Miracle-dz/UniFAR-FTUs huggingface dataset:
Pyrola crypta: A Pacific Northwest species belonging to the Pyrola picta species complex

A new species of Pyrola was discovered from the Pacific Northwest Physiographic Province of western North America. This new species belongs to the Pyrola picta species complex (section Scotophylla), comprising the closely related P. picta, P. dentata, and P. aphylla. The new species shares a similar habitat with other members of the species complex, but differs in that its geographic range is restricted to the Pacific Northwest. The other species have wider ranges throughout montane western North America. Although the new species is phylogenetically sister to P. dentata, it is nearly indistinguishable morphologically from P. picta, which poses a challenge to species diagnosis. Phylogenetic analyses based on genetic markers from the chloroplast and nuclear genomes show no evidence of recent hybridization between the new species and any of its close congeners. Additionally, morphometric analyses show that the new species has larger floral bracts and sepals relative to plants of P. picta, P. dentata, and P. aphylla from the same geographic region. We provide justification for recognizing a new species, P. crypta, and briefly discuss the importance of recognizing cryptic species.
Early Ocean Life History of Harrison River Sockeye Salmon and their Contribution to the Biodiversity of Sockeye Salmon in the Fraser River, British Columbia, Canada

AbstractDuring a study of the early marine survival of Chinook Salmon Oncorhynchus tshawytscha and Coho Salmon O. kisutch in the Strait of Georgia from 1998 to 2010, moderate abundances of juvenile Sockeye Salmon O. nerka were observed to remain in the strait much longer than previously thought. In 2008, DNA stock identification showed that these juveniles were from the Harrison River, a population with a sea-type life history in which juveniles enter the ocean during the year of emergence from the gravel. Using information collected in 1998–2010, we describe the early ocean life and production of Harrison River sea-type Sockeye Salmon. Juveniles entered the Strait of Georgia from the Fraser River over an extended period, with most entering after mid-July—about 8 weeks later than lake-type juveniles, which had virtually all left the strait by that time. The September diets of sea-type juveniles were highly selective for hyperiid amphipods, which were not abundant in the zooplankton. Interaction with juven.
Understanding transmission of traditional knowledge across north‐western South America: a cross‐cultural study in palms (Arecaceae)

The transmission of traditional knowledge (TK) depends largely on the ability of people to preserve and learn new knowledge. Different and opposing evidence about loss, persistence and generation of TK has been reported, but cross-cultural comparisons are notably missing. We interviewed 2050 informants at 25 localities in Colombia, Ecuador, Peru and Bolivia, across three ecoregions (Amazon, Andes, Choco) and three cultural groups (Indigenous, Mestizos, African-Americans). Our main aims were to: (1) explore the transmission of palm use knowledge for 10 use categories across five age cohorts; and (2) identify the use categories in which knowledge is widely shared by all age cohorts or unique to one cohort. TK was heterogeneous between different age cohorts in the Amazon and the Choco and increased with age. TK in the Andes was more evenly distributed between generations, with divergent tendencies in relation to age. TK about the categories Human food and Construction was widely distributed. TK in the categories Medicinal and veterinary, Utensils and tools and Cultural uses were more narrowly distributed. Our cross-cultural and multiple-scale study indirectly shows that the maintenance of TK relies on multiple variables, including ecological, social, cultural and economical factors. Our results provide a strong argument that conservation of TK should be embedded in local strategies that recognize all possible influences on knowledge transmission.
VIBES: Visual Binary Exoplanet survey with SPHERE

Recent surveys indicate that planets in binary systems are more abundant than previously thought, which is in agreement with theoretical work on disc dynamics and planet formation in binaries. In order to measure the abundance and physical characteristics of wide-orbit giant exoplanets in binary systems, we have designed the 'VIsual Binary Exoplanet survey with Sphere' (VIBES) to search for planets in visual binaries. It uses the SPHERE instrument at VLT to search for planets in 23 visual binary and four visual triple systems with ages of <145 Myr and distances of <150 pc. We used the IRDIS dual-band imager on SPHERE to acquire high-contrast images of the sample targets. For each binary, the two components were observed at the same time with a coronagraph masking only the primary star. For the triple star, the tight components were treated as a single star for data reduction. This enabled us to effectively search for companions around 50 individual stars in binaries and four binaries in triples. We derived upper limits of $<$13.7\% for the frequency of sub-stellar companions around primaries in visual binaries, $<$26.5\% for the fraction of sub-stellar companions around secondaries in visual binaries, and an occurrence rate of $<$9.0\% for giant planets and brown dwarfs around either component of visual binaries. We have combined our observations with literature measurements to astrometrically confirm, for the first time, that 20 binaries and two triple systems, which were previously known, are indeed physically bound. Finally, we discovered a third component of the binary HD~121336. The upper limits we derived are compatible with planet formation through the core accretion and the gravitational instability processes in binaries. These limits are also in line with limits found for single star and circumbinary planet search surveys.
Improving generalization of deep fault detection models in the presence of mislabeled data

Mislabeled samples are ubiquitous in real-world datasets as rule-based or expert labeling is usually based on incorrect assumptions or subject to biased opinions. Neural networks can "memorize" these mislabeled samples and, as a result, exhibit poor generalization. This poses a critical issue in fault detection applications, where not only the training but also the validation datasets are prone to contain mislabeled samples. In this work, we propose a novel two-step framework for robust training with label noise. In the first step, we identify outliers (including the mislabeled samples) based on the update in the hypothesis space. In the second step, we propose different approaches to modifying the training data based on the identified outliers and a data augmentation technique. Contrary to previous approaches, we aim at finding a robust solution that is suitable for real-world applications, such as fault detection, where no clean, "noise-free" validation dataset is available. Under an approximate assumption about the upper limit of the label noise, we significantly improve the generalization ability of the model trained under massive label noise.
Impact of heart failure on functional recovery after hip fracture

[Purpose] Heart failure has been identified as a risk factor for reduced physical function and falls; however, the impact of heart failure on functional recovery after a hip fracture is unclear. This study aimed to examine how heart failure and pre-fracture physical function affect recovery after a hip fracture. [Participants and Methods] The study population consisted of 122 patients with sub-acute hip fracture (mean age 81.7 ± 9.7 years, 18.9% male) who were divided into two groups: heart failure and non-heart failure. The outcome measurement was the functional independence measure effectiveness. A two-way analysis of variance was performed to investigate how heart failure and ambulatory ability prior to hip fracture were related to the functional independence measure effectiveness. [Results] Seventeen patients (13.9%) had a history of heart failure. The two-way analysis of variance showed the two independent variables (heart failure and ambulatory ability before fracture) had significant main effects; however, their interaction effect was not significant. [Conclusion] Heart failure affects functional recovery after hip fracture independent of the pre-fracture physical function, and vice versa. Further research on rehabilitation in hip fracture patients with heart failure is required to develop strategies to overcome poor functional recovery in such patients.
Toward Privacy and Utility Preserving Image Representation

Face images are rich data items that are useful and can easily be collected in many applications, such as in 1-to-1 face verification tasks in the domain of security and surveillance systems. Multiple methods have been proposed to protect an individual's privacy by perturbing the images to remove traces of identifiable information, such as gender or race. However, significantly less attention has been given to the problem of protecting images while maintaining optimal task utility. In this paper, we study the novel problem of creating privacy-preserving image representations with respect to a given utility task by proposing a principled framework called the Adversarial Image Anonymizer (AIA). AIA first creates an image representation using a generative model, then enhances the learned image representations using adversarial learning to preserve privacy and utility for a given task. Experiments were conducted on a publicly available data set to demonstrate the effectiveness of AIA as a privacy-preserving mechanism for face images.
EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data

In this paper, we discuss how modern deep learning approaches can be applied to the credit scoring of bank clients. We show that information about connections between clients based on money transfers between them allows us to significantly improve the quality of credit scoring compared to the approaches using information about the target client solely. As a final solution, we develop a new graph neural network model EWS-GCN that combines ideas of graph convolutional and recurrent neural networks via attention mechanism. The resulting model allows for robust training and efficient processing of large-scale data. We also demonstrate that our model outperforms the state-of-the-art graph neural networks achieving excellent results.
X-ray spectra and light curves of cooling novae and a nova like

We present X-ray observations of novae V2491 Cyg and KT Eri about 9 years post-outburst, of the dwarf nova and post-nova candidate EY Cyg, and of a VY Scl variable. The first three objects were observed with XMM-Newton, KT Eri also with the Chandra ACIS-S camera, V794 Aql with the Chandra ACIS-S camera and High Energy Transmission Gratings. The two recent novae, similar in outburst amplitude and light curve, appear very different at quiescence. Assuming half of the gravitational energy is irradiated in X-rays, V2491 Cyg is accreting at $\dot{m}=1.4\times10^{-9}-10^{-8}M_\odot/yr$, while for KT Eri, $\dot{m}<2\times10^{-10}M_\odot/yr$. V2491 Cyg shows signatures of a magnetized WD, specifically of an intermediate polar. A periodicity of ~39 minutes, detected in outburst, was still measured and is likely due to WD rotation. EY Cyg is accreting at $\dot{m}\sim1.8\times10^{-11}M_\odot/yr$, one magnitude lower than KT Eri, consistently with its U Gem outburst behavior and its quiescent UV flux. The X-rays are modulated with the orbital period, despite the system's low inclination, probably due to the X-ray flux of the secondary. A period of ~81 minutes is also detected, suggesting that it may also be an intermediate polar. V794 Aql had low X-ray luminosity during an optically high state, about the same level as in a recent optically low state. Thus, we find no clear correlation between optical and X-ray luminosity: the accretion rate seems unstable and variable. The very hard X-ray spectrum indicates a massive WD.
A Vietnamese Dataset for Evaluating Machine Reading Comprehension

Over 97 million inhabitants speak Vietnamese as the native language in the world. However, there are few research studies on machine reading comprehension (MRC) in Vietnamese, the task of understanding a document or text, and answering questions related to it. Due to the lack of benchmark datasets for Vietnamese, we present the Vietnamese Question Answering Dataset (UIT-ViQuAD), a new dataset for the low-resource language as Vietnamese to evaluate MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. In particular, we propose a new process of dataset creation for Vietnamese MRC. Our in-depth analyses illustrate that our dataset requires abilities beyond simple reasoning like word matching and demands complicate reasoning such as single-sentence and multiple-sentence inferences. Besides, we conduct experiments on state-of-the-art MRC methods in English and Chinese as the first experimental models on UIT-ViQuAD, which will be compared to further models. We also estimate human performances on the dataset and compare it to the experimental results of several powerful machine models. As a result, the substantial differences between humans and the best model performances on the dataset indicate that improvements can be explored on UIT-ViQuAD through future research. Our dataset is freely available to encourage the research community to overcome challenges in Vietnamese MRC.
Ensemble Multi-Source Domain Adaptation with Pseudolabels

Given multiple source datasets with labels, how can we train a target model with no labeled data? Multi-source domain adaptation (MSDA) aims to train a model using multiple source datasets different from a target dataset in the absence of target data labels. MSDA is a crucial problem applicable to many practical cases where labels for the target data are unavailable due to privacy issues. Existing MSDA frameworks are limited since they align data without considering conditional distributions p(x|y) of each domain. They also miss a lot of target label information by not considering the target label at all and relying on only one feature extractor. In this paper, we propose Ensemble Multi-source Domain Adaptation with Pseudolabels (EnMDAP), a novel method for multi-source domain adaptation. EnMDAP exploits label-wise moment matching to align conditional distributions p(x|y), using pseudolabels for the unavailable target labels, and introduces ensemble learning theme by using multiple feature extractors for accurate domain adaptation. Extensive experiments show that EnMDAP provides the state-of-the-art performance for multi-source domain adaptation tasks in both of image domains and text domains.
Joint Contrastive Learning with Infinite Possibilities

This paper explores useful modifications of the recent development in contrastive learning via novel probabilistic modeling. We derive a particular form of contrastive loss named Joint Contrastive Learning (JCL). JCL implicitly involves the simultaneous learning of an infinite number of query-key pairs, which poses tighter constraints when searching for invariant features. We derive an upper bound on this formulation that allows analytical solutions in an end-to-end training manner. While JCL is practically effective in numerous computer vision applications, we also theoretically unveil the certain mechanisms that govern the behavior of JCL. We demonstrate that the proposed formulation harbors an innate agency that strongly favors similarity within each instance-specific class, and therefore remains advantageous when searching for discriminative features among distinct instances. We evaluate these proposals on multiple benchmarks, demonstrating considerable improvements over existing algorithms. Code is publicly available at: this https URL.
Zero Net Flux MRI Turbulence in Disks: Sustenance Scheme and Magnetic Prandtl Number Dependence

We investigate sustenance and dependence on magnetic Prandtl number (Pm) for magnetorotational instability (MRI)-driven turbulence in Keplerian disks with zero net magnetic flux using standard shearing box simulations. We focus on the turbulence dynamics in Fourier space, capturing specific/noncanonical anisotropy of nonlinear processes due to disk flow shear. This is a new type of nonlinear redistribution of modes over wavevector orientations in Fourier space—the nonlinear transverse cascade—which is generic to shear flows and fundamentally different from the usual direct/inverse cascade. The zero flux MRI has no exponentially growing modes, so its growth is transient, or nonmodal. Turbulence self-sustenance is governed by constructive cooperation of the transient growth of MRI and the nonlinear transverse cascade. This cooperation takes place at small wavenumbers (on the flow size scales) referred to as the vital area in Fourier space. The direct cascade transfers mode energy from the vital area to larger wavenumbers. At large Pm, the transverse cascade prevails over the direct one, keeping most of modes’ energy contained in small wavenumbers. With decreasing Pm, however, the action of the transverse cascade weakens and can no longer oppose the action of the direct cascade, which more efficiently transfers energy to higher wavenumbers, leading to increased resistive dissipation. This undermines the sustenance scheme, resulting in the turbulence decay. Thus, the decay of zero net flux MRI turbulence with decreasing Pm is attributed to the topological rearrangement of the nonlinear processes when the direct cascade begins to prevail over the transverse cascade.
Thunder-cell as Source of Energetic Protons

In this article we present the following hypothesis: thunder-cell ejects highly energetic protons, each of which creates a tree-structure of weakly ionized trajectories that can develop into a lightning channel. The tree-structure and the channel have the same geometry so the mean free path of a proton corresponds to the average length of the channel between two successive nodes (branching points). We show this length is around 660 m in lower Earth atmosphere. Effects of Coulomb interaction and various outcomes of proton-nucleus reaction are taken into account. A prediction of CG/CC ratio that follows agrees well with the available data, but only measurements of lightning geometry can reveal whether the hypothesis is any closer to the correct explanation of the phenomenon.
Depletion of Bright Red Giants in the Galactic Center during Its Active Phases

Observations in the near-infrared domain showed the presence of the flat core of bright late-type stars inside ∼0.5 pc from the Galactic center supermassive black hole (Sgr A*), while young massive OB/Wolf–Rayet stars form a cusp. Several dynamical processes were proposed to explain this apparent paradox of the distribution of the Galactic center stellar populations. Given the mounting evidence on the significantly increased activity of Sgr A* during the past million years, we propose a scenario based on the interaction between the late-type giants and a nuclear jet, whose past existence and energetics can be inferred from the presence of γ-ray Fermi bubbles and bipolar radio bubbles. Extended, loose envelopes of red giant stars can be ablated by the jet with kinetic luminosity in the range of Lj ≈ 1041– within the inner ∼0.04 pc of Sgr A* (S-cluster region), which would lead to their infrared luminosity decrease after several thousand jet–star interactions. The ablation of the atmospheres of red giants is complemented by the process of tidal stripping that operates at distances of ≲1 mpc, and by the direct mechanical interaction of stars with a clumpy disk at ≳0.04 pc, which can explain the flat density profile of bright late-type stars inside the inner half parsec from Sgr A*.
Spectral and imaging properties of Sgr A* from high-resolution 3D GRMHD simulations with radiative cooling

The candidate supermassive black hole in the Galactic Centre, Sagittarius A* (Sgr A*), is known to be fed by a radiatively inefficient accretion flow (RIAF), inferred by its low accretion rate. Consequently, radiative cooling has in general been overlooked in the study of Sgr A*. However, the radiative properties of the plasma in RIAFs are poorly understood. In this work, using full 3D general–relativistic magnetohydrodynamical simulations, we study the impact of radiative cooling on the dynamical evolution of the accreting plasma, presenting spectral energy distributions and synthetic sub-millimetre images generated from the accretion flow around Sgr A*. These simulations solve the approximated equations for radiative cooling processes self-consistently, including synchrotron, bremsstrahlung, and inverse Compton processes. We find that radiative cooling plays an increasingly important role in the dynamics of the accretion flow as the accretion rate increases: the mid-plane density grows and the infalling gas is less turbulent as cooling becomes stronger. The changes in the dynamical evolution become important when the accretion rate is larger than $10^{-8}\, M_{\odot }~{\rm yr}^{-1}$ ($\gtrsim 10^{-7} \dot{M}_{\rm Edd}$, where $\dot{M}_{\rm Edd}$ is the Eddington accretion rate). The resulting spectra in the cooled models also differ from those in the non-cooled models: the overall flux, including the peak values at the sub-mm and the far-UV, is slightly lower as a consequence of a decrease in the electron temperature. Our results suggest that radiative cooling should be carefully taken into account in modelling Sgr A* and other low-luminosity active galactic nuclei that have a mass accretion rate of $\dot{M} \gt 10^{-7}\, \dot{M}_{\rm Edd}$.
Learning Hard Retrieval Cross Attention for Transformer

The Transformer translation model that based on the multi-head attention mechanism can be parallelized easily and lead to competitive performance in machine translation. The multi-head attention network performs the scaled dot-product attention function in parallel, empowering the model by jointly attending to information from different representation subspaces at different positions. Though its advantages in parallelization, many previous works suggest the computation of the attention mechanism is not sufficiently efficient, especially when processing long sequences, and propose approaches to improve its efficiency with long sentences. In this paper, we accelerate the inference of the scaled dot-product attention in another perspective. Specifically, instead of squeezing the sequence to attend, we simplify the computation of the scaled dot-product attention by learning a hard retrieval attention which only attends to one token in the sentence rather than all tokens. Since the hard attention mechanism only attends to one position, the matrix multiplication between attention probabilities and the value sequence in the standard scaled dot-product attention can be replaced by a simple and efficient retrieval operation. As a result, our hard retrieval attention mechanism can empirically accelerate the scaled dot-product attention for both long and short sequences by 66.5%, while performing competitively in a wide range of machine translation tasks when using for cross attention networks.