Miracle-dz/UniFAR-FTUs huggingface dataset:
On the use of the first-order moment approach for measurements of Heff from LSD profiles

The big majority of the reported measurements of the stellar magnetic fields that have analysed spectropolarimetric data have employed the least-squares deconvolution method (LSD) and the first-order moment approach. We present a series of numerical tests in which we review some important aspects of this technique. First, we show that the selection of the profile widths, i.e. integration range in the first-order moment equation, is independent of the accuracy of the magnetic measurements, meaning that for any arbitrary profile width it is always possible to properly determine the longitudinal magnetic field. We also study the interplay between the line depth limit adopted in the line mask and the normalization values of the LSD profiles. We finally show that the rotation of the stars has to be considered to correctly infer the intensity of the magnetic field, something that has been neglected up to now. We show that the latter consideration is crucial, and our test shows that the magnetic intensities differ by a factor close to 3 for a moderate fast rotator star with vsini of 50 ${\rm km\, s^{-1}}$. Therefore, it is expected that in general the stellar magnetic fields reported for fast rotators are stronger than what was believed. All the previous results shows that the first-order moment can be a very robust tool for measurements of magnetic fields, provided that the weak magnetic field approximation is secured. We also show that when the magnetic field regime breaks down, the use of the first-order moment method becomes uncertain.
Lossless Attention in Convolutional Networks for Facial Expression Recognition in the Wild

Unlike the constraint frontal face condition, faces in the wild have various unconstrained interference factors, such as complex illumination, changing perspective and various occlusions. Facial expressions recognition (FER) in the wild is a challenging task and existing methods can't perform well. However, for occluded faces (containing occlusion caused by other objects and self-occlusion caused by head posture changes), the attention mechanism has the ability to focus on the non-occluded regions automatically. In this paper, we propose a Lossless Attention Model (LLAM) for convolutional neural networks (CNN) to extract attention-aware features from faces. Our module avoids decay information in the process of generating attention maps by using the information of the previous layer and not reducing the dimensionality. Sequentially, we adaptively refine the feature responses by fusing the attention map with the feature map. We participate in the seven basic expression classification sub-challenges of FG-2020 Affective Behavior Analysis in-the-wild Challenge. And we validate our method on the Aff-Wild2 datasets released by the Challenge. The total accuracy (Accuracy) and the unweighted mean (F1) of our method on the validation set are 0.49 and 0.38 respectively, and the final result is 0.42 (0.67 F1-Score + 0.33 Accuracy).
Consensus-based Optimization on the Sphere II: Convergence to Global Minimizers and Machine Learning

We present the implementation of a new stochastic Kuramoto-Vicsek-type model for global optimization of nonconvex functions on the sphere. This model belongs to the class of Consensus-Based Optimization. In fact, particles move on the sphere driven by a drift towards an instantaneous consensus point, which is computed as a convex combination of particle locations, weighted by the cost function according to Laplace's principle, and it represents an approximation to a global minimizer. The dynamics is further perturbed by a random vector field to favor exploration, whose variance is a function of the distance of the particles to the consensus point. In particular, as soon as the consensus is reached the stochastic component vanishes. The main results of this paper are about the proof of convergence of the numerical scheme to global minimizers provided conditions of well-preparation of the initial datum. The proof combines previous results of mean-field limit with a novel asymptotic analysis, and classical convergence results of numerical methods for SDE. We present several numerical experiments, which show that the algorithm proposed in the present paper scales well with the dimension and is extremely versatile. To quantify the performances of the new approach, we show that the algorithm is able to perform essentially as good as ad hoc state of the art methods in challenging problems in signal processing and machine learning, namely the phase retrieval problem and the robust subspace detection.
Effects of non-uniform magnetic field on the spin current of strongly dissipatively driven XXZ spin chains

In XXZ chains, spin transport can be significantly suppressed when the anisotropy in the chain and the bias of the dissipative driving are large enough. This phenomenon of negative differential conductance is caused by the formation of two oppositely polarized ferromagnetic domains at the edges of the chain. Here we consider the effect of a non-uniform, or disordered, magnetic field. We show that, while in general disorder reduces the current, in the regime of negative differential conductance disorder can enhance currents. This effect is limited and more pronounced in shorter spin chains. We consider different types of disorder distributions: uniform, algebraic and dichotomous. Analyzing in detail the effects of dichotomous disorder we find a resonant behavior with peaks of currents related to avoided crossings in the energy spectrum of the spin chain. Furthermore, by studying the effect of all possible configurations of the magnetic field, we find a configuration which results in a very strong spin current rectification, thus turning the spin chain into an effective diode.
Optimal rates of convergence for quenched central limit theorem rates of one-dimensional random walks in random environments

We consider the rates of convergence of the quenched central limit theorem for hitting times of one-dimensional random walks in a random environment. Previous results had identified polynomial upper bounds for the rates of decay which are sometimes slower than $n^{-1/2}$ (the optimal rate in the classical Berry-Esseen estimates). Here we prove that the previous upper bounds are in fact the best possible polynomial rates for the quenched CLT.
Efficient Differentially Private F0 Linear Sketching

A powerful feature of linear sketches is that from sketches of two data vectors, one can compute the sketch of the difference between the vectors. This allows us to answer fine-grained questions about the difference between two data sets. In this work, we consider how to achieve this kind of property with sketches that are differentially private. We describe how to compute linear sketches for $F_0$, the number of distinct elements, that can efficiently be made differentially private. Specifically, we consider a sketch that is linear over GF(2), mapping a vector $x\in \{0,1\}^u$ to $Hx\in\{0,1\}^\tau$ for a matrix $H$ sampled from a suitable distribution $\mathcal{H}$. Differential privacy is achieved by using randomized response, flipping each bit of $Hx$ with probability $p<1/2$. That is, for a vector $\varphi \in \{0,1\}^\tau$ where $\Pr[(\varphi)_j = 1] = p$ independently for each entry $j$, we consider the noisy sketch $Hx + \varphi$, where the addition of noise happens over GF(2). We show that for every choice of $0<\beta, \varepsilon < 1$ there exists $p<1/2$ and a distribution $\mathcal{H}$ of linear sketches of size $\tau = O(\log^4(u)\varepsilon^{-2}\beta^{-2})$ such that: 1) For random $H\sim\mathcal{H}$ and noise vector $\varphi$, given $Hx + \varphi$ we can compute an estimate of $\Vert x\Vert_0$ that is accurate within a factor $1\pm\beta$, plus an additive error $O(\log^3(u)\varepsilon^{-2}\beta^{-2})$, with high probability, and 2) For every $H\sim\mathcal{H}$, $Hx + \varphi$ is $\varepsilon$-differentially private over the randomness in $\varphi$. Previously, Mir et al.~(PODS 2011) had described a private way of sketching $F_0$, but their noise vector $\varphi$ is constructed using the exponential mechanism, and is not computationally efficient (quasipolynomial time in the sketch size).
Essentially optimal sparse polynomial multiplication

We present a probabilistic algorithm to compute the product of two univariate sparse polynomials over a field with a number of bit operations that is quasi-linear in the size of the input and the output. Our algorithm works for any field of characteristic zero or larger than the degree. We mainly rely on sparse interpolation and on a new algorithm for verifying a sparse product that has also a quasi-linear time complexity. Using Kronecker substitution techniques we extend our result to the multivariate case.
Noise2Inverse: Self-supervised deep convolutional denoising for linear inverse problems in imaging

Recovering a high-quality image from noisy indirect measurement is an important problem with many applications. For such inverse problems, supervised deep convolutional neural network (CNN)-based denoising methods have shown strong results, but their success critically depends on the availability of a high-quality training dataset of similar measurements. For image denoising, methods are available that enable training without a separate training dataset by assuming that the noise in two different pixels is uncorrelated. However, this assumption does not hold for inverse problems, resulting in artifacts in the output of existing methods. Here, we propose Noise2Inverse, a deep CNN-based denoising method for linear inverse problems in imaging that does not require any additional clean or noisy data. Training a CNN-based denoiser is enabled by exploiting the noise model to compute multiple statistically independent reconstructions. We develop a theoretical framework which shows that such training indeed obtains a denoising CNN, assuming the measured noise is element-wise independent and zero-mean. On simulated CT datasets, Noise2Inverse demonstrates a substantial improvement in peak signal-to-noise ratio (> 2dB) and structural similarity index (> 30%) compared to image denoising methods and conventional reconstruction methods, such as Total-Variation Minimization. We also demonstrate that the method is able to significantly reduce noise in challenging real-world experimental datasets.
Moir\'e potential, lattice corrugation, and band gap spatial variation in a twist-free MoS2/MoTe2 heterobilayer.

To have a fully ab initio description of the Moire pattern in a transition metal dichalcogenide heterobilayer, we have carried out density functional theory calculations, taking accounts of both atomic registry in and the lattice corrugation out of the monolayers, on a MoTe2(9*9)/MoS2(10*10) system which has a moderate size of superlattice larger than an exciton yet not large enough to justify a continuum model treatment. We find that the local potential in the midplane of the bilayer displays a conspicuous Moire pattern. It further leads us to reveal that the variation of the average local potential near Mo atoms in both MoTe2 and MoS2 layers make intralayer Moire potentials. They are the result of mutual modulation and correlate directly with the spatial variation of the valence band maximum and conduction band minimum. The interlayer Moire potential, defined as the difference between the two intralayer Moire potentials, has a depth of 0.11 eV and changes roughly in proportion to the band gap variation in the Moire cell, which has an amplitude of 0.04 eV. We find the lattice corrugation is significant in both MoTe2 (0.30A) and MoS2 (0.77A) layers, yet its effect on the electronic properties is marginal. The wrinkling of the MoTe2/MoS2 bilayer enhances the spatial variation of the local band gap by 5 meV, while its influence on the global band gap is within 1 meV. A simple intralayer band-coupling model is proposed to understand the correlation of Moire potential and spatial variation of the band gap.
The classical Boussinesq system revisited

In this work, we revisit the study by M. E. Schonbek [11] concerning the problem of existence of global entropic weak solutions for the classical Boussinesq system, as well as the study of the regularity of these solutions by C. J. Amick [1]. We propose to regularize by a "fractal" operator (i.e. a differential operator defined by a Fourier multiplier of type e |ξ|^λ , (e, λ) ∈ R + ×]0, 2]). We first show that the regularized system is globally unconditionally well-posed in Sobolev spaces of type H s (R), s > 1 2 , uniformly in the regularizing parameters (e, λ) ∈ R + ×]0, 2]. As a consequence we obtain the global well-posedness of the classical Boussinesq system at this level of regularity as well as the convergence in the strong topology of the solution of the regularized system towards the solution of the classical Boussinesq equation as the parameter e goes to 0. In a second time, we prove the existence of low regularity entropic solutions of the Boussinesq equations emanating from u_0 ∈ H 1 and ζ_0 in an Orlicz class as weak limits of regular solutions.
Boosting simple learners

Boosting is a celebrated machine learning approach which is based on the idea of combining weak and moderately inaccurate hypotheses to a strong and accurate one. We study boosting under the assumption that the weak hypotheses belong to a class of bounded capacity. This assumption is inspired by the common convention that weak hypotheses are “rules-of-thumbs” from an “easy-to-learn class”. (Schapire and Freund ’12, Shalev-Shwartz and Ben-David ’14.) Formally, we assume the class of weak hypotheses has a bounded VC dimension. We focus on two main questions: (i) Oracle Complexity: How many weak hypotheses are needed in order to produce an accurate hypothesis? We design a novel boosting algorithm and demonstrate that it circumvents a classical lower bound by Freund and Schapire (’95, ’12). Whereas the lower bound shows that Ω(1/γ2) weak hypotheses with γ-margin are sometimes necessary, our new method requires only Õ(1/γ) weak hypothesis, provided that they belong to a class of bounded VC dimension. Unlike previous boosting algorithms which aggregate the weak hypotheses by majority votes, the new boosting algorithm uses more complex (“deeper”) aggregation rules. We complement this result by showing that complex aggregation rules are in fact necessary to circumvent the aforementioned lower bound. (ii) Expressivity: Which tasks can be learned by boosting weak hypotheses from a bounded VC class? Can complex concepts that are “far away” from the class be learned? Towards answering the first question we identify a combinatorial-geometric parameter which captures the expressivity of base-classes in boosting. As a corollary we provide an affirmative answer to the second question for many well-studied classes, including half-spaces and decision stumps. Along the way, we establish and exploit connections with Discrepancy Theory.
A Review of Personality in Human‒Robot Interactions

Personality has been identified as a vital factor in understanding the quality of human robot interactions. Despite this the research in this area remains fragmented and lacks a coherent framework. This makes it difficult to understand what we know and identify what we do not. As a result our knowledge of personality in human robot interactions has not kept pace with the deployment of robots in organizations or in our broader society. To address this shortcoming, this paper reviews 83 articles and 84 separate studies to assess the current state of human robot personality research. This review: (1) highlights major thematic research areas, (2) identifies gaps in the literature, (3) derives and presents major conclusions from the literature and (4) offers guidance for future research.
Learning Perception and Planning With Deep Active Inference

Active inference is a process theory of the brain that states that all living organisms infer actions in order to minimize their (expected) free energy. However, current experiments are limited to predefined, often discrete, state spaces. In this paper we use recent advances in deep learning to learn the state space and approximate the necessary probability distributions to engage in active inference.
Interaction between low-level jets and wind farms in a stable atmospheric boundary layer

Low-level jets (LLJs) are the wind maxima in a weak or moderately stable atmospheric boundary layer. The momentum in the LLJs can serve as a source of high capacity energy for wind turbines. In this work, large-eddy simulations are used to study the effect of LLJs and low-lying temperature inversions on the power production of wind farms by systematically increasing the stability of the boundary layer. The LLJs increase the shear in the boundary layer and enhance power production. Flow visualization reveals that the low-lying capping inversion limits the growth of the wind farm internal boundary layer and forces the wind to go around the wind farm. This effect is stronger at higher stratification; i.e. more wind goes around the wind farm. An energy budget analysis is carried out to analyze the effect of different flow phenomena on turbine power production. The budget analysis shows elevated entrainment rates in the presence of LLJs, emphasizing the effect of jets as an additional momentum source. Furthermore, the height of the LLJ determines the wake recovery and power production in the wind farm. When the jets are far above the wind turbines, higher entrainment in the rear of the wind farm causes an increase in power production. If the jets are at the same height as the top of the wind turbines, the wind turbine wake meandering in the vertical direction can directly extract the energy in the jets. At high stratification, the combined effect of buoyancy destruction and turbulence dissipation is more than the turbulent entrainment, and causes a slower wake recovery.
Origin of the Electron–Phonon Interaction of Topological Semimetal Surfaces Measured with Helium Atom Scattering

He atom scattering has been demonstrated to be a sensitive probe of the electron–phonon interaction parameter λ at metal and metal-overlayer surfaces. Here it is shown that the theory linking λ to the thermal attenuation of atom scattering spectra (the Debye–Waller factor) can be applied to topological semimetal surfaces, such as the quasi-one-dimensional charge-density-wave system Bi(114) and the layered pnictogen chalcogenides. The electron−phonon coupling, as determined for several topological insulators belonging to the class of bismuth chalcogenides, suggests a dominant contribution of the surface quantum well states over the Dirac electrons in terms of λ.
Clustering and Recognition of Spatiotemporal Features Through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks

Encoder-decoder recurrent neural network models (RNN Seq2Seq) have achieved success in ubiquitous areas of computation and applications. They were shown to be effective in modeling data with both temporal and spatial dependencies for translation or prediction tasks. In this study, we propose an embedding approach to visualize and interpret the representation of data by these models. Furthermore, we show that the embedding is an effective method for unsupervised learning and can be utilized to estimate the optimality of model training. In particular, we demonstrate that embedding space projections of the decoder states of RNN Seq2Seq model trained on sequences prediction are organized in clusters capturing similarities and differences in the dynamics of these sequences. Such performance corresponds to an unsupervised clustering of any spatio-temporal features and can be employed for time-dependent problems such as temporal segmentation, clustering of dynamic activity, self-supervised classification, action recognition, failure prediction, etc. We test and demonstrate the application of the embedding methodology to time-sequences of 3D human body poses. We show that the methodology provides a high-quality unsupervised categorization of movements. The source code with examples is available in a Github repository1.
Neuroticism and home bias

In this paper I investigate whether neuroticism has a negative impact on investment in foreign securities, exacerbating the ‘home bias’ present while investing in stocks and bonds. This hypothesis is motivated by research which: (1) established a link between neuroticism and fear of uncertainty, (2) revealed that investors feel greater uncertainty when considering investment in foreign securities, and (3) shown that negative affect impacts in-group bias. I explore this hypothesis on both the country level (using cross-country observational data) and individual level (using the responses of students that were given a hypothetical investment scenario). Additionally, I explore whether the predicted relationship is further exacerbated when uncertainty or anxiety is high, or when the investment destination is dissimilar from the investors’ own country. The country-level analysis reveals that investors from high-neuroticism countries generally invest less in foreign equities than investors from other countries, and provides some support that this tendency can become more pronounced when investing in times of high uncertainty or in culturally dissimilar countries. The hypothesized relationship is also present in the responses of male students in a high-anxiety (stressful) condition. Given that professional investors are predominantly male, and work under high stress, this explains to some extent why investors have a bias towards domestic securities.
Simulation of shape memory cycle of a polymeric beam undergoing large deflection using a simple incremental approach

Shape memory polymers are a group of polymers which can store a temporary shape at a relatively cold temperature and recover when heated above its glass transition temperature. Various constitutive models exist in literature to simulate the uniaxial shape memory cycle. One such popular model is based on the concept of multiple natural configurations. In the present work, the stress-strain-based model is adapted for bending application by converting it into a scalar moment-curvature-based relationship. The adapted model is used in a numerical framework for large deflection of beams to simulate the shape memory cycle. The employed numerical framework is based on linearising the non-linear governing differential equation and subsequently solving it in steps by numerical integration. The results are presented in suitable non-dimensional form for generality. Since coefficient of thermal expansion plays a minimal role in bending, it is found that the bending results are considerably different from their uniaxial counterpart. The approach may be claimed to be computationally economic as compared to finite element method because here large matrix inversion is avoided.