal miRNA, miR-124, is expressed much earlier in development than the mature functional miRNA. Conversion of this primary RNA into its active form is blocked by the RNA binding protein PTBP1, whose down-regulation upon neuronal differentiation allows mature miR-124 expression. To understand the need for this regulation, we developed a mathematical model of the PTBP1/miR-124 regulatory circuit, finding that early expression of unprocessed miR-124 creates a sharp transition in gene expression during neuronal differentiation, making this developmental decision less affected by random variation and noise. MicroRNA (miRNA)-124 is expressed in neurons, where it represses genes inhibitory for neuronal differentiation, including the RNA binding protein PTBP1. PTBP1 maintains nonneuronal splicing patterns of mRNAs that switch to neuronal isoforms upon neuronal differentiation. We find that primary (pri)-miR-124-1 is expressed in mouse embryonic stem cells where mature miR-124 is absent. PTBP1 binds to this precursor RNA upstream of the miRNA stem–loop to inhibit mature miR-124 expression in vivo and DROSHA cleavage of pri-miR-124-1 in vitro. This function for PTBP1 in repressing miR-124 biogenesis defines an additional regulatory loop in the already intricate interplay between these two molecules. Applying mathematical modeling to examine the dynamics of this regulation, we find that the pool of pri-miR-124 whose maturation is blocked by PTBP1 creates a robust and self-reinforcing transition in gene expression as PTBP1 is depleted during early neuronal differentiation. While interlocking regulatory loops are often found between miRNAs and transcriptional regulators, our results indicate that miRNA targeting of posttranscriptional regulators also reinforces developmental decisions. Notably, induction of neuronal differentiation observed upon PTBP1 knockdown likely results from direct derepression of miR-124, in addition to indirect effects previously described.
Personas with knowledge and cognitive process: tools for teaching conceptual design

Persona is a tool within User Centered Design principles to assist students to be mindful of the users of the system during the conceptual phase of the design. We conducted a series of empirical studies, involving over 200 information systems students who performed design activities, four groups of students were provided with four Personas authored to be different in knowledge and cognitive processes and a control group who received a list of requirements. We found that students' performance, while designing to a Persona are as good as, or better than using the list of requirements. The students who were given one of the Personas thought of a user which had traits similar to the Personas' traits but the students who received the list of requirements thought of themselves as users of the product. Our results indicate that Persona assists students to think of the target users during their design activity.