Miracle-dz/UniFAR-FTUs huggingface dataset:
COVID-19 puzzle in China: a serendipitous interplay between transmissibility and social distancing measures

A number of models in mathematical epidemiology have been developed to account for control measures such as vaccination or quarantine. However, COVID-19 has brought unprecedented social distancing measures, with a challenge on how to include these in a manner that can explain the data, but avoid overfitting in parameter inference. We here develop a simple non-homologous model, where social distancing effects are introduced analogous to course grained models of gene expression control in systems biology. We apply our approach to understand drastic differences in COVID-19 infection and fatality counts (observed between Hubei (Wuhan) and other Mainland China provinces), which we explain through interplay of transmissibility and effective protection differences. That is, we obtain a negative feedback (also commonly encountered in systems biology) between transmissibility and effects of social distancing, with Hubei being an outlier with both large transmissibility and lower social distancing effects. While Case Fatality Rate is significantly larger for Hubei, we find that Infection Fatality Rates (IFRs) are much more uniform/consistent across all the provinces. Even for Wuhan, our inferred Infection Attack Rate is much below the herd immunity threshold, raising a major reemission risk. The obtained results demonstrate applicability of our developed method to extract key infection parameters directly from the data (publicly available for a number of countries), so that it can be applied globally to potential future outbreaks of COVID-19, or other infectious diseases.
Fast, Simpler and More Accurate Hybrid ASR Systems Using Wordpieces

In this work, we first show that on the widely used LibriSpeech benchmark, our transformer-based context-dependent connectionist temporal classification (CTC) system produces state-of-the-art results. We then show that using wordpieces as modeling units combined with CTC training, we can greatly simplify the engineering pipeline compared to conventional frame-based cross-entropy training by excluding all the GMM bootstrapping, decision tree building and force alignment steps, while still achieving very competitive word-error-rate. Additionally, using wordpieces as modeling units can significantly improve runtime efficiency since we can use larger stride without losing accuracy. We further confirm these findings on two internal \emph{VideoASR} datasets: German, which is similar to English as a fusional language, and Turkish, which is an agglutinative language.
Topology of leaves for minimal laminations by non-simply-connected hyperbolic surfaces

We give the topological obstructions to be a leaf in a minimal lamination by hyperbolic surfaces whose generic leaf is homeomorphic to a Cantor tree. Then, we show that all allowed topological types can be simultaneously embedded in the same lamination. This result, together with results of Alvarez-Brum-Martinez-Potrie and Blanc, complete the panorama of understanding which topological surfaces can be leaves in minimal hyperbolic surface laminations when the topology of the generic leaf is given. In all cases, all possible topologies can be realized simultaneously.
Tropical and Extratropical Cyclone Detection Using Deep Learning

Extracting valuable information from large sets of diverse meteorological data is a time-intensive process. Machine-learning methods can help to improve both speed and accuracy of this process. Specifically, deep-learning image-segmentation models using the U-Net structure perform faster and can identify areas that are missed by more restrictive approaches, such as expert hand-labeling and a priori heuristic methods. This paper discusses four different state-of-the-art U-Net models designed for detection of tropical and extratropical cyclone regions of interest (ROI) from two separate input sources: total precipitable water output from the Global Forecast System (GFS) model and water vapor radiance images from the Geostationary Operational Environmental Satellite (GOES). These models are referred to as International Best Track Archive for Climate Stewardship (IBTrACS)-GFS, Heuristic-GFS, IBTrACS-GOES, and Heuristic-GOES. All four U-Nets are fast information extraction tools and perform with an ROI detection accuracy ranging from 80% to 99%. These are additionally evaluated with the Dice and Tversky intersection-over-union (IoU) metrics, having Dice coefficient scores ranging from 0.51 to 0.76 and Tversky coefficients ranging from 0.56 to 0.74. The extratropical cyclone U-Net model performed 3 times as fast as the comparable heuristic model used to detect the same ROI. The U-Nets were specifically selected for their capabilities in detecting cyclone ROI beyond the scope of the training labels. These machine-learning models identified more ambiguous and active ROI missed by the heuristic model and hand-labeling methods that are commonly used in generating real-time weather alerts, having a potentially direct impact on public safety.
From Rayleigh–Bénard convection to porous-media convection: how porosity affects heat transfer and flow structure

We perform a numerical study of the heat transfer and flow structure of Rayleigh–Bénard (RB) convection in (in most cases regular) porous media, which are comprised of circular, solid obstacles located on a square lattice. This study is focused on the role of porosity $\unicode[STIX]{x1D719}$ in the flow properties during the transition process from the traditional RB convection with $\unicode[STIX]{x1D719}=1$ (so no obstacles included) to Darcy-type porous-media convection with $\unicode[STIX]{x1D719}$ approaching 0. Simulations are carried out in a cell with unity aspect ratio, for Rayleigh number $Ra$ from $10^{5}$ to $10^{10}$ and varying porosities $\unicode[STIX]{x1D719}$, at a fixed Prandtl number $Pr=4.3$, and we restrict ourselves to the two-dimensional case. For fixed $Ra$, the Nusselt number $Nu$ is found to vary non-monotonically as a function of $\unicode[STIX]{x1D719}$; namely, with decreasing $\unicode[STIX]{x1D719}$, it first increases, before it decreases for $\unicode[STIX]{x1D719}$ approaching 0. The non-monotonic behaviour of $Nu(\unicode[STIX]{x1D719})$ originates from two competing effects of the porous structure on the heat transfer. On the one hand, the flow coherence is enhanced in the porous media, which is beneficial for the heat transfer. On the other hand, the convection is slowed down by the enhanced resistance due to the porous structure, leading to heat transfer reduction. For fixed $\unicode[STIX]{x1D719}$, depending on $Ra$, two different heat transfer regimes are identified, with different effective power-law behaviours of $Nu$ versus $Ra$, namely a steep one for low $Ra$ when viscosity dominates, and the standard classical one for large $Ra$. The scaling crossover occurs when the thermal boundary layer thickness and the pore scale are comparable. The influences of the porous structure on the temperature and velocity fluctuations, convective heat flux and energy dissipation rates are analysed, further demonstrating the competing effects of the porous structure to enhance or reduce the heat transfer.
Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text

Although temporal tagging is still dominated by rule-based systems, there have been recent attempts at neural temporal taggers. However, all of them focus on monolingual settings. In this paper, we explore multilingual methods for the extraction of temporal expressions from text and investigate adversarial training for aligning embedding spaces to one common space. With this, we create a single multilingual model that can also be transferred to unseen languages and set the new state of the art in those cross-lingual transfer experiments.
Patch based Colour Transfer using SIFT Flow

We propose a new colour transfer method with Optimal Transport (OT) to transfer the colour of a sourceimage to match the colour of a target image of the same scene that may exhibit large motion changes betweenimages. By definition OT does not take into account any available information about correspondences whencomputing the optimal solution. To tackle this problem we propose to encode overlapping neighborhoodsof pixels using both their colour and spatial correspondences estimated using motion estimation. We solvethe high dimensional problem in 1D space using an iterative projection approach. We further introducesmoothing as part of the iterative algorithms for solving optimal transport namely Iterative DistributionTransport (IDT) and its variant the Sliced Wasserstein Distance (SWD). Experiments show quantitative andqualitative improvements over previous state of the art colour transfer methods.
Regularization Methods for Generative Adversarial Networks: An Overview of Recent Studies

Despite its short history, Generative Adversarial Network (GAN) has been extensively studied and used for various tasks, including its original purpose, i.e., synthetic sample generation. However, applying GAN to different data types with diverse neural network architectures has been hindered by its limitation in training, where the model easily diverges. Such a notorious training of GANs is well known and has been addressed in numerous studies. Consequently, in order to make the training of GAN stable, numerous regularization methods have been proposed in recent years. This paper reviews the regularization methods that have been recently introduced, most of which have been published in the last three years. Specifically, we focus on general methods that can be commonly used regardless of neural network architectures. To explore the latest research trends in the regularization for GANs, the methods are classified into several groups by their operation principles, and the differences between the methods are analyzed. Furthermore, to provide practical knowledge of using these methods, we investigate popular methods that have been frequently employed in state-of-the-art GANs. In addition, we discuss the limitations in existing methods and propose future research directions.
Gas-driven Inspiral of Binaries in Thin Accretion Disks

Numerical studies of gas accretion onto supermassive black hole binaries have generally been limited to conditions where the circumbinary disk (CBD) is 10–100 times thicker than expected for disks in active galactic nuclei. This discrepancy arises from technical limitations, and also from publication bias toward replicating fiducial numerical models. Here we present the first systematic study of how the binary’s orbital evolution varies with disk scale height. We report three key results: (1) binary orbital evolution switches from outspiraling for warm disks (aspect ratio h/r ∼ 0.1), to inspiraling for more realistic cooler, thinner disks at a critical value of h/r ∼ 0.04, corresponding to orbital Mach number . (2) The net torque on the binary arises from a competition between positive torque from gas orbiting close to the black holes, and negative torque from the inner edge of the CBD, which is denser for thinner disks. This leads to increasingly negative net torques on the binary for increasingly thin disks. (3) The accretion rate is modestly suppressed with increasing Mach number. We discuss how our results may influence modeling of the nano-Hz gravitational-wave background, as well as estimates of the Laser Interferometer Space Antenna merger event rate.
Spectropolarimetric follow-up of 8 rapidly rotating, X-ray bright FK Comae candidates

Our understanding of the evolved, rapidly rotating, magnetically active, and apparently single FK Comae stars is significantly hindered by their extreme rarity: only two stars in addition to FK Com itself are currently considered to be members of this class. Recently, a sample of more than 20 candidate FK Comae type stars was identified within the context of the \emph{Kepler-Swift} Active Galaxies and Stars (KSwAGS) survey. We present an analysis of high-resolution Stokes $V$ observations obtained using ESPaDOnS@CFHT for 8 of these candidates. We found that none of these targets can be considered members of the FK Comae class based primarily on their inferred rotational velocities and on the detection of spectroscopic binary companions. However, 2 targets show evidence of magnetic activity and have anomalously high projected rotational velocities ($v\sin{i}$) relative to typical values associated with stars of similar evolutionary states. EPIC 210426551 has a $v\sin{i}=209\,{\rm km\,s}^{-1}$, an estimated mass of $1.07\,M_\odot$, and, based in part on its derived metallicity of ${\rm [M/H]}=-0.4$, it is either an evolved main sequence (MS) star or a pre-MS star. KIC 7732964 has a mass of $0.84\,M_\odot$, lies near the base of the red giant branch, and exhibits a $v\sin{i}=23\,{\rm km\,s}^{-1}$. We find that these two objects have similar characteristics to FK Com (albeit less extreme) and that their rapid rotation may be inconsistent with that predicted for a single star evolutionary history. Additional observations are necessary in order to better constrain their evolutionary states and whether they have short-period binary companions.
Gorenstein objects in the n-Trivial extensions of abelian categories

Given an abelian category, we introduce a categorical concept of (strongly) Gorenstein projective (resp., injective) objects, by defining a new special class of objects. Then we study the transfer of these properties when passing to an abelian category and its n-trivial extension category and also give a characterization of Gorenstein object over it. We give, at the end, applications of this study on the category of modules over an associative ring and triangular matrix rings.
The Effects of Smartphones on Well-Being: Theoretical Integration and Research Agenda

As smartphones become ever more integrated in peoples lives, a burgeoning new area of research has emerged on their well-being effects. We propose that disparate strands of research and apparently contradictory findings can be integrated under three basic hypotheses, positing that smartphones influence well-being by (1) replacing other activities (displacement hypothesis), (2) interfering with concurrent activities (interference hypothesis), and (3) affording access to information and activities that would otherwise be unavailable (complementarity hypothesis). Using this framework, we highlight methodological issues and go beyond net effects to examine how and when phones boost versus hurt well-being. We examine both psychological and contextual mediators and moderators of the effects, thus outlining an agenda for future research.
Large facing tuples and a strengthened sector lemma

We prove a strengthened sector lemma for irreducible, finite-dimensional, locally finite, essential, cocompact CAT(0) cube complexes under the additional hypothesis that the complex is \emph{hyperplane-essential}; we prove that every quarterspace contains a halfspace. In aid of this, we present simplified proofs of known results about loxodromic isometries of the contact graph, avoiding the use of disc diagrams. This paper has an expository element; in particular, we collect results about cube complexes proved by combining Ramsey's theorem and Dilworth's theorem. We illustrate the use of these tricks with a discussion of the Tits alternative for cubical groups, and ask some questions about ``quantifying'' statements related to rank-rigidity and the Tits alternative.
ENHANCED ADAPTIVE SURROGATE MODELS WITH APPLICATIONS IN UNCERTAINTY QUANTIFICATION FOR NANOPLASMONICS

We propose an efficient surrogate modeling technique for uncertainty quantification. The method is based on a well-known dimension-adaptive collocation scheme. We improve the scheme by enhancing sparse polynomial surrogates with conformal maps and adjoint error correction. The methodology is applied to Maxwell's source problem with random input data. This setting comprises many applications of current interest from computational nanoplasmonics, such as grating couplers or optical waveguides. Using a non-trivial benchmark model we show the benefits and drawbacks of using enhanced surrogate models through various numerical studies. The proposed strategy allows us to conduct a thorough uncertainty analysis, taking into account a moderately large number of random parameters.
Testing generalised uncertainty principles through quantum noise

Motivated by several approaches to quantum gravity, there is a considerable literature on generalised uncertainty principles particularly through modification of the canonical position–momentum commutation relations. Some of these modified relations are also consistent with general principles that may be supposed of any physical theory. Such modified commutators have significant observable consequences. Here we study the noisy behaviour of an optomechanical system assuming a certain commonly studied modified commutator. From recent observations of radiation pressure noise in tabletop optomechanical experiments as well as the position noise spectrum of advanced LIGO we derive bounds on the modified commutator. We find how such experiments can be adjusted to provide significant improvements in such bounds, potentially surpassing those from sub-atomic measurements.
Try This Instead: Personalized and Interpretable Substitute Recommendation

As a fundamental yet significant process in personalized recommendation, candidate generation and suggestion effectively help users spot the most suitable items for them. Consequently, identifying substitutable items that are interchangeable opens up new opportunities to refine the quality of generated candidates. When a user is browsing a specific type of product (e.g., a laptop) to buy, the accurate recommendation of substitutes (e.g., better equipped laptops) can offer the user more suitable options to choose from, thus substantially increasing the chance of a successful purchase. However, existing methods merely treat this problem as mining pairwise item relationships without the consideration of users' personal preferences. Moreover, the substitutable relationships are implicitly identified through the learned latent representations of items, leading to uninterpretable recommendation results. In this paper, we propose attribute-aware collaborative filtering (A2CF) to perform substitute recommendation by addressing issues from both personalization and interpretability perspectives. In A2CF, instead of directly modelling user-item interactions, we extract explicit and polarized item attributes from user reviews with sentiment analysis, whereafter the representations of attributes, users, and items are simultaneously learned. Then, by treating attributes as the bridge between users and items, we can thoroughly model the user-item preferences (i.e., personalization) and item-item relationships (i.e., substitution) for recommendation. In addition, A2CF is capable of generating intuitive interpretations by analyzing which attributes a user currently cares the most and comparing the recommended substitutes with her/his currently browsed items at an attribute level. The recommendation effectiveness and interpretation quality of A2CF are further demonstrated via extensive experiments on three real-life datasets.
The collinear helium atom and two-electron ions

Collinear configurations of the helium-like atomic systems, relevant, e.g., for the quasifree mechanism of the double photoionization of helium, are studied, parameterized by the single scalar parameter $-1\leq \lambda\leq1$ ("collinear parameter") where $\lambda=0$ corresponds to the electron-nucleus ($\textbf{e-n}$) coalescence and $\lambda=1$ corresponds to the electron-electron ($\textbf{e-e}$) coalescence. In general, $\lambda>0$ corresponds to the \textbf{n-e-e} configuration, and $\lambda<0$ to the \textbf{e-n-e} configuration. Simple mathematical representations of the expectation values of the Dirac delta function relevant for the collinear configurations are derived and calculated from fully three-body dynamics without approximation for the two-electron atomic wave functions with nuclear charge $1\leq Z\leq5$. Simple formulas for calculating the expectation values of the kinetic and potential energy operators in collinear configurations are derived. Unusual physical properties of the \textbf{n-e-e} collinear configurations found for certain ranges of $\lambda$ are presented. The first few angular Fock coefficients for collinear configurations are derived as functions of $\lambda$. Highly accurate model wave functions describing the ground states of the two-electron atoms with collinear arrangement of the particles are constructed. All results are illustrated by tables and figures.
Broadband X-Ray Observation of Broad-line Radio Galaxy 3C 109

We present a study of the central engine in the broad-line radio galaxy 3C 109. To investigate the immediate surrounding of this accreting, supermassive black hole, we perform a multiepoch broadband spectral analysis of a joint NuSTAR/XMM observation (2017), an archival XMM observation (2005) and the 105 month averaged Swift-BAT data. We are able to clearly separate the spectrum into a primary continuum, neutral and ionized absorption, and a reflection component. The photon index of the primary continuum has changed since 2005 ( ), while other components remain unchanged, indicative of minimal geometric changes to the central engine. We constrain the high-energy cutoff of 3C 109 (E keV ) for the first time. The reflector is found to be ionized (log ξ = ) but no relativistic blurring is required by the data. Spectral energy distribution (SED) analysis confirms the super-Eddington nature of 3C 109 initially (λEdd > 2.09). However, we do not find any evidence for strong reflection (R = ) or a steep power-law index, as expected from a super-Eddington source. This puts the existing virial mass estimate of 2 ×108M⊙ into question. We explore additional ways of estimating the Eddington ratio, some of which we find to be inconsistent with our initial SED estimate. We obtain a new black hole mass estimate of 9.3 ×108M⊙, which brings all Eddington ratio estimates into agreement and does not require 3C 109 to be super-Eddington.
CO+ first-negative band emission: A tracer for CO in the Martian upper atmosphere

Context. Recently, the Imaging Ultraviolet Spectrograph (IUVS) on board the Mars Atmosphere and Volatile EvolutioN (MAVEN) satellite observed CO+ first-negative band limb emission in the Martian upper atmosphere. Aims. We aim to explore the photochemical processes in the Martian upper atmosphere, which drive this band emission. Methods. A photochemical model was developed to study the excitation processes of CO+ first-negative band emission (B2Σ+ → X2Σ+) in the upper atmosphere of Mars. The number density profiles of CO2 and CO from two different models, namely, Mars Climate Database (MCD) and Mars Global Ionosphere-Thermosphere (MGIT), were used to determine the limb intensity of this band emission. Results. By increasing the CO density by a factor of 4 and 8 in MCD and MGIT models, respectively, the modelled CO+ first-negative band limb intensity profile is found to be consistent with the IUVS/MAVEN observation. In this case, the intensity of this band emission is significantly determined by the ionisation of CO by solar photons and photoelectrons, and the role of dissociative ionisation of CO2 is negligible. Conclusions. Since CO is the major source of the CO+(B2Σ+), we suggest that the observed CO+ first-negative band emission intensity can be used to retrieve the CO density in the Martian upper atmosphere for the altitudes above 150 km.