arXiv subjects
Publications and source records attributed to Francesca Rossi.
The purpose of this paper is to elucidate the theory and mathematical modelling behind the sheaf neural network (SNN) algorithm and then show how SNN can effectively answer to biomedical questions in a concrete case study and outperform the most popular graph neural networks (GNNs) as graph convolutional networks (GCNs), graph attention networks (GAT) and GraphSage.
Modern power networks face increasing vulnerability to cascading failures due to high complexity and the growing penetration of intermittent resources, necessitating rigorous security assessment beyond the conventional $N-1$ criterion. Current approaches often struggle to achieve the computational tractability required for exhaustive $N-2$ contingency analysis integrated with complex stability evaluations like small-signal stability. Addressing this computational bottleneck and the limitations of deterministic screening, this paper presents a scalable methodology for the vulnerability assessment of modern power networks, integrating $N-2$ contingency analysis with small-signal stability evaluation. To prioritize critical components, we propose a probabilistic \textbf{Risk Index ($R_i$)} that weights the deterministic \textit{severity} of a contingency (including optimal power flow divergence, islanding, and oscillatory instability) by the \textit{failure frequency} of the involved elements based on reliability data. The proposed framework is implemented using High-Performance Computing (HPC) techniques through the PyCOMPSs parallel programming library, orchestrating optimal power flow simulations (VeraGrid) and small-signal analysis (STAMP) to enable the exhaustive exploration of massive contingency sets. The methodology is validated on the IEEE 118-bus test system, processing more than \num{57000} scenarios to identify components prone to triggering cascading failures. Results demonstrate that the risk-based approach effectively isolates critical assets that deterministic $N-1$ criteria often overlook. This work establishes a replicable and efficient workflow for probabilistic security assessment, suitable for large-scale networks and capable of supporting operator decision-making in near real-time environments.
The increasing penetration of inverter-based resources (IBRs) is fundamentally reshaping power system dynamics and creating new challenges for stability assessment. Data-driven approaches, and in particular machine learning models, require large and representative datasets that capture how system stability varies across a wide range of operating conditions and control settings. This paper presents an open-source, high-performance computing framework for the systematic generation of such datasets. The proposed tool defines a scalable operating space for large-scale power systems, explores it through an adaptive sampling strategy guided by sensitivity analysis, and performs small-signal stability assessments to populate a high-information-content dataset. The framework efficiently targets regions near the stability margin while maintaining broad coverage of feasible operating conditions. The workflow is fully implemented in Python and designed for parallel execution. The resulting tool enables the creation of high-quality datasets that support data-driven stability studies in modern power systems with high IBR penetration.
Flexible intelligence relies on the ability to reuse previously acquired behaviors and combine them differently as circumstances change. In biological and artificial systems, this ability is often attributed to gating mechanisms that determine how much each available behavior should contribute at a given time. Yet these gating rules, the dynamics that compute them, and the neural circuits that may implement them are usually introduced separately, leaving unclear whether they reflect a common underlying principle. Here, we show that they can all be derived from a single variational principle for behavioral composition. The resulting mechanism naturally gives rise to softmax gating, evolves as an energy-based dynamical system with guaranteed convergence, and admits a recurrent neural network instantiation featuring context-dependent and local interactions. Across collective behavior, human decision-making, and layered control, the same mechanism reproduces characteristic behavioral patterns, provides interpretable accounts of how different behaviors are combined, and matches or outperforms established approaches. These results provide a unified account of how behavioral composition can emerge from a common principle, with implications for understanding flexible behavior in natural systems and for designing artificial agents that can adapt by recombining existing capabilities.
This study proposes a control strategy to ensure the safe operation of modern power systems with high penetration of inverter-based resources (IBRs) within an optimal operation framework. The objective is to obtain operating points that satisfy the optimality conditions of a predefined problem while guaranteeing small-signal stability. The methodology consists of two stages. First, an offline analysis of a set of operating points is performed to derive a data-driven regression-based expression that captures a damping-based stability index as a function of the operating conditions. Second, an Online Feedback Optimization (OFO) controller is employed to drive the system toward an optimal operating point while maintaining a secure distance from the instability region. The proposed strategy is evaluated on an academic test case based on a modified version of the IEEE 9-bus system, in which synchronous generators are replaced by IBRs operating under both grid-following and grid-forming control modes. The results demonstrate the effectiveness of the method and are discussed in detail.
Large language models (LLMs) excel in speed and adaptability across various reasoning tasks, but they often struggle when strict logic or constraint enforcement is required. In contrast, Large Reasoning Models (LRMs) are specifically designed for complex, step-by-step reasoning, although they come with significant computational costs and slower inference times. To address these trade-offs, we employ and generalize the SOFAI (Slow and Fast AI) cognitive architecture into SOFAI-LM, which coordinates a fast LLM with a slower but more powerful LRM through metacognition. The metacognitive module actively monitors the LLM's performance and provides targeted, iterative feedback with relevant examples. This enables the LLM to progressively refine its solutions without requiring the need for additional model fine-tuning. Extensive experiments on graph coloring and code debugging problems demonstrate that our feedback-driven approach significantly enhances the problem-solving capabilities of the LLM. In many instances, it achieves performance levels that match or even exceed those of standalone LRMs while requiring considerably less time. Additionally, when the LLM and feedback mechanism alone are insufficient, we engage the LRM by providing appropriate information collected during the LLM's feedback loop, tailored to the specific characteristics of the problem domain and leads to improved overall performance. Evaluations on two contrasting domains: graph coloring, requiring globally consistent solutions, and code debugging, demanding localized fixes, demonstrate that SOFAI-LM enables LLMs to match or outperform standalone LRMs in accuracy while maintaining significantly lower inference time.
Perovskite-based nanoscintillators, such as CsPbBr3 nanocrystals (NCs), are emerging as promising candidates for ionizing radiation detection, thanks to their high emission efficiency, rapid response, and facile synthesis. However, their nanoscale dimensions - smaller than the mean free path of secondary carriers - and relatively low emitter density per unit volume, limited by their high molecular weight and reabsorption losses, restrict efficient secondary carrier conversion and hamper their practical deployment. In this work, we introduce a strategy to enhance scintillation performance by organizing NCs into densely packed domains within porous SiO2 mesospheres (MSNs). This engineered architecture achieves up to a 40-fold increase in radioluminescence intensity compared to colloidal NCs, driven by improved retention and conversion of secondary charges, as corroborated by electron release measurements. This approach offers a promising pathway toward developing next-generation nanoscintillators with enhanced performance, with potential applications in high-energy physics, medical imaging, and space technologies.
This paper studies equality-constrained composite minimization problems. This class of problems, capturing regularization terms and inequality constraints, naturally arises in a wide range of engineering and machine learning applications. To tackle these optimization problems, inspired by recent results, we introduce the \emph{proportional--integral proximal gradient dynamics} (PI--PGD): a closed-loop system where the Lagrange multipliers are control inputs and states are the problem decision variables. First, we establish the equivalence between the stationary points of the minimization problem and the equilibria of the PI--PGD. Then for the case of affine constraints, by leveraging tools from contraction theory we give a comprehensive convergence analysis for the dynamics, showing convergence to a stationary point. Moreover, under suitable assumptions, we show linear--exponential convergence towards the equilibrium. That is, the distance between each solution and the equilibrium is upper bounded by a function that first decreases linearly and then exponentially. Our findings are illustrated numerically on a set of representative examples, which include an exploratory application to nonlinear equality constraints.
Hybrid AC/DC transmission grids incorporate Modular Multilevel Converters functioning as Interconnecting Power Converters (IPCs). The control role assigned to each converter significantly influences grid dynamics. Traditionally, these converters operate with static control roles, but recent studies have proposed scheduling their roles based on day-ahead forecasts to enhance stability performance. However, in systems with high renewable energy penetration, forecast deviations can render scheduled control assignments suboptimal or even lead to instability. To address this challenge, this work proposes an online scheduling recalculation algorithm that dynamically adapts IPC control roles during system operation. The approach leverages a data-driven multi-criteria decision-making framework, integrating surrogate models of conventional small-signal stability analysis tools to enable a fast computation of system stability and stability performance indicators.
We propose a computationally straightforward test for the linearity of a spatial interaction function. Such functions arise commonly, either as practitioner imposed specifications or due to optimizing behaviour by agents. Our conditional heteroskedasticity robust test is nonparametric, but based on the Lagrange Multiplier principle and reminiscent of the Ramsey RESET approach. This entails estimation only under the null hypothesis, which yields an easy to estimate linear spatial autoregressive model. Monte Carlo simulations show excellent size control and power. An empirical study with Finnish data illustrates the test's practical usefulness, shedding light on debates on the presence of tax competition among neighbouring municipalities.
We consider the problem of optimally composing a set of primitives to tackle planning and control tasks. To address this problem, we introduce a free energy computational model for planning and control via policy composition: Free-Gate. Within Free-Gate, control primitives are combined via a gating mechanism that minimizes variational free energy. This composition problem is formulated as a finite-horizon optimal control problem, which we prove remains convex even when the cost is not convex in states/actions and the environment is nonlinear, stochastic and non-stationary. We develop an algorithm that computes the optimal primitives composition and demonstrate its effectiveness via in-silico and hardware experiments on an application involving robot navigation in an environment with obstacles. The experiments highlight that Free-Gate enables the robot to navigate to the destination despite only having available simple motor primitives that, individually, could not fulfill the task.
Constraint Satisfaction Problems (CSPs) present significant challenges to artificial intelligence due to their intricate constraints and the necessity for precise solutions. Existing symbolic solvers are often slow, and prior research has shown that Large Language Models (LLMs) alone struggle with CSPs because of their complexity. To bridge this gap, we build upon the existing SOFAI architecture (SOFAI_v1), which adapts Daniel Kahneman's ''Thinking, Fast and Slow'' cognitive model to AI. Our enhanced architecture, SOFAI_v2, integrates refined metacognitive governance mechanisms to improve adaptability across complex domains, specifically tailored here for solving the graph coloring problem, a specific type of CSP. SOFAI_v2 combines a fast System 1 (S1), leveraging LLMs, with a deliberative System 2 (S2), governed by a metacognition module. S1's initial solutions, often limited by constraint adherence issues, are improved through targeted feedback and examples from metacognition, aligning S1 more closely with CSP requirements. If S1 fails to resolve the problem, metacognition strategically invokes S2, ensuring accurate and reliable solutions. Our empirical results demonstrate that SOFAI_v2 achieves a 10.5% higher success rate and is up to 30% faster than a traditional symbolic solver in solving graph coloring problems.
Lead halide perovskite nanocrystals (LHP-NCs) embedded in a plastic matrix are highly promising for a variety of photonic technologies and are quickly gaining attention as ultrafast, radiation-resistant nanoscintillators for radiation detection. However, advancements in LHP-NC-based photonics are hindered by their well-known thermal instability, which makes them unsuitable for industrial thermally activated mass polymerization processes - crucial for creating polystyrene-based scintillating nanocomposites. In this study, we address this challenge by presenting the first thermal nanocomposite scintillators made from CsPbBr3 NCs passivated with fluorinated ligands that remain attached to the particles surfaces even at high temperatures, enabling their integration into mass-cured polyvinyl toluene without compromising optical properties. Consequently, these nanocomposites demonstrate scintillation light yields reaching 10,400 photons/MeV, sub-nanosecond scintillation kinetics, and remarkable radiation resilience, able to withstand gamma radiation doses of up to 1 MGy. This performance not only meets but also exceeds the scintillation of plastic scintillators, despite the radiation-induced damage to the host matrix.
Lead halide perovskite nanocrystals (LHP-NCs) embedded in polymeric hosts are gaining attention as scalable and low-cost scintillation detectors for technologically relevant applications. Despite rapid progress, little is currently known about the scintillation properties and stability of LHP-NCs prepared by the ligand assisted reprecipitation (LARP) method, which allows mass scalability at room temperature unmatched by any other type of nanostructure, and the implications of incorporating LHP-NCs into polyacrylate hosts are still largely debated. Here, we show that LARP-synthesized CsPbBr3 NCs are comparable to particles from hot-injection routes and unravel the dual effect of polyacrylate incorporation, where the partial degradation of LHP-NCs luminescence is counterbalanced by the passivation of electron-poor defects by the host acrylic groups. Experiments on NCs with tailored surface defects show that the balance between such antithetical effects of polymer embedding is determined by the surface defect density of the NCs and provide guidelines for further material optimization.
Lead halide perovskite nanocrystals (LHP-NCs) embedded in polymer matrices are gaining traction for next-generation radiation detectors. While progress has been made on green-emitting CsPbBr3 NCs, scant attention has been given to the scintillation properties of CsPbCl3 NCs, which emit size-tunable UV-blue light matching the peak efficiency of ultrafast photodetectors. In this study, we explore the scintillation characteristics of CsPbCl3 NCs produced through a scalable method and treated with CdCl2. Spectroscopic, radiometric and theoretical analysis on both untreated and treated NCs uncover deep hole trap states due to surface undercoordinated chloride ions, eliminated by Pb to Cd substitution. This yields near-perfect efficiency and resistance to polyacrylate mass-polymerization. Radiation hardness tests demonstrate stability to high gamma doses while time-resolved experiments reveal ultrafast radioluminescence with an average lifetime as short as 210 ps. These findings enhance our comprehension of LHP NCs' scintillation properties, positioning CsPbCl3 as a promising alternative to conventional fast scintillators.
Automated Planning and Scheduling is among the growing areas in Artificial Intelligence (AI) where mention of LLMs has gained popularity. Based on a comprehensive review of 126 papers, this paper investigates eight categories based on the unique applications of LLMs in addressing various aspects of planning problems: language translation, plan generation, model construction, multi-agent planning, interactive planning, heuristics optimization, tool integration, and brain-inspired planning. For each category, we articulate the issues considered and existing gaps. A critical insight resulting from our review is that the true potential of LLMs unfolds when they are integrated with traditional symbolic planners, pointing towards a promising neuro-symbolic approach. This approach effectively combines the generative aspects of LLMs with the precision of classical planning methods. By synthesizing insights from existing literature, we underline the potential of this integration to address complex planning challenges. Our goal is to encourage the ICAPS community to recognize the complementary strengths of LLMs and symbolic planners, advocating for a direction in automated planning that leverages these synergistic capabilities to develop more advanced and intelligent planning systems.
We propose a Holistic Return on Ethics (HROE) framework for understanding the return on organizational investments in artificial intelligence (AI) ethics efforts. This framework is useful for organizations that wish to quantify the return for their investment decisions. The framework identifies the direct economic returns of such investments, the indirect paths to return through intangibles associated with organizational reputation, and real options associated with capabilities. The holistic framework ultimately provides organizations with the competency to employ and justify AI ethics investments.
Nudging is a behavioral strategy aimed at influencing people's thoughts and actions. Nudging techniques can be found in many situations in our daily lives, and these nudging techniques can targeted at human fast and unconscious thinking, e.g., by using images to generate fear or the more careful and effortful slow thinking, e.g., by releasing information that makes us reflect on our choices. In this paper, we propose and discuss a value-based AI-human collaborative framework where AI systems nudge humans by proposing decision recommendations. Three different nudging modalities, based on when recommendations are presented to the human, are intended to stimulate human fast thinking, slow thinking, or meta-cognition. Values that are relevant to a specific decision scenario are used to decide when and how to use each of these nudging modalities. Examples of values are decision quality, speed, human upskilling and learning, human agency, and privacy. Several values can be present at the same time, and their priorities can vary over time. The framework treats values as parameters to be instantiated in a specific decision environment.