Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling the potential impacts of climate change on the water table level of selected forested wetlands in the southeastern United States

Abstract. The southeastern United States hosts extensive forested wetlands, providing ecosystem services including carbon sequestration, water quality improvement, groundwater recharge, and wildlife habitat. However, these wetland ecosystems are dependent on local climate and hydrology, and are therefore at risk due to climate and land use change. This study develops site-specific empirical hydrologic models for five forested wetlands with different characteristics by analyzing long-term observed meteorological and hydrological data. These wetlands represent typical cypress ponds/swamps, Carolina bays, pine flatwoods, drained pocosins, and natural bottomland hardwood ecosystems. The validated empirical models are then applied at each wetland to predict future water table changes using climate projections from 20 general circulation models (GCMs) participating in Coupled Model Inter-comparison Project 5 (CMIP5) under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 scenarios. We show that combined future changes in precipitation and potential evapotranspiration would significantly alter wetland hydrology including groundwater dynamics by the end of the 21st century. Compared to the historical period, all five wetlands are predicted to become drier over time. The mean water table depth is predicted to drop by 4 to 22 cm in response to the decrease in water availability (i.e., precipitation minus potential evapotranspiration) by the year 2100. Among the five examined wetlands, the depressional wetland in hot and humid Florida appears to be most vulnerable to future climate change. This study provides quantitative information on the potential magnitude of wetland hydrological response to future climate change in typical forested wetlands in the southeastern US.
STUDY OF DYNAMICS OF MICROSTRUCTURAL TRANSFORMATIONS IN CRYSTALLINE YTTRIA NANOPOWDER

PACS 61.05. CThis paper is a continuation of a previously reported study of the microstructure transformations in crystalline yttria nanopowder prepared by soft chemistry via precipitation from aqueous acidic nitrates. We have observed the aforementioned transformations over extended periods of time at 900 C and 1100 C (isothermal annealing) with the use of X-ray diffraction analysis (full profile analysis of reflections, etc.).
Molecular and serological infection marker screening in blood donors indicates high endemicity of hepatitis E virus in Poland

Until now, markers of hepatitis E virus (HEV) infection have not been studied in blood donors throughout Poland, and no acute case of HEV infection has been closely documented or confirmed by HEV RNA detection. The prevalence of HEV infection markers, including HEV RNA in Polish blood donors and virus genotypes was investigated.
Flow-induced micro- and nano-fiber suspensions in short-fiber reinforced composite materials processing

Short-fiber reinforced polymer composites enjoy widespread industrial applications due to their high strength-to-weight ratios and versatile manufacturing processes. The mechanical, electrical and thermal properties of short-fiber reinforced composite systems are tremendously dependent on fiber orientations within the polymer matrix during the manufacturing process. However, the commonly used melt flow simulation tools employ simplified empirically-derived models that have recently been shown to over-predict the rate of fiber alignment. Therefore, a physical understanding of fiber suspensions during the injection molding process is critical. The main objective of this research project is to develop a systematic methodology to predict fiber orientations during the manufacture of polymer composites through the numerical simulation. The focus is to address such issues as the effect of fiber shape, fiber-fiber interactions, Brownian motions of nano-fibers and fiber suspensions in various solvents, such as inhomogeneous flows. We develop a stand-alone Finite Element Method (FEM) for calculating hydrodynamic forces and torques exerted on fibers. For nano-fibers, the Brownian forces and torques are modeled using a Gaussian distribution function. Our approach seeks fibers’ velocities that zero the net torques and forces acting on the fibers by the surrounding bulk fluid. Fiber motions are then computed using a 4-order Runge-Kutta method to update fiber positions and orientations as functions of time. The successful completion of this project provides a systematic computational approach capable of addressing issues that are currently unresolved in the critical area of manufacturing. Extension of the approach to other areas such as drug delivery and blood cell motion is an additional benefit of this research work.
Baby Morse Theory for Statistical Inference from Point Cloud Data

A methodology is proposed for inferring the topology underlying point cloud data directly from point cloud data. The approach employs basic elements of Morse theory, and is capable of producing not only a point estimate of various topological quantities (e.g., genus), but it can also assesses their sampling uncertainty in a probabilistic fashion. Several examples of point cloud data in three dimensions are utilized to demonstrate how the method yields interval estimates for the topology of the data as a 2-dimensional surface embedded in R3.
Memristor‐Based Analog Computation and Neural Network Classification with a Dot Product Engine

Using memristor crossbar arrays to accelerate computations is a promising approach to efficiently implement algorithms in deep neural networks. Early demonstrations, however, are limited to simulations or small‐scale problems primarily due to materials and device challenges that limit the size of the memristor crossbar arrays that can be reliably programmed to stable and analog values, which is the focus of the current work. High‐precision analog tuning and control of memristor cells across a 128 × 64 array is demonstrated, and the resulting vector matrix multiplication (VMM) computing precision is evaluated. Single‐layer neural network inference is performed in these arrays, and the performance compared to a digital approach is assessed. Memristor computing system used here reaches a VMM accuracy equivalent of 6 bits, and an 89.9% recognition accuracy is achieved for the 10k MNIST handwritten digit test set. Forecasts show that with integrated (on chip) and scaled memristors, a computational efficiency greater than 100 trillion operations per second per Watt is possible.
Detection of overlapping acoustic events based on NMF with shared basis vectors

Acoustic Event Detection plays an important role for computational acoustic scene analysis. Although we would face with a sound overlapping problem in a real situation, conventional methods do not consider the problem enough. In this paper, we propose a new overlapped acoustic event detection technique combined a source separation technique of Non-negative Matrix Factorization with shared basis vectors and a deep neural network based acoustic model to improve the detection performance. Our approach showed 20.0% absolute higher performance than the best result achieved in the D-CASE 2012 challenge on the frame based F-measure.
Adaptive dynamic network architectures for companion systems

Companion systems act in and interact with changing environments continuously and in an online manner. Therefore, they are required to adapt to their context of operation in several ways, for example by learning to respond to new input categories. Likewise, reliable tuning of behavior to the current context and to expected future events is necessary. Both types of learning require a trade-off between plasticity (acquiring new concepts or behaviors) and stability (retaining previous knowledge). We outline how dynamic hierarchical networks equipped with a small set of canonical operations can be used to build neural architectures which demonstrate some of the capabilities necessary to fulfill such constraints.
Tweeting for Nutrition: Feasibility and Efficacy Outcomes of a 6-Week Social Media-Based Nutrition Education Intervention for Student-Athletes.

The main objective of this study was to determine the feasibility and efficacy of a social media-based nutrition intervention using Twitter on nutrition knowledge, dietary practices, body mass index, self-efficacy, and social support among student-athletes. Participants included 50 male and female NCAA Division I student-athletes between the ages of 18-24 years old. Data were collected in October 2014. The study design employed a six-week, social media intervention using Twitter©, to increase nutrition knowledge, self-efficacy, social support and dietary adherence to the Dietary Guidelines for Americans recommendations. Descriptive statistics were calculated for all study variables and pre-post differences were assessed using paired t-tests. The results indicate a 6-week nutrition intervention delivered solely through social media resulted in increased nutrition knowledge (t=-2.23; p=0.035), reduced fat intake (t=- 1.57; p=0.13) and decreased BMI (t=2.32; p=0.027) in student athletes. In conclusion, social-media based nutrition education may provide university-based strength and conditioning coaches and other sports practitioners with a tool for widespread and timely access to students in order to facilitate healthy dietary behaviors.
Left-Center-Right Separated Neural Network for Aspect-based Sentiment Analysis with Rotatory Attention

Deep learning techniques have achieved success in aspect-based sentiment analysis in recent years. However, there are two important issues that still remain to be further studied, i.e., 1) how to efficiently represent the target especially when the target contains multiple words; 2) how to utilize the interaction between target and left/right contexts to capture the most important words in them. In this paper, we propose an approach, called left-center-right separated neural network with rotatory attention (LCR-Rot), to better address the two problems. Our approach has two characteristics: 1) it has three separated LSTMs, i.e., left, center and right LSTMs, corresponding to three parts of a review (left context, target phrase and right context); 2) it has a rotatory attention mechanism which models the relation between target and left/right contexts. The target2context attention is used to capture the most indicative sentiment words in left/right contexts. Subsequently, the context2target attention is used to capture the most important word in the target. This leads to a two-side representation of the target: left-aware target and right-aware target. We compare our approach on three benchmark datasets with ten related methods proposed recently. The results show that our approach significantly outperforms the state-of-the-art techniques.
Text Annotation Graphs: Annotating Complex Natural Language Phenomena

This paper introduces a new web-based software tool for annotating text, Text Annotation Graphs, or TAG. It provides functionality for representing complex relationships between words and word phrases that are not available in other software tools, including the ability to define and visualize relationships between the relationships themselves (semantic hypergraphs). Additionally, we include an approach to representing text annotations in which annotation subgraphs, or semantic summaries, are used to show relationships outside of the sequential context of the text itself. Users can use these subgraphs to quickly find similar structures within the current document or external annotated documents. Initially, TAG was developed to support information extraction tasks on a large database of biomedical articles. However, our software is flexible enough to support a wide range of annotation tasks for any domain. Examples are provided that showcase TAG's capabilities on morphological parsing and event extraction tasks. The TAG software is available at: this https URL CreativeCodingLab/TextAnnotationGraphs.
A Light-Weight Multimodal Framework for Improved Environmental Audio Tagging

The lack of strong labels has severely limited the state-of-the-art fully supervised audio tagging systems to be scaled to larger dataset. Meanwhile, audio-visual learning models based on unlabeled videos have been successfully applied to audio tagging, but they are inevitably resource hungry and require a long time to train. In this work, we propose a light-weight, multimodal framework for environmental audio tagging. The audio branch of the framework is a convolutional and recurrent neural network (CRNN) based on multiple instance learning (MIL). It is trained with the audio tracks of a large collection of weakly labeled YouTube video excerpts; the video branch uses pretrained state-of-the-art image recognition networks and word embeddings to extract information from the video track and to map visual objects to sound events. Experiments on the audio tagging task of the DCASE 2017 challenge show that the incorporation of video information improves a strong baseline audio tagging system by 5.3% in terms of F1 score. The entire system can be trained within 6 hours on a single GPU, and can be easily carried over to other audio tasks such as speech sentimental analysis.
Effect of methamphetamine dependence on heart rate variability

Methamphetamine (METH) is an increasing popular and highly addictive stimulant associated with autonomic nervous system (ANS) dysfunction, cardiovascular pathology and neurotoxicity. Heart rate variability (HRV) has been used to assess autonomic function and predict mortality in cardiac disorders and drug intoxication, but has not been characterized in METH use. We recorded HRV in a sample of currently abstinent individuals with a history of METH dependence compared to age‐ and gender‐matched drug‐free comparison subjects. HRV was assessed using time domain, frequency domain, and non‐linear entropic analyses in 17 previously METH‐dependent and 21 drug‐free comparison individuals during a 5 minute rest period. The METH‐dependent group demonstrated significant reduction in HRV, reduced parasympathetic activity, and diminished heartbeat complexity relative to comparison participants. More recent METH use was associated with increased sympathetic tone. Chronic METH exposure may be associated with decreased HRV, impaired vagal function, and reduction in heart rate complexity as assessed by multiple methods of analysis. We discuss and review evidence that impaired HRV may be related to the cardiotoxic or neurotoxic effects of prolonged METH use.
Influence of nanotube dispersion and spinning conditions on nanofibre nanocomposites of polypropylene and multi-walled carbon nanotubes produced through ForcespinningTM

Melt centrifugal spinning has been used to successfully produce nanofibres from compounds of polypropylene (PP) and multi-walled carbon nanotubes (MWNTs) at a concentration of 1%. The compounds were prepared either via twin screw compounding or by dissolution in decalin with sonication. Nanofibre production was conducted by centrifugal spinning in a Forcespinner™, a technology capable of producing nanofibres with a high material throughput. Processing via dissolution resulted in a reduction in the size of the MWNT agglomerations in the polymer, which led to a more uniform fibre morphology and a reduced incidence of bead defects as compared to products produced from the melt extrusion compound. The addition of a nonionic surfactant (Triton X-100) to the compound solution aided dispersion of the MWNTs as determined by optical light microscopy of thin cast films and produced fibres with the lowest mean diameter. The mean fibre diameter in the as-spun webs prepared by dissolution of PP in decalin with sonication was found to decrease with increasing spinneret speed; however, a similar trend was not observed for fibres generated from the melt compounded material.
Energy-Efficient Broadcast and Multicast Trees in Wireless Networks

The wireless networking environment presents formidable challenges to the study of broadcasting and multicasting problems. In this paper we focus on the problem of multicast tree construction, and we introduce and evaluate algorithms for tree construction in infrastructureless, all-wireless applications. The performance metric used to evaluate broadcast and multicast trees is energy-efficiency. We develop the Broadcast Incremental Power (BIP) algorithm, and adapt it to multicast operation by introducing the Multicast Incremental Power (MIP) algorithm. These algorithms exploit the broadcast nature of the wireless communication environment, and address the need for energy-efficient operation. We demonstrate that our algorithms provide better performance than algorithms that have been developed for the link-based, wired environment.
Renin Inhibition by Aliskiren Protects Rats Against Isoproterenol Induced Myocardial Infarction

Abstract Objective Cardioprotective potential of aliskiren against isoproterenol-induced myocardial infarction was evaluated in this study. Methods Wistar albino rats (n=36) were randomly divided into six groups. Normal control rats received 1 ml saline, orally for 14 days and 0.1 ml saline, subcutaneously on 13th and 14th treatment day while the toxic group animals were administered with saline orally for 14 days and isoproterenol (85 mg/kg, subcutaneously) on 13th and 14th day. In different pre-treatment groups, aliskiren (30, 50 & 100 mg/kg, orally) was administered for 14 days along with isoproterenol (85 mg/kg, subcutaneously) on the last two days. Per se group animals were given aliskiren (100 mg/kg orally) for 14 days. Twenty-four hours after the last drug treatment, cardiac marker enzymes (creatine kinase-MB and lactate dehydrogenase), apoptotic marker enzyme (caspase-3) and antioxidative enzymes (malondialdehyde, reduced glutathione, superoxide dismutase and catalase) were estimated, along with histopathological analysis of myocardium. Results Isoproterenol treatment caused a significant increase in cardiac markers, malondialdehyde, and caspase-3 levels with a considerable decrease in reduced glutathione, superoxide dismutase and catalase enzyme levels. Aliskiren at 50 and 100 mg/kg doses significantly alleviated the increase of caspase-3 and cardiac marker enzymes as well as the changes in antioxidant enzymes, which was confirmed by histopathological analysis. Aliskiren (100 mg/kg) produced more pronounced protective effects than its other two doses. Moreover, the changes in per se group were insignificant as compared to normal control group. Conclusion The present study thus provides an evidence for the protective effects of aliskiren in isoproterenol-induced myocardial infarction.
Fast alignment-free sequence comparison using spaced-word frequencies

Motivation: Alignment-free methods for sequence comparison are increasingly used for genome analysis and phylogeny reconstruction; they circumvent various difficulties of traditional alignment-based approaches. In particular, alignment-free methods are much faster than pairwise or multiple alignments. They are, however, less accurate than methods based on sequence alignment. Most alignment-free approaches work by comparing the word composition of sequences. A well-known problem with these methods is that neighbouring word matches are far from independent. Results: To reduce the statistical dependency between adjacent word matches, we propose to use ‘spaced words’, defined by patterns of ‘match’ and ‘don’t care’ positions, for alignment-free sequence comparison. We describe a fast implementation of this approach using recursive hashing and bit operations, and we show that further improvements can be achieved by using multiple patterns instead of single patterns. To evaluate our approach, we use spaced-word frequencies as a basis for fast phylogeny reconstruction. Using real-world and simulated sequence data, we demonstrate that our multiple-pattern approach produces better phylogenies than approaches relying on contiguous words. Availability and implementation: Our program is freely available at http://spaced.gobics. de/. Contact: chris.leimeister@stud.uni-goettingen.de Supplementary information: Supplementary data are available at Bioinformatics online.
Designing, Analyzing and Exploiting Stake-Based Social Networks

It is widely recognized that stakeholder information can provide important knowledge about stock investments, and an increasing number of countries require that such information is publicly available. In this paper, we present a novel way to exploit stakeholder information by using it to construct stake-based social networks, namely, StakeNet. We also provide a visualization tool that displays socio-centric and ego-centric views of the networks. In addition, we analyze stakeholders’ static and dynamic behavior patterns in StakeNet, and demonstrate that most of StakeNet’s properties are similar to those of a typical social network, except that the in-degree distribution does not follow a power law distribution. Finally, we demonstrate two applications of StakeNet by exploiting it to identify important companies and to group companies together. The experiments show that our results are highly consistent with the outcomes generated by human experts. Source code, dataset, and resources are available at http://www.csie. ntu.edu. tw/~d97944007/stakenet/
FDM-Structured Preamble Optimization for Channel Estimation in MIMO-OQAM/FBMC Systems

In this paper, we propose a preamble optimization method for the frequency-division multiplexing (FDM)-structured preamble in multiple-input multiple-output systems employing offset quadrature amplitude modulation-based filter bank multicarrier. Specifically, we formulate an optimization problem based on the periodic preamble structure to minimize the mean square error (MSE) of the channel estimation. For two transmit antennas, we find the relationship between preambles and intrinsic interferences from neighboring symbols to achieve the minimum MSE, and derive the optimal closed-form solution. For more than two transmit antennas, we convert the original optimization problem into a quadratically constrained quadratic program and obtain the suboptimal solution by relaxing the nonconvex constraint. The simulation results demonstrate that, in terms of MSE and bit error rate performances, the proposed method outperforms the conventional FDM method at all signal-to-noise ratio (SNR) regimes and outperforms the interference approximation method-complex method at low-to-medium SNR regimes with lower preamble overhead.