Combining edge detection and colour segmentation in the Four-Legged League

Humans process images with apparent ease, quickly filtering out useless information and identifying objects based on their shape and colour. In the Four-Legged League of RoboCup, the focus of most vision systems has been on using colour to recognise objects. This single-mindedness has many disadvantages, but edge detection and shape recognition are not panaceas as their computational requirements are too high. This work focuses on a technique to efficiently combine the use of colour and edge detection in order to better recognise the field landmarks and objects found in the controlled environment of the Four-Legged League of RoboCup

Combining Edge Detection and Colour Segmentation in the Four-Legged League

Humans process images with apparent ease, quickly filtering out useless information and identifying objects based on their shape and colour. In the Four-Legged League of RoboCup, the focus of most vision systems has been on using colour to recognise objects. This single-mindedness has many disadvantages, but edge detection and shape recognition are not panaceas as their computational requirements are too high. This work focuses on a technique to efficiently combine the use of colour and edge detection in order to better recognise the field landmarks and objects found in the controlled environment of the Four-Legged League of RoboCup

Machine learning in the four-legged league

Abstract: The aim of this work is to contribute some insights and a partial overview of how machine learning methods are used in robotics. We first discuss typical general issues in the relationship between robotics and machine learning. Then we focus on projects associated with the RoboCup competition and symposium, and review the extent to which machine learning approaches have been used in the 4-legged league at RoboCup during the years 1998–2003. Further, we summarise the machine learning methods that were used by our own RoboCup team—the NUbots—in 2002/2003

Machine learning with AIBO robots in the four legged league of RoboCup

Robot learning is a growing area of research at the intersection of robotics and machine learning. The main contributions of this paper include a review of how machine learning has been used on Sony AIBO robots and at RoboCup, with a focus on the four-legged league during the years 1998-2004. The review shows that the application-oriented use of machine learning in the four-legged league was still conservative and restricted to a few well-known and easy-to-use methods such as standard decision trees, evolutionary hill climbing, and support vector machines. Method-oriented spin-off studies emerged more frequently and increasingly addressed new and advanced machine learning techniques. Further, the paper presents some details about the growing impact of machine learning in the software system developed by the authors' robot soccer team - the NUbots

Regime type and international conflict: towards a general model

The authors take a new look at the relationship between regime type and deadly militarized conflict among pairs of states (dyads) in the international system. With the goal of describing the general functional form, they evaluate three perspectives: democratic peace, regime similarity and regime rationality. They employ both standard logistic regression (logit) and a recently developed machine learning technique, a support vector machine (SVM). Logit is dependent on assumptions that limit flexibility and make it difficult to discern the appropriate functional form. SVM estimation, on the other hand, is highly flexible and appears capable of discovering a relationship that is contingent on other variables in the model. SVM results indicate that regime similarity and joint democracy are important in most dyadic interactions. However, for the special but important case of the most dangerous dyads, regime rationality plays a role and the democratic peace effect is dominant. The results suggest that models of international conflict excluding distinct indicators for political similarity, joint democracy and joint autocracy may be misspecified. SVMs are an especially useful complement to conventional statistical methods

Blood leukocyte transcriptional modules and differentially expressed genes associated with disease severity and age in COVID-19 patients

Abstract Since the molecular mechanisms determining COVID-19 severity are not yet well understood, there is a demand for biomarkers derived from comparative transcriptome analyses of mild and severe cases, combined with patients’ clinico-demographic and laboratory data. Here the transcriptomic response of human leukocytes to SARS-CoV-2 infection was investigated by focusing on the differences between mild and severe cases and between age subgroups (younger and older adults). Three transcriptional modules correlated with these traits were functionally characterized, as well as 23 differentially expressed genes (DEGs) associated to disease severity. One module, correlated with severe cases and older patients, had an overrepresentation of genes involved in innate immune response and in neutrophil activation, whereas two other modules, correlated with disease severity and younger patients, harbored genes involved in the innate immune response to viral infections, and in the regulation of this response. This transcriptomic mechanism could be related to the better outcome observed in younger COVID-19 patients. The DEGs, all hyper-expressed in the group of severe cases, were mostly involved in neutrophil activation and in the p53 pathway, therefore related to inflammation and lymphopenia. These biomarkers may be useful for getting a better stratification of risk factors in COVID-19