Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of An Efficient Tool to Convert Regular Expressions to NFA

—In the computing theory, while the term “Language” specifies the string set, the term “Regular Expressions” means the notation that builds, creates and generates these languages. Also, the term “Regular Expressions” creates the characters that structure and compose, which refers to the given strings, in order to search patterns for sample matching. In this context, this article tries to show how to convert “Regular Expressions” that is made up of characters into Nondeterministic Finite Automata (NFA), which is a character matching and character searching tool, by giving related algorithms and methods with their explanations in detail. Moreover, in this study, a new and efficient tool has been designed and developed in order to convert regular expressions to NFA. By the contribution of this application, an original conversion tool will have been gained in the computation area for benefiting it. As a natural result of this, an original NFA modelling tool will have been placed in the literature.
JURNAL NUSAMBA VOL.3 NO.1 APRIL

E-ISSN : 2528-0929 P-ISSN : 2549 5291 Diterima: 16 Februari 2018 Revisi : 16 Maret 2018 Disetujui: 18 April 2018 httpsdoi.org10. 29407nusamba. v3i1.12026 Abstract This study was conducted to measure customer ratings of brand image, word of mouth, purchasing decisions.Data collection techniques in this research is to spread the questionnaire to 100 respondents. The sampling technique used in this research is through non probability sampling approach with sampling method using accidental sampling. In the analysis phase validity and reliability test, normality test, multiple linear regression test, hypothesis test, and coefficient of determination test.T test results show that the created brand image and word of mouth activities have a positive impact on the strengthening of purchasing decisions of fashion products. F test results show that the creation of brand image and word of mouth are done correctly have a positive impact on the strengthening of the purchase decision of fashion products.
Shear‐Wave Anisotropy in the Earth's Inner Core

Earth's inner core (IC) anisotropy is widely used to infer the deep Earth's evolution and present dynamics. Many compressional‐wave anisotropy models have been proposed based on seismological observations. In contrast, inner‐core shear‐wave (J‐wave) anisotropy—on a par with the compressional‐wave anisotropy—has been elusive. Here, we present a new class of the J‐wave anisotropy observations utilizing earthquake coda‐correlation wavefield. We establish that the coda‐correlation feature I2‐J, sensitive to J‐wave speed, exhibits time and amplitude changes when sampling the IC differently. J‐waves traversing the IC near its center travel faster for the oblique than equatorial angles relative to the Earth's rotation axis by at least ∼5 s. The simplest explanation is the J‐wave cylindrical anisotropy with a minimum strength of ∼0.8%, formed through the lattice‐preferred‐orientation mechanism of iron. Although we cannot uniquely determine its stable iron phase, the new observations rule out one of the body‐centered‐cubic iron models.
Ferroelectric HfO2-based synaptic devices: recent trends and prospects

Neuro-inspired deep learning algorithms have shown promising futures in artificial intelligence. Despite the remarkable progress in software-based neural networks, the traditional von-Neumann hardware architecture has suffered from limited energy efficiency while facing unprecedented large amounts of data. To meet the performance requirements of neuro-inspired computing, large-scale vector-matrix multiplication is preferred to be performed in situ, namely compute-in-memory. Non-volatile memory devices with different materials have been proposed for weight storage as synaptic devices. Among them, HfO2-based ferroelectric devices have attracted great attention because of their low energy consumption, good complementary-metal-oxide-semiconductor (CMOS) compatibility and multi-bit per cell potential. In this review, recent trends and prospects of the ferroelectric synaptic devices are surveyed. First, we present the three-terminal synaptic devices based on the ferroelectric field effect transistor (FeFET), and discuss the switching physics of the intermediate states, the back-end-of-line integration and the 3D NAND architecture design. Then, we introduce a hybrid precision synapse concept that leverages the volatile charges on the gate capacitor of the FeFET and the non-volatile polarization on the gate dielectric of the FeFET. Lastly, we review two-terminal synaptic devices using the ferroelectric tunnel junction (FTJ) and ferroelectric capacitor (FeCAP). The design margins of the crossbar array with FTJ and FeCAP analyzed.
Identification of Massoia Lactone and its Hydroxy-derivative from Kabatiella caulivora, an Endophyte of the Terrestrial Plant Alyxia reinwardtii

Massoia lactone ( 1 ) and its derivative, 3,5-dihydroxydecanoic acid- d -lactone ( 2 ), were identified from an ethyl acetate extract of the endophytic fungus Kabatiella caulivora , isolated from its host Alyxia reinwardtii. The compounds were identified by GC-MS. To the best of our knowledge, this is the first report of the identification of massoia lactone and its derivative from the endophytic K. c aulivora fungus .
Pervasive Downward Bias in Estimates of Liability Scale Heritability in GWAS Meta-Analysis: A Simple Solution

SNP heritability is a fundamental quantity in the genetic analysis of complex traits. For binary phenotypes, in which the continuous distribution of risk in the population is unobserved, observed-scale heritabilities must be transformed to the more interpretable liability-scale. We demonstrate here that the field standard approach for performing the liability conversion can downwardly bias estimates by as much as ~20% in simulation and ~30% in real data. These attenuated estimates stem from the standard approach failing to appropriately account for varying levels of ascertainment across the cohorts comprising the meta-analysis. We formally derive a simple procedure for incorporating cohort-specific ascertainment based on the summation of effective sample sizes across the contributing cohorts, and confirm via simulation that it produces unbiased estimates of liability-scale heritability.
Novel protein-altering variants associated with serum apolipoprotein and lipid levels

Dyslipidemia is a major risk factor for cardiovascular disease. While common genetic variants are known to modestly affect the serum lipid concentrations, rare genetic mutations can cause monogenic forms of hypercholesteremia and other genetic disorders of lipid metabolism. Aiming to identify low-frequency protein-altering variants (PAVs) affecting lipoprotein and lipid traits, we analyzed whole-exome and whole-genome sequencing data of 481 and 573 individuals with type 1 diabetes, respectively. The phenotypic data consisted of 97 serum lipid, apolipoprotein, or other metabolic phenotypes obtained with clinical laboratory measurements and nuclear magnetic resonance (NMR) technology. Single variant analysis identified a novel association between LIPC p.Thr405Met (rs113298164) and serum apolipoprotein-A1 levels (p=7.8x10-8). In the APOB gene, we identified novel associations at two protein-truncating variants (PTVs) resulting in lower serum apolipoprotein B levels (p=5.6x10-4). The burden of PAVs was significantly associated with lipid phenotypes in LIPC, RBM47, TRMT5, and, GTF3C5 (p<2.9x10-6). The RBM47 gene is required for apolipoprotein-B post-translational modifications, and in our data, the association between RBM47 and apolipoprotein C-III levels was led by a rare 21 base pair Ala496-Ala502 deletion; as replication, the burden of rare deleterious variants in RBM47 was associated with TG-to-HDLC ratio in WES of 20,917 individuals (p=0.0093). Two PAVs in GTF3C5 were highly Finnish-enriched and associated with cardiovascular phenotypes in external data, whereby the TRMT5 p.Ser185Cys lead variant was associated with stroke phenotypes. Altogether, we identified both novel variant associations in known lipid genes, as well as novel genes implicated in lipoprotein metabolism.
The impact of stress on the mental health of people: Advancing our understanding of race-related stressors

This study provides a summary of study on racial stress factors which may impact the wellbeing of communally stressed race-related individuals and also cultural populaces. That starts with evaluating the assessment on self-exposed parting and mental wellness. Even though the information which is segregation is the mainly observed part of mental wellness, discrimination can also affect mental wellness with most important elements and discrimination which is extremely bordered in the larger society. The main essentials for study include more purposeful consideration of stress development measures as a result of institutional racism, the evaluation of difficult understanding related to normal and synthetic all-natural emergencies, archiving and recognizing the wellness influences of aggressiveness opposite to immigrants, listing and also assessing defensive possessions, and also boosting our comprehension of the bewildering partnership amongst physiological wellness.
Shock Thy Neighbor : Economic Uncertainty and Exchange Rate

The financial press linked Trump‘s political rhetoric to the depreciation of the Mexican peso. The massive trade volume of the peso/dollar currency pair obliges to investigate whether Trump‘s presidential campaign and regime, through uncertainty, cause fluctuations in the currency pair. This study finds that economic policy uncertainty (EPU) does not Granger cause the changes in the peso. However, a shock to the EPU produces a short-lived, statistically insignificant variation in the exchange rate. Also, EPU does not have a significant effect on the exchange rate beyond the first moment. EPU‘s lack of focus on trade, a central economic link between the US and Mexico, is a plausible reason for the result. The absence of an effect of EPU on the peso prevents any policy measure.
Leveraging Firm's Innovation Performance Through Big Data Analytics: An Absorptive Capacity View

Previous research has confirmed the effectiveness of utilizing big data benefits to enhance product innovation performance and improve new product success. However, there is little knowledge on mechanisms of firms generate insights from applying big data to boost innovation performance. Based on knowledge absorptive capacity theory, we conceptualized that big data analytics (BDA) can be regarded as one general absorptive capacity that can create value in the process of innovation. We hypothesis that BDA is one general absorptive capacity that can directly influence product innovation performance moderated by environmental turbulences. We empirically test our hypotheses by applying structural equation modeling. The major theoretical implication of this study is conceptualizing and identifying the critical role of big data analytics capabilities on innovation performance.
Regulation of information flows as Central Bank functions? Implications from the treatment of Account Aggregators by the Reserve Bank of India

This paper seeks to understand the role of the Indian Central Bank in regulating information flows through Account Aggregators. These are licensed entities exclusively dedicated to collecting, retrieving and sharing customers’ financial information with other financial entities with the customers’ consent. By regulating Account Aggregators as non-bank providers, the Reserve Bank of India (RBI) has opened up many foundational questions for Central Banking regulation. This paper investigates the issues that emerge for regulatory consideration, by tracing the evolution of the RBI’s regulatory approach to Account Aggregators. It then considers the regulatory approach taken by the Kingdom of Bahrain and the European Union (EU) to regulate “account information services” pursuant to broader Open Banking mandates. The analysis is used to respond to the central question driving this enquiry: Should Central Banks regulate and enable the flow of personal information? In doing so, the paper addresses the RBI’s approach in the Master Directions on Non-Banking Financial Company - Account Aggregator, 2016. We propose specific changes to anchor the Master Directions to the Central Bank’s core mandate and objectives, and to harmonise it with the broader regulatory rubric for data protection in India.
Structured Additive Regression and Tree Boosting

Structured additive regression (STAR) models are a rich class of regression models that include the generalized linear model (GLM) and the generalized additive model (GAM). STAR models can be fitted by Bayesian approaches, component-wise gradient boosting, penalized least-squares, and deep learning. Using feature interaction constraints, we show that such models can be implemented also by the gradient boosting powerhouses XGBoost and LightGBM, thereby benefiting from their excellent predictive capabilities. Furthermore, we show how STAR models can be used for supervised dimension reduction and explain under what circumstances covariate effects of such models can be described in a transparent way. We illustrate the methodology with case studies pertaining to house price modeling, with very encouraging results regarding both interpretability and predictive performance.
Is Bitcoin a Hedge? How Extreme Volatility Can Destroy the Hedge Property

A large literature tests whether Bitcoin can hedge portfolio risk, i.e. reduce the risk if added to a portfolio. Intuitively, given the extreme volatility and thus risk of Bitcoin and cryptocurrencies the idea that Bitcoin is a hedge may be puzzling. Indeed, we show that for extreme levels of volatility and correlations equal to zero, Bitcoin does not reduce the risk if added to a standard risky equity portfolio. This is not only true on average but also holds for sub-samples including the COVID-19 crisis period. We conclude that a focus on correlations is not sufficient for extreme levels of volatility.
Risk Allocation and Financial Intermediation

The classic Arrow-Debreu framework requires a very large number of specific securities to reach Pareto optimality. The present paper shows that financial intermediation can play an important role in maintaining a more parsimonious market framework while still obtaining Pareto optimality. In the framework developed, the aggregate risk components of individual risks are exchanged through a highly reduced set of nonspecific securities, while the idiosyncratic risk components are insured through financial intermediation. The role of financial intermediation is complementary to the role of security derivatives and dynamic trading.
What To Target? Insights from a lab experiment

This paper compares alternative monetary policy regimes within a controlled lab environment, where groups of participants are tasked with repeatedly fore- casting inflation in a simple macroeconomic model featuring only the dynamics of interest rates, inflation and inflation expectations. Average-inflation targeting can approximate the price path observed under price-level targeting in the pres- ence of disinflationary shocks and enable subjects to coordinate on simple heuris- tics that reflect the concern of the central bank for past inflation gaps. However, this depends on the exact specification of the policy rule. In particular, if the central bank considers more than two lags, subjects fail to form expectations that are consistent with the monetary policy rule, which results in greater inflation volatility. Reinforcing communication around the target helps somewhat anchor long- run inflation expectations
The economics of movies (revisited): A decade of literature in review

Twenty years ago, there were all but a handful of scholarly studies published about the economics of the motion picture industry. Over the first decade of the new millennium, this changed dramatically and many studies began to appear in economics journals and those of cognate disciplines. The following, and most recent, decade has seen this trend continue and the literature on the `economics of movies' has well and truly matured. While economics and marketing disciplines still generate the most output, newer data-orientated disciplines have increasingly turned their attention towards the industry and its abundance of rich and relatively-accessible data. This survey endeavours to concisely but comprehensively review recent literature related to this eternally fascinating industry.
Unobserved Heterogeneity in Choice Sets as Additively Separable Disturbances

IT IS POSSIBLE TO REPRESENT UNOBSERVED HETEROGENEITY in choice sets through additively separable disturbances. In a classic random utility model with Ui(c)=Wi(c)+ ic , one may let ic ∈ {−∞ 0} for each alternative c ∈ D and allow ic to be correlated with ic′ for any two alternatives c c′ ∈ D. One would then posit that: if κ = |D|, then ic = 0 for each alternative c ∈D; if κ = |D| − 1, then ic = −∞ for at most one alternative in D (the identity of which is left unspecified); if κ = |D| − 2, then ic = −∞ for at most two alternatives in D (the identities of which are left unspecified); and so forth. This model yields that alternative c is not chosen if ic = −∞, which is analogous to alternative c not being chosen when it is not contained in the agent’s choice set.
A two-step machine learning approach for crop disease detection: an application of GAN and UAV technology

Automated plant diagnosis is a technology that promises large increases in cost-efficiency for agriculture. However, multiple problems reduce the effectiveness of drones, including the inverse relationship between resolution and speed and the lack of adequate labeled training data. This paper presents a two-step machine learning approach that analyzes low-fidelity and high-fidelity images in sequence, preserving efficiency as well as accuracy. Two data-generators are also used to minimize class imbalance in the high-fidelity dataset and to produce low-fidelity data that is representative of UAV images. The analysis of applications and methods is conducted on a database of high-fidelity apple tree images which are corrupted with class imbalance. The application begins by generating high-fidelity data using generative networks and then uses this novel data alongside the original high-fidelity data to produce low-fidelity images. A machine-learning identifier identifies plants and labels them as potentially diseased or not. A machine learning classifier is then given the potentially diseased plant images and returns actual diagnoses for these plants. The results show an accuracy of 96.3% for the high-fidelity system and a 75.5% confidence level for our low-fidelity system. Our drone technology shows promising results in accuracy when compared to labor-based methods of diagnosis.
Secure PAC Bayesian Regression via Real Shamir Secret Sharing

A common approach of system identification and machine learning is to generate a model by using training data to predict the test data instances as accurately as possible. Nonetheless, concerns about data privacy are increasingly raised, but not always addressed. We present a secure protocol for learning a linear model relying on a recently described technique called real number secret sharing. We take as our starting point the PAC Bayesian bounds and deduce a closed form for the model parameters which depends on the data and the prior from the PAC Bayesian bounds. To obtain the model parameters one needs to solve a linear system. However, we consider the situation where several parties hold different data instances and they are not willing to give up the privacy of the data. Hence, we suggest to use real number secret sharing and multiparty computation to share the data and solve the linear regression with a secure distributed Gaussian elimination protocol such that privacy of the data is preserved. The benefit of using secret sharing directly on real numbers is reflected in the simplicity of the protocols and the number of rounds needed. However, this comes with the drawback that a share might leak a small amount of information, but in our analysis we argue that the leakage is small.
Necessary and Sufficient Conditions for State Feedback Equivalence to Negative Imaginary Systems

In this paper, we present necessary and sufficient conditions under which a linear time-invariant (LTI) system is state feedback equivalent to a negative imaginary (NI) system. More precisely, we show that a minimal LTI strictly proper system can be rendered NI using full state feedback if and only if it can be output transformed into a system, which has relative degree less than or equal to two and is weakly minimum phase. We also considered the problems of state feedback equivalence to output strictly negative imaginary systems and strongly strict negative imaginary systems. Then we apply the NI state feedback equivalence result to robustly stabilize an uncertain system with strictly negative imaginary uncertainty. An example is provided to illustrate the proposed results, for the purpose of stabilizing an uncertain system.
How much "human-like" visual experience do current self-supervised learning algorithms need to achieve human-level object recognition?

This paper addresses a fundamental question: how good are our current self-supervised visual representation learning algorithms relative to humans? More concretely, how much “human-like”, natural visual experience would these algorithms need in order to reach human-level performance in a complex, realistic visual object recognition task such as ImageNet? Using a scaling experiment, here we estimate that the answer is on the order of a million years of natural visual experience, in other words several orders of magnitude longer than a human lifetime. However, this estimate is quite sensitive to some underlying assumptions, underscoring the need to run carefully controlled human experiments. We discuss the main caveats surrounding our estimate and the implications of this rather surprising result.
Complexity of Magnetic-field Turbulence at Reconnection Exhausts in the Solar Wind at 1 au

Magnetic reconnection is a complex mechanism that converts magnetic energy into particle kinetic energy and plasma thermal energy in space and astrophysical plasmas. In addition, magnetic reconnection and turbulence appear to be intimately related in plasmas. We analyze the magnetic-field turbulence at the exhaust of four reconnection events detected in the solar wind using the Jensen–Shannon complexity-entropy index. The interplanetary magnetic field is decomposed into the LMN coordinates using the hybrid minimum variance technique. The first event is characterized by an extended exhaust period that allows us to obtain the scaling exponents of higher-order structure functions of magnetic-field fluctuations. By computing the complexity-entropy index we demonstrate that a higher degree of intermittency is related to lower entropy and higher complexity in the inertial subrange. We also compute the complexity-entropy index of three other reconnection exhaust events. For all four events, the B L component of the magnetic field displays a lower degree of entropy and higher degree of complexity than the B M and B N components. Our results show that coherent structures can be responsible for decreasing entropy and increasing complexity within reconnection exhausts in magnetic-field turbulence.