Miracle-dz/UniFAR-FTUs huggingface dataset:
Orbital coupled dipolar fermions in an asymmetric optical ladder

We study a quantum ladder of interacting fermions with coupled s and p orbitals. Such a model describes dipolar molecules or atoms loaded into a double-well optical lattice, dipole moments being aligned by an external field. The two orbital components have distinct hoppings. The tunneling between them is equivalent to a partial Rashba spin-orbital coupling when the orbital space (s, p) is identified as spanned by pseudo-spin 1/2 states. A rich phase diagram, including incommensurate orbital density wave, pair density wave and other exotic superconducting phases, is proposed with bosonization analysis. In particular, superconductivity is found in the repulsive regime.
Discovery of carbon monoxide in the upper atmosphere of Pluto

Pluto’s icy surface has changed colour and its atmosphere has swelled since its last closest approach to the Sun in 1989. The thin atmosphere is produced by evaporating ices, and so can also change rapidly, and in particular carbon monoxide should be present as an active thermostat. Here we report the discovery of gaseous CO via the 1.3-mm wavelength J=2–1 rotational transition, and find that the line-centre signal is more than twice as bright as a tentative result obtained in 2000 by Bock´ elee-Morvan et al. Greater surface ice evaporation over the last decade could explain this, or increased pressure could have caused the atmosphere to expand. The gas must be cold, with a narrow line-width consistent with temperatures around 50 K, as predicted for the very high atmosphere, and the line brightness implies that CO molecules extend up to ≈3 Pluto radii above the surface. The upper atmosphere must have changed markedly over only a decade since the prior search, and more alterations could occur by the arrival of the New Horizons mission in 2015.
Wetting and Diffusion of Water on Pristine and Strained Phosphorene

Phosphorene, a newly fabricated two-dimensional (2D) nanomaterial, have exhibited promising application prospect in biology. Nonetheless, the wetting and diffusive properties of bio-fluids on phosphorene are still elusive. In this study, using molecular dynamics (MD) simulations, we investigated the structural and dynamic properties of water on pristine and strained phosphorene. The MD simulations illustrated that the diffusion of water molecules on the phosphorene surface is anisotropic, while strain-enhanced diffusion is clearly present which arises from strain-induced smooth of the energy landscape. The contact angle of water droplet on phosphorene exhibited a nonmonotonic variation with the transverse strain. The structure of water on transverse stretched phosphorene was demonstrated to be different from that on longitudinal stretched phosphorene. Moreover, we discovered that the contact angle of water on strained phosphorene is proportional to the quotient of longitudinal and transverse diffusion coefficients of interfacial water. These findings would offer helpful insights in potential ways of manipulating the wetting and transport of water at nanoscale, and in future bio-applications of phosphorene.
Statistical physics of social dynamics

Statistical physics has proven to be a fruitful framework to describe phenomena outside the realm of traditional physics. Recent years have witnessed an attempt by physicists to study collective phenomena emerging from the interactions of individuals as elementary units in social structures. A wide list of topics are reviewed ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social spreading. The connections between these problems and other, more traditional, topics of statistical physics are highlighted. Comparison of model results with empirical data from social systems are also emphasized.
Light propagation in the gravitational field of N arbitrarily moving bodies in the 1.5PN approximation for high-precision astrometry

The light-trajectory in the gravitational field of N extended bodies in arbitrary motion is determined in the first post-Newtonian approximation. According to the theory of reference systems, the gravitational fields of these massive bodies are expressed in terms of their intrinsic multipoles, allowing for arbitrary shape and inner structure of these bodies. The results of this investigation aim towards a consistent general-relativistic theory of light propagation in the Solar system for high-precision astrometry at sub-micro-arcsecond level of accuracy.
Average $r$--rank Artin Conjecture

Let $\Gamma\subset\mathbb{Q}^*$ be a finitely generated subgroup and let $p$ be a prime such that the reduction group $\Gamma_p$ is a well defined subgroup of the multiplicative group $\mathbb{F}_p^*$. We prove an asymptotic formula for the average of the number of primes $p\le x$ for which the index $[\mathbb{F}_p^*:\Gamma_p]=m$. The average is performed over all finitely generated subgroups $\Gamma=\langle a_1,\dots,a_r \rangle\subset\mathbb{Q}^*$, with $a_i\in\mathbb{Z}$ and $a_i\le T_i$ with a range of uniformity: $T_i>\exp(4(\log x \log\log x)^{\frac{1}{2}})$ for every $i=1,\dots,r$. We also prove an asymptotic formula for the mean square of the error terms in the asymptotic formula with a similar range of uniformity. The case of rank $1$ and $m=1$ corresponds to the classical Artin conjecture for primitive roots and has already been considered by Stephens in 1969.
Flow modelling in gas transmission networks. Part II – Method of solution and algorithms

In the first part, the mathematical problem of flow modelling in gas transmission network was formulated. In the paper, the method of solution is explained for the model with the postulate $T$=const. The method of automatic generation of the system of algebraic equations is described. The results, good and bad experiences are described.
Loss evaluation of induction motor by using vector magnetic hysteresis "E&S/sup 2/" model

Summary form only given. It is well known that the magnetic field intensity vector H is not parallel to the magnetic flux density vector B in soft magnetic materials under two-directional excitation. This property is called the "vector magnetic property". Introduction of this property into numerical analysis is very important to carry out accurate magnetic field analysis. We have analyzed a three-phase induction motor model by using the finite element method considering the vector magnetic properties. From analyzed results, we can calculate directly the iron loss distributions in the motor core. This method is very useful in designing low loss and highly efficiency motors. In this paper, we present a new core model that has low loss.
Understanding the Effects of Energy from the Solar Wind to the Magnetosphere Ionosphere-Thermosphere System

Abstract : Recent studies have shown that sudden enhancement of solar wind dynamic pressure (Psw) is a significant driver of energy transfer to the magnetosphere-ionosphere (MI) system, generating strong responses such as increase in the cross polar cap potential (CPCP), reduction of the polar cap area, expansion of the auroral oval, etc. This study investigates where, when, and how solar wind energy is deposited into the MI system during sudden solar wind dynamic pressure enhancement, like shocks. We analyze three unique events that occurred during strongly southward, near-zero Bz, and northward IMF by simulating the MI responses with the OpenGGCM coupled magnetosphere-ionosphere-thermosphere model. We examine the behavior of dayside and nightside reconnection, and quantify their respective contribution to CPCP, a proxy of ionospheric flow convection.
de Sitter invariance of the dS graviton vacuum

The two-point function of linearized gravitons on de Sitter space is infrared divergent in the standard transverse traceless synchronous gauge defined by k = 0 cosmological coordinates (also called conformal or Poincaré coordinates). We show that this divergence can be removed by adding a linearized diffeomorphism to each mode function, i.e. by an explicit change of gauge. It follows that the graviton vacuum state is well defined and de Sitter invariant in agreement with various earlier arguments.
Neutral stability height correction for ocean winds

Adjusting ocean wind observations to a standard height, usually 10 m, requires the use of a boundary layer model, and knowledge of the thermodynamical variables. Height adjustment is complicated by the fact that a necessary parameter, the roughness height, cannot be given in a closed form solution. If only the wind and reporting height are known, the best that can be done is to assume neutral stability. The determination of roughness height is analyzed and a simple approximation (used by Atlas et al. 2011) is derived in detail. This approximation is accurate for winds in the range of 1 - 30 m/s for neutral stratification and would be an excellent initial estimate for a Newton iteration to determine the roughness height precisely, whether or not neutral stability is assumed.
Analyse numerique et simulation de problemes d'interaction fluide-structure en regime incompressible

Cette etude a pour objet d'analyser, de modeliser et de simuler differents problemes d'interaction fluide-structure. Les deplacements de la structure ne sont pas negligeables et induisent une reelle deformation du domaine fluide. Le fluide est suppose incompressible et newtonien ; il est represente dans une configuration d'euler par les equations de navier-stokes. La structure est supposee elastique, homogene et isotrope ; elle est decrite dans une configuration de lagrange, par les equations de l'elasticite linearisee. Une methode ale (arbitrary lagrangian eulerian method) permet de resoudre les equations du probleme fluide dans un domaine mobile s'adaptant aux deformations de la structure. Des conditions limites a l'interface traduisent l'interaction : il y a egalite des vitesses et egalite des contraintes normales. Sur un modele simplifie d'interaction entre un fluide et une structure monodimensionnels, l'analyse numerique en termes de stabilite, consistence et convergence est presentee pour un traitement implicite des conditions limites a l'interface. La stabilite de deux algorithmes explicites est etudiee pour ce modele en vue d'aborder la simulation des problemes 2d ou 3d. En effet, afin de coupler deux codes numeriques existants sans avoir de transformation fondamentale a effectuer, l'utilisation d'algorithmes decales est preconisee et donne des resultats satisfaisants sur des modeles d'interaction 2d-fluide/1d-structure et 3d-fluide/2d-structure. L'avantage de tels algorithmes est de pouvoir facilement modifier les codes fluide ou structure afin d'envisager des lois de comportement differentes en fluide ou en structure.
Cosmological free-free emission from dark matter halos in the 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi mathvariant="normal">Λ</mml:mi><mml:mi>CDM</mml:mi></mml:mrow></mml:math>
 model

We study the diffuse background free-free emission induced by dark matter halos. Since dark matter halos host ionized thermal plasma, they could be an essential source of cosmological free-free emission. We evaluate the global background intensity and the anisotropy of this free-free emission. We show that the dominant contribution comes from dark matter halos with a mass close to the Jeans mass, $M_{\mathrm{halo}}\sim 10^{10} M_\odot$, around the redshift $z \sim 3$. Therefore, the intensity of the free-free emission is sensitive to the small-scale primordial curvature perturbations that form such small-mass dark matter halos. Although our obtained intensity of the global and anisotropic free-free emission is smaller than the $10\%$ level of the free-free emission observed in the high galactic region, we find that the free-free emission signal is modified by $\sim 20 \%$ even in the parameter set of the spectral index and the running, which is consistent with the recent Planck result. Therefore, the measurement of the cosmological free-free signals has the potential to provide more stringent constraints on the abundance of small-mass dark matter halos and the curvature perturbations including the spectral index and the running, while carefully removing the Galactic free-free emission is required through the multifrequency radio observation or the cross-correlation study with the galaxy surveys or 21-cm intensity map.
Tourism's trend Ranking on Social Media Data Using Fuzzy-AHP vs. AHP

Tourism is an exciting thing to be visited by people in the world. Search for attractive and popular places can be done through social media. Data from social media or websites can be used as a reference to find current travel trends and get information about reviews, stories, likes, forums, blogs, and feedback from a place. However, if the search is done manually one by one, it takes a long time, and it becomes interesting to do research. So, searching based on current trends will be easier and faster. For this reason, this study uses a computer base to search by ranking tourist facilities from social media data or websites using the multi-criteria decision-making (MCDM) method. The implementation of the method used in finding the trend is the Fuzzy-AHP method in comparison with the AHP. The data used is data reviews, stories, likes, forums, blogs, and feedback from the web or social media. Because with these components, tourism can be developed according to visitors’ wishes. The research aims to rank facilities’ tourism attractions (trends) and development priorities. The priority and ranking used the fuzzy-AHP and AHP method to determine weight criteria and the ranking process. The highest ranking is on the Parks/Picnic Spots attraction, and make it a priority to develop. The methods have an average value MSE of all data is ≈ 0.0002, which can be used for this ranking.
Factor Graph Fusion of Raw GNSS Sensing with IMU and Lidar for Precise Robot Localization without a Base Station

Accurate localization is a core component of a robot's navigation system. To this end, global navigation satellite systems (GNSS) can provide absolute measurements outdoors and, therefore, eliminate long-term drift. However, fusing GNSS data with other sensor data is not trivial, especially when a robot moves between areas with and without sky view. We propose a robust approach that tightly fuses raw GNSS receiver data with inertial measurements and, optionally, lidar observations for precise and smooth mobile robot localization. A factor graph with two types of GNSS factors is proposed. First, factors based on pseudoranges, which allow for global localization on Earth. Second, factors based on carrier phases, which enable highly accurate relative localization, which is useful when other sensing modalities are challenged. Unlike traditional differential GNSS, this approach does not require a connection to a base station. On a public urban driving dataset, our approach achieves accuracy comparable to a state-of-the-art algorithm that fuses visual inertial odometry with GNSS data-despite our approach not using the camera, just inertial and GNSS data. We also demonstrate the robustness of our approach using data from a car and a quadruped robot moving in environments with little sky visibility, such as a forest. The accuracy in the global Earth frame is still 1–2 m, while the estimated trajectories are discontinuity-free and smooth. We also show how lidar measurements can be tightly integrated. We believe this is the first system that fuses raw GNSS observations (as opposed to fixes) with lidar in a factor graph.
R2C-GAN: Restore-to-Classify GANs for Blind X-Ray Restoration and COVID-19 Classification

Restoration of poor quality images with a blended set of artifacts plays a vital role for a reliable diagnosis. Existing studies have focused on specific restoration problems such as image deblurring, denoising, and exposure correction where there is usually a strong assumption on the artifact type and severity. As a pioneer study in blind X-ray restoration, we propose a joint model for generic image restoration and classification: Restore-to-Classify Generative Adversarial Networks (R2C-GANs). Such a jointly optimized model keeps any disease intact after the restoration. Therefore, this will naturally lead to a higher diagnosis performance thanks to the improved X-ray image quality. To accomplish this crucial objective, we define the restoration task as an Image-to-Image translation problem from poor quality having noisy, blurry, or over/under-exposed images to high quality image domain. The proposed R2C-GAN model is able to learn forward and inverse transforms between the two domains using unpaired training samples. Simultaneously, the joint classification preserves the disease label during restoration. Moreover, the R2C-GANs are equipped with operational layers/neurons reducing the network depth and further boosting both restoration and classification performances. The proposed joint model is extensively evaluated over the QaTa-COV19 dataset for Coronavirus Disease 2019 (COVID-19) classification. The proposed restoration approach achieves over 90% F1-Score which is significantly higher than the performance of any deep model. Moreover, in the qualitative analysis, the restoration performance of R2C-GANs is approved by a group of medical doctors. We share the software implementation at https://github.com/meteahishali/R2C-GAN.
Deconstructing the structure of online conversations on Reddit

The Internet has made it easier for social scientists to study human behav-ior by analyzing their interactions on social media platforms. Many of these platforms characterize conversations among users via threads , which induce a tree-like structure. The structural properties of these discussion trees , such as their width, depth, and size, can be used to make inferences regarding user discussion patterns and conversation dynamics. In this paper, we seek to understand the structure of these online discussions on Reddit. We characterize the structure of these discussions via a set of global and local discussion-tree properties. The global features constitute information regarding the commu-nity/subreddit of a given post, whereas the local features are comprised of the properties of the post itself. We perform various statistical analyses on a year’s worth of Reddit data containing a quarter of a million posts and several million comments. These analyses allow us to tease apart the relative contribution of a discussion post’s global and local properties and characterize the importance of speciﬁc individual features in determining the discussions’ structural patterns. Our results indicate that both local and global features explain a signiﬁcant amount of structural variation. Local features are collectively more important as they explain signiﬁcantly more variation in the discussion trees’ structural properties than global features. However, there is signiﬁcant heterogeneity in the impact of the various features. Several global features, e.g., the topic, age, popularity, and the redundancy of content in a subreddit, also play a crucial role in understanding the speciﬁc properties of discussion trees.
Strong to ultrastrong coherent coupling measurements in a YIG/cavity system at room temperature

We present an experimental study of the strong to ultra-strong coupling regimes at room temperature in frequency-reconfigurable 3D re-entrant cavities coupled with a YIG slab. The observed coupling rate, defined as the ratio of the coupling strength to the cavity frequency of interest, ranges from 12% to 59%. We show that certain considerations must be taken into account when analyzing the polaritonic branches of a cavity spintronic device where the RF field is highly focused in the magnetic material. Our observations are in excellent agreement with electromagnetic finite element simulations in the frequency domain.
Farnet-II: An improved solar far-side active region detection method

Context. Activity on the far side of the Sun is routinely studied through the analysis of the seismic oscillations detected on the near side using helioseismic techniques such as phase shift sensitive holography. Recently, the neural network FarNet was developed to improve these detections. Aims. We aim to create a new machine learning tool, FarNet II, which further increases the scope of FarNet, and to evaluate its performance in comparison to FarNet and the standard helioseismic method for detecting far side activity. Methods. We developed FarNet II, a neural network that retains some of the general characteristics of FarNet but improves the detections in general, as well as the temporal coherence among successive predictions. The main novelties are the implementation of attention and convolutional long short term memory (ConvLSTM) modules. A cross validation approach, training the network 37 times with a different validation set for each run, was employed to leverage the limited amount of data available. We evaluate the performance of FarNet II using three years of extreme ultraviolet observations of the far side of the Sun acquired with the Solar Terrestrial Relations Observatory (STEREO) as a proxy of activity. The results from FarNet II were compared with those obtained from FarNet and the standard helioseismic method using the Dice coefficient as a metric. Results. FarNet II achieves a Dice coefficient that improves that of FarNet by over 0.2 points for every output position on the sequences from the evaluation dates. Its improvement over FarNet is higher than that of FarNet over the standard method. Conclusions. The new network is a very promising tool for improving the detection of activity on the far side of the Sun given by pure helioseismic techniques. Space weather forecasts can potentially benefit from the higher sensitivity provided by this novel method.
Reconstruction-guided attention improves the robustness and shape processing of neural networks

Many visual phenomena suggest that humans use top-down generative or reconstructive processes to create visual percepts (e.g., imagery, object completion, pareidolia), but little is known about the role reconstruction plays in robust object recognition. We built an iterative encoder-decoder network that generates an object reconstruction and used it as top-down attentional feedback to route the most relevant spatial and feature information to feed-forward object recognition processes. We tested this model using the challenging out-of-distribution digit recognition dataset, MNIST-C, where 15 different types of transformation and corruption are applied to handwritten digit images. Our model showed strong generalization performance against various image perturbations, on average outperforming all other models including feedforward CNNs and adversarially trained networks. Our model is particularly robust to blur, noise, and occlusion corruptions, where shape perception plays an important role. Ablation studies further reveal two complementary roles of spatial and feature-based attention in robust object recognition, with the former largely consistent with spatial masking benefits in the attention literature (the reconstruction serves as a mask) and the latter mainly contributing to the model's inference speed (i.e., number of time steps to reach a certain confidence threshold) by reducing the space of possible object hypotheses. We also observed that the model sometimes hallucinates a non-existing pattern out of noise, leading to highly interpretable human-like errors. Our study shows that modeling reconstruction-based feedback endows AI systems with a powerful attention mechanism, which can help us understand the role of generating perception in human visual processing.