ChebNet, one of the earliest spectral GNNs, has largely been overshadowed by Message Passing Neural Networks (MPNNs), which gained popularity for their simplicity and effectiveness in capturing local graph structure. Despite their success, MPNNs are limited in their ability to capture long-range dependencies between nodes. This has led researchers to adapt MPNNs through *rewiring* or make use of *Graph Transformers*, which compromise the computational efficiency that characterized early spatial message passing architectures, and typically disregard the graph structure. Almost a decade after its original introduction, we revisit ChebNet to shed light on its ability to model distant node interactions. We find that out-of-box, ChebNet already shows competitive advantages relative to classical MPNNs and GTs on long-range benchmarks, while maintaining good scalability properties for high-order polynomials. However, we uncover that this polynomial expansion leads ChebNet to an unstable regime during training. To address this limitation, we cast ChebNet as a stable and non-dissipative dynamical system, which we coin Stable-ChebNet. Our Stable-ChebNet model allows for stable information propagation, and has controllable dynamics which do not require the use of eigendecompositions, positional encodings, or graph rewiring. Across several benchmarks, Stable-ChebNet achieves near state-of-the-art performance.

Generating diverse, all‐atom conformational ensembles of dynamic proteins such as G‐protein‐coupled receptors (GPCRs) is critical for understanding their function, yet most generative models simplify atomic detail or ignore conformational diversity altogether. We present latent diffusion for full protein generation (LD-FPG), a framework that constructs complete all‐atom protein structures, including every side‐chain heavy atom, directly from molecular dynamics (MD) trajectories. LD-FPG employs a Chebyshev graph neural network (ChebNet) to obtain low‐dimensional latent embeddings of protein conformations, which are processed using three pooling strategies: blind, sequential and residue‐based. A diffusion model trained on these latent representations generates new samples that a decoder, optionally regularized by dihedral‐angle losses, maps back to Cartesian coordinates. Using D2R-MD, a $2\mu\text{s}$ MD trajectory (12 000 frames) of the human dopamine D$2$ receptor in a membrane environment, the sequential and residue-based pooling strategies reproduce the reference ensemble with high structural fidelity (all‐atom lDDT \~ $0.7$; $C\alpha$-lDDT \~ $0.8$) and recovers backbone and side‐chain dihedral‐angle distributions with a Jensen–Shannon divergence $<0.03$ compared to the MD data. LD-FPG thereby offers a practical route to system‐specific, all‐atom ensemble generation for large proteins, providing a promising tool for structure‐based therapeutic design on complex, dynamic targets. The D2R-MD dataset and our implementation are freely available to facilitate further research.

Access and usage control have evolved to include obligations, which are mandatory actions that must be fulfilled as part of authorization decisions. However, standards such as Abbreviated Language For Authorization (ALFA) and eXtensible Access Control Markup Language (XACML) specify that Policy Enforcement Points (PEPs) are responsible for enforcing obligations but leave execution aspects unspecified. They assume that obligations will be fulfilled without addressing how enforcement should be carried out. This paper introduces an enforcement framework based on structured enforcement messages to address these challenges. It defines two types of messages to coordinate enforcement execution across PEPs: Declaration and Execution Records. These records provide information about PEP capabilities, action dependencies, and fallback strategies for synchronized enforcement. Secondly, we propose a hierarchical policy model to separate concerns between the functionality of policy logic and enforcement. The model is composed of Governance, Authorization, and Enforcement Policies. Constraints flow across these three levels, allowing policy selection and execution to adapt to the authorization context and enforcement. Finally, we illustrate a high-level architecture that integrates the three policy layers with distributed enforcement logic across multiple PEPs.

Obligations were introduced in access and usage control as a mechanism to specify mandatory actions to be fulfilled as part of authorization. In this paper, we address challenges related to obligation management in access and usage control, focusing on the Abbreviated Language For Authorization (ALFA) and eXtensible Access Control Markup Language (XACML) standards. Firstly, we provide a comprehensive analysis of Combining Algorithms (CAs) to determine their influence on the selection and ordering of obligations and identify nondeterminism. We then propose solutions to eliminate such nondeterminism enabling policy authors to explicitly specify the intended behavior. Secondly, we discuss the recurrence of obligations in usage control that occurs due to policy re-evaluations, highlighting the need to execute some obligations only once. We address this problem by introducing a parameter that enables policy authors to explicitly specify whether they intend an obligation to recur or not. Thirdly, we highlight an ambiguity in obligation applicability to lifecycle phases (e.g., ongoing) in usage control, arising from the lack of explicit associations between obligations and phases in particular cases. To address this issue, we introduce a parameter that explicitly specifies the scope of an obligation, allowing policy authors to restrict obligations to a single phase or apply them to the entire authorization. Finally, we extend the functionality of the Obligation Manager (OM) component to combine all three solutions, providing deterministic obligation management.

Access control has been traditionally used to protect data and privacy. Traditional access control models (e.g., ABAC, RBAC) cannot meet modern security requirements as technologies spread over heterogeneous and dynamic environments that need continuous monitoring. Modern models such as Usage Control (UCON) introduced the concept of continuous authorisation that has a lifecycle consisting of a series of phases through which the authorisation passes during its lifetime. However, such models assume a fixed lifecycle for all authorisations, so they cannot satisfy emerging technologies (e.g., smart vehicles, zero-trust, data flow), which require various and fine-grained lifecycles. Researchers have extended existing models to meet such requirements, but all solutions remain restrictive, as they are specially tailored for specific use-cases. In this paper, we propose an extensible model for continuous authorisations and usage control. The model enables its users to customise and dynamically configure the authorisation lifecycle as required by the use-case. This adds a layer of abstraction, forming a metamodel that can be instantiated into different flavours of continuous authorisation models, each addressing specific requirements. We also show that the authorisation lifecycle can be modelled as Deterministic Finite Automaton (DFA) and expressed in a structured language used by an evaluation engine to dynamically enact and manage the lifecycle. We layout the building blocks of the proposed metamodel and devise future research directions.

Wi-Fi connectivity, a necessity in this digital era, requires careful infrastructure planning and tuning during installation. In this paper, we propose “Aparecium Wi-Fi Planner”, an open-source and cross-platform, Wi-Fi planning software tool based on an enhanced empirical path loss model. The proposed “Enhanced Solah's model” delivers more accurate results than existing models. And hence, “Aparecium Wi-Fi Planner” accurately estimates path loss attenuation, absorption attenuation by obstacles and fading effect. Moreover, the tool integrates the design and development of several algorithms to find full-coverage and optimal access point location with reduced complexity. Aparecium has been thoroughly tested and validated using both simulation and real testbed scenarios.