Miracle-dz/UniFAR-FTUs huggingface dataset:
A solution to the proplyd lifetime problem

Protoplanetary discs (PPDs) in the Orion Nebula Cluster (ONC) are irradiated by UV fields from the massive star θ1C. This drives thermal winds, inducing mass-loss rates of up to $\dot{M}_\mathrm{wind}\sim 10^{-7}\, \mathrm{M}_\odot$ yr−1 in the ‘proplyds’ (ionized PPDs) close to the centre. For the mean age of the ONC and reasonable initial PPD masses, such mass-loss rates imply that discs should have been dispersed. However, $\sim 80{\,{\rm {per\, cent}}}$ of stars still exhibit a near-infrared excess, suggesting that significant circumstellar mass remains. This ‘proplyd lifetime problem’ has persisted since the discovery of photoevaporating discs in the core of the ONC by O’Dell & Wen (1994). In this work, we demonstrate how an extended period of star formation can solve this problem. Coupling N-body calculations and a viscous disc evolution model, we obtain high disc fractions at the present day. This is partly due to the migration of older stars outwards, and younger stars inwards such that the most strongly irradiated PPDs are also the youngest. We show how the disc mass distribution can be used to test the recent claims in the literature for multiple stellar populations in the ONC. Our model also explains the recent finding that host mass and PPD mass are only weakly correlated, in contrast with other regions of similar age. We conclude that the status of the ONC as the archetype for understanding the influence of environment on planet formation is undeserved; the complex star formation history (involving star formation episodes within ∼0.8 Myr of the present day) results in confusing signatures in the PPD population.
Are gravitating magnetic monopoles stable?

The gravitating Julia-Zee dyon is a particle-like solution with both electric and magnetic charge. It is found in the Einstein-Yang-Mills-Higgs system of SU(2) with a scalar field in the adjoint representation coupled to gravity. Within the magnetic ansatz this system is reduced from describing dyons to describing the gravitating 't Hooft-Polyakov magnetic monopole. The stability of the well-known static gravitating magnetic monopole solutions with respect to perturbations within the magnetic ansatz--so-called magnetic perturbations--is well studied, but their stability with respect to perturbations outside the magnetic ansatz--so-called sphaleronic perturbations--is not. I undertake a purely numerical study by adding sphaleronic perturbations to gravitating magnetic monopole solutions and then dynamically evolving the system. For large perturbations I find that the system heads toward a dyon configuration, as expected. For sufficiently small perturbations, however, the system oscillates about the magnetic ansatz in a manner consistent with oscillations about a stable equilibrium.
BERT-Based Arabic Social Media Author Profiling

We report our models for detecting age, language variety, and gender from social media data in the context of the Arabic author profiling and deception detection shared task (APDA). We build simple models based on pre-trained bidirectional encoders from transformers (BERT). We first fine-tune the pre-trained BERT model on each of the three datasets with shared task released data. Then we augment shared task data with in-house data for gender and dialect, showing the utility of augmenting training data. Our best models on the shared task test data are acquired with a majority voting of various BERT models trained under different data conditions. We acquire 54.72% accuracy for age, 93.75% for dialect, 81.67% for gender, and 40.97% joint accuracy across the three tasks.
HALOGAS: the properties of extraplanar HI in disc galaxies

We present a systematic study of the extraplanar gas (EPG) in a sample of 15 nearby late-type galaxies at intermediate inclinations using publicly available, deep interferometric H I data from the Hydrogen Accretion in LOcal GAlaxieS (HALOGAS) survey. For each system we masked the H I emission coming from the regularly rotating disc and used synthetic datacubes to model the leftover “anomalous” H I flux. Our model consists of a smooth, axisymmetric thick component described by three structural and four kinematical parameters, which are fit to the data via a Markov chain Monte Carlo (MCMC) based Bayesian method. We find that extraplanar H I is nearly ubiquitous in disc galaxies as we fail to detect it in only two of the systems with the poorest spatial resolution. The EPG component encloses ∼5−25% of the total H I mass with a mean value of 14%, and has a typical thickness of a few kpc which is incompatible with expectations based on hydrostatic equilibrium models. The EPG kinematics is remarkably similar throughout the sample, and consists of a lagging rotation with typical vertical gradients of ∼ − 10 km s−1 kpc−1, a velocity dispersion of 15−30 km s−1, and, for most galaxies, a global inflow in both the vertical and radial directions with speeds of 20−30 km s−1. The EPG H I masses are in excellent agreement with predictions from simple models of the galactic fountain that are powered by stellar feedback. The combined effect of photo-ionisation and interaction of the fountain material with the circumgalactic medium can qualitatively explain the kinematics of the EPG, but dynamical models of the galactic fountain are required to fully test this framework.
Coronal Response to Magnetically Suppressed CME Events in M-dwarf Stars

We report the results of the first state-of-the-art numerical simulations of coronal mass ejections (CMEs) taking place in realistic magnetic field configurations of moderately active M-dwarf stars. Our analysis indicates that a clear, novel, and observable, coronal response is generated due to the collapse of the eruption and its eventual release into the stellar wind. Escaping CME events, weakly suppressed by the large-scale field, induce a flare-like signature in the emission from coronal material at different temperatures due to compression and associated heating. Such flare-like profiles display a distinctive temporal evolution in their Doppler shift signal (from red to blue), as the eruption first collapses toward the star and then perturbs the ambient magnetized plasma on its way outwards. For stellar fields providing partial confinement, CME fragmentation takes place, leading to rise and fall flow patterns which resemble the solar coronal rain cycle. In strongly suppressed events, the response is better described as a gradual brightening, in which the failed CME is deposited in the form of a coronal rain cloud leading to a much slower rise in the ambient high-energy flux by relatively small factors (∼2–3). In all the considered cases (escaping/confined) a fractional decrease in the emission from midrange coronal temperature plasma occurs, similar to the coronal dimming events observed on the Sun. Detection of the observational signatures of these CME-induced features requires a sensitive next generation X-ray space telescope.
Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers

We provide adaptive inference methods, based on l1 regularization methods, for regular (semi-parametric) and non-regular (nonparametric) linear functionals of the conditional expectation function. Examples of regular functionals include average treatment effects, policy effects from covariate distribution shifts and stochastic transformations, and average derivatives. Examples of non-regular functionals include the local linear functionals defined as local averages that approximate perfectly localized quantities: average treatment, average policy effects, and average derivatives, conditional on a covariate subvector fixed at a point. Our construction relies on building Neyman orthogonal equations for the target parameter that are approximately invariant to small perturbations of the nuisance parameters. To achieve this property we include the linear Riesz representer for the functionals in the equations as the additional nuisance parameter. We use l1-regularized methods to learn approximations to the regression function and the linear representer, in settings where dimension of (possibly overcomplete) dictionary of basis functions P is much larger than N. We then estimate the linear functional by the solution to the empirical analog of the orthogonal equations. Our key result is that under weak assumptions the estimator of the functional concentrates in a L/root(n) neighborhood of the target with deviations controlled by the Gaussian law, provided L/root(n) \to 0; L is the operator norm of the functional, measuring the degree of its non-regularity, with L diverging for local functionals (or under weak identification of the global functionals).
Pose Agnostic Cross-spectral Hallucination via Disentangling Independent Factors

The cross-sensor gap is one of the challenges that arise much research interests in Heterogeneous Face Recognition (HFR). Although recent methods have attempted to fill the gap with deep generative networks, most of them suffered from the inevitable misalignment between different face modalities. Instead of imaging sensors, the misalignment primarily results from geometric variations (e.g., pose and expression) on faces that stay independent from spectrum. Rather than building a monolithic but complex structure, this paper proposes a Pose Agnostic Cross-spectral Hallucination (PACH) approach to disentangle the independent factors and deal with them in individual stages. In the first stage, an Unsupervised Face Alignment (UFA) network is designed to align the near-infrared (NIR) and visible (VIS) images in a generative way, where 3D information is effectively utilized as the pose guidance. Thus the task of the second stage becomes spectrum transform with paired data. We develop a Texture Prior Synthesis (TPS) network to accomplish complexion control and consequently generate more realistic VIS images than existing methods. Experiments on three challenging NIR-VIS datasets verify the effectiveness of our approach in producing visually appealing images and achieving state-of-the-art performance in cross-spectral HFR.
Coherent states for exactly solvable time-dependent oscillators generated by Darboux transformations

The Darboux method is commonly used in the coordinate variable to produce new exactly solvable (stationary) potentials in quantum mechanics. In this work we follow a variation introduced by Bagrov, Samsonov, and Shekoyan (BSS) to include the time-variable as a parameter of the transformation. The new potentials are nonstationary and define Hamiltonians which are not integrals of motion for the system under study. We take the stationary oscillator of constant frequency to produce nonstationary oscillators, and provide an invariant that serves to define uniquely the state of the system. In this sense our approach completes the program of the BSS method since the eigenfunctions of the invariant form an orthonormal basis for the space of solutions of the related Schrödinger equation. The orthonormality holds when the involved functions are evaluated at the same time. The dynamical algebra of the nonstationary oscillators is generated by properly chosen ladder operators and coincides with the Heisenberg algebra. The related coherent states are constructed and it is shown that they form an overcomplete set that minimizes the quadratures defined by the ladder operators. The time-dependence of these states relies on the basis of states and not on the complex eigenvalue that labels them. Some concrete examples are provided.
VACL: Variance-Aware Cross-Layer Regularization for Pruning Deep Residual Networks

Improving weight sparsity is a common strategy for producing light-weight deep neural networks. However, pruning models with residual learning is more challenging. In this paper, we introduce a novel approach to address this problem. Our method puts the ith filters of layers connected by skip-connections into one regularization group. Additionally, we define Variance-Aware Cross-Layer (VACL) regularization which takes into account both the first and second-order statistics of the connected layers to constrain the variance within a group. Our approach can effectively improve the structural sparsity of residual models. For CIFAR10, the proposed method reduces a ResNet model by up to 79.5% with no accuracy drop, and reduces a ResNeXt model by up to 82% with < 1% accuracy drop. For ImageNet, it yields a pruned ratio of up to 63:3% with < 1% top-5 accuracy drop. Our experimental results show that the proposed approach significantly outperforms other state-of-the-art methods in terms of overall model size and accuracy.
SN2018kzr: A Rapidly Declining Transient from the Destruction of a White Dwarf

We present SN2018kzr, the fastest declining supernova-like transient, second only to the kilonova, AT2017gfo. SN2018kzr is characterized by a peak magnitude of Mr = −17.98, a peak bolometric luminosity of ∼1.4 × 1043 erg s−1, and a rapid decline rate of 0.48 ± 0.03 mag day−1 in the r band. The bolometric luminosity evolves too quickly to be explained by pure 56Ni heating, necessitating the inclusion of an alternative powering source. Incorporating the spin-down of a magnetized neutron star adequately describes the lightcurve and we estimate a small ejecta mass of Mej = 0.10 ± 0.05 M⊙. Our spectral modeling suggests the ejecta is composed of intermediate mass elements including O, Si, and Mg and trace amounts of Fe-peak elements, which disfavors a binary neutron star merger. We discuss three explosion scenarios for SN2018kzr, given the low ejecta mass, intermediate mass element composition, and high likelihood of additional powering—the core collapse of an ultra-stripped progenitor, the accretion induced collapse (AIC) of a white dwarf, and the merger of a white dwarf and neutron star. The requirement for an alternative input energy source favors either the AIC with magnetar powering or a white dwarf–neutron star merger with energy from disk wind shocks.
Generalized List Decoding

This paper concerns itself with the question of list decoding for general adversarial channels, e.g., bit-flip (XOR) channels, erasure channels, AND (Z-) channels, OR (ℤ-) channels, real adder channels, noisy typewriter channels, etc. We precisely characterize when exponential-sized (or positive rate) (L − 1)-list decodable codes (where the list size L is a universal constant) exist for such channels. Our criterion essentially asserts that:For any given general adversarial channel, it is possible to construct positive rate (L − 1)-list decodable codes if and only if the set of completely positive tensors of order-L with admissible marginals is not entirely contained in the order-L confusability set associated to the channel.The sufficiency is shown via random code construction (combined with expurgation or time-sharing). The necessity is shown by1. extracting approximately equicoupled subcodes (generalization of equidistant codes) from any sequence of "large" codes using hypergraph Ramsey’s theorem, and2. significantly extending the classic Plotkin bound in coding theory to list decoding for general channels using duality between the completely positive tensor cone and the copositive tensor cone.In the proof, we also obtain a new fact regarding asymmetry of joint distributions, which may be of independent interest.Other results include1 List decoding capacity with asymptotically large L for general adversarial channels;2 A tight list size bound for most constant composition codes (generalization of constant weight codes);3 Rederivation and demystification of Blinovsky’s [9] characterization of the list decoding Plotkin points (threshold at which large codes are impossible) for bit-flip channels;4 Evaluation of general bounds ([43]) for unique decoding in the error correction code setting.
Energy‐efficient power allocation for device‐to‐device communications underlaid cellular networks using stochastic geometry

In this paper, we study an energy efficiency maximization problem in uplink for device‐to‐device (D2D) communications underlaid with cellular networks on multiple bands. Utilizing stochastic geometry, we derive closed‐form expressions for the average sum rate, successful transmission probability, and energy efficiency of cellular and D2D users. Then, we formulate an optimization problem to jointly maximize the energy efficiency of D2D and cellular users and obtain optimum transmission power of both D2D and cellular users. In the optimization problem, we guarantee the quality‐of‐service of users by taking into account the success transmission probability on each link. To solve the problem, first we convert the problem into canonical convex form. Afterwards, we solve the problem in two phases: energy efficiency maximization of devices and energy efficiency maximization of cellular users. In the first phase, we maximize the energy efficiency of D2D users and feed the solution to the second phase where we maximize the energy efficiency of cellular users. Simulation results reveal that significant energy efficiency can be attained, eg, 10% energy efficiency improvement compared to fix transmission power in a high‐density scenario.
(GL_k x S_n)-modules and nabla of hook-indexed Schur functions

The aim of this paper is to describe structural properties of spaces of diagonal rectangular harmonic polynomials in several sets (say $k$) of $n$ variables, both as $GL_k$-modules and $S_n$-modules. We construct explicit such modules associated to any hook shape partitions. For the two sets of variables case, we conjecture that the associated graded Frobenius characteristic corresponds to the effect of the operator Nabla on the corresponding hook-indexed Schur function, up to a usual renormalization. We prove identities that give indirect support to this conjecture, and show that its restriction to one set of variables holds. We further give indications on how the several sets context gives a better understanding of questions regarding the structures of these modules and the links between them.
Efficient nonmyopic Bayesian optimization and quadrature

Finite-horizon sequential decision problems arise naturally in many machine learning contexts; examples include Bayesian optimization and Bayesian quadrature. Computing the optimal policy for such problems requires solving Bellman equations, which are generally intractable. Most existing work resorts to myopic approximations by limiting the horizon to only a single time-step, which can perform poorly in balancing exploration and exploitation. We propose a general framework for efficient, nonmyopic approximation of the optimal policy by drawing a connection between the optimal adaptive policy and its non-adaptive counterpart. Our proposal is to compute an optimal batch of points, then select a single point from within this batch to evaluate. We realize this idea for both Bayesian optimization and Bayesian quadrature and demonstrate that our proposed method significantly outperforms common myopic alternatives on a variety of tasks.
A new result for 2D boundedness of solutions to a chemotaxis–haptotaxis model with/without sub-logistic source

We consider the Neumann problem for a coupled chemotaxis–haptotaxis model of cancer invasion with/without kinetic source in a 2D bounded and smooth domain. For a large class of cell kinetic sources including zero source and sub-logistic sources, we detect an explicit condition involving the chemotactic strength, the asymptotic ‘damping’ rate, and the initial mass of cells to ensure uniform-in-time boundedness for the corresponding Neumann problem. Our finding significantly improves existing 2D global existence and boundedness in related chemotaxis–haptotaxis systems.
Video Representation Learning by Dense Predictive Coding

The objective of this paper is self-supervised learning of spatio-temporal embeddings from video, suitable for human action recognition. We make three contributions: First, we introduce the Dense Predictive Coding (DPC) framework for self-supervised representation learning on videos. This learns a dense encoding of spatio-temporal blocks by recurrently predicting future representations; Second, we propose a curriculum training scheme to predict further into the future with progressively less temporal context. This encourages the model to only encode slowly varying spatial-temporal signals, therefore leading to semantic representations; Third, we evaluate the approach by first training the DPC model on the Kinetics-400 dataset with self-supervised learning, and then finetuning the representation on a downstream task, i.e. action recognition. With single stream (RGB only), DPC pretrained representations achieve state-of-the-art self-supervised performance on both UCF101(75.7% top1 acc) and HMDB51(35.7% top1 acc), outperforming all previous learning methods by a significant margin, and approaching the performance of a baseline pre-trained on ImageNet.
LOWER BOUNDS FOR PERIODS OF DUCCI SEQUENCES

A Ducci sequence is a sequence of integer $n$-tuples obtained by iterating the map $$\begin{eqnarray}D:(a_{1},a_{2},\ldots ,a_{n})\mapsto (|a_{1}-a_{2}|,|a_{2}-a_{3}|,\ldots ,|a_{n}-a_{1}|). \end{eqnarray}$$ Such a sequence is eventually periodic and we denote by $P(n)$ the maximal period of such sequences for given odd $n$. We prove a lower bound for $P(n)$ by counting certain partitions. We then estimate the size of these partitions via the multiplicative order of two modulo $n$.
Product of masses on boundedness, gradient estimate, blow-up and convergence in a two-species and two-stimuli chemotaxis system with/without loop

In this work, we study dynamic properties of classical solutions to a homogenous Neumann initial-boundary value problem (IBVP) for a two-species and two-stimuli chemotaxis model with/without chemical signalling loop in a 2D bounded and smooth domain. We successfully detect the product of two species masses as a feature to determine boundedness, gradient estimates, blow-up and $W^{j,\infty}(1\leq j\leq 3)$-exponential convergence of classical solutions for the corresponding IBVP. More specifically, we first show generally a smallness on the product of both species masses, thus allowing one species mass to be suitably large, is sufficient to guarantee global boundedness, higher order gradient estimates and $W^{j,\infty}$-convergence with rates of convergence to constant equilibria; and then, in a special case, we detect a straight line of masses on which blow-up occurs for large product of masses. Our findings provide new understandings about the underlying model, and thus, improve and extend greatly the existing knowledge relevant to this model.
Revisiting MOA 2013-BLG-220L: A Solar-type Star with a Cold Super-Jupiter Companion

We present the analysis of high-resolution images of MOA-2013-BLG-220, taken with the Keck adaptive optics system six years after the initial observation, identifying the lens as a solar-type star hosting a super-Jupiter-mass planet. The masses of planets and host stars discovered by microlensing are often not determined from light-curve data, while the star–planet mass ratio and projected separation in units of Einstein ring radius are well measured. High-resolution follow-up observations after the lensing event is complete can resolve the source and lens. This allows direct measurements of flux, and the amplitude and direction of proper motion, giving strong constraints on the system parameters. Due to the high relative proper motion, mas yr−1, the source and lens were resolved in 2019, with a separation of 77.1 ± 0.5 mas. Thus, we constrain the lens flux to . By combining constraints from the model and Keck flux, we find the lens mass to be at . With a mass ratio of the planet’s mass is determined to be at a separation of . The lens mass is much higher than the prediction made by Bayesian analysis that assumes all stars have an equal probability to host a planet of the measured mass ratio, and suggests that planets with mass ratios of a few times 10−3 are more common orbiting massive stars. This demonstrates the importance of high-resolution follow-up observations for testing theories like these.