Miracle-dz/UniFAR-FTUs huggingface dataset:
Multilingual Culture-Independent Word Analogy Datasets

In text processing, deep neural networks mostly use word embeddings as an input. Embeddings have to ensure that relations between words are reflected through distances in a high-dimensional numeric space. To compare the quality of different text embeddings, typically, we use benchmark datasets. We present a collection of such datasets for the word analogy task in nine languages: Croatian, English, Estonian, Finnish, Latvian, Lithuanian, Russian, Slovenian, and Swedish. We designed the monolingual analogy task to be much more culturally independent and also constructed cross-lingual analogy datasets for the involved languages. We present basic statistics of the created datasets and their initial evaluation using fastText embeddings.
When Does Non-Orthogonal Tensor Decomposition Have No Spurious Local Minima?

We study the optimization problem for decomposing $d$ dimensional fourth-order Tensors with $k$ non-orthogonal components. We derive \textit{deterministic} conditions under which such a problem does not have spurious local minima. In particular, we show that if $\kappa = \frac{\lambda_{max}}{\lambda_{min}} < \frac{5}{4}$, and incoherence coefficient is of the order $O(\frac{1}{\sqrt{d}})$, then all the local minima are globally optimal. Using standard techniques, these conditions could be easily transformed into conditions that would hold with high probability in high dimensions when the components are generated randomly. Finally, we prove that the tensor power method with deflation and restarts could efficiently extract all the components within a tolerance level $O(\kappa \sqrt{k\tau^3})$ that seems to be the noise floor of non-orthogonal tensor decomposition.
Screening from 
eg
 states and antiferromagnetic correlations in 
d(1,2,3)
 perovskites: A 
GW+EDMFT
 investigation

We perform a systematic {\it ab initio} study of the electronic structure of Sr(V,Mo,Mn)O$_3$ perovskites, using the parameter-free $GW$+EDMFT method. This approach self-consistently calculates effective interaction parameters, taking into account screening effects due to nonlocal charge fluctuations. Comparing the results of a 3-band ($t_{2g}$) description to those of a 5-band ($t_{2g}$+$e_g$) model, it is shown that the $e_g$ states have little effect on the low-energy properties and the plasmonic features for the first two compounds but play a more active role in SrMnO$_3$. In the case of SrMnO$_3$ paramagnetic $GW$+EDMFT yields a metallic low-temperature solution on the verge of a Mott transition, while antiferromagnetic $GW$+EDMFT produces an insulating solution with the correct gap size. We discuss the possible implications of this result for the nature of the insulating state above the Neel temperature, and the reliability of the $GW$+EDMFT scheme.
On the singularity structure of the discrete KdV equation

The discrete KdV (dKdV) equation, the pinnacle of discrete integrability, is often thought to possess the singularity confinement property because it confines on an elementary quadrilateral. Here we investigate the singularity structure of the dKdV equation through reductions of the equation, obtained for initial conditions on a staircase with height 1, and show that it is much more subtle than one might assume. We first study the singularities for the mappings obtained after reduction and contrast these with the singularities that arise in non-integrable generalizations of those mappings. We then show that the so-called ‘express method’ for obtaining dynamical degrees for second order mappings can be succesfully applied to all the higher order mappings we derived. Finally, we use the information obtained on the singularity structure of the reductions to describe an important subset of singularity patterns for the dKdV equation and we present an example of a non-confining pattern and explain why its existence does not contradict the integrability of the dKdV equation.
Instance Cross Entropy for Deep Metric Learning

Loss functions play a crucial role in deep metric learning thus a variety of them have been proposed. Some supervise the learning process by pairwise or tripletwise similarity constraints while others take advantage of structured similarity information among multiple data points. In this work, we approach deep metric learning from a novel perspective. We propose instance cross entropy (ICE) which measures the difference between an estimated instance-level matching distribution and its ground-truth one. ICE has three main appealing properties. Firstly, similar to categorical cross entropy (CCE), ICE has clear probabilistic interpretation and exploits structured semantic similarity information for learning supervision. Secondly, ICE is scalable to infinite training data as it learns on mini-batches iteratively and is independent of the training set size. Thirdly, motivated by our relative weight analysis, seamless sample reweighting is incorporated. It rescales samples' gradients to control the differentiation degree over training examples instead of truncating them by sample mining. In addition to its simplicity and intuitiveness, extensive experiments on three real-world benchmarks demonstrate the superiority of ICE.
Lévy flights for light in ordered lasers

L\'evy flights for light have been demonstrated in disordered systems with and without optical gain, and remained unobserved in ordered ones. In the present letter, we investigate, numerically and experimentally, L\'evy flights for light in ordered systems due to an ordered (conventional) laser. The statistical analysis was performed on the intensity fluctuations of the output spectra upon repeated identical experimental realizations. We found out that the optical gain and the mirrors reflectivity are critical parameters governing the fluctuation statistics. We identified L\'evy regimes for gain around the laser threshold, and Gaussian-L\'evy-Gaussian crossovers were unveiling when increasing the gain from below to above the threshold. The experimental results were corroborated by Monte Carlo simulations, and the fluctuations were associated to a Langevin noise source that takes into account the randomness of the spontaneous emission, which seeds the laser emission and can cause large fluctuations of the output spectra from shot-to-shot under identical experimental realizations.
Recent update of management of obstetric cholestasis

Introduction Obstetric cholstasis is the most common liver disease in pregnancy. Fortunately this condition is rare but it is often interrupted by distressing and dangerous complications both medical and obstetric. Generalised pruritus occuring in late pregnancy in abscence of dermatological abnormalities is usually due to Obstetric Cholestasis (OC). Classically the patient present with generalised itch typically worse at night and lack of sleep between (28-34) weeks gestation. Jaundice is not necessary for the diagnosis but often followed in more severe cases. The incidence is highly variable probably reflects genetic, geographic and other environmental factors. Asiatic is known to have high incidence. The most dangers of the disease are increased risk of Intrauterine Death (IUD) classically from 37 weeks, Stillborn, Featal distress, Mechoneum stain and increased incidence of preterm laboure (PTL). Pregnant women suffer from sleep disturbence and postpartum haemorrage. Also the recurrence rate of OC is very high (80%).
Both starvation and outflows drive galaxy quenching

Star-forming galaxies can in principle be transformed into passive systems by a multitude of processes that quench star formation, such as the halting of gas accretion (starvation) or the rapid removal of gas in AGN-driven outflows. However, it remains unclear which processes are the most significant, primary drivers of the SF-passive bimodality. We address this key issue in galaxy evolution by studying the chemical properties of 80 000 local galaxies in Sloan Digital Sky Survey Data Release 7. In order to distinguish between different quenching mechanisms, we analyse the stellar metallicities of star-forming, green valley, and passive galaxies. We find that the significant difference in stellar metallicity between passive galaxies and their star-forming progenitors implies that for galaxies at all masses, quenching must have involved an extended phase of starvation. However, some form of gas ejection also has to be introduced into our models to best match the observed properties of local passive galaxies, indicating that, while starvation is likely to be the prerequisite for quenching, it is the combination of starvation and outflows that is responsible for quenching the majority of galaxies. Closed-box models indicate that the duration of the quenching phase is 2–3 Gyr, with an e-folding time of 2–4 Gyr, after which further star formation is prevented by an ejective/heating mode. Alternatively, leaky-box models find a longer duration for the quenching phase of 5–7 Gyr and an e-folding time of ∼1 Gyr, with outflows becoming increasingly important with decreasing stellar mass. Finally, our analysis of local green valley galaxies indicates that quenching is slower in the local Universe than at high redshift.
The ASAS-SN catalogue of variable stars – VII. Contact binaries are different above and below the Kraft break

We characterize ${\sim } 71\, 200$ W Ursae Majoris (UMa) type (EW) contact binaries, including ${\sim } 12\, 600$ new discoveries, using All-Sky Automated Survey for SuperNovae (ASAN-SN)V-band all-sky light curves along with archival data from Gaia, 2MASS, AllWISE, LAMOST, GALAH, RAVE, and APOGEE. There is a clean break in the EW period–luminosity relation at $\rm \log (\it P/{\rm d})\,{\simeq }\,{\rm -0.30}$, separating the longer period, early-type EW binaries from the shorter period, late-type systems. The two populations are even more cleanly separated in the space of period and effective temperature, by $T_{\rm eff}=6710\,{\rm K}-1760\,{\rm K}\, \log (P/0.5\,{\rm d})$. Early-type and late-type EW binaries follow opposite trends in Teff with orbital period. For longer periods, early-type EW binaries are cooler, while late-type systems are hotter. We derive period–luminosity relationships in the WJK, V, Gaia DR2 G, J, H, Ks, and W1 bands for the late-type and early-type EW binaries separated by both period and effective temperature, and by period alone. The dichotomy of contact binaries is almost certainly related to the Kraft break and the related changes in envelope structure, winds, and angular momentum loss.
Luminosity Models and Density Profiles for Nuclear Star Clusters for a Nearby Volume-limited Sample of 29 Galaxies

Nuclear star clusters (NSCs) are dense stellar clusters that are found at the centers of a majority of galaxies. In this paper, we study the density profiles for 29 galaxies in a volume-limited survey within 10 Mpc to characterize their NSCs. These galaxies span a 3 × 108–8 × 1010 M⊙ and a wide range of Hubble types. We use high-resolution Hubble Space Telescope archival data to create luminosity models for the galaxies using Sérsic profiles to parameterize the NSCs. We also provide estimates for photometric masses of NSCs and their host galaxies using color–M/L relationships and examine their correlation. We use the multi-Gaussian expansion to derive the NSC densities and their 3D mass-density profiles. The 3D density profiles characterize the NSC densities on scales as small as ∼1 pc, approaching the likely spheres of influence for BHs in these objects. We find that these densities correlate with galaxy mass, with NSC density profiles becoming both denser and flatter at higher galaxy masses. Most galaxy NSCs are denser than typical globular clusters. We parameterize the 3D NSC density profiles and their scatter and slope as a function of galaxy stellar mass to enable the construction of realistic nuclear mass profiles. Our fitted profiles and the derived relations are useful in predicting the rate of tidal disruption events in galaxies. We will verify the results of this paper in a follow-up paper that presents the dynamical modeling of the same sample of NSCs.
Examining the physical conditions of a warm corona in active galactic nuclei accretion discs

A warm corona at the surface of an accretion disc has been proposed as a potential location for producing the soft excess commonly observed in the X-ray spectra of active galactic nuclei (AGNs). In order to fit the observed data, the gas must be at temperatures of ∼1 keV and have an optical depth of τT ≈ 10–20. We present one-dimensional calculations of the physical conditions and emitted spectra of a τT = 10 or 20 gas layer subject to illumination from an X-ray power law (from above), a blackbody (from below), and a variable amount of internal heating. The models show that a warm corona with kT ∼ 1 keV can develop, producing a strong Comptonized soft excess, but only if the internal heating flux is within a relatively narrow range. Similarly, if the gas density of the layer is too large then efficient cooling will stop a warm corona from forming. The radiation from the hard X-ray power law is crucial in producing a warm corona, indicating that a warm and hot corona may coexist in AGN accretion discs, and their combined effect leads to the observed soft excess. Intense heating of a warm corona leads to steep X-ray spectra with ionized Fe K α lines, similar to those seen in some narrow-line Seyfert 1 galaxies.
Estimation of baryon asymmetry from dark matter decaying into IceCube neutrinos

The recent results of IceCube Neutrino Observatory include an excess of PeV neutrino events which appear to follow a broken power-law different from the other lower energy neutrinos detected by IceCube. The possible astrophysical source of these neutrinos is still unknown. One possible source of such neutrinos could be the decay of nonthermal, long-lived heavy mass dark matter, whose mass should be [Formula: see text] GeV and could have produced at the very early Universe. They can undergo cascading decay via both hadronic and leptonic channels to finally produce such high energy neutrinos. This possibility has been explored in this work by studying the decay flux of these dark matter candidates. The mass and lifetime of such dark matter particles have been obtained by performing a [Formula: see text] fit with the PeV neutrino data of IceCube. We finally estimate the baryon asymmetry produced in the Universe due to such dark matter decay.
Making sustainable choices easier

Summary As policy makers grapple with the challenge of producing more food sustainably, consumers will increasingly be affected in terms of the food on offer and the choices they are encouraged to make. Which? research shows little awareness among consumers of the environmental impact of what they eat, but many would be open to making more sustainable choices if it was easy to do so. Simpler, user-friendly information and advice about how to make more sustainable choices is necessary. Much can be learned from efforts to enable healthier choices, including the lack of consistent, user-friendly labelling. But consumers' choices are determined by many factors and conflicting demands as well as synergies need to be made more explicit. Many of our choices are determined before food reaches the supermarket and so it is essential that consumers' views and expectations are also taken into account when decisions are made about the future direction of food policy and production.
Bootstrap inference for multiple imputation under uncongeniality and misspecification

Multiple imputation has become one of the most popular approaches for handling missing data in statistical analyses. Part of this success is due to Rubin’s simple combination rules. These give frequentist valid inferences when the imputation and analysis procedures are so-called congenial and the embedding model is correctly specified, but otherwise may not. Roughly speaking, congeniality corresponds to whether the imputation and analysis models make different assumptions about the data. In practice, imputation models and analysis procedures are often not congenial, such that tests may not have the correct size, and confidence interval coverage deviates from the advertised level. We examine a number of recent proposals which combine bootstrapping with multiple imputation and determine which are valid under uncongeniality and model misspecification. Imputation followed by bootstrapping generally does not result in valid variance estimates under uncongeniality or misspecification, whereas certain bootstrap followed by imputation methods do. We recommend a particular computationally efficient variant of bootstrapping followed by imputation.
Cosmic ray transport in starburst galaxies

Starburst galaxies are efficient γ-ray producers, because their high supernova rates generate copious cosmic ray (CR) protons, and their high gas densities act as thick targets off which these protons can produce neutral pions and thence γ-rays. In this paper, we present a first-principles calculation of the mechanisms by which CRs propagate through such environments, combining astrochemical models with analysis of turbulence in weakly ionized plasma. We show that CRs cannot scatter off the strong large-scale turbulence found in starbursts, because efficient ion-neutral damping prevents such turbulence from cascading down to the scales of CR gyroradii. Instead, CRs stream along field lines at a rate determined by the competition between streaming instability and ion-neutral damping, leading to transport via a process of field line random walk. This results in an effective diffusion coefficient that is nearly energy independent up to CR energies of ∼1 TeV. We apply our computed diffusion coefficient to a simple model of CR escape and loss, and show that the resulting γ-ray spectra are in good agreement with the observed spectra of the starbursts NGC 253, M82, and Arp 220. In particular, our model reproduces these galaxies’ relatively hard GeV γ-ray spectra and softer TeV spectra without the need for any fine-tuning of advective escape times or the shape of the CR injection spectrum.
Artificial Neural Networks in Action for an Automated Cell-Type Classification of Biological Neural Networks

Identification of different neuronal cell types is critical for understanding their contribution to brain functions. Yet, automated and reliable classification of neurons remains a challenge, primarily because of their biological complexity. Typical approaches include laborious and expensive immunohistochemical analysis while feature extraction algorithms based on cellular characteristics have recently been proposed. The former rely on molecular markers, which are often expressed in many cell types, while the latter suffer from similar issues: finding features that are distinctive for each class has proven to be equally challenging. Moreover, both approaches are time consuming and demand a lot of human intervention. In this work we establish the first, automated cell-type classification method that relies on neuronal activity rather than molecular or cellular features. We test our method on a real-world dataset comprising of raw calcium activity signals for four neuronal types. We compare the performance of three different deep learning models and demonstrate that our method can achieve automated classification of neuronal cell types with unprecedented accuracy.
Two-Dimensional Quantum-Link Lattice Quantum Electrodynamics at Finite Density

We present a tree tensor network approach to the study of lattice gauge theories in two spatial dimensions showing how to perform numerical simulations of theories in presence of fermionic matter and four-body magnetic terms, at zero and finite density, with periodic and closed boundary conditions. We exploit the quantum link representation of the gauge fields and demonstrate that a fermionic rishon representation of the quantum links allows us to efficiently handle the fermionic matter while finite densities are naturally enclosed in the tensor network description. We explicit perform calculations for quantum electrodynamics in the spin-one quantum link representation on lattice sizes of up to $16\times16$ sites, detecting and characterizing different quantum phases. In particular, at finite density, we detect a phase separation behaviour as a function of the bare mass values at different filling densities. The presented approach can be extended straightforwardly to three spatial dimensions.
MIMAMO Net: Integrating Micro- and Macro-motion for Video Emotion Recognition

Spatial-temporal feature learning is of vital importance for video emotion recognition. Previous deep network structures often focused on macro-motion which extends over long time scales, e.g., on the order of seconds. We believe integrating structures capturing information about both micro- and macro-motion will benefit emotion prediction, because human perceive both micro- and macro-expressions. In this paper, we propose to combine micro- and macro-motion features to improve video emotion recognition with a two-stream recurrent network, named MIMAMO (Micro-Macro-Motion) Net. Specifically, smaller and shorter micro-motions are analyzed by a two-stream network, while larger and more sustained macro-motions can be well captured by a subsequent recurrent network. Assigning specific interpretations to the roles of different parts of the network enables us to make choice of parameters based on prior knowledge: choices that turn out to be optimal. One of the important innovations in our model is the use of interframe phase differences rather than optical flow as input to the temporal stream. Compared with the optical flow, phase differences require less computation and are more robust to illumination changes. Our proposed network achieves state of the art performance on two video emotion datasets, the OMG emotion dataset and the Aff-Wild dataset. The most significant gains are for arousal prediction, for which motion information is intuitively more informative. Source code is available at https://github.com/wtomin/MIMAMO-Net.
Learning to Caption Images with Two-Stream Attention and Sentence Auto-Encoder

Automatically generating natural language descriptions from an image is a challenging problem in artificial intelligence that requires a good understanding of the correlations between visual and textual cues. To bridge these two modalities, state-of-the-art methods commonly use a dynamic interface between image and text, called attention, that learns to identify related image parts to estimate the next word conditioned on the previous steps. While this mechanism is effective, it fails to find the right associations between visual and textual cues when they are noisy. In this paper we propose two novel approaches to address this issue - (i) a two-stream attention mechanism that can automatically discover latent categories and relate them to image regions based on the previously generated words, (ii) a regularization technique that encapsulates the syntactic and semantic structure of captions and improves the optimization of the image captioning model. Our qualitative and quantitative results demonstrate remarkable improvements on the MSCOCO dataset setting and lead to new state-of-the-art performances for image captioning.