Miracle-dz/UniFAR-FTUs huggingface dataset:
De la constitution d’un corpus arboré à l’analyse syntaxique du serbe [From the constitution of a treebank to the syntactic analysis of the Serbian language]

RÉSUMÉ. Cet article retrace une expérience de constitution d’un corpus arboré pour le serbe, conçu dans le but de doter cette langue des instruments nécessaires à l’analyse syntaxique et, plus généralement, de favoriser des recherches plus systématiques aussi bien en TAL (traitement automatique des langues) qu’en linguistique serbe. Au-delà de la description des résultats de ce projet, nous présentons une méthode de confection d’un corpus arboré qui vise à optimiser les ressources, par déﬁnition rares, dont on dispose dans le cas d’une langue peu dotée, qu’il s’agisse de moyens matériels (corpus et outils) ou humains. Nous montrons comment tirer au mieux parti de l’existant pour faciliter le travail des annotateurs humains et accélérer l’enri-chissement du corpus, tout en garantissant la validité de l’annotation produite. Cette méthode, basée sur des principes transposables à d’autres langues, a vocation à faciliter la création des corpus arborés pour les langues sous-dotées en général. ABSTRACT. In this paper we describe our work on a treebank for Serbian, which aims to provide this language with tools and resources needed for parsing and, more globally, to encourage research on this language both in NLP (natural language processing) and in theoretical linguistics. Beyond the results of this resource-building project, we also provide a description of a treebank-building method that optimizes the limited resources available for an under-resourced language, both from the technical point of view (tools and corpora) and from that of human resources (annotation process). We show how best to take advantage of what is available in order to facilitate the manual work and accelerate the corpus enrichment process, all the while maintaining a high-quality annotation. Being based on language-independent principles, this method should help forward the creation of treebanks for other under-resourced languages.
ICON: Building a Large-Scale Benchmark Constituency Treebank for the Indonesian Language

Constituency parsing is an important task of informing how words are combined to form sentences. While constituency parsing in English has seen significant progress in the last few years, tools for constituency parsing in Indonesian remain few and far between. In this work, we publish ICON (Indonesian CONstituency treebank), the hitherto largest publicly-available manually-annotated benchmark constituency treebank for the Indonesian language with a size of 10,000 sentences and approximately 124,000 constituents and 182,000 tokens, which can support the training of state-of-the-art transformer-based models. We establish strong baselines on the ICON dataset using the Berkeley Neural Parser with transformer-based pre-trained embeddings, with the best performance of 88.85% F1 score coming from our own version of SpanBERT (IndoSpanBERT). We further analyze the predictions made by our best-performing model to reveal certain idiosyncrasies in the Indonesian language that pose challenges for constituency parsing.
Universalising Latin Universal Dependencies: a harmonisation of Latin treebanks in UD

This paper presents the harmonisation process carried out on the five treebanks available for Latin in Universal Dependencies, with the aim of eliminating the discrepancies in their annotation styles. Indeed, this is the first issue to be addressed when parsing Latin, as significant drops in parsing accuracy on different Latin treebanks have been repeatedly observed. Latin syntactic variability surely accounts for this, but parsing results are as well affected by divergent annotation choices. By analysing where annotations differ, we propose a Python-based alignment of the five UD treebanks. Consequently, the impact of annotation choices on accuracy scores is assessed by performing parsing experiments with UDPipe and Stanza.
Spanish Verbal Synonyms in the SynSemClass Ontology

This paper presents ongoing work in the expansion of the multilingual semantic event-type ontology SynSemClass (Czech-English-German) to include Spanish. As in previous versions of the lexicon, Spanish verbal synonyms have been collected from a sentence-aligned parallel corpus and classified into classes based on their syntactic-semantic properties. Each class member is linked to a number of syntactic and/or semantic resources specific to each language, thus enriching the annotation and enabling interoperability. This paper describes the procedure for the data extraction and annotation of Spanish verbal synonyms in the lexicon.
Rule-based semantic interpretation for Universal Dependencies

In this paper, we present a system for generating semantic representations from Universal Dependencies syntactic parses. The foundation of our pipeline is a rule-based interpretation system, designed to be as universal as possible, which produces the correct semantic structure; the content of this structure can then be filled in by additional (sometimes language-specific) post-processing. The rules which generate semantic resources rely as far as possible on the UD parse alone, so that they can apply to any language for which such a parse can be given (a much larger number than the number of languages for which detailed semantically annotated corpora are available). We discuss our general approach, and highlight areas where the UD annotation scheme makes semantic interpretation less straightforward. We compare our results with the Parallel Meaning Bank, and show that when it comes to modelling semantic structure, our approach shows potential, but also discuss some areas for expansion.
Real-time emotion recognition using end-to-end attention-based fusion network

Abstract. Real-time emotion detection based on facial expression is an innovative research field that has been applied in several areas, such as health, human–machine vision, and autonomous safety. Researchers in object detection are involved in developing methods to interpret, code facial expressions, and extract these features to be better predicted by machines. Furthermore, the success of deep learning with different architectures is exploited to achieve better performance. But these methods drastically fail in excessive sweating in different health conditions. We aim to create a dataset in different health conditions and detect facial emotion using the encoder and decoder-based deep learning methodology. The proposed architecture and the dataset present the progress made by comparing the other proposed methods and the quantitative and qualitative results obtained. The major benefit of our study is to enhance the emotion detection efficiency with other proposed methods and real-time applications for different health conditions. We propose the application of feature extraction of facial expressions with an end-to-end attention module-based fusion network for detecting different facial emotions (happy, angry, neutral, surprised, etc.) with an accuracy of 99.68%. The proposed system depends upon the human face; as we know, the face reflects human brain activities or emotions.
Estimating Passive Dynamics Distributions and State Costs in Linearly Solvable Markov Decision Processes during Z Learning Execution

Although the framework of linearly solvable Markov decision processes (LMDPs) reduces the computational complexity in reinforcement learning, it requires the knowledge of the state-transition probability in the absence of control or passive dynamics. The passive dynamics can be estimated by a temporal difference method called Z learning if the environment obeys the passive dynamics. However, it leads to a slow convergence of learning since no control is allowed during learning. This paper proposed a method to estimate the passive dynamics using Z learning under a different statetransition probability from the passive dynamics. The proposed method requires only the knowledge on what states can be visited from each possible state, and estimates the state-transition probability as well as the immediate cost of the states from the constraints they should satisfy. The computer experiments showed that the proposed method remains more efficient than Q learning with successful estimation of the passive dynamics and state costs and has a comparable convergence speed with the traditional Z learning.
A Critical Study of the Radiative Transfer Approach for the Remote Sensing of Layered Random Media

Abstract : The Radiative Transfer (RT) approach is widely used in applications involving scattering from layered random media with rough interfaces. Although this approach involves approximations, in most applications they are not explicitly stated or well understood. In order to better understand the RT approach to our problem we adopt a statistical wave approach and then transition to the RT equations. The geometry of our problem consists of a multi-layer discrete random medium with rough boundaries which are planar on the average. The random medium in each layer consists of a homogeneous background medium in which discrete scatterers are randomly distributed. The regions above and below the random medium stack are homogeneous. Using the Greens functions of the problem without the volumetric fluctuations we represent our problem as a system of integral equations. Employing the T-matrix description we first average with respect to volumetric fluctuations and then use the Twersky approximation to obtain a system of integral equations. We next average with respect to surface fluctuations, apply the weak surface correlation approximation and arrive at a closed system of integral equations for the first and second moments of the electric fields. We use the Wigner transforms to translate the coherence functions to radiant intensities, which are the fundamental quantities in the RT approach. On applying the quasi-static field approximation we hence arrive at a system of equations identical to those used in the RT approach. From this study we learn that there are more conditions involved in the RT approach than widely believed to be sufficient. This means that the applicability of the RT approach is much smaller than what is generally conceived by the remote sensing community.
Healthcare experience of adults with COPD during the COVID-19 pandemic: a rapid review of international literature

Background People living with chronic obstructive pulmonary disease (COPD) are a group who may be particularly vulnerable to COVID-19. This vulnerability has been associated with increased anxiety or fear about exposure to the virus, which may also impact upon experience in healthcare settings. Aim/objectives The aim of this narrative mixed-methods review was to systematically scope, identify and synthesise findings from peer-reviewed qualitative, quantitative and mixed-methods studies published in academic journals describing the healthcare experiences of adults living with COPD independently in the community, following the emergence of COVID-19 in December 2019–June 2022. Methods Databases including Ovid MEDLINE, PsychINFO, Ovid Emcare and CINAHL Plus were searched. Studies were uploaded to Covidence to support selection and appraisal of studies. Studies were appraised for quality using the Mixed Methods Appraisal Tool. A narrative synthesis of these themes was provided, and qualitative and quantitative findings are interpreted together in the discussion. Findings The quality and experience of care for patients with COPD was impacted through the COVID-19 pandemic. Innovations and adoption of technologies such as telehealth and telerehabilitation were well received and mitigated the potential implications of severe disruption to care access to some extent. Patients feared feeling forgotten and experienced isolation and anxiety; however, telerehabilitation and exercise through modalities such as Zoom classes help support social connection and physical activity. Implications These innovations are likely to be useful to be offered to patients on an ongoing basis, and education and standardised protocols around their use will benefit healthcare providers and patients alike. PROSPERO registration number CRD42022341168.
Association between early life exposure to the great famine and possible sarcopenia in older Chinese adults: a national cross-sectional study

Objectives We used data from the China Health and Retirement Longitudinal Study (CHARLS) to investigate how an early life famine exposure affected possible sarcopenia (PS) and to explore the extent to which a sex difference exists in the association among older Chinese adults, as well as whether risk factors modify the association. Design Cross-sectional study. Setting 28 provinces of China. Participants Considering that the Great Chinese Famine lasted from the spring of 1959 to the fall of 1961, 3557 participants were selected and categorised into four subgroups based on their date of birth: unexposed group (1 October 1962 to 30 September 1964), fetal exposed group (1 October 1959 to 30 September 1961), infant exposed group (1 January 1958 to 31 December 1958) and preschool exposed group (1 January 1956 to 31 December 1957). Outcome measure PS was defined as having low muscle strength or low physical performance. Methods We used multivariable logistic models to analyse the association between early life famine exposure and the risk of PS in elderly life. Results The prevalences of PS among individuals in the unexposed, fetal, infant and preschool exposed groups were 15.1%, 14.4%, 23.6% and 21.9%, respectively. Compared with the unexposed group, the infant (OR: 1.55; 95% CI 1.17 to 2.05) and preschool exposed (OR: 1.46; 95% CI 1.17 to 1.82) groups exhibited significantly higher risks of PS. In men, the infant (OR: 2.15; 95% CI 1.40 to 3.31) and preschool exposed (OR: 1.78; 95% CI 1.23 to 2.57) groups were more likely to have PS, but no significant increase was seen in women. In both sexes, prevalence of PS was unrelated to early life famine exposure in the urban, underweight and normal weight subgroups. Conclusions Early life exposure to the Great Chinese Famine was associated with a higher risk of PS in older adults. Keeping normal nutritional status in elderly life might help avoid the risk of PS, whatever the effect of early famine exposure.
SYS — Ein benutzerfreundliches System zur logischen Prüfung und Auswertung von Datenbeständen

Mit der wachsenden Bedeutung der Datenverarbeitung in der Medizin entsteht die Notwendigkeit benutzerfreundlicher Software. SYS ist ein aus 3 Programmen bestehendes System, das für eine logische und statistische Analyse von Daten geeignet ist. Es wird durch eine Problemsprache gesteuert, die eine direkte Übersetzung vorliegender Probleme gestattet und dabei irgendwelche formalen oder syntaktischen Beschränkungen vermeidet, die dem Laien die Anwendung erschweren könnten. Das System hat sich in einer Vielzahl von Anwendungen bewährt.
A New Diffusion Scheme for Numerical Models Based on Full Irreversibility

Abstract In this work, the forecast accuracy of a numerical weather prediction model is improved by emulating physical dissipation as suggested by the second law of thermodynamics, which controls the irreversible evolutionary direction of a many-body system like the atmosphere. The ability of the new physics-based scheme to improve model accuracy is demonstrated via the case of the one-dimensional viscous Burgers equation and the one-dimensional diffusion equation, as well as a series of numerical simulations of the well-known 1998 successive torrential rains along the Yangtze River valley and 365 continuous 24-h simulations during 2005–06 with decreased root-mean-square errors and improved forecasts in all of the simulations.
Deciphering a Rosetta stone: The nebula MA 1 in M33

We present a new longslit CCD spectrum and Hα imaging of the nebula MA 1 in the Local Group spiral galaxy M33, one of McCall's four «Rosetta Stone» extragalactic H II regions. Our Hα image shows MA 1 to be a complex of arcs, loops, and filaments having a diameter of about 45 arcsec. Our spectrum does not confirm McCall's suggestion that MA 1 is a matter-bounded nebula; rather, the spatial profile of the emission lines shows that [O II] is quite weak near the central stellar object, but becomes more nearly normal in strength in the outer parts of the nebula
Palladium-catalyzed cross-coupling of benzene sulfonhydrazides and benzyltrimethylammonium salts: Synthesis of diarylmethanes

Abstract A palladium-catalyzed cross-coupling of benzene sulfonyl hydrazides with benzyltrimethylammonium salts via C–S bond and C–N bond cleavage is reported. This reaction proceeds under mild reaction conditions and provides a new strategy for the synthesis of various diarylmethanes with broad functional group tolerance in moderate to good yields. Graphical Abstract
JETTO simulations of Te/Ti effects on plasma confinement

A series of hot-electron plasmas were produced at JET with the aim of clarifying the dependence of the energy confinement time on the electron to ion temperature ratio, Te/Ti. Although these plasmas showed a clear increase in the ion heat diffusivity with higher Te/Ti, their energy confinement time remained constant. In this paper we propose that these observations are consistent with the predictions of the Weiland model of energy transport. A feedback loop amplifying the ion diffusivity simultaneously flattens the ion temperature profile, and keeps the ion heat flux constant. The electron transport shows no dependence on Te/Ti, and hence there is no scaling of the confinement time with the temperature ratio. To support these claims we show a series of numerical simulations using the Weiland model. For scaling law purposes it is also interesting to note that the energy confinement time does not show any dependence on the effective input power ratio, Pe/Pi, either.
Tight hydrophobic core and flexible helices yield MscL with a high tension gating threshold and a membrane area mechanical strain buffer

The mechanosensitive (MS) channel of large conductance, MscL, is the high-tension threshold osmolyte release valve that limits turgor pressure in bacterial cells in the event of drastic hypoosmotic shock. Despite MscL from M. tuberculosis (TbMscL) being the first structurally characterized MS channel, its protective mechanism of activation at nearly-lytic tensions has not been fully understood. Here, we describe atomistic simulations of expansion and opening of wild-type (WT) TbMscL in comparison with five of its gain-of-function (GOF) mutants. We show that under far-field membrane tension applied to the edge of the periodic simulation cell, WT TbMscL expands into a funnel-like structure with trans-membrane helices bent by nearly 70 degrees, but does not break its ‘hydrophobic seal’ within extended 20 μs simulations. GOF mutants carrying hydrophilic substitutions in the hydrophobic gate of increasing severity (A20N, V21A, V21N, V21T and V21D) also quickly transition into funnel-shaped conformations but subsequently fully open within 1-8 us. This shows that solvation of the de-wetted (vapor-locked) constriction is the rate-limiting step in the gating of TbMscL preceded by area-buffering silent expansion. Pre-solvated gates in severe V21N and V21D mutants eliminate this barrier. We predict that the asymmetric shape-change of the periplasmic side of the channel during the silent expansion provides strain-buffering to the outer leaflet thus re-distributing the tension to the inner leaflet, where the gate resides.
NOVEL APPROACH EXPLAINS SPATIO-SPECTRAL INTERACTIONS IN RAW ELECTROENCEPHALOGRAM DEEP LEARNING CLASSIFIERS

The application of deep learning classifiers to resting-state electroencephalography (rs-EEG) data has become increasingly common. However, relative to studies using traditional machine learning methods and extracted features, deep learning methods are less explainable. A growing number of studies have presented explainability approaches for rs-EEG deep learning classifiers. However, to our knowledge, no approaches give insight into spatio-spectral interactions (i.e., how spectral activity in one channel may interact with activity in other channels). In this study, we combine gradient and perturbation-based explainability approaches to give insight into spatio-spectral interactions in rs-EEG deep learning classifiers for the first time. We present the approach within the context of major depressive disorder (MDD) diagnosis identifying differences in frontal δ activity and reduced interactions between frontal electrodes and other electrodes. Our approach provides novel insights and represents a significant step forward for the field of explainable EEG classification.
A novel antifolate suppresses growth of FPGS-deficient cells and overcomes methotrexate resistance

Cancer cells make extensive use of the folate cycle to sustain increased anabolic metabolism. Multiple chemotherapeutic drugs interfere with the folate cycle, including methotrexate and 5-fluorouracil that are commonly applied for the treatment of leukemia and colorectal cancer (CRC), respectively. Despite high success rates, therapy-induced resistance causes relapse at later disease stages. Depletion of folylpolyglutamate synthase (FPGS), which normally promotes intracellular accumulation and activity of both natural folates and methotrexate, is linked to methotrexate and 5-fluorouracil resistance and its association with relapse illustrates the need for improved intervention strategies. In this study, we characterize a novel antifolate (C1) that, like methotrexate, potently inhibits dihydrofolate reductase (DHFR) and downstream one-carbon metabolism. Contrary to methotrexate, however, C1 displays optimal efficacy in FPGS-deficient contexts, due to decreased competition with intracellular folate concentrations for interaction with DHFR. Indeed, we show that FPGS-deficient patient-derived CRC organoids display enhanced sensitivity to C1-induced growth inhibition, while FPGS-high CRC organoids are more sensitive to methotrexate. Our results thus argue that polyglutamylation-independent antifolates can be applied to exert selective pressure on FPGS-deficient cells during chemotherapy, employing a vulnerability created by polyglutamylation deficiency.
The Wolbachia WalE1 effector alters Drosophila endocytosis

The most common intracellular bacterial infection is Wolbachia pipientis, a microbe that manipulates host reproduction and is used in control of insect vectors. Phenotypes induced by Wolbachia have been studied for decades and range from sperm-egg incompatibility to male killing. How Wolbachia alters host biology is less well understood. Previously, we characterized the first Wolbachia effector – WalE1, which encodes a synuclein domain at the N terminus. Purified WalE1 sediments with and bundles actin and when heterologously expressed in flies, increases Wolbachia titer in the developing oocyte. In this work, we first identify the native expression WalE1 by Wolbachia infecting both fly cells and whole animals. WalE1 appears as aggregates, separate from Wolbachia cells. We next show that WalE1 co-immunoprecipitates with the host protein Past1 and that WalE1 manipulates host endocytosis. Yeast expressing WalE1 show deficiency in uptake of FM4-64 dye, and flies harboring mutations in Past1 or overexpressing WalE1 are sensitive to AgNO3, a hallmark of endocytosis defects. Finally, we also show that Past1 null flies harbor more Wolbachia overall and in late egg chambers. Our results identify interactions between a Wolbachia secreted effector and a host protein and point to yet another important host cell process impinged upon by Wolbachia.