Miracle-dz/UniFAR-FTUs huggingface dataset:
Bi-fluctuation in Na0.5Bi0.5TiO3 ferroelectric ceramics with abnormal relaxor behaviour

ABSTRACT The existence of Bi-fluctuation dispersing in Na0.5Bi0. 5TiO3 (NBT) relaxor ferroelectric is hinted in other recent studies. However, this fluctuation has not been directly observed yet. We introduce the Bi-rich nano-regions with different sizes in a series of NBT ceramics by the slight excess of Bi3+ content. The crystal symmetries of the Bi-rich nano-regions and the NBT matrix are rhombohedral. The lattice parameters of the nano-regions are larger than those of the matrix in NBT ceramics, which were confirmed by the X-ray diffraction Rietveld refinement, TEM techniques and first-principles calculation. Also, the disorder-induced nano-regions appearing as Bi-fluctuation are associated with the complex phase transitions and the high-frequency relaxor behaviour of NBT suggested by the dielectric measurements and Raman spectra.
Temporal Question Generation from History Text

Temporal analysis of history text has always held special significance to students, historians and the Social Sciences community in general. We observe from experimental data that existing deep learning (DL) models of ProphetNet and UniLM for question generation (QG) task do not perform satisfactorily when used directly for temporal QG from history text. We propose linguistically motivated templates for generating temporal questions that probe different aspects of history text and show that finetuning the DL models using the temporal questions significantly improves their performance on temporal QG task. Using automated metrics as well as human expert evaluation, we show that performance of the DL models finetuned with the template-based questions is better than finetuning done with temporal questions from SQuAD.
Training data reduction for multilingual Spoken Language Understanding systems

Fine-tuning self-supervised pre-trained language models such as BERT has significantly improved state-of-the-art performance on natural language processing tasks. Similar finetuning setups can also be used in commercial large scale Spoken Language Understanding (SLU) systems to perform intent classification and slot tagging on user queries. Finetuning such powerful models for use in commercial systems requires large amounts of training data and compute resources to achieve high performance. This paper is a study on the different empirical methods of identifying training data redundancies for the fine tuning paradigm. Particularly, we explore rule based and semantic techniques to reduce data in a multilingual fine tuning setting and report our results on key SLU metrics. Through our experiments, we show that we can achieve on par/better performance on fine-tuning using a reduced data set as compared to a model finetuned on the entire data set.
Towards Explainable Dialogue System: Explaining Intent Classification using Saliency Techniques

Deep learning based methods have shown tremendous success in several Natural Language Processing (NLP) tasks. The recent trends in the usage of Deep Learning based models for natural language tasks have definitely produced incredible performance for several application areas. However, one major problem that most of these models face is the lack of transparency, i.e. the actual decision process of the underlying model is not explainable. In this paper, at first we solve a very fundamental problem of Natural Language Understanding (NLU), i.e. intent detection using a Bi-directional Long Short Term Memory (BiLSTM). In order to determine the defining features that lead to a specific intent class, we use the Layerwise Relevance Propagation (LRP) algorithm to find the defining feature(s). In the process, we conclude that saliency method of eLRP (epsilon Layerwise Relevance Propagation) is a prominent process for highlighting the important features of the input responsible for the current classification which results in significant insights to the inner workings, such as the reasons for misclassification by the black box model.
Research on Spatial Clustering Algorithm based on Data Mining

We extended the online learning strategy and scalable clustering technique to soft subspace clustering, and propose two online soft subspace clustering methods, OFWSC and OEWSC. The proposed evolving soft subspace clustering algorithms can not only reveal the important local subspace characteristics of high dimensional data, but also leverage on the effectiveness of online learning scheme, as well as the ability of scalable clustering methods for the large or streaming data. Furthermore, we apply our proposed algorithms to text clustering of information retrieval, gene expression data clustering, face image classification and the problem of predicting disulfide connectivity.
Transferring Modern Named Entity Recognition to the Historical Domain: How to Take the Step?

Named entity recognition is of high interest to digital humanities, in particular when mining historical documents. Although the task is mature in the field of NLP, results of contemporary models are not satisfactory on challenging documents corresponding to out-of-domain genres, noisy OCR output, or old-variants of the target language. In this paper we study how model transfer methods, in the context of the aforementioned challenges, can improve historical named entity recognition according to how much effort is allocated to describing the target data, manually annotating small amounts of texts, or matching pre-training resources. In particular, we explore the situation where the class labels, as well as the quality of the documents to be processed, are different in the source and target domains. We perform extensive experiments with the transformer architecture on the LitBank and HIPE historical datasets, with different annotation schemes and character-level noise. They show that annotating 250 sentences can recover 93% of the full-data performance when models are pre-trained, that the choice of self-supervised and target-task pre-training data is crucial in the zero-shot setting, and that OCR errors can be handled by simulating noise on pre-training data and resorting to recent character-aware transformers.
High-frequency monitoring of the occurrence of preferential flow on hillslopes and its relationship with rainfall features, soil moisture and landscape

ABSTRACT High-frequency monitoring was conducted to quantify the frequency and controlling factors of preferential flow (PF) in a monsoon-influenced sub-humid mountainous catchment (6.48 km2) of Northern China. Rainfall was measured using nine bucket raingauges. Soil moisture probes were set up at 12 sites to observe the PF. Overall, 129 rainfall events were identified during the years 2014–2016. The average PF occurrence was 41%, which increased to 71% during heavy rainfall events (>20 mm) revealing a strong influence of the amount and intensity of rainfall. The study also revealed that the PF increased with antecedent soil moisture. Soil moisture was much higher on flat sites compared to sloping sites, providing evidence that the topography has a strong influence on rainfall infiltration and runoff which, subsequently, influence soil moisture variation and the occurrence of PF. Our findings provide valuable insights into the hydrological processes for studies in regions with similar environmental conditions.
South‐North Hemispheric Asymmetry of the FAE Distribution Around the Cusp Region: Cluster Observation

Cluster data from late July to early October were used to study the distribution of field‐aligned electron (FAE) events around the two cusps. An FAE event was defined as electron parallel flux >3 × 108 (cm2 s)−1. The total number of FAE events around the two cusps was basically identical, but downward FAE events prevailed in the south and upward FAE events in the north. In the southern cusp, the peak of the FAE events distribution versus altitude was about 1.3 RE higher and the peak of the FAE events distribution versus invariant latitude (ILAT) was about 4° ILAT lower. Only the downward FAEs around the southern cusp had a second ILAT peak, which was located about 11° higher than the main peak. The normalized number of FAEs showed nearly the same features as the unnormalized number of the FAEs events. These results indicated a north‐south asymmetry of the FAE distribution around the two cusps. Some causes for the asymmetry are discussed, the main ones being the asymmetry of the magnetospheric configuration resulting from geomagnetic dipolar tilt and solar wind flows, the interplanetary magnetic field asymmetry related to the magnetosphere, and the difference of ionospheric conductivity in the two hemispheres. Various solar wind‐magnetosphere interaction processes, such as quasi‐viscous interaction and reconnection, are responsible for the asymmetry, too. The second distribution peak (at higher ILAT) of the downward FAE events around the southern cusp corresponded to high solar wind speed and may be associated with the northward interplanetary magnetic field Bz field‐aligned current at low altitude. This requires further studies, however.
CC2431 based intelligent space locating system

A Z-stack based intelligent space locating system was built on an existing wireless sensor networks platform in intelligent space with the support of CC2431.The working process was analyzed for all kinds of nodes,and the command cluster based operational mechanism of the locating system was described in detail.Information format was also illustrated for the command cluster of locating system.Experimental results showed that this locating system could satisfy the application requirements in intelligent space.
Variation of Anomalous Convergence Around Kalimantan Island in Lower Troposphere and Its Role in Connecting the East Asian Summer Monsoon and Australian Winter Monsoon

Using the data from ERA‐interim, the Hadley Centre Global Sea Surface Temperature data set, and the Climate Prediction Center Merged Analysis of Precipitation, after removing the simultaneous El Niño and Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) signals, we have investigated variations of anomalous convergence around Kalimantan Island in lower troposphere in associations with the East Asian summer and Australian winter monsoons. Results indicate that when negative sea surface temperature anomalies appear in the western Pacific on both sides of the equator, the atmospheric cooling over the northwestern Pacific and from the South Pacific Convergence Zone (SPCZ) to Australia is favorable for the maintenance of anticyclonic anomalies over the areas from the western Pacific to the South China Sea and from the south of the equator to the northeast of Australia. These anomalous anticyclonic circulations can induce equatorial easterly anomalies, which subsequently facilitate the maintenance of anomalous convergence around Kalimantan Island. In turn, the anomalous convergence in Kalimantan Island is favorable for the maintenance of anomalous anticyclonic over the northwestern Pacific and SPCZ and thereby intensifies both the western Pacific subtropical high and the Australian High, inducing concurrent intensifications of the East Asian summer monsoon and Australian winter monsoon. Related to the anomalous convergence over Kalimantan Island, precipitation and temperature decrease from the western Pacific to SPCZ but increase from the Indian Ocean to western Maritime Continent and Australia, while temperature is lower than normal in Kalimantan. The above results have remarkable implications for in‐depth understanding of the mechanism behind anomalies of the East Asian summer monsoon and Australian winter monsoon.
The economic and environmental implications of incorporating composting barns into New Zealand dairy systems

The Economic and Environmental Implications of Incorporating Composting Barns into New Zealand Dairy Systems by Rachel Susanna Syben Durie The New Zealand dairy industry is challenged with the task of improving their environmental performance while at the same time maintaining or increasing farm productivity and profitability. Composting barns have been suggested as one potential solution to improve a farm’s environmental and financial performance largely through improved productivity and duration-controlled grazing. This research project used farm systems modelling utilising Overseer® and Excel to identify the economic and environmental implications of incorporating a composting barn system on a Canterbury dairy farm. Preliminary work from this study suggested that composting barns could improve both the economic and environmental performance of a dairy farm, however a large number of assumptions had to be made to obtain these results. Thus, the key findings from this study was not the overall outcome, but rather the identification of the critical components of the system that affected nitrogen (N) leaching, greenhouse gas emissions and farm profitability. The incorporation of composting barns on the dairy farm altered the N leaching profile from one dominated by urinary N leaching to one dominated by N loss from fertiliser and soil organic matter mineralisation. The inability of Overseer® to model effluent and composting processes in the composting barn impacted on the nitrous oxide (N2O) results and were deemed not representative of the system. A small decrease in methane (CH4) emissions were observed with the incorporation of the composting barn on farm and was an indirect result of an improvement in feed conversion efficiency from incorporating supplementary feeding within the composting barn system. Economic profitability appeared to increase with the incorporation of composting barns on farm. The internal rate of return (IRR) before interest and tax increased from 8.45% without the barn to 11.62% with the barn. The marginal return was valued at 27.6%. Milk payout was identified as the key component
An Urban Sociohydrologic Model for Exploration of Beijing's Water Sustainability Challenges and Solution Spaces

The mismatch between water demand and water availability in many megacities poses vexing water management challenges. Managers are forced to take remedial efforts to address these challenges, often with a heavy focus on infrastructure solutions such as building reservoirs or interbasin transfers to meet demand, which may in fact exacerbate the problem through unintended consequences that arise from neglect of social, economic, and environmental factors. Such a situation awaits Beijing, China, which faces major water management challenges in spite of the addition of a large interbasin transfer to meet increasing demand. In this study, a sociohydrologic model is developed for investigating Beijing's future water sustainability from a holistic and dynamic perspective. Using the model, we first explore the sociohydrologic mechanisms that contributed to Beijing's worsening water situation during 1988–2014. We then use the model to assess possible future impacts of the South to North Water Diversion Project on Beijing's water supply prospects for the 2015–2035 period. Alternative futures are explored by combining three different sustainable management strategies. The model results show that the source of Beijing's dominant water pressure experienced a transformation from productive to domestic water use over the last 30 years. They also indicate that the transfer water via South to North Water Diversion Project cannot fundamentally reverse Beijing's water shortage in the long term and that demand‐oriented management measures will be required for alleviating the city's water stress. These findings provide guidance not only for Beijing's water management but also for other less developed cities around the world.
Integration of Global and Local Feature for Face Recognition

This paper proposes to combine the global and local facial features in both serial and parallel manner.Firstly,global features are used for coarse classification.Then,global and local features are integrated for fine classification.In the proposed method,global and local features are extracted by Discrete Fourier Transform(DFT) and Gabor Wavelets Transform(GWT) respectively.Experiments on two large scale face databases(FERET and FRGC v2.0) validate that the proposed method can not only greatly increase the system accuracy but also improve the system speed.
Daily Flow Forecasting Based on Genetic Algorithm and Support Vector Machine

This paper applied a genetic algorithm(GA) to optimize the parameters of Support vector machine(SVM) for daily flow forecasting in Chickasaw creek at Mobile County.To investigate the impact of variable enabling/disabling of streamflow,rainfall and evapotranspiration on model prediction accuracy,four modelling structures with different input vectors were developed and their performance was evaluated in terms of the mean square error and the coefficient of determination.The results show that the third modelling structure consisting of the past 3 days' streamflow,the past rainfall and evapotranspiration as the inputs is superior to other model structure in performance.Compared with the BP neural network,experimental results show that accuracy of the proposed SVM model is slightly better than the latter in this article and can be used for forecasting the daily flow of reservoir or hydropower plant.
Characterizing Twitter Data using Sentiment Analysis and Topic Modeling

As the global community becomes increasingly connected, it gets more and more common to express thoughts and opinions through social networking websites. Twitter, currently the largest microblog website in the world, is heavily used for this purpose. Well known politicians, comedians and trending persons use this medium to express their minds through 140-character messages. This makes Twitter one of the platforms being most influential on the global web communities’ way of thinking. This thesis combines topic modeling and sentiment analysis in order to obtain information from tweets. While sentiment analysis seeks to find out what opinions people have, topic modeling tries to find out what they talk about. Convential topic modeling schemes, such as Latent Dirichlet Allocation, are known to perform inadequately when applied to tweets, due to the sparsity of short documents. To alleviate these disadvantages, we apply several pooling techniques, aggregating similar tweets into individual documents. We specifically study the aggregation of tweets sharing authors or hashtags. Our Twitter Sentiment Analysis system is comprised of seven di erent machine learning classifiers. These aim to predict whether a message’s polarity is of neutral, negative or positive sentiment. Four machine learning algorithms, Maximum Entropy, Naïve Bayes, Support Vector Machines and Stochastic Gradient Descent, have been proposed for performing sentiment classification in this thesis. The classifiers were trained through experiments of extensive grid searches on a parameter space and preprocessing methods in order to achieve optimal classification scores. To combine topic modeling with sentiment analysis, a visualization application called TweetMoods was built. TweetMoods simultaneously examines the topics contained in a Twitter corpus retrieved by a search query, and the sentiments expressed in these tweets. Our topic modeling results show that aggregating similar tweets into individual documents increases the topic coherence significantly. On performing message polarity classification on tweets, the Maximum Entropy classifier yielded results outperforming most earlier submitted work to the International Workshop on Semantic Evaluation of 2015. This proves the importance of our extensive grid searches on optimizing the parameter space of the classifiers.
Whose line is it anyway? Automatic Multilingual Emotion Annotation of Movie Dialogues

In this work we take advantage of both semantic networks provided by BabelNet [3] and distributed word representations trained by skip-gram model with negative sampling in an effort to obtain vector representations of 8 emotional states. Subsequently, we have employed abovemention emotion vectors to identify emotional scenes in several contemporary movies spanning different genres and languages (Polish and English).
Multi-objective particle swarm optimization and simulated annealing in practice

Several automatic clustering multi-objective algorithms have been proposed in literature to solve the clustering problem. Recently, MultiObjective Particle Swarm Optimization and Simulated Annealing Algorithm (MOPSOSA) has been proposed. The aim of (MOPSOSA) is to estimate the appropriate number of clusters and appropriately partition a data set into these clusters without the need to know the actual number of clusters. In this work, the efficiency of the MOPSOSA algorithm is studied, which is based on parameters of particles’ velocity. Some of the artificial and real-life datasets are used to illustrate the impact of velocity parameters in the efficiency of MOPSOSA algorithm. The 2088 Ahmad Abubaker et al. results show that the suitable values of velocity parameters have almost the same range for the datasets used during the experiments.
Updated climate database and impacts on WEPP model predictions

CLImate GENerator (CLIGEN) (v5.3), a stochastic weather generator, is widely used in conjunction with the Water Erosion Prediction Project (WEPP) model for runoff and soil loss predictions. CLIGEN generates daily estimates of weather based on long-term observed weather station data statistics. For the United States, the original CLIGEN database released with WEPP in 1995 was derived using inconsistent periods of climate records through 1992 that could lead to significant variations in runoff and soil loss predictions on spatial and temporal scales. To achieve more reliable estimates of runoff and soil loss, an updated climate database was derived from a consistent 40 years of recent climate records of 1974 to 2013 in the United States. The objectives of this study were to (1) examine the spatial patterns in trends of differences in precipitation and maximum and minimum temperatures between the two databases, and (2) evaluate the impacts on WEPP-predicted mean annual runoff and soil loss, from the original to the updated databases. For runoff and soil loss estimates, WEPP simulations were conducted under a tilled fallow condition for 1,600 CLIGEN locations in the contiguous United States for a slope profile of 22.1 m length, 9% slope gradient, and silt loam soil. Comparison of precipitation and maximum and minimum temperatures between the original and updated databases showed variations in spatial patterns both annually and seasonally. Annual precipitation and minimum temperature generally increased across most of the country while maximum temperature increased in the western half of the United States and parts of the Northeast. Seasonally, increases in precipitation are evident in the Midwest in spring, fall, and winter, the Northwest in spring, and the Southeast in fall. Maximum daily temperature has increased in the western half of United States and parts of the Northeast in the winter, fall, and spring, whereas minimum daily temperature has increased in all seasons across the United States. Changes in WEPP-simulated mean annual runoff and soil loss from the use of the original to the updated CLIGEN database showed increases in runoff and soil loss in most of the United States. However, some stations showed either increases or decreases in runoff and/or soil loss with the updated database primarily because of differences in monthly precipitation and intensity values in the two databases. Understanding the impacts of the use of the updated database on runoff and soil loss from this study will help in making informed decisions for conservation planning and management when utilizing the WEPP erosion model.