Coronavirus cases in the U.S. have skyrocketed in the last several weeks, reaching more than 11 million cases nationwide. As Americans see rising numbers, Fox News contributor Dr. Nicole Saphier broke down the accuracy of available coronavirus testing on "Fox & Friends Weekend." Recently, more patients have reported testing to be inaccurate, including figures from Tesla CEO Elon Musk, who tweeted Friday that after taking four tests for COVID-19, two were negative and two came back positive. Saphier explained these inaccuracies occur mostly from rapid testing – an antigen test screening for virus proteins – since they could have a false negative rate of up to 50%. "It's far more likely to have a false negative of that antigen test than it is to have a false positive," she explained.

Data Pre-processing - Data Preprocessing is that step in which the data gets transformed, or Encoded, to bring it to such a state that now the machine can easily parse it.

With the growth of sensing, control and robotic technologies, autonomous underwater vehicles (AUVs) have become useful assistants to human divers for performing various underwater operations. In the current practice, the divers are required to carry expensive, bulky, and waterproof keyboards or joystick-based controllers for supervision and control of AUVs. Therefore, diver action-based supervision is becoming increasingly popular because it is convenient, easier to use, faster, and cost effective. However, the various environmental, diver and sensing uncertainties present underwater makes it challenging to train a robust and reliable diver action recognition system. In this regard, this paper presents DARE, a diver action recognition system, that is trained based on Cognitive Autonomous Driving Buddy (CADDY) dataset, which is a rich set of data containing images of different diver gestures and poses in several different and realistic underwater environments. DARE is based on fusion of stereo-pairs of camera images using a multi-channel convolutional neural network supported with a systematically trained tree-topological deep neural network classifier to enhance the classification performance. DARE is fast and requires only a few milliseconds to classify one stereo-pair, thus making it suitable for real-time underwater implementation. DARE is comparatively evaluated against several existing classifier architectures and the results show that DARE supersedes the performance of all classifiers for diver action recognition in terms of overall as well as individual class accuracies and F1-scores.

The acquisition of commonsense knowledge is an important open challenge in artificial intelligence. In this work-in-progress paper, we study the task of automatically augmenting commonsense knowledge bases (KBs) with novel statements. We show empirically that obtaining meaningful negative samples for the completion task is nontrivial, and propose NegatER, a framework for generating negative commonsense knowledge, to address this challenge. In our evaluation we demonstrate the intrinsic value and extrinsic utility of the knowledge generated by NegatER, opening up new avenues for future research in this direction.

The classification of imbalanced data has presented a significant challenge for most well-known classification algorithms that were often designed for data with relatively balanced class distributions. Nevertheless skewed class distribution is a common feature in real world problems. It is especially prevalent in certain application domains with great need for machine learning and better predictive analysis such as disease diagnosis, fraud detection, bankruptcy prediction, and suspect identification. In this paper, we propose a novel tree-based algorithm based on the area under the precision-recall curve (AUPRC) for variable selection in the classification context. Our algorithm, named as the "Precision-Recall Curve classification tree", or simply the "PRC classification tree" modifies two crucial stages in tree building. The first stage is to maximize the area under the precision-recall curve in node variable selection. The second stage is to maximize the harmonic mean of recall and precision (F-measure) for threshold selection. We found the proposed PRC classification tree, and its subsequent extension, the PRC random forest, work well especially for class-imbalanced data sets. We have demonstrated that our methods outperform their classic counterparts, the usual CART and random forest for both synthetic and real data. Furthermore, the ROC classification tree proposed by our group previously has shown good performance in imbalanced data. The combination of them, the PRC-ROC tree, also shows great promise in identifying the minority class.

With growing awareness of societal impact of artificial intelligence, fairness has become an important aspect of machine learning algorithms. The issue is that human biases towards certain groups of population, defined by sensitive features like race and gender, are introduced to the training data through data collection and labeling. Two important directions of fairness ensuring research have focused on (i) instance weighting in order to decrease the impact of more biased instances and (ii) adversarial training in order to construct data representations informative of the target variable, but uninformative of the sensitive attributes. In this paper we propose a Fair Adversarial Instance Re-weighting (FAIR) method, which uses adversarial training to learn instance weighting function that ensures fair predictions. Merging the two paradigms, it inherits desirable properties from both -- interpretability of reweighting and end-to-end trainability of adversarial training. We propose four different variants of the method and, among other things, demonstrate how the method can be cast in a fully probabilistic framework. Additionally, theoretical analysis of FAIR models' properties have been studied extensively. We compare FAIR models to 7 other related and state-of-the-art models and demonstrate that FAIR is able to achieve a better trade-off between accuracy and unfairness. To the best of our knowledge, this is the first model that merges reweighting and adversarial approaches by means of a weighting function that can provide interpretable information about fairness of individual instances.

This will be a journey into predicting whether or not observations, made by high powered telescopes on Earth and potentially deep space probes in the future, are pulsars. Before we jump into the machine learning model I have developed to help identify pulsars, let's talk a bit about what pulsars, or'pulsar stars', actually are since they aren't pulsating and actually aren't technically stars (anymore). Consider, for the sake of explanation, that stars have a life. If they are less massive, between 7 and 25 solar masses (7–25 times the mass of our sun) or maybe a bit larger if they are especially metal-rich, they then become neutron stars, a super-dense mass only around 10 kilometers in radius but so dense that a teaspoon full of their mass would be as heavy as Mt. Everest if placed on Earth.

The NBA isn't going to have much of an offseason. The league is preparing to start the 2020–21 season on Dec. 22, just two months after the Los Angeles Lakers left the Orlando bubble as champions. But the bubble is no more. Teams will be playing games in their normal arenas, and the NBA sent a memo to its 30 organizations with an outline of protocols for hosting reduced-capacity crowds. The NBA has sent its 30 teams a memo with protocols for eligible markets to host fans, requiring people within 30 feet of court to register negative coronavirus test two days prior to game or rapid test on day of game, sources tell @TheAthleticNBA @Stadium.

We propose an improved estimator for the multi-task averaging problem, whose goal is the joint estimation of the means of multiple distributions using separate, independent data sets. The naive approach is to take the empirical mean of each data set individually, whereas the proposed method exploits similarities between tasks, without any related information being known in advance. First, for each data set, similar or neighboring means are determined from the data by multiple testing. Then each naive estimator is shrunk towards the local average of its neighbors. We prove theoretically that this approach provides a reduction in mean squared error. This improvement can be significant when the dimension of the input space is large, demonstrating a "blessing of dimensionality" phenomenon. An application of this approach is the estimation of multiple kernel mean embeddings, which plays an important role in many modern applications. The theoretical results are verified on artificial and real world data.

Machine Learning is a subfield within Artificial Intelligence that builds algorithms that allow computers to learn to perform tasks from data instead of being explicitly programmed. We can make machines learn to do things! The first time I heard that, it blew my mind. That means that we can program computers to learn things by themselves! The ability of learning is one of the most important aspects of intelligence. Translating that power to machines, sounds like a huge step towards making them more intelligent. And in fact, Machine Learning is the area that is making most of the progress in Artificial Intelligence today; being a trendy topic right now and pushing the possibility to have more intelligent machines.