Miracle-dz/UniFAR-FTUs huggingface dataset:
Corpus linguistic and experimental studies on the meaning-preserving hypothesis in Indonesian voice alternations

Abstract One essential feature of voice alternation is that active and passive clauses centred around a given verb express the same meaning: the “meaning-preserving” hypothesis. One effect of the alternation is the different linking of grammatical relations and semantic roles, which affects the identity of the subject. This paper investigates the meaning-preserving hypothesis in voice alternation in Indonesian from a quantitative usage-based perspective by combining corpus-based data with sentence-production experiment data. It analysed Indonesian caused forward/backward motion verbs and the distribution of their (non-)metaphoric senses in active and passive. The findings demonstrate the frequency effects and sense-sensitivity of voice alternation, such that a given voice type of a verb is strongly associated with certain senses. This finding provides initial support for a previous study on voice alternation in an Austronesian language, predicting that the verb’s semantic properties may condition the statistical bias of the verb towards a particular voice. Some convergence between experimental and corpus findings indicates that participants demonstrate some representation of the strong association between a given voice form of the verb and the sense predominantly expressed in that form, highlighting the notion of item-specific representations of linguistic knowledge as found in construction grammar.
Taxation, Inequality, and Poverty: Evidence from Ukraine

Abstract The aim of the article is to assess the impact of taxes on poverty and inequality in Ukraine and provide recommendations on how taxation should be used to address problems of inequality and poverty. The research methodology is based on a combination of linear regression and commitment to equity (CEQ) methodology, which was designed by Lustig to analyse the impact of taxation and social spending on inequality and poverty in individual countries. The dataset consists of data from the World Inequality Data Base and data from State Statistic Service of Ukraine. The analysis shows that income tax reform in Ukraine should not take place in the context of changing tax rates and tax periods but in the context of shifting the tax burden from the poor to the rich and preventing aggressive tax planning. Also, the results of the analysis show that the Ukrainian government’s policy of reducing free education and health services may contribute to poverty if the government does not change its redistributive policies. The article contributes to the academic literature on the impact of taxation on poverty and inequality in developing countries. The practical results obtained in the paper are useful for developing countries’ governments to design poverty- and inequality-sensitive tax policies.
Gleasonian structure in the helminth metacommunity of the opossum Didelphis albiventris in two extremes of the Atlantic Forest

Abstract Didelphis albiventris is the opossum with the largest geographic distribution in Brazil and has a wide spectrum of helminth parasites. This study aimed to describe the species composition and analyse the structure of helminth communities of D. albiventris in two extremes of the Atlantic Forest in Brazil. The influence of host sex, body mass, age, helminth species richness and locality on the abundance and prevalence of the most prevalent helminth species was investigated. Ninety-five per cent of the animals were infected with at least one species. Nine helminth species were found: the nematodes Aspidodera raillieti, Cruzia tentaculata, Trichuris didelphis and Trichuris minuta in the large intestine; Turgida turgida in the stomach; Travassostrongylus orloffi and Viannaia hamata; and the trematodes Brachylaima advena and Rhopalias coronatus in the small intestine. Three helminth morphospecies were also recovered: the nematodes Hoineffia sp. and Viannaia sp. and a cestode in the small intestine. Cruzia tentaculata and V. hamata were the species with the highest mean abundances and intensities, while the most prevalent species were A. raillieti, C. tentaculata, T. turgida and V. hamata, forming the central nucleus of the helminth component community. The analysis of the helminth metacommunity structure indicated a Gleasonian pattern for the total set of infracommunities, corroborating the beta diversity indices, which indicated more species replacement than loss between localities and infracommunities, although at a low level. The results indicated a stronger influence of host attributes than geographical distance on the community structure.
xDeepFIG: An eXtreme Deep Model with Feature Interactions and Generation for CTR Prediction

In this paper, we propose an eXtreme deep model with feature interactions and generation for CTR prediction, called xDeepFIG. The feature generation module fully leverages some advantages of convolutional neural network (CNN) to generate new local and global features, and concatenates them with raw features. Such new fully fused features are shared by both the deep neural network (DNN) and compressed interaction network (CIN), which can learn both implicit and explicit high-order feature interactions automatically. Numerical results on two benchmark datasets for CTR demonstrates such feature fusion can bring some advantages and the xDeepFIG outperforms recent baseline models.
How to Develop an Intuition for Risk... and Other Invisible Phenomena (Invited Talk)

The study of quantitative risk in security systems is often based around complex and subtle mathematical ideas involving probabilities. The notations for these ideas can pose a communication barrier between collaborating researchers even when those researchers are working within a similar framework. This paper describes the use of geometrical representation and reasoning as a way to share ideas using the minimum of notation so as to build intuition about what kinds of properties might or might not be true. We describe a faithful geometrical setting for the channel model of quantitative information flow (QIF) and demonstrate how it can facilitate “proofs without words” for problems in the QIF setting.
Realising Intensional S4 and GL Modalities

There have been investigations into type-theoretic foundations for metaprogramming, notably Davies and Pfenning’s (2001) treatment in S4 modal logic, where code evaluating to values of type A is given the modal type Code A ( □ A in the original paper). Recently Kavvos (2017) extended PCF with Code A and intensional recursion, understood as the deductive form of the GL (Gödel-Löb) axiom in provability logic, but the resulting type system is logically inconsistent. Inspired by staged computation, we observe that a term of type Code A is, in general, code to be evaluated in a next stage, whereas S4 modal type theory is a special case where code can be evaluated in the current stage, and the two types of code should be discriminated. Consequently, we use two separate modalities ⊠ and □ to model S4 and GL respectively in a unified categorical framework while retaining logical consistency. Following Kavvos’ (2017) novel approach to the semantics of intensionality, we interpret the two modalities in the P -category of assemblies and trackable maps. For the GL modality □ in particular, we use guarded type theory to articulate what it means by a “next” stage and to model intensional recursion by guarded recursion together with Kleene’s second recursion theorem. Besides validating the S4 and GL axioms, our model better captures the essence of intensionality by refuting congruence (so that two extensionally equal terms may not be intensionally equal) and internal quoting (both A → □ A and A → ⊠ A ). Our results are developed in (guarded) homotopy type theory and formalised in Agda .
Defining Meditation: Foundations for an Activity-Based Phenomenological Classification System

Classifying different meditation techniques is essential for the progress of meditation research, as this will enable discerning which effects are associated with which techniques, in addition to supporting the development of increasingly effective and efficient meditation-based training programs and clinical interventions. However, both the task of defining meditation itself, as well as defining specific techniques, faces many fundamental challenges. Here we describe problems involved in this endeavor and suggest an integrated model for defining meditation. For classifying different meditation techniques, we draw on classical, contemporary, and holistic systems of classification. We analyze different techniques and propose that all meditation techniques are based on a specific set of activities, that is: focusing, releasing, imagining, and moving in relation to an object of meditation, including fields of experience. Meditative activities can be combined and unified in the activities of observing, producing, and being aware. All meditative activities are unified in awareness of awareness. Defining specific meditation techniques may be done by specifying which activities and objects are involved. The advantage of our approach is that it can potentially account for the inner workings of all current systems of classification and hence it lays the foundation for formulating an overarching system of meditation that can guide future research and practice.
Pituitary Adenylate Cyclase-Activating Polypeptide (PACAP) Protects Striatal Cells and Improves Motor Function in Huntington’s Disease Models: Role of PAC1 Receptor

Huntington’s disease (HD) is a hereditary neurodegenerative disorder caused by the expression of mutant huntingtin (mHtt). One of the main features of HD is the degeneration of the striatum that leads to motor discoordination. Pituitary adenylate cyclase-activating polypeptide (PACAP) is a neuropeptide that acts through three receptors named PAC1R, VPAC1R, and VPAC2R. In the present study, we first investigated the effect of PACAP on STHdhQ7/Q7 and STHdhQ111/Q111 cells that express wild-type Htt with 7 and mHtt with 111 glutamines, respectively. Then we explored the capacity of PACAP to rescue motor symptoms in the R6/1, a murine model of HD. We found that PACAP treatment (10–7 M) for 24 h protects STHdhQ111/Q111 cells from mHtt-induced apoptosis. This effect is associated with an increase in PAC1R transcription, phosphorylation of ERK and Akt, and an increase of intracellular c-fos, egr1, CBP, and BDNF protein content. Moreover, the use of pharmacological inhibitors revealed that activation of ERK and Akt mediates these antiapoptotic and neurotrophic effects of PACAP. To find out PAC1R implication, we treated STHdh cells with vasoactive intestinal peptide (VIP), which exhibits equal affinity for VPAC1R and VPAC2R, but lower affinity for PAC1R, in contrast to PACAP which has same affinity for the three receptors. VIP reduced cleaved caspase-3 protein level, without promoting the expression of c-fos, egr1, CBP, and the neurotrophin BDNF. We next measured the protein level of PACAP receptors in the striatum and cortex of R6/1 mice. We observed a specific reduction of PAC1R at the onset of motor symptoms. Importantly, the intranasal administration of PACAP to R6/1 animals restored the motor function and increased the striatal levels of PAC1R, CBP, and BDNF. In conclusion, PACAP exerts antiapoptotic and neurotrophic effects in striatal neurons mainly through PAC1R. This effect in HD striatum allows the recovery of motor function and point out PAC1R as a therapeutic target for treatment of HD.
Fruits-Based Critical Nitrogen Dilution Curve for Diagnosing Nitrogen Status in Cotton

Estimating the precise nutritional status of crop nitrogen (N) after flowering period is not only important to predict deficiency but the excess that could be revised by fertilization in future crops. Critical N dilution curves describing the critical N concentration ([N]c) in plant tissues during crop growth have been used to estimate the N status of whole plants in cotton. Little is known, however, about the critical N dilution curve for specific plant organs such as cotton fruits. The objective of this study was to verify the feasibility of fruits-based critical N dilution curve as a useful diagnostic tool for diagnosing the N status of cotton crops. A 3-year field experiment was conducted with seven N application rates (0–360 kg N ha–1) using the high-yielding cultivars Jimian 228 and Lumian 28, which differ in maturity. The relationship between fruits dry mass (DM) and N concentration ([N]) was analyzed, and a model of [N]c for cotton fruits was constructed and validated. The results showed that fruits [N]c decreased with increasing fruits DM. The critical N dilution curve based on cotton fruits was described by the equation [N]c = 2.49 × DM–0.12 (R2 = 0.649, P < 0.0001) across cultivar-years. The N nutrition index (NNI) of the fruits (NNIf) with the N dilution curve was significantly related to the NNI of shoot DM, relative yield (RY), and boll density at most sampling dates. For an NNIf of approximately 1, the RY was nearly 95%, while it decreased with a decreasing NNIf below 1. The petiole nitrate-N (NO3-N) concentration was also linearly related to the NNIf, suggesting that the NO3-N concentration in the petiole was a good predictor of the NNIf. Therefore, fruits-based critical N dilution curve and the derived NNIf values will serve as a useful diagnostic tool for diagnosing N status in cotton crops.
Exploring the Nuanced Links Between Internet Use and Subjective Well-Being Among Older Adults: A Nordic Population-Based Study

Aim The aim was to explore the various associations between subjective well-being and internet use among older adults in two regions in Finland and Sweden. Methods The data was collected through a population-based survey (N = 9,386) as part of the GERDA project conducted in 2016. The connection between subjective well-being (measured by perceived meaningfulness, happiness and life satisfaction) and internet use (distinguishing between internet users, non-users and users with support, and diverse internet activities) was studied by conducting binary regression analyses, calculating odds ratios with 95% confidence intervals. The analyses also controlled for key subjective well-being covariates. Results Statistically significant associations were found between perceived life meaningfulness and internet use. When looking into the specific internet-based activities under study, activities related to leisure and entertainment showed statistically significant associations to perceived meaningfulness as well as perceived happiness, also after controlling for potential covariates. However, internet use and the different internet activities failed to show statistical significant associations to life satisfaction in the adjusted regression model. Conclusion The things we do on the internet (the activities) as well as how we conceptualize and measure subjective well-being in this type of research studies seem to matter when it comes to the relationship between subjective well-being and internet use in later life. Internet use and internet activities displayed various connections to the subjective well-being proxies used in this study. Therefore, the complexity and multidimensionality of both subjective well-being and internet use and related links need to be carefully explored in order to deepen our understanding of experienced well-being among older adults in a digitized world.
Heart Rate Information-Based Machine Learning Prediction of Emotions Among Pregnant Women

In this study, the extent to which different emotions of pregnant women can be predicted based on heart rate-relevant information as indicators of autonomic nervous system functioning was explored using various machine learning algorithms. Nine heart rate-relevant autonomic system indicators, including the coefficient of variation R-R interval (CVRR), standard deviation of all NN intervals (SDNN), and square root of the mean squared differences of successive NN intervals (RMSSD), were measured using a heart rate monitor (MyBeat) and four different emotions including “happy,” as a positive emotion and “anxiety,” “sad,” “frustrated,” as negative emotions were self-recorded on a smartphone application, during 1 week starting from 23rd to 32nd weeks of pregnancy from 85 pregnant women. The k-nearest neighbor (k-NN), support vector machine (SVM), logistic regression (LR), random forest (RF), naïve bayes (NB), decision tree (DT), gradient boosting trees (GBT), stochastic gradient descent (SGD), extreme gradient boosting (XGBoost), and artificial neural network (ANN) machine learning methods were applied to predict the four different emotions based on the heart rate-relevant information. To predict four different emotions, RF also showed a modest area under the receiver operating characteristic curve (AUC-ROC) of 0.70. CVRR, RMSSD, SDNN, high frequency (HF), and low frequency (LF) mostly contributed to the predictions. GBT displayed the second highest AUC (0.69). Comprehensive analyses revealed the benefits of the prediction accuracy of the RF and GBT methods and were beneficial to establish models to predict emotions based on autonomic nervous system indicators. The results implicated SDNN, RMSSD, CVRR, LF, and HF as important parameters for the predictions.
Prevalence and Associated Risk Factors of Cognitive Frailty: A Systematic Review and Meta-Analysis

Objective Currently, the prevalence of CF (Cognitive Frailty) is not very clear, and the relationship between CF and its associated risk factors has not been accurately evaluated. Therefore, it is necessary to conduct a systematic review and meta-analysis further to understand CF's prevalence and associated factors. Methods Embase, PubMed, Web of Science, Ovid, and Cochrane were systematically searched for articles exploring the prevalence of CF, the deadline of searching date was up to March 2021. For the prevalence of CF, the events of CF and the total number of patients in every included study were extracted to estimate the prevalence of CF. For associated factors of CF, Odds Ratios (ORs) with (corresponding) 95% confidence intervals (CIs) were used for estimations. Results Firstly, the estimated prevalence of CF I (Cognitive Frailty in the model I) was 16%, 95% CI (0.13–0.19), and the estimated prevalence of CF II (Cognitive Frailty in model II) was 6%, 95% CI (0.05–0.07). Secondly, both lower engagement in activities and age were calculated to be independent risk factors of CF, and the OR (95% CI) was 3.31 (2.28–4.81) and 1.10 (1.04–1.16), respectively. Finally, depression was also a prominent risk factor of CF, with the overall OR (95% CI) as 1.57 (1.32–1.87). Conclusion CF was a high prevalence in community older. The various assessment scales and the different cutoff values of diagnostic criteria would affect the prevalence of CF. Lower engagement in activities, age, and depression was the risky factor of CF. Systematic Review Registration http://www.crd. york.ac. uk/PROSPERO/, identifier: CRD42019121369.
Mechanical Punctate Pain Thresholds in Patients With Migraine Across Different Migraine Phases: A Narrative Review

Purpose of the Review We reviewed the studies of mechanical punctate pain thresholds (MPTs) in patients with migraine and summarized their findings focusing on the differences in MPT measurement and MPTs in different phases of migraine. Methods We searched the English-written articles that investigate the MPTs in the migraine population published in peer-reviewed journals with full-text using the PubMed, Web of Science, and Google Scholar databases. Moreover, we manually searched the references from the articles for possibly related studies. Main Findings We collected 276 articles and finally included twelve studies in this review. Most of the studies that included MPTs were measured with traditional von Frey filaments. The cephalic areas were always included in the assessment. Most studies compared the inter-ictal MPT in patients with migraine to controls. Among them, the majority found no significant differences; however, there were studies found either higher or lower levels of MPTs in migraine. Even though the studies provided the criteria to define the inter-ictal phase, not all of them followed up with the subjects regarding the next migraine attack. In studies that compared MPT between phases, lower MPTs were found during peri-ictal phases. Summary Changes to MPT in migraine patients were inconclusive. The selection of measurement methods as well as properly defined migraine phases should be considered for future studies.
Global Patterns of Coastal Cephalopod Diversity Under Climate Change

Coastal marine ecosystems are currently being exposed to climate change at a much faster rate than many other ecosystems, with coastal species being exposed to multiple stressors. Cephalopod mollusks play a pivotal role in marine trophic webs, and most are “keystone” species due to their influence on ecosystem dynamics. In this study, we characterized the global patterns of coastal cephalopod diversity and present, for the first time, a global forecast of potential changes in richness, mean body size, and assemblage composition (i.e., species replacement, nestedness, and combinations of both) for 161 coastal cephalopod species under climate change, using an ensemble of ecological niche models (ENMs) for an end of the century mitigation scenario. We have shown that, for the baseline period, coastal cephalopod diversity is higher in the Central Indo-Pacific area and that body size patterns follow the temperature-size rule, with larger animals occurring at higher latitudes. The end-century projections of habitat suitability show a different picture, with 96% of cephalopod species predicted to experience range contraction and 15% completely losing their environmental space. Nestedness is projected to be the main effect of species compositional change. Maximum body size is projected to increase in 44% of the pixels and decrease in 37%. Regarding fisheries, the projected changes are more favorable to the countries at higher latitudes, although the search for refugia of smaller tropical species might potentially lead to a mitigation of the negative effects of climate change in these areas, as measured by the total capture (ton). While the model has limitations, our findings reflect major climatic drivers of change and highlight the idea that even though cephalopod species seem good candidates to replace overexploited fish stocks in the near future, they may not have the environmental space to do so.