Miracle-dz/UniFAR-FTUs huggingface dataset:
High circular polarization of near-infrared light induced by micron-sized dust grains

We explore the induction of circular polarization (CP) of near-infrared light in star-forming regions using three-dimensional radiative transfer calculations. The simulations trace the change of Stokes parameters at each scattering/absorption process in a dusty gas slab composed of aligned grains. We find that the CP degree enlarges significantly according as the size of dust grains increases and exceeds ∼20 per cent for micron-sized grains. Therefore, if micron-sized grains are dominant in a dusty gas slab, the high CP observed around luminous young stellar objects can be accounted for. The distributions of CP show the asymmetric quadrupole patterns regardless of the grain sizes. Also, we find that the CP degree depends on the relative position of a dusty gas slab. If a dusty gas slab is located behind a star-forming region, the CP reaches ∼60 per cent in the case of 1.0 µm dust grains. Hence, we suggest that the observed variety of CP maps can be explained by different size distributions of dust grains and the configuration of aligned grains.
Integrable generalisations of Dirac magnetic monopole

We classify certain integrable (both classical and quantum) generalisations of Dirac magnetic monopole on topological sphere S 2 with constant magnetic field, completing the previous local results by Ferapontov, Sayles and Veselov. We show that there are two integrable families of such generalisations with integrals, which are quadratic in momenta. The first family corresponds to the classical Clebsch systems, which can be interpreted as Dirac magnetic monopole in harmonic electric field. The second family is new and can be written in terms of elliptic functions on sphere S 2 with very special metrics.
Collective Response to Media Coverage of the COVID-19 Pandemic on Reddit and Wikipedia: Mixed-Methods Analysis

Background The exposure and consumption of information during epidemic outbreaks may alter people’s risk perception and trigger behavioral changes, which can ultimately affect the evolution of the disease. It is thus of utmost importance to map the dissemination of information by mainstream media outlets and the public response to this information. However, our understanding of this exposure-response dynamic during the COVID-19 pandemic is still limited. Objective The goal of this study is to characterize the media coverage and collective internet response to the COVID-19 pandemic in four countries: Italy, the United Kingdom, the United States, and Canada. Methods We collected a heterogeneous data set including 227,768 web-based news articles and 13,448 YouTube videos published by mainstream media outlets, 107,898 user posts and 3,829,309 comments on the social media platform Reddit, and 278,456,892 views of COVID-19–related Wikipedia pages. To analyze the relationship between media coverage, epidemic progression, and users’ collective web-based response, we considered a linear regression model that predicts the public response for each country given the amount of news exposure. We also applied topic modelling to the data set using nonnegative matrix factorization. Results Our results show that public attention, quantified as user activity on Reddit and active searches on Wikipedia pages, is mainly driven by media coverage; meanwhile, this activity declines rapidly while news exposure and COVID-19 incidence remain high. Furthermore, using an unsupervised, dynamic topic modeling approach, we show that while the levels of attention dedicated to different topics by media outlets and internet users are in good accordance, interesting deviations emerge in their temporal patterns. Conclusions Overall, our findings offer an additional key to interpret public perception and response to the current global health emergency and raise questions about the effects of attention saturation on people’s collective awareness and risk perception and thus on their tendencies toward behavioral change.
Mixup Training as the Complexity Reduction

Machine learning has achieved remarkable results in recent years due to the increase in the number of data and the development of computational resources. However, despite such excellent performance, machine learning models often suffer from the problem of over-fitting. Many data augmentation methods have been proposed to tackle such a problem, and one of them is called Mixup. Mixup is a recently proposed regularization procedure, which linearly interpolates a random pair of training examples. This regularization method works very well experimentally, but its theoretical guarantee is not fully discussed. In this study, we aim to find out why Mixup works well from the aspect of computational learning theory. In addition, we reveal how the effect of Mixup changes in each situation. Furthermore, we also investigated the effects of changes in the Mixup's parameter. This contributes to the search for the optimal parameters and to estimate the effects of the parameters currently used. The results of this study provide a theoretical clarification of when and how effective regularization by Mixup is.
Characterizing Structural Regularities of Labeled Data in Overparameterized Models

Human learners appreciate that observations usually form hierarchies of regularities and sub-regularities. For example, English verbs have irregular cases that must be memorized (e.g., go -> went) and regular cases that generalize well (e.g., kiss -> kissed, miss -> missed). Likewise, deep neural networks have the capacity to memorize rare or irregular forms but nonetheless generalize across instances that share common patterns or structures. We analyze how individual instances are treated by a model via a consistency score. The score is the expected accuracy of a particular architecture for a held-out instance on a training set of a given size sampled from the data distribution. We obtain empirical estimates of this score for individual instances in multiple data sets, and we show that the score identifies out-of-distribution and mislabeled examples at one end of the continuum and regular examples at the other end. We explore two categories of proxies to the consistency score: pairwise distance based proxy and the training statistics based proxies. We conclude with two applications using C-scores to help understand the dynamics of representation learning and filter out outliers, and discussions of other potential applications such as curriculum learning, and active data collection.
DNF-Net: A Neural Architecture for Tabular Data

A challenging open question in deep learning is how to handle tabular data. Unlike domains such as image and natural language processing, where deep architectures prevail, there is still no widely accepted neural architecture that dominates tabular data. As a step toward bridging this gap, we present DNF-Net a novel generic architecture whose inductive bias elicits models whose structure corresponds to logical Boolean formulas in disjunctive normal form (DNF) over affine soft-threshold decision terms. In addition, DNF-Net promotes localized decisions that are taken over small subsets of the features. We present an extensive empirical study showing that DNF-Nets significantly and consistently outperform FCNs over tabular data. With relatively few hyperparameters, DNF-Nets open the door to practical end-to-end handling of tabular data using neural networks. We present ablation studies, which justify the design choices of DNF-Net including the three inductive bias elements, namely, Boolean formulation, locality, and feature selection.
Robust model training and generalisation with Studentising flows

Normalising flows are tractable probabilistic models that leverage the power of deep learning to describe a wide parametric family of distributions, all while remaining trainable using maximum likelihood. We discuss how these methods can be further improved based on insights from robust (in particular, resistant) statistics. Specifically, we propose to endow flow-based models with fat-tailed latent distributions such as multivariate Student's $t$, as a simple drop-in replacement for the Gaussian distribution used by conventional normalising flows. While robustness brings many advantages, this paper explores two of them: 1) We describe how using fatter-tailed base distributions can give benefits similar to gradient clipping, but without compromising the asymptotic consistency of the method. 2) We also discuss how robust ideas lead to models with reduced generalisation gap and improved held-out data likelihood. Experiments on several different datasets confirm the efficacy of the proposed approach in both regards.
Exploring Category-Agnostic Clusters for Open-Set Domain Adaptation

Unsupervised domain adaptation has received significant attention in recent years. Most of existing works tackle the closed-set scenario, assuming that the source and target domains share the exactly same categories. In practice, nevertheless, a target domain often contains samples of classes unseen in source domain (i.e., unknown class). The extension of domain adaptation from closed-set to such open-set situation is not trivial since the target samples in unknown class are not expected to align with the source. In this paper, we address this problem by augmenting the state-of-the-art domain adaptation technique, Self-Ensembling, with category-agnostic clusters in target domain. Specifically, we present Self-Ensembling with Category-agnostic Clusters (SE-CC) --- a novel architecture that steers domain adaptation with the additional guidance of category-agnostic clusters that are specific to target domain. These clustering information provides domain-specific visual cues, facilitating the generalization of Self-Ensembling for both closed-set and open-set scenarios. Technically, clustering is firstly performed over all the unlabeled target samples to obtain the category-agnostic clusters, which reveal the underlying data space structure peculiar to target domain. A clustering branch is capitalized on to ensure that the learnt representation preserves such underlying structure by matching the estimated assignment distribution over clusters to the inherent cluster distribution for each target sample. Furthermore, SE-CC enhances the learnt representation with mutual information maximization. Extensive experiments are conducted on Office and VisDA datasets for both open-set and closed-set domain adaptation, and superior results are reported when comparing to the state-of-the-art approaches.
Field-free switching of magnetic tunnel junctions driven by spin–orbit torques at sub-ns timescales

We report time-resolved measurements of magnetization switching by spin-orbit torques in the absence of an external magnetic field in perpendicularly magnetized magnetic tunnel junctions (MTJ). Field-free switching is enabled by the dipolar field of an in-plane magnetized layer integrated above the MTJ stack, the orientation of which determines the switching polarity. Real-time single-shot measurements provide direct evidence of magnetization reversal and switching distributions. Close to the critical switching voltage we observe stochastic reversal events due to a finite incubation delay preceding the magnetization reversal. Upon increasing the pulse amplitude to twice the critical voltage the reversal becomes quasi-deterministic, leading to reliable bipolar switching at sub-ns timescales in zero external field. We further investigate the switching probability as a function of dc bias of the MTJ and external magnetic field, providing insight on the parameters that determine the critical switching voltage.
Phospho- and sphingolipid distribution during processing of milk, butter and whey

Summary Phospho- and sphingolipids (SPHs) were monitored upon processing of raw milk into skimmed milk, cream, butter, buttermilk, fresh cheese, acid whey, anhydrous milk fat and butterserum (=the aqueous phase of butter). These products were analysed on polar lipids, fat and dry matter and corresponding balances were calculated. It was found that polar lipids were preferentially enriched in aqueous phases like skimmed milk, buttermilk and butterserum. Significant differences in relative SPH content were observed. Tangential filtration and thermocalcic aggregation of the acid whey successfully recovered the polar lipids. With 11.5% of polar lipids on a dry matter base, representing 28.4% of the milk polar lipids and only 0.9% of the milk mass, butterserum was found to be most suitable for further purification. This could result in a food (ingredient) with enhanced technological and nutritional properties, as recent studies reveal health-improving capacities of phospho- and SPHs.
Hypernetwork-Based Augmentation

Data augmentation is an effective technique to improve the generalization of deep neural networks. Recently, AutoAugment proposed a well-designed search space and a search algorithm that automatically finds augmentation policies in a data-driven manner. However, AutoAugment is computationally intensive. In this paper, we propose an efficient gradient-based search algorithm, called Hypernetwork-Based Augmentation (HBA), which simultaneously learns model parameters and augmentation hyperparameters in a single training. Our HBA uses a hypernetwork to approximate a population-based training algorithm, which enables us to tune augmentation hyperparameters by gradient descent. Besides, we introduce a weight sharing strategy that simplifies our hypernetwork architecture and speeds up our search algorithm. We conduct experiments on CIFAR-10, CIFAR-100, SVHN, and ImageNet. Our results demonstrate that HBA is significantly faster than state-of-the-art methods while achieving competitive accuracy.
Effects of Gizzard Shad and Threadfin Shad on Zooplankton and Young‐of‐Year White Crappie Production

Abstract We examined the effects of gizzard shad Dorosoma cepedianum and threadfin shad Dorosoma petenense on zooplankton abundance and on reproduction by white crappie Pomoxis annularis in a March–July 1986 Texas pond experiment of factorial design. We used four treatment combinations: no shad, gizzard shad, threadfin shad, and gizzard shad + threadfin shad. Densities of cyclopoid copepodids and Daphnia sp. were suppressed in the presence of threadfin shad but not gizzard shad. The presence of gizzard shad or threadfin shad was associated with decreased total number and biomass of young-of-year white crappies, a shift in white crappie size distributions toward larger individuals, and increased mean weight of white crappies in some size-classes. The biomass of young-of-year gizzard shad decreased in the presence of threadfin shad. Our study suggests that gizzard shad and threadfin shad may decrease the density and biomass of young-of-year white crappies, but increase their mean size.
Deep Differential System Stability - Learning advanced computations from examples

Can advanced mathematical computations be learned from examples? Using transformers over large generated datasets, we train models to learn properties of differential systems, such as local stability, behavior at infinity and controllability. We achieve near perfect estimates of qualitative characteristics of the systems, and good approximations of numerical quantities, demonstrating that neural networks can learn advanced theorems and complex computations without built-in mathematical knowledge.
VINTERGATAN III: how to reset the metallicity of the Milky Way

Using the cosmological zoom simulation VINTERGATAN, we present a new scenario for the onset of star formation at the metal-poor end of the low-[$\alpha$/Fe] sequence in a Milky Way-like galaxy. In this scenario, the galaxy is fueled by two distinct gas flows. One is enriched by outflows from massive galaxies, but not the other. While the former feeds the inner galactic region, the latter fuels an outer gas disk, inclined with respect to the main galactic plane, and with a significantly poorer chemical content. The first passage of the last major merger galaxy triggers tidal compression in the outer disk, which increases the gas density and eventually leads to star formation, at a metallicity 0.75 dex lower than the inner galaxy. This forms the first stars of the low-[$\alpha$/Fe] sequence. These in situ stars have halo-like kinematics, similarly to what is observed in the Milky Way, due to the inclination of the outer disk which eventually aligns with the inner one via gravitational torques. We show that this tilting disk scenario is likely to be common in Milky-Way like galaxies. This process implies that the low-[$\alpha$/Fe] sequence is populated in situ, simultaneously from two formation channels, in the inner and the outer galaxy, with distinct metallicities. This contrasts with purely sequential scenarios for the assembly of the Milky Way disk and could be tested observationally.
Deep Time-Delay Reservoir Computing: Dynamics and Memory Capacity

The deep time-delay reservoir computing concept utilizes unidirectionally connected systems with time-delays for supervised learning. We present how the dynamical properties of a deep Ikeda-based reservoir are related to its memory capacity (MC) and how that can be used for optimization. In particular, we analyze bifurcations of the corresponding autonomous system and compute conditional Lyapunov exponents, which measure generalized synchronization between the input and the layer dynamics. We show how the MC is related to the systems' distance to bifurcations or magnitude of the conditional Lyapunov exponent. The interplay of different dynamical regimes leads to an adjustable distribution between the linear and nonlinear MC. Furthermore, numerical simulations show resonances between the clock cycle and delays of the layers in all degrees of MC. Contrary to MC losses in single-layer reservoirs, these resonances can boost separate degrees of MC and can be used, e.g., to design a system with maximum linear MC. Accordingly, we present two configurations that empower either high nonlinear MC or long time linear MC.
VINTERGATAN – I. The origins of chemically, kinematically, and structurally distinct discs in a simulated Milky Way-mass galaxy

Spectroscopic surveys of the Milky Way’s stars have revealed spatial, chemical, and kinematical structures that encode its history. In this work, we study their origins using a cosmological zoom simulation, VINTERGATAN, of a Milky Way-mass disc galaxy. We find that in connection to the last major merger at z ∼ 1.5, cosmological accretion leads to the rapid formation of an outer, metal-poor, low-[α/Fe] gas disc around the inner, metal-rich galaxy containing the old high-[α/Fe] stars. This event leads to a bimodality in [α/Fe] over a range of [Fe/H]. A detailed analysis of how the galaxy evolves since z ∼ 1 is presented. We demonstrate the way in which inside-out growth shapes the radial surface density and metallicity profile and how radial migration preferentially relocates stars from the inner disc to the outer disc. Secular disc heating is found to give rise to increasing velocity dispersions and scale heights with stellar age, which together with disc flaring explains several trends observed in the Milky Way, including shallower radial [Fe/H] profiles above the mid-plane. We show how the galaxy formation scenario imprints non-trivial mappings between structural associations (i.e. thick and thin discs), velocity dispersions, α-enhancements, and ages of stars; e.g. the most metal-poor stars in the low-[α/Fe] sequence are found to have a scale height comparable to old high-[α/Fe] stars. Finally, we illustrate how at low spatial resolution, comparable to the thickness of the galaxy, the proposed pathway to distinct sequences in [α/Fe]–[Fe/H] cannot be captured.
Helium abundances and its radial gradient from the spectra of H ii regions and ring nebulae of the Milky Way

We determine the radial abundance gradient of helium in the disc of the Galaxy from published spectra of 19 H ii regions and ring nebulae surrounding massive O-type stars. We revise the Galactocentric distances of the objects considering Gaia DR2 parallaxes (Gaia Collaboration 2018) and determine the physical conditions and the ionic abundance of He+ in a homogeneous way, using between 3 and 10 He i recombination lines in each object. We estimate the total He abundance of the nebulae and its radial abundance gradient using four different ionization correction factor (ICF; He) schemes. The slope of the gradient is always negative and weakly dependent on the ICF(He) scheme, especially when only the objects with log(η) < 0.9 are considered. The slope values go from −0.0078 to −0.0044 dex kpc−1, consistent with the predictions of chemical evolution models of the Milky Way and chemodynamical simulations of disc galaxies. Finally, we estimate the abundance deviations of He, O, and N in a sample of ring nebulae around Galactic Wolf–Rayet stars, finding a quite similar He overabundance of about +0.24 ± 0.11 dex in three stellar ejecta ring nebulae.
Uplink and Downlink MIMO-NOMA With Simultaneous Triangularization

In this paper, we consider the uplink and downlink precoder design for two-user power-domain multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) systems. We propose novel uplink and downlink precoding and detection schemes that lower the decoding complexity at the receiver by decomposing the MIMO-NOMA channels of the users into multiple single-input single-output (SISO)-NOMA channels via simultaneous triangularization (ST) of the MIMO channels of the users and low-complexity self-interference cancellation at the receivers. The proposed ST MIMO-NOMA schemes avoid channel inversion at transmitter and receiver and take advantage of the null spaces of the MIMO channels of the users, which is beneficial for the ergodic achievable rate performance. We characterize the maximum ergodic achievable rate regions of the proposed uplink and downlink ST MIMO-NOMA schemes, and compare them with respective upper bounds, baseline MIMO-NOMA schemes, and orthogonal multiple access (OMA). Our results illustrate that the proposed schemes significantly outperform the considered baseline MIMO-NOMA schemes and OMA, and have a small gap to the respective upper bounds for most channel conditions and user rates. Moreover, we show that a hybrid scheme, which performs time sharing between the proposed uplink and downlink ST MIMO-NOMA and single-user MIMO, can improve performance even further.