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Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs

Neural Information Processing Systems

Node centralities play a pivotal role in network science, social network analysis, and recommender systems. In temporal data, static path-based centralities like closeness or betweenness can give misleading results about the true importance of nodes in a temporal graph. To address this issue, temporal generalizations of betweenness and closeness have been defined that are based on the shortest time-respecting paths between pairs of nodes.


Ubisoft cancels projects and announces restructure in fight to stay competitive

The Guardian

Ubisoft, the video games publisher behind the Assassin's Creed series, has cancelled projects and announced a restructuring that will close several studios as a result of several years of weak results and disappointing sales. Ubisoft, the video games publisher behind the Assassin's Creed series, has cancelled projects and announced a restructuring that will close several studios as a result of several years of weak results and disappointing sales. The video game publisher behind the Assassin's Creed series has cancelled six projects including a remake of Prince of Persia: The Sands of Time as it fights to stay competitive in the global gaming market. Ubisoft announced a sweeping reorganisation and said it would cancel six games, sending its shares to their lowest level in more than a decade on Thursday. Ubisoft is abandoning development of six titles, including a highly anticipated remake of Prince of Persia - a series that dates back to 1989 and received an ill-fated Hollywood adaptation in 2010 - and delaying a further seven. Studios in Halifax, Canada and Stockholm are being closed, with restructuring to follow in other countries, it said.


Ubisoft cancels six games including Prince of Persia and closes studios

BBC News

Ubisoft has cancelled six video games - including its long-awaited Prince of Persia: The Sands of Time remake - as part of a major reset of its operations. The French developer and publisher, known for popular games such as Assassin's Creed, Far Cry and Just Dance, has closed two studios and delayed seven titles as part of its changes. Ubisoft boss Yves Guillemot said the move would create the conditions for a return to sustainable growth. The firm's shares plunged by 33% on Thursday morning following the announcement. The move comes at a time when studios are increasingly turning to video game remakes and remasters, with new versions of Super Mario Galaxy, Oblivion and Metal Gear Solid 3 proving popular in 2025.


Understanding Syntactic Generalization in Structure-inducing Language Models

Arps, David, Sajjad, Hassan, Kallmeyer, Laura

arXiv.org Artificial Intelligence

Structure-inducing Language Models (SiLM) are trained on a self-supervised language modeling task, and induce a hierarchical sentence representation as a byproduct when processing an input. SiLMs couple strong syntactic generalization behavior with competitive performance on various NLP tasks, but many of their basic properties are yet underexplored. In this work, we train three different SiLM architectures from scratch: Structformer (Shen et al., 2021), UDGN (Shen et al., 2022), and GPST (Hu et al., 2024b). We train these architectures on both natural language (English, German, and Chinese) corpora and synthetic bracketing expressions. The models are then evaluated with respect to (i) properties of the induced syntactic representations (ii) performance on grammaticality judgment tasks, and (iii) training dynamics. We find that none of the three architectures dominates across all evaluation metrics. However, there are significant differences, in particular with respect to the induced syntactic representations. The Generative Pretrained Structured Transformer (GPST; Hu et al. 2024) performs most consistently across evaluation settings, and outperforms the other models on long-distance dependencies in bracketing expressions. Furthermore, our study shows that small models trained on large amounts of synthetic data provide a useful testbed for evaluating basic model properties.