Miracle-dz/UniFAR-FTUs huggingface dataset:
A Re-evaluation of Knowledge Graph Completion Methods

Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs. A vast number of state-of-the-art KGC techniques have got published at top conferences in several research fields, including data mining, machine learning, and natural language processing. However, we notice that several recent papers report very high performance, which largely outperforms previous state-of-the-art methods. In this paper, we find that this can be attributed to the inappropriate evaluation protocol used by them and propose a simple evaluation protocol to address this problem. The proposed protocol is robust to handle bias in the model, which can substantially affect the final results. We conduct extensive experiments and report performance of several existing methods using our protocol. The reproducible code has been made publicly available.
Xylella fastidiosa from almond in Iran: overwinter recovery and effects of antibiotics

Almond leaf scorch disease (ALSD), caused by Xylella fastidiosa, has been reported from some regions of Iran. Biological traits of isolates the pathogen from almond were investigated in pot and orchard conditions. ALSD killed trees of susceptible cultivars during 3 to 4 years outdoors in pots, but overwintered in root tissues in orchards with winter temperatures below -15°C. Xylella fastidiosa was not detected by culturing or DAS-enzyme-linked immunosorbent assay (DAS-ELISA) in almond leaves until early summer and peaked in early autumn. However, root samples taken in winter (January and February) and early spring (April) reacted positively in DAS-ELISA, culture media and polymerase chain reaction assays. This demonstrates that X. fastidiosa survives in root tissues of almond trees under orchard conditions in very cold (-28°C) winters. Trunk injection of oxytetracycline into leaf scorched almond trees reduced symptoms of the disease, while penicillin applications also reduced symptoms but to a lesser degree.
"Local is the New Organic": Do Consumers Agree?

There have been numerous studies and growing interests to identify consumer preferences and compare willingness-to-pay for different value-added food labels or attributes. This paper uses stated preference data from choice experiments in a mail survey in Kentucky and Ohio to analyze consumer preferences for a host of value-added attributes of processed blackberry jam. Results from the study confirm positive willingness-to-pay for both organic and local attributes as well as other label claims. However, consumers were found to be willing to pay less for organic blackberry jam than jam identified as being produced in smaller state regions. Furthermore, the study found some evidence of substitution between these two attributes, an issue receiving minimal attention in the existing literature.
Limited mitochondrial DNA variation within South Africa's black rhino (Diceros bicornis minor) population and implications for management

The taxonomy of African black rhinoceros (Diceros bicornis) remains unresolved. Maintaining levels of genetic diversity and species rescue by reintroduction and restocking requires its resolution. We compared the sequences of the mitochondrial DNA (mtDNA) control region for a total of 101 D. bicornis from three subspecies: D. b. minor, D. b. michaeli and D. b. bicornis. A single unique haplotype was found within the 65 D. b. minor samples from KwaZulu-Natal (KZN) Province, South Africa, 55 of which came from Hluhluwe-iMfolozi Game Park (HiP) and Mkuzi Game Reserve (MGR) source populations. However, six different haplotypes were represented in eleven D. b. minor samples from Zimbabwe. Similarly, published autosomal microsatellite data indicate low levels of diversity within the KZN D. b. minor populations. The low levels of mtDNA diversity within the KZN metapopulation point to the possible need for genetic supplementation. However, there is a need to determine whether the low levels of genetic variation within KZN D. b. minor are a result of the recent bottleneck or whether KZN historically always had low
The lifecycle of molecular clouds in nearby star-forming disc galaxies

It remains a major challenge to derive a theory of cloud-scale ($\lesssim100$ pc) star formation and feedback, describing how galaxies convert gas into stars as a function of the galactic environment. Progress has been hampered by a lack of robust empirical constraints on the giant molecular cloud (GMC) lifecycle. We address this problem by systematically applying a new statistical method for measuring the evolutionary timeline of the GMC lifecycle, star formation, and feedback to a sample of nine nearby disc galaxies, observed as part of the PHANGS-ALMA survey. We measure the spatially resolved (∼100 pc) CO-to-H α flux ratio and find a universal de-correlation between molecular gas and young stars on GMC scales, allowing us to quantify the underlying evolutionary timeline. GMC lifetimes are short, typically $10\! -\! 30\,{\rm Myr}$, and exhibit environmental variation, between and within galaxies. At kpc-scale molecular gas surface densities $\Sigma _{\rm H_2}\ge 8\,\rm {M_\odot}\,{{\rm pc}}^{-2}$, the GMC lifetime correlates with time-scales for galactic dynamical processes, whereas at $\Sigma _{\rm H_2}\le 8\,\rm {M_\odot}\,{{\rm pc}}^{-2}$ GMCs decouple from galactic dynamics and live for an internal dynamical time-scale. After a long inert phase without massive star formation traced by H α (75–90 per cent of the cloud lifetime), GMCs disperse within just $1\! -\! 5\,{\rm Myr}$ once massive stars emerge. The dispersal is most likely due to early stellar feedback, causing GMCs to achieve integrated star formation efficiencies of 4–10 per cent. These results show that galactic star formation is governed by cloud-scale, environmentally dependent, dynamical processes driving rapid evolutionary cycling. GMCs and H ii regions are the fundamental units undergoing these lifecycles, with mean separations of $100\! -\! 300\,{{\rm pc}}$ in star-forming discs. Future work should characterize the multiscale physics and mass flows driving these lifecycles.
Constraining the efficiency of cosmic ray acceleration by cluster shocks

We study the acceleration of cosmic rays by collisionless structure formation shocks with ENZO grid simulations. Data from the FERMI satellite enable the use of galaxy clusters as a testbed for particle acceleration models. Based on advanced cosmological simulations that include different prescriptions for gas and cosmic rays physics, we use the predicted {\gamma}-ray emission to constrain the shock acceleration efficiency. We infer that the efficiency must be on average <0.1% for cosmic shocks, particularly for the 2<M<5 merger shocks that are mostly responsible for the thermalisation of the intracluster medium. These results emerge, both, from non-radiative and radiative runs including feedback from active galactic nuclei, as well as from zoomed resimulations of a cluster resembling MACSJ1752.0+0440. The limit on the acceleration efficiency we report is lower than what has been assumed in the literature so far. Combined with the information from radio emission in clusters, it appears that a revision of the present understanding of shock acceleration in the ICM is unavoidable.
Association and root colonization of some medicinal plants with Arbuscular Mycorrhizal Fungi

Twelve medicinal plants belonging to eight different families were selected to study the prevalence of AMF colonization. The plant roots and their respective rhizospheric soil were collected from the Garden of Medicinal Plants, University of North Bengal for AMF analysis and spore assessment per 100 gm of soil. The result showed variation in both AMF colonization and spore percentage. All medicinal plant species studied were found to be colonized by AMF. Highest percent colonization was evident in Justicia adhatoda (95±2.00). Highest spore count was found in Abroma augustum (197.4±9.31). Spore characteristics were studied and tried to identify upto species level based on the available standard keys. Histopathological studies revealed presence of abundant vesicles, thin branched as well as coiled arbuscules along with extra and intra radical hyphae. The present study revealed that the genus of Glomus, Gigaspora, Acaulospora were more predominant. Scutellospora and Entrophospora are least amongst the recovered AMF spores.
Graph-to-Graph Transformer for Transition-based Dependency Parsing

We propose the Graph2Graph Transformer architecture for conditioning on and predicting arbitrary graphs, and apply it to the challenging task of transition-based dependency parsing. After proposing two novel Transformer models of transition-based dependency parsing as strong baselines, we show that adding the proposed mechanisms for conditioning on and predicting graphs of Graph2Graph Transformer results in significant improvements, both with and without BERT pre-training. The novel baselines and their integration with Graph2Graph Transformer significantly outperform the state-of-the-art in traditional transition-based dependency parsing on both English Penn Treebank, and 13 languages of Universal Dependencies Treebanks. Graph2Graph Transformer can be integrated with many previous structured prediction methods, making it easy to apply to a wide range of NLP tasks.
Small inter-event times govern epidemic spreading on networks

Just like the degrees of human and animal interaction networks, the distribution of the times between interactions is known to often be right-skewed and fat-tailed. Both these distributions affect epidemic dynamics strongly, but, as we show in this Letter, for very different reasons. Whereas the high degrees of the tail are critical for facilitating epidemics, it is the small interevent times that control the dynamics of epidemics. We investigate this effect both analytically and numerically for different versions of the Susceptible-Infected-Recovered model on different types of networks.
Hybrid Pℓ(k): general, unified, non-linear matter power spectrum in redshift space

Constraints on gravity and cosmology will greatly benefit from performing joint clustering and weak lensing analyses on large-scale structure data sets. Utilising non-linear information coming from small physical scales can greatly enhance these constraints. At the heart of these analyses is the matter power spectrum. Here we employ a simple method, dubbed “Hybrid Pℓ(k)”, based on the Gaussian Streaming Model (GSM), to calculate the quasi non-linear redshift space matter power spectrum multipoles. This employs a fully non-linear and theoretically general prescription for the matter power spectrum. We test this approach against comoving Lagrangian acceleration simulation measurements performed in GR, DGP and f(R) gravity and find that our method performs comparably or better to the dark matter TNS redshift space power spectrum model {for dark matter. When comparing the redshift space multipoles for halos, we find that the Gaussian approximation of the GSM with a linear bias and a free stochastic term, N, is competitive to the TNS model. } Our approach offers many avenues for improvement in accuracy as well as further unification under the halo model.
An investigation into the dark triad of personality and fear and startle response among adults

A repeated measures, within-participants experiment was conducted to ascertain whether levels of Dark Triad personality characteristics (narcissism, psychopathy, Machiavellianism) would predict a fearful and startled response to two categories of images: violence towards others, and a threat of violence towards self. A convenience sample of 29 non clinical participants (13 male, 16 female) was recruited. Personality constructs were measured by the Short Dark Triad (SD3). Fear response was measured by EEG in the left temporal lobe and startle response was measured by Galvanic Skin Response (GSR). Results showed narcissism to be a significant predictor of a fearful response to images of threat of violence towards self. There was no significant difference between the two categories of images. Strengths and limitations of the study are discussed along with future research.
Determinants of access patterns to goods and services from wetlands in Tanzania and the impact on sustainable wetland management

Wetlands perform vital role in providing useful goods and services to mankind. Because of their importance, human beings interactions with the wetlands’ physical environment have caused some of them to be highly degraded. Due to characteristic nature of wetlands being a common pool resource (CPRs), most researchers claim their degradation to be a result of poor institutions governing the interactions. Wetlands in Tanzania are very useful especially, in areas where agriculture is most prevalent. High pressure to exploit fertile and moist land in those areas has caused wetlands to be highly degraded. This study focused on the rules and regulations governing the resources access from the wetlands using Kilombero Valley as a case study. The study used choice modelling to identify the determinants of the access patterns and their impacts on wetland sustainable management. The results showed that physical nature of resources and characteristics of the users such as location, income and education determine what type of access one chooses to accrue products and services from the wetlands. To ensure wetland sustainable management, the policy makers and conservationists should consider these factors in enacting rules and regulation which would govern human interactions in the wetlands. Payment for environmental services could be used to enhance such options. Key words: Wetlands, goods and services, institutions, choice modelling.
Neural Arabic Text Diacritization: State of the Art Results and a Novel Approach for Machine Translation

In this work, we present several deep learning models for the automatic diacritization of Arabic text. Our models are built using two main approaches, viz. Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN), with several enhancements such as 100-hot encoding, embeddings, Conditional Random Field (CRF) and Block-Normalized Gradient (BNG). The models are tested on the only freely available benchmark dataset and the results show that our models are either better or on par with other models, which require language-dependent post-processing steps, unlike ours. Moreover, we show that diacritics in Arabic can be used to enhance the models of NLP tasks such as Machine Translation (MT) by proposing the Translation over Diacritization (ToD) approach.
Income Inequality & Redistributional Spending: An Empirical Investigation of Competing Theories

The link between income inequality and governmental redistribution is still subject to intense research and debate. Starting with the median-voter-hypothesis, a plethora of theoretical models have been developed during the last three decades to identify and explain possible causal relationships. the empirical evidence so far, however, has been mixed. the aim of this paper is to review the existing literature on inequality and redistribution, to explicate the theoretical causal mechanisms identified so far, and to provide a comprehensive and rigorous empirical test that overcomes some of the shortcomings of previous empirical studies. Using panel data on 23 OECD countries over the time period of 1971–2005, we not only look at different spending categories but also at the share gains of different income deciles and the median-to-mean ratio. We find robust evidence that income inequality affects redistribution. However, the precise degree of this relationship differs for different parts of the income distribution.
RESISTIVITY-DRIVEN STATE CHANGES IN VERTICALLY STRATIFIED ACCRETION DISKS

We investigate the effect of shear viscosity, ν, and Ohmic resistivity, η, on the magnetorotational instability (MRI) in vertically stratified accretion disks through a series of local simulations with the Athena code. First, we use a series of unstratified simulations to calibrate physical dissipation as a function of resolution and background field strength; the effect of the magnetic Prandtl number, Pm = ν/η, on the turbulence is captured by ∼32 grid zones per disk scale height, H. In agreement with previous results, our stratified disk calculations are characterized by a subthermal, predominately toroidal magnetic field that produces MRI-driven turbulence for |z| ≲ 2H. Above |z| ∼ 2H, the magnetic pressure dominates and the field is buoyantly unstable. Large-scale radial and toroidal fields are also generated near the mid-plane and subsequently rise through the disk. The polarity of this mean field switches on a roughly 10 orbit period in a process that is well modeled by an α–Ω dynamo. Turbulent stress increases with Pm but with a shallower dependence compared to unstratified simulations. For sufficiently large resistivity, η ∼ csH/1000, where cs is the sound speed, MRI turbulence within 2H of the mid-plane undergoes periods of resistive decay followed by regrowth. This regrowth is caused by amplification of the toroidal field via the dynamo. This process results in large amplitude variability in the stress on 10–100 orbital timescales, which may have relevance for partially ionized disks that are observed to have high- and low-accretion states.
Sectorial descent for wrapped Fukaya categories

We develop a set of tools for doing computations in and of (partially) wrapped Fukaya categories. In particular, we prove (1) a descent (cosheaf) property for the wrapped Fukaya category with respect to so-called Weinstein sectorial coverings and (2) that the partially wrapped Fukaya category of a Weinstein manifold with respect to a mostly Legendrian stop is generated by the cocores of the critical handles and the linking disks to the stop. We also prove (3) a ‘stop removal equals localization’ result, and (4) that the Fukaya–Seidel category of a Lefschetz fibration with Liouville fiber is generated by the Lefschetz thimbles. These results are derived from three main ingredients, also of independent use: (5) a Künneth formula (6) an exact triangle in the Fukaya category associated to wrapping a Lagrangian through a Legendrian stop at infinity and (7) a geometric criterion for when a pushforward functor between wrapped Fukaya categories of Liouville sectors is fully faithful.
International trade flows under flexible exchange rates

World trade has grown rapidly since the breakdown of the Bretton Woods system of fixed exchange rates in early 1973. Although much of the nominal trade growth resulted from inflation, it is apparent that world trade has continued to prosper despite the increase in exchange rate variability permitted by the 1973 shift from fixed to floating exchange rates. Nevertheless, some observers have argued that exchange rate uncertainty may have had an adverse impact on trade. If these observers are correct, the growth in trade of recent years has been the result of favorable influences which have more than offset the adverse impact of exchange rate uncertainty. This article examines the macroeconomic determinants of international trade flows between developed countries, giving special attention to the effects of exchange rate varia bility. The article also analyzes whether and to what extent fluctuations in bilateral exchange rates have had an adverse impact on bilateral trade flows. THE DETERMINANTS OF
Neural Contextual Bandits with Upper Confidence Bound-Based Exploration

We study the stochastic contextual bandit problem, where the reward is generated from an unknown bounded function with additive noise. We propose the NeuralUCB algorithm, which leverages the representation power of deep neural networks and uses a neural network-based random feature mapping to construct an upper confidence bound (UCB) of reward for efficient exploration. We prove that, under mild assumptions, NeuralUCB achieves $\tilde O(\sqrt{T})$ regret, where $T$ is the number of rounds. To the best of our knowledge, our algorithm is the first neural network-based contextual bandit algorithm with near-optimal regret guarantee. Preliminary experiment results on synthetic data corroborate our theory, and shed light on potential applications of our algorithm to real-world problems.
Kalman Filter Models for the Prediction of Individualised Thermal Work Strain

It is important to monitor and assess the physiological strain of individuals working in hot environments to avoid heat illness and performance degradation. The body core temperature (Tc) is a reliable indicator of thermal work strain. However, measuring Tc is invasive and often inconvenient and impractical for real-time monitoring of workers in high heat strain environments. Seeking a better solution, themain aim of the present studywas to investigate the Kalman filter method to enable the estimation of heat strain from non-invasive measurements (heart rate (HR) and chest skin temperature (ST)) obtained ‘online’ via wearable body sensors. In particular, we developed two Kalman filter models. First, an extended Kalman filter (EFK) was implemented in a cubic state space modelling framework (HR versus Tc) with a stage-wise, autoregressive exogenous model (incorporating HR and ST) as the time update model. Under the second model, the online Kalman filter (OFK) approach builds up the time update equation depending only on the initial value of Tc and the latest value of the exogenous variables. Both models were trained and validated using data from laboratoryand outfield-based heat strain profiling studies in which subjects performed a high intensity military foot march. While both the EKF and OKFmodels provided satisfactory estimates of Tc, the results showed an overall superior performance of the OKFmodel (overall root mean square error, RMSE = 0.31 C) compared to the EKF model (RMSE = 0.45 C).