Miracle-dz/UniFAR-FTUs huggingface dataset:
Lexical Normalization for Code-switched Data and its Effect on POS Tagging

Lexical normalization, the translation of non-canonical data to standard language, has shown to improve the performance of many natural language processing tasks on social media. Yet, using multiple languages in one utterance, also called code-switching (CS), is frequently overlooked by these normalization systems, despite its common use in social media. In this paper, we propose three normalization models specifically designed to handle code-switched data which we evaluate for two language pairs: Indonesian-English and Turkish-German. For the latter, we introduce novel normalization layers and their corresponding language ID and POS tags for the dataset, and evaluate the downstream effect of normalization on POS tagging. Results show that our CS-tailored normalization models significantly outperform monolingual ones, and lead to 5.4% relative performance increase for POS tagging as compared to unnormalized input.
How Twitter data sampling biases U.S. voter behavior characterizations

Online social media are key platforms for the public to discuss political issues. As a result, researchers have used data from these platforms to analyze public opinions and forecast election results. The literature has shown that due to inauthentic actors such as malicious social bots and trolls, not every message is a genuine expression from a legitimate user. However, the prevalence of inauthentic activities in social data streams is still unclear, making it difficult to gauge biases of analyses based on such data. In this article, we aim to close this gap using Twitter data from the 2018 U.S. midterm elections. We propose an efficient and low-cost method to identify voters on Twitter and systematically compare their behaviors with different random samples of accounts. We find that some accounts flood the public data stream with political content, drowning the voice of the majority of voters. As a result, these hyperactive accounts are over-represented in volume samples. Hyperactive accounts are more likely to exhibit various suspicious behaviors and to share low-credibility information compared to likely voters. Our work provides insights into biased voter characterizations when using social media data to analyze political issues.
A quantitative compendium of COVID-19 epidemiology

Accurate numbers are needed to understand and predict viral dynamics. Curation of high-quality literature values for the infectious period duration or household secondary attack rate, for example, is especially pressing currently because these numbers inform decisions about how and when to lockdown or reopen societies. We aim to provide a curated source for the key numbers that help us understand the virus driving our current global crisis. This compendium focuses solely on COVID-19 epidemiology. The numbers reported in summary format are substantiated by annotated references. For each property, we provide a concise definition, description of measurement and inference methods, and associated caveats. We hope this compendium will make essential numbers more accessible and avoid common sources of confusion for the many newcomers to the field such as using the incubation period to denote and quantify the latent period or using the hospitalization duration for the infectiousness period duration. This document will be repeatedly updated and the community is invited to participate in improving it.
Neural control variates

We propose neural control variates (NCV) for unbiased variance reduction in parametric Monte Carlo integration. So far, the core challenge of applying the method of control variates has been finding a good approximation of the integrand that is cheap to integrate. We show that a set of neural networks can face that challenge: a normalizing flow that approximates the shape of the integrand and another neural network that infers the solution of the integral equation. We also propose to leverage a neural importance sampler to estimate the difference between the original integrand and the learned control variate. To optimize the resulting parametric estimator, we derive a theoretically optimal, variance-minimizing loss function, and propose an alternative, composite loss for stable online training in practice. When applied to light transport simulation, neural control variates are capable of matching the state-of-the-art performance of other unbiased approaches, while providing means to develop more performant, practical solutions. Specifically, we show that the learned light-field approximation is of sufficient quality for high-order bounces, allowing us to omit the error correction and thereby dramatically reduce the noise at the cost of negligible visible bias.
Ring formation and dust dynamics in wind-driven protoplanetary discs: global simulations

Large-scale vertical magnetic fields are believed to play a key role in the evolution of protoplanetary discs. Associated with non-ideal effects, such as ambipolar diffusion, they are known to launch a wind that could drive accretion in the outer part of the disc (R > 1 AU). They also potentially lead to self-organisation of the disc into large-scale axisymmetric structures, similar to the rings recently imaged by sub-millimetre or near-infrared instruments (ALMA and SPHERE). The aim of this paper is to investigate the mechanism behind the formation of these gaseous rings, but also to understand the dust dynamics and its emission in discs threaded by a large-scale magnetic field. To this end, we performed global magneto-hydrodynamics (MHD) axisymmetric simulations with ambipolar diffusion using a modified version of the PLUTO code. We explored different magnetisations with the midplane β parameter ranging from 105 to 103 and included dust grains -treated in the fluid approximation- ranging from 100 μm to 1 cm in size. We first show that the gaseous rings (associated with zonal flows) are tightly linked to the existence of MHD winds. Secondly, we find that millimetre-size dust is highly sedimented, with a typical scale height of 1 AU at R = 100 AU for β = 104, compatible with recent ALMA observations. We also show that these grains concentrate into pressure maxima associated with zonal flows, leading to the formation of dusty rings. Using the radiative transfer code MCFOST, we computed the dust emission and make predictions on the ring-gap contrast and the spectral index that one might observe with interferometers like ALMA.
The Age Distribution of Stars in the Milky Way Bulge

The age and chemical characteristics of the Galactic bulge link to the formation and evolutionary history of the Galaxy. Data-driven methods and large surveys enable stellar ages and precision chemical abundances to be determined for vast regions of the Milky Way, including the bulge. Here, we use the data-driven approach of The Cannon, to infer the ages and abundances for 125,367 stars in the Milky Way, using spectra from Apache Point Observatory Galaxy Evolution Experiment (apogee) DR14. We examine the ages and metallicities of 1654 bulge stars within . We focus on fields with b < 12°, and out to longitudes of l < 15°. We see that stars in the bulge are about twice as old (τ = 8 Gyr), on average, compared to those in the solar neighborhood (τ = 4 Gyr), with a larger dispersion in [Fe/H] (≈0.38 compared to 0.23 dex). This age gradient comes primarily from the low-α stars. Looking along the Galactic plane, the very central field in the bulge shows by far the largest dispersion in [Fe/H] (σ[Fe/H] ≈ 0.4 dex) and line-of-sight velocity (σvr ≈ 90 km s−1), and simultaneously the smallest dispersion in age. Moving out in longitude, the stars become kinematically colder and less dispersed in [Fe/H], but show a much broader range of ages. We see a signature of the X-shape within the bulge at a latitude of b = 8°, but not at b = 12°. Future apogee and other survey data, with larger sampling, affords the opportunity to extend our approach and study in more detail, to place stronger constraints on models of the Milky Way.
Evaluation of surgical implantation of electronic tags in European eel and effects of different suture materials

Effects of implanting data-storage tags in European eel, Anguilla anguilla, and the suitability of different suture materials (braided permanent silk, permanent monofilament, absorbable and absorbable antibacterial) were examined. The tags consisted of an electronic unit and three floats on a wire, making them flexible and able to follow the swimming movements of the eel. No mortality occurred, and tagged fish did not differ from the control fish in growth. Sutures were shed or dissolved slowly. After 4 weeks, there was no difference among the groups in the proportion ofsutures left.After6months, fishwith braided silkhad largelyshedtheir sutures,fish withmonofilament sutures hadthe majority of sutures left, whereas the fish with absorbable sutures were intermediate in between. Fish with monofilament sutures showed the least-extensive inflammation reactions and fastest wound healing. Antibacterial treatment had no effectoninflammation orhealingrates.After6months,thetag startedtobecomeexpelled throughtheincision infivefish (12%). Theinternalreactionappearedstrongeraroundthefloats,suggestingthatthecoatingmaterialofthefloatscreateda tissue reaction, which should be further investigated. Intraperitoneal implantation appears to be a suitable tagging method for European silver eel, and it is recommended to close incisions using permanent monofilament sutures. Additional keywords: Anguilla anguilla, archival tag, body implant, DST, tagging effect, telemetry.
Common Tern (Sterna hirundo)

Migration: In San Diego County, the Common Tern migrates primarily offshore, but the birds pause to rest on beaches and in coastal wetlands. Fall migrants begin arriving by 12 July (1999, four at the Santa Margarita River mouth, G4, P. A. Ginsburg), peak in late August, then dwindle away through November. In spring, arrival is normally in mid April, but two at the San Diego River mouth (R7) 21 March 1991 were most likely early spring migrants, as none had been in the area earlier in the winter (G. McCaskie, AB 45:496, 1991). Some may still be moving north in early June. Though the Common Tern was long common at the Salton Sea (Patten et al. 2003), the only inland records in San Diego County have been from Lake Hodges (K10) in fall 1979 (two on 5 August, G. McCaskie; one on 2 September, D. M. Parker) and at Bonita (T11) 2 May 1931 (SDNHM 14529). In the 1960s and 70s, 30–60 immature Common Terns typically summered on the Silver Strand (T9), with a maximum of 150 on 23 June 1967 (AFN 21:605, 1967). But the only recent report of a clearly summering bird was of one at the south end of San Diego Bay (V10) 25 June 2002 (M. Sadowski).
Resolving Class Imbalance in Object Detection with Weighted Cross Entropy Losses

Object detection is an important task in computer vision which serves a lot of real-world applications such as autonomous driving, surveillance and robotics. Along with the rapid thrive of large-scale data, numerous state-of-the-art generalized object detectors (e.g. Faster R-CNN, YOLO, SSD) were developed in the past decade. Despite continual efforts in model modification and improvement in training strategies to boost detection accuracy, there are still limitations in performance of detectors when it comes to specialized datasets with uneven object class distributions. This originates from the common usage of Cross Entropy loss function for object classification sub-task that simply ignores the frequency of appearance of object class during training, and thus results in lower accuracies for object classes with fewer number of samples. Class-imbalance in general machine learning has been widely studied, however, little attention has been paid on the subject of object detection. In this paper, we propose to explore and overcome such problem by application of several weighted variants of Cross Entropy loss, for examples Balanced Cross Entropy, Focal Loss and Class-Balanced Loss Based on Effective Number of Samples to our object detector. Experiments with BDD100K (a highly class-imbalanced driving database acquired from on-vehicle cameras capturing mostly Car-class objects and other minority object classes such as Bus, Person and Motor) have proven better class-wise performances of detector trained with the afore-mentioned loss functions.
WikiBERT Models: Deep Transfer Learning for Many Languages

Deep neural language models such as BERT have enabled substantial recent advances in many natural language processing tasks. However, due to the effort and computational cost involved in their pre-training, such models are typically introduced only for a small number of high-resource languages such as English. While multilingual models covering large numbers of languages are available, recent work suggests monolingual training can produce better models, and our understanding of the tradeoffs between mono- and multilingual training is incomplete. In this paper, we introduce a simple, fully automated pipeline for creating language-specific BERT models from Wikipedia data and introduce 42 new such models, most for languages up to now lacking dedicated deep neural language models. We assess the merits of these models using cloze tests and the state-of-the-art UDify parser on Universal Dependencies data, contrasting performance with results using the multilingual BERT (mBERT) model. We find that the newly introduced WikiBERT models outperform mBERT in cloze tests for nearly all languages, and that UDify using WikiBERT models outperforms the parser using mBERT on average, with the language-specific models showing substantially improved performance for some languages, yet limited improvement or a decrease in performance for others. All of the methods and models introduced in this work are available under open licenses from https://github.com/turkunlp/wikibert.
Structure, histochemistry and formation of the podocyst and cuticle of Aurelia aurita

At the junction between the scyphistoma's scyphopharynx-filament complex and its endoderm is a narrow zone of simple columnar cells. These cells can transform into amoebocytes which collect together and work their way to the aboral end of the polyp, where they secrete a cuticular encasement, thus forming the podocyst. Light and electron microscopy, as well as histochemistry, show the podocyst to be a lamellated tanned chitin-protein complex covering the protoplasmic contents, which have a nucleated outer clear cytoplasmic layer and a yolky central zone which may be liquefied in places.
Caspase Activation Involves the Formation of the Aposome, a Large (∼700 kDa) Caspase-activating Complex*

In mammals, apoptotic protease-activating factor 1 (Apaf-1), cytochrome c, and dATP activate caspase-9, which initiates the postmitochondrial-mediated caspase cascade by proteolytic cleavage/activation of effector caspases to form active ∼60-kDa heterotetramers. We now demonstrate that activation of caspases either in apoptotic cells or following dATP activation of cell lysates results in the formation of two large but different sized protein complexes, the “aposome” and the “microaposome”. Surprisingly, most of the DEVDase activity in the lysate was present in the aposome and microaposome complexes with only small amounts of active caspase-3 present as its free ∼60-kDa heterotetramer. The larger aposome complex (M r = ∼ 700,000) contained Apaf-1 and processed caspase-9, -3, and -7. The smaller microaposome complex (M r = ∼ 200,000–300,000) contained active caspase-3 and -7 but little if any Apaf-1 or active caspase-9. Lysates isolated from control THP.1 cells, prior to caspase activation, showed striking differences in the distribution of key apoptotic proteins. Apaf-1 and procaspase-7 may be functionally complexed as they eluted as an ∼200–300-kDa complex, which did not have caspase cleavage (DEVDase) activity. Procaspase-3 and -9 were present as separate and smaller 60–90-kDa (dimer) complexes. During caspase activation, Apaf-1, caspase-9, and the effector caspases redistributed and formed the aposome. This resulted in the processing of the effector caspases, which were then released, possibly bound to other proteins, to form the microaposome complex.
Cytotypes and Meiotic Behavior in Mexican Populations of Three Species of Echeandia(Liliaceae).

(Cruden pers. comm.). They are commonly found in pine and pine/oak forest, grasslands; xerophyte shrublands, and disturbed areas, (Cruden 1981, 1986, 1987, Cruden and McVaugh 1989). Except polyploidy species based on n=8, as E. longipedicellata n=40, (Cruden 1981); E. altipratensis n=24, and 48; E. luteola n=32, and 64; E. venusta n=84, (Cruden 1986, 1994), chromosomes those exist, little information on interspecific or intraspecific variation in karyo types in Echeandia. In 1988, Palomino and Romo described the karyotypes of E. flavescens
Nordhaus-Gaddum inequalities for the number of connected induced subgraphs in graphs

Abstract Let η(G) be the number of connected induced subgraphs in a graph G, and Ḡ the complement of G. We prove that η(G) + η(Ḡ) is minimum, among all n-vertex graphs, if and only if G has no induced path on four vertices. Since the n-vertex star Sn with maximum degree n − 1 is the unique tree of diameter 2, is minimum among all n-vertex trees, while the maximum is shown to be achieved only by the tree whose degree sequence is (⌈n/2⌉, ⌊n/2⌋, 1, . . . , 1). Furthermore, we prove that every graph G of order n ≥ 5 and with maximum η(G) + η(Ḡ) must have diameter at most 3, no cut vertex and the property that G is also connected. In both cases of trees and graphs that have the same order, we find that if η(G) is maximum then η(G) + η(Ḡ) is minimum. As corollaries to our results, we characterise the unique connected graph G of given order and number of vertices of degree 1, and the unique connected graph G of a given order satisfying |V (G)| = |E(G)| that minimises η(G) + η(Ḡ).
The weak imprint of environment on the stellar populations of galaxies

We investigate the environmental dependence of the stellar populations of galaxies in SDSS DR7. Echoing earlier works, we find that satellites are both more metal-rich (<0.1 dex) and older (<2 Gyr) than centrals of the same stellar mass. However, after separating star-forming, green valley and passive galaxies, we find that the true environmental dependence of both stellar metallicity (<0.03 dex) and age (<0.5 Gyr) is in fact much weaker. We show that the strong environmental effects found when galaxies are not differentiated result from a combination of selection effects brought about by the environmental dependence of the quenched fraction of galaxies, and thus we strongly advocate for the separation of star-forming, green valley and passive galaxies when the environmental dependence of galaxy properties are investigated. We also study further environmental trends separately for both central and satellite galaxies. We find that star-forming galaxies show no environmental effects, neither for centrals nor for satellites. In contrast, the stellar metallicities of passive and green valley satellites increase weakly with increasing halo mass, increasing local overdensity and decreasing projected distance from their central; this effect is interpreted in terms of moderate environmental starvation (`strangulation') contributing to the quenching of satellite galaxies. Finally, we find a unique feature in the stellar mass--stellar metallicity relation for passive centrals, where galaxies in more massive haloes have larger stellar mass at constant stellar metallicity; this effect is interpreted in terms of dry merging of passive central galaxies and/or progenitor bias.
Simulating the inflation of bubbles by late jets in core collapse supernova ejecta

We conducted 3D hydrodynamical simulations to study the interaction of two late opposite jets with the ejecta of a core collapse supernova (CCSN), and study the bipolar structure that results from this interaction as the jets inflate hot-low-density bubbles. The newly born central object, a neutron star (NS; or a black hole), launches these jets at about 50 to 100 d after explosion. The bubbles cross the photosphere in the polar directions at much earlier times than the regions at the same radii near the equatorial plane. The hot bubbles releases more radiation and the photosphere recedes more rapidly in the tenuous bubble. Our results strengthen earlier claims that were based on toy models that such an interaction might lead to a late peak in the light curve, and that an equatorial observer might see a rapid drop in the light curve. Our results might have implications to much earlier jets that explode the star, either jets that the newly born NS launches in a CCSN, or jets that an NS companion that merges with the core of a massive star launches in a common envelope jets supernova (CEJSN) event. Our results add indirect support to the CEJSN scenario for fast blue optical transients, e.g. AT2018cow, ZTF18abvkwla, and CSS161010.
Channel Distillation: Channel-Wise Attention for Knowledge Distillation

Knowledge distillation is to transfer the knowledge from the data learned by the teacher network to the student network, so that the student has the advantage of less parameters and less calculations, and the accuracy is close to the teacher. In this paper, we propose a new distillation method, which contains two transfer distillation strategies and a loss decay strategy. The first transfer strategy is based on channel-wise attention, called Channel Distillation (CD). CD transfers the channel information from the teacher to the student. The second is Guided Knowledge Distillation (GKD). Unlike Knowledge Distillation (KD), which allows the student to mimic each sample's prediction distribution of the teacher, GKD only enables the student to mimic the correct output of the teacher. The last part is Early Decay Teacher (EDT). During the training process, we gradually decay the weight of the distillation loss. The purpose is to enable the student to gradually control the optimization rather than the teacher. Our proposed method is evaluated on ImageNet and CIFAR100. On ImageNet, we achieve 27.68% of top-1 error with ResNet18, which outperforms state-of-the-art methods. On CIFAR100, we achieve surprising result that the student outperforms the teacher. Code is available at this https URL.
Analysis of Genotype × Environment Interactions for Grain Yield in Durum Wheat

Genotype x environment (GE) interactions are important sources of variation in crop breeding programs. The objectives of this study were (i) to analyze GE interactions on grain yield of one bread wheat (Triticum aestivum L.) and 19 durum wheat [T. turgidum L. subsp. durum (Desf.) Husn. ] genotypes by the additive main effects and multiplicative interaction (AMMI) model, and (ii) to evaluate genotype (G), environment (E), and GE interactions with the statistical parameters of AMMI statistic coefficient (D), AMMI stability value (ASV), regression coefficient (b) and ecovalence (W 2 ). Main effects due to E, G, and GE interaction as well as first three interaction principal component axes (IPCA 1 to 3) were found to be significant (P < 0.01). The AMMI biplots distinguished genotypes with wide and specific adaptation and environments with high and low genotype discrimination ability. Ranking the statistical parameters indicated that they are similar in general, especially for the genotypes with similar responses and also for the environments with similar discrimination ability. The results show that the statistical parameters as well as the AMMI model are useful for analyzing GE interaction. Based on the AMMI model, the recommended genotypes had more grain yield improvement in favorable than unfavorable environments. The genotypes selected based on stability of grain yield combined high stability and high-yielding performance.