Miracle-dz/UniFAR-FTUs huggingface dataset:
Massive donors in interacting binaries: effect of metallicity

Metallicity is known to significantly affect the radial expansion of a massive star: the lower the metallicity, the more compact the star, especially during its post-main sequence evolution. Our goal is to study this effect in the context of binary evolution. Using the stellar-evolution code MESA, we computed evolutionary tracks of massive stars at six different metallicities between 1.0 Z⊙ and 0.01 Z⊙. We explored variations of factors known to affect the radial expansion of massive stars (e.g., semiconvection, overshooting, or rotation). Using observational constraints, we find support for an evolution in which already at a metallicity Z ≈ 0.2 Z⊙ massive stars remain relatively compact (∼100 R⊙) during the Hertzprung-gap (HG) phase and most of their expansion occurs during core-helium burning (CHeB). Consequently, we show that metallicity has a strong influence on the type of mass transfer evolution in binary systems. At solar metallicity, a case-B mass transfer is initiated shortly after the end of the main sequence, and a giant donor is almost always a rapidly expanding HG star. However, at lower metallicity, the parameter space for mass transfer from a more evolved, slowly expanding CHeB star increases dramatically. This means that envelope stripping and formation of helium stars in low-metallicity environments occurs later in the evolution of the donor, implying a shorter duration of the Wolf-Rayet phase (even by an order of magnitude) and higher final core masses. This metallicity effect is independent of the effect of metallicity-dependent stellar winds. At metallicities Z ≤ 0.04 Z⊙, a significant fraction of massive stars in binaries with periods longer than 100 days engages in the first episode of mass transfer very late into their evolution, when they already have a well-developed CO core. The remaining lifetime (≲104 yr) is unlikely to be long enough to strip the entire H-rich envelope. Cases of unstable mass transfer leading to a merger would produce CO cores that spin fast at the moment of collapse. We find that the parameter space for mass transfer from massive donors (> 40 M⊙) with outer convective envelopes is extremely small or even nonexistent. We briefly discuss this finding in the context of the formation of binary black hole mergers.
NUBes: A Corpus of Negation and Uncertainty in Spanish Clinical Texts

This paper introduces the first version of the NUBes corpus (Negation and Uncertainty annotations in Biomedical texts in Spanish). The corpus is part of an on-going research and currently consists of 29,682 sentences obtained from anonymised health records annotated with negation and uncertainty. The article includes an exhaustive comparison with similar corpora in Spanish, and presents the main annotation and design decisions. Additionally, we perform preliminary experiments using deep learning algorithms to validate the annotated dataset. As far as we know, NUBes is the largest available corpora for negation in Spanish and the first that also incorporates the annotation of speculation cues, scopes, and events.
Phase coherent transport and spin-orbit interaction in GaAs/InSb core/shell nanowires

Low-temperature magnetotransport measurements are performed on GaAs/InSb core–shell nanowires. The nanowires were self-catalyzed grown by molecular beam epitaxy. The conductance measurements as a function of back-gate voltage show an ambipolar behavior comprising an insulating range in between the transition from the p-type to the n-type region. Simulations based on a self-consistent Schrödinger–Poisson solver revealed that the ambipolar characteristics originate from a Fermi level dependent occupation of hole and electron states within the approximately circular quantum well formed in the InSb shell. By applying a perpendicular magnetic field with respect to the nanowire axis, conductance fluctuations were observed, which are used to extract the phase-coherence length. By averaging the magneto-conductance traces at different back-gate voltages, weak antilocalization features are resolved. Regular flux-periodic conductance oscillations are measured when an axial magnetic field is applied. These oscillations are attributed to closed-loop quantized states located in the InSb shell which shift their energetic position periodically with the magnetic flux. Possible reasons for experimentally observed variations in the oscillation patterns are discussed using simulation results.
Status hierarchy and group cooperation: A generalized model of Mark (2018)

Can the status hierarchy facilitate the emergence of group cooperation? In an evolutionary model, Mark (2018) provided a positive answer to the theoretical inquiry. Despite the contribution, we critiqued that there are not only mathematical errors in Mark's model but also limitations in applying it to other hierarchical structures. We present a more generalized model by introducing a novel hierarchy measure to interpolate the cooperativeness of group members in any hierarchy structure of interest. We derive the conditions under which cooperation can emerge and verify our analytical predictions by agent-based computer simulation. In general, our evolutionary model provides stronger evidence than Mark's original model with respect to how status behavior can facilitate the emergence of social cooperation.
Gradient flow step-scaling function for SU(3) with ten fundamental flavors

We calculate the step scaling function, the lattice analog of the renormalization group $\beta$-function, for an SU(3) gauge theory with ten fundamental flavors. We present a detailed analysis including the study of systematic effects of our extensive data set generated with ten dynamical flavors using the Symanzik gauge action and three times stout smeared Mobius domain wall fermions. Using up to $32^4$ volumes, we calculate renormalized couplings for different gradient flow schemes and determine the step-scaling $\beta$ function for a scale change $s=2$ on up to five different lattice volume pairs. In an accompanying paper we discuss that gradient flow can promote lattice dislocations to instanton-like objects, introducing nonperturbative lattice artifacts to the step scaling function. Motivated by the observation that Wilson flow sufficiently suppresses these artifacts, we choose Wilson flow with the Symanzik operator as our preferred analysis. We study systematic effects by calculating the step-scaling function based on alternative flows (Zeuthen or Symanzik), alternative operators (Wilson plaquette, clover), and also explore the effects of the perturbative tree-level improvement. Further we investigate the effects due to the finite value of $L_s$.
Near Optimality and Tractability in Stochastic Nonlinear Control

We consider the problem of nonlinear stochastic optimal control. This is fundamentally intractable owing to Bellman's infamous "curse of dimensionality". We present a "decoupling principle" for the tractable feedback design for such problems, wherein, first, a nominal open-loop problem is solved, followed by a suitable linear feedback design around the open-loop. The performance of the resulting feedback law is shown to be asymptotically close to the true stochastic feedback law to fourth order in a small noise parameter $\epsilon$. The decoupling theory is empirically tested on robotic planning problems under uncertainty.
Ethnobotanical Survey of Medicinal Plants Used Against Fungal Infections in Prefecture of Sotouboua Central Region, Togo

Plants are a major source of active ingredients and are for that fact used to treat many diseases such as fungal infections. The objective of this study was to identify the plants used in traditional medicine to treat fungal diseases in the prefecture of Sotouboua. An ethnobotanical survey was conducted among healers, elderly and knowledgeable villagers. A semi structured questionnaire and pictures showing common and easily recognizable fungal infection symptoms were used to support a live interview. During this study, 37 plant species belonging to 20 families have been identified. Fabaceae were the most represented family (7 species). The most used parts were leaves (43, 24%) followed by the roots (18, 91%). The decoction is the preferred method of preparation while the oral route is the main route of administration. Sotouboua prefecture in Togo has significant plant biodiversity that is used by dwellers in the management of fungal diseases.
Evaluation of Self-Medication Practice among Pharmacy Students in Jordan

Problem: To assess the practice of self-medication, among pharmacy students. Experimental approach: A cross-sectional study was conducted for a period of three months. A pre-validated questionnaire was distributed to pharmacy students at the University of Jordan. A total of 256 students participated in the study. Major Findings: The prevalence of self-medication among pharmacy students was 86.7%. The prevalence of students who obtained their medication from pharmacies was 56.6%, and about 30.5% reported using drugs without a medical referral 5 to 6 times last year. The majority of students (83.2%) read the leaflet about the drug before using it. The two major reasons for self-medication were to save time and perceived non-seriousness of illness. The presence of severe pain and worsening symptoms were the main reasons to seek medical help. Cough and cold preparations, antibiotics and vitamins were the drugs most frequently used in self-medication. Conclusion: Self-medication is highly prevalent among pharmacy students at the University of Jordan. Students were aware of some facts about self-medication, however more effort to educate pharmacy students to promote responsible self-medication use is needed. Potential interventions, including strict policies from health authorities, should be applied to prohibit the provision of medicines without a prescription.
Optimized statistical approach for combining multi-messenger data for neutron star equation of state inference

The neutron star equation of state (EOS) is now being constrained from a diverse set of multi-messenger data, including gravitational waves from binary neutron star mergers, X-ray observations of the neutron star radius, and many types of laboratory nuclear experiments. These measurements are typically mapped to a common domain -- either to a corresponding radius or to a parametrized EOS using a Bayesian inference scheme -- for comparison with one another. We explore here the statistical biases that can arise when such multi-messenger data are mapped to a common domain for comparison. We find that placing Bayesian priors individually in each domain of measurement can transform to biased constraints in the domain of comparison. Using the first two binary neutron star mergers as an example, we show that a uniform prior in the tidal deformability can produce artificial evidence for large radii, which the data do not support. We present a new prescription for defining Bayesian priors in any domain of measurement, that will allow for minimally-biased constraints in the domain of comparison. Finally, using this new prescription, we provide a status update on multi-messenger EOS constraints on the neutron star radius.
Duality between Approximate Bayesian Methods and Prior Robustness

In this paper we show that there is a link between approximate Bayesian methods and prior robustness. We show that what is typically recognized as an approximation to the likelihood, either due to the simulated data as in the Approximate Bayesian Computation (ABC) methods or due to the functional approximation to the likelihood, can instead also be viewed upon as an implicit exercise in prior robustness. We first define two new classes of priors for the cases where the sufficient statistics is available, establish their mathematical properties and show, for a simple illustrative example, that these classes of priors can also be used to obtain the posterior distribution that would be obtained by implementing ABC. We then generalize and define two further classes of priors that are applicable in very general scenarios; one where the sufficient statistics is not available and another where the likelihood is approximated using a functional approximation. We then discuss the interpretation and elicitation aspects of the classes proposed here as well as their potential applications and possible computational benefits. These classes establish the duality between approximate Bayesian inference and prior robustness for a wide category of Bayesian inference methods.
Effect of Annotation Errors on Drone Detection with YOLOv3

Following the recent advances in deep networks, object detection and tracking algorithms with deep learning backbones have been improved significantly; however, this rapid development resulted in the necessity of large amounts of annotated labels. Even if the details of such semi-automatic annotation processes for most of these datasets are not known precisely, especially for the video annotations, some automated labeling processes are usually employed. Unfortunately, such approaches might result with erroneous annotations. In this work, different types of annotation errors for object detection problem are simulated and the performance of a popular state-of-the-art object detector, YOLOv3, with erroneous annotations during training and testing stages is examined. Moreover, some inevitable annotation errors in Anti-UAV Challenge dataset is also examined in this manner, while proposing a solution to correct such annotation errors of this valuable data set.
Model-based disentanglement of lens occlusions

With lens occlusions, naive image-to-image networks fail to learn an accurate source to target mapping, due to the partial entanglement of the scene and occlusion domains. We propose an unsupervised model-based disentanglement training, which learns to disentangle scene from lens occlusion and can regress the occlusion model parameters from target database. The experiments demonstrate our method is able to handle varying types of occlusions (raindrops, dirt, watermarks, etc.) and generate highly realistic translations, qualitatively and quantitatively outperforming the state-of-the-art on multiple datasets.
UCRDNet: Unsupervised Collaborative Representation Deep Network for Clustering

In this paper, we propose an unsupervised collaborative representation deep network (UCRDNet) which consists of novel collaborative representation RBM (crRBM) and collaborative representation GRBM (crGRBM). The UCRDNet is a novel deep collaborative feature extractor for exploring more sophisticated probabilistic models of real-valued and binary data. Unlike traditional representation methods, one similarity relation between the input instances and another similarity relation between the features of the input instances are collaboratively fused together in the representation process of the crGRBM and crRBM models. Here, we use the Locality Sensitive Hashing (LSH) method to divide the input instance matrix into many mini blocks which contain similar instance and local features. Then, we expect the hidden layer feature units of each block gather to block center as much as possible in the training processes of the crRBM and crGRBM. Hence, the correlations between the instances and features as collaborative relations are fused in the hidden layer features. In the experiments, we use K-means and Spectral Clustering (SC) algorithms based on four contrast deep networks to verify the deep collaborative representation capability of the UCRDNet architecture. One architecture of the UCRDNet is composed with a crGRBM and two crRBMs for modeling real-valued data and another architecture of it is composed with three crRBMs for modeling binary data. The experimental results show that the proposed UCRDNet has more outstanding performance than the Autoencoder and DeepFS deep networks (without collaborative representation strategy) for unsupervised clustering on the MSRA-MM2.0 and UCI datasets. Furthermore, the proposed UCRDNet shows more excellent collaborative representation capabilities than the CDL deep collaborative networks for unsupervised clustering.
PaStaNet: Toward Human Activity Knowledge Engine

Existing image-based activity understanding methods mainly adopt direct mapping, i.e. from image to activity concepts, which may encounter performance bottleneck since the huge gap. In light of this, we propose a new path: infer human part states first and then reason out the activities based on part-level semantics. Human Body Part States (PaSta) are fine-grained action semantic tokens, e.g. , which can compose the activities and help us step toward human activity knowledge engine. To fully utilize the power of PaSta, we build a large-scale knowledge base PaStaNet, which contains 7M+ PaSta annotations. And two corresponding models are proposed: first, we design a model named Activity2Vec to extract PaSta features, which aim to be general representations for various activities. Second, we use a PaSta-based Reasoning method to infer activities. Promoted by PaStaNet, our method achieves significant improvements, e.g. 6.4 and 13.9 mAP on full and one-shot sets of HICO in supervised learning, and 3.2 and 4.2 mAP on V-COCO and images-based AVA in transfer learning. Code and data are available at http://hake-mvig.cn/.
Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers

We propose Pixel-BERT to align image pixels with text by deep multi-modal transformers that jointly learn visual and language embedding in a unified end-to-end framework. We aim to build a more accurate and thorough connection between image pixels and language semantics directly from image and sentence pairs instead of using region-based image features as the most recent vision and language tasks. Our Pixel-BERT which aligns semantic connection in pixel and text level solves the limitation of task-specific visual representation for vision and language tasks. It also relieves the cost of bounding box annotations and overcomes the unbalance between semantic labels in visual task and language semantic. To provide a better representation for down-stream tasks, we pre-train a universal end-to-end model with image and sentence pairs from Visual Genome dataset and MS-COCO dataset. We propose to use a random pixel sampling mechanism to enhance the robustness of visual representation and to apply the Masked Language Model and Image-Text Matching as pre-training tasks. Extensive experiments on downstream tasks with our pre-trained model show that our approach makes the most state-of-the-arts in downstream tasks, including Visual Question Answering (VQA), image-text retrieval, Natural Language for Visual Reasoning for Real (NLVR). Particularly, we boost the performance of a single model in VQA task by 2.17 points compared with SOTA under fair comparison.
Nontame Morse–Smale flows and odd Chern–Weil theory

Abstract Using a certain well-posed ODE problem introduced by Shilnikov in the sixties, Minervini proved the currential “fundamental Morse equation” of Harvey–Lawson but without the restrictive tameness condition for Morse gradient flows. Here, we construct local resolutions for the flow of a section of a fiber bundle endowed with a vertical vector field which is of Morse gradient type in every fiber in order to remove the tameness hypothesis from the currential homotopy formula proved by the first author. We apply this to produce currential deformations of odd degree closed forms naturally associated to any hermitian vector bundle endowed with a unitary endomorphism and metric compatible connection. A transgression formula involving smooth forms on a classifying space for odd K-theory is also given.
Pembentukan Karakter Siswa dan Profesionalisme Guru Melalui Budaya Literasi Sekolah

This article aims to reveal and reinforce the importance of cultural literacy at school turn. This article uses literature study to assess the literacy issues in depth. The study showed that reviving the school literacy culture can be capital in improving the competence of students and teachers, assist in the formation of student character, and improve the professionalism of teachers and can deliver effective schools into schools.
Projection pursuit Gaussian process regression

Abstract A primary goal of computer experiments is to reconstruct the function given by the computer code via scattered evaluations. Traditional isotropic Gaussian process models suffer from the curse of dimensionality, when the input dimension is relatively high given limited data points. Gaussian process models with additive correlation functions are scalable to dimensionality, but they are more restrictive as they only work for additive functions. In this work, we consider a projection pursuit model, in which the nonparametric part is driven by an additive Gaussian process regression. We choose the dimension of the additive function higher than the original input dimension, and call this strategy “dimension expansion”. We show that dimension expansion can help approximate more complex functions. A gradient descent algorithm is proposed for model training based on the maximum likelihood estimation. Simulation studies show that the proposed method outperforms the traditional Gaussian process models.
Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation

Utilizing synthetic data for semantic segmentation can significantly relieve human efforts in labelling pixel-level masks. A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i.e. reducing domain shift. The common approach to this problem is to minimize the discrepancy between feature distributions from different domains through adversarial training. However, directly aligning the feature distribution globally cannot guarantee consistency from a local view (i.e. semantic-level). To tackle this issue, we propose a semi-supervised approach named Alleviating Semantic-level Shift (ASS), which can promote the distribution consistency from both global and local views. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes. Extensive experiments demonstrate that: (1) ASS can significantly outperform the current unsupervised state-of-the-arts by employing a small number of annotated samples from the target domain; (2) ASS can beat the oracle model trained on the whole target dataset by over 3 points by augmenting the synthetic source data with annotated samples from the target domain without suffering from the prevalent problem of overfitting to the source domain.