Narrative cartography is a discipline which studies the interwoven nature of stories and maps. However, conventional geovisualization techniques of narratives often encounter several prominent challenges, including the data acquisition & integration challenge and the semantic challenge. To tackle these challenges, in this paper, we propose the idea of narrative cartography with knowledge graphs (KGs). Firstly, to tackle the data acquisition & integration challenge, we develop a set of KG-based GeoEnrichment toolboxes to allow users to search and retrieve relevant data from integrated cross-domain knowledge graphs for narrative mapping from within a GISystem. With the help of this tool, the retrieved data from KGs are directly materialized in a GIS format which is ready for spatial analysis and mapping. Two use cases - Magellan's expedition and World War II - are presented to show the effectiveness of this approach. In the meantime, several limitations are identified from this approach, such as data incompleteness, semantic incompatibility, and the semantic challenge in geovisualization. For the later two limitations, we propose a modular ontology for narrative cartography, which formalizes both the map content (Map Content Module) and the geovisualization process (Cartography Module). We demonstrate that, by representing both the map content and the geovisualization process in KGs (an ontology), we can realize both data reusability and map reproducibility for narrative cartography.

Collaborative AI systems (CAISs) aim at working together with humans in a shared space to achieve a common goal. This critical setting yields hazardous circumstances that could harm human beings. Thus, building such systems with strong assurances of compliance with requirements, domain-specific standards and regulations is of greatest importance. Only few scale impact has been reported so far for such systems since much work remains to manage possible risks. We identify emerging problems in this context and then we report our vision, as well as the progress of our multidisciplinary research team composed of software/systems, and mechatronics engineers to develop a risk-driven assurance process for CAISs.

In this paper, we discover a two-phase phenomenon in the learning of multi-layer perceptrons (MLPs). I.e., in the first phase, the training loss does not decrease significantly, but the similarity of features between different samples keeps increasing, which hurts the feature diversity. We explain such a two-phase phenomenon in terms of the learning dynamics of the MLP. Furthermore, we propose two normalization operations to eliminate the two-phase phenomenon, which avoids the decrease of the feature diversity and speeds up the training process.

Probabilistic circuits (PCs) are a family of generative models which allows for the computation of exact likelihoods and marginals of its probability distributions. PCs are both expressive and tractable, and serve as popular choices for discrete density estimation tasks. However, large PCs are susceptible to overfitting, and only a few regularization strategies (e.g., dropout, weight-decay) have been explored. We propose HyperSPNs: a new paradigm of generating the mixture weights of large PCs using a small-scale neural network. Our framework can be viewed as a soft weight-sharing strategy, which combines the greater expressiveness of large models with the better generalization and memory-footprint properties of small models. We show the merits of our regularization strategy on two state-of-the-art PC families introduced in recent literature -- RAT-SPNs and EiNETs -- and demonstrate generalization improvements in both models on a suite of density estimation benchmarks in both discrete and continuous domains.

The abductive natural language inference task ($\alpha$NLI) is proposed to infer the most plausible explanation between the cause and the event. In the $\alpha$NLI task, two observations are given, and the most plausible hypothesis is asked to pick out from the candidates. Existing methods model the relation between each candidate hypothesis separately and penalize the inference network uniformly. In this paper, we argue that it is unnecessary to distinguish the reasoning abilities among correct hypotheses; and similarly, all wrong hypotheses contribute the same when explaining the reasons of the observations. Therefore, we propose to group instead of ranking the hypotheses and design a structural loss called ``joint softmax focal loss'' in this paper. Based on the observation that the hypotheses are generally semantically related, we have designed a novel interactive language model aiming at exploiting the rich interaction among competing hypotheses. We name this new model for $\alpha$NLI: Interactive Model with Structural Loss (IMSL). The experimental results show that our IMSL has achieved the highest performance on the RoBERTa-large pretrained model, with ACC and AUC results increased by about 1\% and 5\% respectively.

This paper serves to introduce the reader to the field of multi-agent reinforcement learning (MARL) and its intersection with methods from the study of causality. We highlight key challenges in MARL and discuss these in the context of how causal methods may assist in tackling them. We promote moving toward a 'causality first' perspective on MARL. Specifically, we argue that causality can offer improved safety, interpretability, and robustness, while also providing strong theoretical guarantees for emergent behaviour. We discuss potential solutions for common challenges, and use this context to motivate future research directions.

When it comes to the implementation of Artificial Intelligence, a lot of people have started to acknowledge the impact and return it can have on their investments. But when companies actually decide to hop on the train of smart technology, one issue seems to always come up: data quality. Data is the essence of Artificial Intelligence. In itself, AI has existed for some time now. However, the reason it has gained such momentum in the last couple of years is specifically because of the humongous amount of data that is now being collected.

Tomb Raider recently celebrated its 25th anniversary, which means 25 years of articles about how Lara Croft transcended video games to become a global icon even your gran has heard of. As a female games critic, I am personally asked to explain her enduring popularity 25 times an hour, to the point where I have boiled my answer down to this: for many of us, she symbolises a moment in the history of gaming where we saw ourselves represented for the first time. Not as a princess trapped in a castle, but as an enigmatic, acrobatic embodiment of fierceness. Naturally, the adolescent boys of the 90s also regarded her with the same distanced respect, right? Anyway, here's what nobody says they remember fondly about Tomb Raider: the food.

As time goes by, it is more frequent that the use of artificial intelligence supports the activities we perform on a daily basis. Have you done a Google search recently, used Siri on your cell phone in the last few days, watched a movie on Netflix, played online games, listened to music on Spotify, or compared something on Amazon lately? If you've done any of these things, you've certainly come into contact with some artificial intelligence development. Over time, it is more frequent that artificial intelligence supports the activities we do daily. Every day, companies that have access to our data know us better and provide us with a better service.

NovaSignal's AI-driven automated cerebral doppler ultrasound system. Los Angeles based NovaSignal Inc. recently launched a second version of their artificial intelligence (AI)-guided robotic platform for assessing cerebral blood flow in order to guide real-time diagnosis. The platform uses ultrasound to autonomously capture blood flow data, which then gets sent to their HIPAA-compliant cloud system so that clinicians can access the exam data from anywhere on their personal devices. Founded in 2013, the company states they have raised over $25 million in federal research funding and hold 18 patents. They also have over 130 peer-reviewed citations to their work.