Miracle-dz/UniFAR-FTUs huggingface dataset:
Ecological and evolutionary genetics of Puffinus spp.

Three distinct molecular genetic techniques were applied at different levels in the evolutionary hierarchy to investigate the reproductive ecology, population biology and systematics of species in the shearwater genus Puffinus, with particular emphasis on the short-tailed shearwater, P. tenuirostris, or Tasmanian muttonbird. Genetic relationships between mated pairs of adult short-tailed shearwaters and the single offspring in the nest were analysed by multilocus DNA fingerprinting. The human polycore minisatellite probe, 33.6, revealed sufficient variation in shearwater DNA to allow individualspecific identification. In addition this probe hybridised to a large minisatellite restrictionfragment derived from the female W chromosome, which allowed the identification of sex of adults and nestlings in this sexually monomorphic species. Analysis of DNA fingerprint profiles from 107 nestlings and one or both of the attendant adults in each case, in two independent studies, revealed 13 cases where a nestling was not related to one of the attendant adults. Although four of these unrelated adults could be accounted for by sampling errors, the remaining nine cases all involved the male in each nest and were more likely to have resulted from extra-pair copulations involving the attendant female and an unknown, extra-pair male. These results suggest that although short-tailed shearwaters exhibit strong pair fidelity and social monogamy, some birds are engaging in an alternative mating strategy that may substantially enhance both male and female reproductive success. Future estimates of life-time reproductive success in this species will have to allow for the small percentage of paired males that are unrelated to the nestlings that they are providing care for.
Controls on Microbial Production of Methane and Carbon Dioxide in Three Sphagnum-Dominated Peatland Ecosystems as Revealed by a Reciprocal Field Peat Transplant Experiment

We examined controls on mineralization of carbon to methane (CH4) and carbon dioxide (CO2) in Sphagnum (moss)-dominated peatland ecosystems by transplanting surface (5 cm deep) and subsurface (40 cm deep) peat samples reciprocally among three sites for periods ranging from 4 to 25 months. The sites were Big Run Bog in West Virginia, USA, Bog Lake Bog in Minnesota, USA, and Bog 307 in Ontario, Canada. Immediately upon retrieval, we incubated the peat samples in the laboratory at 12 and 22°C under both anoxic and oxic conditions to estimate rates of carbon mineralization. Transplanting affected surface peat more than subsurface peat. Peat incubated within Bog Lake Bog in Minnesota had the highest rates of CH4 production, regardless of origin, whereas transplanting did not affect rates of CO2 production measured concomitantly. Peat that originated in Big Run Bog in West Virginia generally maintained higher rates of CH4 production and CO2 production than peat from the other two sites after incubation in the field. The temperature dependence (Q 10) of CH4 production and CO2 production varied among transplant sites, but not among peat origins, suggesting physiological adaptations of microbial communities to local environmental conditions. Differences in organic matter quality of the peat, particularly lignin chemistry, helped explain the results: (a) CH4 production correlated with fresher lignin derived from Carex sedges, and (b) CO2 production correlated with woody lignin. We concluded that, although both site conditions (climate, nutrient status, and microbial communities) and organic matter quality influence carbon mineralization in peat, interactive effects occur and may differ depending on peat temperature. Moreover, CH4 production and CO2 production respond differently to environmental regulators.
Microbially Mediated Calcium Carbonate Precipitation: Implications for Interpreting Calcite Precipitation and for Solid-Phase Capture of Inorganic Contaminants

Microbial degradation of urea was investigated as a potential geochemical catalyst for Ca carbonate precipitation and associated solid phase capture of common groundwater contaminants (Sr, UO2, Cu) in laboratory batch experiments. Bacterial degradation of urea increased pH and promoted Ca carbonate precipitation in both bacterial control and contaminant treatments. Associated solid phase capture of Sr was highly effective, capturing 95% of the 1 mM Sr added within 24 h. The results for Sr are consistent with solid solution formation rather than discrete Sr carbonate phase precipitation. In contrast, UO2 capture was not as effective, reaching only 30% of the initial 1 mM UO2 added, and also reversible, dropping to 7% by 24 h. These results likely reflect differing sites of incorporation of these two elements-Ca lattice sites for Sr versus crystal defect sites for UO2. Cu sequestration was poor, resulting from toxicity of the metal to the bacteria, which arrested urea degradation and concomitant Ca carbonate precipitation. Scanning electron microscopy (SEM) indicated a variety of morphologies reminiscent of those observed in the marine stromatolite literature. In bacterial control treatments, X-ray diffraction (XRD) analyses indicated only calcite; while in the presence of either Sr or UO2, both calcite and vaterite, a metastable polymorph of Ca carbonate, were identified. Tapping mode atomic force microscopy (AFM) indicated differences in surface microtopography among abiotic, bacterial control, and bacterial contaminant systems. These results indicate that Ca carbonate precipitation induced by passive biomineralization processes is highly effective and may provide a useful bioremediation strategy for Ca carbonate-rich aquifers where Sr contamination issues exist.
Bacterial Mn2+ Oxidizing Systems and Multicopper Oxidases: An Overview of Mechanisms and Functions

Manganese is oxidized by a wide variety of bacteria. The current state of knowledge on mechanisms and functions of Mn2+ oxidation in two strains of Pseudomonas putida, in Leptothrix discophora SS-1, and in Bacillus sp. strain SG-1 is reviewed. In all three species, proteins bearing resemblance to multicopper oxidases appear to be involved in the oxidation process. A short description of the classification of Cu centers is followed by a more detailed review of properties and postulated functions of some well-known multicopper oxidases. Finally, suggestions are made for future research to assess the potential role of multicopper oxidases in bacterial Mn2+ oxidation.
Do rural development programmes promote gender equality on farms? The case of Slovenia

The necessity of gender equality in agriculture has been identified as a relevant political issue and incorporated into strategic documents and programmes of the EU. However, until now, not much has been examined about the actual contribution of these policy actions and programmes to the everyday life of farm women; there is a considerable gap in the data about women in farming across Europe, particularly for new member states. The article addresses this issue by focusing on the situation of gender equality on farms in Slovenia. Pertaining to two measures of The Rural Development Programme 2004–2006 – the Setting Up of Young Farmers and the Early Retirement of Farmers – the aim of the analysis is to compare the beneficiaries and non-beneficiaries of these two measures at family farms in terms of their development capacities and their inclination towards gender equality. Results based on the survey data ‘Generations and Gender Relations on Slovenian Farms 2007’ revealed the above-mentioned measures did indeed reach those family farms that showed better development capacities in terms of economic and demographic conditions. However, the farms did not show significant development in terms of gender equality as examined through division of work and decision-making on family farms. The rigidity in gender statuses and roles on family farms is explained and discussed through the issue of unequal access of women to farmland ownership and agricultural education, and through persistently weak institutional support to increase political participation and power for farm women.
Additional Phenotypic Features of Muenke Syndrome in 2 Dutch Families

In about 30% of the patients with syndromal craniosynostosis, a genetic mutation can be traced. For the purpose of adequate genetic counseling and treatment of these patients, the full spectrum of clinical findings for each specific mutation needs to be appreciated. The Pro250Arg mutation in the FGFR3 gene is found in patients with Muenke syndrome and is one of the most frequently encountered mutations in craniosynostosis syndromes. A number of studies on the relationship between genotype and phenotype concerning this specific mutation have been published. Two Dutch families with Muenke syndrome were screened for the reported characteristics of this syndrome and for additional features. New phenotypical findings were hypoplasia of the frontal sinus, ptosis of the upper eyelids, dysplastic elbow joints with restricted elbow motion, and mild cutaneous syndactyly. Incidentally, polydactyly, severe ankylosis of the elbow, fusion of cervical vertebrae, and epilepsy were found. Upper eyelid ptosis is thought to be pathognomonic for Saethre-Chotzen syndrome but was also observed in our series of patients with Muenke syndrome. Because Muenke and Saethre-Chotzen syndrome can have similar phenotypes, DNA analysis is needed to distinguish between these syndromes, even when a syndrome diagnosis is already made in a family member.
Line x tester analysis for yield and rust resistance in Cowpea (Vigna unguiculata L. Walp)

The combining ability of the parents as well as the hybrids of cowpea (Vigna unguiculata L. Walp) involving three lines and sixteen testers was studied. The parents and their resultant 48 hybrids were evaluated in line x tester mating design and observations were recorded for nine quantitative and one qualitative trait. The GCA/SCA ratio indicated that all the traits studied were found to be predominantly controlled by non additive genes. Based on mean performance and GCA effects, line V 118 and testers IC 202784, IC 202782 and IC 202778 were found to be the best general combiner for seed yield/plant. The hybrids V 118 x IC 202782 and V 118 X IC 202784 were found to be superior for more than one trait based on per se performance and standard heterosis. Besides the heterotic potential, the hybrid V 118 x IC 202784 was found to be moderately resistant to rust.
HST Survey of the Orion Nebula Cluster in the H2O 1.4 μm Absorption Band. II. The Substellar IMF Down to Planetary Masses

We utilize the ability of the Hubble Space Telescope to probe near-infrared water absorption present in the atmosphere of low-mass stars and brown-dwarf and planetary-mass objects to create a pure sample of Orion Nebula Cluster (ONC) members, not affected by contamination from background stars and galaxies that lack water absorption. Thanks to these data, we infer the Initial Mass Function (IMF) of the ONC in the 0.005–1.4M⊙ regime (i.e., down to a few Jupiter masses). The young age of the ONC, ∼1 Myr, provides a snapshot of the outcome of star formation for the present-day conditions (metallicity, temperature, pressure) of typical Milky Way disk molecular clouds. We demonstrate that the IMF of the ONC is well described by either a log-normal function or a broken power law, with parameter values qualitatively in agreement with the canonical Chabrier or Kroupa forms for the Milky Way disk IMF. This continuity in the mass distribution provides clues to the fact that the same physical processes may be regulating the formation of stars, brown dwarfs, and planetary-mass objects. Both the canonical IMF forms underpredict the observed number of very-low-mass members (below 0.1 M⊙), a regime where our data allows more precise constraints. Nevertheless, we do not observe a rise or secondary peak in the brown dwarfs or planetary-mass regimes. Our study thus contradicts findings based on broadband near-infrared ground-based photometry, which predict an extremely high number of free-floating planets, but likely suffer from unaccounted background contamination.
Asymptotic properties of high-dimensional random forests

As a flexible nonparametric learning tool, random forest has been widely applied to various real applications with appealing empirical performance, even in the presence of high-dimensional feature space. Unveiling the underlying mechanisms has led to some important recent theoretical results on consistency under the classical setting of fixed dimensionality or for some modified version of the random forest algorithm. Yet the consistency rates of the original version of the random forest algorithm in a general high-dimensional nonparametric regression setting remain largely unexplored. In this paper, we fill such a gap and build a high-dimensional consistency theory for random forest. Our new theoretical results show that random forest can indeed adapt to high dimensions and also provide some insights into the role of sparsity from the perspective of feature relevance.
A fast methodology for large-scale focusing inversion of gravity and magnetic data using the structured model matrix and the $2D$ fast Fourier transform

We discuss the focusing inversion of potential field data for the recovery of sparse subsurface structures from surface measurement data on a uniform grid. For the uniform grid, the model sensitivity matrices have a block Toeplitz Toeplitz block structure for each block of columns related to a fixed depth layer of the subsurface. Then, all forward operations with the sensitivity matrix, or its transpose, are performed using the 2-D fast Fourier transform. Simulations are provided to show that the implementation of the focusing inversion algorithm using the fast Fourier transform is efficient, and that the algorithm can be realized on standard desktop computers with sufficient memory for storage of volumes up to size n ≈ 106. The linear systems of equations arising in the focusing inversion algorithm are solved using either Golub–Kahan bidiagonalization or randomized singular value decomposition algorithms. These two algorithms are contrasted for their efficiency when used to solve large-scale problems with respect to the sizes of the projected subspaces adopted for the solutions of the linear systems. The results confirm earlier studies that the randomized algorithms are to be preferred for the inversion of gravity data, and for data sets of size m it is sufficient to use projected spaces of size approximately m/8. For the inversion of magnetic data sets, we show that it is more efficient to use the Golub–Kahan bidiagonalization, and that it is again sufficient to use projected spaces of size approximately m/8. Simulations support the presented conclusions and are verified for the inversion of a magnetic data set obtained over the Wuskwatim Lake region in Manitoba, Canada.
Nuclear mass measurements map the structure of atomic nuclei and accreting neutron stars

We present mass excesses (ME) of neutron-rich isotopes of Ar through Fe, obtained via TOF-$B\rho$ mass spectrometry at the National Superconducting Cyclotron Laboratory. Our new results have significantly reduced systematic uncertainties relative to a prior analysis, enabling the first determination of ME for $^{58,59}{\rm Ti}$, $^{62}{\rm V}$, $^{65}{\rm Cr}$, $^{67,68}{\rm Mn}$, and $^{69,70}{\rm Fe}$. Our results show the $N=34$ subshell weaken at Sc and vanish at Ti, along with the absence of an $N=40$ subshell at Mn. This leads to a cooler accreted neutron star crust, highlighting the connection between the structure of nuclei and neutron stars.
Electromagnetic induction heating as a driver of volcanic activity on massive rocky planets

Aims. We investigate possible driving mechanisms of volcanic activity on rocky super-Earths with masses exceeding 3–4 M⊕. Due to high gravity and pressures in the mantles of these planets, melting in deep mantle layers can be suppressed, even if the energy release due to tidal heating and radioactive decay is substantial. Here we investigate whether a newly identified heating mechanism, namely induction heating by the star’s magnetic field, can drive volcanic activity on these planets due to its unique heating pattern in the very upper part of the mantle. In this region the pressure is not yet high enough to preclude the melt formation. Methods. Using the super-Earth HD 3167b as an example, we calculate induction heating in the planet’s interiors assuming an electrical conductivity profile typical of a hot rocky planet and a moderate stellar magnetic field typical of an old inactive star. Then we use a mantle convection code (CHIC) to simulate the evolution of volcanic outgassing with time. Results. We show that although in most cases volcanic outgassing on HD 3167b is not very significant in the absence of induction heating, including this heating mechanism changes the picture and leads to a substantial increase in the outgassing from the planet’s mantle. This result shows that induction heating combined with a high surface temperature is capable of driving volcanism on massive super-Earths, which has important observational implications.
Towards Prediction Explainability through Sparse Communication

Explainability is a topic of growing importance in NLP. In this work, we provide a unified perspective of explainability as a communication problem between an explainer and a layperson about a classifier's decision. We use this framework to compare several prior approaches for extracting explanations, including gradient methods, representation erasure, and attention mechanisms, in terms of their communication success. In addition, we reinterpret these methods at the light of classical feature selection, and we use this as inspiration to propose new embedded methods for explainability, through the use of selective, sparse attention. Experiments in text classification, natural language entailment, and machine translation, using different configurations of explainers and laypeople (including both machines and humans), reveal an advantage of attention-based explainers over gradient and erasure methods. Furthermore, human evaluation experiments show promising results with post-hoc explainers trained to optimize communication success and faithfulness.
Evaluating the Role of Language Typology in Transformer-Based Multilingual Text Classification

As NLP tools become ubiquitous in today's technological landscape, they are increasingly applied to languages with a variety of typological structures. However, NLP research does not focus primarily on typological differences in its analysis of state-of-the-art language models. As a result, NLP tools perform unequally across languages with different syntactic and morphological structures. Through a detailed discussion of word order typology, morphological typology, and comparative linguistics, we identify which variables most affect language modeling efficacy; in addition, we calculate word order and morphological similarity indices to aid our empirical study. We then use this background to support our analysis of an experiment we conduct using multi-class text classification on eight languages and eight models.
Measurements of Single Event Upset in ATLAS IBL

Effects of Single Event Upsets (SEU) and Single Event Transients (SET) are studied in the FE-I4B chip of the innermost layer of the ATLAS pixel system. SEU/SET affect the FE-I4B Global Registers as well as the settings for the individual pixels, causing, among other things, occupancy losses, drops in the low voltage currents, noisy pixels, and silent pixels. Quantitative data analysis and simulations indicate that SET dominate over SEU on the load line of the memory. Operational issues and mitigation techniques are presented.
Evolution of Semantic Similarity—A Survey

Estimating the semantic similarity between text data is one of the challenging and open research problems in the field of Natural Language Processing (NLP). The versatility of natural language makes it difficult to define rule-based methods for determining semantic similarity measures. To address this issue, various semantic similarity methods have been proposed over the years. This survey article traces the evolution of such methods beginning from traditional NLP techniques such as kernel-based methods to the most recent research work on transformer-based models, categorizing them based on their underlying principles as knowledge-based, corpus-based, deep neural network–based methods, and hybrid methods. Discussing the strengths and weaknesses of each method, this survey provides a comprehensive view of existing systems in place for new researchers to experiment and develop innovative ideas to address the issue of semantic similarity.
Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction

As an effective learning paradigm against insufficient training samples, multi-task learning (MTL) encourages knowledge sharing across multiple related tasks so as to improve the overall performance. In MTL, a major challenge springs from the phenomenon that sharing the knowledge with dissimilar and hard tasks, known as negative transfer, often results in a worsened performance. Though a substantial amount of studies have been carried out against the negative transfer, most of the existing methods only model the transfer relationship as task correlations, with the transfer across features and tasks left unconsidered. Different from the existing methods, our goal is to alleviate negative transfer collaboratively across features and tasks. To this end, we propose a novel multi-task learning method called task-feature collaborative learning (TFCL). Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks and suppressing inter-group knowledge sharing. We then propose an optimization method for the model. Extensive theoretical analysis shows that our proposed method has the following benefits: (a) it enjoys the global convergence property and (b) it provides a block-diagonal structure recovery guarantee. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks. We further apply it to the personalized attribute prediction problem with fine-grained modeling of user behaviors. Finally, experimental results on both simulated dataset and real-world datasets demonstrate the effectiveness of our proposed method.
Benchmarking Robustness of Machine Reading Comprehension Models

Machine Reading Comprehension (MRC) is an important testbed for evaluating models' natural language understanding (NLU) ability. There has been rapid progress in this area, with new models achieving impressive performance on various MRC benchmarks. However, most of these benchmarks only evaluate models on in-domain test sets without considering their robustness under test-time perturbations. To fill this important gap, we construct AdvRACE (Adversarial RACE), a new model-agnostic benchmark for evaluating the robustness of MRC models under six different types of test-time perturbations, including our novel superimposed attack and distractor construction attack. We show that current state-of-the-art (SOTA) models are vulnerable to these simple black-box attacks. Our benchmark is constructed automatically based on the existing RACE benchmark, and thus the construction pipeline can be easily adopted by other tasks and datasets. We will release the data and source codes to facilitate future work. We hope that our work will encourage more research on improving the robustness of MRC and other NLU models.