Miracle-dz/UniFAR-FTUs huggingface dataset:
SILCC-Zoom: H2 and CO-dark gas in molecular clouds – the impact of feedback and magnetic fields

We analyse the CO-dark molecular gas content of simulated molecular clouds from the SILCC-Zoom project. The simulations reach a resolution of 0.1 pc and include H2 and CO formation, radiative stellar feedback and magnetic fields. CO-dark gas is found in regions with local visual extinctions $A_\rm {V, 3D} \sim$ 0.2–1.5, number densities of 10–103 cm−3 and gas temperatures of few 10–100 K. CO-bright gas is found at number densities above 300 cm−3 and temperatures below 50 K. The CO-dark gas fractions range from 40 per cent to 95 per cent and scale inversely with the amount of well-shielded gas ($A_\rm {V, 3D}$ ≳ 1.5), which is smaller in magnetized molecular clouds. We show that the density, chemical abundances and $A_\rm {V, 3D}$ along a given line-of-sight cannot be properly determined from projected quantities. As an example, pixels with a projected visual extinction of $A_\rm {V, 2D} \simeq$ 2.5–5 can be both, CO-bright or CO-dark, which can be attributed to the presence or absence of strong density enhancements along the line-of-sight. By producing synthetic CO(1-0) emission maps of the simulations with RADMC-3D, we show that about 15–65 per cent of the H2 is in regions with intensities below the detection limit. Our clouds have $X_\rm {CO}$-factors around 1.5 × 1020 cm−2 (K km s−1)−1 with a spread of up to a factor ∼ 4, implying a similar uncertainty in the derived total H2 masses and even worse for individual pixels. Based on our results, we suggest a new approach to determine the H2 mass, which relies on the availability of CO(1-0) emission and $A_\rm {V, 2D}$ maps. It reduces the uncertainty of the clouds’ overall H2 mass to a factor of ≲ 1.8 and for individual pixels, i.e. on sub-pc scales, to a factor of ≲ 3.
L-SVRG and L-Katyusha with Arbitrary Sampling

We develop and analyze a new family of {\em nonaccelerated and accelerated loopless variance-reduced methods} for finite sum optimization problems. Our convergence analysis relies on a novel expected smoothness condition which upper bounds the variance of the stochastic gradient estimation by a constant times a distance-like function. This allows us to handle with ease {\em arbitrary sampling schemes} as well as the nonconvex case. We perform an in-depth estimation of these expected smoothness parameters and propose new importance samplings which allow {\em linear speedup} when the expected minibatch size is in a certain range. Furthermore, a connection between these expected smoothness parameters and expected separable overapproximation (ESO) is established, which allows us to exploit data sparsity as well. Our results recover as special cases the recently proposed loopless SVRG and loopless Katyusha.
Multitime Distribution in Discrete Polynuclear Growth

We study the multitime distribution in a discrete polynuclear growth model or, equivalently, in directed last‐passage percolation with geometric weights. A formula for the joint multitime distribution function is derived in the discrete setting. It takes the form of a multiple contour integral of a block Fredholm determinant. The asymptotic multitime distribution is then computed by taking the appropriate KPZ‐scaling limit of this formula. This distribution is expected to be universal for models in the Kardar‐Parisi‐Zhang universality class. © 2021 The Authors. Communications on Pure and Applied Mathematics published by Wiley Periodicals LLC.
Virtual hand-button interaction in a generic virtual reality flight simulator

Flight simulators with a physical mock-up are dependent on the aircraft type and have high costs. In order to overcome high cost issues, a generic virtual reality flight simulator is designed. Virtual buttons are used without a physical mock-up to make the virtual reality flight simulator independent of the aircraft type. The classic virtual hand metaphor is employed to interact with the virtual objects. This paper examines the virtual hand-button interaction in the generic virtual reality flight simulator where no haptic feed-back is provided. The effect of the collision volume of a virtual button during the virtual hand-button interaction is determined. It is concluded that a change in the collision volume within aircraft design limits, does not have a significant impact on the interaction. We also investigate different virtual hand avatars. We find that the accuracy of hand-button interaction depends on the hand avatar rather than the collision volume. Representing a smaller part of the hand avatar results in less efficient interaction. This shows the size and shape of hand avatars plays a major role in the virtual reality simulator design. This finding contributes to the various virtual reality applications which exploit the virtual hand metaphor.
Bifurcation analysis of a TaO memristor model

This paper presents a study of bifurcation in the time-averaged dynamics of TaO memristors driven by narrow pulses of alternating polarities. The analysis, based on a physics-inspired model, focuses on the stable fixed points and on how these are affected by the pulse parameters. Our main finding is the identification of a driving regime when two stable fixed points exist simultaneously. To the best of our knowledge, such bistability is identified in a single memristor for the first time. This result can be readily tested experimentally, and is expected to be useful in future memristor circuit designs.
Negative dynamical friction on compact objects moving through dense gas

An overdense wake is created by a gravitating object moving through a gaseous medium, and this wake pulls back on the object and slows it down. This is conventional dynamical friction in a gaseous medium. We argue that if the object drives a sufficiently powerful outflow, the wake is destroyed and instead an extended underdense region is created behind the object. In this case the overall gravitational force is applied in the direction of the object’s motion, producing a negative dynamical friction (NDF). Black holes in dense gas drive powerful outflows and may experience the NDF, although extensive numerical work is probably needed to demonstrate or refute this conclusively. NDF may be important for stellar-mass black holes and neutron stars inside ‘common envelopes’ in binary systems, for stellar mass black holes inside active galactic nucleus discs, or for massive black holes growing through super-Eddington accretion in early Universe.
Fluctuation of the background sky in the Hubble Extremely Deep Field (XDF) and its origin

We performed a fluctuation analysis of the Hubble Extremely Deep Field (XDF) at four optical wavelength bands and found large fluctuations that are significantly brighter than those expected for ordinary galaxies. Good cross-correlations with flat spectra are found down to ${0{^{\prime\prime}_{. }}2}$, indicating the existence of a spatial structure even at the ${0{^{\prime\prime}_{. }}2}$ scale. The detected auto- and cross-correlations provide a lower limit of 24 nW m−2 sr−1 for the absolute sky brightness at 700–900 nm, which is consistent with previous observations. We searched for candidate objects to explain the detected large fluctuation using the catalog of the Hubble Ultra Deep Field (UDF), and found that the surface number density of faint compact objects (FCOs) rapidly increases toward the faint end. Radial profiles of FCOs are indistinguishable from the point spread function (PSF), and the effective radius based on de Vaucouleur’s law is estimated to be smaller than ${0{^{\prime\prime}_{. }}02}$. The spectral energy densities (SEDs) of FCOs follow a power law at optical wavelengths, but show greater emission and structure at $\lambda \gt 1\, \mu$m. Assuming that the FCOs are the cause of the excess brightness and fluctuations, the faint magnitude limit is 34.9 mag for the F775W band, and the surface number density reaches 2.6 × 103 arcsec−2. Recent γ-ray observations require that the redshift of FCOs must be less than 0.1, if FCOs are the origin of the excess optical and infrared background. Assuming that FCOs consist of missing baryons, the mass and luminosity of a single FCO range from 102 to 103 solar units, and the mass-to-luminosity ratio is significantly lower than 1.0 solar unit. The maximum effective radius of an FCO is 4.7 pc. These results and the good correlation between the near-infrared and X-ray background indicate that FCOs could be powered by the gravitational energy associated with black holes.
Interface-resolved simulations of small inertial particles in turbulent channel flow

We present a direct comparison between interface-resolved and one-way-coupled point-particle direct numerical simulations (DNS) of gravity-free turbulent channel flow laden with small inertial particles, with high particle-to-fluid density ratio and diameter of approximately three viscous units. The most dilute flow considered, solid volume fraction $O(10^{-5})$, shows the particle feedback on the flow to be negligible, whereas differences with respect to the unladen case, notably a drag increase of approximately 10 %, are found for a volume fraction $O(10^{-4})$. This is attributed to a dense layer of particles at the wall, caused by turbophoresis, flowing with large particle-to-fluid apparent slip velocity. The most dilute case is therefore taken as the benchmark for assessing the validity of a widely used point-particle model, where the particle dynamics results only from inertial and nonlinear drag forces. In the bulk of the channel, the first- and second-order moments of the particle velocity from the point-particle DNS agree well with those from the interface-resolved DNS. Close to the wall, however, most of the statistics show major qualitative differences. We show that this difference originates from the strong shear-induced lift force acting on the particles in the near-wall region. This mechanism is well captured by the lift force model due to Saffman (J. Fluid Mech., vol. 22 (2), 1965, pp. 385–400), while other widely used, more elaborate, approaches aiming at extending the lift model for a wider range of particle Reynolds numbers can actually underpredict the magnitude of the near-wall particle velocity fluctuations for the cases analysed here.
ChID: A Large-scale Chinese IDiom Dataset for Cloze Test

Cloze-style reading comprehension in Chinese is still limited due to the lack of various corpora. In this paper we propose a large-scale Chinese cloze test dataset ChID, which studies the comprehension of idiom, a unique language phenomenon in Chinese. In this corpus, the idioms in a passage are replaced by blank symbols and the correct answer needs to be chosen from well-designed candidate idioms. We carefully study how the design of candidate idioms and the representation of idioms affect the performance of state-of-the-art models. Results show that the machine accuracy is substantially worse than that of human, indicating a large space for further research.
Rapidly Evolving Disk–Jet Coupling during Re-brightenings in the Black Hole Transient MAXI J1535−571

The main outburst of the candidate black hole low-mass X-ray binary (BH LMXB) MAXI J1535−571 ended in 2018 May and was followed by at least five episodes of re-brightenings. We have monitored this re-brightening phenomenon at X-ray and radio wavelengths using the Neil Gehrels Swift Observatory and Australia Telescope Compact Array, respectively. The first two re-brightenings exhibited a high peak X-ray luminosity (implying a high mass-accretion rate) and were observed to transition from the hard to the soft state. However, unlike the main outburst, these re-brightenings did not exhibit clear hysteresis. During the re-brightenings, when MAXI J1535−571 was in the hard state, we observed the brightening of a compact radio jet that was subsequently quenched when the source transitioned to a similar soft state, as was observed during the main outburst. We report on the first investigation of disk−jet coupling over multiple rapidly evolving re-brightenings in a BH LMXB. We find that the accretion flow properties and the accompanying compact jet evolve on a similarly rapid timescale of ∼days, rather than the typical value of ∼weeks as observed for most other BH LMXBs during their main outburst events.
Active Object Manipulation Facilitates Visual Object Learning: An Egocentric Vision Study

Inspired by the remarkable ability of the infant visual learning system, a recent study collected first-person images from children to analyze the `training data' that they receive. We conduct a follow-up study that investigates two additional directions. First, given that infants can quickly learn to recognize a new object without much supervision (i.e. few-shot learning), we limit the number of training images. Second, we investigate how children control the supervision signals they receive during learning based on hand manipulation of objects. Our experimental results suggest that supervision with hand manipulation is better than without hands, and the trend is consistent even when a small number of images is available.
An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem

This paper introduces a new learning-based approach for approximately solving the Travelling Salesman Problem on 2D Euclidean graphs. We use deep Graph Convolutional Networks to build efficient TSP graph representations and output tours in a non-autoregressive manner via highly parallelized beam search. Our approach outperforms all recently proposed autoregressive deep learning techniques in terms of solution quality, inference speed and sample efficiency for problem instances of fixed graph sizes. In particular, we reduce the average optimality gap from 0.52% to 0.01% for 50 nodes, and from 2.26% to 1.39% for 100 nodes. Finally, despite improving upon other learning-based approaches for TSP, our approach falls short of standard Operations Research solvers.
On numerical modeling of dispersive mechanical waves in lipid bi-layers

We investigate different mechanical effects which accompany the nerve pulse propagation by using mathematical modeling. The propagation process is composed by three connected phenomena: (i) the action potential (electrical signal) which is usually considered when nerve pulses are discussed, (ii) the mechanical wave propagating in the biomembrane and (iii) the pressure wave in the axoplasm inside the axon. The main goal of the present study is to investigate numerically how the mechanical wave is generated by the action potential and how the characteristics of the system are reflected in the emerging wave ensemble. %the existence and characteristics of the mechanical wave influence the parameters of an action potential part of the wave ensemble. The key characteristics for the coupled model system are: (i) the velocity of the peak of the mechanical pulse is associated with the velocity of the action potential regardless of the sound velocity value in the lipid bi-layer, (ii) the velocity of the front and the shape of the mechanical wave depends on sound velocity in the lipid bi-layer, (iii) the shape of the mechanical wave can have an effect on the velocity and shape of the action potential.
8E-6 Extended FEM/SDA Software for Characterising Surface Acoustic Wave Propagation in Multi-Layered Structures

This paper describes the development of the extended version of the FEM/SDA software for the characterisation of SAW propagation in layered substrate and overlayered structures. First, the newly developed software was applied to the characterisation of SAW propagation under an infinitely-long Al interdigital transducer (IDT) on rotated Y-cut LiTaO3/sapphire substrate structure. Because of the finite LiTaO3 thickness, a series of spurious resonances appears. It is shown that the excitation strength of the spurious resonances changes with frequency as well as the rotation angle, which reflects the frequency and rotation angle dependence of the energy leakage. Next, the analysis was carried out for SAWs propagating in an SiO2 layer/Al IDT/42degYX-LiTaO3 structure. It is shown that the influence of the SiO2 layer is significantly dependent on the location where the SiO2 layer is deposited. That is, the SiO2 layer on the IDT electrodes tends to increase the SAW reflectivity, whereas the SiO, layer sandwiching the grating electrodes in reduces the SAW reflectivity.
A detailed study of recurrent neural networks used to model tasks in the cerebral cortex

Recurrent Neural Networks or RNN are frequently used to model different aspects of brain regions. We studied the properties of RNN trained to perform temporal and flow control tasks with temporal stimuli. We present the results regarding three aspects: inner configuration sets, memory capacity with the scale and immunity to induced damage on trained networks. Our results allow us to quantify different aspects of these physical models, which are normally used as black boxes and must be understood previous to modeling the biological response of cerebral cortex.
Localization Requirements for Autonomous Vehicles

Autonomous vehicles require precise knowledge of their position and orientation in all weather and traffic conditions for path planning, perception, control, and general safe operation. Here we derive these requirements for autonomous vehicles based on first principles. We begin with the safety integrity level, defining the allowable probability of failure per hour of operation based on desired improvements on road safety today. This draws comparisons with the localization integrity levels required in aviation and rail where similar numbers are derived at 10^-8 probability of failure per hour of operation. We then define the geometry of the problem, where the aim is to maintain knowledge that the vehicle is within its lane and to determine what road level it is on. Longitudinal, lateral, and vertical localization error bounds (alert limits) and 95% accuracy requirements are derived based on US road geometry standards (lane width, curvature, and vertical clearance) and allowable vehicle dimensions. For passenger vehicles operating on freeway roads, the result is a required lateral error bound of 0.57 m (0.20 m, 95%), a longitudinal bound of 1.40 m (0.48 m, 95%), a vertical bound of 1.30 m (0.43 m, 95%), and an attitude bound in each direction of 1.50 deg (0.51 deg, 95%). On local streets, the road geometry makes requirements more stringent where lateral and longitudinal error bounds of 0.29 m (0.10 m, 95%) are needed with an orientation requirement of 0.50 deg (0.17 deg, 95%).
"Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations

Methods for interpreting machine learning black-box models increase the outcomes' transparency and in turn generates insight into the reliability and fairness of the algorithms. However, the interpretations themselves could contain significant uncertainty that undermines the trust in the outcomes and raises concern about the model's reliability. Focusing on the method "Local Interpretable Model-agnostic Explanations" (LIME), we demonstrate the presence of two sources of uncertainty, namely the randomness in its sampling procedure and the variation of interpretation quality across different input data points. Such uncertainty is present even in models with high training and test accuracy. We apply LIME to synthetic data and two public data sets, text classification in 20 Newsgroup and recidivism risk-scoring in COMPAS, to support our argument.
Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations

Zero-shot translation, translating between language pairs on which a Neural Machine Translation (NMT) system has never been trained, is an emergent property when training the system in multilingual settings. However, naive training for zero-shot NMT easily fails, and is sensitive to hyper-parameter setting. The performance typically lags far behind the more conventional pivot-based approach which translates twice using a third language as a pivot. In this work, we address the degeneracy problem due to capturing spurious correlations by quantitatively analyzing the mutual information between language IDs of the source and decoded sentences. Inspired by this analysis, we propose to use two simple but effective approaches: (1) decoder pre-training; (2) back-translation. These methods show significant improvement (4 22 BLEU points) over the vanilla zero-shot translation on three challenging multilingual datasets, and achieve similar or better results than the pivot-based approach.
Back Attention Knowledge Transfer for Low-resource Named Entity Recognition

In recent years, great success has been achieved in the field of natural language processing (NLP), thanks in part to the considerable amount of annotated resources. For named entity recognition (NER), most languages do not have such an abundance of labeled data as English, so the performances of those languages are relatively lower. To improve the performance, we propose a general approach called Back Attention Network (BAN). BAN uses a translation system to translate other language sentences into English and then applies a new mechanism named back attention knowledge transfer to obtain task-specific information from pre-trained high-resource languages NER model. This strategy can transfer high-layer features of well-trained model and enrich the semantic representations of the original language. Experiments on three different language datasets indicate that the proposed approach outperforms other state-of-the-art methods.
Probing the existence of very massive first stars

We present a novel approach aimed at identifying the key chemical elements to search for the (missing) descendants of very massive first stars exploding as Pair Instability Supernovae (PISN). Our simple and general method consists in a parametric study accounting for the unknowns related to early cosmic star-formation and metal-enrichment. Our approach allow us to define the most likely [Fe/H] and abundance ratios of long-lived stars born in inter-stellar media polluted by the nucleosynthetic products of PISN at a > 90%, 70%, and 50% level. In agreement with previous works, we show that the descendants of very massive first stars can be most likely found at [Fe/H]~ -2. Further, we demonstrate that to search for an under-abundance of [(N, Cu, Zn)/Fe] < 0 is the key to identify these rare descendants. The "killing elements" N, Zn, and Cu are not produced by PISN, so that their sub-Solar abundance with respect to iron persists in environments polluted by further generations of normal core-collapse supernovae up to a 50% level. We show that the star BD +80 245, which has [Fe/H]= -2.2, [N/Fe]= -0.79, [Cu/Fe]=-0.75, and [Zn/Fe]= -0.12 can be the smoking gun of the chemical imprint from very massive first stars. To this end we acquired new spectra for BD +80 245 and re-analysed those available from the literature accounting for Non-Local Thermodynamic Equilibrium corrections for Cu. We discuss how to find more of these missing descendants in ongoing and future surveys to tightly constrain the mass distribution of the first stars.
Relational Word Embeddings

While word embeddings have been shown to implicitly encode various forms of attributional knowledge, the extent to which they capture relational information is far more limited. In previous work, this limitation has been addressed by incorporating relational knowledge from external knowledge bases when learning the word embedding. Such strategies may not be optimal, however, as they are limited by the coverage of available resources and conflate similarity with other forms of relatedness. As an alternative, in this paper we propose to encode relational knowledge in a separate word embedding, which is aimed to be complementary to a given standard word embedding. This relational word embedding is still learned from co-occurrence statistics, and can thus be used even when no external knowledge base is available. Our analysis shows that relational word vectors do indeed capture information that is complementary to what is encoded in standard word embeddings.