Miracle-dz/UniFAR-FTUs huggingface dataset:
Distantly Supervised Relation Extraction in Federated Settings

This paper investigates distantly supervised relation extraction in federated settings. Previous studies focus on distant supervision under the assumption of centralized training, which requires collecting texts from different platforms and storing them on one machine. However, centralized training is challenged by two issues, namely, data barriers and privacy protection, which make it almost impossible or cost-prohibitive to centralize data from multiple platforms. Therefore, it is worthy to investigate distant supervision in the federated learning paradigm, which decouples the model training from the need for direct access to the raw data. Overcoming label noise of distant supervision, however, becomes more difficult in federated settings, since the sentences containing the same entity pair may scatter around different platforms. In this paper, we propose a federated denoising framework to suppress label noise in federated settings. The core of this framework is a multiple instance learning based denoising method that is able to select reliable instances via cross-platform collaboration. Various experimental results on New York Times dataset and miRNA gene regulation relation dataset demonstrate the effectiveness of the proposed method.
Applying GeoChip analysis to disparate microbial communities

Microorganisms, the most diverse group of life, play integral roles in ecosystem functions and biogeochemical cycling of carbon, nitrogen, sulfur, phosphorus, and various metals. Microorganisms typically form complex communities whose structure, functions, interactions, and dynamics are critical to our lives. For instance, dramatic changes involving microbial communities from epidemics, plant or animal disease outbreaks, biological terrorist attacks, or atmospheric alterations from global climate change could prove a disaster for the environment and us. Although it is critical to understand which microorganisms are present and what they are doing, counting them is difficult, making it a major challenge to address such issues in quantitative ways and, thus, to make accurate predictions.
Neutrino-dominated Accretion Flows: A Second Nucleosynthesis Factory in Core-collapse Supernovae and Regulating the Iron Markets in Galaxies

Cosmic metals are widely believed to be produced by supernovae (SNe) and compact-object mergers. Here, we discuss the nucleosynthesis of neutrino-dominated accretion flows (NDAFs) with outflows in the centers of core-collapse SNe (CCSNe), and show that the outflows from NDAFs can have a significant contribution to the 56Ni abundances of faint explosions if the masses of the progenitor stars are within about 25–50 M ⊙. Less-massive progenitor stars can produce more 56Ni than their more-massive counterparts in the NDAF outflow nucleosynthesis channel. Therefore, we find that the total (i.e., CCSNe and NDAF outflows) 56Ni mass per CCSN depends only weakly upon the mass of the progenitor star. In terms of metallicity evolution, the ratio of 56Fe (produced by the decay of 56Ni) mass to the initial total gas mass can increase by ∼1.95 times if the upper limits of the nucleosynthesis yields from NDAF outflows and CCSNe are considered. Our results might have significant implications for the chemical evolution of the solar neighborhood, galaxies, and active galactic nuclei.
Aerodynamically driven rupture of a liquid film by turbulent shear flow

The rupture of a liquid film due to co-flowing turbulent shear flows in the gas phase is studied using a volume-of-fluid method. To simulate this multiphase problem, we use a simplified numerical setup where the liquid film is 'sandwiched' between two fully developed boundary layers from a turbulent channel simulation. The film deforms and eventually ruptures within the shear zone created by the co-flows. This efficient setup allows systematic variation of physical parameters to gauge their role in the aerodynamically-driven deformation and rupture of a liquid film under fully developed sheared turbulence. The present work presents a detailed study of the developing pressure field over the deforming film and related aerodynamic effects, as previously suggested by other authors, in particular the role of the inviscid lift and drag forces. A cumulative lift force is introduced to capture the effect of the alternating pressure minima and maxima forming over the film which amplify and eventually rupture the film. A velocity scale derived from the lift-induced drag force reflects the state of the turbulent boundary layer over the film and collapses the temporal development of this cumulative lift force as well as the amplitude of film deformation with some success for the different film thicknesses and Reynolds numbers.
Early science with SPIRou: near-infrared radial velocity and spectropolarimetry of the planet-hosting star HD 189733

SPIRou is the newest spectropolarimeter and high-precision velocimeter that has recently been installed at the Canada-France-Hawaii Telescope on Maunakea, Hawaii. It operates in the near-infrared and simultaneously covers the 0.98–2.35 μm domain at high spectral resolution. SPIRou is optimized for exoplanet search and characterization with the radial-velocity technique, and for polarization measurements in stellar lines and subsequent magnetic field studies. The host of the transiting hot Jupiter HD 189733 b has been observed during early science runs. We present the first near-infrared spectropolarimetric observations of the planet-hosting star as well as the stellar radial velocities as measured by SPIRou throughout the planetary orbit and two transit sequences. The planetary orbit and Rossiter-McLaughlin anomaly are both investigated and modeled. The orbital parameters and obliquity are all compatible with the values found in the optical. The obtained radial-velocity precision is compatible with about twice the photon-noise estimates for a K2 star under these conditions. The additional scatter around the orbit, of about 8 m s−1, agrees with previous results that showed that the activity-induced scatter is the dominant factor. We analyzed the polarimetric signal, Zeeman broadening, and chromospheric activity tracers such as the 1083nm HeI and the 1282nm Paβ lines to investigate stellar activity. First estimates of the average unsigned magnetic flux from the Zeeman broadening of the FeI lines give a magnetic flux of 290 ± 58 G, and the large-scale longitudinal field shows typical values of a few Gauss. These observations illustrate the potential of SPIRou for exoplanet characterization and magnetic and stellar activity studies.
ASAP-Net: Attention and Structure Aware Point Cloud Sequence Segmentation

Recent works of point clouds show that mulit-frame spatio-temporal modeling outperforms single-frame versions by utilizing cross-frame information. In this paper, we further improve spatio-temporal point cloud feature learning with a flexible module called ASAP considering both attention and structure information across frames, which we find as two important factors for successful segmentation in dynamic point clouds. Firstly, our ASAP module contains a novel attentive temporal embedding layer to fuse the relatively informative local features across frames in a recurrent fashion. Secondly, an efficient spatio-temporal correlation method is proposed to exploit more local structure for embedding, meanwhile enforcing temporal consistency and reducing computation complexity. Finally, we show the generalization ability of the proposed ASAP module with different backbone networks for point cloud sequence segmentation. Our ASAP-Net (backbone plus ASAP module) outperforms baselines and previous methods on both Synthia and SemanticKITTI datasets (+3.4 to +15.2 mIoU points with different backbones). Code is availabe at this https URL
Influence of Marker Genes on the Expression of Somatic Mutations in Barley.

Mutagenic agents have been evaluated in terms of their effects on germ cells. The study of induced somatic mutations is a faster, more economical, and less laborious method than the traditional ones. The induction of somatic sectors by x-rays, ethyl methanesulfonate, and sodium azide was studied in cultivated barley. Four stocks were used: a normal one, a balanced lethal genotype carrying two chlorophyll-deficiency genes of different expression (albina and golden), and two (semidominant) chlorophyll mutants of the same dominant gene, which in the progeny of selling segregate 1 normal green: 2 light green: 1 yellow seedlings. Treatments were applied to seeds, and the fourth, fifth, and sixth leaves of the main shoot were examined in the M1 generation for the presence of sectors showing conspicuous changes in chlorophyll content. Highly significant frequencies of dark green sectors were induced by all three mutagens in the (semidominant) heterozygous plants, whereas evidence of somatic crossing over or mitotic nondisjunction was very rare in this material and in the balanced lethal genotype. In all cases, the frequencies of induced sectors without chlorophyll were much higher in the heterozygotes than in the normal homozygotes, with each mutagen showing typical dose-response curves. A notable interaction between mutagen and genotype was found. The variation in the relative frequencies of sector types in the various genotypes points to the differential influence of the marker genes.
Phase stability of Au-Li binary systems studied using neural network potential

The miscibility of Au and Li exhibits a potential application as an adhesion layer and electrode material in secondary batteries. Here, to explore alloying properties, we constructed a neural network potential (NNP) of Au-Li binary systems based on density functional theory (DFT) calculations. To accelerate construction of NNPs, we proposed an efficient and inexpensive method of structural dataset generation. The predictions by the constructed NNP on lattice parameters and phonon properties agree well with those obtained by DFT calculations. We also investigated the mixing energy of Au$_{1-x}$Li$_{x}$ with fine composition grids, showing excellent agreement with DFT verifications. We found the existence of various compositions with structures on and slightly above the convex hull, which can explain the lack of consensus on the Au-Li stable phases in previous studies. Moreover, we newly found Au$_{0.469}$Li$_{0.531}$ as a stable phase, which has never been reported elsewhere. Finally, we examined the alloying process starting from the phase separated structure to the complete mixing phase. We found that when multiple adjacent Au atoms dissolved into Li, the alloying of the entire Au/Li interface started from the dissolved region. This paper demonstrates the applicability of NNPs toward miscible phases and provides the understanding of the alloying mechanism.
Improving Stability of LS-GANs for Audio and Speech Signals

In this paper we address the instability issue of generative adversarial network (GAN) by proposing a new similarity metric in unitary space of Schur decomposition for 2D representations of audio and speech signals. We show that encoding departure from normality computed in this vector space into the generator optimization formulation helps to craft more comprehensive spectrograms. We demonstrate the effectiveness of binding this metric for enhancing stability in training with less mode collapse compared to baseline GANs. Experimental results on subsets of UrbanSound8k and Mozilla common voice datasets have shown considerable improvements on the quality of the generated samples measured by the Frechet inception distance. Moreover, reconstructed signals from these samples, have achieved higher signal to noise ratio compared to regular LS-GANs.
Walking on TacTip toes: A tactile sensing foot for walking robots

Little research into tactile feet has been done for walking robots despite the benefits such feedback could give when walking on uneven terrain. This paper describes the development of a simple, robust and inexpensive tactile foot for legged robots based on a high-resolution biomimetic TacTip tactile sensor. Several design improvements were made to facilitate tactile sensing while walking, including the use of phosphorescent markers to remove the need for internal LED lighting. The usefulness of the foot is verified on a quadrupedal robot performing a beam walking task and it is found the sensor prevents the robot falling off the beam. Further, this capability also enables the robot to walk along the edge of a curved table. This tactile foot design can be easily modified for use with any legged robot, including much larger walking robots, enabling stable walking in challenging terrain.
The network footprint of replication in popular DBMSs

Database replication is an important component of reliable, disaster tolerant and highly available distributed systems. However, data replication also causes communication and processing overhead. Quantification of these overheads is crucial in choosing a suitable DBMS form several available options and capacity planning. In this paper, we present results from a comparative empirical analysis of replication activities of three commonly used DBMSs - MySQL, PostgreSQL and Cassandra under text as well as image traffic. In our experiments, the total traffic with two replicas (which is the norm) was as much as $300$\% higher than the total traffic with no replica. Furthermore, activation of the compression option for replication traffic, built into MySQL, reduced the total network traffic by as much as $20$\%. We also found that average CPU utilization and memory utilization were not impacted by the number of replicas or the dataset.
Object detection for graphical user interface: old fashioned or deep learning or a combination?

Detecting Graphical User Interface (GUI) elements in GUI images is a domain-specific object detection task. It supports many software engineering tasks, such as GUI animation and testing, GUI search and code generation. Existing studies for GUI element detection directly borrow the mature methods from computer vision (CV) domain, including old fashioned ones that rely on traditional image processing features (e.g., canny edge, contours), and deep learning models that learn to detect from large-scale GUI data. Unfortunately, these CV methods are not originally designed with the awareness of the unique characteristics of GUIs and GUI elements and the high localization accuracy of the GUI element detection task. We conduct the first large-scale empirical study of seven representative GUI element detection methods on over 50k GUI images to understand the capabilities, limitations and effective designs of these methods. This study not only sheds the light on the technical challenges to be addressed but also informs the design of new GUI element detection methods. We accordingly design a new GUI-specific old-fashioned method for non-text GUI element detection which adopts a novel top-down coarse-to-fine strategy, and incorporate it with the mature deep learning model for GUI text detection.Our evaluation on 25,000 GUI images shows that our method significantly advances the start-of-the-art performance in GUI element detection.
Establishment of a novel technique to study G protein-coupled receptor activation

G protein-coupled receptors (GPCRs) constitute the largest receptor family in mammals and represent important drug targets. Signaling through GPCRs mediates physiological effects that are strongly dependent on the cellular context. Therefore, the availability of assays monitoring GPCR activation applicable in different cell types could help to better understand GPCR functions and to realize the potential of known substances as well as novel ones. Here, we introduce a split-TEV (tobacco etch virus) assay to monitor GPCR activation through the stimulation-dependent recruitment of β-arrestin 2. Inactive N- and C-terminal fragments of the TEV protease are coupled to a GPCR and β-arrestin 2, respectively. Ligand-dependent interaction of the two fusion proteins leads to functional complementation of the TEV protease, followed by the cleavage of an artificial transcription factor and successive reporter gene activation. The presented split-TEV assay system is highly sensitive and was successfully applied in heterologous cell lines as well as in primary cultured neuronal and glial cells. We show that assay performance strongly depends on the endogenous properties of different cell types. The sensitivity and flexibility make split-TEV assays a valuable tool to analyze GPCR activation in different cell types in a rapid and cost-effective way.
BVRI Photometric Observations, Light Curve Solutions and Orbital Period Analysis of BF Pav

A new ephemeris, period change analysis and light curve modeling of the W UMa-type eclipsing binary BF Pav are presented in this study. Light curves of the system taken in BVRI filters from two observatories, in Australia and Argentina, were modeled using the Wilson-Devinney code. The results of this analysis demonstrate that BF Pav is a contact binary system with a photometric mass ratio q = 1.460 ± 0.014, a fillout factor f = 12.5%, an inclination of 87.97 ± 0.45 deg and a cold spot on the secondary component. By applying the distance modulus formula, the distance of BF Pav was calculated to be d = 268 ± 18 pc which is in good agreement with the Gaia EDR3 distance. We obtain an orbital period increase at a rate of 0.142 s century−1 due to a quadratic trend in the O – C diagram. Also, an alternative sudden period jump probably occurred which could be interpreted as a rapid mass transfer from the lower mass star to its companion of about Δ M = 2.45 × 10−6 M ⊙. Furthermore, there is an oscillatory behavior with a period of 18.3 ± 0.3 yr. Since BF Pav does not seem to have significant magnetic activity, this behavior could be interpreted as the light-time effect caused by an undetected third body in this system. In this case, the probability for the third body to be a low mass star with M ≥ 0.075 M ⊙ or a brown dwarf is 5.4% and 94.6% respectively. If we assume i′ = 90°, a 3 = 8.04 ± 0.33 AU. The mass of the secondary component was also determined following two different methods which result close to each other.
Open Source Software Development Process: A Systematic Review

Open Source Software (OSS) has been recognized by the software development community as an effective way to deliver software. Unlike traditional software development, OSS development is driven by collaboration among developers spread geographically and motivated by common goals and interests. Besides this fact, it is recognized by the OSS community the need to understand OSS development process and its activities. Our goal is to investigate the state-of-art about OSS process through conducting a systematic literature review providing an overview of how the OSS community has been investigating OSS process over past years. We identified and summarized OSS process activities and their characteristics and translated them into an OSS macro process using BPMN notation. As a result, we systematically analyzed 33 studies presenting an overview of the OSS process research and a generalized OSS development macro process represented by BPMN notation with a detailed description of each OSS process activity and roles in OSS environment. We conclude that OSS process can be in practice further investigated by researchers. In addition, the presented OSS process can be used as a guide for OSS projects and be adapted according to each OSS project reality. It provides insights to managers and developers who want to improve their development process even in OSS and traditional environments. Finally, recommendations for OSS community regarding OSS process activities are provided.
CFHT MegaPrime/MegaCam u-band source catalogue of the AKARI North Ecliptic Pole Wide field

The $AKARI$ infrared (IR) space telescope conducted two surveys (Deep and Wide) in the North Ecliptic Pole (NEP) field to find more than 100 000 IR sources using its infrared camera (IRC). IRC’s nine filters, which cover wavebands from 2 to 24 μm continuously, make $AKARI$ unique in comparison with other IR observatories such as $Spitzer$ or $WISE$. However, studies of the $AKARI$ NEP-Wide field sources had been limited due to the lack of follow-up observations in the ultraviolet (UV) and optical. In this work, we present the Canada–France–Hawaii Telescope MegaPrime/MegaCam u-band source catalogue of the $AKARI$ NEP-Wide field. The observations were taken in seven nights in 2015 and 2016, resulting in 82 observed frames covering 3.6 deg$^2$. The data reduction, image processing, and source extraction were performed in a standard procedure using the elixir pipeline and the astromatic software, and eventually 351 635 sources have been extracted. The data quality is discussed in two regions (shallow and deep) separately, due to the difference in the total integration time (4520 and 13 910 s). The 5$\sigma$ limiting magnitude, seeing full width at half-maximum, and the magnitude at 50 per cent completeness are 25.38 mag (25.79 mag in the deep region), 0.82 arcsec (0.94 arcsec), and 25.06 mag (25.45 mag), respectively. The u-band data provide us with critical improvements to photometric redshifts and UV estimates of the precious infrared sources from the $AKARI$ space telescope.
Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity

Relevance ranking and result diversification are two core areas in modern recommender systems. Relevance ranking aims at building a ranked list sorted in decreasing order of item relevance, while result diversification focuses on generating a ranked list of items that covers a broad range of topics. In this paper, we study an online learning setting that aims to recommend a ranked list with K items that maximizes the ranking utility, i.e., a list whose items are relevant and whose topics are diverse. We formulate it as the cascade hybrid bandits (CHB) problem. CHB assumes the cascading user behavior, where a user browses the displayed list from top to bottom, clicks the first attractive item, and stops browsing the rest. We propose a hybrid contextual bandit approach, called , for solving this problem. models item relevance and topical diversity using two independent functions and simultaneously learns those functions from user click feedback. We conduct experiments to evaluate on two real-world recommendation datasets: MovieLens and Yahoo music datasets. Our experimental results show that outperforms the baselines. In addition, we prove theoretical guarantees on the n-step performance demonstrating the soundness of .
Mathematical Reasoning via Self-supervised Skip-tree Training

We examine whether self-supervised language modeling applied to mathematical formulas enables logical reasoning. We suggest several logical reasoning tasks that can be used to evaluate language models trained on formal mathematical statements, such as type inference, suggesting missing assumptions and completing equalities. To train language models for formal mathematics, we propose a novel skip-tree task. We find that models trained on the skip-tree task show surprisingly strong mathematical reasoning abilities, and outperform models trained on standard skip-sequence tasks. We also analyze the models' ability to formulate new conjectures by measuring how often the predictions are provable and useful in other proofs.