Miracle-dz/UniFAR-FTUs huggingface dataset:
Ramification has little impact on shoot hydraulic efficiency in the sexually dimorphic genus Leucadendron (Proteaceae)

Despite the diversity of branching architectures in plants, the impact of this morphological variation on hydraulic efficiency has been poorly studied. Branch junctions are commonly thought to be points of high hydraulic resistance, but adjustments in leaf area or xylem conduit abundance or dimensions could compensate for the additional hydraulic resistance of nodal junctions at the level of the entire shoot. Here we used the sexually dimorphic genus Leucadendron (Proteaceae) to test whether variation in branch ramification impacts shoot hydraulic efficiency. We found that branch ramification was related to leaf traits via Corner’s rules such that more highly ramified shoots had smaller leaves, but that branch ramification had little consistent impact on shoot hydraulic efficiency, whether measured on a leaf area or stem cross-sectional area basis. These results suggest that the presumed increase in resistance associated with branching nodes can be compensated by other adjustments at the shoot level (e.g. leaf area adjustments, increased ramification to add additional branches in parallel rather than in series) that maintain hydraulic efficiency at the level of the entire shoot. Despite large morphological differences between males and females in the genus Leucadendron, which are due to differences in pollination and reproduction between the sexes, the physiological differences between males and females are minimal.
Anti‐JC virus antibodies: Implications for PML Risk Stratification

A study was undertaken to establish an enzyme‐linked immunosorbent assay (ELISA) to detect JC virus (JCV)‐specific antibodies in multiple sclerosis (MS) patients, and to evaluate its potential utility for identifying patients at higher or lower risk (ie, risk stratification) of developing progressive multifocal leukoencephalopathy (PML).
Blockchain Based Access Control Services

This paper presents a new design approach for Access Control services leveraging smart contracts provided by blockchain technology. The key idea of our proposal is to codify Access Control policies as executable smart contracts on a blockchain. This transforms the policy evaluation process into completely distributed smart contract executions. In our fully blockchain based approach also the Attribute Managers required for the evaluation of the Access Control policies are managed by the blockchain, i.e., they are implemented as smart contracts as well. To study the feasibility of our proposal we present a working reference implementation using XACML policies and Solidity written smart contracts deployed on Ethereum. Finally we evaluate the advantages and drawbacks of the proposal, making also use of experimental results of our reference implementation.
The dust content of the Crab Nebula

We have modelled the near-infrared to radio images of the Crab Nebula with a Bayesian SED model to simultaneously fit its synchrotron, interstellar (IS), and supernova dust emission. We infer an IS dust extinction map with an average AV = 1.08 ± 0.38 mag, consistent with a small contribution (${\lesssim }22{{\ \rm per\ cent}}$) to the Crab’s overall infrared emission. The Crab’s supernova dust mass is estimated to be between 0.032 and 0.049 M⊙ (for amorphous carbon grains) with an average dust temperature Tdust = 41 ± 3 K, corresponding to a dust condensation efficiency of 8–12 ${{\ \rm per\ cent}}$. This revised dust mass is up to an order of magnitude lower than some previous estimates, which can be attributed to our different IS dust corrections, lower SPIRE flux densities, and higher dust temperatures than were used in previous studies. The dust within the Crab is predominantly found in dense filaments south of the pulsar, with an average V-band dust extinction of AV = 0.20–0.39 mag, consistent with recent optical dust extinction studies. The modelled synchrotron power-law spectrum is consistent with a radio spectral index αradio = 0.297 ± 0.009 and an infrared spectral index αIR = 0.429 ± 0.021. We have identified a millimetre excess emission in the Crab’s central regions, and argue that it most likely results from two distinct populations of synchrotron emitting particles. We conclude that the Crab’s efficient dust condensation (8–12 ${{\ \rm per\ cent}}$) provides further evidence for a scenario where supernovae can provide substantial contributions to the IS dust budgets in galaxies.
Inferring black hole spins and probing accretion/ejection flows in AGNs with the Athena X-ray Integral Field Unit

Context. Active galactic nuclei (AGNs) display complex X-ray spectra that exhibit a variety of emission and absorption features. These are commonly interpreted as a combination of (i) a relativistically smeared reflection component, resulting from the irradiation of an accretion disk by a compact hard X-ray source; (ii) one or several warm or ionized absorption components produced by AGN-driven outflows crossing our line of sight; and (iii) a nonrelativistic reflection component produced by more distant material. Disentangling these components via detailed model fitting could be used to constrain the black hole spin, geometry, and characteristics of the accretion flow, as well as of the outflows and surroundings of the black hole. Aims. We investigate how a high-throughput high-resolution X-ray spectrometer such as the Athena X-ray Integral Field Unit (X-IFU) can be used to this aim, using the state-of-the-art reflection model relxill in a lamp-post geometrical configuration. Methods. We simulated a representative sample of AGN spectra, including all necessary model complexities, as well as a range of model parameters going from standard to more extreme values, and considered X-ray fluxes that are representative of known AGN and quasar populations. We also present a method to estimate the systematic errors related to the uncertainties in the calibration of the X-IFU. Results. In a conservative setting, in which the reflection component is computed self consistently by the relxill model from the pre-set geometry and no iron overabundance, the mean errors on the spin and height of the irradiating source are < 0.05 and ∼0.2 Rg (in units of gravitational radius). Similarly, the absorber parameters (column density, ionization parameter, covering factor, and velocity) are measured to an accuracy typically less than ∼5% over their allowed range of variations. Extending the simulations to include blueshifted ultra-fast outflows, we show that X-IFU could measure their velocity with statistical errors < 1%, even for high-redshift objects (e.g., at redshifts ∼2.5). Conclusion. The simulations presented here demonstrate the potential of the X-IFU to understand how black holes are powered and how they shape their host galaxies. The accuracy in recovering the physical model parameters encoded in their X-ray emission is reached thanks to the unique capability of X-IFU to separate and constrain narrow and broad emission and absorption components.
Machine Learning and Visualization in Clinical Decision Support: Current State and Future Directions

Deep learning, an area of machine learning, is set to revolutionize patient care. But it is not yet part of standard of care, especially when it comes to individual patient care. In fact, it is unclear to what extent data-driven techniques are being used to support clinical decision making (CDS). Heretofore, there has not been a review of ways in which research in machine learning and other types of data-driven techniques can contribute effectively to clinical care and the types of support they can bring to clinicians. In this paper, we consider ways in which two data driven domains - machine learning and data visualizations - can contribute to the next generation of clinical decision support systems. We review the literature regarding the ways heuristic knowledge, machine learning, and visualization are - and can be - applied to three types of CDS. There has been substantial research into the use of predictive modeling for alerts, however current CDS systems are not utilizing these methods. Approaches that leverage interactive visualizations and machine-learning inferences to organize and review patient data are gaining popularity but are still at the prototype stage and are not yet in use. CDS systems that could benefit from prescriptive machine learning (e.g., treatment recommendations for specific patients) have not yet been developed. We discuss potential reasons for the lack of deployment of data-driven methods in CDS and directions for future research.
A Novel Trust Management Scheme for Defending Against On-Off Attack Based on Gaussian Distribution

A Gaussian trusted scheme with adaptive factors defending against On-off attack for sensor networks (GTS) was proposed, with the tests between Gaussian probability distribution and Beta reputation distribution. Meanwhile, the adaptive control factor and the time slot are introduced to verify and restrict the compromised modes, which refer to those nodes' reputation value changes are caused by On-off attacks. Through some preliminary simulation results, this model is more stable to describe reputation against other distributions and easier to display the trust directly and sensitively, and it also has the powerful ability to prevent from On-off attacks.
Canny SLIC to Compute Content-Sensitive Superpixels

Superpixel segmentation is becoming a ubiquitous initial preprocessing step in computer vision applications. It is important that the resulting superpixels preserve image boundaries. The simple linear iterative clustering (SLIC) algorithm is a popular method for superpixel segmentation. However, in the case of content-sensitive superpixels, which are located in small structured -dense regions with high color variation, SLIC may not generate boundary-preserving superpixels. More complex methods have alleviated this problem by, e.g., using distance measures other than Euclidean distance. However, as an initial preprocessing step, the simplicity of superpixel segmentation is crucial. We propose a relatively simple method that uses the Canny edge detector in combination with SLIC to generate superpixels that tend to preserve image boundaries.
Energy Efficient Caching in Backhaul-Aware Ultra-Dense Cellular Networks

Caching popular contents at base stations (BSs) has been regarded as an effective approach to alleviate the backhaul load and to improve the quality of service. To meet the explosive data traffic demand and to save energy consumption, energy efficiency (EE) has become an extremely important performance index for the 5th generation (5G) cellular networks. In general, there are two ways for improving the EE, i.e., improving the cache hit rate and optimizing the cache size. In this work, we investigate the energy efficient caching problem in backhaul-aware cellular networks jointly considering these two approaches. Note that the content popularity distribution changes with time slowly. To cache the most popular content, we consider a static content catalog, ranking from the most popular to the least popular based on the Zipf popularity distribution. Then we propose a distributed caching policy to improve the cache hit rate. Furthermore, we analyze the tradeoff between energy efficiency and cache capacity for which an optimization is formulated. We prove its convexity and derive a closed-form optimal cache capacity for maximizing the EE. Simulation results validate the proposed scheme and show that EE can be improved with appropriate choice of cache capacity.
Comparison of the Reflectance Method (Reflotron® Reflectance Photometer) with the Absorbance Method (Automatic Analysers) for the Determination of Cholesterol

The European Atherosclerosis Society (1) and the Expert Panel of the US National Cholesterol Education Program (2) have issued detailed guide values for recognition and management of hyperlipidaemia in adults. In these guidelines, the diagnosis of dyslipidaemia based on the measurements of total cholesterol, triacylglycerols, HDL and LDL cholesterol plays an important role. A prerequisite for the desired success of interventive measures is the reliability of the analytical data. The aim of this study was to investigate the precision and accuracy of Reflotron Cholesterol, a method based on the dry chemistry principle. Accuracy was assessed by establishing the correlation with the standardized automated methods used in routine lipid diagnosis. In addition, it was also examined whether the Reflotron Cholesterol results in plasma and blood are comparable. The Reflotron cholesterol (sample: blood) showed a good correlation with the CHOD/PAP method on a Hitachi 737 instrument (sample: plasma). The median value of the differences of the test results was -0.4%. Similarly, the method comparison of Reflotron Cholesterol (sample: blood) versus CHOD/PAP method on a SMAC instrument (sample: plasma) showed that Reflotron produces slightly (1.8%) higher results. The Reflotron Cholesterol values obtained from blood samples were slightly lower than those from plasma samples (median value of the differences: -2.2%). The results suggest that for routine purposes Reflotron Cholesterol provides results which are in good agreement with those obtained by standardized wet chemistry methods.
Unfolding the Clickbait: a Siren's Call in the Attention Economy

The media consumption moving online was supposed to enable people to access information more effectively, instead, to some extent, people were yet overwhelmed. Clickbait, defined as “(On the Internet) content whose main purpose is to attract attention and encourage visitors to click on a link to a particular web page” (in Oxford English Dictionary), prevails and restrains people retrieve their desirable information effectively. In this exploratory study, we proposed two intriguing research conjectures, i.e. how rhetorical features in the clickbait influence the visiting traffic to the publisher at different levels and attempted to understand the antecedents and consequences of the prevalence of clickbait. In collaboration with a leading digital media company, we longitudinally collected a massive dataset in 2017. To test the research conjectures, we designed and developed a series of text mining methods and applied econometric analysis for empirical validation. The findings revealed 1) the rhetorical characteristics (hyperbole, insinuation, and visual rhetoric) could entice individual to click the baited headlines, 2) there was a quadratic (inverted U-shaped) relationship between number of clickbait posted by publisher and its visit traffic, and 3) such non-linear relationship was moderated by publisher’s age.
SParC: Cross-Domain Semantic Parsing in Context

We present SParC, a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences (12k+ individual questions annotated with SQL queries). It is obtained from controlled user interactions with 200 complex databases over 138 domains. We provide an in-depth analysis of SParC and show that it introduces new challenges compared to existing datasets. SParC demonstrates complex contextual dependencies, (2) has greater semantic diversity, and (3) requires generalization to unseen domains due to its cross-domain nature and the unseen databases at test time. We experiment with two state-of-the-art text-to-SQL models adapted to the context-dependent, cross-domain setup. The best model obtains an exact match accuracy of 20.2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research. The dataset, baselines, and leaderboard are released at https://yale-lily.github. io/sparc.
Estimation and Tracking of a Moving Target by Unmanned Aerial Vehicles

An image-based control strategy along with estimation of target motion is developed to track dynamic targets without motion constraints. To the best of our knowledge, this is the first work that utilizes a bounding box as image features for tracking control and estimation of dynamic target without motion constraint. The features generated from a You-Only-Look-Once (YOLO) deep neural network can relax the assumption of continuous availability of the feature points in most literature and minimize the gap for applications. The challenges are that the motion pattern of the target is unknown and modeling its dynamics is infeasible. To resolve these issues, the dynamics of the target is modeled by a constant-velocity model and is employed as a process model in the Unscented Kalman Filter (UKF), but process noise is uncertain and sensitive to system instability. To ensure convergence of the estimate error, the noise covariance matrix is estimated according to history data within a moving window. The estimated motion from the UKF is implemented as a feedforward term in the developed controller, so that tracking performance is enhanced. Simulations are demonstrated to verify the efficacy of the developed estimator and controller.
Skill Confidence Ratings in a MOOC: Examining the Link between Skill Confidence and Learner Development

This paper explores the development of perceived learner skill confidence in a programming MOOC by applying and analyzing a new Skill Confidence Rating (SCR) survey. After cleaning datasets, we analyze a sample of n = 1689 for the first course module and n = 1147 for the second course module. Results show that on average, learners perceive their skills more confidently after taking a module. The initial confidence per module differs. We could not find a correlation between perceived learner confidence and learner performance in this course.
Effects of Soil Residues of Sulfosulfuron and Mesosulfuron Methyl + Iodosulfuron Methyl Sodium on Sunflower Varieties

Using field assays, we studied how the residues of sulfosulfuron and mesosulfuron methyl + iodosulfuron methyl sodium (ME+IMS) applied at two spraying times and three application rates affected two sunflower varieties seeded 12 months after treatment (MAT) in Central Anatolian Region, Turkey. Specifically, we studied their effects on the shoot length, seed yield, and yield components, including plant length, head diameter (HD), and 1000-seed weight. Sunflower yield was found to be the most sensitive biological parameter to the herbicidal residue in the soil, while the 1000-seed weight was the least sensitive. Sulfosulfuron previously applied to winter wheat at two sites greatly reduced subsequent sunflower yield depending on application time, rate, and sunflower variety. At site 1, all sunflower seedlings planted in several plots that were treated with doubled rates of sulfosulfuron died. Similar to the effects of sulfosulfuron residue, ME+IMS previously applied to winter wheat also greatly reduced sunflower yield. Generally, the sunflower yields decreased as the dose of the chosen herbicide increased at both sites. More herbicide molecules were leached or degraded by a variety of pathways when sulfosulfuron and ME+IMS were applied early. The responses of sunflower cultivars to residues of sulfosulfuron and ME+IMS were very similar. Sulfosulfuron residue was more phytotoxic to the sunflower varieties than was ME+IMS at the both sites.
Expression of ghrelin receptor mRNA in the rat and the mouse brain

Ghrelin is a hormone that stimulates growth hormone secretion and signals energy insufficiency via interaction with its receptor, the growth hormone secretagogue receptor (GHSR). The GHSR is located in both the central nervous system and the periphery. Its distribution in the CNS, as assessed by in situ hybridization histochemistry (ISHH), has been described previously in a few mammalian species, although these studies were limited by either the detail provided or the extent of the regions examined. In the present study, we systematically examined the distribution of GHSR mRNA in the adult rat and mouse brains and cervical spinal cords by using ISHH with novel cRNA probes specific for the mRNA encoding functional GHSR (the type 1a variant). We confirmed GHSR mRNA expression in several hypothalamic nuclei, many of which have long been recognized as playing roles in body weight and food intake. GHSR also was found in several other regions previously unknown to express GHSR mRNA, including many parasympathetic preganglionic neurons. Additionally, we found GHSR mRNA within all three components of the dorsal vagal complex, including the area postrema, the nucleus of the solitary tract, and the dorsal motor nucleus of the vagus. Finally, we examined the coexpression of GHSR with tyrosine hydroxylase and cholecystokinin and demonstrate a high degree of GHSR mRNA expression within dopaminergic, cholecystokinin‐containing neurons of the substantia nigra and ventral tegmental area. J. Comp. Neurol. 494:528–548, 2006. © 2005 Wiley‐Liss, Inc.
Understanding, Categorizing and Predicting Semantic Image-Text Relations

Two modalities are often used to convey information in a complementary and beneficial manner, e.g., in online news, videos, educational resources, or scientific publications. The automatic understanding of semantic correlations between text and associated images as well as their interplay has a great potential for enhanced multimodal web search and recommender systems. However, automatic understanding of multimodal information is still an unsolved research problem. Recent approaches such as image captioning focus on precisely describing visual content and translating it to text, but typically address neither semantic interpretations nor the specific role or purpose of an image-text constellation. In this paper, we go beyond previous work and investigate, inspired by research in visual communication, useful semantic image-text relations for multimodal information retrieval. We derive a categorization of eight semantic image-text classes (e.g., "illustration" or "anchorage") and show how they can systematically be characterized by a set of three metrics: cross-modal mutual information, semantic correlation, and the status relation of image and text. Furthermore, we present a deep learning system to predict these classes by utilizing multimodal embeddings. To obtain a sufficiently large amount of training data, we have automatically collected and augmented data from a variety of datasets and web resources, which enables future research on this topic. Experimental results on a demanding test set demonstrate the feasibility of the approach.