Miracle-dz/UniFAR-FTUs huggingface dataset:
xDBTagger: Explainable Natural Language Interface to Databases Using Keyword Mappings and Schema Graph

Translating natural language queries (NLQ) into structured query language (SQL) in interfaces to relational databases is a challenging task that has been widely studied by researchers from both the database and natural language processing communities. Numerous works have been proposed to attack the natural language interfaces to databases (NLIDB) problem either as a conventional pipeline-based or an end-to-end deep-learning-based solution. Nevertheless, regardless of the approach preferred, such solutions exhibit black-box nature, which makes it difficult for potential users targeted by these systems to comprehend the decisions made to produce the translated SQL. To this end, we propose xDBTagger, an explainable hybrid translation pipeline that explains the decisions made along the way to the user both textually and visually. We also evaluate xDBTagger quantitatively in three real-world relational databases. The evaluation results indicate that in addition to being fully interpretable, xDBTagger is effective in terms of accuracy and translates the queries more efficiently compared to other state-of-the-art pipeline-based systems up to 10000 times.
Baryonic Effects on Lagrangian Clustering and Angular Momentum Reconstruction

Recent studies illustrate the correlation between the angular momenta of cosmic structures and their Lagrangian properties. However, only baryons are observable and it is unclear whether they reliably trace the cosmic angular momenta. We study the Lagrangian mass distribution, spin correlation, and predictability of dark matter, gas, and stellar components of galaxy–halo systems using IllustrisTNG, and show that the primordial segregations between components are typically small. Their protoshapes are also similar in terms of the statistics of moment of inertia tensors. Under the common gravitational potential they are expected to exert the same tidal torque and the strong spin correlations are not destroyed by the nonlinear evolution and complicated baryonic effects, as confirmed by the high-resolution hydrodynamic simulations. We further show that their late-time angular momenta traced by total gas, stars, or the central galaxies, can be reliably reconstructed by the initial perturbations. These results suggest that baryonic angular momenta can potentially be used in reconstructing the parameters and models related to the initial perturbations.
Existence of invariant volumes in nonholonomic systems subject to nonlinear constraints

We derive conditions for a nonholonomic system subject to nonlinear constraints (obeying Chetaev's rule) to preserve a smooth volume form. When applied to affine constraints, these conditions dictate that a basic invariant density exists if and only if a certain 1-form is exact and a certain function vanishes (this function automatically vanishes for linear constraints). Moreover, this result can be extended to geodesic flows for arbitrary metric connections and the sufficient condition manifests as integrability of the torsion. As a consequence, volume-preservation of a nonholonomic system is closely related to the torsion of the nonholonomic connection. Examples of nonlinear/affine/linear constraints are considered.
Primordial black holes from stochastic tunnelling

If the inflaton gets trapped in a local minimum of its potential shortly before the end of inflation, it escapes by building up quantum fluctuations in a process known as stochastic tunnelling. In this work we study cosmological fluctuations produced in such a scenario, and how likely they are to form Primordial Black Holes (PBHs). This is done by using the stochastic-δ N formalism, which allows us to reconstruct the highly non-Gaussian tails of the distribution function of the number of e-folds spent in the false-vacuum state. We explore two different toy models, both analytically and numerically, in order to identify which properties do or do not depend on the details of the false-vacuum profile. We find that when the potential barrier is small enough compared to its width, ΔV/V < Δϕ 2/M Pl 2, the potential can be approximated as being flat between its two local extrema, so results previously obtained in a “flat quantum well” apply. Otherwise, when Δ V/V < V/M Pl 4, the PBH abundance depends exponentially on the height of the potential barrier, and when Δ V/V > V/M Pl it depends super-exponentially (i.e. as the exponential of an exponential) on the barrier height. In that later case PBHs are massively produced. This allows us to quantify how much flat inflection points need to be fine-tuned. In a deep false vacuum, we also find that slow-roll violations are typically encountered unless the potential is close to linear. This motivates further investigations to generalise our approach to non–slow-roll setups.
Theory of optical activity in doped systems with application to twisted bilayer graphene

We theoretically study the optical activity in a doped system and derive the optical activity tensor from a light wavevector-dependent linear optical conductivity. Although the light-matter interaction is introduced through the velocity gauge from a minimal coupling Hamiltonian, we find that the well-known ``false divergences'' problem can be avoided in practice if the electronic states are described by a finite band effective Hamiltonian, such as a few-band tight-binding model. The expression we obtain for the optical activity tensor is in good numerical agreement with a recent theory derived for an undoped topologically trivial gapped system. We apply our theory to the optical activity of a gated twisted bilayer graphene, with a detailed discussion of the dependence of the results on twist angle, chemical potential, gate voltage, and location of rotation center forming the twisted bilayer graphene.
Over-the-Air Split Machine Learning in Wireless MIMO Networks

In split machine learning (ML), different partitions of a neural network (NN) are executed by different computing nodes, requiring a large amount of communication cost. As over-the-air computation (OAC) can efficiently implement all or part of the computation at the same time of communication, thus by substituting the wireless transmission in the traditional split ML framework with OAC, the communication load can be eased. In this paper, we propose to deploy split ML in a wireless multiple-input multiple-output (MIMO) communication network utilizing the intricate interplay between MIMO-based OAC and NN. The basic procedure of the OAC split ML system is first provided, and we show that the inter-layer connection in a NN of any size can be mathematically decomposed into a set of linear precoding and combining transformations over a MIMO channel carrying out multi-stream analog communication. The precoding and combining matrices which are regarded as trainable parameters, and the MIMO channel matrix, which are regarded as unknown (implicit) parameters, jointly serve as a fully connected layer of the NN. Most interestingly, the channel estimation procedure can be eliminated by exploiting the MIMO channel reciprocity of the forward and backward propagation, thus greatly saving the system costs and/or further improving its overall efficiency. The generalization of the proposed scheme to the conventional NNs is also introduced, i.e., the widely used convolutional NNs. We demonstrate its effectiveness under both the static and quasi-static memory channel conditions with comprehensive simulations.
SelfMix: Robust Learning against Textual Label Noise with Self-Mixup Training

The conventional success of textual classification relies on annotated data, and the new paradigm of pre-trained language models (PLMs) still requires a few labeled data for downstream tasks. However, in real-world applications, label noise inevitably exists in training data, damaging the effectiveness, robustness, and generalization of the models constructed on such data. Recently, remarkable achievements have been made to mitigate this dilemma in visual data, while only a few explore textual data. To fill this gap, we present SelfMix, a simple yet effective method, to handle label noise in text classification tasks. SelfMix uses the Gaussian Mixture Model to separate samples and leverages semi-supervised learning. Unlike previous works requiring multiple models, our method utilizes the dropout mechanism on a single model to reduce the confirmation bias in self-training and introduces a textual level mixup training strategy. Experimental results on three text classification benchmarks with different types of text show that the performance of our proposed method outperforms these strong baselines designed for both textual and visual data under different noise ratios and noise types. Our anonymous code is available at https://github.com/noise-learning/SelfMix.
On the role of the Γ - λEdd relation on the X-ray Baldwin effect in active galactic nuclei

The X-ray Baldwin effect is the inverse correlation between the equivalent width (EW) of the narrow component of the iron Kalpha line and the X-ray luminosity of active galactic nuclei (AGN). A similar trend has also been observed between Fe Kalpha EW and the Eddington ratio (lambda_Edd). Using Chandra/HEG results of Shu et al. (2010) and bolometric corrections we study the relation between EW and the lambda_Edd, and find that log EW = (-0.13+/-0.03)log(lambda_Edd) + 1.47. We explore the role of the known positive correlation between the photon index of the primary X-ray continuum (Gamma) and lambda_Edd on the X-ray Baldwin effect. We simulate the iron Kalpha line emitted by populations of unabsorbed AGN considering 3 different geometries of the reflecting material: toroidal, spherical-toroidal and slab. We find that the Gamma-lambda_Edd correlation cannot account for the whole X-ray Baldwin effect, unless a strong dependence of Gamma on lambda_Edd, such as the one recently found by Risaliti et al. (2009) and Jin et al. (2012), is assumed. No clear correlation is found between EW and Gamma. We conclude that a good understanding of the slope of the Gamma-lambda_Edd relation is critical to assess whether the trend plays a leading or rather a marginal role in the X-ray Baldwin effect.
A Transformer-based deep neural network model for SSVEP classification

Steady-state visual evoked potential (SSVEP) is one of the most commonly used control signals in the brain-computer interface (BCI) systems. However, the conventional spatial filtering methods for SSVEP classification highly depend on the subject-specific calibration data. The need for the methods that can alleviate the demand for the calibration data becomes urgent. In recent years, developing the methods that can work in inter-subject scenario has become a promising new direction. As a popular deep learning model nowadays, Transformer has been used in EEG signal classification tasks owing to its excellent performance. Therefore, in this study, we proposed a deep learning model for SSVEP classification based on Transformer architecture in inter-subject scenario, termed as SSVEPformer, which was the first application of Transformer on the SSVEP classification. Inspired by previous studies, we adopted the complex spectrum features of SSVEP data as the model input, which could enable the model to simultaneously explore the spectral and spatial information for classification. Furthermore, to fully utilize the harmonic information, an extended SSVEPformer based on the filter bank technology (FB-SSVEPformer) was proposed to improve the classification performance. Experiments were conducted using two open datasets (Dataset 1: 10 subjects, 12 targets; Dataset 2: 35 subjects, 40 targets). The experimental results show that the proposed models could achieve better results in terms of classification accuracy and information transfer rate than other baseline methods. The proposed models validate the feasibility of deep learning models based on Transformer architecture for SSVEP data classification, and could serve as potential models to alleviate the calibration procedure in the practical application of SSVEP-based BCI systems.
The Closure Relations in Optical Afterglow of Gamma-Ray Bursts

Gamma-ray bursts (GRBs) are extremely high-energy events that can be observed at very high redshift. In addition to γ rays, they can emit in X-ray, optical, and sometimes radio wavelengths. Here, following the approach in Srinivasaragavan et al.; Dainotti et al.; and Dainotti et al., we consider 82 GRBs from Dainotti et al. that have been observed in optical wavelengths and fitted with a broken power law. We consider the relations between the spectral and temporal indices (closure relations; CRs) according to the synchrotron forward-shock model evolving in the constant-density interstellar medium (ISM; k = 0) and the stellar wind environment (k = 2) in both slow- and fast-cooling regimes, where the density profile is defined as n ∝ r −k . We find the ν>max{νc,νm} regime is most favored, where ν c and ν m are the cooling and characteristic frequencies, respectively. Finally, we test the 2D Dainotti correlation between the rest-frame end time of the plateau and the luminosity at that time on GRBs that fulfill the most-favored CRs. When we compare the intrinsic scatter σ int of those 2D correlations to the scatter presented in Dainotti et al., we see the scatters of our correlations generally agree with the previous values within 1σ, both before and after correction for selection bias. This new information has helped us to pinpoint subsamples of GRBs with features that could drive the GRB emission mechanism, and eventually allow for GRBs to be used as standard candles.
Mirror Mode Storms Observed by Solar Orbiter

Mirror modes (MMs) are ubiquitous in space plasma and grow from pressure anisotropy. Together with other instabilities, they play a fundamental role in constraining the free energy contained in the plasma. This study focuses on MMs observed in the solar wind by Solar Orbiter (SolO) for heliocentric distances between 0.5 and 1 AU. Typically, MMs have timescales from several to tens of seconds and are considered quasi‐MHD structures. In the solar wind, they also generally appear as isolated structures. However, in certain conditions, prolonged and bursty trains of higher frequency MMs are measured, which have been labeled previously as MM storms. At present, only a handful of existing studies have focused on MM storms, meaning that many open questions remain. In this study, SolO has been used to investigate several key aspects of MM storms: their dependence on heliocentric distance, association with local plasma properties, temporal/spatial scale, amplitude, and connections with larger‐scale solar wind transients. The main results are that MM storms often approach local ion scales and can no longer be treated as quasi‐magnetohydrodynamic, thus breaking the commonly used long‐wavelength assumption. They are typically observed close to current sheets and downstream of interplanetary shocks. The events were observed during slow solar wind speeds and there was a tendency for higher occurrence closer to the Sun. The occurrence is low, so they do not play a fundamental role in regulating ambient solar wind but may play a larger role inside transients.
Multi-modal fusion transformer for visual question answering in remote sensing

With the new generation of satellite technologies, the archives of remote sensing (RS) images are growing very fast. To make the intrinsic information of each RS image easily accessible, visual question answering (VQA) has been introduced in RS. VQA allows a user to formulate a free-form question concerning the content of RS images to extract generic information. It has been shown that the fusion of the input modalities (i.e., image and text) is crucial for the performance of VQA systems. Most of the current fusion approaches use modalityspecific representations in their fusion modules instead of joint representation learning. However, to discover the underlying relation between both the image and question modality, the model is required to learn the joint representation instead of simply combining (e.g., concatenating, adding, or multiplying) the modality-specific representations. We propose a multi-modal transformer-based architecture to overcome this issue. Our proposed architecture consists of three main modules: i) the feature extraction module for extracting the modality-specific features; ii) the fusion module, which leverages a user-defined number of multi-modal transformer layers of the VisualBERT model (VB); and iii) the classification module to obtain the answer. In contrast to recently proposed transformer-based models in RS VQA, the presented architecture (called VBFusion) is not limited to specific questions, e.g., questions concerning pre-defined objects. Experimental results obtained on the RSVQAxBEN and RSVQA-LR datasets (which are made up of RGB bands of Sentinel-2 images) demonstrate the effectiveness of VBFusion for VQA tasks in RS. To analyze the importance of using other spectral bands for the description of the complex content of RS images in the framework of VQA, we extend the RSVQAxBEN dataset to include all the spectral bands of Sentinel-2 images with 10m and 20m spatial resolution. Experimental results show the importance of utilizing these bands to characterize the land-use land-cover classes present in the images in the framework of VQA. The code of the proposed method is publicly available at https://git.tu-berlin.de/rsim/multimodal- fusion-transformer-for-vqa-in-rs.
TIMING THE EVOLUTION OF QUIESCENT AND STAR-FORMING LOCAL GALAXIES

Constraining the star formation histories (SFHs) of individual galaxies is crucial for understanding the mechanisms that regulate their evolution. Here, we combine multi-wavelength (ultraviolet, optical, and infrared) measurements of a very large sample of galaxies (∼230,000) at z < 0.16, with physically motivated models of galaxy spectral energy distributions to extract constraints on galaxy physical parameters (such as stellar mass and star formation rate) as well as individual SFHs. In particular, we set constraints on the timescales in which galaxies form a certain percentage of their total stellar mass (namely, 10%, 50%, and 90%). The large statistics allows us to average such measurements over different populations of galaxies (quiescent and star-forming) and in narrow ranges of stellar mass. As in the downsizing scenario, we confirm that low-mass galaxies have more extended SFHs than high-mass galaxies. We also find that at the same observed stellar mass, galaxies that are now quiescent evolve more rapidly than galaxies that are currently still forming stars. This suggests that stellar mass is not the only driver of galaxy evolution, but plays along with other factors such as merger events and other environmental effects.
Coherent forward scattering as a robust probe of multifractality in critical disordered media

We study coherent forward scattering (CFS) in critical disordered systems, whose eigenstates are multifractals. We give general and simple arguments that make it possible to fully characterize the dynamics of the shape and height of the CFS peak. We show that the dynamics is governed by multifractal dimensions D_1D1 and D_2D2, which suggests that CFS could be used as an experimental probe for quantum multifractality. Our predictions are universal and numerically verified in three paradigmatic models of quantum multifractality: Power-law Random Banded Matrices (PRBM), the Ruijsenaars-Schneider ensembles (RS), and the three-dimensional kicked-rotor (3DKR). In the strong multifractal regime, we show analytically that these universal predictions exactly coincide with results from standard perturbation theory applied to the PRBM and RS models.
Strong Gravitational Lensing Parameter Estimation with Vision Transformer

Quantifying the parameters and corresponding uncertainties of hundreds of strongly lensed quasar systems holds the key to resolving one of the most important scientific questions: the Hubble constant ($H_{0}$) tension. The commonly used Markov chain Monte Carlo (MCMC) method has been too time-consuming to achieve this goal, yet recent work has shown that convolution neural networks (CNNs) can be an alternative with seven orders of magnitude improvement in speed. With 31,200 simulated strongly lensed quasar images, we explore the usage of Vision Transformer (ViT) for simulated strong gravitational lensing for the first time. We show that ViT could reach competitive results compared with CNNs, and is specifically good at some lensing parameters, including the most important mass-related parameters such as the center of lens $\theta_{1}$ and $\theta_{2}$, the ellipticities $e_1$ and $e_2$, and the radial power-law slope $\gamma'$. With this promising preliminary result, we believe the ViT (or attention-based) network architecture can be an important tool for strong lensing science for the next generation of surveys. The open source of our code and data is in \url{https://github.com/kuanweih/strong_lensing_vit_resnet}.
Density functional theory screened-exchange approach for investigating electronical properties of graphene-related materials

We present ab initio calculations of the bandstructure of graphene and of short zigzag graphene nanoribbons by the screened-exchange-LDA method (sX-LDA) within the framework of density functional theory (DFT). The inclusion of non-local electron-electron interactions in this approach results in a renormalization of the electronic bandstructure and the Fermi velocity compared to calculations within local density approximation (LDA) gives good agreement with experiment. Similarly, the band gaps in zigzag nanoribbons (ZGNR) are widened by more than 200%, being of similar magnitude than bandgaps from past studies based on quasiparticle bandstructures. We found a noticeable effect of non-local exchange on the spin-polarization of the electronic ground state of ZGNRs, compared to LDA and GGA-PW91 calculations.
Spectrum analysis of bright Kepler late B- to early F-stars

The Kepler satellite mission was designed to search for transiting exoplanets and delivers single band-pass light curves of a huge number of stars observed in the CygnusLyra region. At the same time, it opens a new window for asteroseismology. In order to accomplish one of the required preconditions for the asteroseismic modelling of the stars, namely knowledge of their precise fundamental parameters, ground-based spectroscopic and/or photometric follow-up observations are needed. We aim to derive fundamental parameters and individual abundances for a sample of 18 Dor/� Sct and 8 SPB/� Cep candidate stars in the Kepler satellite field of view. We use the spectral synthesis method to model newly obtained, high-resolution spectra of 26 stars in order to derive their fundamental parameters like Teff, logg, v sini, �, [M/H], and individual abundances with high accuracy. The stars are then placed into the log(Teff)– log(g) diagram and the obtained spectroscopic classification is compared to the existing photometric one. For most A- and F-type stars, the derived Teff values agree within the measurement errors with the values given in the Kepler Input Catalog (KIC). For hot stars, the KIC temperatures appear to be systematically underestimated, in agreement with previous findings. We also find that the temperatures derived from our spectra agree reasonably well with those derived from the SED fitting. According to their position in the log(Teff)–log(g) diagram, two stars are expected Dor stars,