Concordia University in Irvine will discontinue its use of antigen testing for asymptomatic students and employees, after more than 50 false positives prompted unwarranted concern about a possible major coronavirus outbreak. As of Wednesday, university officials said there were six active cases -- four students and two employees -- on campus as opposed to the more than 60 infections reported two days ago. Testing in another six cases has not been confirmed, and 55 students and employees have been confirmed as negative for the virus, they said. Campus officials had canceled athletic practices and urged against out-of-state travel for Thanksgiving because of the erroneous test results, which were preliminary pending confirmation from an outside lab. The university previously had been posting only confirmed test results on its COVID-19 dashboard, but made an exception for the unconfirmed numbers because of the indication of a "potential outbreak."

The aim of this paper is to develop and test advanced analytical methods to improve the prediction accuracy of Credit Risk Models, preserving at the same time the model interpretability. In particular, the project focuses on applying an explainable machine learning model to PSD2-related databases. The input data were obtained solely from synthetic account transactions generated from a pool of commercial banks from a pool of Italian commercial banks. Over the total proven models, CatBoost has shown the highest performance. The algorithm implementation produces a GINI of 0.45 after tuning the hyper-parameters combined with their inherent class-weight resampling method. SHAP package is used to provide a global and local interpretation of the model predictions to formulate a human-comprehensive approach to understanding the decision-maker algorithm. The 20 most important features are selected using the Shapley values to present a full human-understandable model that reveals how the attributes of an individual are related to its model prediction.

The emergence of COVID-19 has necessitated many efforts by the scientific community for its proper management. An urgent clinical reaction is required in the face of the unending devastation being caused by the pandemic. These efforts include technological innovations for improvement in screening, treatment, vaccine development, contact tracing and, survival prediction. The use of Deep Learning (DL) and Artificial Intelligence (AI) can be sought in all of the above-mentioned spheres. This paper aims to review the role of Deep Learning and Artificial intelligence in various aspects of the overall COVID-19 management and particularly for COVID-19 detection and classification. The DL models are developed to analyze clinical modalities like CT scans and X-Ray images of patients and predict their pathological condition. A DL model aims to detect the COVID-19 pneumonia, classify and distinguish between COVID-19, Community-Acquired Pneumonia (CAP), Viral and Bacterial pneumonia, and normal conditions. Furthermore, sophisticated models can be built to segment the affected area in the lungs and quantify the infection volume for a better understanding of the extent of damage. Many models have been developed either independently or with the help of pre-trained models like VGG19, ResNet50, and AlexNet leveraging the concept of transfer learning. Apart from model development, data preprocessing and augmentation are also performed to cope with the challenge of insufficient data samples often encountered in medical applications. It can be evaluated that DL and AI can be effectively implemented to withstand the challenges posed by the global emergency

We propose a unified multi-tasking framework to represent the complex and uncertain causal process of financial market dynamics, and then to predict the movement of any type of index with an application on the monthly direction of the S&P500 index. our solution is based on three main pillars: (i) the use of transfer learning to share knowledge and feature (representation, learning) between all financial markets, increase the size of the training sample and preserve the stability between training, validation and test sample. (ii) The combination of multidisciplinary knowledge (Financial economics, behavioral finance, market microstructure and portfolio construction theories) to represent a global top-down dynamics of any financial market, through a graph. (iii) The integration of forward looking unstructured data, different types of contexts (long, medium and short term) through latent variables/nodes and then, use a unique VAE network (parameter sharing) to learn simultaneously their distributional representation. We obtain Accuracy, F1-score, and Matthew Correlation of 74.3 %, 67 % and 0.42 above the industry and other benchmark on 12 years test period which include three unstable and difficult sub-period to predict.

Recently, the reciprocal recommendation, especially for online dating applications, has attracted more and more research attention. Different from conventional recommendation problems, the reciprocal recommendation aims to simultaneously best match users' mutual preferences. Intuitively, the mutual preferences might be affected by a few key attributes that users like or dislike. Meanwhile, the interactions between users' attributes and their key attributes are also important for key attributes selection. Motivated by these observations, in this paper we propose a novel reinforced random convolutional network (RRCN) approach for the reciprocal recommendation task. In particular, we technically propose a novel random CNN component that can randomly convolute non-adjacent features to capture their interaction information and learn feature embeddings of key attributes to make the final recommendation. Moreover, we design a reinforcement learning based strategy to integrate with the random CNN component to select salient attributes to form the candidate set of key attributes. We evaluate the proposed RRCN against a number of both baselines and the state-of-the-art approaches on two real-world datasets, and the promising results have demonstrated the superiority of RRCN against the compared approaches in terms of a number of evaluation criteria.

Distinguishing COVID-19 from other flu-like illnesses can be difficult due to ambiguous symptoms and still an initial experience of doctors. Whereas, it is crucial to filter out those sick patients who do not need to be tested for SARS-CoV-2 infection, especially in the event of the overwhelming increase in disease. As a part of the presented research, logistic regression and XGBoost classifiers, that allow for effective screening of patients for COVID-19, were generated. Each of the methods was tuned to achieve an assumed acceptable threshold of negative predictive values during classification. Additionally, an explanation of the obtained classification models was presented. The explanation enables the users to understand what was the basis of the decision made by the model. The obtained classification models provided the basis for the DECODE service (decode.polsl.pl), which can serve as support in screening patients with COVID-19 disease. Moreover, the data set constituting the basis for the analyses performed is made available to the research community. This data set consisting of more than 3,000 examples is based on questionnaires collected at a hospital in Poland.

Learning to detect real-world anomalous events through videolevel labels is a challenging task due to the rare occurrence of anomalies as well as noise in the labels. In this work, we propose a weakly supervised anomaly detection method which has manifold contributions including 1) a random batch based training procedure to reduce inter-batch correlation, 2) a normalcy suppression mechanism to minimize anomaly scores of the normal regions of a video by taking into account the overall information available in one training batch, and 3) a clustering distance based loss to contribute towards mitigating the label noise and to produce better anomaly representations by encouraging our model to generate distinct normal and anomalous clusters. The proposed method obtains 83.03% and 89.67% frame-level AUC performance on the UCF-Crime and ShanghaiTech datasets respectively, demonstrating its superiority over the existing state-of-the-art algorithms.

Pull requests are a key part of the collaborative software development and code review process today. However, pull requests can also slow down the software development process when the reviewer(s) or the author do not actively engage with the pull request. In this work, we design an end-to-end service, Nudge, for accelerating overdue pull requests towards completion by reminding the author or the reviewer(s) to engage with their overdue pull requests. First, we use models based on effort estimation and machine learning to predict the completion time for a given pull request. Second, we use activity detection to reduce false positives. Lastly, we use dependency determination to understand the blocker of the pull request and nudge the appropriate actor(author or reviewer(s)). We also do a correlation analysis to understand the statistical relationship between the pull request completion times and various pull request and developer related attributes. Nudge has been deployed on 147 repositories at Microsoft since 2019. We do a large scale evaluation based on the implicit and explicit feedback we received from sending the Nudge notifications on 8,500 pull requests. We observe significant reduction in completion time, by over 60%, for pull requests which were nudged thus increasing the efficiency of the code review process and accelerating the pull request progression.

There is significant growth and interest in the use of synthetic data as an enabler for machine learning in environments where the release of real data is restricted due to privacy or availability constraints. Despite a large number of methods for synthetic data generation, there are comparatively few results on the statistical properties of models learnt on synthetic data, and fewer still for situations where a researcher wishes to augment real data with another party's synthesised data. We use a Bayesian paradigm to characterise the updating of model parameters when learning in these settings, demonstrating that caution should be taken when applying conventional learning algorithms without appropriate consideration of the synthetic data generating process and learning task. Recent results from general Bayesian updating support a novel and robust approach to Bayesian synthetic-learning founded on decision theory that outperforms standard approaches across repeated experiments on supervised learning and inference problems.

With a ROC curve, you're trying to find a good model that optimizes the trade off between the False Positive Rate (FPR) and True Positive Rate (TPR). What counts here is how much area is under the curve (Area under the Curve AuC). The ideal curve in the left image fills in 100%, which means that you're going to be able to distinguish between negative results and positive results 100% of the time (which is almost impossible in real life). The further you go to the right, the worse the detection. The ROC curve to the far right does a worse job than chance, mixing up the negatives and positives (which means you likely have an error in your setup).