Miracle-dz/UniFAR-FTUs huggingface dataset:
Evidence for an Aspherical Population III Supernova Explosion Inferred from the Hyper-metal-poor Star HE 1327–2326

We present observational evidence that an aspherical supernova explosion could have occurred in the first stars in the early universe. Our results are based on the first determination of a Zn abundance in a Hubble Space Telescope/Cosmic Origins Spectrograph high-resolution UV spectrum of a hyper-metal-poor (HMP) star, HE 1327−2326, with . We determine [Zn/Fe] = 0.80 ± 0.25 from a UV Zn i line at 2138 Å, detected at 3. 4σ. Yields of a 25 M⊙ aspherical supernova model with artificially modified densities exploding with E = 5 × 1051 erg best match the entire abundance pattern of HE 1327−2326. Such high-entropy hypernova explosions are expected to produce bipolar outflows, which could facilitate the external enrichment of small neighboring galaxies. This has already been predicted by theoretical studies of the earliest star-forming minihalos. Such a scenario would have significant implications for the chemical enrichment across the early universe, as HMP carbon-enhanced metal-poor (CEMP) stars such as HE 1327−2326 might have formed in such externally enriched environments.
Frontline information disclosure and street-level bureaucrats’ willingness to follow the rules: evidence from local regulatory agencies in China

Abstract Frontline information disclosure refers to the endeavor to make the information of interactions between frontline officials and their clients available to policy stakeholders. This study proposes two sets of competing hypotheses to examine the relationship between frontline information disclosure and the willingness of street-level bureaucrats to follow the rules: facilitation effect and inhibition effect hypotheses. The facilitation effect assumes that frontline information disclosure may increase street-level bureaucrats’ willingness to follow the rules, while the inhibition effect expects bureaucrats will be reluctant to follow the rules under circumstances where more frontline information can be disclosed. These hypotheses are tested using data from a survey of 133 managers and 1,610 Chengguan officials in local regulatory agencies from 24 cities in China. Our statistical analyses consistently show that street-level bureaucrats who are exposed to a higher level of objective information disclosure or perceive a higher level of subjective information disclosure are more willing to follow the rules during regulatory enforcement. This finding supports facilitation effect hypotheses. The key implications of our research are discussed in the conclusion.
Publicly Available Clinical BERT Embeddings

Contextual word embedding models such as ELMo and BERT have dramatically improved performance for many natural language processing (NLP) tasks in recent months. However, these models have been minimally explored on specialty corpora, such as clinical text; moreover, in the clinical domain, no publicly-available pre-trained BERT models yet exist. In this work, we address this need by exploring and releasing BERT models for clinical text: one for generic clinical text and another for discharge summaries specifically. We demonstrate that using a domain-specific model yields performance improvements on 3/5 clinical NLP tasks, establishing a new state-of-the-art on the MedNLI dataset. We find that these domain-specific models are not as performant on 2 clinical de-identification tasks, and argue that this is a natural consequence of the differences between de-identified source text and synthetically non de-identified task text.
Le groupe de Selmer des isog\'enies de hauteur un

Soit V un schéma intègre, qui est quasi-projectif et lisse sur un corps parfait k0 de caractéristique p > 0. Soit K le corps de fonctions de V . Soit π : A → V (resp. ρ : B → V ) un schéma semiabélien sur V . On notera A (resp. B) sa fibre au-dessus du point générique de V . Soit Vab ⊆ V l’ensemble des points v ∈ V tels que Av est une variété abélienne sur κ(v). On suppose que Vab est un ouvert non vide et que son complément E := V \Vab (muni de sa structure de sous-schéma fermé réduit) est un diviseur à croisements normaux au-dessus de k0, au sens de [7].
A Flexible, Parallel, Adaptive Geometric Multigrid Method for FEM

We present the design and implementation details of a geometric multigrid method on adaptively refined meshes for massively parallel computations. The method uses local smoothing on the refined part of the mesh. Partitioning is achieved by using a space filling curve for the leaf mesh and distributing ancestors in the hierarchy based on the leaves. We present a model of the efficiency of mesh hierarchy distribution and compare its predictions to runtime measurements. The algorithm is implemented as part of the deal.II finite-element library and as such available to the public.
The Effects of Social and Spatial Presence on Engagement in a 3D Virtual Reality Environment: An Empirical Investigation

Virtual reality (VR) provides opportunities for businesses to innovatively engage customers. Based on presence theory, a research model was developed to test the influence of two major components of presence, social presence and spatial presence, on users’ perceptions of hedonic value, utilitarian value, and engagement. An experiment was conducted on two conditions of a VR application (low vs. high social presence) to test the hypotheses in the research model. The results reveal that social presence and spatial presence can improve hedonic value of VR. However, inconsistent with previous studies, our findings reveal a negative relationship between spatial presence and engagement. Theoretical and practical implications, as well as future research directions are subsequently discussed.
‘Human rights and democracy are not self-evident’ : Finnish student teachers’ perceptions on democracy and human rights education

This article discusses democracy and human rights education (DHRE) in Finnish teacher education, drawing on existing literature, curricula and a survey of student teachers’ perceptions. Earlier studies suggested that DHRE in Finnish teacher education is unsystematic, implicit, and dependent on the teacher’s individual interests. These studies highlight a sense of national exceptionalism, where DHRE is assumed to be self-evident. In 2019, we conducted a survey of student teachers (n=300) in one university. Data content analysis reveals that student teachers now see DHRE as relevant and timely, and by no means self-evident. Student teachers believe that DHRE needs to be explicit and part of their professional education. Although the Finnish national curriculum addresses DHRE explicitly, there is a lack of implementation and explicit DHRE teacher education. We contend that the data reflects societal change, and that the notion that democracy and human rights are self-evident needs to be challenged in Finland.
Semi-Supervised Few-Shot Learning for Dual Question-Answer Extraction

This paper addresses the problem of key phrase extraction from sentences. Existing state-of-the-art supervised methods require large amounts of annotated data to achieve good performance and generalization. Collecting labeled data is, however, often expensive. In this paper, we redefine the problem as question-answer extraction, and present SAMIE: Self-Asking Model for Information Ixtraction, a semi-supervised model which dually learns to ask and to answer questions by itself. Briefly, given a sentence $s$ and an answer $a$, the model needs to choose the most appropriate question $\hat q$; meanwhile, for the given sentence $s$ and same question $\hat q$ selected in the previous step, the model will predict an answer $\hat a$. The model can support few-shot learning with very limited supervision. It can also be used to perform clustering analysis when no supervision is provided. Experimental results show that the proposed method outperforms typical supervised methods especially when given little labeled data.
Online Reviews on Online Travel Agency: Understanding Tourists’ Perceived Attributes of Taipei’s Economy Hotels

ABSTRACT This study assesses the attributes and performance of economy hotels as perceived by tourists and examines the differences in the importance ascribed to the perceived attributes among different tourist types, hotel star ratings, and tourist nationalities. Using the content analysis method, 1,545 online reviews of Taipei’s economy hotels posted on Agoda and Booking.com were collected. Overall accommodation conditions, transportation convenience, breakfast service, room facilities, and staff attitude were the top five hotel attributes that tourists were found to value and mostly rate positively. Overall, bathroom quality, hotel design, noise level, room facilities, decoration, and design were found to be negatively rated, with improvements suggested for economy hotels.
Eye Contact Between Pedestrians and Drivers

When asked, a majority of people believe that, as pedestrians, they make eye contact with the driver of an approaching vehicle when making their crossing decisions. This work presents evidence that this widely held belief is false. We do so by showing that, in majority of cases where conflict is possible, pedestrians begin crossing long before they are able to see the driver through the windshield. In other words, we are able to circumvent the very difficult question of whether pedestrians choose to make eye contact with drivers, by showing that whether they think they do or not, they can't. Specifically, we show that over 90\% of people in representative lighting conditions cannot determine the gaze of the driver at 15m and see the driver at all at 30m. This means that, for example, that given the common city speed limit of 25mph, more than 99% of pedestrians would have begun crossing before being able to see either the driver or the driver's gaze. In other words, from the perspective of the pedestrian, in most situations involving an approaching vehicle, the crossing decision is made by the pedestrian solely based on the kinematics of the vehicle without needing to determine that eye contact was made by explicitly detecting the eyes of the driver.
A monodromy criterion for existence of Neron models of abelian schemes in characteristic zero

We consider the problem of existence of Neron models for a family of abelian varieties over a base of dimension greater than 1. We show that when S is of equicharacteristic zero, the condition of toric additivity introduced in [Ore18] is sufficient for the existence of a Neron model, and also necessary under some extra assumptions. Furthermore, we give an equivalent formulation of toric additivity in terms of monodromy action on the l-adic Tate module.
GELP: GAN-Excited Liner Prediction for Speech Synthesis from Mel-spectrogram

Recent advances in neural network -based text-to-speech have reached human level naturalness in synthetic speech. The present sequence-to-sequence models can directly map text to mel-spectrogram acoustic features, which are convenient for modeling, but present additional challenges for vocoding (i.e., waveform generation from the acoustic features). High-quality synthesis can be achieved with neural vocoders, such as WaveNet, but such autoregressive models suffer from slow sequential inference. Meanwhile, their existing parallel inference counterparts are difficult to train and require increasingly large model sizes. In this paper, we propose an alternative training strategy for a parallel neural vocoder utilizing generative adversarial networks, and integrate a linear predictive synthesis filter into the model. Results show that the proposed model achieves significant improvement in inference speed, while outperforming a WaveNet in copy-synthesis quality.
Jointly Extracting and Compressing Documents with Summary State Representations

We present a new neural model for text summarization that first extracts sentences from a document and then compresses them. The pro-posed model offers a balance that sidesteps thedifficulties in abstractive methods while gener-ating more concise summaries than extractivemethods. In addition, our model dynamically determines the length of the output summary based on the gold summaries it observes during training and does not require length constraints typical to extractive summarization. The model achieves state-of-the-art results on the CNN/DailyMail and Newsroom datasets, improving over current extractive and abstractive methods. Human evaluations demonstratethat our model generates concise and informa-tive summaries. We also make available a new dataset of oracle compressive summaries derived automatically from the CNN/DailyMailreference summaries.
Multi-Label Image Recognition With Graph Convolutional Networks

The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model the label dependencies to improve the recognition performance. To capture and explore such important dependencies, we propose a multi-label classification model based on Graph Convolutional Network (GCN). The model builds a directed graph over the object labels, where each node (label) is represented by word embeddings of a label, and GCN is learned to map this label graph into a set of inter-dependent object classifiers. These classifiers are applied to the image descriptors extracted by another sub-net, enabling the whole network to be end-to-end trainable. Furthermore, we propose a novel re-weighted scheme to create an effective label correlation matrix to guide information propagation among the nodes in GCN. Experiments on two multi-label image recognition datasets show that our approach obviously outperforms other existing state-of-the-art methods. In addition, visualization analyses reveal that the classifiers learned by our model maintain meaningful semantic topology.
AGN Disks Harden the Mass Distribution of Stellar-mass Binary Black Hole Mergers

The growing number of stellar-mass binary black hole mergers discovered by Advanced LIGO and Advanced Virgo are starting to constrain the binaries’ origin and environment. However, we still lack sufficiently accurate modeling of binary formation channels to obtain strong constraints, or to identify subpopulations. One promising formation mechanism that could result in different black hole properties is binaries merging within the accretion disks of active galactic nuclei (AGNs). Here we show that the black holes’ orbital alignment with the AGN disks preferentially selects heavier black holes. We carry out Monte Carlo simulations of orbital alignment with AGN disks, and find that AGNs harden the initial black hole mass function. Assuming an initial power-law mass distribution , we find that the power-law index changes by Δβ ∼ 1.3, resulting in a more top-heavy population of merging black holes. This change is independent of the mass of, and accretion rate onto, the supermassive black hole in the center of the AGN. Our simulations predict an AGN-assisted merger rate of ∼4 Gpc−3 yr−1. With its hardened mass spectra, the AGN channel could be responsible for 10%–50% of gravitational-wave detections.
Comparison of the Performance of K-Nearest Neighbours and Generalized Neural Network in Construction Crew Productivity Prediction

Insaat islerinde ekip verimliliginin tahmini, insaat suresi ve maliyetinin gercekci tahminini etkileyen en onemli faktorlerden biridir. Ekip verimliligi tahmini icin Ileri Besleme Sinir Agi, Karinca Kolonisi, Yapay Ari Kolonisi, Parcacik Suru Optimizasyonu, Radyal Tabanli Sinir Aglari ve Kendi Kendini Duzenleyen Haritalar gibi farkli arama algoritmalarinin kullanimi onceki calismalarda tartisilmistir. Ancak, bu calismalarda tahmin performansinda kullanilan yontemin ozellikleri ile verinin niteligi arasindaki tutarliligin etkisi genellikle ihmal edilmistir. Dolayisiyla mevcut arastirmanin amaci, iki cagdas ogrenme algoritmasi olan K- En Yakin Komsu (K-NN) ve Genellestirilmis Sinir Agi (GRNN) kullanilarak farkli dagilim ozelliklerine sahip uc farkli ekibe (kalip, doseme ve duvar) ait verimlilikle ilgili veri seti icin tahmin performansini analiz etmektir. Her iki yontemin performansi da, degerlerin degisen katsayilari icin farklilik gostermistir. K-NN, tum veri setleri icin GRNN'den daha iyi performans gostermis olup, her iki yontem de en yuksek varyansa sahip veri kumesinde en kotu performansa sahiptir.
Modeling Word Emotion in Historical Language: Quantity Beats Supposed Stability in Seed Word Selection

To understand historical texts, we must be aware that language—including the emotional connotation attached to words—changes over time. In this paper, we aim at estimating the emotion which is associated with a given word in former language stages of English and German. Emotion is represented following the popular Valence-Arousal-Dominance (VAD) annotation scheme. While being more expressive than polarity alone, existing word emotion induction methods are typically not suited for addressing it. To overcome this limitation, we present adaptations of two popular algorithms to VAD. To measure their effectiveness in diachronic settings, we present the first gold standard for historical word emotions, which was created by scholars with proficiency in the respective language stages and covers both English and German. In contrast to claims in previous work, our findings indicate that hand-selecting small sets of seed words with supposedly stable emotional meaning is actually harm- rather than helpful.
Cluster Developing 1-Bit Matrix Completion

1-bit matrix completion plays a significant role in recommender systems. However, conventional 1-bit methods do not consider the interactive clustering nature of customers and products. To tackle the gap, we introduce Group-Specific 1-bit Matrix Completion (GS1MC) by first-time consolidating cluster effects in 1-bit matrix completion tasks. To empower GS1MC even when grouping information is unobtainable, GS1MC is extended as Cluster Developing Matrix Completion (CDMC) by integrating subspace clustering techniques. CDMC can group entities and utilize their cluster effects simultaneously. Experiments show that GS1MC outperforms current 1-bit matrix completion methods and CDMC successfully captures items’ genre features only with sparse binary rating data. Notably, GS1MC provides a new insight to incorporate and evaluate the efficacy of different clustering methods while CDMC can be served as a new tool to explore unrevealed social behavior or market phenomena.
On Topological and Metrical Properties of Stabilizing Feedback Gains: the MIMO Case

In this paper, we discuss various topological and metrical aspects of the set of stabilizing static feedback gains for multiple-input-multiple-output (MIMO) linear-time-invariant (LTI) systems, in both continuous and discrete-time. Recently, connectivity properties of this set (for continuous time) have been reported in the literature, along with a discussion on how this connectivity is affected by restricting the feedback gain to linear subspaces. We show that analogous to the continuous-time case, one can construct instances where the set of stabilizing feedback gains for discrete time LTI systems has exponentially many connected components.
Cross-task learning for audio tagging, sound event detection and spatial localization: DCASE 2019 baseline systems

The Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 challenge focuses on audio tagging, sound event detection and spatial localisation. DCASE 2019 consists of five tasks: 1) acoustic scene classification, 2) audio tagging with noisy labels and minimal supervision, 3) sound event localisation and detection, 4) sound event detection in domestic environments, and 5) urban sound tagging. In this paper, we propose generic cross-task baseline systems based on convolutional neural networks (CNNs). The motivation is to investigate the performance of a variety of models across several tasks without exploiting the specific characteristics of the tasks. We looked at CNNs with 5, 9, and 13 layers, and found that the optimal architecture is task-dependent. For the systems we considered, we found that the 9-layer CNN with average pooling is a good model for a majority of the DCASE 2019 tasks.
Melting curve analysis for detection and identification of ghost parasitoids in host carcasses a month after host death

Incidence of parasitism is often underestimated because ‘ghost’ parasitoids (dead unemerged parasitoids or those that have emerged leaving the host carcasses) are difficult to detect and identify. This study demonstrates that the use of melting curve analysis (MCA) of host carcasses can detect and identify DNA of ghost parasitoids even a month after host death. The coccinellid hosts Cycloneda sanguinea, Eriopis connexa, Harmonia axyridis and Hippodamia convergens were sampled from cole crops in 2017 and 2018 in the Midwest of Brazil, and reared and observed daily for parasitoid emergence. Dead coccinellids were held for 30 days after death before storage and only host carcasses with parasitoid emergence observed were analysed. Species‐specific primers were designed for the identification of the parasitoid species that emerged during host rearing: Dinocampus coccinellae, Homalotylus mirabilis, Ho. terminalis, Strongygaster triangulifera and Phalacrotophora sp. The melting temperatures (Tm) of their amplicons were used as positive controls in MCA post‐amplification in qPCR. Detection of parasitoid DNA in host carcasses was possible for D. coccinellae, Ho. mirabilis and Phalacrotophora sp. with a limit of detection (LOD) for all the parasitoids <1 pg of DNA, except for Phalacroptopthora sp. (LOD = 1.9 ng). Parasitoids were detected in 31 out of 70 host carcasses (44.3%) with detection ranging from 0% to 71%, depending on the parasitoid species. Synthesis and applications. This work demonstrates that MCA could be used to detect and rapidly identify the DNA of some parasitoid species (e.g. Ho. mirabilis, D. coccinellae and Phalacrotophora sp.) in host carcasses up to a month after parasitoid emergence with high sensitivity and specificity. MCA can be used on any field‐collected host and, for some parasitoid species, it should lead to more accurate estimates of the incidence of parasitism. Our approach can be easily adapted and applied to study other parasitoid–host interactions.
Determining the recurrence time-scale of long-lasting YSO outbursts

We have determined the rate of large accretion events in class I and II young stellar objects (YSOs) by comparing the all-sky digitised photographic plate surveys provided by SuperCOSMOS with the latest data release from Gaia (DR2). The long mean baseline of 55 years along with a large sample of class II YSOs ($\simeq$15,000) allows us to study approximately 1 million YSO-years. We find 139 objects with $\Delta R\geq1$~mag, most of which are found at amplitudes between 1 and 3 mag. The majority of YSOs in this group show irregular variability or long-lasting fading events, which is best explained as hot spots due to accretion or by variable extinction. There is a tail of YSOs at $\Delta R\geq3$~mag and they seem to represent a different population. Surprisingly many objects in this group show high-amplitude irregular variability over timescales shorter than 10 years, in contrast with the view that high-amplitude objects always have long outbursts. However, we find 6 objects that are consistent with undergoing large, long lasting accretion events, 3 of them previously unknown. This yields an outburst recurrence timescale of 112 kyr, with a 68\% confidence interval [74 to 180] kyr. This represents the first robust determination of the outburst rate in class II YSOs and shows that YSOs in their planet-forming stage do in fact undergo large accretion events, and with timescales of $\simeq$100,000 years. In addition, we find that outbursts in the class II stage are $\simeq$10 times less frequent than during the class I stage.