Miracle-dz/UniFAR-FTUs huggingface dataset:
Photometric Redshifts with the LSST. II. The Impact of Near-infrared and Near-ultraviolet Photometry

Accurate photometric redshift (photo-z) estimates are essential to the cosmological science goals of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). In this work, we use simulated photometry for mock galaxy catalogs to explore how LSST photo-z estimates can be improved by the addition of near-infrared (NIR) and/or ultraviolet (UV) photometry from the Euclid, Wide-Field InfrarRed Survey Telescope (WFIRST), and/or Cosmological Advanced Survey Telescope for Optical and ultraviolet Research (CASTOR) space telescopes. Generally, we find that deeper optical photometry can reduce the standard deviation of the photo-z estimates more than adding NIR or UV filters but that additional filters are the only way to significantly lower the fraction of galaxies with catastrophically under- or overestimated photo-z. For Euclid, we find that the addition of JH 5σ photometric detections can reduce the standard deviation for galaxies with z > 1 (z > 0.3) by ∼20% (∼10%), and the fraction of outliers by ∼40% (∼25%). For WFIRST, we show how the addition of deep YJHK photometry could reduce the standard deviation by ≳50% at z > 1.5 and drastically reduce the fraction of outliers to just ∼2% overall. For CASTOR, we find that the addition of its UV- and u-band photometry could reduce the standard deviation by ∼30% and the fraction of outliers by ∼50% for galaxies with z < 0.5. We also evaluate the photo-z results within sky areas that overlap with both the NIR and UV surveys and when spectroscopic training sets built from the surveys’ small-area deep fields are used.
Stellar angular momentum distribution linked to galaxy morphology

We study the spatially-resolved stellar specific angular momentum j∗ in a high-quality sample of 24 CALIFA galaxies covering a broad range of visual morphology, accounting for stellar velocity and velocity dispersion. The shape of the spaxel-wise probability density function of normalised s = j∗/j∗mean, PDF(s), deviates significantly from the near-universal initial distribution expected of baryons in a dark matter halo and can be explained by the expected baryonic effects in galaxy formation that remove and redistribute angular momentum. Further we find that the observed shape of the PDF(s) correlates significantly with photometric morphology, where late-type galaxies have a PDF(s) that is similar to a normal distribution, whereas early types have a strongly-skewed PDF(s) resulting from an excess of low-angular momentum material. Galaxies that are known to host pseudobulges (bulge S´ersic index nb < 2.2) tend to have less skewed bulge PDF(s), with skewness (b1r b ) . 0.8. The PDF(s) encodes both kinematic and photometric information and appears to be a robust tracer of morphology. Its use is motivated by the desire to move away from traditional component-based classifications which are subject to observer bias, to classification on a galaxy’s fundamental (stellar mass, angular momentum) properties. In future, PDF(s) may also be useful as a kinematic decomposition tool.
Transform and Tell: Entity-Aware News Image Captioning

We propose an end-to-end model which generates captions for images embedded in news articles. News images present two key challenges: they rely on real-world knowledge, especially about named entities; and they typically have linguistically rich captions that include uncommon words. We address the first challenge by associating words in the caption with faces and objects in the image, via a multi-modal, multi-head attention mechanism. We tackle the second challenge with a state-of-the-art transformer language model that uses byte-pair-encoding to generate captions as a sequence of word parts. On the GoodNews dataset, our model outperforms the previous state of the art by a factor of four in CIDEr score (13 to 54). This performance gain comes from a unique combination of language models, word representation, image embeddings, face embeddings, object embeddings, and improvements in neural network design. We also introduce the NYTimes800k dataset which is 70% larger than GoodNews, has higher article quality, and includes the locations of images within articles as an additional contextual cue.
Early forecasts of the evolution of the COVID-19 outbreaks and quantitative assessment of the effectiveness of countering measures.

We discovered that the time evolution of the inverse fractional daily growth of new infections, N/dN, in the current outbreak of COVID-19 is accurately described by a universal function, namely the two-parameter Gumbel cumulative function, in all countries that we have investigated. While the two Gumbel parameters, as determined bit fits to the data, vary from country to country (and even within different regions of the same country), reflecting the diversity and efficacy of the adopted containment measures, the functional form of the evolution of N/dN appears to be universal. The result of the fit in a given region or country appears to be stable against variations of the selected time interval. This makes it possible to robustly estimate the two parameters from the data data even over relatively small time periods. In turn, this allows one to predict with large advance and well-controlled confidence levels, the time of the peak in the daily new infections, its magnitude and duration (hence the total infections), as well as the time when the daily new infections decrease to a pre-set value (e.g. less than about 2 new infections per day per million people), which can be very useful for planning the reopening of economic and social activities. We use this formalism to predict and compare these key features of the evolution of the COVID-19 disease in a number of countries and provide a quantitative assessment of the degree of success in in their efforts to countain the outbreak.
Results from the First All-Sky Search for Continuous Gravitational Waves from Small-Ellipticity Sources.

We present the results of an all-sky search for continuous gravitational-wave signals with frequencies in the 500-1700 Hz range targeting neutron stars with ellipticity of 10^{-8}. The search is done on LIGO O2 data using the Falcon analysis pipeline. The results presented here double the sensitivity over any other result on the same data [B. P. Abbott et al. (LIGO Scientific Collaboration and Virgo Collaboration), Phys. Rev. D 100, 024004 (2019)PRVDAQ2470-001010.1103/PhysRevD.100. 024004, C. Palomba et al., Phys. Rev. Lett. 123, 171101 (2019)PRLTAO0031-900710.1103/PhysRevLett.123. 171101]. The search is capable of detecting low-ellipticity sources up to 170 pc. We establish strict upper limits which hold for worst-case signal parameters. We list outliers uncovered by the search, including several which we cannot associate with any known instrumental cause.
Common origin of neutrino masses and 
RD(*)
, 
RK(*)
 anomalies

In this work, we present a solution to the persistent tensions in the decay observables $R_{D^{(\ast)}}$ and $R_{K^{(\ast)}}$ by introducing a $SU(2)_L$ doublet and a $SU(2)_L$ triplet scalar leptoquarks (LQs) that reside at the TeV energy scale. Neutrinos that remain massless in the Standard Model receive naturally small masses at one-loop level via the propagation of the same LQs inside the loop. Such a common origin of apparently disjoint phenomenological observations is appealing, and we perform a comprehensive analysis of this set-up. We identify the minimal Yukawa textures required to accommodate these flavor anomalies and to successfully incorporate neutrino oscillation data while being consistent with all experimental constraints. This scenario has the potential to be tested at the experiments by the future improved measurements in the lepton flavor violating processes. Furthermore, proper explanations of these flavor anomalies predict TeV scale LQs that are directly accessible at the LHC.
Coronavirus (COVID-19): ARIMA-based Time-series Analysis to Forecast near Future and the Effect of School Reopening in India

COVID-19, a novel coronavirus, is currently a major worldwide threat. It has infected more than a million people globally leading to hundred-thousands of deaths. In such grave circumstances, it is very important to predict future scenario to support prevention and recurrence of the disease, aid in healthcare service preparation and help in decision making process. Following that notion, a model has been developed for forecasting future COVID-19 cases in India. The time series analysis indicates that the cases will keep on increasing in India in the coming month as the peak has not been attained until now. A statistical analysis based on the effect of reopening of schools has also been performed. It is revealed that there will be a minor increase in the active cases when pre-/primary schools are opened. The present prediction models will assist the government and medical personnel in gaining insight and planning for forthcoming conditions.
Bridging Anaphora Resolution as Question Answering

Most previous studies on bridging anaphora resolution (Poesio et al., 2004; Hou et al., 2013b; Hou, 2018a) use the pairwise model to tackle the problem and assume that the gold mention information is given. In this paper, we cast bridging anaphora resolution as question answering based on context. This allows us to find the antecedent for a given anaphor without knowing any gold mention information (except the anaphor itself). We present a question answering framework (BARQA) for this task, which leverages the power of transfer learning. Furthermore, we propose a novel method to generate a large amount of “quasi-bridging” training data. We show that our model pre-trained on this dataset and fine-tuned on a small amount of in-domain dataset achieves new state-of-the-art results for bridging anaphora resolution on two bridging corpora (ISNotes (Markert et al., 2012) and BASHI (Ro ̈siger, 2018)).
Spectral Gap for the Growth-Fragmentation Equation via Harris's Theorem

We study the long-time behaviour of the growth-fragmentation equation, a nonlocal linear evolution equation describing a wide range of phenomena in structured population dynamics. We show the existence of a spectral gap under conditions that generalise those in the literature by using a method based on Harris's theorem, a result coming from the study of equilibration of Markov processes. The difficulty posed by the non-conservativeness of the equation is overcome by performing an $h$-transform, after solving the dual Perron eigenvalue problem. The existence of the direct Perron eigenvector is then a consequence of our methods, which prove exponential contraction of the evolution equation. Moreover the rate of convergence is explicitly quantifiable in terms of the dual eigenfunction and the coefficients of the equation.
Detection and Estimation of Local Signals

We study the maximum score statistic to detect and estimate local signals in the form of change-points in the level, slope, or other property of a sequence of observations, and to segment the sequence when there appear to be multiple changes. We find that when observations are serially dependent, the change-points can lead to upwardly biased estimates of autocorrelations, resulting in a sometimes serious loss of power. Examples involving temperature variations, the level of atmospheric greenhouse gases, suicide rates and daily incidence of COVID-19 illustrate the general theory.
Using single-cell entropy to describe the dynamics of reprogramming and differentiation of induced pluripotent stem cells

Induced pluripotent stem cells (iPSCs) provide a great model to study the process of stem cell reprogramming and differentiation. Single-cell RNA sequencing (scRNA-seq) enables us to investigate the reprogramming process at single-cell level. Here, we introduce single-cell entropy (scEntropy) as a macroscopic variable to quantify the cellular transcriptome from scRNA-seq data during reprogramming and differentiation of iPSCs. scEntropy measures the relative order parameter of genomic transcriptions at single cell level during the process of cell fate changes, which show increase tendency during differentiation, and decrease upon reprogramming. Hence, scEntropy provides an intrinsic measurement of the cell state, and can be served as a pseudo-time of the stem cell differentiation process. Moreover, based on the evolutionary dynamics of scEntropy, we construct a phenomenological Fokker-Planck equation model and the corresponding stochastic differential equation for the process of cell state transitions during pluripotent stem cell differentiation. These equations provide further insights to infer the processes of cell fates changes and stem cell differentiation. This study is the first to introduce the novel concept of scEntropy to quantify the biological process of iPSC, and suggests that the scEntropy can provide a suitable macroscopic variable for single cells to describe cell fate transition during differentiation and reprogramming of stem cells.
The socio-economic determinants of the coronavirus disease (COVID-19) pandemic

The magnitude of the coronavirus disease (COVID-19) pandemic has an enormous impact on the social life and the economic activities in almost every country in the world. Besides the biological and epidemiological factors, a multitude of social and economic criteria also govern the extent of the coronavirus disease spread in the population. Consequently, there is an active debate regarding the critical socio-economic determinants that contribute to the resulting pandemic. In this paper, we contribute towards the resolution of the debate by leveraging Bayesian model averaging techniques and country level data to investigate the potential of 35 determinants, describing a diverse set of socio-economic characteristics, in explaining the coronavirus pandemic outcome.
Conservative Plane Releasing for Spatial Privacy Protection in Mixed Reality

Augmented reality (AR) or mixed reality (MR) platforms require spatial understanding to detect objects or surfaces, often including their structural (i.e. spatial geometry) and photometric (e.g. color, and texture) attributes, to allow applications to place virtual or synthetic objects seemingly "anchored" on to real world objects; in some cases, even allowing interactions between the physical and virtual objects. These functionalities require AR/MR platforms to capture the 3D spatial information with high resolution and frequency; however, these pose unprecedented risks to user privacy. Aside from objects being detected, spatial information also reveals the location of the user with high specificity, e.g. in which part of the house the user is. In this work, we propose to leverage spatial generalizations coupled with conservative releasing to provide spatial privacy while maintaining data utility. We designed an adversary that builds up on existing place and shape recognition methods over 3D data as attackers to which the proposed spatial privacy approach can be evaluated against. Then, we simulate user movement within spaces which reveals more of their space as they move around utilizing 3D point clouds collected from Microsoft HoloLens. Results show that revealing no more than 11 generalized planes--accumulated from successively revealed spaces with large enough radius, i.e. $r\leq1.0m$--can make an adversary fail in identifying the spatial location of the user for at least half of the time. Furthermore, if the accumulated spaces are of smaller radius, i.e. each successively revealed space is $r\leq 0.5m$, we can release up to 29 generalized planes while enjoying both better data utility and privacy.
Finite-cutoff JT gravity and self-avoiding loops

We study quantum JT gravity at finite cutoff using a mapping to the statistical mechanics of a self-avoiding loop in hyperbolic space, with positive pressure and fixed length. The semiclassical limit (small $G_N$) corresponds to large pressure, and we solve the problem in that limit in three overlapping regimes that apply for different loop sizes. For intermediate loop sizes, a semiclassical effective description is valid, but for very large or very small loops, fluctuations dominate. For large loops, this quantum regime is controlled by the Schwarzian theory. For small loops, the effective description fails altogether, but the problem is controlled using a conjecture from the theory of self-avoiding walks.
Understanding the Difficulty of Training Transformers

Transformers have been proved effective for many deep learning tasks. Training transformers, however, requires non-trivial efforts regarding carefully designing learning rate schedulers and cutting-edge optimizers (the standard SGD fails to train Transformers effectively). In this paper, we study Transformer training from both theoretical and empirical perspectives. Our analysis reveals that unbalanced gradients are not the root cause of the instability of training. Instead, we identify an amplification effect that substantially influences training. Specifically, we observe that for each layer in a multi-layer Transformer model, heavy dependency on its residual branch makes training unstable since it amplifies small parameter perturbations (e.g., parameter updates) and result in significant disturbances in the model output, yet a light dependency limits the potential of model training and can lead to an inferior trained model. Inspired by our analysis, we propose Admin ($\mathbf{Ad}$aptive $\mathbf{m}$odel $\mathbf{in}$itialization) to stabilize the training in the early stage and unleash its full potential in the late stage. Extensive experiments show that Admin is more stable, converges faster, and leads to better performance.
RPnet: A Deep Learning approach for robust R Peak detection in noisy ECG

Automatic detection of R-peaks in an Electrocardiogram signal is crucial in a multitude of applications including Heart Rate Variability (HRV) analysis and Cardio Vascular Disease(CVD) diagnosis. Although there have been numerous approaches that have successfully addressed the problem, there has been a notable dip in the performance of these existing detectors on ECG episodes that contain noise and HRV Irregulates. On the other hand, Deep Learning(DL) based methods have shown to be adept at modelling data that contain noise. In image to image translation, Unet is the fundamental block in many of the networks. In this work, a novel application of the Unet combined with Inception and Residual blocks is proposed to perform the extraction of R-peaks from an ECG. Furthermore, the problem formulation also robustly deals with issues of variability and sparsity of ECG R-peaks. The proposed network was trained on a database containing ECG episodes that have CVD and was tested against three traditional ECG detectors on a validation set. The model achieved an F1 score of 0.9837, which is a substantial improvement over the other beat detectors. Furthermore, the model was also evaluated on three other databases. The proposed network achieved high F1 scores across all datasets which established its generalizing capacity. Additionally, a thorough analysis of the model’s performance in the presence of different levels of noise was carried out.
KIC 8975515: A fast-rotating (γ Dor – δ Sct) hybrid star with Rossby modes and a slower δ Sct companion in a long-period orbit

Aims. KIC 8975515 is a double-lined spectroscopic binary system with hybrid nature. Regular monitoring with the échelle spectrograph HERMES show that both components have matching atmospheric properties (Teff ∼ 7400 K), and that one of them is a fast rotator (v sin i = 162 versus 32 km s−1). Our aim is to study the Kepler light curve in order to determine the frequencies of the pulsations, to search for regular spacing patterns in the Fourier spectrum, if any, and to discuss their origin in the context of binarity and fast rotation. Methods. In this paper, we study the properties of the stellar pulsations based on a careful analysis of the Fourier spectrum. This was done by performing repeated frequency-search analyses with successive prewhitenings of all the significant frequencies detected in the spectrum. Moreover, we searched for regular period spacing among the g modes as well as frequency splitting among the g and p modes in frequency. Results. In the low-frequency regime, one series of prograde g modes and four series of retrograde r modes, were detected. The r modes are well-distributed with respect to the harmonics of the rotational frequency of the fast-rotating star frot = 1.647 d−1. The dominant g mode is f2 = 2.37 d−1. The strongest p mode is f1 = 13.97 d−1. We identified two multiplets of regularly split p modes with the mean values of 0.42 d−1 and 1.65 d−1. In addition, we also identified a few incidences of mode coupling (between f1 and a g or r mode). Conclusions. We detected five series of retrograde r and prograde g modes as well as two multiplets of p modes with rotational frequency splitttings related to both components of KIC 8975515. We identified the fast-rotating component as a hybrid pulsator with r modes, and the slowly-rotating component as a δ Sct pulsator.
Bayesian inference for high-dimensional decomposable graphs

In this paper, we consider high-dimensional Gaussian graphical models where the true underlying graph is decomposable. A hierarchical $G$-Wishart prior is proposed to conduct a Bayesian inference for the precision matrix and its graph structure. Although the posterior asymptotics using the $G$-Wishart prior has received increasing attention in recent years, most of results assume moderate high-dimensional settings, where the number of variables $p$ is smaller than the sample size $n$. However, this assumption might not hold in many real applications such as genomics, speech recognition and climatology. Motivated by this gap, we investigate asymptotic properties of posteriors under the high-dimensional setting where $p$ can be much larger than $n$. The pairwise Bayes factor consistency, posterior ratio consistency and graph selection consistency are obtained in this high-dimensional setting. Furthermore, the posterior convergence rate for precision matrices under the matrix $\ell_1$-norm is derived, which turns out to coincide with the minimax convergence rate for sparse precision matrices. A simulation study confirms that the proposed Bayesian procedure outperforms competitors.
Quantifying excess power from radio frequency interference in Epoch of Reionization measurements

We quantify the effect of radio frequency interference (RFI) on measurements of the 21-cm power spectrum during the Epoch of Reionization (EoR). Specifically, we investigate how the frequency structure of RFI source emission generates contamination in higher-order wave modes that is much more problematic than smooth-spectrum foreground sources. Using a relatively optimistic EoR model, we find that even a single relatively dim RFI source can overwhelm the EoR power spectrum signal of $\sim10\text{ mK}^2$ for modes $0.1 \text{ }h\text{ Mpc}^{-1} < k < 2 \text{ }h\text{ Mpc}^{-1}$. If total apparent RFI flux density in the final power spectrum integration is kept below 1 mJy, an EoR signal resembling this optimistic model should be detectable for modes $k < 0.9\text{ }h\text{ Mpc}^{-1}$, given no other systematic contaminants and an error tolerance as high as 10%. More pessimistic models will be more restrictive. These results emphasize the need for highly effective RFI mitigation strategies for telescopes used to search for the EoR.