Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical thermodynamics and weighted topology of radial networks

We propose a method of characterizing radial networks based on a partition function associated with the structural triangulation of the network. The internal energy, Helmholtz free energy, and entropy derived from the partition function are used to group similar networks together and to interrogate the history of their development. We illustrate our methodology for a model for optimal transport in tree leaves.
Soil Moisture Prediction from Remote Sensing Images Coupled with Climate, Soil Texture and Topography via Deep Learning

Soil moisture (SM) is an important biophysical parameter by which to evaluate water resource potential, especially for agricultural activities under the pressure of global warming. The recent advancements in different types of satellite imagery coupled with deep learning-based frameworks have opened the door for large-scale SM estimation. In this research, high spatial resolution Sentinel-1 (S1) backscatter data and high temporal resolution soil moisture active passive (SMAP) SM data were combined to create short-term SM predictions that can accommodate agricultural activities in the field scale. We created a deep learning model to forecast the daily SM values by using time series of climate and radar satellite data along with the soil type and topographic data. The model was trained with static and dynamic features that influence SM retrieval. Although the topography and soil texture data were taken as stationary, SMAP SM data and Sentinel-1 (S1) backscatter coefficients, including their ratios, and climate data were fed to the model as dynamic features. As a target data to train the model, we used in situ measurements acquired from the International Soil Moisture Network (ISMN). We employed a deep learning framework based on long short-term memory (LSTM) architecture with two hidden layers that have 32 unit sizes and a fully connected layer. The accuracy of the optimized LSTM model was found to be effective for SM prediction with the coefficient of determination (R2) of 0.87, root mean square error (RMSE) of 0.046, unbiased root mean square error (ubRMSE) of 0.045, and mean absolute error (MAE) of 0.033. The model’s performance was also evaluated concerning above-ground biomass, land cover classes, soil texture variations, and climate classes. The model prediction ability was lower in areas with high normalized difference vegetation index (NDVI) values. Moreover, the model can better predict in dry climate areas, such as arid and semi-arid climates, where precipitation is relatively low. The daily prediction of SM values based on microwave remote sensing data and geophysical features was successfully achieved by using an LSTM framework to assist various studies, such as hydrology and agriculture.
Representative‐discriminative dictionary learning algorithm for human action recognition using smartphone sensors

With the advancement of mobile computing, understanding, and interpretation of human activities has become increasingly popular as an innovative human computer interaction application over the past few decades. This article presents a new scheme for action recognition based on sparse representation theory using a novel dictionary learning algorithm. This system employs two types of inertial signals from smartphones namely, accelerometer and gyroscope sensory data. Attainment of higher values of classification accuracy depends on the creation of effective dictionaries that completely retain the important features of every action while maintaining the least correlation with the features of other actions. Accordingly, in this research, we propose a new algorithm for learning dictionaries with two levels of dictionary training that aims at learning a compact, representative, and discriminative dictionary for each class. Unlike typical dictionary learning algorithms that aim at the creation of dictionaries that best represents the features of each class, our proposed algorithm incorporates a discriminative criterion that eventually produces better classification results. To validate the proposed framework, all the experiments were performed using three publicly available datasets.
Identifying tools for comparing simulations and observations of spectral-line data cubes

We present a statistical framework to compare spectral-line data cubes of molecular clouds and use the framework to perform an analysis of various statistical tools developed from methods proposed in the literature. We test whether our methods are sensitive to changes in the underlying physical properties of the clouds or whether their behaviour is governed by random fluctuations. We perform a set of 32 self-gravitating magnetohydrodynamic simulations that test all combinations of five physical parameters -- Mach number, plasma parameter, virial parameter, driving scales, and solenoidal driving fraction -- each of which can be set to a low or high value. We create mock observational data sets of ${\rm ^{13}CO}$(1-0) emission from each simulation. We compare these mock data to a those generated from a set of baseline simulations using pseudo-distance metrics based on 18 different statistical techniques that have previously been used to study molecular clouds. We analyze these results using methods from the statistical field of experimental design and find that several of the statistics can reliably track changes in the underlying physics. Our analysis shows that the interactions between parameters are often among the most significant effects. A small fraction of statistics are also sensitive to changes in magnetic field properties. We use this framework to compare the set of simulations to observations of three nearby star-forming regions: NGC 1333, Oph A, and IC 348. We find that no one simulation agrees significantly better with the observations, although there is evidence that the high Mach number simulations are more consistent with the observations.
Control System for Mobile Robot using FPGA-Based Q-Learning Accelerator

Q-Learning algorithm is used in smart navigation, path planning and others. The output of the Q-Learning cannot be directly used to drive motors or control robot to specific function in the testing area. To convert Q-Learning action into low level signal, we need a controller that organize the task of the sensors, the actuators and the main accelerator. In this paper, we present the control system for a mobile robot that uses FPGA-Based Q-Learning Accelerator. The control system is connected to the sensors, actuators and the Q-Learning Accelerators. The control system is successfully connected to the FPGA development board and deployed to the mobile robot. Moreover, the simulation result of the control for the actuator shows stability. The control system is useful for designing application systems such as smart navigation, IoT, and smart communication.
Pulsars are cool. Seriously.

Abstract Ever since the first pulsar was discovered by Bell and Hewish over 40 years ago, we've known that not only are pulsars fascinating and truly exotic objects, but that we can use them as powerful tools for basic physics and astrophysics as well. Taylor and Hulse hammered these views home with their discovery and timing of the spectacular “binary pulsar” in the 1970s and 1980s. In the last two decades a host of surprises and a promise of phenomenal scientific riches in the future has come from the millisecond pulsars. As our instrumentation has become more sensitive and better suited to measuring the pulses from these objects, they've given us new tests of general relativity, fantastic probes of the interstellar medium, constraints on the physics of ultra-dense matter, new windows into binary and stellar evolution, and the promise of a direct detection of gravitational waves. These things really are cool, and there is much more we will do with them in the future.
Axisymmetric and stationary structures of magnetized barotropic stars with extremely strong magnetic fields deep inside

We have succeeded in obtaining magnetized star models that have extremely strong magnetic fields in the interior of the star. In our formulation, arbitrary functions of the magnetic flux function appear in the expression for the current density. By appropriately choosing the functional form for one of the arbitrary functions that corresponds to the distribution of the toroidal current density, we have obtained configurations with magnetic field distributions that are highly localized within the central part and near the magnetic axis region. The absolute values of the central magnetic fields are stronger than those of the surface region by two orders of magnitude. By applying our results to magnetars, the internal magnetic poloidal fields could be 1017 G, although the surface magnetic fields are about 1015 G in the case of magnetars. For white dwarfs, the internal magnetic poloidal fields could be 1012 G, when the surface magnetic fields are 109–1010 G .
Arabic Language Sentiment Analysis using Bidirectional Long Short Term Memory

The amount of data generated in the digital era is huge since the super growth of social networks. Sentiment analysis (SA) seeks to extract opinions from a text and determine the polarity (positive, negative, or neutral). SA is widely used to refer to English. The topic of this study is SA in the Arabic language. There is an amalgamation between Word2Vec and Bidirectional Long-Short Time Memory (BLSTM) used in this paper. Firstly, words in reviews are transferred into their corresponding vectors with word representation models. Secondly, the sequence of words in the sentences passes as input to BLSTM. BLSTM not only captures long-range information and solves the gradient attenuation problem, but it also better represents the future semantics of the word sequence. The polarity was calculated using Word2Vec representation models, which rely on meaning and context. A BLSTM-based deep learning (DL) architecture is proposed. The result shows that the BLSTM Model Architecture surpasses ML, CNN, and LSTM Architectures with a maximum accuracy of 94.88 percent.
The Contextual Analysis of HRM Practices in Multinational Companies in the Czech Republic

This paper aims to analyze the current state of the Czech Republic’s socioeconomic environment and the HRM practices of MNCs. The Contextual Analysis was made by using data records from 2015 to 2020 from the Czech Statistical Office, Santander Trade’s website, and CzechInvest. By using Hofstede’s six dimensions, the Czech Republic was compared with its neighbor countries: Germany, Poland, and Slovakia. Literature review was done by using articles from the prestigious scientific journals. The study found that the Czech Republic is an attractive foreign investment market because of the followings: it has one of Europe's most strategic geographical locations. Its excellent infrastructure contributes to the Czech Republic's positioning as an important crossroads for European business corridors; its FDI policies and investor-friendly regulations, which offer a number of advantages for foreign investors; and its socioeconomic environment, which is also suitable for the establishment of new businesses in the country. At the end, the study recommends further research on the effects of the COVID-19 pandemic on MNCs, FDI, and the Czech Republic’s policies on how to eliminate these effects.
Photometric and spectroscopic variability of the B5IIIe star HD 171219

We analyzed the star HD 171219, one of the relatively bright Be stars observed in the seismo field of the CoRoT satellite, in order to determine its physical and pulsation characteristics. Classical Be stars are main-sequence objects of mainly B-type, whose spectra show, or had shown at some epoch, Balmer lines in emission and an infrared excess. Both characteristics are attributed to an equatorially concentrated circumstellar disk fed by non-periodic mass-loss episodes (outbursts). Be stars often show nonradial pulsation gravity modes and, as more recently discovered, stochastically excited oscillations. Applying the CLEANEST algorithm to the high-cadence and highly photometrically precise measurements of the HD 171219 light curve led us to perform an unprecedented detailed analysis of its nonradial pulsations. Tens of frequencies have been detected in the object compatible with nonradial g-modes. Additional high-resolution ground-based spectroscopic observations were obtained at La Silla (HARPS) and Haute Provence (SOPHIE) observatories during the month preceding CoRoT observations. Additional information was obtained from low-resolution spectra from the BeSS database. From spectral line fitting we determined physical parameters of the star, which is seen equator-on. We also found in the ground data the same frequencies as in CoRoT data. Additionally, we analyzed the circumstellar activity through the traditional method of V/R emission H{\alpha} line variation. A quintuplet was identified at approximately 1.113 c/d (12.88 {\mu}Hz) with a separation of 0.017 c/d that can be attributed to a pulsation degree l~2. The light curve shows six small- to medium-scale outbursts during the CoRoT observations. The intensity of the main frequencies varies after each outburst, suggesting a possible correlation between the nonradial pulsations regime and the feeding of the envelope.
Artificial Intelligence-Based Support System for the Diagnosis of Cervical Lesions

Cervical cancer affects the whole world today, being the fourth most common type of cancer diagnosed. The prevention methods applied standardized to the female population contribute to the reduction of incidence and mortality. Medical approaches include colposcopy, a way of visual inspection of the cervix, and identification of cervical, benign or malignant lesions. Being an analysis that depends entirely on the expertise of the medical professional who performs it, it can be improved by artificial methods. Deep learning has been successfully applied in the field of medical imaging and also in the classification of cervical lesions. In the presented paper, a set of convolutional networks for deep learning was used, consisting of a network with residues and a dense network, pre-trained on an extensive database and applied to a limited dataset. The assembly achieved accuracy on the validation set of 69%, improving the performance of individual models by 2 and 4 percent, respectively. The presented approach classifies 5 types of cervical lesions, the novelty lies in the extensive number of categories, since the literature addresses a more limited number.
The shear dynamo problem for small magnetic Reynolds numbers

We study large-scale kinematic dynamo action due to turbulence in the presence of a linear shear flow in the low-conductivity limit. Our treatment is non-perturbative in the shear strength and makes systematic use of both the shearing coordinate transformation and the Galilean invariance of the linear shear flow. The velocity fluctuations are assumed to have low magnetic Reynolds number (Rem), but could have arbitrary fluid Reynolds number. The equation for the magnetic fluctuations is expanded perturbatively in the small quantity, Rem. Our principal results are as follows: (i) the magnetic fluctuations are determined to the lowest order in Rem by explicit calculation of the resistive Green's function for the linear shear flow; (ii) the mean electromotive force is then calculated and an integro-differential equation is derived for the time evolution of the mean magnetic field. In this equation, velocity fluctuations contribute to two different kinds of terms, the ‘C’ and ‘D’ terms, respectively, in which first and second spatial derivatives of the mean magnetic field, respectively, appear inside the space–time integrals; (iii) the contribution of the D term is such that its contribution to the time evolution of the cross-shear components of the mean field does not depend on any other components except itself. Therefore, to the lowest order in Rem, but to all orders in the shear strength, the D term cannot give rise to a shear-current-assisted dynamo effect; (iv) casting the integro-differential equation in Fourier space, we show that the normal modes of the theory are a set of shearing waves, labelled by their sheared wavevectors; (v) the integral kernels are expressed in terms of the velocity-spectrum tensor, which is the fundamental dynamical quantity that needs to be specified to complete the integro-differential equation description of the time evolution of the mean magnetic field; (vi) the C term couples different components of the mean magnetic field, so they can, in principle, give rise to a shear-current-type effect. We discuss the application to a slowly varying magnetic field, where it can be shown that forced non-helical velocity dynamics at low fluid Reynolds number does not result in a shear-current-assisted dynamo effect.
Asymptotic local hypothesis testing between a pure bipartite state and the completely mixed state

In this paper, we treat an asymptotic hypothesis testing (or state discrimination with asymmetric treatment of errors) between an arbitrary fixed bipartite pure state and the completely mixed state by one-way LOCC, two-way LOCC, and separable POVMs. As a result, we derive single-letterized formulas for the Stein's lemma type of optimal error exponents under one-way LOCC, two-way LOCC and separable POVMs, the Chernoff bounds under one-way LOCC POVMs and separable POVMs, and the Hoeffding bounds under one-way LOCC POVMs in the whole region of a parameter and under separable POVMs on a restricted region of a parameter. We also numerically calculate the Chernoff and the Hoeffding bounds under a class of three-step LOCC protocols in low-dimensional systems and show that these bounds not only outperform the bounds for one-way LOCC POVMs but also almost approximates the bounds for separable POVMs in the parameter region where analytical bounds for separable POVMs are derived.
A VLA Study of High-redshift GRBs. II. The Complex Radio Afterglow of GRB 140304A: Shell Collisions and Two Reverse Shocks

We present detailed multifrequency, multiepoch radio observations of GRB 140304A at z = 5.283 from 1 to 86 GHz and from 0.45 to 89 days. The radio and millimeter data exhibit unusual multiple spectral components, which cannot be simply explained by standard forward and reverse shock scenarios. Through detailed multiwavelength analysis spanning radio to X-rays, we constrain the forward shock parameters to Ek,iso ≈ 4.9 × 1054 erg, A * ≈ 2.6 × 10−2, ϵ e ≈ 2.5 × 10−2, ϵ B ≈ 5.9 × 10−2, p ≈ 2.6, and θ jet ≈ 1. °1, yielding a beaming-corrected γ-ray and kinetic energy, E γ ≈ 2.3 × 1049 erg and E K ≈ 9.5 × 1050 erg, respectively. We model the excess radio emission as due to a combination of a late-time reverse shock (RS) launched by a shell collision, which also produces a rebrightening in the X-rays at ≈0.26 days, and either a standard RS or diffractive interstellar scintillation (ISS). Under the standard RS interpretation, we invoke consistency arguments between the forward and reverse shocks to derive a deceleration time, tdec ≈ 100 s, the ejecta Lorentz factor, Γ(tdec) ≈ 300, and a low RS magnetization, RB ≈ 0.6. Our observations highlight both the power of radio observations in capturing RS emission and thus constraining the properties of GRB ejecta and central engines and the challenge presented by ISS in conclusively identifying RS emission in GRB radio afterglows.
Aplicación de herramientas de neuromarketing a la investigación de mercados

El neuromarketing ha conformado un área emergente capaz de establecer vínculos entre el estudio del comportamiento del consumidor y la neurociencia, basándose en innovaciones tecnológicas y tecnologías de neuroimagen capaces de obtener datos del cerebro. La aplicación de las tecnologías de neuroimagen en la investigación de mercados ha supuesto un nuevo universo de actuación en términos de recogida de información, en base a ello, el presente estudio tiene como objetivo realizar un análisis de las diferentes herramientas de neuroimagen, basándose en una metodología de análisis de elementos teóricos combinada con estudio de casos concretos.
Effect of strain on hyperfine-induced hole-spin decoherence in quantum dots

We theoretically consider the effect of strain on the spin dynamics of a single heavy hole (HH) confined to a self-assembled quantum dot and interacting with the surrounding nuclei via hyperfine interaction. Confinement and strain hybridize the HH states, which show an exponential decay for a narrowed nuclear spin bath. For different strain configurations within the dot, the dependence of the spin decoherence time T-2 on external parameters is shifted and the nonmonotonic dependence of the peak is altered. Application of external strain yields considerable shifts in the dependence of T-2 on external parameters. We find that external strain affects mostly the effective hyperfine coupling strength of the conduction band (CB), indicating that the CB admixture of the hybridized HH states plays a crucial role in the sensitivity of T-2 on strain.
JCMT HARP CO 3-2 Observations of Molecular Outflows in W5

New JCMT HARP CO 3-2 observations of the W5 star forming complex are presented, totaling an area of 12000 arcmin 2 with sensitivity better than 0.1 K per 0.4 km s 1 channel. We discovered 55 CO outow candidates, of which 40 are associated with W5 and 15 are more distant than the Perseus arm. Most of the outows are located on the periphery of the W5 HII region. However, two outow clusters are > 5 pc from the ionization fronts, indicating that their driving protostars formed without directly being triggered by the O-stars in W5. We compare the derived outow properties to those in Perseus and nd that the total W5 outow mass is surprisingly low given the cloud masses. The outow mass deciency in the more massive W5 cloud (M(H2) 5 10 4 M ) can be explained if ionizing radiation dissociates molecules as they break out of their host cloud cores. Although CO J=3-2 is a good outow tracer,
THE PROPERTIES OF TWO LOW-REDSHIFT O vi ABSORBERS AND THEIR ASSOCIATED GALAXIES TOWARD 3C 263,

Ultraviolet observations of the QSO 3C 263 (zem = 0.652) with Cosmic Origins Spectrograph and FUSE reveal O vi absorption systems at z = 0.06342 and 0.14072. WIYN multi-object spectrograph observations provide information about the galaxies associated with the absorbers. The multi-phase system at z = 0.06342 traces cool photoionized gas and warm collisionally ionized gas associated with an L ∼ 0.31 L* compact spiral emission line galaxy with an impact parameter of 63 kpc. The cool photoionized gas in the absorber is well modeled, with log U ∼ −2.6, log N(H) ∼ 17.8, log n(H) ∼ −3.3 and [Si/H] = −0.14 ± 0.23. The collisionally ionized gas containing C iv and O vi probably arises in cooling shock-heated transition temperature gas with log T ∼ 5.5. The absorber is likely tracing circumgalactic gas enriched by gas ejected from the spiral emission line galaxy. The simple system at z = 0.14072 only contains O vi and broad and narrow H i. The O vi with b = 33.4 ± 11.9 km s−1 is likely associated with the broad H i λ1215 absorption, with b = 86.7 ± 15.4 km s−1. The difference in Doppler parameters implies the detection of a very large column of warm gas with log T = 5.61(+0.16, −0.25), log N(H) = 19.54(+0.26, −0.44), and [O/H] = −1.48 (+0.46, −0.26). This absorber is possibly associated with a 1.6 L* absorption line galaxy with an impact parameter of 617 kpc, although an origin in warm filament gas or in the halo of a fainter galaxy is more likely.