Miracle-dz/UniFAR-FTUs huggingface dataset:
Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation

Learning disentangled representations of realworld data is a challenging open problem. Most previous methods have focused on either supervised approaches which use attribute labels or unsupervised approaches that manipulate the factorization in the latent space of models such as the variational autoencoder (VAE) by training with task-specific losses. In this work, we propose polarized-VAE, an approach that disentangles select attributes in the latent space based on proximity measures reflecting the similarity between data points with respect to these attributes. We apply our method to disentangle the semantics and syntax of sentences and carry out transfer experiments. Polarized-VAE outperforms the VAE baseline and is competitive with state-of-the-art approaches, while being more a general framework that is applicable to other attribute disentanglement tasks.
Alternating links on surfaces and volume bounds

Weakly generalised alternating knots are knots with an alternating projection onto a closed surface in a compact irreducible 3-manifold, and they share many hyperbolic geometric properties with usual alternating knots. For example, usual alternating knots have volume bounded above and below by the twist number of the alternating diagram due to Lackenby. Howie and Purcell showed that a similar lower bound holds for weakly generalised alternating knots. In this paper, we show that a generalisation of the upper volume bound does not hold, by producing a family of weakly generalised alternating knots in the 3-sphere with fixed twist number but unbounded volumes. As a corollary, generalised alternating knots can have arbitrarily small cusp density, in contrast with usual alternating knots whose cusp densities are bounded away from zero due to Lackenby and Purcell. On the other hand, we show that the twist number of a weakly generalised alternating projection does gives two sided linear bounds on volume inside a thickened surface; we state some related open questions.
Ultradiffuse galaxies in the IC 1459 group from the VEGAS survey

Using deep g,r,i imaging from the VEGAS survey, we have searched for ultra diffuse galaxies (UDGs) in the IC 1459 group. Assuming they are group members, we identify 9 galaxies with physical sizes and surface brightnesses that match the UDG criteria within our measurement uncertainties. They have mean colours of g--i = 0.6 and stellar masses of $\sim$10$^8$ M$_{\odot}$. Several galaxies appear to have associated systems of compact objects, e.g. globular clusters. Two UDGs contain a central bright nucleus, with a third UDG revealing a remarkable double nucleus. This appears to be the first reported detection of a double nucleus in a UDG - its origin is currently unclear.
Chandra High Energy Transmission Gratings Spectra of V3890 Sgr

The recurrent nova (RN) V3890 Sgr was observed during the seventh day after the onset of its most recent outburst, with the Chandra ACIS-S camera and High Energy Transmission Gratings. A rich emission line spectrum was detected, due to transitions of Fe-L and K-shell ions ranging from neon to iron. The measured absorbed flux is ≈10−10 erg cm−2 s−1 in the 1.4–15 Å range (0.77–8.86 keV). The line profiles are asymmetric, blueshifted, and skewed toward the blue side, as if the ejecta moving toward us are less absorbed than the receding ejecta. The full width at half-maximum of most emission lines is 1000–1200 km s−1, with some extended blue wings. The spectrum is thermal and consistent with a plasma in collisional ionization equilibrium with column density 1.3 × 1022 cm−2 and at least two components at temperatures of about 1 and 4 keV, possibly a forward and a reverse shock, or regions with differently mixed ejecta and a red giant wind. The spectrum is remarkably similar to the symbiotic RNe V745 Sco and RS Oph, but we cannot distinguish whether the shocks occurred at a distance of a few au from the red giant, or near the giant’s photosphere, in a high-density medium containing only a low mass. The ratios of the flux in lines of aluminum, magnesium, and neon relative to the flux in lines of silicon and iron probably indicate a carbon–oxygen white dwarf.
Excess registered deaths in England and Wales during the COVID-19 pandemic, March 2020 and April 2020

Official counts of COVID-19 deaths have been criticized for potentially including people who did not die of COVID-19 but merely died with COVID-19. I address that critique by fitting a generalized additive model to weekly counts of all deaths registered in England and Wales during the 2010s. The model produces baseline rates of death registrations expected without the COVID-19 pandemic, and comparing those baselines to recent counts of registered deaths exposes the emergence of excess deaths late in March 2020. By April's end, England and Wales registered 45,300 $\pm$ 3200 excess deaths of adults aged 45+. Through 22 May, the last day of available all-deaths data, 56,600 $\pm$ 4400 were registered (about 53% of which were of men). Both the ONS's corresponding count of 43,205 death certificates which mention COVID-19, and the Department of Health and Social Care's count of 33,671 deaths, are appreciably less, implying that their counting methods have underestimated, not overestimated, the pandemic's true death toll. If underreporting rates have held steady during May, about 59,000 direct and indirect COVID-19 deaths might have been registered through the end of May but not yet publicly reported in full.
How $\mathcal N=1$, $D=4$ SYM domain walls look like

We review main features of the pure $\mathcal N=1$, $D=4$ SYM and its effective description by the Veneziano-Yankielowicz generalized sigma-model. We then indicate that the construction of BPS domain walls interpolating between different SYM vacua requires the presence of a dynamical membrane source. We will show how such a membrane is coupled to the SYM and present the explicit form of BPS domain walls which it creates in the Veneziano-Yankielowicz effective theory. In particular, we will describe 1/2 BPS domain wall configurations with $|k|\leq N/3$, where $k$ is the membrane charge that sets the "distance" between two distinct SUSY vacua.
Estimate of emissions on road networks via Generic Second Order Models

In this paper we provide emission estimates due to vehicular traffic via Generic Second Order Models. We generalize them to model road networks with merge and diverge junctions. The procedure consists on solving the Riemann Problem at junction assuming the maximization of the flow and a priority rule for the incoming roads. We provide some numerical results for a single-lane roundabout and we propose an application of the given procedure to estimate the production of nitrogen oxides (NOx) emission rates. In particular, we show that the presence of a traffic lights produces a 28% increase in the NOx emissions with respect to the roundabout.
Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies

Organisations disclose their privacy practices by posting privacy policies on their websites. Even though internet users often care about their digital privacy, they usually do not read privacy policies, since understanding them requires a significant investment of time and effort. Natural language processing has been used to create experimental tools to interpret privacy policies, but there has been a lack of large privacy policy corpora to facilitate the creation of large-scale semi-supervised and unsupervised models to interpret and simplify privacy policies. Thus, we present the PrivaSeer Corpus of 1,005,380 English language website privacy policies collected from the web. The number of unique websites represented in PrivaSeer is about ten times larger than the next largest public collection of web privacy policies, and it surpasses the aggregate of unique websites represented in all other publicly available privacy policy corpora combined. We describe a corpus creation pipeline with stages that include a web crawler, language detection, document classification, duplicate and near-duplicate removal, and content extraction. We employ an unsupervised topic modelling approach to investigate the contents of policy documents in the corpus and discuss the distribution of topics in privacy policies at web scale. We further investigate the relationship between privacy policy domain PageRanks and text features of the privacy policies. Finally, we use the corpus to pretrain PrivBERT, a transformer-based privacy policy language model, and obtain state of the art results on the data practice classification and question answering tasks.
Erratum: Hypercompact stellar clusters: morphological renditions and spectrophotometric models

Numerical relativity predicts that the coalescence of a black hole (BH) binary causes the newly formed BH to recoil, and evidence for such recoils has been found in the gravitational waves observed during the merger of stellar-mass BHs. Recoiling (super)massive BHs are expected to reside in hypercompact stellar clusters (HCSCs). Simulations of galaxy assembly predict that hundreds of HCSCs should be present in the halo of a Milky Way (MW)-type galaxy, and a fraction of those around the MW should have magnitudes within the sensitivity limit of existing surveys. However, recoiling BHs and their HCSCs are still waiting to be securely identified. With the goal of enabling searches through recent and forthcoming data bases, we improve over existing literature to produce realistic renditions of HCSCs bound to BHs with a mass of 105 M⊙. Including the effects of a population of blue stragglers, we simulate their appearance in Pan-STARRS and in forthcoming Euclid images. We also derive broad-band spectra and the corresponding multiwavelength colours, finding that the great majority of the simulated HCSCs fall on the colour–colour loci defined by stars and galaxies, with their spectra resembling those of giant K-type stars. We discuss the clusters properties, search strategies, and possible interlopers.
Quantifying torque from the Milky Way bar using Gaia DR2

We determine the mass of the Milky Way bar and the torque it causes, using Gaia DR2, by applying the orbital arc method. Based on this, we have found that the gravitational acceleration is not directed towards the centre of our Galaxy but a few degrees away from it. We propose that the tangential acceleration component is caused by the bar of the Galaxy. Calculations based on our model suggest that the torque experienced by the region around the Sun is $\approx 2400\, {\rm km^2\, s^{-2}}$ per solar mass. The mass estimate for the bar is $\sim 1.6\pm 0.3\times 10^{10}\, \mathrm{M_\odot }$. Using greatly improved data from Gaia DR2, we have computed the acceleration field to great accuracy by adapting the orbital Probability Density Function (oPDF) method (Han et al. 2016) locally and used the phase space coordinates of ∼4 × 105 stars within a distance of 0.5 kpc from the Sun. In the orbital arc method, the first step is to guess an acceleration field and then reconstruct the stellar orbits using this acceleration for all the stars within a specified region. Next, the stars are redistributed along orbits to check if the overall phase space distribution has changed. We repeat this process until we find an acceleration field that results in a new phase space distribution that is the same as the one that we started with; we have then recovered the true underlying acceleration.
Power Calculations for Replication Studies

The reproducibility crisis has led to an increasing number of replication studies being conducted. Sample sizes for replication studies are often calculated using conditional power based on the effect estimate from the original study. However, this approach is not well suited as it ignores the uncertainty of the original result. Bayesian methods are used in clinical trials to incorporate prior information into power calculations. We propose to adapt this methodology to the replication framework and promote the use of predictive instead of conditional power in the design of replication studies. Moreover, we describe how extensions of the methodology to sequential clinical trials can be tailored to replication studies. Conditional and predictive power calculated at an interim analysis are compared and we argue that predictive power is a useful tool to decide whether to stop a replication study prematurely. A recent project on the replicability of social sciences is used to illustrate the properties of the different methods.
YOLOv4: Optimal Speed and Accuracy of Object Detection

There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy. Practical testing of combinations of such features on large datasets, and theoretical justification of the result, is required. Some features operate on certain models exclusively and for certain problems exclusively, or only for small-scale datasets; while some features, such as batch-normalization and residual-connections, are applicable to the majority of models, tasks, and datasets. We assume that such universal features include Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT) and Mish-activation. We use new features: WRC, CSP, CmBN, SAT, Mish activation, Mosaic data augmentation, CmBN, DropBlock regularization, and CIoU loss, and combine some of them to achieve state-of-the-art results: 43.5% AP (65.7% AP50) for the MS COCO dataset at a realtime speed of ~65 FPS on Tesla V100. Source code is at this https URL
Supervised Contrastive Learning

Cross entropy is the most widely used loss function for supervised training of image classification models. In this paper, we propose a novel training methodology that consistently outperforms cross entropy on supervised learning tasks across different architectures and data augmentations. We modify the batch contrastive loss, which has recently been shown to be very effective at learning powerful representations in the self-supervised setting. We are thus able to leverage label information more effectively than cross entropy. Clusters of points belonging to the same class are pulled together in embedding space, while simultaneously pushing apart clusters of samples from different classes. In addition to this, we leverage key ingredients such as large batch sizes and normalized embeddings, which have been shown to benefit self-supervised learning. On both ResNet-50 and ResNet-200, we outperform cross entropy by over 1%, setting a new state of the art number of 78.8% among methods that use AutoAugment data augmentation. The loss also shows clear benefits for robustness to natural corruptions on standard benchmarks on both calibration and accuracy. Compared to cross entropy, our supervised contrastive loss is more stable to hyperparameter settings such as optimizers or data augmentations.
Astraeus – II. Quantifying the impact of cosmic variance during the Epoch of Reionization

Next generation telescopes such as the James Webb Space Telescope (JWST) and the Wide Field Infrared Survey Telescope (WFIRST) will enable us to study the first billion years of our Universe in unprecedented detail. In this work we use the ASTRAEUS (semi-numerical rAdiative tranSfer coupling of galaxy formaTion and Reionization in N-body dArk mattEr simUlationS) framework, that couples galaxy formation and reionization (for a wide range of reionization feedback models), to estimate the cosmic variance expected in the UV Luminosity Function (UV LF) and the Stellar Mass Function (SMF) in JWST surveys. We find that different reionization scenarios play a minor role in the cosmic variance. Most of the cosmic variance is completely driven by the underlying density field and increases above 100% for M$_{UV} \sim$ -17.5 (-20) at z = 12 (6) for the JADES-deep survey (the deep JWST Advanced Extragalactic Survey with an area of 46 arcmin$^2$); the cosmic variance decreases with an increasing survey area roughly independently of redshift. We find that the faint-end slope of the Lyman Break Galaxies (LBGs) UV LF becomes increasingly shallower with increasing reionization feedback and show how JWST observations will be able to distinguish between different models of reionization feedback at z > 9, even accounting for cosmic variance. We also show the environments (in terms of density and ionization fields) of Lyman Break Galaxies during the EoR. Finally, we also provide a public software tool to allow interested readers to compute cosmic variance for different redshifts and survey areas.
Superradiant pion clouds around primordial black holes

We show that highly spinning primordial black holes of mass M ∼ 1012 kg, potentially born in a matter-dominated era after inflation, can produce clouds of pions in their vicinity via the superradiant instability, with densities up to that of nuclear matter. We discuss the electromagnetic signatures of this process, via neutral pion decay and charged pion annihilation into photons, computing in particular their contribution to the isotropic gamma-ray background. This allows us to place upper bounds on the abundance of such primordial black holes that are comparable to the ones obtained from Hawking evaporation. We also discuss the possibility of directly observing such clouds in high-redshift superclusters.
PolyLaneNet: Lane Estimation via Deep Polynomial Regression

One of the main factors that contributed to the large advances in autonomous driving is the advent of deep learning. For safer self-driving vehicles, one of the problems that has yet to be solved completely is lane detection. Since methods for this task have to work in real-time (+30 FPS), they not only have to be effective (i.e., have high accuracy) but they also have to be efficient (i.e., fast). In this work, we present a novel method for lane detection that uses as input an image from a forward-looking camera mounted in the vehicle and outputs polynomials representing each lane marking in the image, via deep polynomial regression. The proposed method is shown to be competitive with existing state-of-the-art methods in the TuSimple dataset while maintaining its efficiency (115 FPS). Additionally, extensive qualitative results on two additional public datasets are presented, alongside with limitations in the evaluation metrics used by recent works for lane detection. Finally, we provide source code and trained models that allow others to replicate all the results shown in this paper, which is surprisingly rare in state-of-the-art lane detection methods.
Partner support during pregnancy mediates social inequalities in maternal postpartum depression for non-migrant and first generation migrant women

Background An advantaged socioeconomic position (SEP) and satisfying social support during pregnancy (SSP) have been found to be protective factors of maternal postpartum depression (PDD). An advantaged SEP is also associated with satisfying SSP, making SSP a potential mediator of social inequalities in PPD. SEP, SSP and PPD are associated with migrant status. The aim of this study was to quantify the mediating role of SSP in social inequalities in PPD regarding mother’s migrant status. Methods A sub-sample of 15,000 mothers from the French nationally-representative ELFE cohort study was used for the present analyses. SEP was constructed as a latent variable measured with educational attainment, occupational grade, employment, financial difficulties and household income. SSP was characterized as perceived support from partner (good relation, satisfying support and paternal leave) and actual support from midwives (psychosocial risk factors assessment and antenatal education). Mediation analyses with multiple mediators, stratified by migrant status were conducted. Results Study population included 76% of non-migrant women, 12% of second and 12% of first generation migrant. SEP was positively associated with support from partner, regardless of migrant status. Satisfying partner support was associated with a 8 (non-migrant women) to 11% (first generation migrant women) reduction in PPD score. Limitations History of depression was not reported. Conclusions Partner support could reduce social inequalities in PPD. This work supports the need of interventions, longitudinal and qualitative studies including fathers and adapted to women at risk of PPD to better understand the role of SSP in social inequalities in PPD.
Learning Dialog Policies from Weak Demonstrations

Deep reinforcement learning is a promising approach to training a dialog manager, but current methods struggle with the large state and action spaces of multi-domain dialog systems. Building upon Deep Q-learning from Demonstrations (DQfD), an algorithm that scores highly in difficult Atari games, we leverage dialog data to guide the agent to successfully respond to a user’s requests. We make progressively fewer assumptions about the data needed, using labeled, reduced-labeled, and even unlabeled data to train expert demonstrators. We introduce Reinforced Fine-tune Learning, an extension to DQfD, enabling us to overcome the domain gap between the datasets and the environment. Experiments in a challenging multi-domain dialog system framework validate our approaches, and get high success rates even when trained on out-of-domain data.
Syntactic Structure from Deep Learning

Modern deep neural networks achieve impressive performance in engineering applications that require extensive linguistic skills, such as machine translation. This success has sparked interest in probing whether these models are inducing human-like grammatical knowledge from the raw data they are exposed to and, consequently, whether they can shed new light on long-standing debates concerning the innate structure necessary for language acquisition. In this article, we survey representative studies of the syntactic abilities of deep networks and discuss the broader implications that this work has for theoretical linguistics.
OF-VO: Reliable Navigation among Pedestrians Using Commodity Sensors

We present a novel algorithm for safe navigation of a mobile robot among pedestrians. Our approach uses commodity visual sensors, including RGB-D cameras and a 2D lidar, for explicitly predicting the velocities and positions of surrounding obstacles through optical flow estimation and object detection. Given these partial observations of the environment, we present a modified velocity-obstacle (VO) algorithm to compute collision-free trajectories for the robot. A key aspect of our work is the coupling between the perception (OF: optical flow) and planning (VO) components for reliable navigation. Overall, our OF-VO algorithm is a hybrid combination of learning-based and model-based methods and offers better performance over prior algorithms in terms of navigation time and success rate of collision avoidance. We highlight the realtime performance of OF-VO in simulated and real-world dynamic scenes on a Turtlebot robot navigating among pedestrians with commodity sensors. A demo video is available at \url{this https URL}
Prevalence and risk factors of low back pain and disability index among operating room nurses

Occupational LBP is a common health problem worldwide. Health care workers (HCWs) are at a higher risk of developing LBP due to a variety of factors6. This problem is associated with major consequences in terms of disability and frequent absence6. LBP may lead to activity limitation and sick leave for more than 50 per cent of nurses7. Female gender, advanced age and high Body Mass Index (BMI) are some examples of risk factors commonly associated with LBP8–13. Sport and regular physical activity were found to decrease LBP14. Working in the operating room (OR) carries its own risk for developing LBP due to exposure to additional risk factors such as prolonged standing and awkward posture during surgeries6,15–17.