arXiv subjects

Mirko Degli Esposti

Publications and source records attributed to Mirko Degli Esposti.

At least 19 recordsLinked to original sources

arXiv2026

Animarium: an open, reproducible pipeline for synthetic populations of Italian cities, from ISTAT sources to open data (Tech Report v1)

Synthetic populations of eleven Italian municipalities (1,814,317 individuals in 887,937 households) generated from published aggregates alone: ISTAT census and register tables, census-section counts, the national civic-address register, public-use survey microdata, and six municipal open-data portals, every source certified in a registry with licence, fingerprint and declared affordances. Four rings give every attribute a declared place: a maximum-entropy joint model of up to nine demographic attributes; whole-vector donation of twenty-three attitudinal and health variables from survey respondents; placement to census section, single year of age and address; and households constrained by the census size distribution per section. Every downstream layer (detailed titles, work, names, biographies) is a declared derivation adding no information. The pipeline is deterministic to the byte: regenerating all eleven municipalities from the tagged commit reproduces every file of every ring bit for bit, in 33 minutes on one workstation. Populations are released in a public regime enforced in the data (no names, no addresses, coordinates randomised within census section), browsable in Animarium, a dependency-free web viewer where every number carries its comparison and every view is a citable URL, and downloadable as an open dataset. The report documents the architecture, the sources and their certification, the reproducibility and quality measurements at the release tag, the viewer, and the narrative layer that renders records into personas for LLM-driven simulation, with the platform's controllability demonstrated in companion experiments, and validation explicitly out of scope.

cs.CY↗

arXiv2026

Watershed vs. Region Growing for Individual Tree Segmentation from Airborne LiDAR: An Urban Case Study in Bologna

We present a LiDAR-based pipeline for the segmentation and structural characterisation of tall urban vegetation in Bologna. Starting from airborne LiDAR point clouds previously classified as high vegetation by a Random Forest model, we implement and compare two individual-tree segmentation strategies: a watershed algorithm applied to the Canopy Height Model and a point-wise region growing algorithm operating directly on the three-dimensional cloud. Over an area of 12 tiles covering the Talea district, the watershed method detects a larger number of trees (7589), dominated by short trees with a height peak around 5 meters, whereas region growing yields fewer trees (6432) but retains the highest returns, yielding individuals above 40 meters, reflecting the absence of a smoothing step in the latter. We further confront the segmentation results with the municipal Open Data catalogue Alberi in manutenzione: on a sample tile roughly half of the LiDAR-detected trees turn out to be absent from the catalogue, several catalogued positions correspond to locations where no tree is observed, and the majority of the height records date back about two decades, which makes the dataset unsuitable as a ground truth reference. From the segmented trees we extract height and crown radius and use them to compute preliminary vegetation indicators, namely above-ground biomass and carbon storage through allometric relations and annual pollen production through species-specific inflorescence coefficients. All results are collected in an interactive per-tree metadata map. The pipeline is modular and can be recalibrated as improved classifications, field campaigns, or complementary sensing technologies become available, providing a scalable basis for data-driven urban greenery planning.

physics.app-ph↗

arXiv2026

Calibrating the Instrument: Controllability of an LLM-Driven Synthetic Population

Generative Synthetic Populations (GSP) -- the convergence of population synthesis, agent-based modelling, and LLM agents -- are attracting growing interest for urban simulation and institutional communication research. Before any GSP instrument is used on a real population, a more basic question must be answered: does it respond to stimuli of known valence in an ordered, replicable, group-structured way? We call this controllability. We ask not whether a synthetic population tracks humans, but whether it tracks itself: whether the latent structure we impose on it is recovered in its own responses. This internal-validity question is logically prior to any claim about external validity, just as characterising an instrument's response function must precede using it to test a theory. We report SIVE (Synthetic Instrument Validation Experiment): a fictional municipality (Montelago) with 120 synthetic personas of known latent structure, exposed to seven conditions spanning strongly positive to strongly negative institutional communications about a water network. Seven pre-registered criteria, evaluated across a temperature sweep, jointly assess fidelity, stability, noise floor, specificity, sensitivity, and ordering. All seven pass at every temperature. A central finding turns a calibration failure into a diagnostic success: a message designed as "weakly positive" was identified by the instrument as functionally negative, traced to unresolved problems, uncertainty, and institutional passivity in its text; a redesigned version restored the expected ordering and interacts with agents' latent trust in unanticipated ways. A noise sub-experiment shows the instrument's intrinsic noise is roughly half the cross-agent estimate and stable across temperatures. Individual trajectories reveal coherent micro-dynamics that summary statistics obscure. Full data are available via an interactive explorer.

cs.MA↗

arXiv2026

Emergence of power laws in hierarchical dynamics on multi-level graphs

Power-law distributions are widely recognized in complex systems physics as indicative of underlying complexity in interaction networks and critical macroscopic behavior. Previous studies, notably those of Newman and others, have emphasized the importance of network structure and dynamics in understanding the emergence of such statistical patterns and predicting extreme events. In this study, we investigate the emergence of power-law behavior in delay distributions within a multi-level hierarchical network of agents governed by simple priority rules. Using railway systems as a case study, we model the dynamics of high-speed and local trains agents assigned distinct priority levels-operating within a simplified hierarchical network framework. By introducing Laplacian-distributed stochastic fluctuations into scheduled travel times, derived from empirical data, we observe that local trains exhibit a markedly higher incidence of higher delays than high-speed trains. To account for this phenomenon, we propose a queue-based dynamical model, calibrated using Italian railway data, and validate our findings through comparative analysis with both Italian and German datasets. The model accurately reproduces the empirically observed power-law exponent associated with the Italian local train delays. Furthermore, we analyze the influence of operational policies, such as priority assignment and delay compensation thresholds, revealing distinct cut-offs in delay distributions at 30 and 60 minutes for high-speed and local trains, respectively-corresponding to refund eligibility criteria in Italy. Such cut-offs are absent in the German case, where no comparable priority-change policies are in effect. These results underscore the capacity of simple hierarchical structures and rule-based dynamics to generate complex statistical behaviors without necessitating intricate interaction networks.

physics.soc-ph↗

arXiv2026

Scalable Maximum Entropy Population Synthesis via Persistent Contrastive Divergence

Maximum entropy (MaxEnt) modelling provides a principled framework for generating synthetic populations from aggregate census data, without access to individual-level microdata. The bottleneck of exact-enumeration approaches is expectation computation by explicit summation over the full tuple space $\cX$, which becomes infeasible for more than $K \approx 20$ categorical attributes; sampling-based alternatives exist but rely on Metropolis-type schemes that require proposal tuning and rejection steps. We propose \emph{GibbsPCDSolver}, a stochastic replacement for this computation based on Persistent Contrastive Divergence (PCD): a persistent pool of $N$ synthetic individuals is updated by Gibbs sweeps at each gradient step, providing a stochastic approximation of the model expectations without ever materialising $\cX$. We validate the approach on controlled benchmarks and on \emph{Syn-ISTAT}, a $K{=}15$ Italian demographic benchmark with analytically exact marginal targets derived from ISTAT-inspired conditional probability tables. Scaling experiments across $K \in \{12, 20, 30, 40, 50\}$ confirm that GibbsPCDSolver maintains $\MRE \in [0.010, 0.018]$ while $|\cX|$ grows eighteen orders of magnitude, with runtime scaling as $O(K)$ rather than $O(|\cX|)$. On Syn-ISTAT, GibbsPCDSolver reaches $\MRE{=}0.03$ on training constraints and -- crucially -- produces populations with effective sample size $\Neff = N$ versus $\Neff \approx 0.012\,N$ for generalised raking, an $86.8{\times}$ diversity advantage that is essential for agent-based urban simulations.

cs.LG↗

arXiv2026

A Zipf-preserving, long-range correlated surrogate for written language and other symbolic sequences

Symbolic sequences such as written language and genomic DNA display characteristic frequency distributions and long-range correlations extending over many symbols. In language, this takes the form of Zipf's law for word frequencies together with persistent correlations spanning hundreds or thousands of tokens, while in DNA it is reflected in nucleotide composition and long-memory walks under purine-pyrimidine mappings. Existing surrogate models usually preserve either the frequency distribution or the correlation properties, but not both simultaneously. We introduce a surrogate model that retains both constraints: it preserves the empirical symbol frequencies of the original sequence and reproduces its long-range correlation structure, quantified by the detrended fluctuation analysis (DFA) exponent. Our method generates surrogates of symbolic sequences by mapping fractional Gaussian noise (FGN) onto the empirical histogram through a frequency-preserving assignment. The resulting surrogates match the original in first-order statistics and long-range scaling while randomising short-range dependencies. We validate the model on representative texts in English and Latin, and illustrate its broader applicability with genomic DNA, showing that base composition and DFA scaling are reproduced. This approach provides a principled tool for disentangling structural features of symbolic systems and for testing hypotheses on the origin of scaling laws and memory effects across language, DNA, and other symbolic domains.

cs.CL↗

arXiv2026

Trailer Reimagined: An Innovative, Llm-DRiven, Expressive Automated Movie Summary framework (TRAILDREAMS)

This paper introduces TRAILDREAMS, a framework that uses a large language model (LLM) to automate the production of movie trailers. The purpose of LLM is to select key visual sequences and impactful dialogues, and to help TRAILDREAMS to generate audio elements such as music and voiceovers. The goal is to produce engaging and visually appealing trailers efficiently. In comparative evaluations, TRAILDREAMS surpasses current state-of-the-art trailer generation methods in viewer ratings. However, it still falls short when compared to real, human-crafted trailers. While TRAILDREAMS demonstrates significant promise and marks an advancement in automated creative processes, further improvements are necessary to bridge the quality gap with traditional trailers.

cs.MM↗

arXiv2026

An Automatic Deep Learning Approach for Trailer Generation through Large Language Models

Trailers are short promotional videos designed to provide audiences with a glimpse of a movie. The process of creating a trailer typically involves selecting key scenes, dialogues and action sequences from the main content and editing them together in a way that effectively conveys the tone, theme and overall appeal of the movie. This often includes adding music, sound effects, visual effects and text overlays to enhance the impact of the trailer. In this paper, we present a framework exploiting a comprehensive multimodal strategy for automated trailer production. Also, a Large Language Model (LLM) is adopted across various stages of the trailer creation. First, it selects main key visual sequences that are relevant to the movie's core narrative. Then, it extracts the most appealing quotes from the movie, aligning them with the trailer's narrative. Additionally, the LLM assists in creating music backgrounds and voiceovers to enrich the audience's engagement, thus contributing to make a trailer not just a summary of the movie's content but a narrative experience in itself. Results show that our framework generates trailers that are more visually appealing to viewers compared to those produced by previous state-of-the-art competitors.

cs.MM↗

arXiv2025

Urban Buildings Energy Consumption Estimation Using HPC: A Case Study of Bologna

Urban Building Energy Modeling (UBEM) plays a central role in understanding and forecasting energy consumption at the city scale. In this work, we present a UBEM pipeline that integrates EnergyPlus simulations, high-performance computing (HPC), and open geospatial datasets to estimate the energy demand of buildings in Bologna, Italy. Geometric information including building footprints and heights was obtained from the Bologna Open Data portal and enhanced with aerial LiDAR measurements. Non-geometric attributes such as construction materials, insulation characteristics, and window performance were derived from regional building regulations and the European TABULA database. The computation was carried out on Leonardo, the Cineca-hosted supercomputer, enabling the simulation of approximately 25,000 buildings in under 30 minutes.

cs.DC↗

arXiv2025

Urban Complexity through Vision Intelligence: Variance, Gradients, and Correlations across Six Italian Cities

This paper introduces a scalable methodology for the objective analysis of quality metrics across six major Italian metropolitan areas: Rome, Bologna, Florence, Milan, Naples, and Palermo. Leveraging georeferenced Street View imagery and an advanced Urban Vision Intelligence system, we systematically classify the visual environment, focusing on key metrics such as the Pavement Condition Index (PCI) and the Façade Degradation Score (FDS). The findings quantify Structural Heterogeneity (Spatial Variance), revealing significant quality dispersion (e.g., Milan $σ^2_{\mathrm{PCI}}=1.52$), and confirm that the classical Urban Gradient -- quality variation as a function of distance from the core -- is consistently weak across all sampled cities ($R^2 < 0.03$), suggesting a complex, polycentric, and fragmented morphology. In addition, a Cross-Metric Correlation Analysis highlights stable but modest interdependencies among visual dimensions, most notably a consistent positive association between façade quality and greenery ($ρ\approx 0.35$), demonstrating that structural and contextual urban qualities co-vary in weak yet interpretable ways. Together, these results underscore the diagnostic potential of Vision Intelligence for capturing the integrated spatial and morphological structure of Italian cities and motivate a large national-scale analysis.

physics.soc-ph↗

arXiv2024

Congestion transition on random walks on graphs

The congestion formation on a urban road network is one of the key issue for the development of a sustainable mobility in the future smart cities. In this work we propose a reductionist approach studying the stationary states of a simple transport model using of a random process on a graph, where each node represents a location and the weight links give the transition rates to move from one node to another that represent the mobility demand. Each node has a finite transport capacity and a maximum load capacity and we assume that the average. In the approximation of the single step process we are able to analytically characterize the traffic load distribution on the single nodes, using a local Maximum Entropy Principle. Our results explain how the congested nodes emerge when the total traffic load increases in analogous way to a percolation transition where the appearance of a congested node is a independent random event, However, using numerical simulations, we show that in the more realistic case of the synchronous dynamics for the nodes, there are entropic forces that introduce correlation among the node state and favor the clustering of the empty and congested nodes. Our aim is to highlight universal properties of the congestion formation and, in particular, to understand the role traffic load fluctuations as a possible precursor of congestion in a transport network.

physics.soc-ph↗

arXiv2022

Recurrence times, waiting times and universal entropy production estimators

The universal typical-signal estimators of entropy and cross entropy based on the asymptotics of recurrence and waiting times play an important role in information theory. Building on their construction, we introduce and study universal typical-signal estimators of entropy production in the context of nonequilibrium statistical mechanics of one-sided shifts over finite alphabets.

math-ph↗

arXiv2022

On a waiting-time result of Kontoyiannis: mixing or decoupling?

We introduce conditions of lower decoupling to the study of waiting-time estimations of the cross entropy between two mutually independent stationary stochastic processes. Although similar decoupling conditions have been used in the literature on large deviations and statistical mechanics, they appear largely unexplored in information theory. Building on a result of Kontoyiannis, namely Theorem 4 in [Kontoyiannis, J. Theor. Probab., 1998], and replacing the $ψ$-mixing condition in this result with a lower decoupling condition, we considerably extend the validity of waiting-time estimation of cross entropy.

math.PR↗

arXiv2020

Dynamics of transposable elements generates structure and symmetries in genetic sequences

Genetic sequences are known to possess non-trivial composition together with symmetries in the frequencies of their components. Recently, it has been shown that symmetry and structure are hierarchically intertwined in DNA, suggesting a common origin for both features. However, the mechanism leading to this relationship is unknown. Here we investigate a biologically motivated dynamics for the evolution of genetic sequences. We show that a metastable (long-lived) regime emerges in which sequences have symmetry and structure interlaced in a way that matches that of extant genomes.

q-bio.GN↗

arXiv2019

Natural Language Statistical Features of LSTM-generated Texts

Long Short-Term Memory (LSTM) networks have recently shown remarkable performance in several tasks dealing with natural language generation, such as image captioning or poetry composition. Yet, only few works have analyzed text generated by LSTMs in order to quantitatively evaluate to which extent such artificial texts resemble those generated by humans. We compared the statistical structure of LSTM-generated language to that of written natural language, and to those produced by Markov models of various orders. In particular, we characterized the statistical structure of language by assessing word-frequency statistics, long-range correlations, and entropy measures. Our main finding is that while both LSTM and Markov-generated texts can exhibit features similar to real ones in their word-frequency statistics and entropy measures, LSTM-texts are shown to reproduce long-range correlations at scales comparable to those found in natural language. Moreover, for LSTM networks a temperature-like parameter controlling the generation process shows an optimal value---for which the produced texts are closest to real language---consistent across all the different statistical features investigated.

cs.CL↗

arXiv2018

The common origin of symmetry and structure in genetic sequences

Biologists have long sought a way to explain how statistical properties of genetic sequences emerged and are maintained through evolution. On the one hand, non-random structures at different scales indicate a complex genome organisation. On the other hand, single-strand symmetry has been scrutinised using neutral models in which correlations are not considered or irrelevant, contrary to empirical evidence. Different studies investigated these two statistical features separately, reaching minimal consensus despite sustained efforts. Here we unravel previously unknown symmetries in genetic sequences, which are organized hierarchically through scales in which non-random structures are known to be present. These observations are confirmed through the statistical analysis of the human genome and explained through a simple domain model. These results suggest that domain models which account for the cumulative action of mobile elements can explain simultaneously non-random structures and symmetries in genetic sequences.

q-bio.QM↗

arXiv2016

Resonances in the Two-Centers Coulomb Systems

We investigate the existence of resonances for two-centers Coulomb systems with arbitrary charges in two dimensions, defining them in terms of generalised complex eigenvalues of a non-selfadjoint deformation of the two-centers Schrödinger operator. We construct the resolvent kernels of the operators and prove that they can be extended analytically to the second Riemann sheet. The resonances are then analysed by means of perturbation theory and numerical methods.

math-ph↗

arXiv2015

Characterization of DNA methylation as a function of biological complexity via dinucleotide inter-distances

We perform a statistical study of the distances between successive occurrencies of a given dinucleotide in the DNA sequence for a number of organisms of different complexity. Our analysis highlights peculiar features of the dinucleotide CG distribution in mammalian DNA, pointing towards a connection with the role of such dinucleotide in DNA methylation. While the CG distributions of mammals exhibit exponential tails with comparable parameters, the picture for the other organisms studied (e.g., fish, insects, bacteria and viruses) is more heterogeneous, possibly because in these organisms DNA methylation has different functional roles. Our analysis suggests that the distribution of the distances between dinucleotides CG provides useful insights in characterizing and classifying organisms in terms of methylation functionalities.

q-bio.QM↗