Miracle-dz/UniFAR-FTUs huggingface dataset:
No Calm Before the Storm: Low-Income Latina Immigrant and Citizen Mothers Before and After COVID-19

Government pandemic provisions occurred alongside a safety net that excludes or dissuades Latina mothers from participation. These families are also disproportionately exposed to punitive immigration policies and rhetoric that may shape their views on such provisions and, in turn, influence their post-pandemic well-being. To understand these complexities, we draw on interviews before and after COVID-19 with thirty-eight Latina immigrant and citizen mothers, most of whom are undocumented (N = 29). We find that pre-pandemic distrust of public institutions and the safety net was common, increased after COVID-19, and negatively affected undocumented respondents’ post-pandemic circumstances relative to that of citizen mothers. Findings suggest that safety net expansion on its own will not offset pandemic effects for these families without addressing exclusion from public benefits and alienation from and distrust of government.
What do Humor Classifiers Learn? An Attempt to Explain Humor Recognition Models

Towards computational systems capable of dealing with complex and general linguistic phenomena, it is essential to understand figurative language, which verbal humor is an instance of. This paper reports state-of-the-art results for Humor Recognition in Portuguese, specifically, an F1-score of 99.64% with a BERT-based classifier. However, following the surprising high performance in such a challenging task, we further analyzed what was actually learned by the classifiers. Our main conclusions were that classifiers based on content-features achieve the best performance, but rely mostly on stylistic aspects of the text, not necessarily related to humor, such as punctuation and question words. On the other hand, for humor-related features, we identified some important aspects, such as the presence of named entities, ambiguity and incongruity.
Is German secretly a Slavic language? What BERT probing can tell us about language groups

In the light of recent developments in NLP, the problem of understanding and interpreting large language models has gained a lot of urgency. Methods developed to study this area are subject to considerable scrutiny. In this work, we take a closer look at one such method, the structural probe introduced by Hewitt and Manning (2019). We run a series of experiments involving multiple languages, focusing principally on the group of Slavic languages. We show that probing results can be seen as a reflection of linguistic classification, and conclude that multilingual BERT learns facts about languages and their groups.
Findings from the Bambara - French Machine Translation Competition (BFMT 2023)

Orange Silicon Valley hosted a low-resource machine translation (MT) competition with monetary prizes. The goals of the competition were to raise awareness of the challenges in the low-resource MT domain, improve MT algorithms and data strategies, and support MT expertise development in the regions where people speak Bambara and other low-resource languages. The participants built Bambara to French and French to Bambara machine translation systems using data provided by the organizers and additional data resources shared amongst the competitors. This paper details each team’s different approaches and motivation for ongoing work in Bambara and the broader low-resource machine translation domain.
Numerical Simulation of Laminar and Turbulent Mixed Convection in Rectangular Enclosure with Hot upper Moving Wall

In recent year, the evolutionary structural optimization (ESO) method, has been applied as an effective tool for structures design. The concept of ESO method is to use the finite element results in each iteration of process and removal of inefficient material based on These results. In this method there are several experimental parameters such as rejection rate and evolutionary rate that are effective in results. This paper studies the influence of elements according to orders and in this regard has been applied eight node element and in addition to that it compares whit four node element results, is investigated influence in stress value, volume reduction, other parameters such as evolutionary rate and rejection rate. the results show that some parameters for low order elements are not suitable in high order elements problems and there for, has represented adequate value parameters. several examples are provided whit various boundary condition.
Patient adherence to tuberculosis treatment in the Indian subcontinent: systematic review and meta-synthesis of qualitative research

Objectives How well patients adhere to their tuberculosis (TB) treatment influences their recovery and development of drug resistance, but influences on adherence are multiple and often competing. We synthesised qualitative studies from our setting in the Indian subcontinent to understand the dimensions and dynamics involved to help inform service provision. Design Qualitative synthesis comprising inductive coding, thematic analysis and forming a conceptual framework. Data sources Medline (OVID), Embase (OVID), CINAHL (EBSCOHost), PsycINFO (EBSCOHost), Web of Science Core Collection, Cochrane Library and Epistemonikos were databases searched on 26 March 2020 for studies published since 1 January 2000. Eligibility criteria for selecting studies We included reports in English from the Indian subcontinent that used qualitative or mixed-methodology designs and reported findings around adherence to TB treatment. Full texts meeting eligibility were sampled based on ‘thickness’ (the richness of the qualitative data reported). Data extraction and synthesis Two reviewers used standardised methods to screen abstracts and code. Included studies were assessed for reliability and quality using a standard tool. Qualitative synthesis was performed by inductive coding, thematic analysis and developing conceptual framework. Results Of 1729 abstracts screened from initial search, 59 were shortlisted for full-text review. Twenty-four studies that qualified as ‘thick’ were included in the synthesis. Studies were set in India (12), Pakistan (6), Nepal (3), Bangladesh (1) or in two or more of these countries (2). Of the 24 studies, all but one included people who were taking TB treatment (1 study included only healthcare providers), and 17 included healthcare workers, community members or both. We identified three themes: (1) personal influences on the people with TB include interconnections between their social role in the family unit, their own priorities in day-to-day living and their experience to date with the disease; (2) adherence is profoundly influenced by how individual healthcare providers interact with patients on treatment and address their needs; (3) adherence is influenced across communities by structural, social, economic and cultural factors related to treatment. Conclusion Staff in TB programmes require an understanding of the various competing influences on individuals undergoing treatment. Programmes need to have more flexible and people-centred approaches to service provision in order to achieve adherence, and thus improve treatment outcomes. PROSPERO registration number CRD42020171409.
Evolution of galaxies studied by the COMBO-17+4 survey

In this thesis the evolution of galaxies at redshift z < 2 was analyzed, using the COMBO-17+4 (Classifying Objects by Medium-Band Observations) survey. This survey used optical as well as near-infrared data from three different fields of view with a total area of 0.625 square degree. On the basis of an H band selected catalog the photometric redshifts, rest-frame colors and masses of 42,134 galaxies to a limiting magnitude of H = 21.7 mag were calculated. Using the color bimodality in (U280 −V) the galaxies were divided in a red (passive) and a blue (active) population, which were analyzed separately. The results verify the existence of a bimodality up to a redshift of z = 1.6. Further the evolution of the luminosity function for both galaxy populations was analyzed in ten redshift bins. In both populations a significant decrease in the characteristic magnitude M∗B from z = 2 to z = 0.2 was measured. Moreover the number density of red galaxies showed a slight increase during this time, whereas the number density of blue galaxies was decreasing. An analysis of the mass function showed a constant value of the characteristic mass for both populations and an increase of the mass density for red galaxies from z = 2 to z = 0.2.
Lean Management Implementation in Malaysian Public Sectors: Critical Success Factors and Performance Implications

Abstract This study aims to investigate the effects of lean management implementation on public sector performance along with the critical success factors of its implementation. The cross-sectional quantitative survey collected 131 responses from public organisations in Malaysia selected through a stratified random sampling procedure. Data were analysed using the structural equation modelling (SEM) approach with SmartPLS 4 professional. Lean resources, lean culture, and lean knowledge management are critical success factors for implementing lean management in the Malaysian public sector. Subsequently, the holistic implementation of lean management will enhance their performance. This study provides a practical viewpoint for practitioners to understand and validate the potential benefits lean management can sustain if implemented with the critical success factors. As a result, practitioners and policymakers could use the insight from this study to tailor strategies for leveraging public sectors’ performance in Malaysia.
Structural change and industrial linkages: a perspective on China’s growth pattern, 1995-2009

ABSTRACT China has presented one of the most noticeable growth experiences in economic history. High growth rates in the post-1978 reform period have been marked by deep structural changes in the Chinese economy. This paper aims to discuss China’s long-term economic growth from a Kaldorian-Structuralist framework that emphasises the importance of a large, diversified and integrated industrial base as a central engine of economic growth that may prevent balance-of-payments constraints. This study applies input-output indicators to reveal key sectoral transformations of the Chinese productive structure and changes in interindustry linkages during the 1990s and 2000s. Results provide evidence that: (i) the Chinese sustained growth pattern has relied on a diversified and increasingly integrated domestic industrial production; and (ii) most sectors have been able to generate through exports enough foreign exchange to pay for import needs.
HRM work and open innovation: evidence from a case study

Abstract This paper proposes a framework for firm use of HRM when engaging in open innovation. Whereas open innovation has gained wide recognition in the innovation management field, as firms open their boundaries to knowledge inflow and outflow to advance innovation, very few empirical papers link the HRM literature to this phenomenon. We base our analysis on an exploratory qualitative study of the pharmaceutical corporation AstraZeneca and its implementation of an open innovation initiative called BioVentureHub. We identify three main areas of HRM work: inbound, outbound, and coupled HRM work. Furthermore, we illustrate how these HRM activities relate to the development of the open innovation initiative and to current HRM and open innovation literature. The framework identifies HRM activities that target not only internal employees, but also external human resources engaged in the open innovation initiative. This HRM work is mainly conducted through informal means, separate from the host corporation’s business as usual. Our empirical study contributes to the limited and mainly conceptual research connecting open innovation with HRM, increasing our knowledge of how corporations use HRM work to manage open innovation initiatives in practice.
Asymptotic final-size distribution for some chain-binomial processes

The classical Reed-Frost process is generalized by allowing infection probabilities to depend on current epidemic size. Such a process can be imbedded in a simple Markov process derived from i.i. d. waiting times. The final size of the epidemic has the same distribution as the time for the first crossing of a certain linear barrier of the imbedding process. The asymptotic distribution of the final size can be derived from some weak convergence results for the imbedding process. The existence of a distribution determining set of harmonic functions for these chain-binomial processes is also established.
An E-I Firing Rate Model for Gamma Oscillations

Firing rate models for describing the mean-field activities of neuronal ensembles can be used effectively to study network function and dynamics, including synchronization and rhythmicity of excitatory-inhibitory populations. However, traditional Wilson-Cowan-like models, being amenable to mathematical analysis, are found unable to capture some dynamics such as Interneuronal Network Gamma oscillations (ING) although use of an explicit delay can help. We resolve this issue by introducing a mean-voltage variable (v) that considers the subthreshold integration of inputs and works as an effective delay in the negative feedback loop between firing rate (r) and synaptic gating of inhibition (s). Here we describe a three-variable r-s-v firing rate model for I-I networks, which is biophysically interpretable and capable of generating ING-like oscillations. Linear stability analysis, numerical branch-tracking and simulations show that the rate model captures many of the common features of spiking network models for ING. We also propose an alternative r-u-s model for ING which considers an implicit delay by a pre-synaptic variable u. Furthermore, we extend the framework to E-I networks. With our six-variable r-s-v model, we describe for the first time in firing rate models the transition from Pyramidal-Interneuronal Network Gamma (PING) to ING by increasing the external drive to the inhibitory population without adjusting synaptic weights. Having PING and ING available in a single network, without invoking synaptic blockers, is plausible and natural for explaining the emergence and transition of two different types of gamma oscillations. Author Summary Gamma rhythm (30-90 Hz) is a neuronal oscillation broadly observed across various animal species. It is associated with cognitive functions and gamma oscillation dysfunction has been found in neurological diseases. Traditional studies of gamma oscillations are primarily conducted in detailed but computational demanding spiking network models. Motivated by the growing size of accessible datasets and large-scale modeling, we develop mean-field representations of gamma oscillations that are computationally lightweight and mathematically analyzable. We resolve the incapability of generating oscillation in inhibitory network with traditional Wilson-Cowan firing rate model by introducing implicit delay with a mean membrane potential variable and generalize the model for excitatory-inhibitory network to capture richer dynamics. Our work provides a potential building block for multi-area large-scale modeling to help understanding the functional roles of circuits at different levels.
Rapid effects of valproic acid on the fetal brain transcriptome: Implications for brain development and autism

There is an increased incidence of autism among the children of women who take the anti-epileptic, mood-stabilizing drug, valproic acid (VPA) during pregnancy; moreover, exposure to VPA in utero causes autistic-like symptoms in rodents and non-human primates. Analysis of RNA-seq data obtained from E12.5 fetal mouse brains 3 hours after VPA administration to the pregnant dam revealed that VPA rapidly and significantly increased or decreased the expression of approximately 7,300 genes. No significant sex differences in VPA-induced gene expression were observed. Expression of 399 autism risk genes was significantly altered by VPA as was expression of 255 genes that have been reported to play fundamental roles in fetal brain development but are not otherwise linked to autism. Expression of genes associated with intracellular signaling pathways, neurogenesis, and excitation-inhibition balance as well as synaptogenesis, neuronal fate determination, axon and dendritic development, neuroinflammation, circadian rhythms, and epigenetic modulation of gene expression was dysregulated by VPA. The goal of this study was to identify mouse genes that are: (a) significantly up- or down-regulated by VPA in the fetal brain and (b) known to be associated with autism and/or to play a role in embryonic neurodevelopmental processes, perturbation of which has the potential to alter brain connectivity and, consequently behavior, in the adult. The set of genes meeting these criteria provides potential targets for future hypothesis-driven studies to elucidate the proximal causes of errors in brain connectivity underlying neurodevelopmental disorders such as autism.
Molecule-Morphology Contrastive Pretraining for Transferable Molecular Representation

Image-based profiling techniques have become increasingly popular over the past decade for their applications in target identification, mechanism-of-action inference, and assay development. These techniques have generated large datasets of cellular morphologies, which are typically used to investigate the effects of small molecule perturbagens. In this work, we extend the impact of such dataset to improving quantitative structure-activity relationship (QSAR) models by introducing Molecule-Morphology Contrastive Pretraining (MoCoP), a framework for learning multi-modal representation of molecular graphs and cellular morphologies. We scale MoCoP to approximately 100K molecules and 600K morphological profiles using data from the JUMP-CP Consortium and show that MoCoP consistently improves performances of graph neural networks (GNNs) on molecular property prediction tasks in ChEMBL20 across all dataset sizes. The pretrained GNNs are also evaluated on internal GSK pharmacokinetic data and show an average improvement of 2.6% and 6.3% in AUPRC for full and low data regimes, respectively. Our findings suggest that integrating cellular morphologies with molecular graphs using MoCoP can significantly improve the performance of QSAR models, ultimately expanding the deep learning toolbox available for QSAR applications.
Navigating sex and sex roles: deciphering sex-biased gene expression in a species with sex-role reversal (Syngnathus typhle)

Sexual dimorphism, the divergence in morphological traits between males and females of the same species, is often accompanied by sex-biased gene expression. However, the majority of research has focused on species with conventional sex roles, where females have the highest energy burden with both egg production and parental care, neglecting the diversity of reproductive roles found in nature. We investigated sex-biased gene expression in the broadnosed pipefish (Syngnathus typhle), a sex-role reversed species with male pregnancy, allowing us to separate these two female traits. Employing RNA sequencing, we examined gene expression across organs (brain, head kidney, gonads) at various life stages, encompassing differences in age, sex, and reproductive status. While some gene groups were more strongly associated with sex roles, such as stress resistance and immune defence, others were driven by biological sex, such as energy and lipid storage regulation in an organ- and age-specific manner. By investigating how genes regulate and are regulated by changing reproductive roles and resource allocation in a model system with unconventional life-history strategy, we aim to enhance our understanding of the importance of sex and sex role in regulating gene expression patterns, broadening the scope of this discussion to encompass a wide range of organisms.
Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data

As the field of deep learning has grown in recent years, its application to the domain of raw resting-state electroencephalography (EEG) has also increased. Relative to traditional machine learning methods or deep learning methods applied to manually engineered features, there are fewer methods for developing deep learning models on small raw EEG datasets. One potential approach for enhancing deep learning performance, in this case, is the use of transfer learning. While a number of studies have presented transfer learning approaches for manually engineered EEG features, relatively few approaches have been developed for raw resting-state EEG. In this study, we propose a novel EEG transfer learning approach wherein we first train a model on a large publicly available single-channel sleep stage classification dataset. We then use the learned representations to develop a classifier for automated major depressive disorder diagnosis with raw multichannel EEG. Statistical testing reveals that our approach significantly improves the performance of our model (p < 0.05), and we also find that the performance of our approach exceeds that of many previous studies using both engineered features and raw EEG. We further examine how transfer learning affected the representations learned by the model through a pair of explainability analyses, identifying key frequency bands and channels utilized across models. Our proposed approach represents a significant step forward for the domain of raw resting-state EEG classification and has broader implications for use with other electrophysiology and time-series modalities. Importantly, it has the potential to expand the use of deep learning methods across a greater variety of raw EEG datasets and lead to the development of more reliable EEG classifiers.
A 2D convolutional neural network for taxonomic classification applied to viruses in the phylum Cressdnaviricota

Taxonomy, defined as the classification of different objects/organisms into defined stable hierarchical categories (taxa), is fundamental for proper scientific communication. In virology, taxonomic assignments based on sequence alone are now possible and their use may contribute to a more precise and comprehensive framework. The current major challenge is to develop tools for the automated classification of the millions of putative new viruses discovered in metagenomic studies. Among the many tools that have been proposed, those applying machine learning (ML), mainly in the deep learning branch, stand out with highly accurate results. One ML tool recently released that uses k-mers, VirusTaxo, was the first one to be applied with success, 93% average accuracy, to all types of viruses. Nevertheless, there is a demand for new tools that are less computationally intensive. Viruses classified in the phylum Cressdnaviricota, with their small and compact genomes, are good subjects for testing these new tools. Here we tested the usage of 2D convolutional neural networks for the taxonomic classification of cressdnaviricots, also testing the effect of data imbalance and two augmentation techniques by benchmarking against VirusTaxo. We were able to get perfect classification during k-fold test evaluations for balanced taxas, and more than 98% accuracy in the final pipeline tested for imbalanced datasets. The mixture of augmentation on more imbalanced groups and no augmentation for more balanced ones achieved the best score in the final test. These results indicate that these architectures can classify DNA sequences with high precision.