Archivio della ricerca - Fondazione Bruno Kessler
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Unsupervised deep learning for semantic segmentation of multispectral LiDAR forest point clouds
No full textPoint clouds captured with laser scanning systems from forest environments can be utilized in a wide variety of applications within forestry and plant ecology, such as the estimation of tree stem attributes, leaf angle distribution, and above-ground biomass. However, effectively utilizing the data in such tasks requires the semantic segmentation of the data into wood and foliage points, also known as leaf–wood separation.
The traditional approach to leaf–wood separation has been geometry- and radiometry-based unsupervised algorithms, which tend to perform poorly on data captured with airborne laser scanning (ALS) systems, even
with a high point density (> 1, 000 points/m2). While recent machine and deep learning approaches achieve great results even on sparse point clouds, they require manually labeled training data, which is often extremely laborious to produce. Multispectral (MS) information has been demonstrated to have potential for improving the accuracy of leaf–wood separation, but quantitative assessment of its effects has been lacking. This study proposes a fully unsupervised deep learning method, GrowSP-ForMS, which is specifically designed for leaf–wood separation of high-density MS ALS point clouds (acquired with wavelengths 532, 905, and 1550 nm) and based on the GrowSP architecture. GrowSP-ForMS achieved a mean accuracy of 84.3% and a mean intersection over union (mIoU) of 69.6% on our MS test set, outperforming the unsupervised reference methods by a significant margin. When compared to supervised deep learning methods, our model performed similarly to the slightly older PointNet architecture but was outclassed by more recent approaches. Finally, two ablation studies were conducted, which demonstrated that our proposed changes increased the test set mIoU of GrowSPForMS
by 29.4 percentage points (pp) in comparison to the original GrowSP model, and that utilizing MS data improved the mIoU by 5.6 pp from the monospectral case. For reproducibility, we release the GrowSP-ForMS
source code and pretrained weights (https://github.com/ruoppa/GrowSP-ForMS), along with the multispectral data set (https://zenodo.org/records/15913427)
Linking dynamic connectivity states to cognitive decline and anatomical changes in Alzheimer's disease
No full textAlterations in brain connectivity provide early indications of neurodegenerative diseases like Alzheimer’s disease (AD). Here, we present a novel framework that integrates a Hidden Markov Model (HMM) within the architecture of a convolutional neural network (CNN) to analyze dynamic functional connectivity (dFC) in resting-state functional magnetic resonance imaging (rs-fMRI). Our unsupervised approach captures recurring connectivity states in a large cohort of subjects spanning the Alzheimer’s disease continuum, including healthy controls, individuals with mild cognitive impairment (MCI), and patients with clinically diagnosed AD.
We propose a deep neural model with embedded HMM dynamics to identify stable recurring brain states from resting-state fMRI. These states exhibit distinct connectivity patterns and are differentially expressed across the Alzheimer’s disease continuum. Our analysis shows that the fraction of time each state is active varies systematically with disease severity, highlighting dynamic network alterations that track neurodegeneration.
Our findings suggest that the disruption of dynamic connectivity patterns in AD may follow a two-stage trajectory, where early shifts toward integrative network states give way to reduced connectivity organization as the disease progresses. This framework offers a promising tool for early diagnosis and monitoring of AD, and may have broader applications in the study of other neurodegenerative conditions
A Discriminating Discussion of RF-MEMS. Past, Present and Future of a Repeatedly Hyping Technology, at the Dawn of 6G and Future Networks
No full textThis book offers a unique bottom-up perspective of a technology for the manufacturing of low-complexity hardware components, which elevates this to a key enabling technology of 6G and future networks. This MEMS-based technology enables the fabrication of highly miniaturized, high-performance, widely tunable/reconfigurable and frequency agile passives, among which are low-loss/high-isolation micro-switches, multi-state RF power attenuators, delay lines and impedance tuners, high-order switching matrices, tunable filters, and more. The ultimate target of the book is increasing awareness of RF-MEMS technology and its possibilities, breaking the usual boundaries of the restricted scientific community dedicated to microsystems, and engaging scientists, developers and enthusiasts involved in other fields of technology. This includes 6G and future networks, as well as the unprecedented bottom-up design and conceptualization approaches that will be crucial to turn such paradigms into reality. The book addresses a broad audience, both technical and non-technical, spread across multiple modern disciplines, encompassing microelectronics, micro-technologies, telecommunications, RF and microwave engineering
GamiDOC: The Importance of Designing Gamification in a Proper Way
No full textGamification, commonly described as the use of game design elements in non-game contexts, is frequently adopted to enhance users' motivation, engagement, and happiness while supporting them in reaching different objectives, related to learning activities and behavioral changes. Despite being a widely used approach, several studies show that the final outcomes following gamification use are not always positive. To face this problem, we developed a tool called GamiDOC composed of different features aimed at facing the existing problems in the gameful systems design process and, at the same time, guiding designers and practitioners in the design and evaluation of gamified solutions. In this paper, we present the elements that make gameful systems design a challenging process, the state of the art in gamification design, and the issues that are still open in the design of gameful systems. Finally, we provide a detailed description of GamiDOC and why the tool stands as a valuable solution to guide users across all the stages of gameful systems design, development, and evaluation. Finally, we present a usability evaluation of GamiDOC and a use-case scenario
Glasses and Glass-Ceramics: Functionalization and Biomedical Applications
No full textchapter 7. Glasses and Glass-Ceramics: Functionalization and Biomedical Application
Identity by Design? Evaluating Gender Conditioning in LLM-Generated Agent Identity Profile
No full textIn multi-agent reasoning frameworks powered by large language models, agent roles are often instantiated
through identity descriptions that condition their behavior. This paper investigates whether and how the
gender assigned to the agent responsible for defining role-specific identity profiles affects the linguistic identity,
sentiment, and gender expression of downstream agents. We introduce an extensive corpus of agent identity
descriptions generated under controlled combinations of frameworks, roles, models, and gender conditions.
Through quantitative and qualitative linguistic analysis, we observe a consistent skew toward female identity
across models and roles when gender is unspecified, along with varying degrees of polarity and subjectivity
depending on the description framework. Notably, cognitively-oriented frameworks suppress affective expression,
while trait-based frameworks amplify gender alignment. These results reveal that identity conditioning is not
solely determined by prompt parameters, but emerges through a layered interaction of model priors, framework
semantics, and role-specific expressive constraints
Characterization of an Ion Beam for Quantum Applications Using the ARCADIAMD3 Fully-Depleted CMOS Monolithic Sensor
No full textEditorial / Editoriale
No full textPresentazione dei curatori del numero 'Umwelt und Region / Ambiente e regione', pubblicato in 'Geschichte und Region/Storia e regione', 34. Jahrgang, 2025, Heft 2 – anno XXXIV, 2025, n.
