The recognition or understanding of the scenes observed with a SAR system requires a broader range of cues, beyond the spatial context. These encompass but are not limited to: imaging geometry, imaging mode, properties of the Fourier spectrum of the images or the behavior of the polarimetric signatures. In this paper, we propose a change of paradigm for explainability in data science for the case of Synthetic Aperture Radar (SAR) data to ground the explainable AI for SAR. It aims to use explainable data transformations based on well-established models to generate inputs for AI methods, to provide knowledgeable feedback for training process, and to learn or improve high-complexity unknown or un-formalized models from the data. At first, we introduce a representation of the SAR system with physical layers: i) instrument and platform, ii) imaging formation, iii) scattering signatures and objects, that can be integrated with an AI model for hybrid modeling. Successively, some illustrative examples are presented to demonstrate how to achieve hybrid modeling for SAR image understanding. The perspective of trustworthy model and supplementary explanations are discussed later. Finally, we draw the conclusion and we deem the proposed concept has applicability to the entire class of coherent imaging sensors and other computational imaging systems.

Keyphrase identification and classification is a Natural Language Processing and Information Retrieval task that involves extracting relevant groups of words from a given text related to the main topic. In this work, we focus on extracting keyphrases from scientific documents. We introduce TA-DA, a Topic-Aware Domain Adaptation framework for keyphrase extraction that integrates Multi-Task Learning with Adversarial Training and Domain Adaptation. Our approach improves performance over baseline models by up to 5% in the exact match of the F1-score.

In my previous article I mentioned for the first time that a classical neural network may have quantum properties as its own structure may be entangled. The question one may ask now is whether such a quantum property can be used to entangle other systems? The answer should be yes, as shown in what follows.

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people's lives. Whether in areas of Automated Driving, Electric & Connected Mobility, IoT or Connectivity, our ideas make driving safer and more comfortable than ever before. This is only possible with the help of our colleagues from Bosch Engineering Center - with offices in Cluj and Bucharest - specialized in software, hardware & mechanics and reliability engineering. We work closely together with other international mobility development teams and with the local Bosch Cluj Plant in order to offer unique products and AIoT solutions to our clients from around the world. So, are you ready to shape the future of the mobility together with us?

What makes Netcentric a great fit for you? At Cognizant Netcentric, we come to work every day with the mission of leveraging cutting-edge technology to create memorable digital experiences for the world's leading brands. And we do it as a diverse, global community of over 1500 Adobe experts collaborating across countries, cultures, languages and technologies. We're energized by an inclusive and responsive organizational culture that brings together the entrepreneurial spirit of a startup with the resources, growth opportunities and stability of a larger global organization. Holacracy is the cornerstone of how we work, empowering every individual with the power to make an impact within our organization.

Medical image segmentation is an actively studied task in medical imaging, where the precision of the annotations is of utter importance towards accurate diagnosis and treatment. In recent years, the task has been approached with various deep learning systems, among the most popular models being U-Net. In this work, we propose a novel strategy to generate ensembles of different architectures for medical image segmentation, by leveraging the diversity (decorrelation) of the models forming the ensemble. More specifically, we utilize the Dice score among model pairs to estimate the correlation between the outputs of the two models forming each pair. To promote diversity, we select models with low Dice scores among each other. We carry out gastro-intestinal tract image segmentation experiments to compare our diversity-promoting ensemble (DiPE) with another strategy to create ensembles based on selecting the top scoring U-Net models. Our empirical results show that DiPE surpasses both individual models as well as the ensemble creation strategy based on selecting the top scoring models.

The theory of algorithms with predictions [1, 2, 3] is, without a doubt, one of the most exciting recent research directions in algorithmics: when supplemented by a (correct) predictor, often based on machine learning, the newly-developed algorithms are capable of outcompeting their worst-case classical counterparts. A desirable feature of such algorithms is, of course, to perform comparably to the (worst-case) algorithms when the predictors are really bad. This requirement often results [2] in tradeoffs between two measures of algorithm performance, robustness and consistency. A significant amount of subsequent research has followed, summarized by the algorithms with predictions webpage [3]. Recently, the idea of augmenting algorithms by predictions has been adapted to the game-theoretic setting of mechanism design [4, 5, 6, 7]: indeed, strategyproof mechanisms often yield solutions that are only approximately optimal [8]. On the other hand, if the designer has access to a predictor for the desired outcome it could conceivably take advantage of this information by creating mechanisms that lead to an improved approximation ratio, compared to their existing (worst-case) counterparts. Tradeoffs between robustness and consistency similar to the ones from [2] apply to this setting as well.

An important component of an automated fact-checking system is the claim check-worthiness detection system, which ranks sentences by prioritising them based on their need to be checked. Despite a body of research tackling the task, previous research has overlooked the challenging nature of identifying check-worthy claims across different topics. In this paper, we assess and quantify the challenge of detecting check-worthy claims for new, unseen topics. After highlighting the problem, we propose the AraCWA model to mitigate the performance deterioration when detecting check-worthy claims across topics. The AraCWA model enables boosting the performance for new topics by incorporating two components for few-shot learning and data augmentation. Using a publicly available dataset of Arabic tweets consisting of 14 different topics, we demonstrate that our proposed data augmentation strategy achieves substantial improvements across topics overall, where the extent of the improvement varies across topics. Further, we analyse the semantic similarities between topics, suggesting that the similarity metric could be used as a proxy to determine the difficulty level of an unseen topic prior to undertaking the task of labelling the underlying sentences.

We present a novel corpus for French dialect identification comprising 413,522 French text samples collected from public news websites in Belgium, Canada, France and Switzerland. To ensure an accurate estimation of the dialect identification performance of models, we designed the corpus to eliminate potential biases related to topic, writing style, and publication source. More precisely, the training, validation and test splits are collected from different news websites, while searching for different keywords (topics). This leads to a French cross-domain (FreCDo) dialect identification task. We conduct experiments with four competitive baselines, a fine-tuned CamemBERT model, an XGBoost based on fine-tuned CamemBERT features, a Support Vector Machines (SVM) classifier based on fine-tuned CamemBERT features, and an SVM based on word n-grams. Aside from presenting quantitative results, we also make an analysis of the most discriminative features learned by CamemBERT. Our corpus is available at https://github.com/MihaelaGaman/FreCDo.

Sunquakes are seismic emissions visible on the solar surface, associated with some solar flares. Although discovered in 1998, they have only recently become a more commonly detected phenomenon. Despite the availability of several manual detection guidelines, to our knowledge, the astrophysical data produced for sunquakes is new to the field of Machine Learning. Detecting sunquakes is a daunting task for human operators and this work aims to ease and, if possible, to improve their detection. Thus, we introduce a dataset constructed from acoustic egression-power maps of solar active regions obtained for Solar Cycles 23 and 24 using the holography method. We then present a pedagogical approach to the application of machine learning representation methods for sunquake detection using AutoEncoders, Contrastive Learning, Object Detection and recurrent techniques, which we enhance by introducing several custom domain-specific data augmentation transformations. We address the main challenges of the automated sunquake detection task, namely the very high noise patterns in and outside the active region shadow and the extreme class imbalance given by the limited number of frames that present sunquake signatures. With our trained models, we find temporal and spatial locations of peculiar acoustic emission and qualitatively associate them to eruptive and high energy emission. While noting that these models are still in a prototype stage and there is much room for improvement in metrics and bias levels, we hypothesize that their agreement on example use cases has the potential to enable detection of weak solar acoustic manifestations.