12 research outputs found
Scrittore stoico anonimo, Opera incerta (PHerc. 1020), coll. 104-112. Edizione, introduzione e commento
No full textPHerc. 1020 (SVF 2. 131 = FDS 88) è uno dei sette papiri di sicura o probabile paternità stoica conservati nella collezione ercolanese. Esso è privo di subscriptio, per cui dell’opera in esso conservata si ignorano autore e titolo. Svariati elementi sembrano corroborare la tesi, risalente a Hans von Arnim, che PHerc. 1020 contenga parte di un’opera risalente a Crisippo o a uno dei suoi immediati successori. A favore della paternità crisippea vengono qui forniti nuovi argomenti, che si aggiungono a quelli già addotti da von Arnim, Pohlenz e Keil. Per quanto riguarda il contenuto del libro, non siamo autorizzati a concludere né che esso equivalesse a uno scritto di tipo esclusivamente morale, piuttosto che logico o epistemologico, né che trattasse unicamente del sapiente stoico. Al contrario, dall’esame puntuale del testo, volto in particolare a comprenderlo in relazione alle altre numerose testimonianze sullo Stoicismo antico in nostro possesso, è emerso che esso presentava una singolare compenetrazione di logica, etica ed epistemologia. Facendo uso di nuove metodologie in campo papirologico, i due editori hanno ricostruito per la prima volta l’anatomia del rotolo e la sequenza dei frammenti e hanno ristabilito il testo con nuovi criteri editoriali basandosi sull’autopsia del manoscritto originale. Il presente lavoro consiste in una nuova edizione critica delle ultime otto colonne del papiro (coll. 104-112 Alessandrelli-Ranocchia), le meglio conservate e le uniche sinora edite dagli studiosi, e si inquadra nell’edizione complessiva di PHerc. 1020 programmata nell’ambito del progetto ERC Starting Grant 241184-PHerc finanziato dalla Commissione Europea (FP7, Ideas, www.pherc.eu)PHerc. 1020 (SVF 2. 131 = FDS 88) is one of the seven certain or probable Stoic papyri of the Herculaneum collection. Since the papyrus has no subscriptio, the author and the title of the work contained in it are unknown. Several elements seem to corroborate Hans von Arnim’s thesis that PHerc. 1020 hands down a work by either Chrysippus or one of his immediate successors. New arguments are advanced here in favour of this authorship beside those formerly adduced by von Arnim, Pohlenz and Keil. As far as the book’s content is concerned, we are not allowed to conclude that it was merely ethical, rather than purely logical or epistemological, nor that it only focused on the Stoic sage. On the contrary, from a detailed exegetical analysis and a comparison with the other evidence on Early Stoicism available to us it emerges that the work displayed a unique combination of ethics, logic and epistemology. By using new methods for the reading and editing of Herculaneum papyri, the editors have reconstructed for the first time the anatomy of the roll and the sequence of the fragments, while also establishing the Greek text on the basis of personal inspection of the original manuscript. This study is a new critical edition of the last eight columns of the papyrus (coll. 104-112 Alessandrelli-Ranocchia) – the best preserved columns and the only ones to have been studied by scholars so far – and constitutes the first part of the comprehensive edition of PHerc. 1020 included in the Project ERC Starting Grant 241184-PHerc funded by the European Commission (FP7, Ideas, www.pherc.eu)
Identification of Characteristic Points in Multivariate Physiological Signals by Sensor Fusion and Multi-Task Deep Networks
No full textIdentification of characteristic points in physiological signals, such as the peak of the R wave in the electrocardiogram and the peak of the systolic wave of the photopletismogram, is a fundamental step for the quantification of clinical parameters, such as the pulse transit time. In this work, we presented a novel neural architecture, called eMTUnet, to automate point identification in multivariate signals acquired with a chest-worn device. The eMTUnet consists of a single deep network capable of performing three tasks simultaneously: (i) localization in time of characteristic points (labeling task), (ii) evaluation of the quality of signals (classification task); (iii) estimation of the reliability of classification (reliability task). Preliminary results in overnight monitoring showcased the ability to detect characteristic points in the four signals with a recall index of about 1.00, 0.90, 0.90, and 0.80, respectively. The accuracy of the signal quality classification was about 0.90, on average over four different classes. The average confidence of the correctly classified signals, against the misclassifications, was 0.93 vs. 0.52, proving the worthiness of the confidence index, which may better qualify the point identification. From the achieved outcomes, we point out that high-quality segmentation and classification are both ensured, which brings the use of a multi-modal framework, composed of wearable sensors and artificial intelligence, incrementally closer to clinical translation
SLEEP-SEE-THROUGH: Explainable Deep Learning for Sleep Event Detection and Quantification From Wearable Somnography
No full textEvidence is rapidly accumulating that multifactorial nocturnal monitoring, through the coupling of wearable devices and deep learning, may be disruptive for early diagnosis and assessment of sleep disorders. In this work, optical, differential air-pressure and acceleration signals, acquired by a chest-worn sensor, are elaborated into five somnographic-like signals, which are then used to feed a deep network. This addresses a three-fold classification problem to predict the overall signal quality (normal, corrupted), three breathing-related patterns (normal, apnea, irregular) and three sleep-related patterns (normal, snoring, noise). In order to promote explainability, the developed architecture generates additional information in the form of qualitative (saliency maps) and quantitative (confidence indices) data, which helps to improve the interpretation of the predictions. Twenty healthy subjects enrolled in this study were monitored overnight for approximately ten hours during sleep. Somnographic-like signals were manually labeled according to the three class sets to build the training dataset. Both record- and subject-wise analyses were performed to evaluate the prediction performance and the coherence of the results. The network was accurate (0.96) in distinguishing normal from corrupted signals. Breathing patterns were predicted with higher accuracy (0.93) than sleep patterns (0.76). The prediction of irregular breathing was less accurate (0.88) than that of apnea (0.97). In the sleep pattern set, the distinction between snoring (0.73) and noise events (0.61) was less effective. The confidence index associated with the prediction allowed us to elucidate ambiguous predictions better. The saliency map analysis provided useful insights to relate predictions to the input signal content. While preliminary, this work supported the recent perspective on the use of deep learning to detect particular sleep events in multiple somnographic signals, thus representing a step towards bringing the use of AI-based tools for sleep disorder detection incrementally closer to clinical translation
Multimodal sleep analysis: deep learning for wearables and cardiovascular dynamics of auditory-evoked NREM patterns
No full textDOTTORATONegli ultimi anni, il rapido progresso dei dispositivi indossabili e della tecnologia computazionale ha aperto nuove prospettive per la comprensione dei processi fisiologici durante il sonno. Questo lavoro si muove lungo due direzioni parallele e complementari: da un lato la progettazione di strumenti basati su deep learning per l’elaborazione multimodale dei segnali del sonno; dall’altro l’indagine delle dinamiche cardiovascolari nel sonno NREM, con particolare attenzione al ruolo della stimolazione acustica nella modulazione delle slow waves.
La prima linea di ricerca ha portato allo sviluppo di modelli capaci di analizzare in modo automatico i dati raccolti, in ambiente domestico, da un dispositivo toracico indossabile. Le architetture di deep learning proposte assolvono simultaneamente più compiti: valutazione della qualità del segnale, rilevazione di eventi respiratori e correlati al sonno, oltre alla stima preliminare della pressione arteriosa. I risultati dimostrano la fattibilità di un monitoraggio del sonno accurato, interpretabile e scalabile anche al di fuori del contesto clinico.
Il secondo filone di ricerca ha portato alla definizione di un modello analitico per quantificare il legame tra slow waves e risposte cardiovascolari durante il sonno. Grazie a registrazioni ad alta risoluzione di EEG, ECG e pressione arteriosa, ottenute in condizioni controllate con stimolazione acustica a ciclo chiuso, le slow waves sono state classificate in base al grado di sincronizzazione e analizzate rispetto alle risposte autonomiche evento-correlate. È emerso che le onde più sincronizzate, soprattutto se potenziate acusticamente, producono variazioni più marcate della frequenza cardiaca e della pressione sanguigna — segnali associati a un miglioramento della funzione cardiaca osservato al risveglio. L’approccio evento-centrico adottato ha permesso di scomporre le dinamiche di interazione cervello-cuore durante il sonno, offrendo un quadro quantitativo dell’impatto di specifici pattern elettroencefalografici sulla regolazione cardiovascular.
Nel loro insieme, questi contributi delineano un percorso che va dall’innovazione tecnologica alla scoperta fisiologica, rafforzando gli strumenti per studiare il sonno come interfaccia dinamica tra sistema nervoso e apparato cardiovascolare. Integrando sviluppo algoritmico e comprensione biologica, la tesi si colloca nel più ampio contesto della ricerca traslazionale, contribuendo a chiarire come la stimolazione acustica e i diversi fenotipi delle slow waves influenzino l’equilibrio autonomico e la funzione cardiaca.Recent progress in wearable technologies and computational modeling has created new pathways for deriving meaningful physiological insights from sleep. This research follows two interconnected avenues: developing deep learning methods for analyzing multimodal sleep signals, and examining cardiovascular dynamics during NREM sleep influenced by auditory stimulation.
The wearable-focused investigations aimed to extract sleep-related information using a chest-mounted device in real-world, home settings. Custom deep learning models were crafted for multitask classification, addressing signal quality evaluation, detection of respiratory and sleep-associated events, and early-stage, cuffless blood pressure estimation. These solutions showcased promising capabilities for delivering precise, explainable, and scalable sleep monitoring beyond traditional clinical environments.
The second part of the work established an analytical pipeline to quantify how discrete slow wave events interact with cardiovascular dynamics during sleep. Using high-resolution EEG, ECG, and blood pressure signals collected under closed-loop auditory stimulation, slow waves were classified by synchronization type and analyzed in relation to wave-locked autonomic responses. This approach revealed that highly synchronized slow waves, when enhanced through auditory stimulation, elicited stronger heart rate and blood pressure modulations — physiological signatures that correlated with the next-morning cardiac performance. These results highlight how event-based analysis of multimodal sleep signals can disentangle the dynamics of neural-cardiovascular coupling, offering a quantitative framework to study how specific sleep patterns influence cardiovascular physiology.
Taken together, these contributions advance the methodological and conceptual tools needed to investigate sleep as a dynamic interface between neural and cardiovascular systems. By bridging algorithmic development with physiological insight, this work contributes to a growing effort to characterize and contextualize sleep as a window into cardiovascular function — both through scalable monitoring tools and deeper understanding of how auditory stimulation and slow wave dynamics shape autonomic regulation.DIPARTIMENTO DI ELETTRONICA, INFORMAZIONE E BIOINGEGNERIA37ALIVERTI, ANDREADELLACA', RAFFAEL
Online advertising campaign management for hotel booking
No full textLAUREA MAGISTRALEIl mercato pubblicitario online statunitense ha registrato entrate pari a 126.4 miliardi di dollari a fine 2019, per poi raggiungerne 139.8 l'anno seguente. Inoltre, il valore di mercato mondiale è attualmente pari a 378 miliardi di dollari ed si stima che possa raggiungere i 646 miliardi nel 2024, a ulteriore prova della costante crescita di questo settore. Nel corso dell'ultimo decennio, le numerose opportunità introdotte da tale mercato hanno alimentato l'interesse della comunità scientifica. In particolare, il campo della Intelligenza Artificiale (IA) ha avuto un ruolo fondamentale nel fornire meccanismi automatici a supporto sia degli inserzionisti, sia di coloro che forniscono spazi per annunci online. In quanto segue, il nostro focus è rivolto agli inserzionisti, i quali hanno il compito di ottimizzare le proprie campagne pubblicitarie online. L'obiettivo di questo lavoro consiste nell'applicare tecnologie appartenenti allo stato dell'arte della IA per dare supporto all'azienda AdsHotel, la quale si occupa della gestione di campagne pubblicitarie online per molteplici hotel in tutto il mondo. Sfruttando alcune delle metodologie esistenti nel campo dell'IA, abbiamo realizzato un sistema in grado di eseguire un'ottimizzazione sicura delle bid, garantendo quindi di superare una determinata soglia minima di ritorno sugli investimenti (ROI) all'azienda. Inoltre, abbiamo esteso tali metodi per adattare il sistema ai cambiamenti dell'ambiente a esso circostante; questo è infatti di estrema importanza date le svariate ragioni per cui esso può risultare non stazionario nel tempo, tra cui: stagionalità, festività nazionali ed eventi imprevedibili come la diffusione del COVID-19. In aggiunta, abbiamo integrato in tale sistema una strategia per espandere le attuali campagne pubblicitarie. Nello specifico, essa consiste nel suggerire a un hotel l'apertura delle sotto-campagne attualmente non attive che però risultano essere le più promettenti. Infine, implementando il nostro sistema all'interno dell'ambiente di AdsHotel, ne valutiamo le capacità e le prestazioni su reali campagne pubblicitarie relative ad hotel.The Online Advertising revenue in the United States increased from 126.4 billion in 2019 to 139.8 billion U.S. dollars in 2020. A further sign of its growth is that the worldwide spending in 2020 has been estimated to be 378 billion U.S. dollars and is forecast to increase in the following years, reaching 646 billion by 2024. All the opportunities that Online Advertising has brought for the advertising market have drawn a lot of attention from the scientific community. In particular, the Artificial Intelligence (AI) field played a crucial role in providing automatic mechanisms to support both the publishers and advertisers in their tasks. We specifically focus our attention on the advertiser's task, consisting in optimizing an online advertising campaign. The goal of this work is to apply state-of-the-art AI technologies in a real-world Online Advertising scenario for the company AdsHotel, which is responsible for the optimization of the advertising campaigns of multiple hotels all around the world. We built a real-world system exploiting existing AI's methods to perform safe bid optimization (i.e., guaranteeing a given return-on-investment (ROI) constraint) and extending them to adapt their behavior as the hotel environment changes. This is crucial due to the many reasons why it might present non-stationarity over time (e.g., seasonality, national holidays, and unpredictable events like COVID-19). Finally, we integrate into the system a strategy to provide suggestions on how to expand an ongoing campaign by selecting the most promising sub-campaigns a hotel might open. By implementing our system in the AdsHotel environment, we evaluate its capabilities and measure its performance over multiple real-world hotel advertising campaigns
15-17 sept. 2016 // International workshop about PHerc. 1020 (Naples)
No full textFrom 15 to 17 September an international workshop about PHerc. 1020 ([Early Stoic Author], Unknown Work) will take place in Naples (see poster attached). We shall discuss together the new critical text of several inedited columns of this valuable text reconstructed by Graziano Ranocchia and Michele Alessandrelli in the framework of the ERC Starting Grant 241184-PHerc (www.pherc.eu). http://www.pherc.eu
Entre autobiographisme et autogenèse du texte, Le Vent Paraclet de Michel Tournier
No full textAvec Le Vent Paraclet, Michel Tournier compose un ouvrage à mi-chemin entre le récit autobiographique et l’essai. Dans ce texte qui s’articule sur le double registre de la narration d’anecdotes personnelles et des commentaires, l’interprète n’assume le parti pris de l’objectivité que pour mieux brouiller les relations entre discours fictif et métadiscours critique. Cette étude veut montrer la manière dans laquelle, tout en évoquant la thèse barthésienne de la « mort de l’auteur », le Vent Paraclet finit par montrer plutôt l’urgence de l’expression d’un moi de l’écrivain qui ne cesse d’empiéter sur le projet d’auto-dénégation ironique du critique.With Le Vent Paraclet, Michel Tournier creates a work of art which lies halfway between an autobiographical and an essayistic discourse. As the text builds upon a double register, entailing a mixture of personal anecdotes and commentary, the narrative voice assumes an objective stance only to further blur the boundaries between critical metadiscourse and fiction. This paper aims to highlight how Le Vent Paraclet, although referring to Barthes’s theory of the “death of the author”, ends up revealing the writer’s drive towards self-expression, which constantly exceeds the project of the ironic self-denial of the critic
Parution : G. Ranocchia, Scrittore stoico anonimo, Opera incerta PHerc. 1020, coll. 104-112
No full textVient de paraître (open-access): Scrittore stoico anonimo, Opera incerta PHerc. 1020, coll. 104-112 Introduzione, edizione e commento di M. Alessandrelli e G. Ranocchia «ILIESI digitale. Testi e tradizioni» 1 Roma 2017 http://www.iliesi.cnr.it/scheda.php?id=227&cl=I/T&lan= PHerc. 1020 (SVF 2. 131 = FDS 88) is one of the seven certain or probable Stoic papyri of the Herculaneum collection. Since the papyrus has no subscriptio, the author and the title of the work contained in it are unknown. S..
Virtual unrolling and deciphering of Herculaneum papyri by X-ray phase-contrast tomography
No full textA collection of more than 1800 carbonized papyri, discovered in the Roman 'Villa dei Papiri' at Herculaneum is the unique classical library survived from antiquity. These papyri were charred during 79 A.D. Vesuvius eruption, a circumstance which providentially preserved them until now. This magnificent collection contains an impressive amount of treatises by Greek philosophers and, especially, Philodemus of Gadara, an Epicurean thinker of 1st century BC. We read many portions of text hidden inside carbonized Herculaneum papyri using enhanced X-ray phase-contrast tomography non-destructive technique and a new set of numerical algorithms for 'virtual-unrolling'. Our success lies in revealing the largest portion of Greek text ever detected so far inside unopened scrolls, with unprecedented spatial resolution and contrast, all without damaging these precious historical manuscripts. Parts of text have been decoded and the 'voice' of the Epicurean philosopher Philodemus is brought back again after 2000 years from Herculaneum papyri
IDPlanT : The Italian Database of Plant Translocation
No full textIDPlanT is the Italian Database of Plant Translocation, an initiative of the Nature Conservation Working Group of the Italian Botanical Society. IDPlanT currently includes 185 plant translocations. The establishment of a national database on plant translocation is a key step forward in data sharing and techniques improvement in this field of plant conservation
