Abstract
Continuous-time dynamic graphs (CTDGs) are essential for modeling interconnected, evolving systems. Traditional methods for extracting knowledge from these graphs often depend on feature engineering or deep learning. Feature engineering is limited by the manual and time-intensive nature of crafting features, while deep learning approaches suffer from high inference latency, making them impractical for real-time applications. This paper introduces Deep-Graph-Sprints (DGS), a novel deep learning architecture designed for efficient representation learning on CTDGs with low-latency inference requirements. We benchmark DGS against state-of-the-art (SOTA) feature engineering and graph neural network methods using five diverse datasets. The results indicate that DGS achieves competitive performance while inference speed improves between 4x and 12x compared to other deep learning approaches on our benchmark datasets. Our method effectively bridges the gap between deep representation learning and low-latency application requirements for CTDGs.

Abstract
Team Automata is a formalism for interacting component-based systems proposed in 1997, whereby multiple sending and receiving actions from concurrent automata can synchronise. During the past 25+ years, team automata have been studied and applied in many different contexts, involving 25+ researchers and resulting in 25+ publications. In this paper, we first revisit the specific notion of synchronisation and composition of team automata, relating it to other relevant coordination models, such as Reo, BIP, Contract Automata, Choreography Automata, and Multi-Party Session Types. We then identify several aspects that have recently been investigated for team automata and related models. These include communication properties (which are the properties of interest?), realisability (how to decompose a global model into local components?) and tool support (what has been automatised or implemented?). Our presentation of these aspects provides a snapshot of the most recent trends in research on team automata, and delineates a roadmap for future research, both for team automata and for related formalisms.

Abstract
Maintaining process quality is one of the biggest challenges manufacturing industries face, as production processes have become increasingly complex and difficult to monitor effectively in today's manufacturing contexts. Reliance on skilled operators can result in suboptimal solutions, impacting process quality. In doing so, the importance of quality monitoring and diagnosis methods cannot be undermined. Existing approaches have limitations, including assumptions, prior knowledge requirements, and unsuitability for certain data types. To address these challenges, we present a novel unsupervised monitoring and detection methodology to monitor and evaluate the evolution of a quality characteristic's degradation. To measure the degradation we created a condition index that effectively captures the quality characteristic's mean and scale shifts from the company's specification levels. No prior knowledge or data assumptions are required, making it highly flexible and adaptable. By transforming the unsupervised problem into a supervised one and utilising historical production data, we employ logistic regression to predict the quality characteristic's conditions and diagnose poor condition moments by taking advantage of the model's interpretability. We demonstrate the methodology's application in a glass container production process, specifically monitoring multiple defective rates. Nonetheless, our approach is versatile and can be applied to any quality characteristic. The ultimate goal is to provide decision-makers and operators with a comprehensive view of the production process, enabling better-informed decisions and overall product quality improvement.

Abstract
This paper addresses the challenges of integrating large shares of renewable energy sources into the power system, focusing on managing operational reserves in multi-area systems and their long-term adequacy. Unlike previous studies, this paper investigates the long-term impact of procurement and activation of operational reserve in adjacent areas, considering energy scheduling and interconnection line constraints. Three procurement schemes for multi-area energy and reserve exchanges are proposed and analyzed through Sequential Monte Carlo Simulation. These schemes vary in their approach to interconnection line capacity constraints and the simultaneous or phased procurement of energy and synchronized reserve. The mathematical operationalization of these schemes is achieved through simple linear programming models, facilitating the quantification of marginal prices for both products. The impact of these schemes on operational reserve adequacy, marginal prices, and interconnection line utilization is demonstrated using configurations of the IEEE RTS 96 system. This analysis incorporates long-term uncertainty and diverse operational conditions and provides valuable insights into the interplay between energy and reserve procurement strategies in multi-area systems.

Abstract
Context. Since 2019, GRAVITY has provided direct observations of giant planets and brown dwarfs at separations of down to 95 mas from the host star. Some of these observations have provided the first direct confirmation of companions previously detected by indirect techniques (astrometry and radial velocities). Aims. We want to improve the observing strategy and data reduction in order to lower the inner working angle of GRAVITY in dual-field on-axis mode. We also want to determine the current limitations of the instrument when observing faint companions with separations in the 30-150 mas range. Methods. To improve the inner working angle, we propose a fiber off-pointing strategy during the observations to maximize the ratio of companion-light-to-star-light coupling in the science fiber. We also tested a lower-order model for speckles to decouple the companion light from the star light. We then evaluated the detection limits of GRAVITY using planet injection and retrieval in representative archival data. We compare our results to theoretical expectations. Results. We validate our observing and data-reduction strategy with on-sky observations; first in the context of brown dwarf follow-up on the auxiliary telescopes with HD 984 B, and second with the first confirmation of a substellar candidate around the star Gaia DR3 2728129004119806464. With synthetic companion injection, we demonstrate that the instrument can detect companions down to a contrast of 8 x 10(-4) (Delta K = 7.7 mag) at a separation of 35 mas, and a contrast of 3 x 10(-5) (Delta K = 11 mag) at 100 mas from a bright primary (K < 6.5), for 30 min exposure time. Conclusions. With its inner working angle and astrometric precision, GRAVITY has a unique reach in direct observation parameter space. This study demonstrates the promising synergies between GRAVITY and Gaia for the confirmation and characterization of substellar companions.

Abstract