Miracle-dz/UniFAR-FTUs huggingface dataset:
Superflares and variability in solar-type stars with TESS in the Southern hemisphere

Superflares on solar-type stars have been a rapidly developing field ever since the launch of Kepler. Over the years, there have been several studies investigating the statistics of these explosive events. In this study, we present a statistical analysis of stellar flares on solar-type stars made using photometric data in 2-min cadence from Transiting Exoplanet Survey Satellite of the whole Southern hemisphere (sectors 1–13). We derive rotational periods for all the stars in our sample from rotational modulations present in the light curve as a result of large star-spot(s) on the surface. We identify 1980 stellar flares from 209 solar-type stars with energies in the range of 1031–1036 erg (using the solar flare classification, this corresponds to X1–X100 000) and conduct an analysis into their properties. We investigate the rotational phase of the flares and find no preference for any phase, suggesting the flares are randomly distributed. As a benchmark, we use GOES data of solar flares to detail the close relationship between solar flares and sunspots. In addition, we also calculate approximate spot areas for each of our stars and compare this to flare number, rotational phase, and flare energy. Additionally, two of our stars were observed in the continuous viewing zone with light-curves spanning 1 yr; as a result we examine the stellar variability of these stars in more detail.
Parametric Lyapunov exponents

In an algebraic family of rational maps of P1 , we show that, for almost every parameter for the trace of the bifurcation current of a marked critical value, the critical value is Collet–Eckmann. This extends previous results of Graczyk and Świa̧tek in the unicritical family, using Makarov theorem. Our methods are based instead on ideas of laminar currents theory.
Spatio-Temporal Graph for Video Captioning With Knowledge Distillation

Video captioning is a challenging task that requires a deep understanding of visual scenes. State-of-the-art methods generate captions using either scene-level or object-level information but without explicitly modeling object interactions. Thus, they often fail to make visually grounded predictions, and are sensitive to spurious correlations. In this paper, we propose a novel spatio-temporal graph model for video captioning that exploits object interactions in space and time. Our model builds interpretable links and is able to provide explicit visual grounding. To avoid unstable performance caused by the variable number of objects, we further propose an object-aware knowledge distillation mechanism, in which local object information is used to regularize global scene features. We demonstrate the efficacy of our approach through extensive experiments on two benchmarks, showing our approach yields competitive performance with interpretable predictions.
Second-Order Guarantees in Centralized, Federated and Decentralized Nonconvex Optimization

Rapid advances in data collection and processing capabilities have allowed for the use of increasingly complex models that give rise to nonconvex optimization problems. These formulations, however, can be arbitrarily difficult to solve in general, in the sense that even simply verifying that a given point is a local minimum can be NP-hard [1]. Still, some relatively simple algorithms have been shown to lead to surprisingly good empirical results in many contexts of interest. Perhaps the most prominent example is the success of the backpropagation algorithm for training neural networks. Several recent works have pursued rigorous analytical justification for this phenomenon by studying the structure of the nonconvex optimization problems and establishing that simple algorithms, such as gradient descent and its variations, perform well in converging towards local minima and avoiding saddle-points. A key insight in these analyses is that gradient perturbations play a critical role in allowing local descent algorithms to efficiently distinguish desirable from undesirable stationary points and escape from the latter. In this article, we cover recent results on second-order guarantees for stochastic first-order optimization algorithms in centralized, federated, and decentralized architectures.
COVID-19 epidemic outcome predictions based on logistic fitting and estimation of its reliability

Since the first outbreak of the COVID-19 epidemic at the end of 2019, data has been made available on the number of infections, deaths and recoveries for all countries of the World, and that data can be used for statistical analysis. The primary interest of this paper is how well the logistic equation can predict the outcome of COVID-19 epidemic in any regions of the World assuming that the methodology of the testing process, namely the data collection method and social behavior is not changing over the course of time. Besides the social relevance, this study has two scientific purposes: we investigate if a simple saturation model can describe the trend of the COVID-19 epidemic and if so, we would like to determine, from which point during the epidemic the fitting parameters provide reliable predictions. We also give estimations for the outcome of this epidemic in several countries based on the logistic model and the data available on 27 March, 2020. Based on the saturated cases in China, we have managed to find some criteria to judge the reliability of the predictions.
A new set of atmosphere and evolution models for cool T–Y brown dwarfs and giant exoplanets

We present a new set of solar metallicity atmosphere and evolutionary models for very cool brown dwarfs and self-luminous giant exoplanets, which we term ATMO 2020. Atmosphere models are generated with our state-of-the-art 1D radiative-convective equilibrium code ATMO, and are used as surface boundary conditions to calculate the interior structure and evolution of 0.001–0.075 M⊙ objects. Our models include several key improvements to the input physics used in previous models available in the literature. Most notably, the use of a new H–He equation of state including ab initio quantum molecular dynamics calculations has raised the mass by ~1−2% at the stellar–substellar boundary and has altered the cooling tracks around the hydrogen and deuterium burning minimum masses. A second key improvement concerns updated molecular opacities in our atmosphere model ATMO, which now contains significantly more line transitions required to accurately capture the opacity in these hot atmospheres. This leads to warmer atmospheric temperature structures, further changing the cooling curves and predicted emission spectra of substellar objects. We present significant improvement for the treatment of the collisionally broadened potassium resonance doublet, and highlight the importance of these lines in shaping the red-optical and near-infrared spectrum of brown dwarfs. We generate three different grids of model simulations, one using equilibrium chemistry and two using non-equilibrium chemistry due to vertical mixing, all three computed self-consistently with the pressure-temperature structure of the atmosphere. We show the impact of vertical mixing on emission spectra and in colour-magnitude diagrams, highlighting how the 3.5−5.5 μm flux window can be used to calibrate vertical mixing in cool T–Y spectral type objects.
Strong in-plane anisotropy in the electronic structure of fixed-valence 
β−LuAlB4

The origin of intrinsic quantum criticality in the heavy-fermion superconductor $\beta$-YbAlB$_4$ has been attributed to strong Yb valence fluctuations and its peculiar crystal structure. Here, we assess these contributions individually by studying the isostructural but fixed-valence compound $\beta$-LuAlB$_4$. Quantum oscillation measurements and DFT calculations reveal a Fermi surface markedly different from that of $\beta$-YbAlB$_4$, consistent with a `large' Fermi surface there. We also find an unexpected in-plane anisotropy of the electronic structure, in contrast to the isotropic Kondo hybridization in $\beta$-YbAlB$_4$.
Unstructured Space-Time Finite Element Methods for Optimal Sparse Control of Parabolic Equations

We consider a space-time finite element method on fully unstructured simplicial meshes for optimal sparse control of semilinear parabolic equations. The objective is a combination of a standard quadratic trackingtype functional including a Tikhonov regularization term and of the Lnorm of the control that accounts for its spatio-temporal sparsity. We use a space-time Petrov-Galerkin finite element discretization for the firstorder necessary optimality system of the associated discrete optimal sparse control problem. The discretization is based on a variational formulation that employs piecewise linear finite elements simultaneously in space and time. Finally, the discrete nonlinear optimality system that consists of coupled forward-backward state and adjoint state equations is solved by a semismooth Newton method.
Robust predictive model for Carriers, Infections and Recoveries (CIR): predicting death rates for CoVid-19 in Spain

This article presents a new model to predict the evolution of infective diseases under uncertainty or low-quality information, just as it has happened in the initial scenario during the CoVid-19 spread in China and Europe. The model has been used to predict the death rate in Spain but can be used to predict the demand of ICUs or mechanical ventilators under different restraint policies. The main novelty of the model is that it keeps track of the date of infection of a single individual and uses stochastic distributions to aggregate individuals who share the same date of infection. In addition, it uses two types of infections, mild and serious, with a different recovery time. These features are implemented in a set of differential equations which determine the number of Carriers, Infections, Recoveries, Hospitalized and Deaths. Comparison with real data shows good agreement.
Multimodal Interfaces for Effective Teleoperation

Research in multi-modal interfaces aims to provide solutions to immersion and increase overall human performance. A promising direction is combining auditory, visual and haptic interaction between the user and the simulated environment. However, no extensive comparisons exist to show how combining audiovisuohaptic interfaces affects human perception reflected on task performance. Our paper explores this idea. We present a thorough, full-factorial comparison of how all combinations of audio, visual and haptic interfaces affect performance during manipulation. We evaluate how each interface combination affects performance in a study (N=25) consisting of manipulating tasks of varying difficulty. Performance is assessed using both subjective, assessing cognitive workload and system usability, and objective measurements, incorporating time and spatial accuracy-based metrics. Results show that regardless of task complexity, using stereoscopic-vision with the VRHMD increased performance across all measurements by 40% compared to monocular-vision from the display monitor. Using haptic feedback improved outcomes by 10% and auditory feedback accounted for approximately 5% improvement.
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition

Spatial-temporal graphs have been widely used by skeleton-based action recognition algorithms to model human action dynamics. To capture robust movement patterns from these graphs, long-range and multi-scale context aggregation and spatial-temporal dependency modeling are critical aspects of a powerful feature extractor. However, existing methods have limitations in achieving (1) unbiased long-range joint relationship modeling under multi-scale operators and (2) unobstructed cross-spacetime information flow for capturing complex spatial-temporal dependencies. In this work, we present (1) a simple method to disentangle multi-scale graph convolutions and (2) a unified spatial-temporal graph convolutional operator named G3D. The proposed multi-scale aggregation scheme disentangles the importance of nodes in different neighborhoods for effective long-range modeling. The proposed G3D module leverages dense cross-spacetime edges as skip connections for direct information propagation across the spatial-temporal graph. By coupling these proposals, we develop a powerful feature extractor named MS-G3D based on which our model outperforms previous state-of-the-art methods on three large-scale datasets: NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400.
Extending the Thermal Near Field Through Compensation in Hyperbolic Waveguides

A promising method to leverage near-field power densities without the use of nanoscale vacuum gaps is through hyperbolic metamaterial (HMM) waveguides. When placed between a hot and cold reservoir, an ideal HMM can transmit surface waves across several microns, enabling an extension of near-field enhancements. However, when accounting for transmission loss due to realistic levels of absorption within the waveguide, previous studies have shown that the enhancements are significantly curtailed at wide separations. In our study, we investigate the role of internal sources within realistic non-isothermal HMMs. We demonstrate that, in some cases, the emission from the HMM accounts for over 90% of the total heat transfer to the receiver, and that these additional sources can largely compensate for optical losses associated with decreased transmission from the emitter to the receiver. Lastly, we investigate the spectral transport in a realistic 3-body system which has mismatching optical properties between the boundaries (emitter and receiver) and the waveguide (HMM). Our model shows that the near-field thermal transport remains spectrally selective to the boundaries, even as major radiative contributions come from the waveguide. This work may enable the design of non-isothermal emitter-waveguide-receiver systems that transmit near-field power levels over wider separations.
The dynamics of the γ Vel cluster and nearby Vela OB2 association

The kinematics of low-mass stars in nearby OB associations can provide clues about their origins and evolution. Combining the precise positions, proper motions and parallaxes given in the second Gaia Data Release with radial velocity measurements obtained with the Hermes spectrograph at the Anglo-Australian Telescope, we have an opportunity to study in detail the kinematics of low-mass stars belonging to the nearby $\gamma$ Vel cluster and the Vela OB2 association it is projected against. The presence of lithium is used to confirm the youth of our targets. We separate our sample into the cluster and association populations based on the membership probabilities of Jeffries et al. 2014, their parallaxes, and kinematics. We find strong evidence for expansion in the OB association population with at least 4$\sigma$ significance along all three axes, though the expansion is notably anisotropic. We discuss these results in the context of cluster and association dispersal theories.
On the Integration of LinguisticFeatures into Statistical and Neural Machine Translation

New machine translations (MT) technologies are emerging rapidly and with them, bold claims of achieving human parity such as: (i) the results produced approach "accuracy achieved by average bilingual human translators" (Wu et al., 2017b) or (ii) the "translation quality is at human parity when compared to professional human translators" (Hassan et al., 2018) have seen the light of day (Laubli et al., 2018). Aside from the fact that many of these papers craft their own definition of human parity, these sensational claims are often not supported by a complete analysis of all aspects involved in translation. Establishing the discrepancies between the strengths of statistical approaches to MT and the way humans translate has been the starting point of our research. By looking at MT output and linguistic theory, we were able to identify some remaining issues. The problems range from simple number and gender agreement errors to more complex phenomena such as the correct translation of aspectual values and tenses. Our experiments confirm, along with other studies (Bentivogli et al., 2016), that neural MT has surpassed statistical MT in many aspects. However, some problems remain and others have emerged. We cover a series of problems related to the integration of specific linguistic features into statistical and neural MT, aiming to analyse and provide a solution to some of them. Our work focuses on addressing three main research questions that revolve around the complex relationship between linguistics and MT in general. We identify linguistic information that is lacking in order for automatic translation systems to produce more accurate translations and integrate additional features into the existing pipelines. We identify overgeneralization or 'algorithmic bias' as a potential drawback of neural MT and link it to many of the remaining linguistic issues.
Structural covariance networks across the lifespan, from 6-94 years of age

Structural covariance examines covariation of grey matter morphology between brain regions and across individuals. Despite significant interest in the influence of age on structural covariance patterns, no study to date has provided a complete lifespan perspective—bridging childhood with early, middle, and late adulthood—on the development of structural covariance networks. Here, we investigate the lifespan trajectories of structural covariance in six canonical neurocognitive networks: default, dorsal attention, frontoparietal control, somatomotor, ventral attention, and visual. By combining data from five open access data sources, we examine the structural covariance trajectories of these networks from 6-94 years of age in a sample of 1580 participants. Using partial least squares, we show that structural covariance patterns across the lifespan exhibit two significant, age-dependent trends. The first trend is a stable pattern whose integrity declines over the lifespan. The second trend is an inverted-U that differentiates young adulthood from other age groups. Hub regions, including posterior cingulate cortex and anterior insula, appear particularly influential in the expression of this second age-dependent trend. Overall, our results suggest that structural covariance provides a reliable definition of neurocognitive networks across the lifespan and reveal both shared and network-specific trajectories. Author Summary The importance of lifespan perspectives is increasingly apparent in understanding normative interactions of large-scale neurocognitive networks. Although recent work has made significant strides in understanding the functional and structural connectivity of these networks, there has been comparatively little attention to lifespan trajectories of structural covariance networks. In this study we examine patterns of structural covariance across the lifespan for six neurocognitive networks. Our results suggest that networks exhibit both network-specific stable patterns of structural covariance as well as shared age-dependent trends. Previously identified hub regions seem to show a strong influence on the expression of these age-related trajectories. These results provide initial evidence for a multi-modal understanding of structural covariance in network structure-function interaction across the life course.
FaceScape: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction

In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and propose a novel algorithm that is able to predict elaborate riggable 3D face models from a single image input. FaceScape dataset provides 18,760 textured 3D faces, captured from 938 subjects and each with 20 specific expressions. The 3D models contain the pore-level facial geometry that is also processed to be topologically uniformed. These fine 3D facial models can be represented as a 3D morphable model for rough shapes and displacement maps for detailed geometry. Taking advantage of the large-scale and high-accuracy dataset, a novel algorithm is further proposed to learn the expression-specific dynamic details using a deep neural network. The learned relationship serves as the foundation of our 3D face prediction system from a single image input. Different than the previous methods, our predicted 3D models are riggable with highly detailed geometry under different expressions. The unprecedented dataset and code will be released to public for research purpose.
Isoniazid preventive therapy protects against tuberculosis among household contacts of isoniazid-resistant patients

Background World Health Organization recommend the use of isoniazid (INH) only, or INH and rifapentine therapy, to treat the latent tuberculosis infection (LTBI). The recent rise of isoniazid and multi-drug resistant (MDR) tuberculosis has complicated the choice of LTBI treatment regimen. The current lack of evidence on optimal regimens prevents the formulation of definitive recommendations for latent drug-resistant tuberculosis. We examine the risk of disease progression of individuals exposed to sensitive, INH, or MDR tuberculosis who received INH as part of routine tuberculosis management. Methods This study is a prospective cohort study conducted in Lima, Peru. Between September 2009 and August 2012, we identified and enrolled 4,500 tuberculosis patients and their 14,044 household contacts. We measured the incident tuberculosis of the household contacts (HHCs) over a one-year follow-up. We used a Cox frailty proportional hazards model to evaluate whether the effect of INH preventive therapy (IPT) on tuberculosis progression varied by the resistance profile of the index case. Findings We restricted the analyses to 4,216 HHCs who were ≤ 19 years old. 2,106 HHCs (50%) had initiated isoniazid prevention therapy at enrollment. We found that the protective effect of INH against tuberculosis was stronger in HHCs exposed to drug-sensitive or MDR TB than in those exposed to mono-INH-resistant strains (IPT vs. No-IPT aHR[95% confidence interval]: 0.32 [0.20-0.50] in INH-sensitive subgroup; 0.26 [0.08-0.77] in MDR; 0.80 [0.23 to 2.79] in mono-INH-resistant). When we further restricted the analyses to those who received a ≥ 3 months of INH, the protective effect of IPT became even stronger across all three groups (0.2 [0.1 to 0.4] in INH-sensitive subgroup; 0.16 [0.02-1.27] in MDR; 0.72 [0.16-3.16] in mono-INH-resistant). Interpretation We found that INH prevention therapy protected against TB among contacts of INH-resistant TB patients. This finding suggests that INH may have a role in the management of MDR-LTBI. Funding National Institutes of Health and the National Institute of Allergy and Infectious Diseases CETR (U19AI109755) and TBRU (U19AI111224)
Smoking cessation services provided by the National Health Insurance Service

흡연은 반복적인 중재가 필요한 만성적 질환으로, 세계보 건기구에 따르면 흡연으로 인해 향후 50년간 4억5천 명의 사망자가 생길 거라고 추정한다[1]. 담배를 사용하는 모든 환자들을 선별하여 행동상담과 약물치료를 제공하고, 금연 을 하도록 하는 것은 일차진료에서 가장 중요한 예방서비 스 영역 중의 하나이다. 세계보건기구의 담배규제기본협약 (Framework Convention on Tobacco Control, FCTC)에서 는 ‘담배 의존 및 금연에 관한 수요 감소 조치’의 일환으로 금 연과 담배 의존 치료를 촉진하기 위한 효과적인 조치를 취하 라고 협약 당사국들에게 권고하고 있다. 따라서 대부분의 국 가에서 금연 관련 서비스를 일정부분 제공하고 있다. 우리나 라는 FCTC에 2005년 참여하였고, 이 가이드라인의 14조에 근거하여 흡연규제 및 금연지원 서비스를 제공하여 왔다[2]. 흡연의 위험성에 대한 인식 제고와 강화된 금연정책에 따라 대다수 흡연자들은 금연을 원하고 있으며, 외국의 사례를 살 펴보면, 흡연자의 절반 이상이 지난 1년간 금연을 한 차례 이상 시도한다[3]. 우리나라의 경우 성인 흡연자 중 80% 이 상이 한 번 이상 금연 시도를 한 적이 있고, 과거 금연을 시 도한 평균 횟수는 4. 6회 정도인 것으로 추정된다[4]. 하지만 국민건강보험공단 금연치료지원사업