Understanding High Dimensional Spaces through Visual Means Employing Multidimensional Projections

Data visualisation helps understanding data represented by multiple variables, also called features, stored in a large matrix where individuals are stored in lines and variable values in columns. These data structures are frequently called multidimensional spaces.In this paper, we illustrate ways of employing the visual results of multidimensional projection algorithms to understand and fine-tune the parameters of their mathematical framework. Some of the common mathematical common to these approaches are Laplacian matrices, Euclidian distance, Cosine distance, and statistical methods such as Kullback-Leibler divergence, employed to fit probability distributions and reduce dimensions. Two of the relevant algorithms in the data visualisation field are t-distributed stochastic neighbourhood embedding (t-SNE) and Least-Square Projection (LSP). These algorithms can be used to understand several ranges of mathematical functions including their impact on datasets. In this article, mathematical parameters of underlying techniques such as Principal Component Analysis (PCA) behind t-SNE and mesh reconstruction methods behind LSP are adjusted to reflect the properties afforded by the mathematical formulation. The results, supported by illustrative methods of the processes of LSP and t-SNE, are meant to inspire students in understanding the mathematics behind such methods, in order to apply them in effective data analysis tasks in multiple applications

LDPP at the FinNLP-2022 ERAI task: Determinantal point processes and variational auto-encoders for identifying high-quality opinions from a pool of social media posts

Social media and online forums have made it easier for people to share their views and opinions on various topics in society. In this paper, we focus on posts discussing investment related topics. When it comes to investment , people can now easily share their opinions about online traded items and also provide rationales to support their arguments on social media. However, there are millions of posts to read with potential of having some posts from amateur investors or completely unrelated posts. Identifying the most important posts that could lead to higher maximal potential profit (MPP) and lower maximal loss for investment is not a trivial task. In this paper, propose to use determinantal point processes and variational autoencoders to identify high quality posts from the given rationales. Experimental results suggest that our method mines quality posts compared to random selection and also latent variable modeling improves improves the quality of selected posts

The State of Global Air Quality Funding 2022

The only global snapshot of clean air funding from donor governments and philanthropic foundations. This report highlights funding trends and gaps in 2015-2021, as well as recommendations for smarter investment for people and planet.99% of the world's population breathes air that exceeds World Health Organization air quality guidelines. Cleaning the air is a massive opportunity to improve public health and climate change. Because air pollution and climate change are mainly caused by burning fossil fuels, these problems can be tackled together. By addressing these issues in isolation, funders and policymakers drastically overlook the potential of clean air to realise multiple health, social and sustainable economic benefits.Our fourth annual report is the only global snapshot of projects funded by international development funders and philanthropic foundations to tackle air pollution. We identify gaps in funding, and opportunities for strategic investment and collaboration for systemic change.?As the world prepares for COP27 in Egypt, we call for more joined up policies and funding to address air pollution, climate change and unsustainable economic growth simultaneously. This report provides recommendations for decision makers, policy makers and philanthropic foundations

GGNN@Causal News Corpus 2022: Gated graph neural networks for causal event classification from social-political news articles

The discovery of causality mentions from text is a core cognitive concept and appears in many natural language processing (NLP) applications. In this paper, we study the task of Event Causality Identification (ECI) from social-political news. The aim of the task is to detect causal relationships between event mention pairs in text. Although deep learning models have recently achieved a state-of-the-art performance on many tasks and applications in NLP, most of them still fail to capture rich semantic and syntactic structures within sentences which is key for causality classification. We present a solution for causal event detection from social-political news that captures semantic and syntactic information based on gated graph neural networks (GGNN) and contextualized language embeddings. Experimental results show that our proposed method outperforms the baseline model (BERT (Bidirectional Embeddings from Transformers) in terms of f1-score and accuracy

UCCNLP@SMM4H’22:Label distribution aware long-tailed learning with post-hoc posterior calibration applied to text classification

The paper describes our submissions for the Social Media Mining for Health (SMM4H) workshop 2022 shared tasks. We participated in 2 tasks: (1) classification of adverse drug events (ADE) mentions in english tweets (Task-1a) and (2) classification of self-reported intimate partner violence (IPV) on twitter (Task 7). We proposed an approach that uses RoBERTa (A Robustly Optimized BERT Pretraining Approach) fine-tuned with a label distribution-aware margin loss function and post-hoc posterior calibration for robust inference against class imbalance. We achieved a 4% and 1 % increase in performance on IPV and ADE respectively when compared with the traditional fine-tuning strategy with unweighted cross-entropy loss

PROGRAMA MULHERES MIL: UM CONVITE À INCLUSÃO SOCIAL, ECONÔMICA E AMBIENTAL, NO IFSC CAMPUS GASPAR/SC

O Programa Nacional Mulheres Mil: Educação, Cidadania e DesenvolvimentoSustentável, integra um conjunto de ações que consolidam as políticas  públicas e diretrizes governamentais de inclusão educacional, social e produtiva de mulheres em situação de vulnerabilidade. O Programa possibilita formação educacional, profissional e tecnológica, que permitam elevação de escolaridade, emancipação e acesso ao mundo do trabalho, por meio doestímulo ao empreendedorismo, às formas associativas solidárias e à empregabilidade

Uso combinado de modelos de estresse no trabalho e a saúde auto-referida na enfermagem

OBJETIVO: Identificar combinaciones de dos modelos de estrés psicossocial del trabajo en equipos de enfermería y su asociación con la salud auto referida. MÉTODOS: Estudio transversal con trabajadoras de tres hospitales públicos del Municipio de Rio de Janeiro, Sureste de Brasil, (N=1307). Se aplicó cuestionario multidimensional que incluyó dos escalas de estrés en el trabajo (modelo demanda-control y desequilibrio esfuerzo-recompensa) en 2006. Se consideraron el modelo demando y control parcial y completo (incluye apoyo social en el trabajo), así como el esfuerzo y recompensa parcial y completo (incluye exceso de compromiso con el trabajo). Se utilizaron modelos estadísticos múltiples para estimar razones de probabilidades ajustadas y sus respectivos intervalos con 95% de confianza. RESULTADOS: Las dimensiones de ambos modelos estuvieron independientemente asociadas con la salud autoreferida, con odds ratios entre 1,70 y 3,37. El modelo parcial demanda-control se mostró menos asociado a la salud (OR=1,79; IC 95% 1,26;2,53) al compararlo con el desequilibrio esfuerzo-recompensa (OR=2,27; IC 95% 1,57;3,30). La incorporación del apoyo social y del exceso de compromiso con el trabajo aumentó la fuerza de asociación de los modelos demanda-control y desequilibrio esfuerzo-recompensa, respectivamente. Se observó aumento en la fuerza de asociación al combinarse los dos modelos parciales. CONCLUSIONES: Los resultados indican mejor desempeño del modelo desequilibrio esfuerzo-recompensa para este grupo específico y para el resultado evaluado y ventaja en el uso de modelos completos o del uso combinado en modelos parciales.OBJETIVO: Identificar combinações de dois modelos do estresse psicossocial do trabalho em equipes de enfermagem e sua associação com a saúde auto-referida. MÉTODOS: Estudo transversal com trabalhadoras de três hospitais públicos do Município do Rio de Janeiro, RJ (N=1307). Foi aplicado questionário multidimensional que incluiu duas escalas de estresse no trabalho (modelo demanda-controle e desequilíbrio esforço-recompensa) em 2006. Foram considerados o modelo demanda e controle parcial e completo (inclui apoio social no trabalho), assim como o esforço e recompensa parcial e completo (inclui excesso de comprometimento com o trabalho). Modelos de regressão múltipla foram utilizados para estimar razões de chances ajustadas e seus respectivos intervalos com 95% de confiança. RESULTADOS: As dimensões de ambos os modelos estiveram independentemente associadas à situação de saúde, com odds ratios entre 1,70 e 3,37. O modelo parcial demanda-controle mostrou-se menos associado à saúde (OR = 1,79; IC95% 1,26;2,53) quando comparado ao de desequilíbrio esforço-recompensa (OR=2,27; IC95% 1,57;3,30). A incorporação do apoio social e do excesso de comprometimento com o trabalho aumentou a força de associação dos modelos demanda-controle e desequilíbrio esforço-recompensa, respectivamente. Foi observado aumento na força de associação quando os dois modelos parciais foram combinados. CONCLUSÕES: Os resultados indicam melhor desempenho do modelo desequilíbrio esforço-recompensa para este grupo específico e para o desfecho avaliado e vantagem do uso de modelos completos ou do uso combinado em modelos parciais.OBJECTIVE: To identify combinations of two models of psychosocial stress at work among nursing teams and their associations with self-rated health. METHODS: This was a cross-sectional study among workers at three public hospitals in the municipality of Rio de Janeiro, Southeastern Brazil (N = 1307). In 2006, a multidimensional questionnaire including two scales for measuring stress at work (demand-control and effort-reward imbalance models) was administered. Partial and complete (including social support at work) demand-control models were considered, along with partial and complete (including excessive commitment to work) effort-reward models. Multiple logistic regression models were used to estimate adjusted odds ratios and their respective 95% confidence intervals. RESULTS: The dimensions of both models were independently associated with self-rated health, with odds ratios between 1.70 and 3.37. The partial demand-control model was less associated with health (OR = 1.79; 95%CI 1.26;2.53) than was the partial effort-reward imbalance model (OR = 2.27; 95%CI 1.57;3.30). Incorporation of social support and excessive commitment to work increased the strength of the demand-control and effort-reward imbalance models, respectively. Increased strength of association was observed when the two partial models were combined. CONCLUSIONS: The results indicate that the effort-reward imbalance model performed better for this specific group and for the outcome evaluated, and that there was an advantage in using complete models or combinations of partial models

UNLPSat TextGraphs-16 Natural Language Premise Selection task: Unsupervised Natural Language Premise Selection in mathematical text using sentence-MPNet

This paper describes our system for the submission to the TextGraphs 2022 shared task at COLING 2022: Natural Language Premise Selection (NLPS) from mathematical texts. The task of NLPS is about selecting mathematical statements called premises in a knowledge base written in natural language and mathematical formulae that are most likely to be used to prove a particular mathematical proof. We formulated this task as an unsupervised semantic similarity task by first obtaining contextualized embeddings of both the premises and mathematical proofs using sentence transformers. We then obtained the cosine similarity between the embeddings of premises and proofs and then selected premises with the highest cosine scores as the most probable. Our system improves over the baseline system that uses bag of words models based on term frequency inverse document frequency in terms of mean average precision (MAP) by about 23.5% (0.1516 versus 0.1228)

Evaluating the Impact of a Critical Time Intervention Adaptation on Health Care Utilization among Homeless Adults with Mental Health Needs in a Large Urban Center

OBJECTIVE: This study evaluated the impact of a critical time intervention (CTI) adaptation on health care utilization outcomes among adults experiencing homelessness and mental health needs in a large urban center. METHODS: Provincial population-based administrative data from Ontario, Canada, were used in a pre-post design for a cohort of 197 individuals who received the intervention between January 2013 and May 2014 and were matched to a cohort of adults experiencing homelessness who did not receive the intervention over the same time period. Changes in health care utilization outcomes in the year pre- and postintervention were evaluated using generalized estimating equations, and post hoc analyses evaluated differences between groups. RESULTS: Pre-post analyses revealed statistically significant changes in health care utilization patterns among intervention recipients, including reduced inpatient service use and increased outpatient service use in the year following the intervention compared to the year prior. However, the matched cohort analysis found nonsignificant differences in health service use changes between a subgroup of intervention recipients and their matched counterparts. CONCLUSIONS: An adapted CTI model was associated with changes in health care utilization among people experiencing homelessness and mental health needs. However, changes were not different from those observed in a matched cohort. Rigorous study designs with adequate samples are needed to examine the effectiveness of CTI and local adaptations in diverse health care contexts

Rastreio da condição neuropsicológica de profissionais de saúde na linha de frente ao combate do COVID-19: dados preliminares / Screening the neuropsychological condition of health professionals on the frontlines of the COVID-19 fight: preliminary data

Introdução: uma pandemia pode promover manifestações cognitivas e comportamentais, afetando especialmente profissionais de saúde no enfrentamento do COVID-19. Objetivo: investigar a condição neuropsicológica de profissionais de saúde na linha de frente ao combate do COVID-19 no sul do estado de Santa Catarina, durante o pico da pandemia, prévio ao período vacinal. Método: aplicação de versão piloto online do NEUROPSIC-R (considerando escore mínimo de 43 pontos para a identificação/suspeita de alterações neuropsicológicas (valores mínimos de 19 e máximo de 76 pontos), respondido voluntariamente, assim como as perguntas acerca de fadiga e contágio por COVID-19. Resultados: 62 profissionais (média 35,7 e desvio padrão de 7,5 anos), ambos os sexos. A maioria eram Enfermeiros (38,7%), seguido de Agentes comunitários, Técnicos, de Médicos, Dentistas e Psicólogos, higienizadores, entre outros (8%). No grande grupo, observou-se escores deficitários no NEUROPSIC-R, especialmente em oscilação do humor e depressão (66% dos sujeitos), dificuldade de concentração (63%), de atenção (56,5%) e em memória recente (55 %). Adicionalmente, questiona-se acerca da preocupação com o avanço da pandemia e sobre a contaminação de familiares pela COVID-19, tendo 100% de respostas afirmativas em ambas. Foi questionado, também, sobre fadiga e cansaço, com afirmação em 84% dos respondentes, tendo uso de psicotrópicos para ansiedade (com 34% de afirmações). Conclusão: evidenciou-se relevante frequência de oscilação do humor, sintomas depressivos e ansiedade, dificuldade de concentração e fadiga, bem como alta preocupação com contágio nestes profissionais, repercutindo na sua condição cognitiva e emocional. Também se constatou a boa aplicabilidade do NEUROPSIC-R, inclusive na versão online