Miracle-dz/UniFAR-FTUs huggingface dataset:
A Blended Grammar Learning System Featuring Unsupervised Pattern Discovery

Recent developments in cognitive and psycholinguistic research postulate that language learning is essentially the learning of grammatical construc-tions. An important type of grammatical construction with wide-ranging pedagogical implications is grammar patterns as laid out in Pattern Gram-mar. While grammar patterns have seen increasing adoption in language pedagogy, existing applications typically follow a paper-based, teacher-centered approach to instruction, which is known to be less effective in grammar learning than blended, learner-centered approaches. In this paper, we propose a blended learning model that integrates web-based technology with classroom-based instruction to facilitate efficient, personalized grammar learning. We present the design and implementation of a blended grammar learning system that provides customizable learning materials for individual learners by discovering important grammar patterns from corpora in an unsupervised manner. Preliminary evaluation shows that the proposed system achieves an accuracy in pattern discovery comparable to systems that rely on manually precompiled pattern lists and hard-coded rules. With a flexible architecture and an easy-to-use interface, the system can play a key role in the creation of a blended learning environment that can be integrated into a wide range of language learning curricula.
Compositional Generation of Images

We propose a novel approach to synthesize images from text descriptions by exploiting the compositionality of language. We decompose the input text to basic visual primitives and dynamically train a network to generate visual attributes for the desired image. By incorporating compositionality into the network design, we aim to make the generative process more interpretable and controllable. We deﬁne a set of compositional blocks and introduce them into generative networks in two different settings–one at the conditioning level and one at the generation level. We evaluate our approaches on the SHAPES dataset and present the qualitative results
Sufficiently Informative and Relevant Features: An Information-Theoretic and Fourier-Based Characterization

A fundamental challenge in learning is the presence of nonlinear redundancies and dependencies in the data. To address this, we propose a Fourier-based approach to characterize feature redundancies, in unsupervised learning, and feature-label dependencies, in the supervised variant of the problem. We first develop a novel Fourier expansion for functions (more generally stochastic mappings) of correlated binary random variables. This is a generalization of the standard Fourier expansion on the Boolean cube beyond product probability spaces. As an important application of this analysis, we investigate learning with feature subset selection. In the unsupervised variant of this problem, we characterize feature redundancies via the Shannon entropy and group the features into sufficiently informative and redundant. Then, we make a connection to the proposed Fourier expansion and derive an upper bound on the joint entropy. Based on that, we propose a measure to quantify feature redundancies and present an unsupervised learning algorithm. We test our method on various real-world and synthetic datasets and demonstrate improvements on conventional unsupervised feature selection techniques. Then, we investigate the supervised feature subset selection and reformulate it in the Fourier domain. Bridging the Bayesian error rate with the Fourier coefficients, we demonstrate that the Fourier expansion provides a powerful tool to characterize nonlinear feature-label dependencies. Further, we introduce a computationally efficient measure for selecting relevant features. Via a theoretical analysis, we show that our proposed measure finds provably asymptotically optimal feature subsets. Lastly, we present an algorithm based on this measure and via numerical experiments demonstrate its improvements on various supervised feature selection algorithms.
Study of retroanalysis of asphal c pavements resilience modules with the use of ar ficial neural networks

Recebido: 13 de julho de 2018 Aceito para publicação: 14 de outubro de 2019 Publicado: 31 de dezembro de 2019 Editor de área: Kamilla Vasconcelos ABSTRACT Knowledge of pavement Resilience Modules (RM) is an important element for the structural assessment of exis ng infrastructures. One way to determine them is through dynamic repeated load tes ng in the laboratory; another way is to use the technique of retroanalysis, which consists in obtaining the RM from the thickness of the layers and the deflec ons measured on the pavement surface. In this sense, this paper aims to present the RM retroanalysis through the technique of Ar ficial Neural Networks (ANN) as an alterna ve to the tradi onal retroanalysis. The results demonstrate that the ANN could predict the RM with results of coefficients of determina on (R2) above 99.9% between the reference and predicted values. Thus, ANN are a poten al alterna ve to obtain this important mechanical property of paving materials compared to tradi onal methods.
Industry 4.0: A Systematic Review of Surveys

The advent of Industry 4.0 has heralded a new era for manufacturing, and hence, the aim of this study was to ascertain the extent to which the phenomenon is being embraced by businesses in different regions of the world. The primary objective of the study was to establish business executives’ perceptions on the extent to which their organisations are aware of and the extent to which their organisations would be influenced by Industry 4.0. The secondary objectives were to ascertain business executives’ perceptions regarding the areas of business that would be most impacted by Industry 4.0 and their organisation’s intention to invest in Industry 4.0. The methodology entailed a systematic literature review (SLR) of 10 industry surveys. The findings from the surveys were synthesised to reveal that there was generally a high degree of awareness regarding the Industry 4.0 phenomenon with the degree of influence being perceived to have the greatest impact on operational effectiveness, productivity and cost reduction. While most organisations have expressed intentions to invest in related technologies, this remains to be translated into equivalent levels of actual investments. The greatest challenge revolves around cyber security followed by the lack of appropriate skills. It is recommended that businesses collaborate with their suppliers and customers who may be more advanced in the digital journey to smoothen the learning curve. It is also recommended that organisations identify individuals within the organisation or recruit individuals who have the necessary skill set to lead change initiatives.
Fabrication of Si Nanoparticles@Carbon Fibers Composites from Natural Nanoclay as an Advanced Lithium-Ion Battery Flexible Anode

In this paper, a cost-effective strategy for fabricating silicon-carbon composites was designed to further improve the electrochemical performance and commercialization prospects of Si anodes for lithium-ion batteries (LIBs). Silicon-carbon fibers (CFs) were prepared by loading Si nanoparticles (SiNPs) on interconnected carbon fibers via an electrospinning technique (SiNPs@CFs). The Si nanoparticles were obtained by the reduction reaction of natural clay minerals. As a flexible anode for LIBs, the SiNPs@CFs anode demonstrated a reversible capacity of 1238.1 mAh·g−1 and a capacity retention of 77% after 300 cycles (in contrast to the second cycle) at a current density of 0.5 A·g−1. With a higher current density of 5.0 A·g−1, the electrode showed a specific capacity of 528.3 mAh·g−1 after 1000 cycles and exhibited a superior rate capability compared to Si nanoparticles. The excellent electrochemical properties were attributed to the construction of flexible electrodes and the composite comprising carbon fibers, which lessened the volume expansion and improved the conductivity of the system.
Improving Clinical Utility with Molecular Profiling – A Review of Critical Success Factors

Giovanni Ussia *, Jaak Janssens, Andrew Dean, William M. Gallagher, Andreas Seeber, Gordon Stamp Department of Medicine and Surgery, OspedaleSant'Orsola, University of Bologna,, Italy. Department of Oncology, Limburg Oncology Center, Belgium. St John of God Hospital, Subiaco, WA 6008, Australia. Imperial College Section of Investigative Medicine, Hammersmith Campus, London, United Kingdom. Department of Haematoloy and Oncology, Innsbruck Medical University, Austria. Imperial College Section of Investigative Medicine, Hammersmith Campus, London, United Kingdom. *Corresponding Author: Giovanni Ussia, Department of Medicine and Surgery, OspedaleSant'Orsola, University of Bologna, Italy Email: info.ussia@gmail.com Received date: May 19, 2018; Accepted date : June 06, 2018; Published date: June 12, 2018. Citation this Article : Giovanni Ussia , Jaak Janssens, Andrew Dean William M. Gallagher, Andreas Seeber, Gordon Stamp. Improving Clinical Utility with Molecular Profiling – A Review of Critical Success Factors. J Surgical Case Reports and Images, Doi : 10.31579/JSCI/2018/001. Copyright: ©2018. Giovanni Ussia. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Interactive comment on “Necessary conditions for algorithmic tuning of weather prediction models using OpenIFS as an example” by Lauri Tuppi et al

(1) The paper is providing a recipe how to optimise parameters within an ensemble NWP system. Such an optimisation is very difficult to realise and the paper, which is based on many years of experience in parameter optimisation, provides essential guidelines how to do it properly and should therefore be published. However, the presentation of the paper can still be improved and I provide suggestions in the following. The English language could also be improved. (2) We thank for the feedback, and we have made numerous modifications according your comments. The comments of the second reviewer in particular helped to improve the English language. We also found some clear errors, and now we hope that readability of the text has improved. (3) Exact locations of modifications to the manuscript can be found in the comments below. The English language has been improved at least on lines 31, 86, 198, 229, 235, 236, 294-304, 312 and 321.
Evaluating landslide response in seismic and rainfall regime: A case study from the SE Carpathians, Romania

Abstract. There have been many studies exploring the rainfall induced slope failures in the earthquake affected terrain. However, studies evaluating the potential effects of both landslide triggering factors; rainfall and earthquake have been infrequent despite the rising global landslide mortality risk. The SE Carpathians, which have been subjected to many large historical earthquakes and changing climate and thus resulting in frequent landslides, is one such region that is least explored in this context. Therefore, a massive (~9.1 Mm2) landslide, situated along the Basca Rozilei River, in the Vrancea Seismic Zone, SE Carpathians is chosen as a case study area to achieve the aforesaid objective. The present state of slope reveals the Factor of Safety in a range of 1.17–1.32 with a static condition displacement of 0.4–4 m that reaches up to 8–60 m under dynamic (earthquake) condition. The Groundwater (GW) effect further decreases the Factor of Safety and increases the displacement. Ground motion amplification enhances the possibility of slope surface deformation and displacements. The debris flow prediction, implying the excessive rainfall effect, reveals a flow having 9.0–26.0 m height and 2.1–3.0 m/sec velocity along the river channel. The predicted extent of potential debris flow is found to follow the trails possibly created by previous debris flow and/or slide events.
An application of neural point processes to geophysical data

The huge amount of temporal data available nowadays in numerous scientiﬁc ﬁelds requires dedicated analysis and prediction methods. Stochastic temporal point processes are certainly one of the popular approaches available to model time series. While point processes have been successfully applied in many application domains, they need strong assumptions. For instance, the conditional intensity is often supposed to follow a particular parametric function, hence ﬁxing a priori the structure of the events distribution: purely random or independent, clustered or regular. Recent papers investigate the use of models from machine learning dedicated to sequential events analysis, namely recurrent neural networks (RNN). These RNNs are expected to be versatile enough to automatically adapt to the data, without the need for a priori choosing the character of the events distribution. This paper presents a brief introduction to the so-called neural point processes and discusses numerical experiments. In particular, the presented real data application considers seismic data from the Guadeloupe region.
Mining of Mineral Deposits

Purpose is the analysis of world practice of applying the best available techniques to increase energy saving and energy efficiency of mining enterprises being the important factor of the economic development. Methods. The research methodology is based on the scientific approaches: historical-evolutionary, interdisciplinary, integration, process, and situational. Methods of retrospective, categorical, and system analysis as well as modeling have been applied. The analysis was performed on the basis of both initial information sources (original texts of legislative documents, strategies) and material of the international analytical organizations (International Energy Agency, World Bank) as well as the data of official statistical records. Findings. For the first time, different variants of the best available techniques in Japan, the USA, and the EU have been analyzed as well as the role of a state in applying institutional measures of energy efficiency support, including mining complex, and prospects of its use in the Russian Federation. It has been determined that energy efficiency is the effective and economically feasible tool in reaching sustainable development of the national economy. It has been demonstrated that energy saving and increased energy efficiency may reduce the needs in investment, raise industrial competitiveness and consumer welfare as well as improve environmental protection owing to the reduced greenhouse gas emissions and decreased air pollution. Energy efficiency growth has direct effect upon the industrial competitiveness; it can be achieved by means of innovations. Originality. Emphasis has been put on the key scientific and technical aspects and approaches stimulating increased energy efficiency in the developed countries under the effect of strong, coordinated, and consistent national policy in different sectors of economy. Practical implications. The carried-out analysis is useful in terms of the innovative and technological development of different industries of the Russian Federation in the context of its complicated cooperation with European countries and the USA, dependence on foreign technologies, and limited access to them.
OTT_A_242854 4305..4318

Hao Wang Jiang Wu Wei Guo 1Department of Neurosurgery, Shenzhen People’s Hospital, The Second Clinical Medical College of Jinan University, Shenzhen, Guangdong, People’s Republic of China; 2Department of Neurosurgery, Hebei General Hospital, Shijiazhuang, Hebei, People’s Republic of China Background: Long non-coding RNAs (lncRNAs) play an imperative role in tumorigenesis, but few lncRNAs have been functionally characterized in glioma. The aim of the present study was to identify the role of long non-coding RNA LINC01614 (LINC01614) in glioma development and explore the underlying mechanisms of LINC01614/miR-383/ADAM12 axis. Patients and Methods: LncRNA expression in glioma specimens was measured by lncRNA microarray and qRT-PCR. The prognostic value of LINC01614 expression was statistically analyzed in 112 glioma patients. Loss-of-function experiments were conducted to investigate the biological functions of LINC01614 in vitro. Luciferase analyses, ChIP assays, and RNA pulldown were performed to determine the underlying LINC01614 mechanisms. Results: We identified a novel glioma-related lncRNA LINC01614 by analyzing TCGA datasets. The distinct upregulation of LINC01614 was observed in both glioma specimens and cell lines using RT-PCR. We also observed that LINC01614 upregulation was induced by nuclear transcription factor SP1. Clinical assays revealed that high levels of LINC01614 were associated with KPS, WHO grade and shorter overall survival of glioma patients. Multivariate analysis further confirmed that LINC01614 was an independent prognostic marker for glioma patients. Besides, functional assays displayed that silence of LINC01614 knockdown distinctly inhibited cell growth, migration and invasion and promoted cell apoptosis in glioma cells. LINC01614 expression was enriched in the cytoplasm of glioma cells. Mechanistic investigation revealed that LINC01614 functioned as a competing endogenous RNA to upregulate a disintegrin and metalloproteinase 12 (ADAM12) by sponging miR-383. Conclusion: Overall, these findings showed that SP1-induced upregulation of LINC01614 promoted glioma malignant progression via modulating the miR-383/ADAM12 axis, which may provide a promising therapy for glioma.
Regression Heuristics by Optimal Tridimensional Features of Electrocardiogram for Arrhythmia Detection

Computer aided predictive analytics are vital in noncommunicable diseases. In particular, early diagnosis of arrhythmia (heart related disease) is crucial to prevent sudden deaths due to heart failure. The critical context to prevent deaths caused by arrhythmia is early prediction of the arrhythmia scope. The clinical experts widely consider the Electro Cardio Gram (ECG) report as primary parameter to scale the scope of arrhythmia. However, the diagnosis accuracy of clinical experts is highly correlate on their expertise. Unlike the other domains, the sensitivity that is the accuracy in disease-prone is very much crucial in clinical practices. Particularly, the accuracy and sensitivity are more vital in computer-aided heart disease prediction methods. Hence, the recent research contributions are quantifying the possibilities of optimizing machine-learning approaches to achieve significance in computer-aided methods to perform predictive analysis on arrhythmia detection. Regarding this context, this manuscript is defining a Regression Heuristics by Tridimensional Features of the electrocardiogram reports, which has intended to perform arrhythmia prediction. The experimental study evincing the significance of the proposed model that scaled against the contemporary methods.
A Case Report of Atypical Hyponatremia Caused by Diarrhea Following a Course of Ferrous Sulfate Supplements

Hyponatremia, defined as a serum sodium level less than 135 mg/mL, can be euvolemic, hypovolemic, or hypervolemic. Its presentation can be subtle or severe depending on whether the hyponatremia is acute or chronic. This case investigates a geriatric patient who suffered diarrhea and hyponatremia following a course of ferrous sulfate pills for anemia treatment. Given that ferrous sulfate pills usually result in constipation, this presentation was abnormal. Literature review and comparison to prior atypical situations resulted in the conclusion that the patient’s diarrhea was likely a result of her Crohn’s diagnosis. The subsequent resulting hyponatremia was likely due to this diarrhea coupled with her prescription thiazide diuretic which can cause hyponatremia as a common side effect. This report allows for physicians to understand potential causes behind atypical electrolyte abnormalities in their patients and hence, find an effective treatment plan.
BR+ for Addressing Imbalanced Multilabel Data Classification Combined with Resampling Technique

BR+ is a multilabel method that transforms multilabel into binary single label and assumes label dependency. BR+ can use any different classification method such as random forest. Random forest is an advantageous classification method. But presence of imbalanced classes, random forest will result in poor performance. Hence, handling imbalanced data can be done by applying resampling techniques consisting of SMOTE-NC and T-Link. The dataset used was adolescent risk behavior of drug abuse and premarital sex based on SKAP. The dataset has two labels means there are multilabel problems and the dataset is imbalanced. Thus, the combination of BR+ (Stat) and resampling techniques will be compared in handling multilabel imbalanced data in the classification of adolescent risk behavior using random forest. The results show that the optimum Mtry is 7 and the combination of BR+ (Stat) and T-Link is the best method to handle the multilabel imbalanced data.
A Study on the Impact of Nodes Density on the Energy Consumption of LoRa

LoRaWAN is one of the leading Low power wide area network (LPWAN) LPWAN technologies that compete for the formation of big scale Internet of Things (IoT). It uses LoRa protocol to achieve long range, low bit rate and low power communication. Large scale LoRaWAN based IoT deployments can consist of battery powered sensor nodes. Therefore, the energy consumption and efficiency of these nodes are crucial factors that can influence the lifetime of the network. However, there is no coherent experimental based research which identifies the factors that influence the LoRa energy efficiency at various nodes density. In this paper, results on measuring the packet delivery ratio, packet loss, data rate and energy consumption ratio ECR to gauge the energy efficiency of LoRa devices at various nodes density are presented. It is shown that the ECR of LoRa is inversely proportional to the nodes density and that the ECR of the network is smaller at higher traffic indicating better network energy efficiency. It is also demonstrated that at high node density, spreading factor SF of 7 and 9 can improve the energy efficiency of the network by 5 and 3 times, respectively, compare to SF 11.
the analysis Of the pOlyamine Oxidase genes in the methylOtrOphic yeast

` Polyamines are present in all living cells and regulate a wide range of biological processes. In Saccharomyces cerevisiae the polyamine oxidase Fms1p converts spermine to spermidine and 3-aminopropionaldehyde, which is necessary for the synthesis of pantothenic acid and hypusination. This paper shows that S. cerevisiae FMS1 gene orthologs are present in all major representatives of the Saccharomycotina subdivision, but their copy numbers are different. In the Komagataella phaffii (Pichia pastoris) yeast, two polyamine oxidase genes (KpFMS1 and KpFMS2) were identified, and the regulation of their promoters activity was studied.