Table 13: ANT comparisons when trained with small car as target on xView in multi-query setting.

Moreover, query results (e.g., detected search and rescue activity in a particular region) are highly informative about the locations of target objects in other regions, for example, due to spatial

Breast cancer is one of the most prevalent cancers worldwide and pathologists are closely involved in establishing a diagnosis. Tools to assist in making a diagnosis are required to manage the increasing workload. In this context, artificial intelligence (AI) and deep-learning based tools may be used in daily pathology practice. However, it is challenging to develop fast and reliable algorithms that can be trusted by practitioners, whatever the medical center. We describe a patch-based algorithm that incorporates a convolutional neural network to detect and locate invasive carcinoma on breast whole-slide images. The network was trained on a dataset extracted from a reference acquisition center. We then performed a calibration step based on transfer learning to maintain the performance when translating on a new target acquisition center by using a limited amount of additional training data. Performance was evaluated using classical binary measures (accuracy, recall, precision) for both centers (referred to as test reference dataset and test target dataset) and at two levels: patch and slide level. At patch level, accuracy, recall, and precision of the model on the reference and target test sets were 92.1\% and 96.3\%, 95\% and 87.8\%, and 73.9\% and 70.6\%, respectively. At slide level, accuracy, recall, and precision were 97.6\% and 92.0\%, 90.9\% and 100\%, and 100\% and 70.8\% for test sets 1 and 2, respectively. The high performance of the algorithm at both centers shows that the calibration process is efficient. This is performed using limited training data from the new target acquisition center and requires that the model is trained beforehand on a large database from a reference center. This methodology allows the implementation of AI diagnostic tools to help in routine pathology practice.

Images of spacecraft photographed from other spacecraft operating in outer space are difficult to come by, especially at a scale typically required for deep learning tasks. Semantic image segmentation, object detection and localization, and pose estimation are well researched areas with powerful results for many applications, and would be very useful in autonomous spacecraft operation and rendezvous. However, recent studies show that these strong results in broad and common domains may generalize poorly even to specific industrial applications on earth. To address this, we propose a method for generating synthetic image data that are labelled for semantic segmentation, generalizable to other tasks, and provide a prototype synthetic image dataset consisting of 2D monocular images of unmanned spacecraft, in order to enable further research in the area of autonomous spacecraft rendezvous. We also present a strong benchmark result (S{\o}rensen-Dice coefficient 0.8723) on these synthetic data, suggesting that it is feasible to train well-performing image segmentation models for this task, especially if the target spacecraft and its configuration are known.

There has been recently a growing interest in studying adversarial examples on natural language models in the black-box setting. These methods attack natural language classifiers by perturbing certain important words until the classifier label is changed. In order to find these important words, these methods rank all words by importance by querying the target model word by word for each input sentence, resulting in high query inefficiency. A new interesting approach was introduced that addresses this problem through interpretable learning to learn the word ranking instead of previous expensive search. The main advantage of using this approach is that it achieves comparable attack rates to the state-of-the-art methods, yet faster and with fewer queries, where fewer queries are desirable to avoid suspicion towards the attacking agent. Nonetheless, this approach sacrificed the useful information that could be leveraged from the target classifier for that sake of query efficiency. In this paper we study the effect of leveraging the target model outputs and data on both attack rates and average number of queries, and we show that both can be improved, with a limited overhead of additional queries.