Miracle-dz/UniFAR-FTUs huggingface dataset:
Mindfulness and emotional regulation: A systematic literature review*

* This article refers to the call “Mindfulness and other contemplative practices”. * Paper extracted from doctoral dissertation “Mindfulness, emotional regulation and academic career: an investigation with postgraduate students”, presented to Instituto de Psicologia, Universidade Federal da Bahia, Salvador, BA, Brazil. 1 Universidade Federal da Bahia, Instituto de Psicologia, Salvador, BA, Brazil. 2 Scholarship holder at the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil. Liana Santos Alves Peixoto1,2 https://orcid.org/0000-0002-6650-2009 Sônia Maria Guedes Gondim1 https://orcid.org/0000-0003-3482-166X Objective: This systematic literature review study sought to analyze the relationship between mindfulness and emotional regulation. Method: Several national and international databases were consulted and 60 articles published in the period from 2009 to 2019 were selected for analysis. Results: In conceptual terms, mindfulness is predominantly considered as a trait, and less as a state or skill. The results of empirical studies suggest that mindfulness is associated with the use of adaptive emotional regulation strategies favoring healthy psychic functioning. The limits and contributions of this review are also pointed out. Conclusion: It is concluded that there is a need for further studies that consider the procedural aspect of mindfulness and that make it possible to capture the subjective experience of the practice, considering the prevalence of quantitative studies that used self-report scales.
Third-party Link Shortener Usage on Twitter

—Twitter has proven a powerful tool to shape peoples’ opinions and thoughts. One efﬁcient way to spread information is with the use of links. In this paper, we characterize the link sharing usage on Twitter, placing particular focus on third-party link shortener services that hide the full URL from the user. First, we present a measurement framework that combines two Twitter APIs and the Bitly API, and allows us to collect detailed statistics about tweets, their posters, their link usage, and the retweets and clicks 24 hours after the original tweet. Second, using two one-week-long datasets, collected one year apart (April 2019 and 2020), we then characterize and analyze important difference in link usage among users, the domains that different users and shorteners (re)direct users too, and compare the click rates of such links with the corresponding retweet rates. The analysis provides insights into link sharing biases on Twitter, skews, and behavioral differences in usage, as well as reveal interesting observations capturing differences in how a tweet containing a link may be retweeted versus how the embedded link is clicked. Finally, we use click-based results for covid-19 tweets to discuss the importance of controlling the spread of (mis)information.
Coherent and incoherent Thomson scattering on an argon/hydrogen microwave plasma torch with transient behaviour

A new method of processing time-integrated coherent Thomson scattering spectra is presented, which provides not only the electron density and temperature but also information about the transient behaviour of the plasma. Therefore, it is an alternative to single-shot Thomson scattering measurements as long as the scattering is coherent. The method is applied to a microwave plasma torch operating in argon or a mixture of argon with hydrogen at atmospheric pressure. Electron densities up to 8⋅1021 m−3 (ionization degree above 10−3) were observed, which is more than two times higher than presented in earlier works on comparable discharges. Additionally, a parametric study with respect to the argon/hydrogen ratio and the input power was carried out and the results are discussed together with earlier Stark broadening measurements on the same plasma.
Finding Stable Groups of Cross-Correlated Features in Two Data Sets With Common Samples

Data sets in which measurements of different types are obtained from a common set of samples appear in many scientific applications. In the analysis of such data, an important problem is to identify groups of features from different data types that are strongly associated. Given two data types, a bimodule is a pair (A,B) of feature sets from the two types such that the aggregate cross-correlation between the features in A and those in B is large. A bimodule (A,B) is stable if A coincides with the set of features that have significant aggregate correlation with the features in B, and vice-versa. We develop an, iterative, testing-based procedure called BSP to identify stable bimodules. BSP relies on approximate p-values derived from the permutation moments of sums of squared sample correlations between a single feature of one type and a group of features of the second type.We carry out a thorough simulation study to assess the performance of BSP, and present an extended application to the problem of expression quantitative trait loci (eQTL) analysis using recent data from the GTEx project. In addition, we apply BSP to climatology data to identify regions in North America where annual temperature variation affects precipitation. Key terms – Iterative testing, permutation distribution, bipartite correlation network, eQTL analysis, temperature and precipitation covariation.
Low-Rank Methods in Event Detection with Subsampled Point-to-Subspace Proximity Tests

Monitoring of streamed data to detect abnormal behaviour (variously known as event detection, anomaly detection, change detection, or outlier detection) underlies many applications, especially within the Internet of Things. There, one often collects data from a variety of sources, with asynchronous sampling, and missing data. In this setting, one can detect abnormal behavior using low-rank techniques. In particular, we assume that normal observations come from a low-rank subspace, prior to being corrupted by a uniformly distributed noise. Correspondingly, we aim to recover a representation of the subspace, and perform event detection by running point-to-subspace distance query for incoming data. We use a variant of low-rank factorisation, which considers interval uncertainty sets around “known entries”, on a suitable flattening of the input data to obtain a low-rank model. On-line, we compute the distance of incoming data to the low-rank normal subspace and update the subspace to keep it consistent with the seasonal changes present. For the distance computation, we consider subsampling. We bound the one-sided error as a function of the number of coordinates employed. In our experimental evaluation, we test the proposed algorithm on induction-loop data from Dublin, Ireland.
Three-Dimensional Central Moment Lattice Boltzmann Method on a Cuboid Lattice for Anisotropic and Inhomogeneous Flows

Lattice Boltzmann (LB) methods are usually developed on cubic lattices that discretize the configuration space using uniform grids. For efficient computations of anisotropic and inhomogeneous flows, it would be beneficial to develop LB algorithms involving the collision-and-stream steps based on orthorhombic cuboid lattices. We present a new 3D central moment LB scheme based on a cuboid D3Q27 lattice. This scheme involves two free parameters representing the ratios of the characteristic particle speeds along the two directions with respect to those in the remaining direction, and these parameters are referred to as the grid aspect ratios. Unlike the existing LB schemes for cuboid lattices, which are based on orthogonalized raw moments, we construct the collision step based on the relaxation of central moments and avoid the orthogonalization of moment basis, which leads to a more robust formulation. Moreover, prior cuboid LB algorithms prescribe the mappings between the distribution functions and raw moments before and after collision by using a moment basis designed to separate the trace of the second order moments (related to bulk viscosity) from its other components (related to shear viscosity), which lead to cumbersome relations for the transformations. By contrast, in our approach, the bulk and shear viscosity effects associated with the viscous stress tensor are naturally segregated only within the collision step and not for such mappings, while the grid aspect ratios are introduced via simpler pre- and post-collision diagonal scaling matrices in the above mappings. These lead to a compact approach, which can be interpreted based on special matrices. It also results in a modular 3D LB scheme on the cuboid lattice, which allows the existing cubic lattice implementations to be readily extended to those based on the more general cuboid lattices. To maintain the isotropy of the viscous stress tensor of the 3D Navier–Stokes equations using the cuboid lattice, corrections for eliminating the truncation errors resulting from the grid anisotropy as well as those from the aliasing effects are derived using a Chapman–Enskog analysis. Such local corrections, which involve the diagonal components of the velocity gradient tensor and are parameterized by two grid aspect ratios, augment the second order moment equilibria in the collision step. We present a numerical study validating the accuracy of our approach for various benchmark problems at different grid aspect ratios. In addition, we show that our 3D cuboid central moment LB method is numerically more robust than its corresponding raw moment formulation. Finally, we demonstrate the effectiveness of the 3D cuboid central moment LB scheme for the simulations of anisotropic and inhomogeneous flows and show significant savings in memory storage and computational cost when used in lieu of that based on the cubic lattice.
A Core Mass Function Indistinguishable from the Salpeter Stellar Initial Mass Function Using 1000 au Resolution ALMA Observations

We present the core mass function (CMF) of the massive star-forming clump G33.92 + 0.11 using 1.3 mm observations obtained with the Atacama Large Millimeter/submillimeter Array (ALMA). With a resolution of 1000 au, this is one of the highest resolution CMF measurements to date. The CMF is corrected by flux and number incompleteness to obtain a sample that is complete for gas masses M ≳ 2.0 M ⊙. The resulting CMF is well represented by a power-law function ( dN/dlogM∝MΓ ), whose slope is determined using two different approaches: (i) by least-squares fitting of power-law functions to the flux- and number-corrected CMF, and (ii) by comparing the observed CMF to simulated samples with similar incompleteness. We provide a prescription to quantify and correct a flattening bias affecting the slope fits in the first approach, which is caused by small-sample or edge effects when the data are represented by either classical histograms or a kernel density estimate, respectively. The resulting slopes from both approaches are in good agreement each other, with Γ=−1.11−0.11+0.12 being our adopted value. Although this slope appears to be slightly flatter than the Salpeter slope Γ = −1.35 for the stellar initial mass function (IMF), we find from Monte Carlo simulations that the CMF in G33.92 + 0.11 is statistically indistinguishable from the Salpeter representation of the stellar IMF. Our results are consistent with the idea that the form of the IMF is inherited from the CMF, at least at high masses and when the latter is observed at high enough resolution.
Automatic Multi-Stain Registration of Whole Slide Images in Histopathology

Joint analysis of multiple biomarker images and tissue morphology is important for disease diagnosis, treatment planning and drug development. It requires cross-staining comparison among Whole Slide Images (WSIs) of immune-histochemical and hematoxylin and eosin (H&E) microscopic slides. However, automatic, and fast cross-staining alignment of enormous gigapixel WSIs at single-cell precision is challenging. In addition to morphological deformations introduced during slide preparation, there are large variations in cell appearance and tissue morphology across different staining. In this paper, we propose a two-step automatic feature-based cross-staining WSI alignment to assist localization of even tiny metastatic foci in the assessment of lymph node. Image pairs were aligned allowing for translation, rotation, and scaling. The registration was performed automatically by first detecting landmarks in both images, using the scale-invariant image transform (SIFT), followed by the fast sample consensus (FSC) protocol for finding point correspondences and finally aligned the images. The Registration results were evaluated using both visual and quantitative criteria using the Jaccard index. The average Jaccard similarity index of the results produced by the proposed system is 0.942 when compared with the manual registration.
Generic Property of the Partial Calmness Condition for Bilevel Programming Problems

The partial calmness for the bilevel programming problem (BLPP) is an important condition which ensures that a local optimal solution of BLPP is a local optimal solution of a partially penalized problem where the lower level optimality constraint is moved to the objective function and hence a weaker constraint qualification can be applied. In this paper we propose a sufficient condition in the form of a partial error bound condition which guarantees the partial calmness condition. We analyse the partial calmness for the combined program based on the Bouligand (B-) and the Fritz John (FJ) stationary conditions from a generic point of view. Our main result states that the partial error bound condition for the combined programs based on B and FJ conditions are generic for an important setting with applications in economics and hence the partial calmness for the combined program is not a particularly stringent assumption. Moreover we derive optimality conditions for the combined program for the generic case without any extra constraint qualifications and show the exact equivalence between our optimality condition and the one by Jongen and Shikhman given in implicit form. Our arguments are based on Jongen, Jonker and Twilt's generic (five type) classification of the so-called generalized critical points for one-dimensional parametric optimization problems and Jongen and Shikhman's generic local reductions of BLPPs.
Impact of regularization on spectral clustering under the mixed membership stochastic block model

Mixed membership community detection is a challenge problem in network analysis. To estimate the memberships and study the impact of regularized spectral clustering under the mixed membership stochastic block (MMSB) model, this article proposes two efficient spectral clustering approaches based on regularized Laplacian matrix, Simplex Regularized Spectral Clustering (SRSC) and Cone Regularized Spectral Clustering (CRSC). SRSC and CRSC methods are designed based on the ideal simplex structure and the ideal cone structure in the variants of the eigendecomposition of the population regularized Laplacian matrix. We show that these two approaches SRSC and CRSC are asymptotically consistent under mild conditions by providing error bounds for the inferred membership vector of each node under MMSB. Through the theoretical analysis, we give the upper and lower bound for the regularizer τ . By introducing a parametric convergence probability, we can directly see that when τ is large these two methods may still have low error rates but with a smaller probability. Thus we give an empirical optimal choice of τ is O(log(n)) with n the number of nodes to detect sparse networks. The proposed two approaches are successfully applied to synthetic and empirical networks with encouraging results compared with some benchmark methods.
DCT2net: an interpretable shallow CNN for image denoising

This work tackles the issue of noise removal from images, focusing on the well-known DCT image denoising algorithm. The latter, stemming from signal processing, has been well studied over the years. Though very simple, it is still used in crucial parts of state-of-the-art ”traditional” denoising algorithms such as BM3D. For a few years however, deep convolutional neural networks (CNN), especially DnCNN, have outperformed their traditional counterparts, making signal processing methods less attractive. In this paper, we demonstrate that a DCT denoiser can be seen as a shallow CNN and thereby its original linear transform can be tuned through gradient descent in a supervised manner, improving considerably its performance. This gives birth to a fully interpretable CNN called DCT2net. To deal with remaining artifacts induced by DCT2net, an original hybrid solution between DCT and DCT2net is proposed combining the best that these two methods can offer; DCT2net is selected to process non-stationary image patches while DCT is optimal for piecewise smooth patches. Experiments on artificially noisy images demonstrate that two-layer DCT2net provides comparable results to BM3D and is as fast as DnCNN algorithm.
A More Precise Mass for GJ 1214 b and the Frequency of Multiplanet Systems Around Mid-M Dwarfs

We present an intensive effort to refine the mass and orbit of the enveloped terrestrial planet GJ 1214 b using 165 radial velocity (RV) measurements taken with the HARPS spectrograph over a period of 10 years. We conduct a joint analysis of the RVs with archival Spitzer/IRAC transits and measure a planetary mass and radius of 8.17 ± 0.43 M ⊕ and 2.742−0.053+0.050 R ⊕. Assuming that GJ 1214 b is an Earth-like core surrounded by a H/He envelope, we measure an envelope mass fraction of X env = 5.24−0.29+0.30 %. GJ 1214 b remains a prime target for secondary eclipse observations of an enveloped terrestrial, the scheduling of which benefits from our constraint on the orbital eccentricity of <0.063 at 95% confidence, which narrows the secondary eclipse window to 2.8 hr. By combining GJ 1214 with other mid-M-dwarf transiting systems with intensive RV follow up, we calculate the frequency of mid-M-dwarf planetary systems with multiple small planets and find that 90−21+5 % of mid-M dwarfs with a known planet with mass ∈ [1, 10] M ⊕ and orbital period ∈ [0.5, 50] days, will host at least one additional planet. We rule out additional planets around GJ 1214 down to 3 M ⊕ within 10 days, such that GJ 1214 is a single-planet system within these limits. This result has a 44−5+9 probability given the prevalence of multiplanet systems around mid-M dwarfs. We also investigate mid-M-dwarf RV systems and show that the probability that all reported RV planet candidates are real planets is <12% at 99% confidence, although this statistical argument is unable to identify the probable false positives.
Distributed Event- and Self-Triggered Coverage Control with Speed Constrained Unicycle Robots

Voronoi coverage control is a particular problem of importance in the area of multi-robot systems, which considers a network of multiple autonomous robots, tasked with optimally covering a large area. This is a common task for fleets of fixed-wing Unmanned Aerial Vehicles (UAVs), which are described in this work by a unicycle model with constant forward-speed constraints. We develop event-based control/communication algorithms to relax the resource requirements on wireless communication and control actuators, an important feature for battery-driven or otherwise energy-constrained systems. To overcome the drawback that the event-triggered algorithm requires continuous measurement of system states, we propose a self-triggered algorithm to estimate the next triggering time. Hardware experiments illustrate the theoretical results.
Diffractive dissociation in future electron-ion colliders

We study diffractive scattering cross sections, focusing on the rapidity gap distribution in realistic kinematics at future electron-ion colliders. Our study consists in numerical solutions of the QCD evolution equations in both fixed and running coupling frameworks. The fixed and the running coupling equations are shown to lead to different shapes for the rapidity gap distribution. The obtained distribution when the coupling is fixed exhibits a shape characteristic of a recently developed model for diffractive dissociation, which indicates the relevance of the study of that diffractive observable for the partonic-level understanding of diffraction.
Chiral modes near exceptional points in symmetry broken H1 photonic crystal cavities

The H1 photonic crystal cavity supports two degenerate dipole modes of orthogonal linear polarization which could give rise to circularly polarized ﬁelds when driven with a π /2 phase difference. However, fabrication errors tend to break the symmetry of the cavity which lifts the degeneracy of the modes, rendering the cavity unsuitable for supporting circular polarization. We demonstrate numerically, a scheme that in-duces chirality in the cavity modes, thereby achieving a cavity that supports intrinsic circular polarization. By selectively modifying two air holes around the cavity, the dipole modes could interact via asymmetric coherent backscattering which is a non-Hermitian process. With suitable air hole parameters, the cavity modes approach the exceptional point, coalescing in frequencies and linewidths as well as giving rise to signiﬁcant circular polarization close to unity. The handedness of the chirality can be selected depending on the choice of the modiﬁed air holes. Our results highlight the prospect of using the H1 photonic crystal cavity for chiral-light matter coupling in applications such as valleytronics, spin-photon interfaces and the generation of single photons with well-deﬁned spins.
Self-supervised Transformer for Multivariate Clinical Time-Series with Missing Values

Multivariate time-series (MVTS) data are frequently observed in critical care settings and are typically characterized by excessive missingness and irregular time intervals. Existing approaches for learning representations in this domain handle such issues by either aggregation or imputation of values, which in-turn suppresses the ﬁne-grained information and adds undesirable noise/overhead into the machine learning model. To tackle this challenge, we propose STraTS ( S elf-supervised Tra nsformer for T ime-S eries) model which bypasses these pitfalls by treating time-series as a set of observation triplets instead of using the traditional dense matrix representation. It employs a novel Continuous Value Embedding (CVE) technique to encode continuous time and variable values without the need for discretization. It is composed of a Transformer component with Multi-head attention layers which enables it to learn contextual triplet embeddings while avoiding problems of recurrence and vanishing gradients that occur in recurrent architectures. Many healthcare datasets also suﬀer from the limited availability of labeled data. Our model utilizes self-supervision by leveraging unlabeled data to learn better representations by performing time-series forecasting as a self-supervision task. Experiments on real-world multivariate clinical time-series benchmark datasets show that STraTS shows better prediction performance than state-of-the-art methods for mortality prediction, especially when labeled data is limited. Finally, we also present an interpretable version of STraTS which can identify important measurements in the time-series data.
Neural Network Based Model Predictive Control for an Autonomous Vehicle

We study learning based controllers as a replacement for model predictive controllers (MPC) for the control of autonomous vehicles. We concentrate for the experiments on the simple yet representative bicycle model. We compare training by supervised learning and by reinforcement learning. We also discuss the neural net architectures so as to obtain small nets with the best performances. This work aims at producing controllers that can both be embedded on real-time platforms and amenable to verification by formal methods techniques.