Latent Variable Model for Multi-modal Translation

In this work, we propose to model the interaction between visual and textual features for multi-modal neural machine translation (MMT) through a latent variable model. This latent variable can be seen as a multi-modal stochastic embedding of an image and its description in a foreign language. It is used in a target-language decoder and also to predict image features. Importantly, our model formulation utilises visual and textual inputs during training but does not require that images be available at test time. We show that our latent variable MMT formulation improves considerably over strong baselines, including a multi-task learning approach (Elliott and K\'ad\'ar, 2017) and a conditional variational auto-encoder approach (Toyama et al., 2016). Finally, we show improvements due to (i) predicting image features in addition to only conditioning on them, (ii) imposing a constraint on the minimum amount of information encoded in the latent variable, and (iii) by training on additional target-language image descriptions (i.e. synthetic data).Comment: Paper accepted at ACL 2019. Contains 8 pages (11 including references, 13 including appendix), 6 figure

Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice.

Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today's world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios

Markovian model for forecasting financial time series

The study aims to create a Markovian model for forecasting financial time series and measure its effectiveness on stock prices. In the study, the new forecaster was inspired by several machine learning techniques and included statistical approaches and conditional probabilities. Namely, Markov Chains and Hidden Markov Chains are the main inspiration for machine learning techniques. To be able to process time series with Markov Chains like algorithm, new transformation developed with the usage of daily stock prices. Thirteen years of daily stock prices have been used for the data feed. For measuring the effectiveness of a new predictor, the obtaıned results are compared with conventional methods such as ARIMA, linear regression, decision tree regression and support vector regression predictions. The comparisons presented are based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Error ( RMSE). According to the achieved results, the new predictor performs better than decision tree regression, and ARIMA performs best among them.O estudo tem como objectivo criar um modelo markoviano para a previsão de séries temporais e medir a eficácia deste nas previsões de preços das ações. No estudo, o novo previsor foi inspirado em várias técnicas de aprendizagem de máquinas e incluiu abordagens estatísticas e probabilidades condicionais. Ou seja, as cadeias de Markov são a principal inspiração das técnicas para a aprendizagem das máquinas. Para ser capaz de processar séries temporais com algorítmo do tipo Cadeias de Markov, a nova técnica é desenvolvida com base em preços diários e ações. Foram considerados treze anos de preços diários de ações para teste dos modelos. Para medir a eficácia do novo previsor, foram obtidos resultados comparados com métodos convencionais, como os modelos ARIMA, a regressão linear, a regressão a partir da árvore de decisão. Esta comparação foi efetuada com base no Erro Absoluto Médio Percentual (MAPE) e na Raiz do Erro Quadrático Médio (RMSE). De acordo com os resultados obtidos, o novo previsor tem melhor desempenho do que a regressão da árvore de decisão, e o ARIMA tem o melhor desempenho entre eles

Economic implications of corporate financial reporting in brazilian and european financial markets

The main objective of this study is to determine how the people involved in the accounting process consider the role of accounting information in an economic environment where capital markets play a major role. The study is also aimed at determining whether International Financial Reporting Standards (IFRS) will help fulfill this role. To this end, we compare the perceptions of financial officers, financial analysts and auditors, using Europe as a proxy for a highly developed capital market environment and Brazil as a proxy for a less developed capital market environmentEconomic implications ; corporate financial reporting ; brazil ; europe ; financial markets

Temporal matching between interoception and exteroception: electrophysiological responses in a heartbeat discrimination task

Recent studies on interoception emphasize the importance of multisensory integration between interoception and exteroception. One of the methods frequently applied for assessing interoceptive sensitivity is the heartbeat discrimination task, where individuals judge whether the timing of external stimuli (e.g., tones) are synchronized to their own heartbeat. Despite its extensive use in research, the neural dynamics underlying the temporal matching between interoceptive and exteroceptive stimuli in this task have remained unclear. The present study used electroencephalography (EEG) to examine the neural responses of healthy participants who performed a heartbeat discrimination task. We analyzed the differences between EEG responses to tones, which were likely to be perceived as “heartbeat-synchronous” (200 ms delayed from the R wave) or “heartbeat-asynchronous” (0 ms delayed). Possible associations of these neural differentiations with task performance were also investigated. Compared with the responses to heartbeat-asynchronous tones, heartbeat-synchronous tones caused a relative decrease in early gamma-band EEG response and an increase in later P2 event-related potential (ERP) amplitude. Condition differences in the EEG/ERP measures were not significantly correlated with the behavioral measures. The mechanisms underlying the observed neural responses and the possibility of electrophysiological measurement of interoceptive sensitivity are discussed in terms of two perspectives: the predictive coding framework and the cardiac-phase-dependent baroreceptor function.This version of the article may not completely replicate the final authoritative version published in Journal of Psychophysiology at https://doi.org/10.1027/0269-8803/a000224. It is not the version of record and is therefore not suitable for citation. Please do not copy or cite without the permission of the author(s)

Distribution of discontinuous mudstone beds within wave-dominated shallow-marine deposits : Star Point Sandstone and Blackhawk Formation, Eastern Utah

Acknowledgements Funding for this study was provided from the Research Council of Norway through the Petromaks project 193059 and the FORCE Safari Project. The lidar data was collected by Julien Vallet and Samuel Pitiot of Helimap Systems SA. Riegl LMS GmbH is acknowledged for software support. The first author would like to thank Oliver Severin Tynes for assistance in the field. Tore Grane Klausen and Gijs Allard Henstra are thanked for invaluable discussions. The authors would also like to thank Janok Bhattacharya, Cornel Olariu and one anonymous revier for their insightful comments which improved this paper, and Frances Witehurst for his editorial comments.Peer reviewedPostprin

Geomorphological processes on terrestrial planetary surfaces

This review deals with features and processes on planetary surfaces, first by examining the impact of photographic explorations of Moon, Mars, and Mercury on studies of surface processes on our own planet, and second by treating matters related to current deformation of Earth’s surface

Shape mode analysis exposes movement patterns in biology: flagella and flatworms as case studies

We illustrate shape mode analysis as a simple, yet powerful technique to concisely describe complex biological shapes and their dynamics. We characterize undulatory bending waves of beating flagella and reconstruct a limit cycle of flagellar oscillations, paying particular attention to the periodicity of angular data. As a second example, we analyze non-convex boundary outlines of gliding flatworms, which allows us to expose stereotypic body postures that can be related to two different locomotion mechanisms. Further, shape mode analysis based on principal component analysis allows to discriminate different flatworm species, despite large motion-associated shape variability. Thus, complex shape dynamics is characterized by a small number of shape scores that change in time. We present this method using descriptive examples, explaining abstract mathematics in a graphic way.Comment: 20 pages, 6 figures, accepted for publication in PLoS On

Management of hypertension at the community level in Sub-Saharan Africa (SSA): towards a rational use of available resources

Hypertension is emerging in many developing nations as a leading cause of cardiovascular mortality, morbidity and disability in adults. In sub-Saharan African (SSA) countries it has specificities such as occurring in young and active adults, resulting in severe complications dominated by heart failure and taking place in limited-resource settings in which an individual's access to treatment (affordability) is very limited. Within this context of restrained economic conditions, the greatest gains for SSA in controlling the hypertension epidemic lie in its prevention. Attempts should be made to detect hypertensive patients early before irreversible organ damage becomes apparent, and to provide them with the best possible and affordable non-pharmacological and pharmacological treatment. Therefore, efforts should be made for detection and early management at the community level. In this context, a standardized algorithm of management can help in the rational use of available resources. Although many international and regional guidelines have been published, they cannot apply to SSA settings because the economy of the countries and affordability of the patients do not allow access to advocated treatment. In addition, none of them suggest a clear algorithm of management for limited-resource settings at the community level. In line with available data and analysing existing guidelines, a practical algorithm for management of hypertension at the community level, including treatment affordability, has been suggested in the present work