Miracle-dz/UniFAR-FTUs huggingface dataset:
Search for RR Lyrae stars in DES ultrafaint systems: Grus I, Kim 2, Phoenix II, and Grus II

This work presents the first search for RR Lyrae stars (RRLs) in four of the ultrafaint systems imaged by the Dark Energy Survey using SOAR/Goodman and Blanco/DECam imagers. We have detected two RRLs in the field of Grus I, none in Kim 2, one in Phoenix II, and four in Grus II. With the detection of these stars, we accurately determine the distance moduli for these ultrafaint dwarf satellite galaxies; μ0 = 20.51 ± 0.10 mag (D⊙ = 127 ± 6 kpc) for Grus I and μ0 = 20.01 ± 0.10 mag (D⊙ = 100 ± 5 kpc) for Phoenix II. These measurements are larger than previous estimations by Koposov et al. and Bechtol et al., implying larger physical sizes; 5 per cent for Grus I and 33 per cent for Phoenix II. For Grus II, of the four RRLs detected, one is consistent with being a member of the galactic halo (D⊙ = 24 ± 1 kpc, μ0 = 16.86 ± 0.10 mag), another is at D⊙ = 55 ± 2 kpc (μ0 = 18.71 ± 0.10 mag), which we associate with Grus II, and the two remaining at D⊙ = 43 ± 2 kpc (μ0 = 18.17 ± 0.10 mag). Moreover, the appearance of a subtle red horizontal branch in the colour–magnitude diagram of Grus II at the same brightness level of the latter two RRLs, which are at the same distance and in the same region, suggests that a more metal-rich system may be located in front of Grus II. The most plausible scenario is the association of these stars with the Chenab/Orphan Stream. Finally, we performed a comprehensive and updated analysis of the number of RRLs in dwarf galaxies. This allows us to predict that the method of finding new ultrafaint dwarf galaxies using two or more clumped RRLs will work only for systems brighter than MV ∼ −6 mag.
Encoding High-Level Visual Attributes in Capsules for Explainable Medical Diagnoses

Deep neural networks are often called black-boxes due to their difficult-to-interpret decisions. This is characteristic of a deeper trend in machine learning, where predictive performance typically comes at the cost of interpretability. In some domains, such as image-based diagnostic tasks, understanding the reasons behind machine generated predictions is vital in assessing trust. In this study, we introduce novel designs of capsule networks to provide explainable diagnoses. Our proposed deep explainable capsule architecture, called DX-Caps, can encode high-level visual attributes within the vectors of capsules in order to simultaneously produce malignancy predictions for lung cancer as well as approximations of six visually-interpretable attributes, used by radiologists to explain their predictions. To reduce parameter and memory burden of this deeper network, we introduce a new capsule-average pooling function. With this simple, but fundamental addition, capsule networks can be designed in a deeper fashion than was possible before. Our overall approach can be characterized as multi-task learning; we learn to approximate the six high-level visual attributes of a lung nodule within the vectors of our uniquely constructed deep capsule network, while simultaneously segmenting the nodule and predicting its malignancy potential (diagnosis). Tested on over 1000 CT scans, our experimental results show that our proposed algorithm can approximate the visual attributes of lung nodules far better than a deep multi-path dense 3D CNN. The proposed network also achieves higher diagnostic accuracy than a baseline explainable capsule network X-Caps and CapsNet when applied to this task for the first time as well. To the best of our knowledge, this is the first study to investigate capsule networks for visual attribute prediction in general, and explainable medical image diagnosis in particular.
OCCASO – III. Iron peak and α elements of 18 open clusters. Comparison with chemical evolution models and field stars

The study of open-cluster chemical abundances provides insights on stellar nucleosynthesis processes and on Galactic chemo-dynamical evolution. In this paper we present an extended abundance analysis of 10 species (Fe, Ni, Cr, V, Sc, Si, Ca, Ti, Mg, O) for red giant stars in 18 OCCASO clusters. This represents a homogeneous sample regarding the instrument features, method, line list and solar abundances from confirmed member stars. We perform an extensive comparison with previous results in the literature, and in particular with the Gaia FGK Benchmark stars Arcturus and $\mu$-Leo. We investigate the dependence of [X/Fe] with metallicity, Galactocentric radius (6.5 kpc < RGC < 11 kpc), age (0.3 Gyr < Age < 10 Gyr), and height above the plane (|z| < 1000 pc). We discuss the observational results in the chemo-dynamical framework, and the radial migration impact when comparing with chemical evolution models. We also use APOGEE DR14 data to investigate the differences between the abundance trends in RGC and |z| obtained for clusters and for field stars.
Self-force effects on the marginally bound zoom-whirl orbit in Schwarzschild spacetime

For a Schwarzchild black hole of mass $M$, we consider a test particle falling from rest at infinity and becoming trapped, at late time, on the unstable circular orbit of radius $r=4GM/c^2$. When the particle is endowed with a small mass, $\mu\ll M$, it experiences an effective gravitational self-force, whose conservative piece shifts the critical value of the angular momentum and the frequency of the asymptotic circular orbit away from their geodesic values. By directly integrating the self-force along the orbit (ignoring radiative dissipation), we numerically calculate these shifts to $O(\mu/M)$. Our numerical values are found to be in agreement with estimates first made within the Effective One Body formalism, and with predictions of the first law of black-hole-binary mechanics (as applied to the asymptotic circular orbit). Our calculation is based on a time-domain integration of the Lorenz-gauge perturbation equations, and it is a first such calculation for an unbound orbit. We tackle several technical difficulties specific to unbound orbits, illustrating how these may be handled in more general cases of unbound motion. Our method paves the way to calculations of the self-force along hyperbolic-type scattering orbits. Such orbits can probe the two-body potential down to the "light ring", and could thus supply strong-field calibration data for eccentricity-dependent terms in the Effective One Body model of merging binaries.
End-to-End Neural Speaker Diarization with Permutation-Free Objectives

In this paper, we propose a novel end-to-end neural-network-based speaker diarization method. Unlike most existing methods, our proposed method does not have separate modules for extraction and clustering of speaker representations. Instead, our model has a single neural network that directly outputs speaker diarization results. To realize such a model, we formulate the speaker diarization problem as a multi-label classification problem, and introduces a permutation-free objective function to directly minimize diarization errors without being suffered from the speaker-label permutation problem. Besides its end-to-end simplicity, the proposed method also benefits from being able to explicitly handle overlapping speech during training and inference. Because of the benefit, our model can be easily trained/adapted with real-recorded multi-speaker conversations just by feeding the corresponding multi-speaker segment labels. We evaluated the proposed method on simulated speech mixtures. The proposed method achieved diarization error rate of 12.28%, while a conventional clustering-based system produced diarization error rate of 28.77%. Furthermore, the domain adaptation with real-recorded speech provided 25.6% relative improvement on the CALLHOME dataset. Our source code is available online at this https URL.
Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering

Text-based Question Generation (QG) aims at generating natural and relevant questions that can be answered by a given answer in some context. Existing QG models suffer from a “semantic drift” problem, i.e., the semantics of the model-generated question drifts away from the given context and answer. In this paper, we first propose two semantics-enhanced rewards obtained from downstream question paraphrasing and question answering tasks to regularize the QG model to generate semantically valid questions. Second, since the traditional evaluation metrics (e.g., BLEU) often fall short in evaluating the quality of generated questions, we propose a QA-based evaluation method which measures the QG model’s ability to mimic human annotators in generating QA training data. Experiments show that our method achieves the new state-of-the-art performance w.r. t. traditional metrics, and also performs best on our QA-based evaluation metrics. Further, we investigate how to use our QG model to augment QA datasets and enable semi-supervised QA. We propose two ways to generate synthetic QA pairs: generate new questions from existing articles or collect QA pairs from new articles. We also propose two empirically effective strategies, a data filter and mixing mini-batch training, to properly use the QG-generated data for QA. Experiments show that our method improves over both BiDAF and BERT QA baselines, even without introducing new articles.
Selective Quantum State Tomography.

We introduce the concept of selective quantum state tomography or SQST, a tomographic scheme that enables a user to estimate arbitrary elements of an unknown quantum state using a fixed measurement record. We demonstrate how this may be done with the following notable advantages (i) a number of state copies that depends only on the desired precision of the estimation, rather than the dimension of the unknown state; (ii) a similar reduction in the requisite classical memory and computational cost; (iii) an approach to state tomography using $O(\epsilon^{-2}\log d)$ state copies for maximum norm error $\epsilon$, as well as achieving nearly optimal bounds for full tomography with independent measurements. As an immediate extension to this technique we show that SQST can be used to generate an universal data sample, of fixed and dimension independent size, from which one can extract the mean values of a continuous number of operators on demand.
Cayley path and quantum computational supremacy: A proof of average-case #P-hardness of Random Circuit Sampling with quantified robustness

A one-parameter unitary-valued interpolation between any two unitary matrices is constructed based on the Cayley transformation, which extends our work. The entries of the interpolated unitaries are shown to be low-degree rational functions in the parameter, which we proved can be efficiently determined using an extension of the Berlekamp-Welch algorithm. We prove that this path provides scrambled unitaries with probability distributions arbitrarily close to the Haar measure. We then prove the simplest known average-case #$P$-hardness of random circuit sampling (RCS), which is the task of sampling from the output distribution of a quantum circuit whose local gates are random Haar unitaries, and is the lead candidate for demonstrating quantum supremacy in the NISQ era. We show that a previous work based on the truncations of the power series representation of the exponential function does not provide practical robustness. Explicit bounds on noise resilience are proved, which on a grid of $\sqrt{n}\times\sqrt{n}$ qubits with circuit depth $\sqrt{n}$ is $2^{-\tilde{O}(n^{3})}$, and with constant-depth it is $2^{-\tilde{O}(n^{2})}$. Improvements to $O(2^{-n}/\text{poly}(n))$ would prove the quantum supremacy conjecture, which may be false.
Typical representations via fixed point sets in Bruhat–Tits buildings

<p>For a tame supercuspidal representation <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="pi"> <mml:semantics> <mml:mi>π<! -- π --></mml:mi> <mml:annotation encoding="application/x-tex">\pi</mml:annotation> </mml:semantics> </mml:math> </inline-formula> of a connected reductive <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="p"> <mml:semantics> <mml:mi>p</mml:mi> <mml:annotation encoding="application/x-tex">p</mml:annotation> </mml:semantics> </mml:math> </inline-formula>-adic group <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="upper G"> <mml:semantics> <mml:mi>G</mml:mi> <mml:annotation encoding="application/x-tex">G</mml:annotation> </mml:semantics> </mml:math> </inline-formula>, we establish two distinct and complementary sufficient conditions, formulated in terms of the geometry of the Bruhat–Tits building of <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="upper G"> <mml:semantics> <mml:mi>G</mml:mi> <mml:annotation encoding="application/x-tex">G</mml:annotation> </mml:semantics> </mml:math> </inline-formula>, for the irreducible components of its restriction to a maximal compact subgroup to occur in a representation of <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="upper G"> <mml:semantics> <mml:mi>G</mml:mi> <mml:annotation encoding="application/x-tex">G</mml:annotation> </mml:semantics> </mml:math> </inline-formula> which is not inertially equivalent to <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="pi"> <mml:semantics> <mml:mi>π<! -- π --></mml:mi> <mml:annotation encoding="application/x-tex">\pi</mml:annotation> </mml:semantics> </mml:math> </inline-formula>. The consequence is a set of broadly applicable tools for addressing the branching rules of <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="pi"> <mml:semantics> <mml:mi>π<! -- π --></mml:mi> <mml:annotation encoding="application/x-tex">\pi</mml:annotation> </mml:semantics> </mml:math> </inline-formula> and the unicity of <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="left-bracket upper G comma pi right-bracket Subscript upper G"> <mml:semantics> <mml:mrow> <mml:mo stretchy="false">[</mml:mo> <mml:mi>G</mml:mi> <mml:mo>,</mml:mo> <mml:mi>π<! -- π --></mml:mi> <mml:msub> <mml:mo stretchy="false">]</mml:mo> <mml:mi>G</mml:mi> </mml:msub> </mml:mrow> <mml:annotation encoding="application/x-tex">[G,\pi ]_G</mml:annotation> </mml:semantics> </mml:math> </inline-formula>-types. </p>
Porosity in Conformal Dynamical Systems

Abstract In this paper we study various aspects of porosities for conformal fractals. We first explore porosity in the general context of infinite graph directed Markov systems (GDMS), and we show that their limit sets are porous in large (in the sense of category and dimension) subsets. We also provide natural geometric and dynamic conditions under which the limit set of a GDMS is upper porous or mean porous. On the other hand, we prove that if the limit set of a GDMS is not porous, then it is not porous almost everywhere. We also revisit porosity for finite graph directed Markov systems, and we provide checkable criteria which guarantee that limit sets have holes of relative size at every scale in a prescribed direction. We then narrow our focus to systems associated to complex continued fractions with arbitrary alphabet and we provide a novel characterisation of porosity for their limit sets. Moreover, we introduce the notions of upper density and upper box dimension for subsets of Gaussian integers and we explore their connections to porosity. As applications we show that limit sets of complex continued fractions system whose alphabet is co-finite, or even a co-finite subset of the Gaussian primes, are not porous almost everywhere, while they are uniformly upper porous and mean porous almost everywhere. We finally turn our attention to complex dynamics and we delve into porosity for Julia sets of meromorphic functions. We show that if the Julia set of a tame meromorphic function is not the whole complex plane then it is porous at a dense set of its points and it is almost everywhere mean porous with respect to natural ergodic measures. On the other hand, if the Julia set is not porous then it is not porous almost everywhere. In particular, if the function is elliptic we show that its Julia set is not porous at a dense set of its points.
Mechanisms of a Convolutional Neural Network for Learning Three-dimensional Unsteady Wake Flow

Convolutional neural networks (CNNs) have recently been applied to predict or model fluid dynamics. However, mechanisms of CNNs for learning fluid dynamics are still not well understood, while such understanding is highly necessary to optimize the network or to reduce trial-and-errors during the network optmization. In the present study, a CNN to predict future three-dimensional unsteady wake flow using flow fields in the past occasions is developed. Mechanisms of the developed CNN for prediction of wake flow behind a circular cylinder are investigated in two flow regimes: the three-dimensional wake transition regime and the shear-layer transition regime. Feature maps in the CNN are visualized to compare flow structures which are extracted by the CNN from flow at the two flow regimes. In both flow regimes, feature maps are found to extract similar sets of flow structures such as braid shear-layers and shedding vortices. A Fourier analysis is conducted to investigate mechanisms of the CNN for predicting wake flow in flow regimes with different wave number characteristics. It is found that a convolution layer in the CNN integrates and transports wave number information from flow to predict the dynamics. Characteristics of the CNN for transporting input information including time histories of flow variables is analyzed by assessing contributions of each flow variable and time history to feature maps in the CNN. Structural similarities between feature maps in the CNN are calculated to reveal the number of feature maps that contain similar flow structures. By reducing the number of feature maps that contain similar flow structures, it is also able to successfully reduce the number of parameters to learn in the CNN by 85\% without affecting prediction performances.
Traffic-Aware Two-Stage Queueing Communication Networks: Queue Analysis and Energy Saving

To boost energy saving for the general delay-tolerant IoT networks, a two-stage, and single-relay queueing communication scheme is investigated. Concretely, a traffic-aware $N$ -threshold and gated-service policy are applied at the relay. As two fundamental and significant performance metrics, the mean waiting time and long-term expected power consumption are explicitly derived and related with the queueing and service parameters, such as packet arrival rate, service threshold and channel statistics. Besides, we take into account the electrical circuit energy consumptions when the relay server and access point (AP) are in different modes and energy costs for mode transitions, whereby the power consumption model is more practical. The expected power minimization problem under the mean waiting time constraint is formulated. Tight closed-form bounds are adopted to obtain tractable analytical formulae with less computational complexity. The optimal energy-saving service threshold that can flexibly adjust to packet arrival rate is determined. In addition, numerical results reveal that: 1) sacrificing the mean waiting time not necessarily facilitates power savings; 2) a higher arrival rate leads to a greater optimal service threshold; and 3) our policy performs better than the current state-of-the-art.
Estimating Fisher Information Matrix in Latent Variable Models based on the Score Function

The Fisher information matrix (FIM) is a key quantity in statistics as it is required for example for evaluating asymptotic precisions of parameter estimates, for computing test statistics or asymptotic distributions in statistical testing, for evaluating post model selection inference results or optimality criteria in experimental designs. However its exact computation is often not trivial. In particular in many latent variable models, it is intricated due to the presence of unobserved variables. Therefore the observed FIM is usually considered in this context to estimate the FIM. Several methods have been proposed to approximate the observed FIM when it can not be evaluated analytically. Among the most frequently used approaches are Monte-Carlo methods or iterative algorithms derived from the missing information principle. All these methods require to compute second derivatives of the complete data log-likelihood which leads to some disadvantages from a computational point of view. In this paper, we present a new approach to estimate the FIM in latent variable model. The advantage of our method is that only the first derivatives of the log-likelihood is needed, contrary to other approaches based on the observed FIM. Indeed we consider the empirical estimate of the covariance matrix of the score. We prove that this estimate of the Fisher information matrix is unbiased, consistent and asymptotically Gaussian. Moreover we highlight that none of both estimates is better than the other in terms of asymptotic covariance matrix. When the proposed estimate can not be directly analytically evaluated, we present a stochastic approximation estimation algorithm to compute it. This algorithm provides this estimate of the FIM as a by-product of the parameter estimates. We emphasize that the proposed algorithm only requires to compute the first derivatives of the complete data log-likelihood with respect to the parameters. We prove that the estimation algorithm is consistent and asymptotically Gaussian when the number of iterations goes to infinity. We evaluate the finite sample size properties of the proposed estimate and of the observed FIM through simulation studies in linear mixed effects models and mixture models. We also investigate the convergence properties of the estimation algorithm in non linear mixed effects models. We compare the performances of the proposed algorithm to those of other existing methods.
Effects of magnetic fields on the location of the evaporation valley for low-mass exoplanets

The observed distribution of radii for exoplanets shows a bimodal form that can be explained by mass-loss from planetary atmospheres due to high-energy radiation emitted by their host stars. The location of the minimum of this radius distribution depends on the mass–radius relation, which in turn depends on the composition of planetary cores. Current studies suggest that super-Earth and mini-Neptune planets have iron-rich and hence largely Earth-like composition cores. This paper explores how non-zero planetary magnetic fields can decrease the expected mass-loss rates from these planets. These lower mass-loss rates, in turn, affect the location of the minimum of the radius distribution and the inferred chemical composition of the planetary cores.