Miracle-dz/UniFAR-FTUs huggingface dataset:
Patterns of denitrification associated with land use in 9 midwestern headwater streams

Abstract The effects of land use on the relationships among denitrification, NO3−-N, dissolved organic C (DOC), and other environmental parameters were examined in 9 headwater streams (3 each in forested, agricultural, and urban-dominated subwatersheds) in the Kalamazoo River Watershed, Michigan. Sediment denitrification rates were determined using the chloramphenicol-amended acetylene inhibition technique. Agricultural streams had high concentrations of NO3−-N, DOC, soluble reactive P, and NH4+-N, whereas forested streams had the lowest concentrations of these nutrients, and urban streams generally exhibited intermediate concentrations. Sediment denitrification rates were highest in agricultural streams and lowest in forested streams throughout the study period. Availability of NO3−-N was the dominant environmental predictor of sediment denitrification rates, limiting denitrification when NO3−-N concentrations were below a calculated threshold of 0.4 mg NO3−-N/L. Other potential controlling variables (e.g., DOC, dissolved O2, water temperature, and sediment organic matter content) influenced denitrification rates secondarily. Despite higher denitrification rates in agricultural and urban streams compared to forested streams, sediment denitrification removed a smaller proportion of the stream NO3−-N load in agricultural and urban streams relative to forested streams.
Understanding the population genetic structure of Gleditsia triacanthos L.: seed dispersal and variation in female reproductive success

Fine‐scale genetic structuring is influenced by a variety of ecological factors and can directly affect the evolutionary dynamics of plant populations by influencing effective population size and patterns of viability selection. In many plant species, genetic structuring within populations may result from highly localized patterns of seed dispersal around maternal plants or by the correlated dispersal and recruitment of siblings from the same fruit. This fine‐scale genetic structuring may be enhanced if female parents vary significantly in their reproductive success. To test these hypotheses, we used genetic data from 17 allozyme loci and a maximum‐likelihood, ‘maternity‐analysis’ model to estimate individual female fertilities for maternal trees across a large number of naturally established seedlings and saplings in two populations of Gleditsia triacanthos L. (Leguminosae). Maximum‐likelihood fertility estimates showed that the three highest fertility females accounted for 58% of the 313 progeny at the first site and 46% of the 651 progeny at the second site, whereas 18 of 35 and 16 of 34 females, respectively, had fertility estimates that did not exceed 1%. Additional analyses of the second site found individual female fertility to vary significantly both within and among juvenile age classes. Female fertility at the first site was weakly correlated with maternal tree size and spatial location relative to the open, old‐field portions of the population, where the great majority of seedlings and saplings were growing, but no such correlations were found at the second site. Estimates of realized seed dispersal distances indicated that dispersal was highly localized at the first site, but was nearly random at the second site, possibly reflecting differences between the two sites in the behaviour of animal dispersers. The combined estimates of seed dispersal patterns and fertility variation are sufficient to explain previously described patterns of significant fine‐scale spatial genetic structure in these two populations. In general, our results demonstrate that effective seed dispersal distributions may vary significantly from population to population of a species due to the unpredictable behaviour of secondary dispersers. Consequently, the effects of seed dispersal on realized fine‐scale genetic structure may also be relatively unpredictable.
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure

Abstract In many product development problems, the performance of the product is governed by two types of parameters: design parameters and environmental parameters. While the former is fully controllable, the latter varies depending on the environment in which the product is used. The challenge of such a problem is to find the design parameter that maximizes the probability that the performance of the product will meet the desired requisite level given the variation of the environmental parameter. In this letter, we formulate this practical problem as active learning (AL) problems and propose efficient algorithms with theoretically guaranteed performance. Our basic idea is to use a gaussian process (GP) model as the surrogate model of the product development process and then to formulate our AL problems as Bayesian quadrature optimization problems for probabilistic threshold robustness (PTR) measure. We derive credible intervals for the PTR measure and propose AL algorithms for the optimization and level set estimation of the PTR measure. We clarify the theoretical properties of the proposed algorithms and demonstrate their efficiency in both synthetic and real-world product development problems.
Graph Neural Networks in TensorFlow and Keras with Spektral

In this paper we present Spektral, an open-source Python library for building graph neural networks with TensorFlow and the Keras application programming interface. Spektral implements a large set of methods for deep learning on graphs, including message-passing and pooling operators, as well as utilities for processing graphs and loading popular benchmark datasets. The purpose of this library is to provide the essential building blocks for creating graph neural networks, focusing on the guiding principles of user-friendliness and quick prototyping on which Keras is based. Spektral is, therefore, suitable for absolute beginners and expert deep learning practitioners alike. In this work, we present an overview of Spektral's features and report the performance of the methods implemented by the library in scenarios of node classification, graph classification, and graph regression.
Interactions between nitrifying and denitrifying bacteria in gnotobiotic microcosms planted with the emergent macrophyte Glyceria maxima

The population dynamics of the chemolithoautotrophic nitrifiers Nitrosomonas europaea and Nitrobacter winogradskyi were studied in gnotobiotic microcosms fed with ammonium in response to the presence or absence of the emergent macrophyte Glyceria maxima and the heterotrophic denitrifying bacterium Pseudomonas chlororaphis. By subjecting the plants to different day lengths, the effect of possibly limiting factors (i.e. oxygen and ammonium) on the interactions between the nitrifiers and denitrifying bacterium could be analysed. The presence of the plant had no effect on the growth of nitrifiers suggesting that, in addition to radial oxygen loss from the roots, other non-plant sources of oxygen (e.g. diffusion from the water layer) were important for nitrification. Potential nitrifying activities were suppressed by G. maxima due to ammonium uptake by the plants. Elongation of the day length in combination with the presence of G. maxima led to an increase in the number of P. chlororaphis. The presence of P. chlororaphis suppressed the growth of N. winogradskyi, but the growth of N. europaea and the potential nitrifying activities were not significantly affected. Potential denitrifying activities were stimulated by the plant, but showed no correlations with nitrifier activities or numbers. Apparently ammonium, and not oxygen, was the limiting factor for nitrification in the root zone of G. maxima. However, when the plant did not deplete the ammonium pool, P. chlororaphis could repress the nitrifiers indicating the latter's poor competitive status with respect to oxygen when the presence of root exudates allows for heterotrophic oxygen consumption.
Differing vernalization responses of Veronica spicata 'red fox' and Laurentia axillaris

Many polycarpic herbaceous perennials are known to have a cold-requirement for flowering. To determine the range and relative effectiveness of vernalization temperatures for flower induction, clonally propagated plants of veronica (Veronica spicata L.) 'Red Fox' and laurentia (Laurentia axillaris (Lindl.) E. Wimm. ) were exposed to temperatures from -2.5 to 20 8C at 2.5 8C increments for 0, 2, 4, 6, or 8 weeks (veronica 'Red Fox') and 0, 2.5, 5, 7.5, 10, 12.5, or 15 weeks (laurentia). After treatments, growth and flowering were monitored in a glass greenhouse set at 20 8C with an average daily light integral of '5 molm -2 d -1 . Both veronica 'Red Fox' and laurentia exhibited obligate vernalization requirements for flowering, but the temperature-response curves were distinctly different. A minimum of 4 weeks at -2.5 and 0 8C, 6 weeks at 2.5 8C, and 8 weeks at 5 and 7.5 8C was required for complete (100%) flowering of veronica 'Red Fox', while a minimum of 5 weeks at 5 to 10 8C, 7.5 weeks at 12.5 8C, and 10 weeks at 2.5 8C were required for complete flowering of laurentia. For veronica 'Red Fox', node number under each flower and flower timing were relatively fixed following up to 8 weeks at each temperature, although these values generally decreased at each temperature with extended exposure for laurentia. Based on percent flowering and percentage of lateral nodes flowering, vernalization of veronica 'Red Fox' was most effective at 0 and -2.5 8C, while based on percent flowering and flower number, vernalization of laurentia was most effective at 5 to 10 8C. Many herbaceous winter annual, biennial, and perennial plants require vernalization, exposure to low temperatures, for flower induction (Chouard, 1960; Thomas and Vince-Prue, 1984). While vernalization responses of numerous plant species have been studied, the bulk of our knowledge comes from extensive research on a relatively few plant species. In particular, numerous studies have evaluated the vernalization responses of winter annuals and biennials such as winter wheat (Triticum aestivum L.), 'Petkus' winter rye (Secale cereale L.), carrot (Daucus carota L.), celery (Apium graveolens L.), sugar beet (Beta vulgaris L.), onion (Allium cepa L.), arabidopsis (Arabidopsis thaliana (L.) Heynh. ) var. Stockholm, Hyoscya- mus niger (L.), and Lunaria annua (L.) (Chouard, 1960; Lang, 1952, 1965; Thomas and Vince-Prue, 1984).
High-Precision Digital Traffic Recording with Multi-LiDAR Infrastructure Sensor Setups*

Large driving datasets are a key component in the current development and safeguarding of automated driving functions. Various methods can be used to collect such driving data records. In addition to the use of sensor equipped research vehicles or unmanned aerial vehicles (UAVs), the use of infrastructure sensor technology offers another alternative. To minimize object occlusion during data collection, it is crucial to record the traffic situation from several perspectives in parallel. A fusion of all raw sensor data might create better conditions for multi-object detection and tracking (MODT) compared to the use of individual raw sensor data. So far, no sufficient studies have been conducted to sufficiently confirm this approach. In our work we investigate the impact of fused LiDAR point clouds compared to single LiDAR point clouds. We model different urban traffic scenarios with up to eight 64-layer LiDARs in simulation and in reality. We then analyze the properties of the resulting point clouds and perform MODT for all emerging traffic participants. The evaluation of the extracted trajectories shows that a fused infrastructure approach significantly increases the tracking results and reaches accuracies within a few centimeters.
Factors affecting the nutritional value of canola meal: A review

In this review canola meal (CM), its nutritionally significant components and the availability of its nutrients to poultry, pigs and cattle were discussed. Avenues for possible improvement in available energy, protein and lysine were identified. Recent findings on glucosinolates were also discussed with the conclusion that a further reduction of glucosinolates through plant breeding is both possible and desirable. The role of the anti-nutrients sinapine, tannins and phytic acid were also considered. The potential impact of improvements to CM on its economic value was examined using linear program least-cost ration formulations applied to typical market situations. Key words: Canola meal, energy, protein, carbohydrates, anti-nutritional factors, economics
Personalized PageRank to a Target Node, Revisited

Personalized PageRank (PPR) is a widely used node proximity measure in graph mining and network analysis. Given a source node s and a target node t, the PPR value π(s,t) represents the probability that a random walk from s terminates at t, and thus indicates the bidirectional importance between s and t. The majority of the existing work focuses on the single-source queries, which asks for the PPR value of a given source node s and every node t ∈ V. However, the single-source query only reflects the importance of each node t with respect to s. In this paper, we consider the single-target PPR query, which measures the opposite direction of importance for PPR. Given a target node t, the single-target PPR query asks for the PPR value of every node $s\in V$ to a given target node t. We propose RBS, a novel algorithm that answers approximate single-target queries with optimal computational complexity. We show that RBS improves three concrete applications: heavy hitters PPR query, single-source SimRank computation, and scalable graph neural networks. We conduct experiments to demonstrate that RBS outperforms the state-of-the-art algorithms in terms of both efficiency and precision on real-world benchmark datasets.
Always-On, Sub-300-nW, Event-Driven Spiking Neural Network based on Spike-Driven Clock-Generation and Clock- and Power-Gating for an Ultra-Low-Power Intelligent Device

Always-on artificial intelligent (AI) functions such as keyword spotting (KWS) and visual wake-up tend to dominate total power consumption in ultra-low power devices [1]. A key observation is that the signals to an always-on function are sparse in time, which a spiking neural network (SNN) classifier can leverage for power savings, because the switching activity and power consumption of SNNs tend to scale with spike rate. Toward this goal, we present a novel SNN classifier architecture for always-on functions, demonstrating sub-300nW power consumption at the competitive inference accuracy for a KWS and other always-on classification workloads.
Serum amyloid A (SAA): Biochemistry, genetics and the pathogenesis of AA amyloidosis

Serum amyloid A (apoSAA) is a polymorphic protein encoded by a family of SAA genes in which new members continue to be identi3ed. Those isoforms or allelic forms that are precursors for the amyloidfibril protein A(AA) in reactive (secondary) amyloidosis, are among the acute phase or regulated apoSAA apoproteins complexed with high densig lipoprotein (HDL); other isoforms are expressed constitutively. The chieffunction of apoSAA, which is a well conserved protein found in birds and mammals, has probably not yet been disclosed, but is thought to be part of the host response to infections and tissue damage. Structural and functional aspects of apoSAA. and its role in AA amyloidosis are complex due to the presence of multiple isoforms. Most species appear to possess two main acute phase apoSAA isoforms of hepatic origin in their serum (apoSAA, and apoSAAJ and a third form that has predominant extrahepatic expression (apoSAAJ A single constitutive isoform, apoSAA,, has been identijied in human and apoSAA, has been described in BALB/c mice. Thus, apoSAA proteins can be regarded or subclassified as either acute phase reactants (regulated apoSAA also termed A-SAA) or constitutive proteins (C-SAA).
Studies on the Association of the Ant Oecophylla longinoda (Latr.) (Formicidae) with the Scale Insect Saissetia zanzibarensis Williams (Coccidae).

A close association exists between the ant Oecophylla longinoda (Latr.) and the scale insect Saissetia zanzibarensis Williams. Several factors cause the scale to be rare in the absence of the ant. Contamination by honey-dew and sooty moulds prevents increase of the S. zanzibarensis population above a relatively low level, while, in addition, insect parasites and probably predators virtually exterminate the scale. A fungal parasite may destroy honey-dew-contaminated S. zanzibarensis under humid conditions. The attendant O. longinoda prevents contamination of the scale by honeydew. It gives absolute protection from Coccinellid predators and, although parasitism by Coccophagus spp. and predation by Eublemma spp. is not prevented, the mortality of S. zanzibarensis which they cause is thought to be of little significance. O. longinoda workers benefit S. zanzibarensis in other ways. They remove debris from the scale clusters; they transport the nymphs and establish them at suitable feeding sites. The effect of these benefits is most marked when the ant population is high relative to that of the scale. The silken shelters built by O. longinoda over S. zanzibarensis clusters protect the ant during adverse weather and may only incidentally benefit the scales. S. zanzibarensis and other honey-dew-producing Homoptera are the major food source for O. longinoda . Other sources are sufficient to maintain only relatively low populations of the ant.
A deep convolutional neural network for rapid fluvial flood inundation modelling

The two-dimensional (2D) hydrodynamic models are often infeasible for real-time operations. In this paper, a deep convolutional neural network (CNN)-based method is presented for rapid fluvial flood modelling. The CNN model is trained using outputs from a two-dimensional hydraulic model (i.e. LISFLOOD-FP) to predict water depths. The pre-trained model is then applied to simulate the flooding event that occurred in Carlisle, UK, in January 2005. The predictions are compared against the outputs produced by the calibrated LISFLOOD-FP. The performance of the CNN is also compared with a support vector regression (SVR)-based method. The results show that the CNN model outperforms SVR by a large margin. The model is highly accurate in capturing flooded cells as indicated by several quantitative assessment matrices, e.g., the estimated error for the peak flood depth is 0-0.2 meters for 97% cells of the domain when 99% confidence level is drawn. The proposed method offers great potential for real-time applications considering its simplicity, superior performance and computational efficiency.
Temperature and its impact on predation risk within aquatic ecosystems

Metabolic rates of fish and their activity levels have thermal optima. When environmental temperatures are below these optima, increasing temperature will increase their rates of energy consumption, resulting in a corresponding increase in the risk of starvation. For that reason we predicted that within this temperature range, food is of greater value at higher temperatures so fish should be willing to incur greater costs to obtain it. To test this hypothesis, we measured how the activity and foraging rates of the fathead minnow (Pimephales promelas) changed with temperature at 4, 15, and 24 °C. As expected, fish activity and foraging were greater at higher temperatures. We then measured the impact of predation risk on foraging decisions at 5, 15, and 23 °C. At 5 and 15 °C, the risk of predation had a significant effect on foraging decisions, but there was no effect at 23 °C. These results demonstrate that increasing temperatures below their optimal level diminish the impact of predation risk on foraging .
Generalized Inverses of Tensors Over Rings

Generalized inverses of tensors play increasingly important roles in computational mathematics and numerical analysis. It is appropriate to develop the theory of generalized inverses of tensors within the algebraic structure of a ring. In this paper, we study different generalized inverses of tensors over a commutative ring and a non-commutative ring. Several numerical examples are provided in support of the theoretical results. We also propose algorithms for computing the inner inverses, the Moore-Penrose inverse, and weighted Moore-Penrose inverse of tensors over a non-commutative ring. The prowess of some of the results is demonstrated by applying these ideas to solve an image deblurring problem.
Broadcasting on random recursive trees

We study the broadcasting problem when the underlying tree is a random recursive tree. The root of the tree has a random bit value assigned. Every other vertex has the same bit value as its parent with probability $1-q$ and the opposite value with probability $q$, where $q \in [0,1]$. The broadcasting problem consists in estimating the value of the root bit upon observing the unlabeled tree, together with the bit value associated with every vertex. In a more difficult version of the problem, the unlabeled tree is observed but only the bit values of the leaves are observed. When the underlying tree is a uniform random recursive tree, in both variants of the problem we characterize the values of $q$ for which the optimal reconstruction method has a probability of error bounded away from $1/2$. We also show that the probability of error is bounded by a constant times $q$. Two simple reconstruction rules are analyzed in detail. One of them is the simple majority vote, the other is the bit value of the centroid of the tree. Most results are extended to linear preferential attachment trees as well.
Short-Term Traffic Forecasting Using High-Resolution Traffic Data

This paper develops a data-driven toolkit for traffic forecasting using high-resolution (a.k. a. event-based) traffic data. This is the raw data obtained from fixed sensors in urban roads. Time series of such raw data exhibit heavy fluctuations from one time step to the next (typically on the order of 0.1-1 second). Short-term forecasts (10-30 seconds into the future) of traffic conditions are critical for traffic operations applications (e.g., adaptive signal control). But traffic forecasting tools in the literature deal predominantly with 3-5 minute aggregated data, where the typical signal cycle is on the order of 2 minutes. This renders such forecasts useless at the operations level. To this end, we model the traffic forecasting problem as a matrix completion problem, where the forecasting inputs are mapped to a higher dimensional space using kernels. The formulation allows us to capture both nonlinear dependencies between forecasting inputs and outputs but also allows us to capture dependencies among the inputs. These dependencies correspond to correlations between different locations in the network. We further employ adaptive boosting to enhance the training accuracy and capture historical patterns in the data. The performance of the proposed methods is verified using high-resolution data obtained from a real-world traffic network in Abu Dhabi, UAE. Our experimental results show that the proposed method outperforms other state-of-the-art algorithms.