Intel (Germany)

This article proposes a multimodal approach for the detection of disagreement in dyadic conversations, where disagreement means that people express different opinions about a topic under discussion. The key-assumption underlying the work is that people tend to manifest different emotions depending on whether they are disagreeing or not. Therefore, emotions can provide evidence that disagreement is taking place. The experiments were performed over a corpus of 684 clips involving 60 dyads (120 persons and roughly 8 hours of speech). Each clip revolves around a decision-making task and it is annotated in terms of the percentage of time people spend in disagreement. For the sake of reproducibility, the Glasgow Disagreement Corpus, the data used in the experiments, has been made accessible through a link available in the paper. The results show that a multimodal approach based on language and paralanguage can predict such a percentage with Mean Absolute Error 9.7 and correlation 0.52 between actual and predicted percentage of time spent in disagreement.

Modern cloud infrastructures are increasingly complex, driven by heterogeneity, disaggregation, and dynamic service composition—exposing critical limits in traditional attestation models. These models struggle to scale when trust must span multiple domains and elastic services. We present scale-out attestation, a paradigm decoupling platform trust verification from app-level attestation. Our design introduces a recursive attestation framework leveraging abstract service identities and trusted deployment workflows: a single infrastructure agent verifies platforms via abstract policies, while services derive instance-agnostic identities enabling secure recursive dependency attestation. We implement the system on FractOS, a distributed OS for disaggregated data centers, and plan to extend Confidential Containers for practical deployment. Evaluation shows strong security with minimal overhead, enabling scalable confidential computing across heterogeneous and dynamic cloud environments.

The computing continuum aims to break the isolation of edge and cloud computing, creating a smooth and heterogeneous infrastructure surface for deploying applications. However, the continuum is practically fragmented, with infrastructure managed in isolation by various parties in a vertical dimension, i.e., edge, fog, and cloud layers, and horizontally, i.e., computing, network, and storage domains. This scenario negatively impacts the fulfillment of the application objectives. We propose inCoord, an intent-aware solution that enables the creation of a unified computing continuum by coordinating its instances to fulfill application objectives. Each instance represents a cluster of (compute, storage, or network) nodes with their own manager component. Traditionally, systems take low-level actions on these instances. In contrast, inCoord learns their emerging behaviors and adapts their managers’ objectives to fulfill the application’s intents. Here, through a Reinforcement-Learning-based Proof of Concept, we show the potential of this system to understand emerging behavior and manage multi-domain, independently managed instances.

Physically based differentiable rendering has established itself as key to inverse rendering, in which scenes are recovered from images through gradient-based optimization. Taking the derivative of the rendering equation is made difficult by the presence of discontinuities in the integrand at object silhouettes. To obtain correct derivatives w.r.t. changing geometry, accounting e.g. for changing penumbras or silhouettes in glossy reflections, differentiable renderers must compute an integral over these silhouettes. Prior work proposed importance sampling of silhouette edges for a given shading point. The main challenge is to efficiently reject parts of the mesh without silhouettes during sampling, which has been done using top-down traversal of a tree. Inaccuracies of this existing rejection procedure result in many samples with zero contribution. Thus, variance remains high and subsequent work has focused on alternatives such as area sampling or path space differentiable rendering. We propose an improved rejection test. It reduces variance substantially, which makes edge sampling in a unidirectional path tracer competitive again. Our rejection test relies on two approximations to the triangle planes of a mesh patch: A bounding box in dual space and dual quadrics. Additionally, we improve the heuristics used for stochastic traversal of the tree. We evaluate our method in a unidirectional path tracer and achieve drastic improvements over the original edge sampling and outperform methods based on area sampling.

This paper introduces the concept of seamlessly embedding AI-native Radio Access Networks (RANs) and integrated device-network approaches, aiming to extend AI capabilities across diverse devices - e.g., smartphones, Internet of Things (IoT) sensors, autonomous vehicles - and network infrastructures. By embedding AI deeply within RANs and fostering tight integration between devices and network architectures, we unlock unprecedented levels of intelligence, efficiency, flexibility, and performance in wireless communication systems. This integration enables devices and networks to collaboratively process, adapt, and optimize data in real-time, creating a synergistic AI-enabled ecosystem that forms the backbone of AI-native 6G. Unlike traditional wireless communication systems, which rely on static configurations and manual interventions, AI-native networks dynamically adapt to changing conditions, autonomously optimize resource allocation, and respond to diverse user demands and environmental factors. The 6GARROW project explores these transformative approaches, demonstrating how AI-native RANs and integrated device-network solutions deliver higher throughput, lower latency, and improved reliability. Beyond enhanced performance, this paradigm shift supports a diverse range of innovative services, including spatio-temporal communications, critical and compute-AI applications, omnipresent IoT, immersive user experiences, and global broadband connectivity. This paper outlines the vision, methodologies, and potential impact of the project, highlighting its role in advancing toward a more connected, intelligent, and adaptive wireless ecosystem for the future.

We discuss three SYCL realisations of a simple Finite Volume scheme over multiple Cartesian patches. The realisation flavours differ in the way how they map the compute steps onto loops and tasks: We compare an implementation that is exclusively using a sequence of for-loops to a version that uses nested parallelism, and finally benchmark these against a version modelling the calculations as task graph. Our work proposes realisation idioms to realise these flavours within SYCL. The results suggest that a mixture of classic task and data parallelism performs if we map this hybrid onto a solely data-parallel SYCL implementation, taking into account SYCL specifics and the problem size.

Smart city intersections may support connected automated vehicles in the future and are the core of intelligent traffic management and control. In this work, we consider the traffic surveillance task in multi-camera and event-camera setups at road intersections with a focus on efficient edge computation. We give an overview over different aspects of such systems from the literature, including relevant recognition tasks, architectures, benchmark datasets and algorithms with a focus on their properties like real-time capability and energy consumption where possible.

<p>6G will connect heterogeneous intelligent agents to make them natively operate complex cooperative tasks. When connecting intelligence, two main research questions arise to identify how artificial intelligence and machine learning models behave depending on: i) their input data quality, affected by errors induced by interference and additive noise during wireless communication; ii) their contextual effectiveness and resilience to interpret and exploit the meaning behind the data. Both questions are within the realm of semantic and goal-oriented communications. With this paper we investigate on how to effectively share communication spectrum resources between a legacy communication systems (i.e., data-oriented) and a new goal-oriented edge intelligence one. Specifically, we address the scenario of an enhanced Mobile Broadband (eMBB) service, i.e., a user uploading a video stream to a radio access point, interfering with an edge inference system, in which a user uploads images to a Mobile Edge Host that runs a classification task. Our objective is to achieve, through cooperation, the eMBB service highest data rate, subject to a target goal-effectiveness of the edge inference service, namely the probability of confident inference on time. We first formalize a general definition of goal in the context of wireless communications. This includes the goal-effectiveness, (i.e., the goal achievability rate, or the probability of achieving the goal), as well as that of goal cost (i.e., the network resource consumption needed to achieve the goal with target effectiveness). We argue and show, through numerical evaluations, that communication reliability and goal-effectiveness are not straightforwardly linked. Then, after a performance evaluation aiming to clarify the difference between communication performance and goal-effectiveness, a long-term optimization problem is formulated and solved via Lyapunov stochastic network optimization tools, to guarantee the desired target performance. Finally, our numerical results assess the advantages of the proposed optimization, and the superiority of the goal-oriented strategy against baseline 5G compliant legacy approaches, under both stationary and non-stationary communication (and computation) environments. </p>