Mahmoud Assran et al. "Stochastic Gradient Push for Distributed Deep Learning". Keith Bonawitz et al. "Practical secure aggregation for privacy-preserving machine learning". Pierre Courtiol et al. "Deep learning-based classification of mesothelioma improves prediction "Distributed nonconvex optimization over time-varying networks". "Dual Averaging for Distributed Optimization: Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. "Model inversion attacks that exploit Transactions on The Built Environment 37 (1998). Zhanhong Jiang et al. "Collaborative deep learning in fixed topology networks". Can Karakus et al. "Straggler Mitigation in Distributed Optimization Through Data Encoding". "Federated Optimization:Distributed Optimization Beyond the Datacenter". Jakub Konecný et al. "Federated Optimization: Distributed Machine Learning for On-Device Songze Li et al. "Near-Optimal Straggler Mitigation for Distributed Gradient Methods".

This paper proposes sharp lower bounds for the number of message passing iterations required in graph neural networks (GNNs) when solving partial differential equations (PDE). This significantly reduces the need for exhaustive hyperparameter tuning. Bounds are derived for the three fundamental classes of PDEs (hyperbolic, parabolic and elliptic) by relating the physical characteristics of the problem in question to the message-passing requirement of GNNs. In particular, we investigate the relationship between the physical constants of the equations governing the problem, the spatial and temporal discretisation and the message passing mechanisms in GNNs. When the number of message passing iterations is below these proposed limits, information does not propagate efficiently through the network, resulting in poor solutions, even for deep GNN architectures. In contrast, when the suggested lower bound is satisfied, the GNN parameterisation allows the model to accurately capture the underlying phenomenology, resulting in solvers of adequate accuracy. Examples are provided for four different examples of equations that show the sharpness of the proposed lower bounds.

Developing autonomous driving (AD) systems is challenging due to the complexity of the systems and the need to assure their safe and reliable operation. The widely adopted approach of DevOps seems promising to support the continuous technological progress in AI and the demand for fast reaction to incidents, which necessitate continuous development, deployment, and monitoring. We present a systematic literature review meant to identify, analyse, and synthesise a broad range of existing literature related to usage of DevOps in autonomous driving development. Our results provide a structured overview of challenges and solutions, arising from applying DevOps to safety-related AI-enabled functions. Our results indicate that there are still several open topics to be addressed to enable safe DevOps for the development of safe AD.

Predictive Process Monitoring (PPM) often uses deep learning models to predict the future behavior of ongoing processes, such as predicting process outcomes. While these models achieve high accuracy, their lack of interpretability undermines user trust and adoption. Explainable AI (XAI) aims to address this challenge by providing the reasoning behind the predictions. However, current evaluations of XAI in PPM focus primarily on functional metrics (such as fidelity), overlooking user-centered aspects such as their effect on task performance and decision-making. This study investigates the effects of explanation styles (feature importance, rule-based, and counterfactual) and perceived AI accuracy (low or high) on decision-making in PPM. We conducted a decision-making experiment, where users were presented with the AI predictions, perceived accuracy levels, and explanations of different styles. Users' decisions were measured both before and after receiving explanations, allowing the assessment of objective metrics (Task Performance and Agreement) and subjective metrics (Decision Confidence). Our findings show that perceived accuracy and explanation style have a significant effect.

Abstract: In the present work we address the problem of deploying a team of robots in a scenario where some locations of interest must be reached. Thus, a planning for a deployment is required, before sending the robots. The obstacles, the limited communication range, and the need of communicating to a base station, constrain the connectivity of the team and the deployment planning. We propose a method consisting of three algorithms: a distributed path planner to obtain communication-aware trajectories; a deployment planner providing dual-use of the robots, visiting primary goals and performing connectivity tasks; and a clustering algorithm to allocate the tasks to robots, and obtain the best goal visit order for the mission. Keywords: Multi-robot systems, deployment planning, communication-aware planning 1. INTRODUCTION characterize the signal in the environment, considering the variations suffered by the signal in the propagation media. The deployment of robot teams for exploration or environmental monitoring can be executed in many ways.

In this demo work we develop a method to plan and coordinate a multi-agent team to gather information on demand. The data is periodically requested by a static Operation Center (OC) from changeable goals locations. The mission of the team is to reach these locations, taking measurements and delivering the data to the OC. Due to the limited communication range as well as signal attenuation because of the obstacles, the agents must travel to the OC, to upload the data. The agents can play two roles: ones as workers gathering data, the others as collectors traveling invariant paths for collecting the data of the workers to re-transmit it to the OC. The refreshing time of the delivered information depends on the number of available agents as well as of the scenario. The proposed algorithm finds out the best balance between the number of collectors-workers and the partition of the scenario into working areas in the planning phase, which provides the minimum refreshing time and will be the one executed by the agents.

In the present paper we develop a distributed method to reconnect a multi-robot team after connectivity failures, caused by unpredictable environment changes, i.e. appearance of new obstacles. After the changes, the team is divided into different groups of robots. The groups have a limited communication range and only a partial information in their field of view about the current scenario. Their objective is to form a chain from a static base station to a goal location. In the proposed distributed replanning approach, the robots predict new plans for the other groups from the new observed information by each robot in the changed scenario, to restore the connectivity with a base station and reach the initial joint objective. If a solution exists, the method achieves the reconnection of all the groups in a unique chain. The proposed method is compared with other two cases: 1) when all the agents have full information of the environment, and 2) when some robots must move to reach other waiting robots for reconnection. Numerical simulations are provided to evaluate the proposed approach in the presence of unpredictable scenario changes.

In this paper we develop a method for planning and coordinating a multi-agent team deployment to periodically gather information on demand. A static operation center (OC) periodically requests information from changing goal locations. The objective is to gather data in the goals and to deliver it to the OC, balancing the refreshing time and the total number of information packages. The system automatically splits the team in two roles: workers to gather data, or collectors to retransmit the data to the OC. The proposed three step method: 1) finds out the best area partition for the workers; 2) obtains the best balance between workers and collectors, and with whom the workers must to communicate, a collector or the OC; 3) computes the best tour for the workers to visit the goals and deliver them to the OC or to a collector in movement. The method is tested in simulations in different scenarios, providing the best area partition algorithm and the best balance between collectors and workers.

While Large Language Models (LLMs) have significantly advanced natural language processing, aligning them with human preferences remains an open challenge. Although current alignment methods rely primarily on explicit feedback, eye-tracking (ET) data offers insights into real-time cognitive processing during reading. In this paper, we present OASST-ETC, a novel eye-tracking corpus capturing reading patterns from 24 participants, while evaluating LLM-generated responses from the OASST1 dataset. Our analysis reveals distinct reading patterns between preferred and non-preferred responses, which we compare with synthetic eye-tracking data. Furthermore, we examine the correlation between human reading measures and attention patterns from various transformer-based models, discovering stronger correlations in preferred responses. This work introduces a unique resource for studying human cognitive processing in LLM evaluation and suggests promising directions for incorporating eye-tracking data into alignment methods. The dataset and analysis code are publicly available.