Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of non-native Eucalyptus plantations on epigeal spider communities in the northern Negev desert, Israel

Abstract Plantation forests are being planted at an increasing rate and account for 7% of the global forested area. The majority of planted forests are composed of exotic tree species, and Eucalyptus trees have become the most widely planted hardwood species in the world. While Eucalyptus plantations have economic importance, their role in native biodiversity conservation, especially in areas without naturally occurring forests, is little explored. In the present study, we assessed the impact on biodiversity of replacing natural semi-deserts with Eucalyptus camaldulensis plantations. The impact was evaluated by comparing epigeal spider communities of seven plantations with previously sampled communities of seven natural habitats in the northwestern Negev, Israel. In contrast to our assumptions, spider species richness was higher in Eucalyptus plantations compared to natural semi-deserts. However, substantial differences in species composition between the two habitat types were observed. Few species found in natural semi-deserts were sampled in the plantations, suggesting that Eucalyptus plantations cannot substitute for natural semi-desert habitats.
Automatic Analysis of Linguistic Complexity and Its Application in Language Learning Research

The construct of complexity, together with accuracy and fluency have become the central foci of language learning research in recent years. This dissertation focuses on complexity, a multidimensional construct that has its own working mechanism, cognitive and psycholinguistic processes, and developmental dynamics. Six studies revolving around complexity, including its conceptualization, automatic measurement, and application in language acquisition research are reported. The basis of these studies is the automatic multidimensional analysis of linguistic complexity, which was implemented into a Web platform called Common Text Analysis Platform by making use of state-of-the-art Natural Language Processing (NLP) technologies . The system provides a rich set of complexity measures that are easily accessible by normal users and supports collaborative development of complexity feature extractors. An application study zooming into characterizing the text-level readability with the word-level feature of lexical frequency is reported next. It was found that the lexical complexity measure of word frequency was highly predictive of text readability. Another application study focuses on investigating the developmental interrelationship between complexity and accuracy, an issue that conflicting theories and research results have been reported. Our findings support the simultaneous development account. The other few studies are about applying automatic complexity analysis to promote language development, which involves analyzing both learning input and learner production, as well as linking the two spaces. We first proposed and validated the approach to link input and production with complexity feature vector distances. Then the ICALL system SyB implementing the approach was developed and demonstrated. An effective test of the system was conducted with a randomized control experiment that tested the effects of different levels of input challenge on L2 development. Results of the experiment supported the comprehensible input hypothesis in Second Language Acquisition (SLA) and provided an automatizable operationalization of the theory.
The close binary fraction as a function of stellar parameters in APOGEE: a strong anticorrelation with α abundances

We use observations from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey to explore the relationship between stellar parameters and multiplicity. We combine high-resolution repeat spectroscopy for 41 363 dwarf and subgiant stars with abundance measurements from the APOGEE pipeline and distances and stellar parameters derived using Gaia DR2 parallaxes from Sanders & Das to identify and characterize stellar multiples with periods below 30 yr, corresponding to ΔRVmax ≳ 3 km s−1, where ΔRVmax is the maximum APOGEE-detected shift in the radial velocities. Chemical composition is responsible for most of the variation in the close binary fraction in our sample, with stellar parameters like mass and age playing a secondary role. In addition to the previously identified strong anticorrelation between the close binary fraction and [Fe/H], we find that high abundances of α elements also suppress multiplicity at most values of [Fe/H] sampled by APOGEE. The anticorrelation between α abundances and multiplicity is substantially steeper than that observed for Fe, suggesting C, O, and Si in the form of dust and ices dominate the opacity of primordial protostellar discs and their propensity for fragmentation via gravitational stability. Near [Fe/H] = 0 dex, the bias-corrected close binary fraction (a < 10 au) decreases from ≈100 per cent at [α/H] = −0.2 dex to ≈15 per cent near [α/H] = 0.08 dex, with a suggestive turn-up to ≈20 per cent near [α/H] = 0.2. We conclude that the relationship between stellar multiplicity and chemical composition for sun-like dwarf stars in the field of the Milky Way is complex, and that this complexity should be accounted for in future studies of interacting binaries.
The Landau equation as a Gradient Flow

We propose a gradient flow perspective to the spatially homogeneous Landau equation for soft potentials. We construct a tailored metric on the space of probability measures based on the entropy dissipation of the Landau equation. Under this metric, the Landau equation can be characterized as the gradient flow of the Boltzmann entropy. In particular, we characterize the dynamics of the PDE through a functional inequality which is usually referred as the Energy Dissipation Inequality. Furthermore, analogous to the optimal transportation setting, we show that this interpretation can be used in a minimizing movement scheme to construct solutions to a regularized Landau equation.
Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context

Concerns about the societal impact of AI-based services and systems has encouraged governments and other organisations around the world to propose AI policy frameworks to address fairness, accountability, transparency and related topics. To achieve the objectives of these frameworks, the data and software engineers who build machine-learning systems require knowledge about a variety of relevant supporting tools and techniques. In this paper we provide an overview of technologies that support building trustworthy machine learning systems, i.e., systems whose properties justify that people place trust in them. We argue that four categories of system properties are instrumental in achieving the policy objectives, namely fairness, explainability, auditability and safety & security (FEAS). We discuss how these properties need to be considered across all stages of the machine learning life cycle, from data collection through run-time model inference. As a consequence, we survey in this paper the main technologies with respect to all four of the FEAS properties, for data-centric as well as model-centric stages of the machine learning system life cycle. We conclude with an identification of open research problems, with a particular focus on the connection between trustworthy machine learning technologies and their implications for individuals and society.
The Discovery of a Hidden Broad-line AGN in a Bulgeless Galaxy: Keck NIR Spectroscopic Observations of SDSS J085153.64+392611.76

We report the discovery of a buried, active supermassive black hole (SMBH) in SDSS J085153.64+392611.76, a bulgeless Seyfert 2 (Sy2) galaxy. Keck near-infrared observations reveal a hidden broad-line region, allowing for the rare case where strong constraints can be placed on both the BH mass and bulge component. Using virial mass estimators, we obtain a BH mass of log(MBH/M⊙) = 6.78 ± 0.50. This is one of the only Sy2 active galactic nuclei (AGNs) hosted in a bulgeless galaxy with a virial BH mass estimate and could provide important constraints on the formation scenarios of the BH seed population. The lack of a bulge component suggests that the SMBH has grown quiescently, likely caused by secular processes independent of major mergers. In the absence of a detectable bulge component, we find the MBH–Mstellar relation to be more reliable than the MBH–Mbulge relation. In addition, we detect extended narrow Paα emission that allows us to create a rotation curve where we see counterrotating gas within the central kiloparsec. Possible causes of this counterrotation include a galactic bar or disruption of the inner gas by a recent fly-by of a companion galaxy. This in turn could have triggered accretion onto the central SMBH in the current AGN phase.
Restoration of grazing management and its effect on vegetation in an upland grassland

ABSTRACT Question: How are plant species and functional group composition, and potential sward height affected by implementation of different grazing regimes on previously abandoned semi-natural grassland? Location: The Jizerské mountains, northern Czech Republic. Methods: We established a randomized block experiment with the following treatments: unmanaged control (U), intensive (IG) and extensive (EG) continuous grazing, first cut followed by intensive (ICG) and first cut followed by extensive (ECG) continuous grazing for the rest of the growing season. The percentage cover of all vascular plant species was recorded in 40 permanent plots. Results: Total plant species richness increased in all managed treatments, whereas species number was reduced in U at the end of the experiment. Tall forbs (Aegopodium podagraria, Galium album, Anthriscus sylvestris, Cirsium arvense) as well as tall grasses (Elytrigia repens and Alopecurus pratensis) were more abundant in U. Species associated with both grazing treatments (IG, EG) were Dactylis glomerata, Festuca rubra agg. and Phleum pratense. Agrostis capillaris, Taraxacum spp. , Trifolium repens, Ranunculus repens and Cirsium vulgare were promoted by ECG and ICG. Abundance of tall grasses and tall forbs reflected the intensity of management in the order U>EG, ECG>IG, ICG. Prostrate forbs, on the other hand, increased their cover with increasing intensity: ICG>IG>ECG>EG. Conclusions: Plant species composition of semi-natural grasslands is affected by the defoliation regime. Continuous grazing on abandoned grassland alters the sward structure towards a permanent pasture with short, light-sensitive grasses and prostrate forbs. To maintain or enhance plant species richness in semi-natural grasslands, understanding the effects of different grazing regimes on plant species composition is necessary. Nomenclature: Kubát et al. (2002).
A method for checking high-redshift identification of radio AGNs

In large-scale optical spectroscopic surveys, there are many objects found to have multiple redshift measurements due to the weakness of their emission lines and the different automatic identification algorithms used. These include some suspicious high-redshift (z >= 5) active galactic nuclei (AGNs). Here we present a method for inspecting the high-redshift identification of such sources provided that they are radio-loud and have very long baseline interferometry (VLBI) imaging observations of their milli-arcsec (mas) scale jet structure available at multiple epochs. The method is based on the determination of jet component proper motions, and the fact that the combination of jet physics (the observed maximal values of the bulk Lorentz factor) and cosmology (the time dilation of observed phenomena in the early Universe) constrain the possible values of apparent proper motions. As an example, we present the case of the quasar J2346+0705 that was reported with two different redshifts, $z_{1} = 5.063$ and $z_{2} = 0.171$, in the literature. We measured the apparent proper motions ($\mu$) of three components identified in its radio jet by utilizing VLBI data taken from 2014 to 2018. We obtained $\mu_{J1} = 0.334 \pm 0.099$ mas yr$^{-1}$, $\mu_{J2} = 0.116 \pm 0.029$ mas yr$^{-1}$, and $\mu_{J3} = 0.060 \pm 0.005$ mas yr$^{-1}$. The maximal proper motion is converted to an apparent transverse speed of $\beta_{\rm app} = 41.2\pm12.2\,c$. if the source is at redshift 5.063. This value exceeds the blazar jet speeds known to date. This and other arguments suggest that J2346+0705 is hosted by a low-redshift galaxy. Our method may be applicable for other high-redshift AGN candidates lacking unambiguous spectroscopic redshift determination or having photometric redshift estimates only, but showing prominent radio jets allowing for VLBI measurements of fast jet proper motions.
Isolation of a cell surface receptor protein for laminin from murine fibrosarcoma cells

We used affinity chromatography to isolate a specific laminin-binding protein from murine fibrosarcoma cells. These cells bind exogenous laminin to their surface with high affinity (Kd = 2 X 10(-9)M for laminin) with approximately 5 X 10(4) sites per cell. Laminin affinity chromatography of [35S]methionine-labeled cell extracts produced two distinct proteins. One was identified as Type IV (basement membrane) collagen based on its migration pattern on SDS gels and bacterial collagenase sensitivity. The other protein, which migrates as a single band or closely spaced doublet on reduced SDS gels, has a reduced molecular weight of 69,000. Using a nitrocellulose filter disk assay, we found that the latter protein specifically bound 125I-laminin with the same high affinity (Kd = 2 X 10(-9)M for laminin) as did intact fibrosarcoma cells. By iodinating intact cells, we demonstrated that this laminin-binding protein is on the cell surface. We conclude that this protein with reduced molecular weight of 69,000 is a subunit or component of a larger cell surface receptor protein for laminin in this fibrosarcoma model. This laminin receptor may mediate the interaction of the cell with its extracellular matrix.
Natural language processing and recurrent network models for identifying genomic mutation-associated cancer treatment change from patient progress notes

Abstract Objectives Natural language processing (NLP) and machine learning approaches were used to build classifiers to identify genomic-related treatment changes in the free-text visit progress notes of cancer patients. Methods We obtained 5889 deidentified progress reports (2439 words on average) for 755 cancer patients who have undergone a clinical next generation sequencing (NGS) testing in Wake Forest Baptist Comprehensive Cancer Center for our data analyses. An NLP system was implemented to process the free-text data and extract NGS-related information. Three types of recurrent neural network (RNN) namely, gated recurrent unit, long short-term memory (LSTM), and bidirectional LSTM (LSTM_Bi) were applied to classify documents to the treatment-change and no-treatment-change groups. Further, we compared the performances of RNNs to 5 machine learning algorithms including Naive Bayes, K-nearest Neighbor, Support Vector Machine for classification, Random forest, and Logistic Regression. Results Our results suggested that, overall, RNNs outperformed traditional machine learning algorithms, and LSTM_Bi showed the best performance among the RNNs in terms of accuracy, precision, recall, and F1 score. In addition, pretrained word embedding can improve the accuracy of LSTM by 3.4% and reduce the training time by more than 60%. Discussion and Conclusion NLP and RNN-based text mining solutions have demonstrated advantages in information retrieval and document classification tasks for unstructured clinical progress notes.
New and rare tropical and subtropical fishes from northern New Zealand

Abstract Five fish species (Platax teira, Abudefduf vaigiensis, Chromis flavomaculata, Coris dorsomacula, and Aluterus scriptus) are recorded from New Zealand for the first time, and five species (Forcipiger flavissimus, Chromis vanderbilti, Chrysiptera rapanui, Parma kermadecensis, and Thalassoma lutescens) are newly recorded from mainland New Zealand. Five other species that have been reported previously in the popular press, or by name only, are confirmed as members of the New Zealand fauna (Carcharhinus longimanus, Etelis carbunculus, Istiophorus platypterus, Acanthocybium solandri, and Aseraggodes bahamondei). New observations of 20 previously reported tropical and subtropical species are also presented. Many of the fishes were observed in autumn 1996, 1998, and 1999, indicating recruitment or immigration when sea surface temperature (SST) was higher than average. Major influxes of tropical and subtropical fishes apparently occur during warm summers, although some warm periods were not accompanied by.
Kibble–Zurek mechanism in the Ising Field Theory

The Kibble--Zurek mechanism captures universality when a system is driven through a continuous phase transition. Here we study the dynamical aspect of quantum phase transitions in the Ising Field Theory where the quantum critical point can be crossed in different directions in the two-dimensional coupling space leading to different scaling laws. Using the Truncated Conformal Space Approach, we investigate the microscopic details of the Kibble--Zurek mechanism in terms of instantaneous eigenstates in a genuinely interacting field theory. For different protocols, we demonstrate dynamical scaling in the non-adiabatic time window and provide analytic and numerical evidence for specific scaling properties of various quantities. In particular, we argue that the higher cumulants of the excess heat exhibit universal scaling in generic interacting models for a slow enough ramp.
The Combinatorial Santa Claus Problem or: How to Find Good Matchings in Non-Uniform Hypergraphs

We consider hypergraphs on vertices $P\cup R$ where each hyperedge contains exactly one vertex in $P$. Our goal is to select a matching that covers all of $P$, but we allow each selected hyperedge to drop all but an $(1/\alpha)$-fraction of its intersection with $R$ (thus relaxing the matching constraint). Here $\alpha$ is to be minimized. We dub this problem the Combinatorial Santa Claus problem, since we show in this paper that this problem and the Santa Claus problem are almost equivalent in terms of their approximability. The non-trivial observation that any uniform regular hypergraph admits a relaxed matching for $\alpha = O(1)$ was a major step in obtaining a constant approximation rate for a special case of the Santa Claus problem, which received great attention in literature. It is natural to ask if the uniformity condition can be omitted. Our main result is that every (non-uniform) regular hypergraph admits a relaxed matching for $\alpha = O(\log\log(|R|))$, when all hyperedges are sufficiently large (a condition that is necessary). In particular, this implies an $O(\log\log(|R|))$-approximation algorithm for the Combinatorial Santa Claus problem with large hyperedges.
Algebraic slice spectral sequences

For certain motivic spectra, we construct a square of spectral sequences relating the effective slice spectral sequence and the motivic Adams spectral sequence. We show the square can be constructed for connective algebraic K-theory, motivic Morava K-theory, and truncated motivic Brown-Peterson spectra. In these cases, we show that the $\mathbb{R}$-motivic effective slice spectral sequence is completely determined by the $\rho$-Bockstein spectral sequence. Using results of Heard, we also obtain applications to the Hill-Hopkins-Ravenel slice spectral sequences for connective Real K-theory, Real Morava K-theory, and truncated Real Brown-Peterson spectra.
Compositional Generalization in Semantic Parsing: Pre-training vs. Specialized Architectures

While mainstream machine learning methods are known to have limited ability to compositionally generalize, new architectures and techniques continue to be proposed to address this limitation. We investigate state-of-the-art techniques and architectures in order to assess their effectiveness in improving compositional generalization in semantic parsing tasks based on the SCAN and CFQ datasets. We show that masked language model (MLM) pre-training rivals SCAN-inspired architectures on primitive holdout splits. On a more complex compositional task, we show that pre-training leads to significant improvements in performance vs. comparable non-pre-trained models, whereas architectures proposed to encourage compositional generalization on SCAN or in the area of algorithm learning fail to lead to significant improvements. We establish a new state of the art on the CFQ compositional generalization benchmark using MLM pre-training together with an intermediate representation.
The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Large-scale structure catalogues for cosmological analysis

We present large-scale structure catalogues from the completed extended Baryon Oscillation Spectroscopic Survey (eBOSS). Derived from Sloan Digital Sky Survey (SDSS) IV Data Release 16 (DR16), these catalogues provide the data samples, corrected for observational systematics, and random positions sampling the survey selection function. Combined, they allow large-scale clustering measurements suitable for testing cosmological models. We describe the methods used to create these catalogues for the eBOSS DR16 Luminous Red Galaxy (LRG) and Quasar samples. The quasar catalogue contains 343 708 redshifts with 0.8 < z < 2.2 over 4808 deg2. We combine 174 816 eBOSS LRG redshifts over 4242 deg2 in the redshift interval 0.6 < z < 1.0 with SDSS-III BOSS LRGs in the same redshift range to produce a combined sample of 377 458 galaxy redshifts distributed over 9493 deg2. Improved algorithms for estimating redshifts allow that 98 per cent of LRG observations result in a successful redshift, with less than one per cent catastrophic failures (Δz > 1000 km s−1). For quasars, these rates are 95 and 2 per cent (with Δz > 3000 km s−1). We apply corrections for trends between the number densities of our samples and the properties of the imaging and spectroscopic data. For example, the quasar catalogue obtains a χ2/DoF = 776/10 for a null test against imaging depth before corrections and a χ2/DoF= 6/8 after. The catalogues, combined with careful consideration of the details of their construction found here-in, allow companion papers to present cosmological results with negligible impact from observational systematic uncertainties.
A Framework for Automatic Behavior Generation in Multi-Function Swarms

Multi-function swarms are swarms that solve multiple tasks at once. For example, a quadcopter swarm could be tasked with exploring an area of interest while simultaneously functioning as ad-hoc relays. With this type of multi-function comes the challenge of handling potentially conflicting requirements simultaneously. Using the Quality-Diversity algorithm MAP-elites in combination with a suitable controller structure, a framework for automatic behavior generation in multi-function swarms is proposed. The framework is tested on a scenario with three simultaneous tasks: exploration, communication network creation and geolocation of Radio Frequency (RF) emitters. A repertoire is evolved, consisting of a wide range of controllers, or behavior primitives, with different characteristics and trade-offs in the different tasks. This repertoire enables the swarm to online transition between behaviors featuring different trade-offs of applications depending on the situational requirements. Furthermore, the effect of noise on the behavior characteristics in MAP-elites is investigated. A moderate number of re-evaluations is found to increase the robustness while keeping the computational requirements relatively low. A few selected controllers are examined, and the dynamics of transitioning between these controllers are explored. Finally, the study investigates the importance of individual sensor or controller inputs. This is done through ablation, where individual inputs are disabled and their impact on the performance of the swarm controllers is assessed and analyzed.