Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Stable Isotopes of Water and Strontium to Investigate the Hydrology of a Natural and a Constructed Wetland

Wetlands cannot exist without water, but wetland hydrology is difficult to characterize. As a result, compensatory wetland mitigation often only assumes the proper hydrology has been created. In this study, water sources and mass transfer processes in a natural and constructed wetland complex were investigated using isotopes of water and strontium. Water isotope profiles in the saturated zone revealed that the natural wetland and one site in the constructed wetland were primarily fed by ground water; profiles in another constructed wetland site showed recent rain was the predominant source of water in the root zone. Water isotopes in the capillary fringe indicated that the residence time for rain is less in the natural wetland than in the constructed wetland, thus transpiration (an important water sink) was greater in the natural wetland. Strontium isotopes showed a systematic difference between the natural and constructed wetlands that we attribute to the presence or absence of peat. In the peat‐rich natural wetland, δ87Sr in the pore water increased along the flowline due to preferential weathering of minerals containing radiogenic Sr in response to elevated Fe concentrations in the water. In the constructed wetland, where peat thickness was thin and Fe concentrations in water were negligible, δ87Sr did not increase along the flowline. The source of the peat (on‐site or off‐site derived) applied in the constructed wetland controlled the δ87Sr at the top of the profile, but the effects were restricted by strong cation exchange in the underlying fluvial sediments. Based on the results of this study, neither constructed wetland site duplicated the water source and weathering environment of the adjoining natural wetland. Moreover, stable isotopes were shown to be effective tools for investigating wetlands and gaining insight not easily obtained using non‐isotopic techniques. These tools have potential widespread application to wetlands that have distinct isotopic endmember sources.
EVIDENCE OF CHAOTIC BEHAVIOR IN SINGAPORE RAINFALL 1

ABSTRACT: This paper focuses on the investigation of the existence of chaotic behavior in the Singapore rainfall data. The procedure for the determination of the minimum number of variables essential and the number of variables sufficient to model the dynamics of the rainfall process was studied. An analysis of the rainfall behavior of different time periods was also conducted. The correlation dimension was used as a basis for discriminating stochastic and chaotic behaviors. Daily rainfall records for durations of 30, 20, 10, 5, 4, 3, 2, and 1 years from six stations were analyzed. The delay time for the phase‐space reconstruction was computed using the autocorrelation function approach. The results provide positive evidence of the existence of chaotic behavior in the daily rainfall data. The minimum number of variables essential to model the dynamics of the rainfall process was identified to be 3 while the number of variables sufficient to model the dynamics of the rainfall process ranges from 11 to 18. The results also suggest that the attractor dimensions of rainfall data of longer time periods are higher than that of shorter time periods. The study suggests a minimum number of 1500 data points required for the computation of the correlation dimension of the rainfall data.
Superconducting On-Chip Spectrometery for Millimeter-submillimeter Wave Astronomy

Since the birth of astrophysics, astronomers have been using free-space optics to analyze light falling on Earth. In the future however, thanks to the advances in photonics and nanoscience/nanotechnology, much of the manipulation of light might be carried out using not optics but confined waveguides, or circuits, on a chip. This new generation of instruments will be not only extremely compact, but also powerful in performance because the integration enables a greater degree of multiplexing. The benefit is especially profound for space- or air-borne observatories, where size, weight, and mechanical reliability are of top priority. Recently, several groups around the world are trying to integrate ultra-wideband (UWB), low-resolution spectrometers for millimeter-submillimeter waves onto microchips, using superconducting microelectronics. The scope of this Paper is to provide a general introduction and a review of the state-of-the-art of this rapidly advancing field.
OMG: A Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control

The OpenModelica Microgrid Gym (OMG) toolbox provides a transient simulation framework for local energy grids based on power electronic converters. OpenModelica is used as the backend, allowing users to set up arbitrary electric grid designs via its well-known graphical user interface in a plug-and-play fashion (Fritzson et al., 2018). Simulations can be configured using a python interface, making it easy to integrate software modules for the realization and testing of closed control loops. In addition, the OpenAI Gym interface is provided to connect data-driven reinforcement learning algorithms for investigating intelligent microgrid control approaches (Brockman et al., 2016).
Post-classification change detection with data from different sensors: Some accuracy considerations

Change detection from remote sensing data is often done by simple overlay of classified maps. However, such analyses can contain a significant proportion of boundary errors, especially when combining data from different sensors. This paper presents a protocol that allows reliable post-classification comparisons by taking into account classification accuracies, landscape fragmentation, planimetric accuracies, pixel sizes and grid origins. The proposed protocol has been applied, with little extra effort, in a fragmented agricultural Mediterranean zone using MSS (1970s) and TM (1990s) images. Applying the protocol, change detection had an accuracy of 85.1%, while for a direct overlay it was only 43.9% accurate. The drawback of this method is that it reduces the useful area of comparison. As the accuracy of individual classifications is critical, the paper also describes and tests a hybrid classifier that combines an unsupervised classification approach with training areas. This approach has proved more successful than maximum likelihood classifiers.
Stochastic Game Frameworks for Efficient Energy Management in Microgrid Networks

We consider the problem of energy management in microgrid networks. A microgrid is capable of generating power from a renewable resource and is responsible for handling the demands of its dedicated customers. Owing to the variable nature of renewable generation and the demands of the customers, it becomes imperative that each microgrid optimally manages its energy. This involves intelligently scheduling the demands at the customer side, selling (when there is a surplus) and buying (when there is a deficit) the power from its neighboring microgrids depending on its current and future needs. In this work, we formulate the problems of demand and battery scheduling, energy trading and dynamic pricing (where we allow the microgrids to decide the price of the transaction depending on their current configuration of demand and renewable energy) in the framework of stochastic games. Subsequently, we propose a novel approach that makes use of independent learners Deep Q-learning algorithm to solve this problem.
Data Structures to Represent a Set of k-long DNA Sequences

The analysis of biological sequencing data has been one of the biggest applications of string algorithms. The approaches used in many such applications are based on the analysis of k-mers, which are short fixed-length strings present in a dataset. While these approaches are rather diverse, storing and querying a k-mer set has emerged as a shared underlying component. A set of k-mers has unique features and applications that, over the past 10 years, have resulted in many specialized approaches for its representation. In this survey, we give a unified presentation and comparison of the data structures that have been proposed to store and query a k-mer set. We hope this survey will serve as a resource for researchers in the field as well as make the area more accessible to researchers outside the field.
Carbon and nitrogen cycle dynamics in the O‐CN land surface model: 1. Model description, site‐scale evaluation, and sensitivity to parameter estimates

Nitrogen (N) availability plays a key role in terrestrial biosphere dynamics. To understand and quantify the role of terrestrial N in the Earth system, we developed an advanced terrestrial biogeochemical model O‐CN that mechanistically couples terrestrial energy, water, carbon, and nitrogen fluxes in terrestrial ecosystems. We evaluate this new model against observations from intensive forest monitoring plots at temperate and boreal locations in Europe. O‐CN simulates realistic foliage N concentrations and N cycling rates and reproduces observed diurnal and seasonal cycles of C fluxes as well as observed gradients in vegetation productivity with N availability for the forest sites studied. A sensitivity test reveals that these results are reasonably robust against uncertainties in model parameter estimates. Using this model we quantify the likely contribution of anthropogenic N deposition to present ecosystem C sequestration as 36 (range: 2–79) g C g−1 N in agreement with ecosystem manipulation studies.
Group Size and Incentive to Contribute: A Natural Experiment at Chinese Wikipedia

The literature of private provision of public goods suggests that incentive to contribute is inversely related to group size. This paper empirically tests this relationship using field data from Chinese Wikipedia, an online encyclopedia based entirely on user contributions. The blocking of Wikipedia in mainland China has exogenously reduced the group size of contributors. We exploit this reduction in group size to examine whether individual contributions increase after the block, as predicted in the literature. Our result indicates the opposite: individual contributions of contributors outside mainland China decrease by 41.4% on average as a consequence of the block. We attribute the cause to social effects: a contributor receives social benefits that increase in both the amount of her contributions and group size, and the shrinking group size weakens these social benefits. Consistent with our explanation, we find that the more a contributor values social benefits, the greater the reduction in her contributions after the block. A series of robustness checks appear to support our findings.
Model-Based Method for Social Network Clustering

We propose a novel model-based method for social network clustering in this paper. More precisely, we cluster a set of entities in a social network into disjoint communities based a newly adopted distance function. Our model not only allows mixed membership for each entity, but also provides reliable statistical inference on network structure. We design a Bayesian-based algorithm, the Gibbs sampling, to estimate membership parameters. We evaluate the performance of our algorithm by applying our model to two social network data, the Zachary club data and the bottlenose dolphin network data. Some concluding remarks and future work are discussed briefly at last.
On the Quadratic Decaying Property of the Information Rate Function

The quadratic decaying property of the information rate function states that given a ﬁxed conditional distribution p Y | X , the mutual information between the random variables X and Y decreases at least quadratically in the distance as p X moves away from the capacity-achieving input distributions. It is a fundamental property of the information rate function that is particularly useful in the study of higher order asymptotics and ﬁnite blocklength information theory, where it was ﬁrst used by Strassen [1] and later, more explicitly, Polyanskiy–Poor–Verd´u [2], [3]. Recently, while applying this result in our work, we were not able to close apparent gaps in both of these proofs. This motivates us to provide an alternative proof in this note.
A Computational Cognitive Model of Syntactic Priming

The psycholinguistic literature has identified two syntactic adaptation effects in language production: rapidly decaying short-term priming and long-lasting adaptation. To explain both effects, we present an ACT-R model of syntactic priming based on a wide-coverage, lexicalized syntactic theory that explains priming as facilitation of lexical access. In this model, two well-established ACT-R mechanisms, base-level learning and spreading activation, account for long-term adaptation and short-term priming, respectively. Our model simulates incremental language production and in a series of modeling studies, we show that it accounts for (a) the inverse frequency interaction; (b) the absence of a decay in long-term priming; and (c) the cumulativity of long-term adaptation. The model also explains the lexical boost effect and the fact that it only applies to short-term priming. We also present corpus data that verify a prediction of the model, that is, that the lexical boost affects all lexical material, rather than just heads.
Energetic (0.1‐ to 16‐keV/e) magnetospheric ion composition at different levels of solar F10.7

Data obtained in the near-equatorial magnetosphere, between L = 3 and R = 23 RE, by the plasma composition experiment on ISEE-1 are examined for possible effects of varying solar activity, as measured by the daily F10.7 index. The data consist of velocity moments for H+, He++, He+, and O+ ions, primarily number densities and mean energies, integrated over the 0.1- to 16-keV/e energy range. These are grouped into four ranges of F10.7, less than 100, 100 to 150, 150 to 200, and greater than 200, using two methods. In one the data are averaged over geomagnetic activity, in the other the data are restricted to times of AE 1 cm−3). Both species show a decreasing energy with increasing F10.7 at R 7 and appears to have both solar and terrestrial origins. It does not show a strong increase in density anywhere, however. In the plasma sheet it actually decreases slightly with increasing F10.7 (about 25% over the full range of the F10.7), an effect that is ascribed in part to a known variation of the solar wind proton density and in part to the apparent tendency of the plasma sheet to maintain a constant total pressure (including all ion species). At low L the H+ density appears to be dominated by a terrestrial source, but shows little or no increase with the F10.7, suggesting that this source is essentially saturated with respect to the EUV (F10.7>80).
Scanning microarrays at multiple intensities enhances discovery of differentially expressed genes

MOTIVATION Scanning parameters are often overlooked when optimizing microarray experiments. A scanning approach that extends the dynamic data range by acquiring multiple scans of different intensities has been developed. RESULTS Data from each of three scan intensities (low, medium, high) were analyzed separately using multiple scan and linear regression approaches to identify and compare the sets of genes that exhibit statistically significant differential expression. In the multiple scan approach only one-third of the differentially expressed genes were shared among the three intensities, and each scan intensity identified unique sets of differentially expressed genes. The set of differentially expressed genes from any one scan amounted to < 70% of the total number of genes identified in at least one scan. The average signal intensity of genes that exhibited statistically significant changes in expression was highest for the low-intensity scan and lowest for the high-intensity scan, suggesting that low-intensity scans may be best for detecting expression differences in high-signal genes, while high-intensity scans may be best for detecting expression differences in low-signal genes. Comparison of the differentially expressed genes identified in the multiple scan and linear regression approaches revealed that the multiple scan approach effectively identifies a subset of statistically significant genes that linear regression approach is unable to identify. Quantitative RT-PCR (qRT-PCR) tests demonstrated that statistically significant differences identified at all three scan intensities can be verified. AVAILABILITY The data presented can be viewed at http://www.ncbi. nlm.nih. gov/geo/ under GEO accession no. GSE3017.
Low Rank Directed Acyclic Graphs and Causal Structure Learning

Despite several important advances in recent years, learning causal structures represented by directed acyclic graphs (DAGs) remains a challenging task in high dimensional settings when the graphs to be learned are not sparse. In particular, the recent formulation of structure learning as a continuous optimization problem proved to have considerable advantages over the traditional combinatorial formulation, but the performance of the resulting algorithms is still wanting when the target graph is relatively large and dense. In this paper we propose a novel approach to mitigate this problem, by exploiting a low rank assumption regarding the (weighted) adjacency matrix of a DAG causal model. We establish several useful results relating interpretable graphical conditions to the low rank assumption, and show how to adapt existing methods for causal structure learning to take advantage of this assumption. We also provide empirical evidence for the utility of our low rank algorithms, especially on graphs that are not sparse. Not only do they outperform state-of-the-art algorithms when the low rank condition is satisfied, the performance on randomly generated scale-free graphs is also very competitive even though the true ranks may not be as low as is assumed.
ViNMT: Neural Machine Translation Tookit

We present an open-source toolkit for neural machine translation (NMT). The new toolkit is mainly based on the vaulted Transformer (Vaswani et al., 2017) along with many other improvements detailed below, in order to create a self-contained, simple to use, consistent and comprehensive framework for Machine Translation tasks of various domains. It is tooled to support both bilingual and multilingual translation tasks, starting from building the model from respective corpora, to inferring new predictions or packaging the model to serving-capable JIT format. The source code and data are available at https://github.com/KCDichDaNgu/ MultilingualMT-UET-KC4.0 .
Understanding appropriation of a social media technology to manage chronic illness: The Facebook case

© 2018 authors. With the rapid increase of social media appropriation globally, there has been a surge in the number of chronically ill adults who leverage social media tools as part of their illness management practice. While numerous studies discuss the potential benefits of appropriation there seem to be limited studies that have explored appropriation of social media by investigating how and why these technologies have been appropriated by these patients. This paper applies an interpretive case study with mixed methods to examine appropriation of Facebook by these cohorts. Our results highlight the patterns of social media appropriation: lurking as passive learning; liking and reacting to show support and; borderless appropriation of multiple social media tools. Among a range of influences, crafting a positive illness identity, communal filtering of misinformation were found to be positive influences and barriers like emotional overload were found to influence appropriation for chronically ill adults on social media.
Aproximando el clima de negocios a nivel municipal

This study analyses the more appropriate environment for the business location at the Peruvian municipal level. The first part of this study analyses the conditions to determine the good locations for investing in business, within the framework of the Economic Geography (Venables, 2003; and Burgess and Venables, 2004). The Municipal National Register 2004 and the Population and Household Census 2005 were used to create a Business Climate Indicator with the objective of identifying the municipalities that promote the right environment for business. The results show that, only 27.5% of the municipalities that were studied (464 out of 1686), encourage an adequate business climate in Peru. Said municipalities are located mostly in the coastal regions, standing out the municipalities of Lima and Callao, which have the highest Business Climate Indicators at the national level. The study concludes with some reflections on the need to make more efforts for improving the business climate in Peru.
Water Flux in a Cashew Orchard during a Wet-to-Dry Transition Period: Analysis of Sap Flow and Eddy Correlation Measurements

Abstract Information regarding biosphere–atmosphere interactions is important in the study of a hydrological cycle. To this purpose, xylem sap flow (SF) using the Granier system and evapotranspiration (ET) using the eddy correlation method were measured during a “wet-to-dry” transition period in a young cashew (Anacardium occidentale L.) plantation. Estimates of half-hourly tree transpiration made from stem sap flow measurements and above-canopy eddy correlation measurements of water vapor flux were compared for a period of 22 days of complete records. Cross-correlation analysis was used to estimate the time lags (τ) between the time courses of SF and ET, and between SF and ET with solar radiation (RS) and vapor pressure deficit (D). Applying a simple functional technique, values of τ = 43 min (November), τ = 46 min (December), and τ = 75 min (January) with an overall τ = 53 min (using all data) between the time courses of ET and SF were estimated. A positive lag indicates that SF lags behind ET. However,.
Continuous data assimilation for the three-dimensional Navier-Stokes-α model

Motivated by the presence of a ﬁnite number of determining parameters (degrees of freedom) such as modes, nodes and local spatial averages for dissipative dynamical systems, we present a continuous data assimilation algorithm for the three-dimensional Navier-Stokes- α model. This algorithm consists of introducing a nudging process through general type of approximation interpolation operator (that is constructed from observational measurements) that synchronizes the large spatial scales of the approximate solutions with those of the un-known solutions the Navier-Stokes- α equations that corresponds to these measurements. Our main result provides conditions on the ﬁnite-dimensional spatial resolution of the collected data, suﬃcient to guarantee that the approximating solution, that is obtained from these collected data, converges to the unkown reference solution (physical reality) over time. These conditions are given in terms of some physical parameters, such as kinematic viscosity, the size of the domain and the forcing term.
Concatenated phoneme models for text-variable speaker recognition

Methods that create models to specify both speaker and phonetic information accurately by using only a small amount of training data for each speaker are investigated. For a text-dependent speaker recognition method, in which arbitrary key texts are prompted from the recognizer, speaker-specific phoneme models are necessary to identify the key text and recognize the speaker. Two methods of making speaker-specific phoneme models are discussed: phoneme-adaptation of a phoneme-independent speaker model and speaker-adaptation of universal phoneme models. The authors also investigate supplementing these methods by adding a phoneme-independent speaker model to make up for the lack of speaker information. This combination achieves a rejection rate as high as 98.5% for speech that differs from the key text and a speaker verification rate of 100.0%. <<ETX>>