Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey Paper : Improving Neural Language Modeling with Linguistic Annotation

Sequence modeling task like language modeling has benefited by the recent advances in Recurrent Neural Networks like Long-Short Term Memory networks (LSTMs). However, language models being trained on the raw text has hardly taken any advantage of external linguistic features hidden inside the natural language sentence. In this work, we use additional linguistic feature to existing LSTM-based state-of-the-art systems. The additional feature advances the state-of-the-art on two benchmark datasets Penn Treebank, and WikiText-2. Our experiments on 26 more languages across 8 language families show consistent improvement with an average of 13.5% reduction in perplexity. In this paper, we present the literature survey for the language modeling task.
heat yield response to spatially variable nitrogen fertilizer in editerranean environment

Farmers obtain high yield when proper crop management is matched with favourable weather. Nitrogen (N) fertilization is an important agronomic management practice because it affects profitability and the environment. In rainfed environments, farmers generally apply uniform rates of N without taking into account the spatial variability of soil available water or nutrient availability. Uniform application of fertilizer can lead to over or under-fertilization, decreasing the efficiency of the fertilizer use. The objective of this study was to evaluate the impact of variable rate nitrogen fertilizer application on spatial and temporal patterns of wheat grain yield. The study was conducted during the 2008/2009 and 2009/10 growing seasons in a 12 ha field near Foggia, Italy. The crop planted each year was durum wheat (Triticum durum, Desf.) cultivar Duilio. The field was subdivided into two management zones High (H), and Average (A). Three N rates were identified using a crop model tested on the same field during a previous −1 −1 growing season. The N rates were: low N (T1: 30 kg N ha ), average N (T2: 70 kg N ha ), and high N (T3: 90 kg N ha−1). The ANOVA test showed that there were no effects of the N levels for the first growing season for the H and A zone. For the 2009/10 growing season with higher rainfall there was a significant difference in grain yield for the A zone (2955 kg ha−1), but not in the H zone (3970 kg ha−1). This study demonstrates the optimal amount of N for a given management zone is not fixed but varies with the rainfall amount and distribution during the fallow and growing season.
Slow Learner Learning Facts in Their Inclusion Class

Education is a medium for processing input (students) in order to become a quality output. Reality assumes that existing students are heterogeneous. All kinds of characteristics of students have the same right to gain knowledge in the formal education system. This is where the role of inclusive education needs to be reviewed more in depth. Moreover, based on observations in primary school, many students detected slow learner and joined the regular class. Thus, this study also aims to describe how the implementation of slow learner learning in inclusion settings. The subcomponents include planning, core, and evaluation or followup in learners' learning. The approach used is qualitative descriptive. Research subjects were second grade teachers, special escort teachers, principals, and learner learning learners. Data collection includes observation, interviews, and documentation. Analysis of data used data reduction, data display, and verification. The validity of the data is tested through technique triangulation as well as source. The results showed that the second-grade teacher performed the same learning between regular students and slow learner students. This can be seen from planning, implementation, to evaluation or follow-up. There is no special RPP for slow learner students. Learning components include methods, media, tests/evaluations, assessments used are also the same as other students. One special treatment for slow learner students is the addition of time to complete the task after school. Keywords—learning implementation, slow learner, inclusion.
Modeling Acoustic Interface Wave Dispersion using COMSOL

Measuring geoacoustic parameters of underwater sediments is important for accurate numerical modeling of underwater acoustic propagation. While the density and compressional wave speeds can be directly measured in sediments, shear wave speeds are difficult to measure because they are highly attenuated. However, shear wave speeds can be indirectly determined through measurement of Scholte ocean-seafloor interface wave speeds. When the seafloor is composed of layers of sediments with different shear wave speeds, the Scholte wave speed becomes dispersive. Scholte wave speed dispersion was measured in the Narragansett Bay, Rhode Island [J. Greene et al., SYMPOL, Cochin, India, 2011]. By using an inversion process, each layer’s shear wave speeds were calculated. To validate the inversion method, COMSOL is employed to model the experiment at Narragansett Bay. The modeled and measured interface wave speeds are then compared.
Using R and Google-API tools to estimate geographic features

This technical document is a guide for using Google APIs to find information for research purposes. First, I apply the code to find the elevation at a given set of coordinates for specific locations. Second, I apply the code to find the street distances between two or more sets of coordinates. All the codes and sample files are available in the zip file attached to this guide.
On Spectral Clustering for Sparse Stochastic Block Models

We analyze the performance of a practical spectral clustering algorithm for community extraction in the stochastic block model. The procedure performs k-means clustering on the leading eigenvectors of the adjacency matrix of the observed network. We provide sufficient conditions for consistent community recovery in terms of the overall sparsity, the degree of separation among communities, and the imbalance among the sizes the communities. We show that the algorithm can recover the hidden communities with vanishing misclustering rate even when the expected node degrees grow only logarithmically in the size of the network. We demonstrate rates that are comparable or better than those reported in most existing work, which are often based on more computationally demanding algorithms.
An Efficient Approximation Algorithm for Traffic Engineering in Software Defined Networks

In this paper, we focus on the efficient algorithm for solving the multi-commodity flow problems involved in TE and develop an improved approximation algorithm (i-FPTAS) based on the Fully Polynomial Time Approximation Scheme (FPTAS), with aim of greatly improving computational efficiency without compromising traffic load balancing performance. Numerical results shows that i-FPTAS can achieve close-optimal approximation solution which is much better than that of FPATS with the same approximate parameter, and increasing the approximate parameter of i-FPTAS can lower computational complexity with little loss on close-optimality.
PII: S0048-7333(02)00124-5

11 We combine estimates of the value of patent rights from a survey of patent-holders with a set of indicator variables in order to model the value of patents. Our results suggest that the number of references to the patent literature as well as the citations a patent receives are positively related to its value. References to the non-patent literature are informative about the value of pharmaceutical and chemical patents, but not in other technical fields. Patents which are upheld in opposition and annulment procedures and patents representing large international patent families are particularly valuable. 12
Multi-View Instance Matching for Plant Leaf Modeling

This abstract introduces a work-in-progress report towards detailed modeling of plants from multi-view images. Plant shoot (i.e., leaves and stems) modeling is actively studied among computer vision and graphics fields. Although the state-of-the-art study [2] achieves multi-view reconstruction of 3D branching patterns, detailed modeling of leaves, i.e., estimating location, orientation, and shape of each leaf, still remains an open problem. We propose a multi-view per-leaf 3D reconstruction pipeline depicted in Figure 1(a). The proposed approach finds the correspondences among given leaf instances (e.g., segmented by Mask R-CNN [1]) seen in multi-view images. A set of multi-view correspondences of leaf regions allows 3D reconstruction of each leaf by traditional 3D reconstruction approaches. This abstract summarizes the preliminary report with a focus on a multi-view instance matching method using an epipolar constraint and spectral clustering.
PHYTOPLANKTON COMPOSITION IN A MANGROVE ECOSYSTEM AT SANDSPIT , KARACHI , PAKISTAN

The phytoplankton occurring in the mangrove habitat of Sandspit, Karachi was assessed qualitatively and quantitatively during a period from June 2013 to May 2014. Species of diatoms appeared to be more diverse and dominant forms. In contrast dinoflagellates exhibited very low species diversity and were observed in samples collected from June, August and September. Phytoplankton cell density varied seasonally and their abundance ranged from 0.25 x10-7.044x10 and 0.042x10-5.172x10 cells L ̅ during high and low tide, respectively. Pennate diatoms were highly diversified when compare to centric diatoms (3 centric and 23 pennate). Centric diatoms were dominated by Cyclotella cf. meneghiniana, whereas Cyilndrotheca closterium was the most abundant species in the pennate group followed, by Navicula sp. , Nitzschia sp. , Pleurosigma sp. and Gyrosigma sp. respectively. The dinoflagellates were represented by very few species such as Alexandrium sp. , Prorocentrum micans and Gyrodinium spirale. Seasonal variations in hydrographical parameters were also recorded and the data ranges for salinity (33-42 PSU), temperature (16-32 ̊C), pH (7.0-7.7), dissolved oxygen (0.086.18mg/L), total suspended solids (0.97-13.8 mg/L) and chlorophyll a (0.0006-0.431 μg/L). During the period of this investigation Sandspit backwaters received a load of pollution from the Layari River and therefore the resistant diatom species dominated and dinoflagellates occurred in very low abundance and diversity. Implication of pollution and changing hydrographical parameters in the coastal waters of Karachi with phytoplankton structure need regular monitoring and research.
Block Compressed Sensing-Based Algorithm for Super Resolution Image Reconstruction

Compressed sensing cannot be sampled in real-time and is computationally intensive. This paper deals with image blocks, and makes some improvements for the traditional block approach. Under the same number of measured values we make the image to have higher reconstruction accuracy and shorter reconstitution time. We construct a block compressed sensing algorithm based on classification. First, according to the number of high frequency information, the image block is divided into two types of texture blocks and flat blocks. Then, the texture block adopts more measured values and number of iterations, and the flat block uses less measured value and number of iterations. Finally, an improved total variation algorithm is used to remove the false boundaries between image blocks. Experiments show that the reconstructed images using this method have a good visual effect, which improves the signal to noise ratio by 2.6dB 3.2dB.
Evaluating different Mobility Management Methods

This paper deals with the comparison of different mobility management methods. Several methods from the literature are presented and compared. An assessment of the location algorithms is necessary to find the best methods for the individual behaviour of the subscribers. Since the behaviour is described by the movement pattern and the traffic characteristic, mobility and traffic models are discussed. A cost function is derived as the basis for the comparison to calculate the saving of the different mobility management methods. This cost function takes the air interface and the fixed network links into account. Since the analytic model of the behaviour of the users does not fulfil all the requirements for the estimation of the performance of the location algorithms in reality, simulations are necessary. Simulations are used to find a relation between the user characteristic and the location algorithm. Reference models are proposed as a basis for comparison.
Lazy Classification of Cardiovascular Risk via Multiway Graph Cut Optimization Karma

Cardiovascular disease (CVD) is considered a leading cause of human mortality with rising trends worldwide. Therefore, early identification of seemingly healthy subjects at risk is a priority. For this purpose, we propose a novel classification algorithm that provides a sound individual risk prediction, based on a non-invasive assessment of retinal vascular function. So-called lazy classification methods offer reduced time complexity by saving model construction time and better adapting to newly available instances, when compared to well-known eager methods. Lazy methods are widely used due to their simplicity and competitive performance. However, traditional lazy approaches are more vulnerable to noise and outliers, due to their full reliance on the instances’ local neighbourhood for classification. In this work, a learning method based on Graph Cut Optimization called GCO mine is proposed, which considers both the local arrangements and the global structure of the data, resulting in improved performance relative to traditional lazy methods. We compare GCO mine coupled with genetic algorithms (hGCO mine) with established lazy and eager algorithms to predict cardiovascular risk based on Retinal Vessel Analysis (RVA) data. The highest accuracy of 99.52% is achieved by hGCO mine. The performance of GCO mine is additionally demonstrated on 12 benchmark medical datasets from the UCI repository. In 8 out of 12 datasets, GCO mine outperforms its counterparts. GCO mine is recommended for studies where new instances are expected to be acquired over time, as it saves model creation time and allows for better generalization compared to state of the art methods. © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC B Clicense (https://creativeco ons.org/licenses/by-nc-nd/4.0/) electi a eer-re ie er res si ilit f I ter ati al.
JP 3 . 18 DEVELOPMENT OF A LARGE-SCALE HYDROLOGIC PREDICTION SYSTEM

The Scripps ECPC (Experimental Climate Prediction Center) is developing an experimental off-line hydrologic prediction system for the southwestern United States. The system includes the atmospheric Global and Regional Spectral Model (GSM/RSM) and the Variable Infiltration Capacity (VIC) macro-scale hydrologic land surface model. In particular, GSM/RSM daily forecasts of precipitation, temperature (max and min), wind speed, and solar radiation are used to continuously force water and energy balance modes of the VIC model at sub-daily scales. VIC daily, weekly and monthly forecasts are made from the routine ECPC GSM/RSM forecasts for the Southwest forecasts. Daily Land Data Assimilation System (LDAS) products are used to evaluate the performance of the VIC model. As well, it shows that there are noticeable differences between the streamflow (and other hydrologic variables) forecasts and continuous simulations due to various biases in the forecast variables (especially precipitation). As long as these forecast biases are empirically corrected, useful predictions can be
Twitter Texts’ Quality Classification using Data Mining and Neural Networks

Purpose: This is an attempt to classify the level of noise in twitter texts which is part of social media data analytics problem. Estimations in recent machine learning & data feeding algorithms researches’ assumptions consider high data quality in social media texts, while they actually lack data accuracy, completeness, and overall quality which leads to the principle of “Garbage In Garbage Out” resulting in bizarre statistical findings. The aim of this project is to predict and classify Twitter data noise levels using a labelled dataset. Methodology: After data cleaning, a clustering technique was used to find the major dimensions in the data imported, and a dimension reduction algorithm was ran using PCA Weighting and the Wight Guided Feature Selection algorithms. They resulted into 6 most significant features which were used in the implementation. An artificial neural network model was trained to predict the Tweets’ quality classes using R and RStudio. The ANN used is Neural Network (NN) and Naïve Bayes (NB) for the purpose of predicting the Twitter text quality. There will be a comparison between the 2 ANN used in terms of accuracy and precision.
Sample Size and Reproducibility of Gene Set Analysis 1

Gene set analysis is widely used to gain insight from gene expression data. Achieving reproducible results is a fundamental part of any expression analysis. In this paper, we propose a systematic approach to study the effect of sample sizes on the reproducibility of the results of 10 gene set analysis methods. To do so, we quantify the concept of reproducibility and use real expression datasets of different sizes. Our findings suggest that, as a general pattern, the results of gene set analysis are more reproducible as sample size increases. However, the smallest sample size for achieving reproducible results are variable across gene set analysis methods. Moreover, for some methods, increasing sample size leads to an increase in the number of false positives.
12-14-2018 Enterprise Architecture : A Reconceptualization Is Needed

Enterprise architecture (EA) is a description of an organization from an integrated business and IT perspective. Current literature conceptualizes EA as a comprehensive blueprint of an enterprise organized according to a logical framework and describing its current state, desired future state and migration roadmap. However, the current concept of EA originates from non-empirical sources, lacks demonstrated examples of its successful practical implementation and deviates from the real practical use of EA in organizations in multiple important aspects. Due to these and other problems the notion of EA needs to be reconceptualized in order to more accurately reflect empirical realities. In this paper, based on an extensive EA literature review, I describe the problems with the current concept of EA, demonstrate the critical inconsistencies between this concept and the real practice use of EA in organizations and illustrate them based on a recent exemplary case study of a successful EA practice. Although this paper justifies the need for the reconceptualization of EA and points to the most essential aspects of this reconceptualization, it does not offer an alternative ready-to-use conceptualization and represents only the first step towards developing a new, evidence-based concept of EA.
Post-print version : Data-driven fault diagnosis and robust control : Application to PEM fuel cell systems

A data-driven methodology that includes the unfalsified control concept in the framework of fault diagnosis and isolation (FDI) and fault-tolerant control (FTC) is presented. The selection of the appropriate controller from a bank of controllers in a switching supervisory control setting is performed by using an adequate FDI outcome. By combining simultaneous on-line performance assessment of multiple controllers with the fault diagnosis decision from structured hypothesis tests (SHT), a diagnosis statement regarding what controller is most suitable to deal with the current (nominal or faulty) mode of the plant is obtained. Switching strategies that use the diagnosis statement are also proposed. This approach is applied to a non-linear experimentally validated model of the breathing system of a polymer electrolyte membrane (PEM) fuel cell. The results show the effectiveness of this FDI-FTC data-driven methodology.
Assessing the reliability of pulsatility in four-dimensional digital subtraction angiography time concentration curves

Objective. The time concentration curves (TCCs) from a Four-Dimensional Digital Subtraction Angiography (4D DSA) reconstruction contain cardiac derived pulsatility waveforms. These can be used to quantify blood flow. This work’s purpose was to evaluate the ability to visualize and characterize these waveforms in 4D DSA acquisitions done using a variety of injection protocols. Approach. Forty-two 4D DSA acquisitions were obtained in 6 canines under a variety of injection protocols. Heart rate was recorded during the acquisitions. Two blinded reviewers qualitatively scored the pulsatility waveforms in TCCs obtained from 147 ROIs. A Fourier space based method for automated pulsatility detection was developed to determine the correlation of the Fundamental Frequency (FF) in the TCCs with heart rate. The sensitivity of pulsatility detection using this quantitative method was compared to the observer’s visual detection. Main results. Recorded heart rate and Fourier space calculated heart rates were highly similar (107.0 versus 106.7 p = 0.42). Reviewer scores of 0 (n = 15, pulsatility poorly defined and random), 0.5 (n = 21, pulsatility ambiguous) or 1 (n = 111, pulsatility clear and regular) correlated with the signal strength of the FF. The quantitative algorithm allowed pulsatility detection in 10 of 36 acquisitions where visual assessment was inconclusive. There was no association between injections done with a variety of injection parameters and either the recognition of pulsatility or its link with heart rate. Significance. Cardiac derived pulsatility in the TCCs of a 4D DSA reconstruction may be detected visually as well as automatically using a Fourier space method. FF correlates highly with heart rate. In TCCs where pulsatility was difficult or impossible to recognize visually, this method improved the ability to differentiate the FF from noise. In this study there was no correlation between injection protocols and the quality of the pulsatility waveforms.
1 Absence of complementary sex determination in 2 Trichogramma dendrolimi Matsumura ( Hymenoptera : 3 Trichogrammatidae ) 4 Short running title : Absence of CSD in T . dendrolimi 5

Over 60 species in Hymenoptera have been reported to possess a complementary sex determination (CSD) system. Under CSD, sex is determined by allelic complementation at one or several sex loci. But this mechanism is still uninvestigated in parasitoid wasp Trichogramma dendrolimi, one of the most important biocontrol agents widely used against Lepidopteran pests. We tested CSD in this species by conducting ten consecutive generations of inbreeding, to monitor both direct evidence (diploid male production) and indirect evidence (brood size, sex ratio, mortality). In total 475 males detected from this inbreeding regime, only one was determined as diploidy. The observed proportions of diploid male offspring significantly differed from expected values under CSD model involving up to ten independent loci, allowing us to safely reject CSD in T. dendrolimi. Meanwhile, the possibility of unviable diploid males was excluded by the absence of significant differences in brood size, offspring sex ratio and offspring mortality among different generations. Our study of sex determination in T. dendrolimi provides useful information for the mass rearing conditions in a biofactory and the quality improvement of this biocontrol agent. It also brings necessary background to further study of the sex determination in Trichogramma.
New LMI conditions for the stability of 2D discrete Roesser models

This paper proposes a new LMI condition for the structural stability of 2D discrete Roesser models. The condition is necessary and sufficient. Unlike previous ones, it does not involve a hierarchy level. More precisely, if many of the LMI conditions encountered in the literature are dependent on a hierarchy level which has to tend to infinity or at least to be very large to hope for necessity, the here-presented test is based upon a straightforwardly expressed LMI system in which no hierarchy level has to be guessed.