Miracle-dz/UniFAR-FTUs huggingface dataset:
Slow decline and rise of the broad [O iii] emission line in globular cluster black hole candidate RZ2109

RZ2109 is the first of several extragalactic globular clusters shown to host an ultraluminous X-ray source. RZ2109 is particularly notable because optical spectroscopy shows it has broad, luminous [O iii] λλ4959,5007 emission, while also having no detectable hydrogen emission. The X-ray and optical characteristics of the source in RZ2109 make it a good candidate for being a stellar mass black hole accreting from a white dwarf donor (i.e. an ultracompact black hole X-ray binary). In this paper we present optical spectroscopic monitoring of the [O iii]5007 emission line from 2007 to 2018. We find that the flux of the emission line is significantly lower in recent observations from 2016 to 2018 than it was in earlier observations in 2007–2011. We also explore the behaviour of the emission line shape over time. Both the core and the wings of the emission line decline over time, with some evidence that the core declines more rapidly than the wings. However, the most recent observations (in 2019) unexpectedly show the emission line core rebrightening
Don’t Forget the Long Tail! A Comprehensive Analysis of Morphological Generalization in Bilingual Lexicon Induction

Human translators routinely have to translate rare inflections of words – due to the Zipfian distribution of words in a language. When translating from Spanish, a good translator would have no problem identifying the proper translation of a statistically rare inflection such as habláramos. Note the lexeme itself, hablar, is relatively common. In this work, we investigate whether state-of-the-art bilingual lexicon inducers are capable of learning this kind of generalization. We introduce 40 morphologically complete dictionaries in 10 languages and evaluate three of the best performing models on the task of translation of less frequent morphological forms. We demonstrate that the performance of state-of-the-art models drops considerably when evaluated on infrequent morphological inflections and then show that adding a simple morphological constraint at training time improves the performance, proving that the bilingual lexicon inducers can benefit from better encoding of morphology.
Conditional propagation of chaos for mean field systems of interacting neurons

We study the stochastic system of interacting neurons introduced in De Masi et al. (2015) and in Fournier and L\"ocherbach (2016) in a diffusive scaling. The system consists of $N$ neurons, each spiking randomly with rate depending on its membrane potential. At its spiking time, the potential of the spiking neuron is reset to $0$ and all other neurons receive an additional amount of potential which is a centred random variable of order $ 1 / \sqrt{N}. $ In between successive spikes, each neuron's potential follows a deterministic flow. We prove the convergence of the system, as $N \to \infty$, to a limit nonlinear jumping stochastic differential equation driven by Poisson random measure and an additional Brownian motion $W$ which is created by the central limit theorem. This Brownian motion is underlying each particle's motion and induces a common noise factor for all neurons in the limit system. Conditionally on $W,$ the different neurons are independent in the limit system. This is the {\it conditional propagation of chaos} property. We prove the well-posedness of the limit equation by adapting the ideas of Graham (1992) to our frame. To prove the convergence in distribution of the finite system to the limit system, we introduce a new martingale problem that is well suited for our framework. The uniqueness of the limit is deduced from the exchangeability of the underlying system.
Multi-Granularity Self-Attention for Neural Machine Translation

Current state-of-the-art neural machine translation (NMT) uses a deep multi-head self-attention network with no explicit phrase information. However, prior work on statistical machine translation has shown that extending the basic translation unit from words to phrases has produced substantial improvements, suggesting the possibility of improving NMT performance from explicit modeling of phrases. In this work, we present multi-granularity self-attention (Mg-Sa): a neural network that combines multi-head self-attention and phrase modeling. Specifically, we train several attention heads to attend to phrases in either n-gram or syntactic formalisms. Moreover, we exploit interactions among phrases to enhance the strength of structure modeling – a commonly-cited weakness of self-attention. Experimental results on WMT14 English-to-German and NIST Chinese-to-English translation tasks show the proposed approach consistently improves performance. Targeted linguistic analysis reveal that Mg-Sa indeed captures useful phrase information at various levels of granularities.
Calibration of a Fluid-Structure Problem with Keras

In this short paper we report on an inverse problem issued from a physical system, namely a fluid structure problem where the parameters are the rigidity constant, the solid-fluid density ratio and the fluid viscosity. We have chosen a simple geometry so that the numerical solution of the system is easy. We compare the solution of this inverse problem by a Neural Network with a more classical solution obtained with a genetic algorithm. The Neural Network does much better.
On the k-synchronizability for mailbox systems

Asynchronous bounded or unbounded message passing is ubiquitous in communication-centric systems. When modelling distributed scenarios, it is important to understand whether buffers are bounded or not. In this paper, we work on the notion of k-synchronizability: a system is k-synchronizable if any of its executions, up to reordering causally independent actions, can be divided into a succession of k-bounded interaction phases. We show two results: first, the reachability problem is decidable for k-synchronizable systems; second, the membership problem (whether a given system is k-synchronizable) is decidable as well. Our proofs fix several important issues in previous attempts to prove these two results.
The impact of non-dipolar magnetic fields in core-collapse supernovae

The magnetic field is believed to play an important role in at least some core-collapse supernovae (CCSN) if its magnitude reaches $10^{15}\, \rm {G}$, which is a typical value for a magnetar. In the presence of fast rotation, such a strong magnetic field can drive powerful jet-like explosions if it has the large-scale coherence of a dipole. The topology of the magnetic field is, however, probably much more complex with strong multipolar and small-scale components and the consequences for the explosion are so far unclear. We investigate the effects of the magnetic field topology on the dynamics of CCSN and the properties of the forming proto-neutron star (PNS) by comparing pre-collapse fields of different multipolar orders and radial profiles. Using axisymmetric special relativistic MHD simulations and a two-moment neutrino transport, we find that higher multipolar magnetic configurations lead to generally less energetic explosions, slower expanding shocks, and less collimated outflows. Models with a low order multipolar configuration tend to produce more oblate PNS, surrounded in some cases by a rotationally supported toroidal structure of neutron-rich material. Moreover, magnetic fields which are distributed on smaller angular scales produce more massive and faster rotating central PNS, suggesting that higher order multipolar configurations tend to decrease the efficiency of the magnetorotational launching mechanism. Even if our dipolar models systematically display a far more efficient extraction of the rotational energy of the PNS, fields distributed on smaller angular scales are still capable of powering magnetorotational explosions and shape the evolution of the central compact object.
Improving Neural Machine Translation with Parent-Scaled Self-Attention

Most neural machine translation (NMT) models operate on source and target sentences, treating them as sequences of words and neglecting their syntactic structure. Recent studies have shown that embedding the syntax information of a source sentence in recurrent neural networks can improve their translation accuracy, especially for low-resource language pairs. However, state-of-the-art NMT models are based on self-attention networks (e.g., Transformer), in which it is still not clear how to best embed syntactic information. In this work, we explore different approaches to make such models syntactically aware. Moreover, we propose a novel method to incorporate syntactic information in the self-attention mechanism of the Transformer encoder by introducing attention heads that can attend to the dependency parent of each token. The proposed model is simple yet effective, requiring no additional parameter and improving the translation quality of the Transformer model especially for long sentences and low-resource scenarios. We show the efficacy of the proposed approach on NC11 English-German, WMT16 and WMT17 English-German, WMT18 English-Turkish, and WAT English-Japanese translation tasks.
Randomized methods to characterize large-scale vortical flow networks

We demonstrate the effective use of randomized methods for linear algebra to perform network-based analysis of complex vortical flows. Network theoretic approaches can reveal the connectivity structures among a set of vortical elements and analyze their collective dynamics. These approaches have recently been generalized to analyze high-dimensional turbulent flows, for which network computations can become prohibitively expensive. In this work, we propose efficient methods to approximate network quantities, such as the leading eigendecomposition of the adjacency matrix, using randomized methods. Specifically, we use the Nyström method to approximate the leading eigenvalues and eigenvectors, achieving significant computational savings and reduced memory requirements. The effectiveness of the proposed technique is demonstrated on two high-dimensional flow fields: two-dimensional flow past an airfoil and two-dimensional turbulence. We find that quasi-uniform column sampling outperforms uniform column sampling, while both feature the same computational complexity.
Oral Administration of a 12% Sucrose Solution Did Not Decrease Behavioural Indicators of Distress in Piglets Undergoing Tail Docking, Teeth Clipping and Ear Notching

Sucrose has been shown to attenuate the behavioural response to painful procedures in human infants undergoing circumcision or blood collection via heelstick. Sucrose has also been found to have a behaviour-modifying effect in neonatal rats exposed to a hot plate. The effect was abolished in neonatal rats by injection of the opioid antagonist naltrexone, suggesting that it was mediated by endogenous opioids. In this experiment, the behaviour of 571 newborn Large White x Landrace hybrid piglets in a specific-pathogen-free piggery of the University of Queensland was recorded during and after the routine management practices of tail docking, ear notching and teeth clipping. Piglets were randomly assigned to receive 1.0 ml of a 12% sucrose solution (treatment group) or a placebo (1.0 ml of air) administered via syringe in the mouth, 60 s before commencement of one of the management procedures. Behaviours were recorded at the time of the procedure, and then 2 min after completion of the procedure. Piglets that received the sucrose solution did not behave significantly differently from piglets receiving the placebo. Regardless of whether sucrose or placebo was administered, piglets undergoing the routine management procedures showed significantly greater behavioural responses than piglets undergoing no procedure. It was concluded that under commercial conditions, a 12% sucrose solution administered 1 min prior to surgery was not effective in decreasing the behavioural indicators of distress in piglets undergoing routine management procedures. Further research into methods of minimising distress caused to piglets by these procedures is recommended.
Risk Factors Associated with Obesity among Adolescent Students: A Case Control Study. -

Objective: Obesity has emerged as a global public health problem affecting all ages and both sexes. The seeds of behavioral factors associated with obesity are implanted during childhood and adolescence. The present study was undertaken to identify the behavioral correlates of overweight/obesity among school going adolescents in Hyderabad (India). Methods: A case-control study was carried out in two schools and 57 cases (overweight and obese) and equal number age and sex matched controls were studied. Results: The study revealed that decreased outdoor physical activity, measured by fewer hours of outdoor games, motorized conveyance to school and longer hours of TV watching was significantly associated with overweight/obesity. In addition, higher consumption of junk food, irregularity of meals and lack of parental supervision of diet were also correlated with overweight/obesity. A higher rate of overweight/obesity among children whose mothers were working is also reported. Conclusion: This case-control study confirmed the findings of earlier cross-sectional studies, and also revealed that children of working mothers may be at higher risk of overweight/obesity.
Automatic Argument Quality Assessment - New Datasets and Methods

We explore the task of automatic assessment of argument quality. To that end, we actively collected 6.3k arguments, more than a factor of five compared to previously examined data. Each argument was explicitly and carefully annotated for its quality. In addition, 14k pairs of arguments were annotated independently, identifying the higher quality argument in each pair. In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results. We release the labeled datasets to the community. Furthermore, we suggest neural methods based on a recently released language model, for argument ranking as well as for argument-pair classification. In the former task, our results are comparable to state-of-the-art; in the latter task our results significantly outperform earlier methods.
Polar decomposition of semigroups generated by non-selfadjoint quadratic differential operators and regularizing effects

We study semigroups generated by accretive non-selfadjoint quadratic differential operators. We give a description of the polar decomposition of the associated evolution operators as products of a selfadjoint operator and a unitary operator. The selfadjoint parts turn out to be also evolution operators generated by time-dependent real-valued quadratic forms that are studied in details. As a byproduct of this decomposition, we give a geometric description of the regularizing properties of semigroups generated by accretive non-selfadjoint quadratic operators. Finally, by using the interpolation theory, we take advantage of this smoothing effect to establish subelliptic estimates enjoyed by quadratic operators.
Formation of a two-dimensional single-component correlated electron system and band engineering in the nickelate superconductor 
NdNiO2

Motivated by the discovery of the nickelate superconductor Nd${}_{0.8}$Sr${}_{0.2}$NiO${}_{2}$, the authors construct an effective low-energy Hamiltonian for the parent compound NdNiO${}_{2}$, which is found to be strongly correlated. While high-T${}_{c}$ superconductivity emerges near the Mott-insulating phase in the cuprates, NdNiO${}_{2}$ is not a Mott insulator, because the 3$d$ ${x}^{2}$-${y}^{2}$ band of nickel is self-doped. The authors propose a route for the design of novel nickelates where self-doping is suppressed. If synthesized, such nickelates would provide a playground for the exploration of high-T${}_{c}$ superconductivity.
Neural Conversational QA: Learning to Reason vs Exploiting Patterns

Neural Conversational QA tasks such as ShARC require systems to answer questions based on the contents of a given passage. On studying recent state-of-the-art models on the ShARC QA task, we found indications that the model(s) learn spurious clues/patterns in the data-set. Further, a heuristic-based program, built to exploit these patterns, had comparative performance to that of the neural models. In this paper we share our findings about the four types of patterns in the ShARC corpus and how the neural models exploit them. Motivated by the above findings, we create and share a modified data-set that has fewer spurious patterns than the original data-set, consequently allowing models to learn better.
Estimating the Potential for Shared Autonomous Scooters

Recent technological developments have shown significant potential for transforming urban mobility. Considering first- and last-mile travel and short trips, the rapid adoption of dockless bike-share systems showed the possibility of disruptive change, while simultaneously presenting new challenges, such as fleet management or the use of public spaces. In this paper, we evaluate the operational characteristics of a new class of shared vehicles that are being actively developed in the industry: scooters with self-repositioning capabilities. We do this by adapting the methodology of shareability networks to a large-scale dataset of dockless bike-share usage, giving us estimates of ideal fleet size under varying assumptions of fleet operations. We show that the availability of self-repositioning capabilities can help achieve up to 10 times higher utilization of vehicles than possible in current bike-share systems. We show that actual benefits will highly depend on the availability of dedicated infrastructure, a key issue for scooter and bicycle use. Based on our results, we envision that technological advances can present an opportunity to rethink urban infrastructures and how transportation can be effectively organized in cities.
Secure Radar-Communication Systems With Malicious Targets: Integrating Radar, Communications and Jamming Functionalities

This article studies the physical layer security in a multiple-input-multiple-output (MIMO) dual-functional radar-communication (DFRC) system, which communicates with downlink cellular users and tracks radar targets simultaneously. Here, the radar targets are considered as potential eavesdroppers which might eavesdrop the information from the communication transmitter to legitimate users. To ensure the transmission secrecy, we employ artificial noise (AN) at the transmitter and formulate optimization problems by minimizing the signal-to-noise ratio (SNR) received at radar targets, while guaranteeing the signal-to-interference-plus-noise ratio (SINR) requirement at legitimate users. We first consider the ideal case where both the target angle and the channel state information (CSI) are precisely known. The scenario is further extended to more general cases with target location uncertainty and CSI errors, where we propose robust optimization approaches to guarantee the worst-case performance. Accordingly, the computational complexity is analyzed for each proposed method. Our numerical results show the feasibility of the algorithms with the existence of instantaneous and statistical CSI error. In addition, the secrecy rate of secure DFRC system grows with the increasing angular interval of location uncertainty.
The Role of Magnetic Helicity in Coronal Heating

One of the greatest challenges in solar physics is understanding the heating of the Sun’s corona. Most theories for coronal heating postulate that free energy in the form of magnetic twist/stress is injected by the photosphere into the corona where the free energy is converted into heat either through reconnection or wave dissipation. The magnetic helicity associated with the twist/stress, however, is expected to be conserved and appear in the corona. In previous works, we showed that the helicity associated with the small-scale twists undergoes an inverse cascade via stochastic reconnection in the corona and ends up as the observed large-scale shear of filament channels. Our “helicity condensation” model accounts for both the formation of filament channels and the observed smooth, laminar structure of coronal loops. In this paper, we demonstrate, using helicity- and energy-conserving numerical simulations of a coronal system driven by photospheric motions, that the model also provides a natural mechanism for heating the corona. We show that the heat generated by the reconnection responsible for the helicity condensation process is sufficient to account for the observed coronal heating. We study the role that helicity injection plays in determining coronal heating and find that, crucially, the heating rate is only weakly dependent on the net helicity preference of the photospheric driving. Our calculations demonstrate that motions with 100% helicity preference are least efficient at heating the corona; those with 0% preference are most efficient. We discuss the physical origins of this result and its implications for the observed corona.
GAN-Leaks: A Taxonomy of Membership Inference Attacks against GANs

In recent years, the success of deep learning has carried over from discriminative models to generative models. In particular, generative adversarial networks (GANs) have facilitated a new level of performance ranging from media manipulation to dataset re-generation. Despite the success, the potential risks of privacy breach stemming from GANs are less well explored. In this paper, we focus on membership inference attack against GANs that has the potential to reveal information about victim models' training data. Specifically, we present the first taxonomy of membership inference attacks, which encompasses not only existing attacks but also our novel ones. We also propose the first generic attack model that can be instantiated in various settings according to adversary's knowledge about the victim model. We complement our systematic analysis of attack vectors with a comprehensive experimental study, that investigates the effectiveness of these attacks w.r. t. model type, training configurations, and attack type across three diverse application scenarios ranging from images, over medical data to location data. We show consistent effectiveness in all the setups, which bridges the assumption gap and performance gap in previous study with a complete spectrum of performance across settings. We conclusively remind users to think over before publicizing any part of their models.
Multitype branching process with nonhomogeneous Poisson and generalized Polya immigration

In a multitype branching process, it is assumed that immigrants arrive according to a non-homogeneous Poisson or a generalized Polya process (both processes are formulated as a non-homogeneous birth process with an appropriate choice of transition intensities). Joint asymptotic behaviour of the numbers of objects of the various types alive at time $t$ for supercritical, critical, and subcritical cases are studied under those two different arrival processes. Furthermore, some transient analysis when there are only two types of particles is provided. AMS 2000 subject classifications: Primary 60J80, 60J85; secondary 60K10, 60K25, 90B15.