Miracle-dz/UniFAR-FTUs huggingface dataset:
Crowdsourcing OLiA Annotation Models the Indirect Way

The paper describes a technology to complement established documentation workflows in two linguistic community projects with the possibility to automatically create OLiA Annotation Models, i.e., formal, ontological representations of their annotation schemas. For this purpose, we provide a domain-specific extractor that consumes MediaWiki wikitext, extracts sections headers and tables and produces an OWL2/DL ontology as a result. This ontology can be further processed with standard technology as established in the context of the Linguistic Linked Open Data (LLOD) community. The main contribution we provide effectively eliminates the entry barrier into LLOD technology and OLiA for two potential user communities, and that this setup can be trivially adopted to any comparable community project – as long as it uses Wiki technology and Wiki lists for documenting tags and abbreviations.
ISO-DR-core Plugs into ISO-dialogue Acts for a Cross-linguistic Taxonomy of Discourse Markers

The present paper 1 proposes an interoperable taxonomy to represent the meaning of discourse markers based on ISO DR-core (ISO 24617-8) but with a plug-in to ISO-dialogue acts (ISO 24617-2). The proposed taxonomy encompasses two dimensions: the semantic, with values regarding the discourse relations signalled by discourse markers, and the pragmatic, with values concerning the communicative function realized by discourse markers. We present a proof of concept for this two-dimensional taxonomy in a multilingual parallel dataset in three languages, English, European Portuguese and Bulgarian, comprising 165 textual segments with multiword discourse makers obtained from publicly available TED Talk transcripts. We show that the two-dimensional taxonomy can successfully annotate cross-linguistically the meaning of discourse markers and discuss linguistic evidence where extension of the proposed taxonomy can be relevant.
DBnary2Vec: Preliminary Study on Lexical Embeddings for Downstream NLP Tasks

In this preliminary study, we experiment with the use of DBnary, a big lexical knowledge graph, to create word embeddings that could be used in NLP downstream tasks. Our gamble is that word embeddings created from lexical data (instead of language corpora) may exhibit less biases while still being usable as the first layer of deep learning approaches to NLP tasks. We tried very basic method of embedding creation from lexical graph and evaluate (1) the intrinsic performance of the created embed-dings on word similarity and word analogy test sets and their extrinsic quality in POS tagging and NER downstream tasks, along with (2) the biases they may exhibit. Such embeddings show promising performances outperforming word2vec on few specific tasks, while still not on par on most others, but we confirm that they exhibit less bias overall.
Unifying Emotion Analysis Datasets using Valence Arousal Dominance (VAD)

This paper presents a novel approach to unifying various emotional datasets in Natural Language Processing (NLP) using the Valence Arousal Dominance (VAD) framework. Emotion analysis, which aims to deeply analyse emotions and understand user behaviour, is a complex research area that requires large, standard, and unified datasets. However, the lack of such datasets in NLP has been a challenge in advancing the field. Our approach maps diverse emotions from different datasets into four categories: joy, anger, fear, and sadness using the VAD framework. This process creates multidimensional emotional scores that are consistent across datasets, regardless of the number of emotions included. By unifying these datasets, we were able to train a BERT model on the combined data and improve the performance of emotion detection.
Testing the Continuity Hypothesis: A decompositional approach

The Continuity Hypothesis (CH) predicts that discontinuous discourse relations are harder to process and therefore more marked than continuous ones. To investigate this hypothesis, we annotated a corpus of discourse relations for Givón’s (1993) seven continuity dimensions and also for discourse signalling, widening the perspective to discourse signals in general. Our results show that discourse relations often are simultaneously continuous and discontinuous on different continuity dimensions, and that continuity dimensions behave very differently with respect to discourse marking: Only the temporal dimension (partially) confirms the CH while the perspective dimension provides counter-evidence to the CH. Also, contrary to Givón’s expectation, local discontinuity introduces more marking than global discontinuity.
Making Non-Normalized Content Retrievable – A Tagging Pipeline for a Corpus of Expert–Layperson Texts

Conventional terminology resources reach their limits when it comes to automatic content classification of texts in the domain of expert-layperson communication. This can be attributed to the fact that (non-normalized) language usage does not necessarily reflect the ter-minological elements stored in such resources. We present several strategies to extend a termi-nological resource with term-related elements in order to optimize automatic content classification of expert-layperson texts.
Validation of the Bigger Analogy Test Set Translation into Croatian, Lithuanian and Slovak

This paper presents ongoing work focused on the analysis of translations of the English Big-ger Analogy Test Set (BATS) dataset into three languages: Croatian, Lithuanian, and Slovak. We describe our automatic validation and further manual correction of the translations and analyse the main types of issues encountered in the dataset. The validation process involves checking the translations against morphological databases in order to uncover obvious mistakes or typos. Additionally, the translations are tested for the compliance to some of the formal guidelines for the Bigger Analogy Test Set translations, and for rudimentary grammatical correctness.
Genetic and environmental architecture of synaesthesia and its association with the autism spectrum—a twin study

Synaesthesia is a sensory phenomenon where external stimuli, such as sounds or letters, trigger additional sensations (e.g. colours). Synaesthesia aggregates in families but its heritability is unknown. The phenomenon is more common in people on the autism spectrum compared with the general population and associated with higher autistic traits. Using classical twin design, we assessed the heritability of individual differences in self-reported synaesthesia and the genetic and environmental contributions to their association with autistic traits within a population twin cohort (n = 4262, age = 18 years). We estimated individual differences in synaesthesia to be heritable and influenced by environmental factors not shared between twins. The association between individual differences in synaesthesia and autistic traits was estimated to be predominantly under genetic influence and seemed to be mainly driven by non-social autistic traits (repetitive behaviours, restricted interests and attention to detail). Our study suggests that the link between synaesthesia and autism might reside in shared genetic causes, related to non-social autistic traits such as alterations in perception. Future studies building on these findings may attempt to identify specific groups of genes that influence both autism, synaesthesia and perception.
Stabilization of Bottom Hole Temperature With Finite Circulation Time and Fluid Flow

Summary. As an alternative to finite element or finite difference modelling, analytical solutions are derived by the method of Laplace transformation and numerical results obtained for several models of the bottom hole temperature stabilization. Included in the models are the finite circulation time, the thermal property contrast between the borehole mud and the surrounding formation, and the presence of radial or lateral fluid flow in the formation, all of which are found to have significant effects on the dissipation of the thermal disturbance induced by drilling. The mud circulation is considered to have the effect of either maintaining the borehole mud at a constant temperature or supplying a constant amount of heat per unit length per unit time to the borehole. For small circulation times, the former reduces to the ‘zero circulation’ model in which the mud circulation creates an instantaneous temperature anomaly at the hole bottom; for small borehole radii, the latter reduces to the line source model and the traditional ‘Horner plot’. For typical drilling operations in which the bottom hole temperatures are measured several hours to several tens of hours after the hole is shut in, the new models generally predict higher equilibrium formation temperatures than does the Horner plot. However, predictions from the various models converge if the BHTs are taken after the hole has been shut in for a period which is greater than about five times the circulation period.
Scale-up of the Australian Fans in Training (Aussie-FIT) men’s health behaviour change program: a protocol for a randomised controlled hybrid effectiveness–implementation trial

Introduction Improving physical activity (PA) and healthy eating is critical for primary and secondary prevention of cardiovascular disease (CVD). Behaviour change programmes delivered in sporting clubs can engage men in health behaviour change, but are rarely sustained or scaled-up post trial. Following the success of pilot studies of the Australian Fans in Training (Aussie-FIT) programme, a hybrid effectiveness–implementation trial protocol was developed. This protocol outlines methods to: (1) establish if Aussie-FIT is effective at supporting men with or at risk of CVD to sustain improvements in moderate-to-vigorous PA (primary outcome), diet and physical and psychological health and (2) examine the feasibility and utility of implementation strategies to support programme adoption, implementation and sustainment. Methods and analysis A pragmatic multistate/territory hybrid type 2 effectiveness–implementation parallel group randomised controlled trial with a 6-month wait list control arm in Australia. 320 men aged 35–75 years with or at risk of CVD will be recruited. Aussie-FIT involves 12 weekly face-to-face sessions including coach-led interactive education workshops and PA delivered in Australian Football League (Western Australia, Northern Territory) and rugby (Queensland) sports club settings. Follow-up measures will be at 3 and 6 months (both groups) and at 12 months to assess maintenance (intervention group only). Implementation outcomes will be reported using the Reach, Effectiveness, Adoption, Implementation, Maintenance framework. Ethics and dissemination This multisite study has been approved by the lead ethics committees in the lead site’s jurisdiction, the South Metropolitan Health Service Human Research Ethics Committee (Reference RGS4254) and the West Australian Aboriginal Health Ethics Committee (HREC1221). Findings will be disseminated at academic conferences, peer-reviewed journals and via presentations and reports to stakeholders, including consumers. Findings will inform a blueprint to support the sustainment and scale-up of Aussie-FIT across diverse Australian settings and populations to benefit men’s health. Trial registration number This trial is registered with the Australian New Zealand Clinical Trials Registry (ACTRN12623000437662).
Navigating the Metaverse: UAV-Based Cross-View Geo-Localization in Virtual Worlds

In GPS-denied UAV flight, understanding the visual scene ahead is crucial. This paper focuses on cross-view geo-localization within the realm of the UAV's viewpoint in the Coarse-to-Fine problem setting. Previous research in this area has been limited due to challenges in obtaining images from the drone's perspective. To overcome this limitation, we leverage the flexibility provided by virtual space to create datasets. Moreover, we created datasets changing illumination and weather conditions, which would be cost taking to obtain in real-world scenarios. Additionally, we introduced angle loss and evaluated using these datasets, confirming that the proposed loss function performs effectively on datasets with diverse settings and on an unseen dataset.
Dynamic Network for Language-based Fashion Retrieval

Language-based fashion image retrieval, as a kind of composed image retrieval, presents a substantial challenge in the domain of multi-modal retrieval. This task aims to retrieve the target fashion item in the gallery given a reference image and a modification text. Existing approaches primarily concentrate on developing a static multi-modal fusion module to learn the combined semantics of the reference image and modification text. Despite their commendable advancements, these approaches are still limited by a deficiency in flexibility, which is attributed to the application of a singluar fusion module across diverse input queries. In contrast to static fusion methods, we propose a novel method termed Dynamic Fusion Network (DFN) to compose the multi-granularity features dynamically by considering the consistency of routing path and modality-specific information simultaneously. In specific, our proposed method is consisted of two modules: (1) Dynamic Network. The dynamic network enables a flexible combination of the different operation modules, providing multi-granularity modality interaction for each reference image and modifier text. (2) Modality Specific Routers (MSR). The MSR generates precise routing decisions based on the distinct semantics and distributions of each reference image and modifier text. Extensive experiments on three benchmarks,\textiti.e. , FashionIQ, Shoes and Fashion200K, demonstrate the effectiveness of our proposed model compared with the existing methods.
Unusual Behavior Analysis and Its Application to Surveillance Systems

This paper presents a new posture classification system that can be used to analyze human movements and irregular action directly from video sequences. In order to better characterize a posture in a sequence, we triangulate it into triangular meshes and extract two features, namely, the skeleton feature and the centroid context feature, from the triangulated meshes. The first can be used as a coarse representation, while the second is used to derive a finer description. We adopt a dfs (depth-first search) scheme to extract the skeletal features of a posture from the triangulation result. The proposed skeleton feature extraction scheme is most robust and efficient than conventional silhouette-based approaches. To extract the centroid context feature, we use the skeletal features extracted in the first stage. The centroid context feature is a finer representation that can characterize the shape of a whole body or body parts. The two descriptors working together make human movement analysis a very efficient and accurate process. Experiment results prove that the proposed method is a robust, accurate, and powerful tool for human movement analysis.
Accounting for Heterogeneity in the Measurement of Hospital Performance

With prospective payment of hospitals becoming more common, measuring their performance is gaining in importance. However, the standard cost frontier model yields biased efficiency scores because it ignores technological heterogeneity between hospitals. In this paper, efficiency scores are derived from a random intercept and an extended random parameter frontier model, designed to overcome the problem of unobserved heterogeneity in stochastic frontier analysis. Using a sample of 100 Swiss hospitals covering the years 2004 to 2007 and applying Bayesian inference, significant heterogeneity is found, suggesting rejection of the standard cost frontier model. Estimated inefficiency decreases even below the 14 percent reported by Hollingsworth (2008) for European countries. Accounting for unobserved heterogeneity would make hospitals rated below 85 percent efficiency according to the standard model gain up to 12 percentage points, serving to highlight the importance of heterogeneity correction in the estimation of hospital performance.
Duration Magnitude Uncertainty due to Seismic Noise: Inferences on the Temporal Pattern of G-R b-value at Mt. Vesuvius, Italy

We analyze the statistical properties of seismic noise amplitude at seismic station OVO in the Mt. Vesuvius Volcano (southern Italy) monitoring network. The root mean square of seismic noise amplitude has a lognormal distribution. Using the mean and standard deviation of this distribution, we evaluate the uncertainties in the estimate of duration magnitude ( M D) for the volcano-tectonic seismic events at Mt. Vesuvius. We develop a method based on knowledge of the coda-envelope pattern of local events and find an empirical formula for determining the standard deviation of M D. We then recalculate the b -value time pattern for Mt. Vesuvius from 1972 to the present using a weighted least-squares approach, where the inversion weights are given by the magnitude errors and the number of events included in each magnitude bin. Results are compared to a suite of synthetic catalogs with the same number of events, the same magnitude errors, and the same completeness threshold as the true Mt. Vesuvius catalog. Our results indicate that the method presented in this note has a lower bias than the maximum likelihood approach previously used for calculating b -values at Vesuvius. Propagation of uncertainties in M D measurements into the estimate of b -value indicates that previously determined time changes in b -values at Mt. Vesuvius are not statistically significant. Manuscript received 5 November 2002.
Glaciotectonised Quaternary sediments at Cape Shpindler, Yugorski Peninsula, Arctic Russia: implications for glacial history, ice movements and Kara Sea Ice Sheet configuration

The coastal cliffs of Cape Shpindler, Yugorski Peninsula, Arctic Russia, occupy a key position for recording overriding ice sheets during past glaciations in the Kara Sea area, either from the Kara Sea shelf or the uplands of Yugorski Peninsula/Polar Urals. This study on Late Quaternary glacial stratigraphy and glaciotectonic structures of the Cape Shpindler coastal cliffs records two glacier advances and two ice‐free periods older than the Holocene. During interglacial conditions, a sequence of marine to fluvial sediments was deposited. This was followed by a glacial event when ice moved southwards from an ice‐divide over Novaya Zemlya and overrode and disturbed the interglacial sediments. After a second period of fluvial deposition, under interstadial or interglacial conditions, the area was again subject to glacial overriding, with the ice moving northwards from an inland ice divide. The age‐control suggests that the older glacial event could possibly belong to marine oxygen isotope stage (MOIS) 8, Drenthe (300–250 ka), and that the underlying interglacial sediments might be Holsteinian (>300 ka). One implication of this is that relict glacier ice, buried in sediments and incorporated into the permafrost, may survive several interglacial and interstadial events. The younger glacial event recognised in the Cape Shpindler sequence is interpreted to be of Early‐to‐Middle Weichselian age. It is suggested to correlate to a regional glaciation around 90 or 60 ka. The Cape Shpindler record suggests more complex glacial dynamics during that glaciation than can be explained by a concentric ice sheet located in the Kara Sea, as suggested by recent geological and model studies. Copyright © 2003 John Wiley & Sons, Ltd.
A deep learning model embedded framework to distinguish DNA and RNA mutations directly from RNA-seq

We develop a stepwise computational framework, called DEMINING, to directly detect expressed DNA and RNA mutations in RNA deep sequencing data. DEMINING incorporates a deep learning model named DeepDDR, which facilitates the separation of expressed DNA mutations from RNA mutations after RNA-seq read mapping and pileup. When applied in RNA-seq of acute myeloid leukemia patients, DEMINING uncovered previously-underappreciated DNA and RNA mutations, some associated with the upregulated expression of host genes or the production of neoantigens. Finally, we demonstrate that DEMINING could precisely classify DNA and RNA mutations in RNA-seq data from non-primate species through the utilization of transfer learning.
Socio-Economic Exclusion of Different Religious Communities in Meghalaya

Meghalaya, a state in the North Eastern India, is inhabited by over 2.3 million of population of which 70 percent are Christian, 13 percent are Hindus and a little over 4 percent are Muslims as obtained in the Census 2001. In this study we investigate if numerical dominance of a community leads to socio-economic dominance. We have constructed two composite indices of exclusion by weighted aggregation of 13 socio-economic indicators. The first composite index (I1) is obtained by maximization of the sum of absolute coefficients of correlation of the index with the indicator variables, while the second index (I2) is constructed by the principal components analysis that maximizes the sum of squared coefficients of correlation of the index with the indicator variables. In our judgment, the first index presents the reality more correctly, as a number of indicators undermined by I2 are given their due representation in I1. A perusal of the index (I1) reveals that while the Christian segment of population in the rural areas of Meghalaya is certainly better off than their Hindu or Muslim counterparts, they score comparatively poorly in the urban areas of Meghalaya. In the urban areas, the Muslim segment of the population is in the most advantageous position, followed by the Hindus. The Christians segment of population is more intensively excluded from the benefits of development. Thus, numerical dominance of a particular religious community does not entail socio-economic advantages. The advantages of numerical dominance may well be absorbed by the intra-community inequalities in the command over resources and opportunities.
Assigning credit where it’s due: An information content score to capture the clinical value of Multiplexed Assays of Variant Effect

Background A variant can be pathogenic or benign with relation to a human disease. Current classification categories from benign to pathogenic reflect a probabilistic summary of current understanding. A primary metric of clinical utility for multiplexed assays of variant effect (MAVE) is the number of variants that can be reclassified from uncertain significance (VUS). However, we hypothesized that this measure of utility underrepresents the information gained from MAVEs and that an information theory approach which includes data that does not reclassify variants will better reflect true information gain. We used this information theory approach to evaluate the information gain, in bits, for MAVEs of BRCA1, PTEN, and TP53. Here, one bit represents the amount of information required to completely classify a single variant starting from no information. Results BRCA1 MAVEs produced a total of 831.2 bits of information, 6.58% of the total missense information in BRCA1 and a 22-fold increase over the information that only contributed to VUS reclassification. PTEN MAVEs produced 2059.6 bits of information which represents 32.8% of the total missense information in PTEN and an 85-fold increase over the information that contributed to VUS reclassification. TP53 MAVEs produced 277.8 bits of information which represents 6.22% of the total missense information in TP53 and a 3.5-fold increase over the information that contributed to VUS reclassification. Conclusions An information content approach will more accurately portray information gained through MAVE mapping efforts than counting the number of variants reclassified. This information content approach may also help define the impact of modifying information definitions used to classify many variants, such as guideline rule changes.