Archivio della ricerca della Scuola Superiore Sant'Anna
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Soft Reverse Reconciliation for Discrete Modulations
Get PDFThe performance of the information reconciliation phase is crucial for quantum key distribution (QKD). Reverse reconciliation (RR) is typically preferred over direct reconciliation (DR) because it yields higher secure key rates. However, a significant challenge in continuous-variable (CV) QKD with discrete modulations (such as QAM) is that Alice lacks soft information about the symbol decisions made by Bob. This limitation restricts error correction to hard-decoding methods, with low reconciliation efficiency. This work introduces a reverse reconciliation softening (RRS) procedure designed for CV-QKD scenarios employing discrete modulations. This procedure generates a soft metric that Bob can share with Alice over a public channel, enabling her to perform soft-decoding error correction without disclosing any information to a potential eavesdropper. After detailing the RRS procedure, we investigate how the mutual information between Alice's and Bob's variables changes when the additional metric is shared. We show numerically that RRS improves the mutual information with respect to RR with hard decoding, practically achieving the same mutual information as DR with soft decoding. Finally, we test the proposed RRS for PAM-4 signalling with a rate 1/2 binary LDPC code and bit-wise decoding through numerical simulations, obtaining more than 1dB SNR improvement compared to hard-decoding RR
Clinical Phenotype and Prognosis of Asymptomatic Patients With Transthyretin Cardiac Amyloid Infiltration
Get PDFWhite-Box Validation of Collective Adaptive Systems by Statistical Model Checking and Process Mining
Get PDFModeling and analyzing collective adaptive systems with heterogeneous components poses challenges to language designers, software engineers, and computer scientists interested in the formal verification of the modeled systems. This requires the integration of computational, reasoning, and analysis tools, but also of techniques to validate and shed light on the actual behavior described by a model. We build on a methodology built in collaboration with Rocco De Nicola, based on his influential contributions in the modeling and programming of distributed and adaptive systems. Such methodology allows to model and analyze collective adaptive systems equipped with reasoning capabilities. It combines SCEL, a language for programming collective adaptive systems, with Pirlo, a reasoner that can enrich agents with reasoning capabilities, and MultiVeStA, a statistical model checker that can analyze the obtained models by simulating them. Here, we further enrich this methodology with techniques from process-oriented data science, and in particular process mining, to ease the validation and debugging of such models. This follows recent proposals from the literature that validate models by explaining graphically the behaviors observed by a statistical model checker. We demonstrate our approach by considering a simple collision-avoidance robotic scenario where a group of robots moves in an arena while aiming at minimizing the number of collisions
Post-stroke spontaneous motor recovery in mice can be predicted from acute-phase local field potential using machine learning
Get PDFStroke remains a leading cause of long-term disability, underscoring the urgent need for effective predictors of motor recovery. Understanding the electrophysiological changes underlying spontaneous recovery could offer critical insight into recovery mechanisms and aid in predicting individual rehabilitation trajectories. In this study, we investigated the predictive power of local field potentials recorded 2 days post-stroke to forecast 1 month motor recovery in a mouse model of ischemic stroke. By employing a comprehensive machine learning approach, we identified key electrophysiological features that significantly enhanced prediction accuracy. Through nested leave-one-animal-out cross-validation, we achieved high prediction accuracy, correctly identifying motor recovery status in 15 out of 16 mice. Our findings also revealed that pre-stroke brain activity did not contribute to prediction accuracy, suggesting that post-stroke dynamics are the primary determinants of recovery. Notably, we found that features from the contralesional hemisphere were particularly influential in predicting recovery outcomes, underscoring the critical role of the non-lesioned hemisphere in motor recovery. Our data-driven methodology underscores the importance of balancing feature selection to optimize predictive performance, particularly in the context of spontaneous recovery, where insight into natural recovery processes can guide the development of targeted rehabilitation strategies. Ultimately, our findings advocate for a deeper understanding of post-stroke brain dynamics to improve clinical outcomes for stroke patients
Challenging Behaviors in Children with Nonverbal Autism: A Questionnaire to Guide the Design of a Wearable Device for Biomarker Recording
Get PDFHighlights: Approximately 25% of individuals affected with ASD exhibit severe language impairments and are more likely to have challenging behavioral issues. Recent research focuses on early detection and prevention of crises by utilizing biomarkers measuring stress signals before the onset of the crisis. Regarding design requirements, the trunk appears to be the preferred location for wearable devices in this population. The arm and wrist can be an alternative with encouraging results. These locations offer interesting prospects for integrating devices into clothing or connected bracelets. The utilization of wires should be discouraged in the design of wearable devices for this specific population. Acceptance could be improved through prior desensitization. What are the main findings? A total of 67.5% of respondents support a wearable stress detection device for nmvASD children, preferably integrated into clothing, placed on the trunk, and be wireless. What is the implication of the main finding? These findings guide the development of tailored tools to improve behavioral management in this population. Children with non- or minimally verbal autism (nmvASD) commonly display sensory and emotional dysregulations leading to extremely stressful situations that trigger challenging behaviors which are often difficult to treat. Nonetheless, this population remains rarely studied in clinical research. Recent methods use electrophysiological biomarkers as diagnostic tools to detect stress signals, which may be useful in anticipating situations or conditions leading to challenging behaviors in nmvASD. A specific questionnaire was created in order to identify the characteristics of nmvASD children and gather the opinions of future users (parents and caregivers) on the design of a wearable device able to collect stress-related electrophysiological data. The results indicate that approximately 67.5% of respondents (n = 40) would be interested in such a device, both in outpatient and inpatient settings. In 70% of cases, prolonged contact with an object on the trunk is always well accepted by the child. This location was also preferentially chosen by 57.5% of respondents for such a wearable device. The presence of wires could be problematic in 82.5% of cases. About 65% of respondents find it far better to integrate these wearable devices directly into the clothing. These results will help in the development of devices specifically developed for the nmvASD population to enhance their care for behavioral disorders and based on user-center design
Employment of persons with disabilities: an assessment of the effectiveness of national policies
Get PDFHigh prevalence of wild-type transthyretin cardiac amyloidosis in older adults with carpal tunnel syndrome, heart failure or increased left ventricular mass: The CAPTURE study
Get PDFWavelet Tree, Part II: Text Indexing
Get PDFThe Wavelet Tree data structure introduced in Grossi, Gupta, and Vitter [Grossi et al., 2003] is a space-efficient technique for rank and select queries that generalizes from binary symbols to an arbitrary multisymbol alphabet. Over the last two decades, it has become a pivotal tool in modern full-text indexing and data compression because of its properties and capabilities in compressing and indexing data, with many applications to information retrieval, genome analysis, data mining, and web search. In this paper, we survey the fascinating history and impact of Wavelet Trees; no doubt many more developments are yet to come. Our survey borrows some content from the authors' earlier works.
This paper is divided into two parts: The first part gives a brief history of Wavelet Trees, including its varieties and practical implementations, which appears in the Festschrift dedicated to Roberto Grossi; the second part (this one) deals with Wavelet Tree-based text indexing and is included in the Festschrift dedicated to Giovanni Manzini