Miracle-dz/UniFAR-FTUs huggingface dataset:
Cracking the Contextual Commonsense Code: Understanding Commonsense Reasoning Aptitude of Deep Contextual Representations

Pretrained deep contextual representations have advanced the state-of-the-art on various commonsense NLP tasks, but we lack a concrete understanding of the capability of these models. Thus, we investigate and challenge several aspects of BERT’s commonsense representation abilities. First, we probe BERT’s ability to classify various object attributes, demonstrating that BERT shows a strong ability in encoding various commonsense features in its embedding space, but is still deficient in many areas. Next, we show that, by augmenting BERT’s pretraining data with additional data related to the deficient attributes, we are able to improve performance on a downstream commonsense reasoning task while using a minimal amount of data. Finally, we develop a method of fine-tuning knowledge graphs embeddings alongside BERT and show the continued importance of explicit knowledge graphs.
Mobile Phone Use as Sequential Processes: From Discrete Behaviors to Sessions of Behaviors and Trajectories of Sessions

Mobile phone use is an unfolding process by nature. In this study, it is explicated as two sequential processes: mobile sessions composed of an uninterrupted set of behaviors and mobile trajectories composed of mobile sessions and mobile-off time. A data set of a five-month behavioral logfile of mobile application use by approximately 2,500 users in Hong Kong is used. Mobile sessions are constructed and mined to uncover sequential characteristics and patterns in mobile phone use. Mobile trajectories are analyzed to examine intraindividual change and interindividual differences on mobile re-engagement as indicators of behavioral dynamics in mobile phone use. The study provides empirical support for and expands the boundaries of existing theories about combinatorial use of information and communication technologies (ICTs). Finally, the understanding on mobile temporality is enhanced, that is, mobile temporality is homogeneous across social sectors. Furthermore, mobile phones redefine, rather than blur, the boundary between private and public time.
A simple model of Keratocyte membrane dynamics

We perform an analytical investigation of the cell interface dynamics in the framework of a minimal phase field model of cell motility suggested in [1], which consists of two coupled evolution equations for the order parameter and a two-dimensional vector field describing the actin network polarization (orientation). We derive a closed evolutionary integro-differential equation governing the cell interface dynamics. The equation includes the normal velocity of the membrane, curvature, volume relaxation, and a parameter that is determined by the non-equilibrium effects in the cytoskeleton. This equation can be simplified to obtain a Burgers-like equation. A condition on the system parameters for the existence of a stationary cell shape is obtained.
Evidence for spots on hot stars suggests major revision of stellar physics

It has long been thought that starspots are not present in the A and B stars because magnetic fields cannot be generated in stars with radiative envelopes. Space observations show that a considerable fraction of these stars varies in light with periods consistent with the expected rotation periods. Here we show that the photometric periods are the same as the rotation periods and that starspots are the likely cause for the light variations. This discovery has wide-ranging implications and suggests that a major revision of the physics of hot stellar envelopes may be required.
A tale of two sentiment scales: disentangling short-run and long-run components in multivariate sentiment dynamics

The digitalization of news and social media provides an unprecedented source to investigate the role of information on market dynamics. However, the observed sentiment time-series represent a noisy proxy of the true investor sentiment. Moreover, modeling the joint dynamics of different sentiment series can be beneficial for the assessment of their economic relevance. The main methodological contribution of this paper is twofold: (i) we filter the latent sentiment signals in a genuinely multivariate model; (ii) we propose a decomposition into a long-term random walk component, named long-term sentiment, and a short-term component driven by a stationary Vector Autoregressive process of order one, named short-term sentiment. The proposed framework is a dynamic factor model describing the joint evolution of the observed sentiments of a portfolio of assets. Empirically, we find that the long-term sentiment co-integrates with the market price factor, while the short-term sentiment captures transient and firm-specific swings. By means of quantile regressions, we assess the significance of the explanatory power of filtered present sentiment on future returns. Then, we demonstrate how the lagged relation can be successfully exploited in a portfolio allocation exercise.
Lyman‐alpha Variability During Solar Flares Over Solar Cycle 24 Using GOES‐15/EUVS‐E

The chromospheric Lyman‐alpha line of neutral hydrogen (Lyα; 1216 Å) is the strongest emission line in the solar spectrum. Fluctuations in Lyα are known to drive changes in planetary atmospheres, although few instruments have had the ability to capture rapid Lyα enhancements during solar flares. In this paper, we describe flare‐associated emissions via a statistical study of 477 M‐ and X‐class flares as observed by the Extreme UltraViolet Sensor on board the 15th Geostationary Operational Environmental Satellite, which has been monitoring the full‐disk solar Lyα irradiance on 10‐s timescales over the course of Solar Cycle 24. The vast majority (95%) of these flares produced Lyα enhancements of 10% or less above background levels, with a maximum increase of ∼30%. The irradiance in Lyα was found to exceed that of the 1–8 Å X‐ray irradiance by as much as two orders of magnitude in some cases, although flares that occurred closer to the solar limb were found to exhibit less of a Lyα enhancement. This center‐to‐limb variation was verified through a joint, stereoscopic observation of an X‐class flare that appeared near the limb as viewed from Earth, but close to disk center as viewed by the MAVEN spacecraft in orbit around Mars. The frequency distribution of peak Lyα was found to have a power‐law slope of 2.8±0.27. We also show that increased Lyα flux is closely correlated with induced currents in the ionospheric E‐layer through the detection of the solar flare effect as observed by the Kakioka magnetometer.
From Senseless Swarms to Smart Mobs: Tuning Networks for Prosocial Behaviour

Social media have been seen to accelerate the spread of negative content such as disinformation and hate speech, often unleashing reckless herd mentality within networks, further aggravated by malicious entities using bots for amplification. So far, the response to this emerging global crisis has centred around social media platform companies making reactive moves that appear to have greater symbolic value than practical utility. These include taking down patently objectionable content or manually deactivating the accounts of bad actors, while leaving vast troves of negative content to circulate and perpetuate within social networks. Governments worldwide have thus sought to intervene using regulatory tools, with countries such as France, Germany and Singapore introducing laws to compel technology companies to take down or correct erroneous and harmful content. However, the relentless pace of technological progress enfeebles regulatory measures that seem fated for obsolescence.
Exploring Positive Noise in Estimation Theory

Estimation of the mean of a stochastic variable observed in noise with positive support is considered. It is well known from the literature that order statistics gives one order of magnitude lower estimation variance compared to the best linear unbiased estimator (BLUE). We provide a systematic survey of some common distributions with positive support, and provide derivations of minimum variance unbiased estimators (MVUE) based on order statistics, including BLUE for comparison. The estimators are derived with or without knowledge of the hyperparameters of the underlying noise distribution. Though the uniform, exponential and Rayleigh distributions, respectively, we consider are standard in literature, the problem of estimating the location parameter with additive noise from these distribution seems less studied, and we have not found any explicit expressions for BLUE and MVUE for these cases. In addition to additive noise with positive support, we also consider the mixture of uniform and normal noise distribution for which an order statistics-based unbiased estimator is derived. Finally, an iterative global navigation satellite system (GNSS) localization algorithm with uncertain pseudorange measurements is proposed which relies on the derived estimators for receiver clock bias estimation. Simulation data for GNSS time estimation and experimental GNSS data for joint clock bias and position estimation are used to evaluate the performance of the proposed methods.
Impact of the Changes in the Nifty Index Constituents

The objective of this study is to conduct an empirical examination of the S&P CNX Nifty index reconstitutions, between 2009 and 2018, focusing on both the price and non-price effects and the explanations surrounding them. The event methodology, with multiple abnormal return computational methods, is employed to improve the robustness and reliability of the results. The results show that the Nifty index additions (deletions) are associated with significant positive (negative) permanent abnormal returns. But the evidence of permanent abnormal volume is limited, unlike the developed markets. The evidence in this study favours the downward sloping demand curve hypothesis as the dominant explanation for the permanent abnormal return. This study extends the existing literature to a hitherto unexplored new sample period.
Extracting Patterns of Harmful Expressions for Cyberbullying Detection

Cyberbullying, or humiliating and slandering people through Internet, has been recently noticed as a serious social problem disturbing mental health of Internet users. In Japan, to deal with the problem, voluntary members of Parent-Teacher Association (PTA) manually read through the Web to spot cyberbullying entries. To help PTA members in their uphill task we propose a novel method for automatic detection of malicious contents on the Internet. The method is based on a combinatorial approach resembling brute force search algorithms with application to language classification. The method extracts sophisticated patterns from sentences and uses them in classification. We tested the method on actual data containing cyberbullying provided by Human Rights Center. The results show our method outperformed previous methods. It is also more efficient as it requires minimal human effort.
Epithelial Mesenchymal and Endothelial Mesenchymal Transitions in Hepatocellular Carcinoma: A Review

Purpose To present a comprehensive review of the literature data, published between 2000 and 2019 on the PubMed and Web of Science databases, in the field of the tumor microenvironment in hepatocellular carcinoma (HCC). All the data were combined with the personal experiences of the authors. Design From 1002 representative papers, we selected 86 representative publications which included data on epithelial-to-mesenchymal transition (EMT), angiogenesis, cancer stem-like cells (CSCs), and molecular background of chemoresistance or resistance to radiotherapy. Results Although the central event concerns activation of the Wnt/β-catenin pathway, other signal pathways, such as c-Met/HGF/Snail, Notch-1/NF-κB, TGF-β/SMAD, and basic fibroblast growth factor-related signaling, play a role in the EMT of HCC cells. This pathway is targeted by specific miRNAs and long noncoding RNAs, as explored in this paper. A central player in the tumor microenvironment proved to be the CSCs which can be marked by CD133, CD44, CD90, EpCAM, and CD105. CSCs can induce resistance to cytotoxic therapy or, alternatively, can be synthesized, de novo, after chemo- or radiotherapy, especially after transarterial chemoembolization- or radiofrequency ablation-induced hypoxia. The circulating tumor cells proved to have epithelial, intermediate, or mesenchymal features; their properties have a critical prognostic role. Conclusion The metastatic pathway of HCC seems to be related to the Wnt- or, rather, TGFβ1-mediated inflammation-angiogenesis-EMT-CSCs crosstalk link. Molecular therapy should target this molecular axis controlling the HCC microenvironment.
Ground-Aware Point Cloud Semantic Segmentation for Autonomous Driving

Semantic understanding of 3D scenes is essential for autonomous driving. Although a number of efforts have been devoted to semantic segmentation of dense point clouds, the great sparsity of 3D LiDAR data poses significant challenges in autonomous driving. In this paper, we work on the semantic segmentation problem of extremely sparse LiDAR point clouds with specific consideration of the ground as reference. In particular, we propose a ground-aware framework that well solves the ambiguity caused by data sparsity. We employ a multi-section plane fitting approach to roughly extract ground points to assist segmentation of objects on the ground. Based on the roughly extracted ground points, our approach implicitly integrates the ground information in a weakly-supervised manner and utilizes ground-aware features with a new ground-aware attention module. The proposed ground-aware attention module captures long-range dependence between ground and objects, which significantly facilitates the segmentation of small objects that only consist of a few points in extremely sparse point clouds. Extensive experiments on two large-scale LiDAR point cloud datasets for autonomous driving demonstrate that the proposed method achieves state-of-the-art performance both quantitatively and qualitatively.
Latent Space Modelling of Unsteady Flow Subdomains

Very complex flows can be expensive to compute using current Computational Fluid Dynamics (CFD) techniques. In this thesis, models based on deep learning were used to replace certain parts of the flow domain, with the objective of replacing well-known regions with simplified models to increase efficiency. To keep the error produced by the deep learning model bounded, a traditional CFD model and deep learning model were coupled using a boundary overlap area. In this overlap area, the flow computed by the traditional CFD model was used by the deep learning model as an input. It was demonstrated that since traditional CFD model continuously feeds in reliable information into the deep learning domain, the error remains bounded. Furthermore, it was found that the accuracy of the deep learning models depends significantly on the random initial weights. Therefore, deep learning models trained differently must be carefully compared.
Mediation Effect of Motivation Mediation Effect of Motivation on Learning Achievement

February 2019 ABSTRACT The level of education of parents is a major factor for students’ learning achievement. In addition to education, parents' socioeconomic status, learning environment and learning facilities also affect students learning if these factors are mediated by students’ learning motivation. This study aims to analyze and evaluate the effect of education, socioeconomic status, learning environment, and learning facilities on students’ learning achievement viewed from direct and indirect effects by mediation of learning motivation. The research used explanatory research type involving samples from grade XI students of State Senior High Schools in the City of Jember in the academic year 2017/2018 in total of 206 respondents. The study applied primary data collected by questionnaires, in addition to observation and documentation. The analytical tool used was path analysis to determine the direct and indirect effects. The results of the study showed that parents’ education, socioeconomic status, learning environment, learning environment and learning facilities directly affect learning motivation. Meanwhile, education, socioeconomic status, learning environment, learning facilities and learning motivation directly affect students’ learning achievement. Learning motivation mediates the effect of education, socioeconomic status, learning environment, and learning facilities on students’ achievement.
Fast and Slow Arbitrage: Smart Money, Dumb Money and Mispricing in the Frequency Domain

We conduct a spectral analysis of the relation between capital flows and mispricing. Hedge funds (smart money) and mutual funds (dumb money) both behave as low-pass filters, deploying highfrequency flows towards low-frequency mispricing in opposite directions. But hedge funds attenuate high-frequency flows twice as much as mutual funds do, thus improving market efficiency more slowly than mutual funds exacerbating market inefficiency. Time-series and cross-sectional tests indicate that transaction and implementation costs are the reason hedge funds particularly behave as low-pass filters.
Proctective Effect of Aqueous Extract of Cyperus esculentus on the Flutamide-Induced Testicular Defect of Male Wistar Rats

DOI: 10.21276/sjams.2018. 6.6. 15 Abstract: The Cyperus esculentus (tiger nuts) have long been used as a liver tonic and heat stimulant. This study evaluated effect of aqueous extract of tiger nuts on serum level of testosterone, LH and FSH in male Wistar rats and histological structures on the flutamide-induced testicular defect of male Wistar rats. Tiger nuts was dried and ground and aqueous extract was prepared using distilled water. Sixteen adult male Wistar rats were randomly divided into four (4) groups (n=4), group A-D. Group A was given distilled water only. Group B was given 5mg/kg of flutamide. Groups E was given 150mg/kg of extract of tiger nuts for the period of two weeks before 5mg/kg of flutamide was given for the period of one week. Group D was given 300mg/kg of extract of tiger nuts for the period of two weeks before 5mg/kg of flutamide was given for the period of one week. Rats in group C had a significant increase in level of serum testosterone (p< 0.05) when compared with normal control. Also, there was significant increase in levels of LH in group D when compared with normal and positive controls. There was a significant decrease in FSH levels in all treated groups when compared with normal control this may be due to feed mechanism. The histological evidences of testes in group B (positive control) showed a reduction in linning cells of the seminiferous tubules; however, in the other two treatment groups they were similar to the normal control group. The results suggest that extract of tiger nuts elevates the testosterone and LH and protectes the adverse effects of flutamide on the testes.
Towards Secure Mobile Computing: Employing Power-Consumption Information to Detect Malware on Mobile Devices

—Smartphones and related mobile end-user devices represent key components of mobile computing based solutions and enable end users to conveniently access services and information virtually everywhere and any time. Due to their continuously growing importance and popularity, mobile devices have recently become a common target for malware. Unfortunately, capabilities of malware-detection applications on smartphones are limited, as integrated security features of smartphone platforms such as sandboxing or ﬁne-grained permission models restrict capabilities of third-party applications. These restrictions prevent malware-detection applications from accessing required information for the identiﬁcation of malware. This renders the implementation of reliable malware-detection solutions on smartphones difﬁcult. To overcome this problem, we propose an alternative malware-detection method for smartphones that relies on the smartphone’s measured power consumption. We show that information contained in the measured power consumption of smartphones can in principle be used to identify certain kinds of malware by means of simple threshold-based approaches. We also propose two different machine-learning techniques that allow for a classiﬁcation of applications according to their power consumption in situations, where disturbing inﬂuences prevent an application of simple threshold-based approaches. The capabilities of all proposed techniques have been assessed by means of an evaluation with real-world applications running on physical smartphones. The results of this evaluation process demonstrate the applicability of power consumption based classiﬁcation and malware-detection approaches in general and of the two proposed machine-learning techniques in particular.
US Corporate Tax Rate Cuts: Spillovers to the Irish Economy

We examine spillovers to the Irish economy from US corporate income tax rate cuts and find they lead to a small but persistent increase in Irish economic output. Our analysis of the transmission channels shows that an increase in investment, employment and exports in the externally-financed industrial sector largely drives this expansion. We also find that output spillovers from US corporate income tax cuts are larger when there is slack in the Irish labour market. Our findings suggest that the changing structure of the Irish economy means any spillovers to real economic activity from the recent US corporate tax cuts could be relatively minor. However, the larger presence and shifting focus of foreign multinational corporations’ operations in Ireland means lessons from past US corporate tax cuts may be of limited value in predicting the effects of the recent US tax system reform.
Assessment of community based forest management practices in Benue State, Nigeria

Forests in Benue State are depleting due to anthropogenic activities. Community based forest management had been suggested by various researchers as being capable of stemming the rate of destruction of forests. This study was therefore carried out to assess the presence, mode and level of participation in community based forests practices in Benue State with a view to corroborate or dispute the veracity of the practice. Stratified multistage random sampling method was adopted to select 240 Household Heads who were later interviewed using 240 pre-tested semi-structured questionnaire. Data were analysed using descriptive and inferential statistics. Participatory Index analysis (PI) was employed to determine the extent of peoples’ involvement in various forestry practices. Likert scale rating was equally used to measure the factors motivating and inhibiting respondents’ participation in identified community forestry practices. Modal forestry practice identified was boundary planting (91.2%) with PI of 0.95, followed by home gardens (PI=0.93). Participation in physical execution of work (85.4%) was highest while only 15.8% of the respondents participated in decision making process. Most of the people who participated did so because the practices were relevant to their needs (WMS = 4.30>3.05) followed by the fact that they themselves were part of the planning process (WMS =3.90 > 3.05) while others said that level of literacy was not a hindrance to participation (WMS = 1.55 < 2.95). It was concluded that community based forest management practices existed in Benue State but participation in the practices was most in physical execution of work. It was recommended that people should be involved at the stage of planning, monitoring and evaluation to enhance better participation in Community Based Forest Management by the people.