Miracle-dz/UniFAR-FTUs huggingface dataset:
Different universality classes of isostructural 
UTX
 compounds 
(T=Rh,Co,Co0.98Ru0.02;X=Ga,Al)

Magnetization isotherms of the 5f-electron ferromagnets URhGa, UCoGa and UCo0.98Ru0. 02Al were measured at temperatures in the vicinity of their Curie temperature in order to investigate the critical behavior near the ferromagnetic phase transition. These compounds adopt the layered hexagonal ZrNiAl-type structure and exhibit huge uniaxial magnetocrystalline anisotropy. The critical \b{eta}, {\gamma} and {\delta} exponents were determined by analyzing Arrott-Noakes plots, Kouvel-Fisher plots, critical isotherms, scaling theory and Widom scaling relations. The values obtained for URhGa and UCoGa can be explained by the results of the renormalization group theory for a 2D Ising system with long-range interactions similar to URhAl reported by other investigators. On the other hand, the critical exponents determined for UCo0.98Ru0. 02Al are characteristic of a 3D Ising ferromagnet with short-range interactions suggested in previous studies also for the itinerant 5f-electron paramagnet UCoAl situated near a ferromagnetic transition. The change from the 2D to the 3D Ising system is related to the gradual delocalization of 5f electrons in the series of the URhGa, URhAl, UCoGa to UCo0.98Ru0. 02Al and UCoAl compounds and appears close to the strongly itinerant nonmagnetic limit. This indicates possible new phenomena that may be induced by the change of dimensionality in the vicinity of the quantum critical point.
Exploring 
0νββ
 and leptogenesis in the alternative left-right model

We investigate the possibility of neutrinoless double beta decay ($0\nu\beta\beta$) and leptogenesis within the Alternative Left-Right Model (ALRM). Unlike the usual left-right symmetric model, ALRM features a Majorana right-handed neutrino which does not carry any charge. Further, in this picture the down-type quark and the charged leptons receive mass through the additional left-handed scalar field, rather than the usual doublet. Together, these features conspire to generate significant contributions to the $0\nu\beta\beta$ through vector-scalar ($WH$) mediation. For moderate masses of the relevant charged Higgs boson ($M_{H_1^\pm}\sim 200$ GeV), the half-life of $T_{\frac{1}{2}}^{WH}$ is $\sim 3\times 10^{26}~{\rm yrs}$ for both the case of $^{76}$Ge and $^{136}$Xe, well within the sensitivity expected by future experiments. Invoking the resonant leptogenesis, CP violation arising from the right-handed neutrino decay could be the required order to generate the correct baryogenesis, $\epsilon\sim 10^{-6}$, for small Dirac phases and without any fine tuning.
Orbit Structure of Grassmannian G2,m and a Decoder for Grassmann Code C(2, m)

In this article, we consider decoding Grassmann codes, linear codes associated to the Grassmannian and its embedding in a projective space. We look at the orbit structure of Grassmannian arising from the multiplicative group <inline-formula> <tex-math notation="LaTeX">${\mathbb {F}}_{q^{m}}^{*}$ </tex-math></inline-formula> in <inline-formula> <tex-math notation="LaTeX">$GL_{m}(q)$ </tex-math></inline-formula>. We project the corresponding Grassmann code onto these orbits to obtain a subcode of a <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>–ary Reed-Solomon code. We prove that some of these projections contain an information set of the parent Grassmann code. By improving the decoding capacity of Peterson’s decoding algorithm for the projected subcodes, we prove that one can correct up to <inline-formula> <tex-math notation="LaTeX">$\lfloor (d-1)/2\rfloor $ </tex-math></inline-formula> errors for Grassmann code, where <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> is the minimum distance of Grassmann code.
Orthogonal rational functions with real poles, root asymptotics, and GMP matrices

There is a vast theory of the asymptotic behavior of orthogonal polynomials with respect to a measure on R \mathbb {R} and its applications to Jacobi matrices. That theory has an obvious affine invariance and a very special role for ∞ \infty . We extend aspects of this theory in the setting of rational functions with poles on R ¯ = R ∪ { ∞ } \overline {\mathbb {R}} = \mathbb {R} \cup \{\infty \} , obtaining a formulation which allows multiple poles and proving an invariance with respect to R ¯ \overline {\mathbb {R}} -preserving Möbius transformations. We obtain a characterization of Stahl–Totik regularity of a GMP matrix in terms of its matrix elements; as an application, we give a proof of a conjecture of Simon – a Cesàro–Nevai property of regular Jacobi matrices on finite gap sets.
Joint modeling of time-varying exposure history and subsequent health outcomes: identification of critical windows

Long-term behavioral and health risk factors constitute a primary focus of research on the etiology of chronic diseases. Yet, identifying critical time-windows during which risk factors have the strongest impact on disease risk is challenging. Metrics based on a weighted cumulative exposure index (WCIE) have been proposed, with weights reflecting the relative importance of exposures at different times. However, they are usually restricted to a complete observed error-free exposure. When handling an exposure measured with error, the evaluation of exposure and outcome are concomitant while in some contexts, the time-window of exposure history should precede the outcome window. We thus propose a general joint modelling approach to estimate the dynamics of association between the history of a time-varying exposure intermittently measured with error and a non-concomitant outcome using WCIE methodology. Inference is obtained in two stages: (1) the individual complete exposure history is estimated using a flexible mixed model; (2) the trajectory of association (approximated by cubic splines) between the predicted exposure history and the outcome is then estimated. The methodology is validated in simulations and illustrated in the Nurses' Health Study to investigate the association between body mass index history starting in midlife and subsequent cognitive decline after age 70. We show that while higher mid-life BMI is associated with worse cognitive trajectories, the association is inverted at the approach of cognitive assessment, likely reflecting reverse causation. This approach, applicable with any type of outcome, provides a flexible tool for studying complex dynamic relationships in life-course epidemiology.
On Fake Accuracy Verification

In this paper, we reveal a mechanism behind a fake accuracy verification encountered with unstructured-grid schemes based on solution reconstruction such as UMUSCL. Third- (or higher-) order of accuracy has been reported for the Euler equations in the literature, but UMUSCL is actually second-order accurate at best for nonlinear equations. Fake high-order convergence occurs generally for a scheme that is high order for linear equations but second-order for nonlinear equations. It is caused by unexpected linearization of a target nonlinear equation due to too small of a perturbation added to an exact solution used for accuracy verification. To clarify the mechanism, we begin with a proof that the UMUSCL scheme is third-order accurate only for linear equations. Then, we derive a condition under which the third-order truncation error dominates the second-order error and demonstrate it numerically for Burgers' equation. Similar results are shown for the Euler equations, which disprove some accuracy verification results in the literature. To be genuinely third-order, UMUSCL must be implemented with flux reconstruction.
Relation/Entity-Centric Reading Comprehension

Constructing a machine that understands human language is one of the most elusive and long-standing challenges in artificial intelligence. This thesis addresses this challenge through studies of reading comprehension with a focus on understanding entities and their relationships. More specifically, we focus on question answering tasks designed to measure reading comprehension. We focus on entities and relations because they are typically used to represent the semantics of natural language.
Forecasting system for infection risk of phoma stem canker in selected regions of the Czech Republic in 2009-2011.

Poslusna J., Plachka E. (2014): Forecasting system for infection risk of phoma stem canker in selected regions of the Czech Republic in 2009–2011. Plant Protect. Sci., 50: 8–16. In the period 2009–2011 monitoring of the incidence of phoma stem canker was carried out at selected sites of the Czech Republic (Sumperk and Opava regions in North Moravia). The risk of infection was evaluated by assessing the autumn release of Leptosphaeria spp. ascospores and with the proPlant prediction model. In recent years, the incidence of phoma stem canker has been relatively low and this corresponded with the total count of ascospores released in the autumn period but not with an increased level of infection risk announced by the proPlant model. During the monitored period the ascospore concentration reached maximally 2 ascospores/1 m3 per day. Maximum ascospore release was recorded in 2010 as a result of favourable weather conditions. The first incidence of phoma leaf spot has been observed in mid-October. The higher values of ascospores concentration were recorded in the Opava region, but the total number of the trapped ascospores was more often higher in the Sumperk region.
A comparative study of convolutional neural network models for wind field downscaling

We analyze the applicability of convolutional neural network (CNN) architectures for downscaling of short‐range forecasts of near‐surface winds on extended spatial domains. Short‐range wind forecasts (at the 100 m level) from European Centre for Medium Range Weather Forecasts ERA5 reanalysis initial conditions at 31 km horizontal resolution are downscaled to mimic high resolution (HRES) (deterministic) short‐range forecasts at 9 km resolution. We evaluate the downscaling quality of four exemplary CNN architectures and compare these against a multilinear regression model. We conduct a qualitative and quantitative comparison of model predictions and examine whether the predictive skill of CNNs can be enhanced by incorporating additional atmospheric variables, such as geopotential height and forecast surface roughness, or static high‐resolution fields, like land–sea mask and topography. We further propose DeepRU, a novel U‐Net‐based CNN architecture, which is able to infer situation‐dependent wind structures that cannot be reconstructed by other models. Inferring a target 9 km resolution wind field from the low‐resolution input fields over the Alpine area takes less than 10 ms on our graphics processing unit target architecture, which compares favorably to an overhead in simulation time of minutes or hours between low‐ and high‐resolution forecast simulations.
Estimating Uniqueness of Human Voice UsingI-Vector Representation

We study the individuality of human voice with re-spect to a widely used feature representation of speech utterances,namely, the i-vector model. As a first step toward this goal, wecompare and contrast uniqueness measures proposed consideringdifferent biometric modalities. Then, we introduce a more appro-priate uniqueness measure that evaluates the entropy of i-vectorswhile taking into account speaker level variations. Estimates areobtained on two newly generated datasets designed to capturevariabilities between and within speakers. The first dataset speechsamples of more than 20 thousand speakers obtained fromTEDx Talks videos. The second one includes samples of morethan one and a half thousand actors that are extracted frommovie dialogues. By using this data, we analyzed how severalfactors, such as the number of speakers, number of samples perspeakers, and different levels of within-speaker variation affectestimates. Most notably, we determined that the discretizationof i-vector elements does not necessarily cause a reduction inspeaker recognition performance. Our results show that thedegree of uniqueness offered by i-vector based representationmay reach 43-52 bits in a confined setting; however, under lessconstrained variations estimates reduce significantly to 13-20 bitlevel, depending on coarseness of quantization.
Deep generative models in inversion: a review and development of a new approach based on a variational autoencoder

When solving inverse problems in geophysical imaging, deep generative models (DGMs) may be used to enforce the solution to display highly structured spatial patterns which are supported by independent information (e.g. the geological setting) of the subsurface. In such case, inversion may be formulated in a latent space where a low-dimensional parameterization of the patterns is defined and where Markov chain Monte Carlo or gradient-based methods may be applied. However, the generative mapping between the latent and the original (pixel) representations is usually highly nonlinear which may cause some difficulties for inversion, especially for gradient-based methods. In this contribution we review the conceptual framework of inversion with DGMs and study the principal causes of the nonlinearity of the generative mapping. As a result, we identify a conflict between two goals: the accuracy of the generated patterns and the feasibility of gradient-based inversion. In addition, we show how some of the training parameters of a variational autoencoder, which is a particular instance of a DGM, may be chosen so that a tradeoff between these two goals is achieved and acceptable inversion results are obtained with a stochastic gradient-descent scheme. A test case using truth models with channel patterns of different complexity and cross-borehole traveltime tomographic data involving both a linear and a nonlinear forward operator is used to assess the performance of the proposed approach.
A Survey of Evaluation Metrics Used for NLG Systems

In the last few years, a large number of automatic evaluation metrics have been proposed for evaluating Natural Language Generation (NLG) systems. The rapid development and adoption of such automatic evaluation metrics in a relatively short time has created the need for a survey of these metrics. In this survey, we (i) highlight the challenges in automatically evaluating NLG systems, (ii) propose a coherent taxonomy for organising existing evaluation metrics, (iii) briefly describe different existing metrics, and finally (iv) discuss studies criticising the use of automatic evaluation metrics. We then conclude the article highlighting promising future directions of research.
Traces of Class/Cross-Class Structure Pervade Deep Learning Spectra

Numerous researchers recently applied empirical spectral analysis to the study of modern deep learning classifiers. We identify and discuss an important formal class/cross-class structure and show how it lies at the origin of the many visually striking features observed in deepnet spectra, some of which were reported in recent articles, others are unveiled here for the first time. These include spectral outliers, "spikes", and small but distinct continuous distributions, "bumps", often seen beyond the edge of a "main bulk". The significance of the cross-class structure is illustrated in three ways: (i) we prove the ratio of outliers to bulk in the spectrum of the Fisher information matrix is predictive of misclassification, in the context of multinomial logistic regression; (ii) we demonstrate how, gradually with depth, a network is able to separate class-distinctive information from class variability, all while orthogonalizing the class-distinctive information; and (iii) we propose a correction to KFAC, a well-known second-order optimization algorithm for training deepnets.
Ultrafast photocurrents in MoSe2 probed by terahertz spectroscopy

We use the terahertz (THz) emission spectroscopy to study femtosecond photocurrent dynamics in the prototypical 2D semiconductor, transition metal dichalcogenide MoSe2. We identify several distinct mechanisms producing THz radiation in response to an ultrashort (30 fs) optical excitation in a bilayer (BL) and a multilayer (ML) sample. In the ML, the THz radiation is generated at a picosecond timescale by out-of-plane currents due to the drift of photoexcited charge carriers in the surface electric field. The BL emission is generated by an in-plane shift current. Finally, we observe oscillations at about 23 THz in the emission from the BL sample. We attribute the oscillations to quantum beats between two excitonic states with energetic separation of ∼100 meV.
The spin-orbit alignment of visual binaries

Context. The angle between the stellar spin-axis and the orbital plane of a stellar or planetary companion has important implications for the formation and evolution of such systems. A study by Hale (1994, AJ, 107, 306) found that binaries with separations a ≲ 30 au are preferentially aligned while binaries on wider orbits are frequently misaligned. Aims. We aim to test the robustness of the Hale study results by reanalysing the sample of visual binaries with measured rotation periods using independently derived stellar parameters and a Bayesian formalism. Methods. Our analysis is based on a combination of data from the Hale study and newly obtained spectroscopic data from the Hertzsprung SONG telescope, combined with astrometric data from Gaia DR2 and the Washington Double Star Catalog. We combine measurements of stellar radii and rotation periods to obtain stellar rotational velocities v. Rotational velocities v are combined with measurements of projected rotational velocities v sin i to derive posterior probability distributions of stellar inclination angles i. We determine line-of-sight projected spin-orbit angles by comparing stellar inclination angles with astrometric orbital inclination angles. Results. We find that the precision of the available data is insufficient to make inferences about the spin-orbit alignment of visual binaries. The data are equally compatible with alignment and misalignment at all orbital separations. Conclusions. We conclude that the previously reported trend that binaries with separations a ≲ 30 au are preferentially aligned is spurious. The spin-orbit alignment distribution of visual binaries is unconstrained. Based on simulated observations, we predict that it will be difficult to reach the sufficient precision in v sin i, rotation periods, and orbital inclination required to make robust statistical inferences about the spin-orbit alignment of visual binaries.
An appraisal of sampling methods and effort for investigating moth assemblages in a Fijian forest

The moth assemblages in forest ecosystems are often used as indicators of forest quality and to monitor the effects of habitat degradation or ecological restoration and management. However, to provide meaningful data on nocturnal moth faunas, it is important to evaluate the efficacy of available sampling methods and identify the minimum number of samples needed to obtain a reliable estimate of moth diversity. This study compared three light‐based collecting methods to sample nocturnal moths in Colo‐i‐Suva Forest Reserve, a lowland mixed forest 8 km north of Suva, Fiji Islands. On average, over eight nights collecting, a mercury vapour light (MV) with manual capture obtained approximately 14 times more individuals and five times more species than a white fluorescent light with automatic capture and ultraviolet light with automatic capture. Of the 84 moth taxa recorded in total, only two were not obtained by the MV trap, suggesting the moth assemblages obtained by the fluorescent light and ultraviolet light methods were subsets of the larger MV collection. Using a bootstrap method to estimate the total species collected after successive nights sampling, we found that after four nights almost 90% of the predicted total moth species would be obtained by the MV method. These results identify the MV method as a high‐performing technique to investigate nocturnal moth diversity in Fijian forests, and that a minimum of four nights sampling with this protocol would produce reliable data for use in habitat evaluation.
DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data

This work proposes DeepFolio, a new model for deep portfolio management based on data from limit order books (LOB). DeepFolio solves problems found in the state-of-the-art for LOB data to predict price movements. Our evaluation consists of two scenarios using a large dataset of millions of time series. The improvements deliver superior results both in cases of abundant as well as scarce data. The experiments show that DeepFolio outperforms the state-of-the-art on the benchmark FI-2010 LOB. Further, we use DeepFolio for optimal portfolio allocation of crypto-assets with rebalancing. For this purpose, we use two loss-functions - Sharpe ratio loss and minimum volatility risk. We show that DeepFolio outperforms widely used portfolio allocation techniques in the literature.
A Precise Performance Analysis of Learning with Random Features

We study the problem of learning an unknown function using random feature models. Our main contribution is an exact asymptotic analysis of such learning problems with Gaussian data. Under mild regularity conditions for the feature matrix, we provide an exact characterization of the asymptotic training and generalization errors, valid in both the under-parameterized and over-parameterized regimes. The analysis presented in this paper holds for general families of feature matrices, activation functions, and convex loss functions. Numerical results validate our theoretical predictions, showing that our asymptotic findings are in excellent agreement with the actual performance of the considered learning problem, even in moderate dimensions. Moreover, they reveal an important role played by the regularization, the loss function and the activation function in the mitigation of the "double descent phenomenon" in learning.
Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters

Many key problems in machine learning and data science are routinely modeled as optimization problems and solved via optimization algorithms. With the increase of the volume of data and the size and complexity of the statistical models used to formulate these often ill-conditioned optimization tasks, there is a need for new efficient algorithms able to cope with these challenges. In this thesis, we deal with each of these sources of difficulty in a different way. To efficiently address the big data issue, we develop new methods which in each iteration examine a small random subset of the training data only. To handle the big model issue, we develop methods which in each iteration update a random subset of the model parameters only. Finally, to deal with ill-conditioned problems, we devise methods that incorporate either higher-order information or Nesterov's acceleration/momentum. In all cases, randomness is viewed as a powerful algorithmic tool that we tune, both in theory and in experiments, to achieve the best results. Our algorithms have their primary application in training supervised machine learning models via regularized empirical risk minimization, which is the dominant paradigm for training such models. However, due to their generality, our methods can be applied in many other fields, including but not limited to data science, engineering, scientific computing, and statistics.