Board structure and modified audit opinions: the case of the Portuguese Stock Exchange

Prior research has found evidence that some characteristics of the board of directors influence the quality of accounting information (e.g., Beasley, 1996; Dechow et al., 1996; Klein, 2002a; Xie et al., 2003). In this study we extend the literature by analysing a different dimension of accounting information quality, the probability of a firm receiving a modified audit opinion. Using a sample of companies listed on Euronext Lisbon where firms can publish financial statements not in accordance with GAAP, unlike the current situation in other markets like the US, and 91 firm-year observations for the period 2002-03, we find evidence consistent with the hypotheses that board diligence and independence contribute negatively to the probability of a modified opinion, while board size is not statistically significant. Our results are robust to different specifications and also show that financial health, performance, growth opportunities and the existence of dividend payments are additional factors affecting the likelihood of a modified audit opinion.auditing, modified opinions, accounting quality, board structure, corporate governance, non-executive directors

Importance of long-term cycles for predicting water level dynamics in natural lakes

Lakes are disproportionately important ecosystems for humanity, containing 77% of the liquid surface freshwater on Earth and comprising key contributors to global biodiversity. With an ever-growing human demand for water and increasing climate uncertainty, there is pressing need for improved understanding of the underlying patterns of natural variability of water resources and consideration of their implications for water resource management and conservation. Here we use Bayesian harmonic regression models to characterise water level dynamics and study the influence of cyclic components in confounding estimation of long-term directional trends in water levels in natural Irish lakes. We found that the lakes were characterised by a common and well-defined annual seasonality and several inter-annual and inter-decadal cycles with strong transient behaviour over time. Importantly, failing to account for the longer-term cyclic components produced a significant overall underestimation of the trend effect. Our findings demonstrate the importance of contextualising lake water resource management to the specific physical setting of lakes

Effect of noise in open chaotic billiards

We investigate the effect of white-noise perturbations on chaotic trajectories in open billiards. We focus on the temporal decay of the survival probability for generic mixed-phase-space billiards. The survival probability has a total of five different decay regimes that prevail for different intermediate times. We combine new calculations and recent results on noise perturbed Hamiltonian systems to characterize the origin of these regimes, and to compute how the parameters scale with noise intensity and billiard openness. Numerical simulations in the annular billiard support and illustrate our results.Comment: To appear in "Chaos" special issue: "Statistical Mechanics and Billiard-Type Dynamical Systems"; 9 pages, 5 figure

Evaluating Dominant Land Use/Land Cover Changes and Predicting Future Scenario in a Rural Region Using a Memoryless Stochastic Method

The present study used the o cial Portuguese land use/land cover (LULC) maps (Carta de Uso e Ocupação do Solo, COS) from 1995, 2007, 2010, 2015, and 2018 to quantify, visualize, and predict the spatiotemporal LULC transitions in the Beja district, a rural region in the southeast of Portugal, which is experiencing marked landscape changes. Here, we computed the conventional transition matrices for in-depth statistical analysis of the LULC changes that have occurred from 1995 to 2018, providing supplementary statistics regarding the vulnerability of inter-class transitions by focusing on the dominant signals of change. We also investigated how the LULC is going to move in the future (2040) based on matrices of current states using the Discrete-Time Markov Chain (DTMC) model. The results revealed that, between 1995 and 2018, about 28% of the Beja district landscape changed. Particularly, croplands remain the predominant LULC class in more than half of the Beja district (in 2018 about 64%). However, the behavior of the inter-class transitions was significantly di erent between periods, and explicitly revealed that arable land, pastures, and forest were the most dynamic LULC classes. Few dominant (systematic) signals of change during the 1995–2018 period were observed, highlighting the transition of arable land to permanent crops (5%) and to pastures (2.9%), and the transition of pastures to forest (3.5%) and to arable land (2.7%). Simulation results showed that about 25% of the territory is predicted to experience major LULC changes from arable land (3.81%), permanent crops (+2.93%), and forests (+2.60%) by 2040.info:eu-repo/semantics/publishedVersio

Plant species identification through leaf venation extraction and CNNs

Tese de mestrado em Bioinformática e Biologia Computacional, Universidade de Lisboa, Faculdade de Ciências, 2020O declínio no número de especialistas em taxonomia de plantas é um problema conhecido. Delegar parte do trabalho de identificação dos taxonomistas a modelos de aprendizagem automática ajudaria a reduzir o trabalho do número, cada vez menor, de profissionais disponível. Este projeto tem como objetivo testar a possibilidade de identificar quatro espécies de plantas diferentes exclusivamente pela nervação das suas folhas. Especificamente, criamos uma rede neuronal convolucional de aprendizagem profunda que tenta aprender como distinguir diferentes espécies com base no conjunto de dados aumentado de folhas diafanizadas disponíveis. Folhas diafanizadas são folhas que foram submetidas a processos específicos (como métodos químicos e/ou raio-X) para permitir visualizar não só as nervuras principais, mas também nervuras menores. Devido à escassez de imagens originais de folhas diafanizadas de cada classe, usamos um conjunto de dados aumentado. Os testes foram executados com diferentes parâmetros para testar a capacidade do modelo de prever a classe correta com precisão, e com outras métricas. O modelo foi testado em imagens não utilizadas anteriormente para se assegurar que as imagens de treino não estavam a ser memorizadas. Os resultados obtidos foram positivos para os parâmetros selecionados nos testes de tentativa e erro: uma precisão média de teste de cerca de 79,3% para o conjunto final de parâmetros. Estes resultados sugerem, como aliás outros estudos já o vêm apontando, que pode ser possível utilizar o padrão de nervação como uma característica para a identificação de plantas, embora mais estudos em larga escala, com mais classes e significativamente mais dados, sejam necessários para obter uma resposta mais confiante para a hipótese.The decline in the number of plant taxonomy experts is a known issue. Delegating part of the identification work of taxonomists to machine learning models would help reduce the workload on the dwindling number of available personnel. This project aims to test the concept of classifying four different species of plants solely by the venation network of its leaves. Specifically, we create a convolutional deep learning neural network that attempts to learn how to distinguish the distinct species based on the available augmented dataset of cleared leaves. Cleared leaves are leaves in which the venation network is rendered visible, by specific chemical processes and/or by other methods such as X-ray. We use an augmented dataset because of the scarcity of images of cleared leaves from each class. Tests were run with different parameters to test the model’s ability to predict the correct class with accuracy, and with other metrics. The model was tested on previously unseen images to ensure that it was not memorizing the training images. The results obtained were positive for the parameters selected through trial and error testing: an average testing accuracy of around 79.3% for the final set of parameters. These results further suggest, as other studies before it, that it might be possible to rely on the venation network as an identifying characteristic for plants, although more large scale studies with more classes and significantly more data are necessary to obtain better support for the hypothesis

A comparative study of calibration methods for low-cost ozone sensors in IoT platforms

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper shows the result of the calibration process of an Internet of Things platform for the measurement of tropospheric ozone (O 3 ). This platform, formed by 60 nodes, deployed in Italy, Spain, and Austria, consisted of 140 metal–oxide O 3 sensors, 25 electro-chemical O 3 sensors, 25 electro-chemical NO 2 sensors, and 60 temperature and relative humidity sensors. As ozone is a seasonal pollutant, which appears in summer in Europe, the biggest challenge is to calibrate the sensors in a short period of time. In this paper, we compare four calibration methods in the presence of a large dataset for model training and we also study the impact of a limited training dataset on the long-range predictions. We show that the difficulty in calibrating these sensor technologies in a real deployment is mainly due to the bias produced by the different environmental conditions found in the prediction with respect to those found in the data training phase.Peer ReviewedPostprint (author's final draft

Training samples from open data for satellite imagery classification: Using K-means clustering algorithm

To create a land use/land cover (LULC) map from a satellite image, we can follow a supervised classification approach if we know what classes exist in the study area and if we have representative training samples for each class. However, in heterogeneous biophysical environments, the wide range of spectral signatures among LULC classes can bias the classification results. In this study, we generated training samples from the official 2015 Portuguese Land Cover Map (COS). In spite of the viability of this source of information (official reference data), we faced some problems with corrupted data and an unbalanced number of training samples per class. As such, we explored the K-means clustering technique in order to understand whether the data had critical issues and to select the most representative training samples by class for satellite imagery classification. We investigated the potential of this technique for LULC classification in a predominantly rural region characterized by a mixed agro-silvo-pastoral environment, which means there is a broad range of spectral signatures for each LULC class. Two image classifications for 2015 were performed using the random forest classifier. The first was done by using the most representative training samples selected from the statistical analysis, and the other was done by using the full generated training set (original training set). Ultimately, the present study demonstrates the improvements in overall accuracy between both image classifications (+8%), showing that the applied methodology has a positive impact on the results.info:eu-repo/semantics/publishedVersio