Miracle-dz/UniFAR-FTUs huggingface dataset:
Unify : sharing digital media content from the cloud through physical interaction

Sharing digital media in the cloud between our everyday smart devices has become an essential part of our workflows. The goal of the project was to explore new ways to share and interact with intangible media using specific devices such as smartphones and tablets. Many current commercial and research concepts are often technology-led. The concept presented in this paper, Unify, aims at envisioning how these technologies would fit in the users everyday life, moving from explorative to contextual concepts. Unify introduces specific affordances of the physical world to intangible digital objects by using the physical properties of tangible smart devices. In this way the device becomes a metaphorical physical container in which the digital objects are subject to the law of physics. This offers a different mindset for the user, releasing images of what users consider to be appropriate or standard behavior with smart devices. Through a qualitative evaluation it became apparent that users could envision the concept to be part of their daily routines and they do not need a lot of time to make this switch, which is promising for further development in this field.
Spin glass : an unifinished story

It has been a long and hard way to unravel the fascinating subtleties involved in the physics of spin glasses since the pioneering experimental work of Cannella and ~ ~ d o s h [ l ] in the dilute metallic alloy CuMn with 0.9% Mn. ~ o w a d ' a ~ s these systems comprise a large variety of distinct materials emboding the two basic ingredients: frozen disorder and antagonistic interactions (frustration). For a review see for instance Rammal and ~oule t ie [~] , Binder and ~ o u n & ~ ] , Chowdury and ~ooke r j ee [~ I , Mezard et a1 [51, ~ r a ~ [ ' I and Fisher and ~ e r t z [ ~ ] . The most successful and popular theoretical model to describe the physical properties of spin glasses is the one introduced by Edwards and ~nderson['l whose mean field version was proposed by Sherrington and Kirkpatrick (sK)['~. Even today some aspects of the SI< model remains elusive such as the structure of the free energy barriers[lO], the ordering field of the condensed phase[11~12] and its dynamical properties[13]. In this work a short account of the theoretical development in spin glasses mainly the Ising model, is presented. There is no intention of completeness in this work nor to give any details of the model calculations or a complete list of references but only of providing useful informations concerning some results up to now. In section I1 the mean field SK model and the picture arising from its solution are discussed. In Section I11 the counterpart of some special short range models where exact solutions were obtained are considered as well as some scaling and renormalization group theories. Some concluding remarks are presented.
Sequence SAR Image Classification Based on Bidirectional Convolution-Recurrent Network

Although the deep convolutional neural network (DCNN) has been successfully applied to target classification of military vehicles based on synthetic aperture radar (SAR), most of the available methods do not fully exploit the characteristics of continuous SAR imaging and only utilize single image for recognition. To extract significant identification features contained in the image sequence, this paper proposes a sequence of SAR target classification method based on bidirectional convolution-recurrent network. In this network, we extract spatial features of each image through DCNNs without the fully connected layer, and then learn sequence features by bidirectional long short-term memory networks. Finally, we design the average softmax classifier to obtain the classification results. Compared with the available methods, the proposed network takes advantage of the significant information in the image sequence and achieves higher classification accuracy in the moving and stationary target acquisition and recognition data set. In addition, it has shown robustness to large depression angle variants, configuration variants, and version variants.
Definition of candidate Essential Variables for the monitoring of mineral resource exploitation

ABSTRACT The practice of raw material extraction has a high impact on the environment and represents a potential threat to the health and thriving of local communities. The concept of Extractive Essential Variables (EEVs) are explored in order to propose variables that can be used to quantify the environmental footprint of mineral extraction. Considering the interdependence of mining activities with social, economic and environmental issues, the variables target the development of monitoring tools for the implementation of the Sustainable Development Goals (SDGs). The identification of EEVs is based on the use of Earth Observation products in the field of mineral resources exploitation. A list of variables is proposed based on three classes of Essential Variables (EVs): installation and exploration phase, mineral extraction, and ore processing. These variables take into account the impacts of mining on the hydrology, land, water resources and the atmosphere of the area subjected to mineral exploitation. One of the variables is implemented as an operational workflow addressing SDG15, “life on land”. The workflow is intended to assess the area of forest ecosystem lost due to the presence of a mining site. Geospatial data on the extent of mining concessions and forest cover are combined using ArcGISTM. The workflow is successively translated into a Unix script to automatize the process of data treatment. The script is developed using the Geospatial Data Abstraction Library (GDAL). The use of a Virtual Laboratory Platform (VLab), a web-service-based access platform, increases the accessibility of data and resources and the re-use of the script. This work is a first attempt to propose a framework of EEVs, derived data workflows, while the underlying methodology, partially based on scientific publications and on personal reasoning, still needs to be tested and, improved based on expertise in the sector.
Analysis and design of a novel dual-band coaxial waveguide mode converter for high-power microwave applications

ABSTRACT Based on the derived coupling coefficients of the circular waveguide and coaxial waveguide, a novel dual-band serpentine coaxial mode converter is proposed in this paper. This dual-band converter consists of two parts: an inner inserted circular waveguide TM01-TE11 converter operating at Ka-band, and an outer coaxial waveguide TEM-TE11 converter working at Ku-band. The inner and outer converters have the same curvature distribution along the propagation direction with a structure length of 800 mm. Simulated results exhibit that the maximum conversion efficiencies of the inner and outer converters reach 98.7% and 98.9% at 35 and 15 GHz, respectively. The bandwidths of the inner and outer converter are 1.9 and 2.5 GHz when the conversion efficiency is over 90%. Furthermore, the dual-band mode converter has a symmetry and reciprocity structure with coaxial-parallel input/output port. The good agreement between theoretical calculations and simulation demonstrates the feasibility of this design.
EL ROL DE LA IMMIGRACIÓN EN PERIODOS CORTOS SOBRE LA DINÁMICA DE ENFERMEDADES: UN MODELO SIR CON ESTRUCTURA DE EDAD

Se formula un modelo de ecuaciones diferenciales parciales no lineal y con estructura de edad, que incluye inmigración en un periodo breve de tiempo y sus efectos en la población. Los inmigrantes pueden ser de cualquier clase en el modelo: susceptibles, infectados o recuperados. Se discute además la estabilidad global del equilibrio correspondiente al modelo libre de infección e inmigración. Un método numérico general es discutido, y se exploran escenarios específicos con inmigración en periodos cortos de tiempo.
Numerical study of unsteady flow and heat transfer CNT-based MHD nanofluid with variable viscosity over a permeable shrinking surface

Purpose The purpose of this paper is to present a novel model on the unsteady MHD flow of heat transfer in carbon nanotubes with variable viscosity over a shrinking surface. Design/methodology/approach The temperature-dependent viscosity makes the proposed model non-linear and coupled. Consequently, the resulting non-linear partial differential equations are first reformed into set of ordinary differential equations through appropriate transformations and boundary layer approximation and are then solved numerically by the Keller box method. Findings Graphical and numerical results are executed keeping temperature-dependent viscosity of nanofluid. It is noted that, for diverse critical points, it is found that at one side of these critical values, multiple solutions exist; on the other side, no solution exists. A comparison is also computed for the special case of existing study. The temperature and pressure profiles are also plotted for various effective parameters. Originality/value The work is original.
Differential SAR Tomography Reconstruction Robust to Temporal Decorrelation Effects

Temporal decorrelation is one of the major problems in synthetic aperture radar (SAR) tomography (TomoSAR) of a natural environment that leads to blurring and spreading in focused image space. In the context of spatiotemporal focusing using the multi-temporal multi-baseline (MB) SAR data, a model-based differential TomoSAR is employed. Along this and with the aim of temporal decorrelation-robust focusing, a differential tomography framework based on generalized Capon estimator is investigated. The method can cope with temporal decorrelation of the distributed environment by spatiotemporal focusing with optimal bandwidth of the distributed signal. In addition, the method employs an additional parameter for coherence channel balancing in the model of generalized Capon that benefits from it in characterizing the spatiotemporal backscattering by mitigating the inconsistency between channels. The analysis is performed with a realistic simulation of temporal decorrelation in the presence of different decorrelation sources and taking into account the dependence on the vertical structure of the forested area. Effectiveness of the proposed framework has been assessed on both simulated and real data sets by evaluation and characterization of the canopy and under foliage ground in terms of deviation between the estimated covariance matrix and one of the generalized TomoSAR models.
Identification of continuous-time hybrid “Box–Jenkins” systems with multiple unknown time delays using two-stage parameter estimation algorithm

Purpose This study/paper aims to present a separable identification algorithm for a multiple input single output (MISO) continuous time (CT) hybrid “Box–Jenkins”. Design/methodology/approach This paper proposes an optimal method for the identification of MISO CT hybrid “Box–Jenkins” systems with unknown time delays by using the two-stage recursive least-square (TS-RLS) identification algorithm. Findings The effectiveness of the proposed scheme is shown with application to a simulation example. Originality/value A two-stage recursive least-square identification method is developed for multiple input single output continuous time hybrid “Box–Jenkins” system with multiple unknown time delays from sampled data. The proposed technique allows the division of the global CT hybrid “Box–Jenkins” system into two fictitious subsystems: the first one contains the parameters of the system model, including the multiple unknown time delays, and the second contains the parameters of the noise model. Then the TS-RLS identification algorithm can be applied easily to estimate all the parameters of the studied system.
Approximate Bayesian Computation: a non-parametric perspective

Approximate Bayesian Computation is a family of likelihood-free inference techniques that are tailored to models defined in terms of a stochastic generating mechanism. In a nutshell, Approximate Bayesian Computation proceeds by computing summary statistics from the data and giving more weight to the values of the parameters for which the simulated summary statistics resemble the observed ones. In this paper, we present Approximate Bayesian Computation as a technique of inference that relies on stochastic simulations and non-parametric statistics. We derive the asymptotic bias and variance of the standard estimators of the posterior distribution which are based on rejection sampling and linear adjustment. Additionally, we introduce an original estimator of the posterior distribution based on quadratic adjustment and we show that its bias contains a smaller number of terms than the estimator with linear adjustment. Although we find that the estimators with adjustment are not universally superior to the estimator based on rejection sampling, we find that they can achieve better performance when there is a nearly homoscedastic relationship between the summary statistics and the parameter of interest. Last, we present model selection in Approximate Bayesian Computation and provide asymptotic properties of two estimators of the model probabilities. As for parameter estimation, the asymptotic results raise the importance of the curse of dimensionality in Approximate Bayesian Computation. Performing numerical simulations in a simple normal
Optimal reduced‐order LQG synthesis: Nonseparable control and estimation design

The optimal linear‐quadratic‐Gaussian synthesis design approach and the associated separation principle are investigated for the case where the observer design model is a reduced model of the underlying system model. Performance of the resulting reduced‐order controller in the full‐state system environment is formulated in terms of an augmented state vector consisting of the system state vector and the reduced model state vector. Considering explicitly separated linear control and estimation laws, a calculus of variations/Hamiltonian approach is used to determine the necessary conditions for the optimal controller and observer gains for the simplified algorithms. Results show that the optimal gains are not separable, ie, the optimal controller and observer gains are coupled and cannot be computed independently. Numerical examples of an infinite‐horizon and finite‐horizon control and estimation large‐scale multiagent system problem clearly show the advantages of using the nonseparable coupled solutions.
Advances in population-based imaging using cardiac magnetic resonance

Large population-based studies have helped to identify cardiovascular risk factors and to understand the natural progression of diseases. Cardiac magnetic resonance (CMR) is the reference method for the assessment of ventricular morphology and function given the low variance between scans. In addition, advanced sequences such as MR tagging, T1 mapping, and late gadolinium enhancement allow to assess regional ventricular function and fibrotic changes. MESA was the first study to use CMR on a large scale in a sample of the general population. Subsequent studies focused on cohorts of particular ethnicities or from certain locations with the Jackson Heart Study looking at African-Americans and the Dallas Heart Study at Dallas County Residents. More recently, the German National Cohort and UK Biobank have started to perform CMRs in a significantly larger number of participants (30 000 and 100 000, respectively). The introduction of CMR into prospective cohort studies has allowed to characterize ventricular remodeling in individuals of different age, sex, and gender and has found associations with new environmental exposures. The ability to detect subclinical changes in asymptomatic individuals has also been highlighted by reports of a high number of missed myocardial infarctions (MI) using CMR. In this review, we discuss the use of CMR in the different large population-based studies and compare the various associations found with left and right ventricular structure and function. In addition, we outline automated image analysis strategies aimed at overcoming challenges posed by the large amount of data in population-based studies.
Automatização de testes para plataformas Oracle - Xstore

Uma das atividades fundamentais durante o desenvolvimento de qualquer software e a constante validacao e verificacao ao produto. Ao longo de todo o ciclo de desenvolvimento, sao realizados testes funcionais e nao funcionais, a fim de serem detetados e corrigidos possiveis erros ou desconformidades com os requisitos definidos.O trabalho desta dissertacao foi realizado em parceria com a Retail Consult, uma empresa especialista em sistemas de informacao para o sector do retalho. Apesar das diversas solucoes que a Retail Consult fornece ao setor, esta dissertacao apenas contempla a aplicacao Xstore que e um dos produtos incorporados na suite Oracle Retail Xstore Point-of-Service.Para atender aos requisitos tecnicos e do negocio de cada cliente, e necessario proceder com a configuracao e desenvolvimento de novas funcionalidades. Como se trata de um produto bastante complexo, qualquer modificacao na solucao base requer a realizacao de um conjunto de testes para garantir que nao existem erros ou funcionalidades que nao respeitem os requisitos definidos.Na Retail Consult, apenas sao realizados testes de forma manual, o que representa uma grande dificuldade tendo em conta a elevada quantidade de funcionalidades e casos de teste existentes. Para aumentar o nivel de cobertura dos testes e reduzir o esforco necessario nessa atividade, e proposta a utilizacao de uma ferramenta de automatizacao durante as diversas fases de desenvolvimento.O objetivo desta dissertacao e a implementacao e analise do mecanismo de testes automaticos disponibilizado e integrado no Xstore. Para uma melhor avaliacao ao procedimento, sao descritas as varias etapas necessarias ate a execucao dos testes e a obtencao dos respetivos resultados. Para validar a estrategia de testes apresentada, foi realizado um projeto piloto que se dividiu em duas fases. Inicialmente, foram planeados e executados alguns casos de teste nas funcionalidades mais comuns da aplicacao base. Esta primeira fase permitiu analisar o esforco e conhecimento necessario para o planeamento inicial dos casos de teste, assim como evidenciar as limitacoes existentes no mecanismo de testes. Para a segunda fase, foram realizadas algumas modificacoesno Xstore, de modo a simular os requisitos dos clientes. Com as alteracoes na aplicacao, tornou-se necessaria a correcao e planeamento de novos casos de teste automatizados, na qual permitiu verificar a capacidade de modulacao e adaptacao do mecanismo de testes.
A Potts‐mixture spatiotemporal joint model for combined magnetoencephalography and electroencephalography data

We develop a new methodology for determining the location and dynamics of brain activity from combined magnetoencephalography (MEG) and electroencephalography (EEG) data. The resulting inverse problem is ill‐posed and is one of the most difficult problems in neuroimaging data analysis. In our development we propose a solution that combines the data from three different modalities, magnetic resonance imaging (MRI), MEG and EEG, together. We propose a new Bayesian spatial finite mixture model that builds on the mesostate‐space model developed by Daunizeau & Friston [Daunizeau and Friston, NeuroImage 2007; 38, 67–81]. Our new model incorporates two major extensions: (i) We combine EEG and MEG data together and formulate a joint model for dealing with the two modalities simultaneously; (ii) we incorporate the Potts model to represent the spatial dependence in an allocation process that partitions the cortical surface into a small number of latent states termed mesostates. The cortical surface is obtained from MRI. We formulate the new spatiotemporal model and derive an efficient procedure for simultaneous point estimation and model selection based on the iterated conditional modes algorithm combined with local polynomial smoothing. The proposed method results in a novel estimator for the number of mixture components and is able to select active brain regions, which correspond to active variables in a high‐dimensional dynamic linear model. The methodology is investigated using synthetic data and simulation studies and then demonstrated on an application examining the neural response to the perception of scrambled faces. R software implementing the methodology along with several sample datasets are available at the following GitHub repository https://github.com/v2south/PottsMix. The Canadian Journal of Statistics 47: 688–711; 2019 © 2019 Statistical Society of Canada
A vertical facility based on raster scan configuration for the x-ray scientific calibrations of the ATHENA optics

The ATHENA X-ray observatory is a large-class ESA approved mission, with launch scheduled in 2028. The technology of Silicon Pore Optics (SPO) was selected since 2004 as the baseline for making the X-ray Mirror Assembly. Up to 700 mirror modules to obtain a nested Wolter like optics. The maximum diameter of the shells will be 2.5 m while the focal length is 12 m. The requirements for on-axis angular resolution and effective area at 1 keV are 5 arcsec HEW and 1.4 m2, while the field of view will be 40 arcmin in diameter (50 % vignetting). While in this moment there an on-going effort aiming at demonstrating the feasibility of a so large optics with so stringent scientific requirements, an important aspect to be considered regards the scientific calibrations of the X-ray optics. In this respect, the Point Spread Function and effective area have to be correctly measured and calibrated on-ground at different energies across the entire field of view, with a low vignetting. The approach considered so far foresees the use of a long (several hundreds of meters) facility to allow a full illumination with low divergence of the entire optics module (or at least of large sections of it). The implementation of similar configurations in a completely new facility to be realized in Europe (friendly called "super Panter") or the retrofitting existing facilities like the XRCF at NASA/MSFC are being considered. In both cases the costs and the programmatic risks related to the implementation of these huge facilities, with their special jigs for the alignment of the ATHENA optics, represent important aspects to be considered. Moreover, the horizontal position of the optics to be used in full illumination facilities would determine gravitational deformations, not easy to be removed with actuators or by modeling. In this talk we will discuss a completely different concept, based on the mount of the optics in vertical position and the use of a raster scan of the ATHENA optics with a small (a few cm2 wide) highly collimated (1 arcsec or so) white beam X-ray. This system will allow us to operate a much compact system. The use of a vertical configuration will imply smaller gravitational deformations, that can be controlled with actuators able to compensate them. A proper camera system with a sufficient energy resolution will be able to grant a correct measurement of both PSF and effective area of the Mirror Assembly within the calibration requirements and in a reasonable integration time. Moreover, it may allow us also to perform end-to-end tests using the two flight focal plane instruments of ATHENA. The cost and risks for the implementation would be much lower than for the full illumination systems. The conceptual configuration and preliminary expected performance of the facility will be discussed.
Magnetized Modified Holographic Ricci Dark Energy Cosmological Model in Saez-Ballester Theory of Gravitation

The main purpose of this paper is to explore the solutions of anisotropic Bianchi Type III space time with magnetized modified holographic Ricci dark energy in the framework of SaezBallester theory of gravitation [1]. We have solved Saez- Ballester theory field equations using the hybrid expansion law proposed by Akarsu et al. [2]. We have presented the cosmological model by the proposed expansion law with considering certain parameters and studied their evolutions.We have also discussed physical and kinematical properties of the model.