Objectives. We generate via advanced Deep Learning (DL) techniques artificial leaf images in an automatized way. We aim to dispose of a source of training samples for AI applications for modern crop management. Such applications require large amounts of data and, while leaf images are not truly scarce, image collection and annotation remains a very time--consuming process. Data scarcity can be addressed by augmentation techniques consisting in simple transformations of samples belonging to a small dataset, but the richness of the augmented data is limited: this motivates the search for alternative approaches. Methods. Pursuing an approach based on DL generative models, we propose a Leaf-to-Leaf Translation (L2L) procedure structured in two steps: first, a residual variational autoencoder architecture generates synthetic leaf skeletons (leaf profile and veins) starting from companions binarized skeletons of real images. In a second step, we perform translation via a Pix2pix framework, which uses conditional generator adversarial networks to reproduce the colorization of leaf blades, preserving the shape and the venation pattern. Results. The L2L procedure generates synthetic images of leaves with a realistic appearance. We address the performance measurement both in a qualitative and a quantitative way; for this latter evaluation, we employ a DL anomaly detection strategy which quantifies the degree of anomaly of synthetic leaves with respect to real samples. Conclusions. Generative DL approaches have the potential to be a new paradigm to provide low-cost meaningful synthetic samples for computer-aided applications. The present L2L approach represents a step towards this goal, being able to generate synthetic samples with a relevant qualitative and quantitative resemblance to real leaves.

In clinical practice MR images are often first seen by radiologists long after the scan. If image quality is inadequate either patients have to return for an additional scan, or a suboptimal interpretation is rendered. An automatic image quality assessment (IQA) would enable real-time remediation. Existing IQA works for MRI give only a general quality score, agnostic to the cause of and solution to low-quality scans. Furthermore, radiologists' image quality requirements vary with the scan type and diagnostic task. Therefore, the same score may have different implications for different scans. We propose a framework with multi-task CNN model trained with calibrated labels and inferenced with image rulers. Labels calibrated by human inputs follow a well-defined and efficient labeling task. Image rulers address varying quality standards and provide a concrete way of interpreting raw scores from the CNN. The model supports assessments of two of the most common artifacts in MRI: noise and motion. It achieves accuracies of around 90%, 6% better than the best previous method examined, and 3% better than human experts on noise assessment. Our experiments show that label calibration, image rulers, and multi-task training improve the model's performance and generalizability.

The use of automated decision support and decision-making systems (ADM) (Hardt, Price, and Srebro 2016) in applications with direct impact on people's lives has increasingly become a fact of life, e,g. in criminal justice (Kleinberg, Contributions. We propose a technique that uses Bias Mullainathan, and Raghavan 2016; Jain et al. 2020b; Dressel Parity Score (BPS) measures to characterize fairness and develop and Farid 2018), medical diagnosis (Kleinberg, Mullainathan, a family of corresponding loss functions that are used and Raghavan 2016; Ahsen, Ayvaci, and Raghunathan as regularizers during training of Neural Networks to enhance 2019), insurance (Baudry and Robert 2019), credit fairness of the trained models. The goal here is to permit card fraud detection (Dal Pozzolo et al. 2014), electronic the system to actively pursue fair solutions during training health record data (Gianfrancesco et al. 2018), credit scoring while maintaining as high a performance on the task as (Huang, Chen, and Wang 2007) and many more diverse possible. We apply the approach in the context of several domains. This, in turn, has lead to an urgent need fairness measures and investigate multiple loss function formulations for study and scrutiny of the bias-magnifying effects of machine and regularization weights in order to study the learning and Artificial Intelligence algorithms and thus performance as well as potential drawbacks and deployment their potential to introduce and emphasize social inequalities considerations. In these experiments we show that, if used and systematic discrimination in our society. Appropriately, with appropriate settings, the technique measurably reduces much research is being done currently to mitigate bias race-based bias in recidivism prediction, and demonstrate in AI-based decision support systems (Ahsen, Ayvaci, and on the gender-based Adult Income dataset that the proposed Raghunathan 2019; Kleinberg, Mullainathan, and Raghavan method can outperform state-of-the art techniques aimed at 2016; Noriega-Campero et al. 2019; Feldman 2015; more targeted aspects of bias and fairness.

Antimicrobial resistance (AMR) is a risk for patients and a burden for the healthcare system. However, AMR assays typically take several days. This study develops predictive models for AMR based on easily available clinical and microbiological predictors, including patient demographics, hospital stay data, diagnoses, clinical features, and microbiological/antimicrobial characteristics and compares those models to a naive antibiogram based model using only microbiological/antimicrobial characteristics. The ability to predict the resistance accurately prior to culturing could inform clinical decision-making and shorten time to action. The machine learning algorithms employed here show improved classification performance (area under the receiver operating characteristic curve 0.88-0.89) versus the naive model (area under the receiver operating characteristic curve 0.86) for 6 organisms and 10 antibiotics using the Philips eICU Research Institute (eRI) database. This method can help guide antimicrobial treatment, with the objective of improving patient outcomes and reducing the usage of unnecessary or ineffective antibiotics.

Automatic detection of click-bait and incongruent news headlines is crucial to maintaining the reliability of the Web and has raised much research attention. However, most existing methods perform poorly when news headlines contain contextually important cardinal values, such as a quantity or an amount. In this work, we focus on this particular case and propose a neural attention based solution, which uses a novel cardinal Part of Speech (POS) tag pattern based hierarchical attention network, namely POSHAN, to learn effective representations of sentences in a news article. In addition, we investigate a novel cardinal phrase guided attention, which uses word embeddings of the contextually-important cardinal value and neighbouring words. In the experiments conducted on two publicly available datasets, we observe that the proposed methodgives appropriate significance to cardinal values and outperforms all the baselines. An ablation study of POSHAN shows that the cardinal POS-tag pattern-based hierarchical attention is very effective for the cases in which headlines contain cardinal values.

Learning from class imbalanced datasets poses challenges for many machine learning algorithms. Many real-world domains are, by definition, class imbalanced by virtue of having a majority class that naturally has many more instances than its minority class (e.g. genuine bank transactions occur much more often than fraudulent ones). Many methods have been proposed to solve the class imbalance problem, among the most popular being oversampling techniques (such as SMOTE). These methods generate synthetic instances in the minority class, to balance the dataset, performing data augmentations that improve the performance of predictive machine learning (ML) models. In this paper we advance a novel data augmentation method (adapted from eXplainable AI), that generates synthetic, counterfactual instances in the minority class. Unlike other oversampling techniques, this method adaptively combines exist-ing instances from the dataset, using actual feature-values rather than interpolating values between instances. Several experiments using four different classifiers and 25 datasets are reported, which show that this Counterfactual Augmentation method (CFA) generates useful synthetic data points in the minority class. The experiments also show that CFA is competitive with many other oversampling methods many of which are variants of SMOTE. The basis for CFAs performance is discussed, along with the conditions under which it is likely to perform better or worse in future tests.

We investigate identifying the boundary of a domain from sample points in the domain. We introduce new estimators for the normal vector to the boundary, distance of a point to the boundary, and a test for whether a point lies within a boundary strip. The estimators can be efficiently computed and are more accurate than the ones present in the literature. We provide rigorous error estimates for the estimators. Furthermore we use the detected boundary points to solve boundary-value problems for PDE on point clouds. We prove error estimates for the Laplace and eikonal equations on point clouds. Finally we provide a range of numerical experiments illustrating the performance of our boundary estimators, applications to PDE on point clouds, and tests on image data sets.

As the Covid-19 pandemic continues to evolve, there is a pressing need for a faster diagnostic system. Testing kit shortages, virus mutations, and soaring numbers of cases have overwhelmed health care systems worldwide. Even when a good testing policy is in place, lab testing is arduous, expensive, and time consuming. Cheap antigen tests, which can give results in 30 seconds, are widely available but suffer from low sensitivity; The tests correctly identifying just 75% of Covid-19 cases a week after symptoms start [2]. Shashwat Sanket and colleagues set out to find an easy, fast, and accurate alternative using simple chest X-ray images.

Rare-event simulation techniques, such as importance sampling (IS), constitute powerful tools to speed up challenging estimation of rare catastrophic events. These techniques often leverage the knowledge and analysis on underlying system structures to endow desirable efficiency guarantees. However, black-box problems, especially those arising from recent safety-critical applications of AI-driven physical systems, can fundamentally undermine their efficiency guarantees and lead to dangerous under-estimation without diagnostically detected. We propose a framework called Deep Probabilistic Accelerated Evaluation (Deep-PrAE) to design statistically guaranteed IS, by converting black-box samplers that are versatile but could lack guarantees, into one with what we call a relaxed efficiency certificate that allows accurate estimation of bounds on the rare-event probability. We present the theory of Deep-PrAE that combines the dominating point concept with rare-event set learning via deep neural network classifiers, and demonstrate its effectiveness in numerical examples including the safety-testing of intelligent driving algorithms.

Tracking the behaviour of livestock enables early detection and thus prevention of contagious diseases in modern animal farms. Apart from economic gains, this would reduce the amount of antibiotics used in livestock farming which otherwise enters the human diet exasperating the epidemic of antibiotic resistance - a leading cause of death. We could use standard video cameras, available in most modern farms, to monitor livestock. However, most computer vision algorithms perform poorly on this task, primarily because, (i) animals bred in farms look identical, lacking any obvious spatial signature, (ii) none of the existing trackers are robust for long duration, and (iii) real-world conditions such as changing illumination, frequent occlusion, varying camera angles, and sizes of the animals make it hard for models to generalize. Given these challenges, we develop an end-to-end behaviour monitoring system for group-housed pigs to perform simultaneous instance level segmentation, tracking, action recognition and re-identification (STAR) tasks. We present starformer, the first end-to-end multiple-object livestock monitoring framework that learns instance-level embeddings for grouped pigs through the use of transformer architecture. For benchmarking, we present Pigtrace, a carefully curated dataset comprising video sequences with instance level bounding box, segmentation, tracking and activity classification of pigs in real indoor farming environment. Using simultaneous optimization on STAR tasks we show that starformer outperforms popular baseline models trained for individual tasks.