Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination of irrigation depths using a numerical model of crop growth and quantitative weather forecast and evaluation of its effect through a field experiment for potato

A scheme for determining irrigation depth using a numerical model of crop response to irrigation and quantitative weather forecast was presented. To optimize each irrigation depth, a concept of virtual income, which is proportional to an increment in transpiration amount during an irrigation interval, is introduced. A field experiment was carried out to evaluate effectiveness of the presented scheme in terms of net income considering the price of water. Potato was grown in summer season of 2015 using a drip irrigation system in Arid Land Research Center, Tottori, Japan. Two treatments were conducted: automated irrigation and proposed scheme with two replicates for each. Results indicated that predicted water content agreed well with observation although some underestimation of water content due to overestimation of transpiration was observed. Proposed scheme could save water by 27 %, while yield was increased by 15 %, resulting in higher net income as compared to automated irrigation. Based on these results together with previous works, we can conclude that proposed scheme can at least realize similar net income to automated irrigation systems without high initial investment.
A Survey On Visual Questioning Answering : Datasets, Approaches And Models

: Visual Questioning Answering is a new way of interacting with artificial systems. They are aimed at making interaction with machines similar to interaction with humans. Lots of VQA models are proposed over time which are focused on datasets such as VQA, MS - COCO, Flicker30k etc. These VQA systems are limited to the type of dataset used to trained the system. There are different techniques for different types of images like natural images, artificial images, mathematical plots, etc. The performance of the models also depend on the type of question asked. In this paper we will discuss about different datasets available for VQA tasks and techniques used to build VQA systems on those datasets. We will also discuss the state of art VQA system in detail in the later section of this paper.
A REVIEW ON NANOTECHNOLOGY AND PLANT MEDIATED METAL NANOPARTICLES AND ITS APPLICATIONS

Nanotechnology play a major role in research and this nanotechnology are classified into various types of nanomaterials. During the most recent decades, the green synthesis of metal nanoparticles has huge significance of both academic research and innovation. In drug plant play a major role. The phytochemical present in the plant has efficient in curing and healing human diseases. This traditional method is merged with metal. This mixture of traditional and new method formulates green synthesis. This method has high impact on social needs for the curing of human ills. The phytochemical present in the plant extract act as capping and stabilizing agent with metal to form metal nanoparticles. Here, biological method is carried which is better than physical and chemical method. This method is eco-friendly, nontoxic and easy to synthesize. Their application is extended in various fields and has different topology.
I can’t get (no) satisfaction? A multilevel study on the satisfaction with democracy of individuals, ethnic groups and countries

Democratization research focuses mainly on either countries or individuals. Ethnicity is, however, seen as one of the most relevant factors that hinders democratization. In its most extreme outcome, ethnic tensions cumulate to ethnic violence. But how does ethnic group membership influence democratization? Do individual or group-based factors explain satisfaction with democracy? In this study, the effects of individuals and their membership to ethnic groups are analyzed. Additionally, two power-sharing institutions are included in the analysis. Due to the structure of the Afrobarometer, previous survey studies have not yet incorporated both country and time effects. In this paper, a multilevel approach is used allowing a comparative analysis over time, countries, ethnic groups, and individuals.
Classiﬁcation of Failures in Goat Leather Samples Using Computer Vision and Machine Learning

—Textile industry has used goat skins in manufacturing products that require high quality control. Thus, a specialist performed a skins qualities classiﬁcation to put a price on the goat leather sample, but this evaluation depends on whom evaluate. To reduce these divergences and to increase the productivity on the textile industry area, this paper presents a new approach to detect leather failure using feature extractor and machine learning classiﬁers. Also, a new feature extractor, called of Pixel Intensity Analyzer (PIA), is proposed for this application. Experiments were performed with a real data set comparing PIA with two other features extractors using machine learning classiﬁers with each one. In accuracy, the best approach was LBP with LS-SVM (RBF), but in processing time as a very important factor, since it is a real-time application to the industry, the PIA combined with ELM presents the best cost-effective because it also has excellent accuracy rates.
Some seismic reflections on 3C-3D and anisotropy

In this short note I consider some of the features of different 3-D surveys PP, S-S and P-SV that make them more or less advantageous for anisotropy analysis. The implications of survey type, that is, source and receiver polarizations, upon phenomena such as shear-wave splitting and moveout variation with offset and azimuth are considered. There are apparently many benefits to be derived from the 3C-3D survey with a single (P) source type, both in terms of data acquired and of cost.
Impact of Macroeconomic Variables on Stock Markets: Evidence from Frontier Markets like Pakistan Stock Exchange (PSX)

The macroeconomic version of the APT is of great significance in examining the return on assets. It analyzes the estimated security return with reference to various macroeconomic variables. Despite availability of research studies related to the developed and emerging stock markets of the world, still a research gap exists for exploring the frontier markets like equity market of Pakistan. The study examines the long and short term impact of macroeconomic variables on the KSE 100 index for the period of July 1996 - June 2015. Cointegration technique and VECM models have been applied. Among these variables, GDP, inflation, exchange rate, unemployment rate, labor force cost and stock market of US were found significant for explanation of effects on return of stock market of Pakistan. The study findings have potential implications for both policymakers and investors pertaining to macroeconomic factors and stock market volatility.
Predictive Evaluation of Replication Studies

Throughout the last decade, the so-called replication crisis has stimulated many researchers to conduct large-scale replication projects. With data from four of these projects, we computed probabilistic predictions of the replication outcomes, which we then evaluated regarding discrimination, calibration and sharpness. By using a model of effect sizes which can take into account possible inflation and heterogeneity of effects, it was possible to predict the effect estimate of the replication study with good performance in two of the four data sets. In the other two data sets, predictive performance could still be substantially improved compared to the naive model which does not consider inflation and heterogeneity of effects. The results suggest that many of the estimates from the original studies were too optimistic, possibly caused by publication bias or questionable research practices. Moreover, the results also indicate that the use of statistical significance as the only criterion for replication success may be questionable, since from a predictive viewpoint non-significant replication results are often in agreement with significant results from the original study. Finally, the proposed model could be used to determine the sample size of a new replication study, considering potentially inflated and heterogeneous effect estimates, which seems realistic in view of our results.
On the Equivalence of Bi-Level Optimization and Game-Theoretic Formulations of Invariant Risk Minimization

The standard risk minimization paradigm of machine learning is brittle when operating in environments whose test distributions are different from the training distribution due to spurious correlations. Training on data from many environments and ﬁnding invariant predictors reduces the effect of spurious features by concentrating models on features that have a causal relationship with the outcome. Such invariant risk minimization was posed as a bi-level optimization problem by Arjovsky et al. (2019). In this work, we pose invariant risk minimization as ﬁnding the Nash equilibrium of an ensemble game among several environments and show that the set of Nash equilibria for the proposed game are equivalent to the set of invariant predictors obtained by the bi-level optimization even with nonlinear classiﬁers and transformations.
Rapid high quality next generation sequencing library preparation with Swift 2STM Turbo DNA Library Kits on the Opentrons OT-2

The Swift 2STM Turbo DNA Library Kit was automated with the Opentrons OT-2 for fast and robust preparation of high quality libraries for next generation sequencing. ◾ Libraries prepared with 100 and 1 ng input were prepared from a mixture of bacteria with varied GC content including: Staphylococcus aureus, Escherichia coli, and Streptomyces avermitilis. ◾ Comparable performance was observed for libraries prepared manually and with the Opentrons OT-2 in terms of size and yield. Similar sequencing metrics such as complexity, coverage, and relative representation were also observed. INTRODUCTION Next-generation sequencing (NGS) library preparation requires multiple steps for fragmentation, polishing, adenylation, ligation, and PCR, requiring two to five master-mix additions, incubations, and SPRI clean-ups. Laboratory automation can reduce technical hands-ontime, while increasing the reliability of experiments due to human error and contamination. The Opentrons OT-2 is an affordable liquid handling platform, starting at $5,000, that enables the automation of complex library preparation. The implementation of automation and robust fluidics with the Swift 2S Turbo DNA Library Kits© (1) ensures rapid and consistent performance for next generation sequencing. The two to three hour Turbo protocol allows varied input amounts of DNA (1 to METHODS AND MATERIALS Libraries were prepared with genomic DNA from three bacterial species with varied GC content, Staphylococcus aureus (ATCC 25923D-5), Escherichia coli (ATCC 700926) and Streptomyces avermitilis (ATCC 31267), mixed at equal molar concentrations of DNA. Libraries were prepared manually by an experienced Swift developer with six or eight technical replicates of DNA with 100 ng and 1 ng input into each library prep. Eight technical replicates of both inputs were also prepared on the Opentrons OT-2. The fragmentation master mix used with 100 ng input samples prepared on the OT-2 included Regent DE to slow the fragmentation reaction (2). In detail, 5 ul of Reagent DE was added per reaction, Reagent K2 was not included, and the fragmentation time was increased to 15 min. Libraries were prepared for 350bp aligned insert size. PCR was used to incorporate indexes with 5 and 11 cycles for 100 ng and 1 ng, respectively. Eight libraries were randomly selected for sequencing including libraries prepared from both inputs on the OT-2 and manually. All sequencing was performed on an Illumina MiSeq Standard V2 PEx150. The Opentrons Temperature and Magnetic Modules were used for active cooling of enzymes and magnetic bead clean-up protocols on the OT-2. For comparable analysis, reads were normalized to 1.9 million reads per sample. 250 ng), volumes, and sample types. Demonstrated herein with a mock bacterial community, the Swift 2S Turbo DNA Library Kit© with Opentrons OT-2 is a rapid and ideal library preparation solution for applications such as whole genome sequencing, targeted exome, genotyping, and metagenomics.
Evaluation of IFN-γ and T-bet Expression Levels as Possible Molecular Markers of Schizophrenia

Schizophrenia is an irritating mental disorder that affects around 1% of the world's population. The immune system contributes to the onset of the disease, particularly through production and secretion of some cytokines. In patients with schizophrenia, the balance of Th1 to Th2 ratio is often altered. In the present study, we investigated these changes by measuring the gene expression levels of IFN-γ and T-bet as Th1 indicators, as well as IL-4 and GATA-3 as representatives for Th2. Blood samples of schizophrenic patients (n=25) and healthy individuals (n=10) were obtained. Total RNA was extracted from leukocytes and cDNA synthesis was performed based on provided protocols. Real-time PCR technique was utilized for the assessment of gene expression levels. Results indicated a significant increase in the expression of IFN-γ and its transcription factor, T-bet, while IL-4 gene expression was reduced significantly. The expression level of GATA-3 gene revealed no meaningful changes. Altogether, results confirmed the relative shift of Th1 to Th2 status in the patient with schizophrenia and re-emphasize the importance of the inflammatory events in the incidence of the disease. Moreover, a new index was introduced based on the IFN-γ and T-bet genes expression, which can determine healthy condition with total accuracy of 79%.
Adaptive High-Level Strategy Learning in StarCraft

—Reinforcement learning (RL) is a technique to compute an optimal policy in stochastic settings whereby, actions from an initial policy are simulated (or directly executed) and the value of a state is updated based on the immediate rewards obtained as the policy is executed. Existing efforts model opponents in competitive games as elements of a stochastic environment and use RL to learn policies against such opponents. In this setting, the rate of change for state values monotonically decreases over time, as learning converges. Although this modeling assumes that the opponent strategy is static over time, such an assumption is too strong when human opponents are possible. Consequently, in this paper, we develop a meta-level RL mechanism that detects when an opponent changes strategy and allows the state-values to “deconverge” in order to learn how to play against a different strategy. We validate this approach empirically for high-level strategy selection in the Starcraft: Brood War game.
Quantifying Prejudiced Statistics Information Based On Tweets

: No matter how you look at it utilization of online relational associations (OSNs) to disperse information and exchange appraisals, by the general populace, news media and political entertainers the same, has engaged new streets of research in computational political theory. In this paper, we concentrate on the issue of assessing and assembling the political slanting of Twitter customers. We characterize political slanting induction as an angled streamlining issue that breakers two musings: (a) customers are solid in their exercises of tweeting and retweeting about political issues, and (b) practically identical customers tend to be retweeted by similar get-together of people.our quantitative audit uncovers knowledge into the political economics of the Twitter masses, and the common elements of political polarization as events spread out.
A Parallelization based Data Management Framework for Pervasive IoT Applications

Pervasive Internet of Things (IoT) is a research paradigm that has attracted considerable attention nowadays. The main aim of pervasive IoT is that in the future, the everyday objects (devices) would be accessible, sensed, and interconnected inside the global structure of the Internet. But in most of the pervasive IoT applications, the resources of an IoT device such as storage, processing, and energy are limited; as such there is a need for management of resources in such applications. Multiple aspects related to the data such as the type of data, size of data, number of transmission and reception of data packets, the structure of data, etc are taken into consideration while managing the resources of pervasive IoT applications. Therefore data management is essential for the management of limited resources in such applications. This paper presents the recent studies and related information in data management for pervasive IoT applications having limited resources. This paper also proposes a parallelization based data management framework for resource-constrained pervasive applications of IoT. The comparison of the proposed framework is done with the sequential approach through simulations and empirical data analysis. The results show an improvement in energy, processing, and storage requirements for the processing of data on the IoT device in the proposed framework as compared to the sequential approach.
Skill Discovery for Exploration and Planning using Deep Skill Graphs

We introduce a new skill-discovery algorithm that builds a discrete graph representation of large continuous MDPs, where nodes correspond to skill subgoals and the edges to skill policies. The agent constructs this graph during an unsupervised training phase where it interleaves discovering skills and planning using them to gain coverage over ever-increasing portions of the state-space. Given a novel goal at test time, the agent plans with the acquired skill graph to reach a nearby state, then switches to learning to reach the goal. We show that the resulting algorithm, Deep Skill Graphs , outperforms both ﬂat and existing hierarchical reinforcement learning methods on four difﬁcult continuous control tasks.
Comparison of ENDF/B-VIII.0 and ENDF/B-VII.1 in Criticality and Depletion using PWR Pin Cell by STREAM

This paper presents the criticality and depletion comparison of nuclear data library ENDF/B-VIII.0 and ENDF/B-VII.1 using pressurized light water reactor (PWR) pin cell by STREAM. In this study, several different temperatures of fuel and coolant are used The new version of ENDF/B library, ENDF/B-VIII.0 was released in 2018 [1-2]. ENDF/B is a raw evaluated nuclear data library containing the neutron cross section. Generally, the neutron cross section data is processed by a nuclear data processing code for a neutron transport analysis code. Then, the output of nuclear data processing code is processed again into the data and the format required for the library of a neutron transport analysis code. In this study, NJOY2016 is used to process nuclear data ,and NTOS (NJOY to STREAM) is used to process the output of NJOY into library of STREAM [3]. STREAM has been developing by Computational Reactor Physics and Experiment laboratory (CORE) in Ulsan National Institute Science and Technology (UNIST) [4-5].
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation

Real-world tasks often exhibit a compositional structure that contains a sequence of simpler sub-tasks. For instance, opening a door requires reaching, grasping, rotating, and pulling the door knob. Such compositional tasks require an agent to reason about the sub-task at hand while orchestrating global behavior accordingly. This can be cast as an online task inference problem, where the current task identity, represented by a context variable, is estimated from the agent's past experiences with probabilistic inference. Previous approaches have employed simple latent distributions, e.g., Gaussian, to model a single context for the entire task. However, this formulation lacks the expressiveness to capture the composition and transition of the sub-tasks. We propose a variational inference framework OCEAN to perform online task inference for compositional tasks. OCEAN models global and local context variables in a joint latent space, where the global variables represent a mixture of sub-tasks required for the task, while the local variables capture the transitions between the sub-tasks. Our framework supports flexible latent distributions based on prior knowledge of the task structure and can be trained in an unsupervised manner. Experimental results show that OCEAN provides more effective task inference with sequential context adaptation and thus leads to a performance boost on complex, multi-stage tasks.
S1366728920000358jra 1..14

Numerous studies have argued that bilingualism has effects on cognitive functions. Recently, in light of increasingly mixed empirical results, this claim has been challenged. One might ponder if there is enough evidence to justify a cessation to future research on the topic or, alternatively, how the field could proceed to better understand the phantom-like appearance of bilingual effects. Herein, we attempt to frame this appearance at the crossroads of several factors such as the heterogeneity of the term ‘bilingual’, sample size effects, task effects, and the complex dynamics between an early publication bias that favours positive results and the subsequent Proteus phenomenon. We conclude that any definitive claim on the topic is premature and that research must continue, albeit in a modified way. To this effect, we offer a path forward for future multi-lab work that should provide clearer answers to whether bilingualism has neurocognitive effects, and if so, under what conditions.
Disgust and Deontology

Models of moral judgment have linked generalized emotionality with deontological moral judgment. The evidence, however, is mixed. Other research has linked the specific emotion of disgust with generalized moral condemnation. Here too, the evidence is mixed. We suggest that a synthesis of these two literatures points to one specific emotion (disgust) that reliably predicts one specific type of moral judgment (deontological). In all three studies, we found that trait disgust sensitivity predicted more extreme deontological judgment. In Study 3, with deontological endorsement and consequentialist endorsement operationalized as independent constructs, we found that disgust was positively associated with deontological endorsement but was unrelated to consequentialist endorsement. Across studies, the disgust–deontology link was mediated by individual difference variables related to preference for order (right-wing authoritarianism and intolerance for ambiguity). These data suggest a more precise model of emotion and moral judgment that identifies specific emotions, specific types of moral judgment, and specific motivational pathways.
Making didactic use of augmented reality in regular basic education

We explore the technologies related to Augmented Reality as a type of complementary didactical tool in the classroom. We describe how to integrate Augmented Reality with regular teaching methods. In the experimental phase we used the Augmented Reality in three classrooms from a public institution, with twenty five students in every classroom, the childrens ages are between three years old to five years old. This work used the FLEX BUILDER 3 Framework from the ADOBE Family to develop the software that was applied in the classrooms because other tools do not have a didactic approach. The results show the importance of Augmented Reality in the education. This shows a new way to increase commitment and enthusiasm from students.