More pixels doesn't always mean a better screen. Breakthroughs, discoveries, and DIY tips sent every weekday. Every year, tech and television companies boast their products' latest and greatest, highest-resolution displays. The 4K display--a screen with a horizontal display of approximately 4,000 pixels-- first became widely available around 2014. Barely a decade later, you can purchase a TV with double the resolution .

Breakthroughs, discoveries, and DIY tips sent every weekday. An international team of physicists has developed a new methodology to aid NASA and other government agencies in their ongoing investigations into unidentified aerial phenomena (UAPs). The result is a novel strategy integrating a specially designed artificial intelligence program that was partially inspired by the physicists' own hunt for elusive dark matter. More popularly known as unidentified flying objects or UFOs, UAPs aren't necessarily considered as outlandish as they were decades ago. Setting aside the various theories that point to mysterious visitors from another planet, analysis increasingly centers on determining more worldly explanations.

After the launch of ChatGPT v.4 there has been a global vivid discussion on the ability of this artificial intelligence powered platform and some other similar ones for the automatic production of all kinds of texts, including scientific and technical texts. This has triggered a reflection in many institutions on whether education and academic procedures should be adapted to the fact that in future many texts we read will not be written by humans (students, scholars, etc.), at least, not entirely. In this work it is proposed a new methodology to classify texts coming from an automatic text production engine or a human, based on Sentiment Analysis as a source for feature engineering independent variables and then train with them a Random Forest classification algorithm. Using four different sentiment lexicons, a number of new features where produced, and then fed to a machine learning random forest methodology, to train such a model. Results seem very convincing that this may be a promising research line to detect fraud, in such environments where human are supposed to be the source of texts.

Artificial intelligence (AI) and machine learning models are making progress at an unprecedented rate and have achieved remarkable performance in several specific tasks such as image classification, object detection, automatic control, strategy games, some types of medical diagnoses, and music composition. The exceptional performance of machine learning models in perception tasks makes them very attractive for being adopted in a large variety of autonomous systems, which must process sensory data to understand the environment and react in real time to accomplish a given task. Examples of such autonomous systems include self-driving cars, advanced robots operating in unknown environments, and interplanetary space probes. These systems must not only perceive the objects in the scene and their location with a high accuracy, but they also must predict their trajectories and plan proper actions within stringent timing constraints. Consider, for instance, an autonomous car driving in an urban environment.

Introduction: The Oncotype DX (ODX) test is a commercially available molecular test for breast cancer assay that provides prognostic and predictive breast cancer recurrence information for hormone positive, HER2-negative patients. The aim of this study is to propose a novel methodology to assist physicians in their decision-making. Methods: A retrospective study between 2012 and 2020 with 333 cases that underwent an ODX assay from three hospitals in Bourgogne Franche-Comt{\'e} was conducted. Clinical and pathological reports were used to collect the data. A methodology based on distributional random forest was developed using 9 clinico-pathological characteristics. This methodology can be used particularly to identify the patients of the training cohort that share similarities with the new patient and to predict an estimate of the distribution of the ODX score. Results: The mean age of participants id 56.9 years old. We have correctly classified 92% of patients in low risk and 40.2% of patients in high risk. The overall accuracy is 79.3%. The proportion of low risk correct predicted value (PPV) is 82%. The percentage of high risk correct predicted value (NPV) is approximately 62.3%. The F1-score and the Area Under Curve (AUC) are of 0.87 and 0.759, respectively. Conclusion: The proposed methodology makes it possible to predict the distribution of the ODX score for a patient and provides an explanation of the predicted score. The use of the methodology with the pathologist's expertise on the different histological and immunohistochemical characteristics has a clinical impact to help oncologist in decision-making regarding breast cancer therapy.

Researchers from the University establish a new methodology to improve, from space and through machine learning, the observation and analysis of the terrestrial biosphere. This statistical approach will represent a significant advance in monitoring crops and carbon sinks, as well as in predicting floods and droughts. The work has been published in the journal Science Advances. The new machine learning methodology makes it possible to improve the precision in the prediction of key parameters, such as the leaf area index, the gross primary productivity and the fluorescence of the chlorophyll induced by the sun, among others. The field of applications is huge and will be of great use to improve the monitoring of crops and carbon sinks, detect changes and anomalies, droughts and floods.

Identification of the influential clinical symptoms and laboratory features that help in the diagnosis of dengue fever in early phase of the illness would aid in designing effective public health management and virological surveillance strategies. Keeping this as our main objective we develop in this paper, a new computational intelligence based methodology that predicts the diagnosis in real time, minimizing the number of false positives and false negatives. Our methodology consists of three major components (i) a novel missing value imputation procedure that can be applied on any data set consisting of categorical (nominal) and/or numeric (real or integer) (ii) a wrapper based features selection method with genetic search for extracting a subset of most influential symptoms that can diagnose the illness and (iii) an alternating decision tree method that employs boosting for generating highly accurate decision rules. The predictive models developed using our methodology are found to be more accurate than the state-of-the-art methodologies used in the diagnosis of the dengue fever.

The concept of 'self' has been investigated using many methodologies (e.g. the philosophical approach and the neurobiological approach) that has given rise to issues that yielded popular debates. In this paper, I endeavor to employ virtual reality as a new tool for investigating 'self'. Future directions are provided that could be further helpful in advancing our understanding about the self amidst the complexity of culture.