Miracle-dz/UniFAR-FTUs huggingface dataset:
A diachronic approach to variation and change in the typology of motion event expression.

Most studies dealing with variation and change between Verb-Framed and Satellite-Framed strategies adopt a synchronic approach. In this paper, adopting a diachronic approach, we will see whether it is possible to account for changes driving languages from one prevalent strategy to the other. We will begin with an overview of the main proposals about the typology of motion event encoding from a variationist perspective, the theoretical premises of our approach to the analysis of (diachronic) change, and their relevance for a 'Working Typology', arguing in favour of applying typological classifications to the matching between individual event types and linguistic patterns rather than to languages as a whole. In the second part of this paper, taking Romance languages as a case study of typological change, we argue that "macroscopic" typological shifts may result from "microscopic" changes and that these phenomena cannot be adequately explained within the framework of a holistic typology: the dynamics of change can be understood only through a careful investigation of the different strategies adopted for the encoding of motion and their relationships with different kinds of events. Changes affecting motion event expression may also be the effect of phenomena which are independent from motion encoding. The final part of the paper lists a series of phenomena related to the encoding of path in Romance languages which rather than exceptions to the VF type may be considered more or less significant indicators of the emergence of SF patterns, hence to be interpreted from a methodological point of view as possible relevant signals of ongoing typological change in the preferred way of expressing motion.
Towards a complete picture of stationary covariance functions on spheres cross time

With the advent of wide-spread global and continental-scale spatiotemporal datasets, increased attention has been given to covariance functions on spheres over time. This paper provides results for stationary covariance functions of random fields defined over $d$-dimensional spheres cross time. Specifically, we provide a bridge between the characterization in \cite{berg-porcu} for covariance functions on spheres cross time and Gneiting's lemma \citep{gneiting2002} that deals with planar surfaces. We then prove that there is a valid class of covariance functions similar in form to the Gneiting class of space-time covariance functions \citep{gneiting2002} that replaces the squared Euclidean distance with the great circle distance. Notably, the provided class is shown to be positive definite on every $d$-dimensional sphere cross time, while the Gneiting class is positive definite over $\R^d \times \R$ for fixed $d$ only. In this context, we illustrate the value of our adapted Gneiting class by comparing examples from this class to currently established nonseparable covariance classes using out-of-sample predictive criteria. These comparisons are carried out on two climate reanalysis datasets from the National Centers for Environmental Prediction and National Center for Atmospheric Research. For these datasets, we show that examples from our covariance class have better predictive performance than competing models.
Capture of solids by growing proto-gas giants: effects of gap formation and supply limited growth

Studies of internal structure of gas giant planets suggest that their envelopes are enriched with heavier elements than hydrogen and helium relative to their central stars. Such enrichment likely occurred by solid accretion during late formation stages of gas giant planets in which gas accretion dominates protoplanetary growth. Some previous studies performed orbital integration of planetesimals around a growing protoplanet with the assumption of an uniform circumstellar disc to investigate how efficiently the protoplanet captures planetesimals. However, not only planetesimals but also disc gas are gravitationally perturbed by the protoplanet in its late formation stages, resulting in gap opening in the circumstellar disc. In this study, we investigate the effects of such gap formation on the capture of planetesimals by performing dynamical simulations of planetesimals around a growing proto-gas giant planet. Gap formation reduces the surface density of disc gas, makes a steep pressure gradient and limits the growth rate of the protoplanet. We find that the first effect enhances the capture of planetesimals, while the others reduce it. Consequently the amount of planetesimals captured during the gas accretion is estimated to be at most ~3 Mearth. We conclude that the in-situ capture of planetesimals needs the initial solid surface density more than five times higher than that of the minimum mass solar nebula for explaining the inferred large amount of heavy element in Jupiter. For highly dense warm Jupiters, we would need additional processes enhancing the capture and/or supply of planetesimals.
Hardy Variation Framework for Restoration of Weather Degraded Images

Images captured in fog conditions often suffer from weather of poor visibility which fades the colors and reduces the contrast in the scene. This paper proposes a novel regularization method which utilizes space transformation in order to restore the hidden scene with high dynamic range and enhanced edge information. In order to efficiently improve the visualization, the proposed method is built upon contrast stretching which can obtain better estimation map as well as solve the problem. Using minimum energy constraints, the algorithm recovers scene albedo on a number of haze images. Experimental results show that the method effectively achieves accurate and true representation.
UCAM Biomedical Translation at WMT19: Transfer Learning Multi-domain Ensembles

The 2019 WMT Biomedical translation task involved translating Medline abstracts. We approached this using transfer learning to obtain a series of strong neural models on distinct domains, and combining them into multi-domain ensembles. We further experimented with an adaptive language-model ensemble weighting scheme. Our submission achieved the best submitted results on both directions of English-Spanish.
The Impact of Internal Audit Function Effectiveness on Quality of Financial Reporting and its Implications on Good Government Governance Research on Local Government Indonesia

The purpose of this research is to examine the impact of Internal Audit Function Effectiveness on the Quality of Financial Reporting and Its Implications on Good Government Governance. The research conducted on 70 working unit area device on 7 District Local Government in eks Karesidenan Pekalongan Indonesia uses survey methods. The data is primary data collected through questionnaire. The samples selection uses proportionate stratified random sampling method. The data was processed using structural equation modeling (Partial Least Square). The results of this research show that: (1) From the results of test Krusskal Wallis, there are no significant differences between the effectiveness of the internal audit, quality financial reporting and the good government governance in 7 local government (2 )the effectiveness of the internal audit function has significant effect on the quality of financial reporting. 3) Internal audit function has significant effect on Good Goverment Governance Application, 4) Quality of financial reporting has significant effect on Good Government Governance Application Keywords: Effectiveness of Internal Audit Function, Quality of Financial Reporting, Good Government Governance
Self-organized Criticality in Solar and Stellar Flares: Are Extreme Events Scale-free?

We search for outliers in extreme events of statistical size distributions of astrophysical data sets, motivated by the Dragon-King hypothesis of Sornette, which suggests that the most extreme events in a statistical distribution may belong to a different population, and thus may be generated by a different physical mechanism, in contrast to the strict power-law behavior of self-organized criticality models. Identifying such disparate outliers is important for space weather predictions. Possible physical mechanisms to produce such outliers could be generated by sympathetic flaring. However, we find that Dragon-King events are not common in solar and stellar flares, identified in 4 out of 25 solar and stellar flare data sets only. Consequently, small, large, and extreme flares are essentially scale-free and can be modeled with a single physical mechanism. In very large data sets (N ≳ 104) we find significant deviations from ideal power laws in almost all data sets. Nevertheless, the fitted power-law slopes constrain physical scaling laws in terms of flare areas and volumes, which have the highest nonlinearity in their scaling laws.
Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization

Graph Neural Networks (GNNs) achieve an impressive performance on structured graphs by recursively updating the representation vector of each node based on its neighbors, during which parameterized transformation matrices should be learned for the node feature updating. However, existing propagation schemes are far from being optimal since they do not fully utilize the relational information between nodes. We propose the information maximizing graph neural networks (IGNN), which maximizes the mutual information between edge states and transform parameters. We reformulate the mutual information as a differentiable objective via a variational approach. We compare our model against several recent variants of GNNs and show that our model achieves the state-of-the-art performance on multiple tasks including quantum chemistry regression on QM9 dataset, generalization capability from QM9 to larger molecular graphs, and prediction of molecular bioactivities relevant for drug discovery. The IGNN model is based on an elegant and fundamental idea in information theory as explained in the main text, and it could be easily generalized beyond the contexts of molecular graphs considered in this work. To encourage more future work in this area, all datasets and codes used in this paper will be released for public access.
Error bounds in stochastic-geometric normal approximation

We provide normal approximation error bounds for sums of the form $\sum_x \xi_x$, indexed by the points $x$ of a Poisson process (not necessarily homogeneous) in the unit $d$-cube, with each term $\xi_x$ determined by the configuration of Poisson points near to $x$ in some sense. We consider geometric graphs and coverage processes as examples of our general results.
Emplacement of Cuban Ophiolites

According to their lithological and structural characteristics, three ophiolite bearing structural units are recognized in Cuba: 1) The Northern Ophiolitic Belt, a complex melange that stretches more than 1000 km along the island; 2) The metamorphic basement of the Cretaceous Volcanic Arc Terrane; and 3) The tectonic slices in the high pressure metamorphic Escambray (Guamuhaya) Massif of south central Cuba. The available age constrains indicate that relics of oceanic lithosphere in Cuba are Late Mesozoic in age and probably originated in two different tectonic environments; 1) in a Late Jurassic- Neocomian small oceanic basin resulting from drifting between North America and a southern continent; and 2) in a suprasubduction marginal basin developed between the North American passive margin and an Aptian-Albian volcanic arc. Serpentinite slices and the high pressure amphibolites in the Mesozoic metasedimentary rocks of the Escambray (Guamuhaya) Massif originated during closure of a small oceanic basin, possibly during the Albian. The basement metamorphic rocks of the Volcanic Arc Terrane were derived from Upper Jurassic- Neocomian oceanic crust, metamorphosed during the development on this crust of a Middle Cretaceous volcanic arc. Ophiolites of the northern belt were probably derived from both the formerly mentioned small oceanic basin and suprasubduction marginal basin, and were emplaced in several episodes from the Campanian to the Middle Eocene. The different ophiolite types in Cuba represent relics of the oceanic lithosphere (the Proto-Caribbean oceanic basin) that developed between North and South America after the Pangea breakup. An alternative plate tectonic model is proposed and discussed.
Deep Learning based Emotion Recognition System Using Speech Features and Transcriptions

This paper proposes a speech emotion recognition method based on speech features and speech transcriptions (text). Speech features such as Spectrogram and Mel-frequency Cepstral Coefficients (MFCC) help retain emotion-related low-level characteristics in speech whereas text helps capture semantic meaning, both of which help in different aspects of emotion detection. We experimented with several Deep Neural Network (DNN) architectures, which take in different combinations of speech features and text as inputs. The proposed network architectures achieve higher accuracies when compared to state-of-the-art methods on a benchmark dataset. The combined MFCC-Text Convolutional Neural Network (CNN) model proved to be the most accurate in recognizing emotions in IEMOCAP data.
Medicinal plants and animals sold by the "Yan-Shimfidas" of Sabo Wuse in Niger State, Nigeria.

Sabo Wuse, a resettlement in Tafa Local Government Area of Niger State inhabits the original inhabitants of Wuse in Abuja, the Federal Capital of Nigeria. Despite the close proximity of about 65km to the urban settlement of Abuja, Sabo Wuse is still a relatively remote settlement; their lifestyle remained more or less unchanged and therefore, relied on their traditional knowledge for health care delivery. Recent ethnobotanical survey in Sabo Wuse has documented plants not indigenous to the area. In view of this, a survey was carried out to identify and document plants and animal materials sold by the Yan- shimfidas in the local settlement and to study their uses in treatment of ailments and other socio-cultural problems. The result shows that most of the herb sellers of Sabo Wuse were immigrants from the northern part of the country who had stayed in the village of Sabo Wuse for over twenty years. Thirty-four different plant species in twenty-one different families were documented. These were in the form of stem barks, leaves, roots, fruits, seeds, flower buds and fruit juices. Six different animal fats and skin, dung or other parts of animal of twenty two different species were also documented. All the materials documented were used for the treatment of various diseases and ailments ranging from the physical to the metaphysical. The study uncovered the remains of ancient medical practices which still exist in traditional societies in Nigeria. Therefore, the documentation of these practices can conserve the culture and traditional knowledge of this community. Key words: Indigenous knowledge, ‘Yan-Shimfida’, Sabo-Wuse, Nigeria.
Chemical composition and antioxidant activity of Marrubium vulgare L. essential oil from Tunisia

In this study, the essential oil of the aerial parts of Marrubium vulgare L. obtained by hydrodistillation was analyzed by gas chromatography coupled to mass spectrometry (GC-MS) in order to determine their chemical composition. Thirty-four (34) components in the oil of M. vulgare were identified. The results demonstrated that the major components of the essential oil were �-eudesmol (11.93%), �citronellol (9.90%), citronellyl formate (9.50%) and germacrene D (9.37%). Antioxidant effectiveness was examined by three different methods: The DPPH (1,1-diphenyl-2-picrylhydrazyl) assay, the �-carotene bleaching test and the reducing power assay. The results showed that this oil can be considered an effective source of antioxidants of natural origin. This is the first report on chemical composition of M. vulgare essential oil cultivated in Tunisia and the original study on the antioxidant activity of M. vulgare essential oil.
OEDIPUS: An Experiment Design Framework for Sparsity-Constrained MRI

This paper introduces a new estimation-theoretic framework for experiment design in the context of MR image reconstruction under sparsity constraints. The new framework is called OEDIPUS (Oracle-based Experiment Design for Imaging Parsimoniously Under Sparsity constraints) and is based on combining the constrained Cramér-Rao bound with classical experiment design techniques. Compared to popular random sampling approaches, OEDIPUS is fully deterministic and automatically tailors the sampling pattern to the specific imaging context of interest (i.e., accounting for coil geometry, anatomy, image contrast, etc.). OEDIPUS-based experiment designs are evaluated using retrospectively subsampled in vivo MRI data in several different contexts. The results demonstrate that OEDIPUS-based experiment designs have some desirable characteristics relative to conventional MRI sampling approaches.
Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning

We model human decision-making behaviors in a risk-taking task using inverse reinforcement learning (IRL) for the purposes of understanding real human decision making under risk. To the best of our knowledge, this is the first work applying IRL to reveal the implicit reward function in human risk-taking decision making and to interpret risk-prone and risk-averse decision-making policies. We hypothesize that the state history (e.g. rewards and decisions in previous trials) are related to the human reward function, which leads to risk-averse and risk-prone decisions. We design features that reflect these factors in the reward function of IRL and learn the corresponding weight that is interpretable as the importance of features. The results confirm the sub-optimal risk-related decisions of human-driven by the personalized reward function. In particular, the risk-prone person tends to decide based on the current pump number, while the risk-averse person relies on burst information from the previous trial and the average end status. Our results demonstrate that IRL is an effective tool to model human decision-making behavior, as well as to help interpret the human psychological process in risk decision-making.
Warm Absorber Diagnostics of AGN Dynamics

Warm absorbers and related phenomena are some of the observable manifestations of outflows or winds from active galactic nuclei (AGNs). Warm absorbers are common in low-luminosity AGNs. They have been extensively studied observationally and are well described by simple phenomenological models. However, major open questions remain. What is the driving mechanism? What is the density and geometrical distribution? How much associated fully ionized gas is there? What is the relation to the quasi-relativistic “ultrafast outflows”? In this paper we present synthetic spectra for the observable properties of warm absorber flows and associated quantities. We use ab initio dynamical models, i.e., solutions of the equations of motion for gas in finite difference form. The models employ various plausible assumptions for the origin of the warm absorber gas and the physical mechanisms affecting its motion. The synthetic spectra are presented as an observational test of these models. In this way we explore various scenarios for warm absorber dynamics. We show that observed spectra place certain requirements on the geometrical distribution of the warm absorber gas, and that not all dynamical scenarios are equally successful at producing spectra similar to what is observed.
Grid R-CNN Plus: Faster and Better

Grid R-CNN is a well-performed objection detection framework. It transforms the traditional box offset regression problem into a grid point estimation problem. With the guidance of the grid points, it can obtain high-quality localization results. However, the speed of Grid R-CNN is not so satisfactory. In this technical report we present Grid R-CNN Plus, a better and faster version of Grid R-CNN. We have made several updates that significantly speed up the framework and simultaneously improve the accuracy. On COCO dataset, the Res50-FPN based Grid R-CNN Plus detector achieves an mAP of 40.4%, outperforming the baseline on the same model by 3.0 points with similar inference time. Code is available at this https URL .
Decentralised multi-demic evolutionary approach to the dynamic multi-agent travelling salesman problem

This paper looks to use both centralised and decentralised implementations of Evolutionary Algorithms to solve a dynamic variant of the Multi-Agent Travelling Salesman Problem. The problem is allocating an active set of tasks to a set of agents whilst simultaneously planning the route for each agent. The allocation and routing are closely coupled parts of the same problem, this paper attempts to align the real world implementation demands of a decentralised solution by using multiple populations with well defined interactions to exploit the problem structure.
Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective

In the past decade, deep neural networks (DNNs) came to the fore as the leading machine learning algorithms for a variety of tasks. Their raise was founded on market needs and engineering craftsmanship, the latter based more on trial and error than on theory. While still far behind the application forefront, the theoretical study of DNNs has recently made important advancements in analyzing the highly over-parametrized regime where some exact results have been obtained. Leveraging these ideas and adopting a more physics-like approach, here we construct a versatile field-theory formalism for supervised deep learning, involving renormalization group, Feynmann diagrams, and replicas. In particular, we show that our approach leads to highly accurate predictions of learning curves of truly deep DNNs trained on polynomial regression problems. It also explains in a concrete manner why DNNs generalize well despite being highly over-parametrized, this due to an entropic bias to simple functions -- low order polynomials in the input vector, for the case of fully-connected DNNs. Being a complex interacting system of artificial neurons, we believe that such tools and methodologies borrowed from condensed matter physics would prove essential for obtaining an accurate quantitative understanding of deep learning.
Mentalization, specific attachment, and relational satisfaction from the intrapsychic and interpersonal perspectives

Mentalization is a process of social cognition that involves making inferences about one’s own behavior and the behavior of other people on the basis of unobservable mental states. Particularly in psychodynamic approaches, mentalization is conceptualized in the context of activation of internal representation of emotional relationship. In this study, we checked whether mentalization constitutes a predictor of relational satisfaction in the context of one’s own and the partner’s specific attachment. The research sample was composed of 32 heterosexual couples living together for at least two years. Three tools were applied: the Mental State Task, Experiences in Close Relationships—Relationship Structures, and the Relationship Assessment Scale. The results indicate a strong negative effect of one’s own attachment anxiety and avoidance, and a strong effect of the partner’s attachment anxiety on the relationship satisfaction for both men and women. In women, the incremental validity of mentalization in predicting satisfaction in the relationship beyond the attachment of their partners has been observed. The results are discussed in the context of the intrapsychic and interpersonal aspects of mentalization and attachment.