Case study: DBRS sovereign rating of Portugal - analysis of rating methodology and rating decisions

This paper analyzes and assesses the DBRS sovereign credit rating methodology and its rating decisions on Portugal. A replicated rating model on Portugal allows to assess the DBRS rating methodology and to identify country-specific risk factors. An OLS regression compares rating effects of ten fundamental variables among S&P, Moody’s, Fitch Ratings and DBRS. Further, a rating scale model fractionally disentangles DBRS rating grades into their subjective and objective rating components. Both qualitative and empirical findings attest DBRS a comparably lenient rating behavior on Portugal –in comparison to other rating agencies as well as within the DBRS cross-country rating decisions

Fast Context Adaptation via Meta-Learning

We propose CAVIA for meta-learning, a simple extension to MAML that is less prone to meta-overfitting, easier to parallelise, and more interpretable. CAVIA partitions the model parameters into two parts: context parameters that serve as additional input to the model and are adapted on individual tasks, and shared parameters that are meta-trained and shared across tasks. At test time, only the context parameters are updated, leading to a low-dimensional task representation. We show empirically that CAVIA outperforms MAML for regression, classification, and reinforcement learning. Our experiments also highlight weaknesses in current benchmarks, in that the amount of adaptation needed in some cases is small.Comment: Published at the International Conference on Machine Learning (ICML) 201

Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning

To rapidly learn a new task, it is often essential for agents to explore efficiently -- especially when performance matters from the first timestep. One way to learn such behaviour is via meta-learning. Many existing methods however rely on dense rewards for meta-training, and can fail catastrophically if the rewards are sparse. Without a suitable reward signal, the need for exploration during meta-training is exacerbated. To address this, we propose HyperX, which uses novel reward bonuses for meta-training to explore in approximate hyper-state space (where hyper-states represent the environment state and the agent's task belief). We show empirically that HyperX meta-learns better task-exploration and adapts more successfully to new tasks than existing methods.Comment: Published at the International Conference on Machine Learning (ICML) 202

VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning

Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning. A Bayes-optimal policy, which does so optimally, conditions its actions not only on the environment state but on the agent's uncertainty about the environment. Computing a Bayes-optimal policy is however intractable for all but the smallest tasks. In this paper, we introduce variational Bayes-Adaptive Deep RL (variBAD), a way to meta-learn to perform approximate inference in an unknown environment, and incorporate task uncertainty directly during action selection. In a grid-world domain, we illustrate how variBAD performs structured online exploration as a function of task uncertainty. We further evaluate variBAD on MuJoCo domains widely used in meta-RL and show that it achieves higher online return than existing methods.Comment: Published at ICLR 202

Environmental contamination and hygienic measures after feline calicivirus field strain infections of cats in a research facility

Feline calicivirus (FCV) can cause painful oral ulcerations, salivation, gingivitis/stomatitis, fever and depression in infected cats; highly virulent virus variants can lead to fatal epizootic outbreaks. Viral transmission occurs directly or indirectly via fomites. The aim of this study was to investigate the presence and viability of FCV in the environment after sequential oronasal infections of specified pathogen-free cats with two FCV field strains in a research facility. Replicating virus was detected in saliva swabs from all ten cats after the first and in four out of ten cats after the second FCV exposure using virus isolation to identify FCV shedders. In the environment, where cleaning, but no disinfection took place, FCV viral RNA was detectable using RT-qPCR on all tested items and surfaces, including cat hair. However, only very limited evidence was found of replicating virus using virus isolation. Viral RNA remained demonstrable for at least 28 days after shedding had ceased in all cats. Disinfection with 5% sodium bicarbonate (and IncidinTM Plus) and barrier measures were effective in that no viral RNA was detectable outside the cat rooms. Our findings are important for any multicat environment to optimize hygienic measures against FCV infection

Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging

Purpose: To optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by exploring manually adjusted versus automated window-setting optimization (WSO) on computed tomography (CT) images. Methods: 7,194 CT images (3,597 with COPD; 3,597 healthy controls) from 78 subjects (43 with COPD; 35 healthy controls) were selected retrospectively (10.2018-12.2019) and preprocessed. For each image, intensity values were manually clipped to the emphysema window setting and a baseline 'full-range' window setting. Class-balanced train, validation, and test sets contained 3,392, 1,114, and 2,688 images. The network backbone was optimized by comparing various CNN architectures. Furthermore, automated WSO was implemented by adding a customized layer to the model. The image-level area under the Receiver Operating Characteristics curve (AUC) [lower, upper limit 95% confidence] and P-values calculated from one-sided Mann-Whitney U-test were utilized to compare model variations. Results: Repeated inference (n=7) on the test set showed that the DenseNet was the most efficient backbone and achieved a mean AUC of 0.80 [0.76, 0.85] without WSO. Comparably, with input images manually adjusted to the emphysema window, the DenseNet model predicted COPD with a mean AUC of 0.86 [0.82, 0.89] (P=0.03). By adding a customized WSO layer to the DenseNet, an optimal window in the proximity of the emphysema window setting was learned automatically, and a mean AUC of 0.82 [0.78, 0.86] was achieved. Conclusion: Detection of COPD with DenseNet models was improved by WSO of CT data to the emphysema window setting range

Effects of selenium supplementation on oxidative stress in the brain

Selenium is known to produce great effect on the oxidative stress of cells, both systemic and cerebral. Brain degeneration occurs as a result of several factors, one of them is the oxidative stress. We aim To analyze selenium effect on rats’ brains, investigating serum and immunohistochemical oxidative stress markers, and the neural structural pattern.  Selenium supplementation with 48 μg chelated selenium and with 96 μg chelated selenium for 60 days, followed by an evaluation of oxidative stress markers, as well as immunohistochemical markers.  Lower TBARS was observed in the rats subjected to 44mcg Se supplementation when compared to the other groups. Among the other oxidative stress markers, and immunohistochemical markers, little variation was observed. Improvement was observed in TBARS levels. However, the other measurements showed little statistical relevance. This raises some questioning regarding the dose used or the formulation and its bioavailability as influential factors in the oxidative response