1,721,023 research outputs found
(recensione a) Daniele Miano, Fortuna. Deity and concept in Archaic and Republican Italy, Oxford, Oxford University Press, 2018, pp. XIV + 242. ISBN: 9780198786566. € 60,00.
No full textLa recensione attua un breve e positiva valutazione del volume edito dalla OUP a firma del prof. Daniele MianoThe volume of prof. Miano, published in 2018 by the OUP, is briefly and positively reviewed
Recensione a Giovanni Brizzi, 70 d.C. La conquista di Gerusalemme. I Robinson/Letture. Roma; Bari: GLF editori Laterza, 2015. Pp. xi, 426. ISBN 9788858119792. €24,00.
No full textRecensione a Giovanni Brizzi, 70 d.C. La conquista di Gerusalemme. I Robinson/Letture. Roma; Bari: GLF editori Laterza, 2015. Pp. xi, 426
No full textRecensione del libro di G. Brizzi, 70 d.C. La conquista di Gerusalemme, Roma-Bari 2015Book review on G. Brizzi, 70 d.C. La conquista di Gerusalemme, Roma-Bari 201
Atti diplomatici romani, 338-270 a.C. Cronologia e contesto storico
Full text linkIl soggetto della tesi è l'analisi degli atti diplomatici romani stipulati durante la conquista romana dell'Italia. Questi atti sono principalmente foedera, paces, societates e amicitiae, con i loro equivalenti greci.
A partire dall'analisi sistematica degli atti diplomatici, ho dimostrato che l'attività diplomatica roman portò alla conquista dell'Italia tanto quanto l'attività militare. L'azione diplomatica dei Romani nel panorama politico italico fu differente da quella di altre potenze; di conseguenza, gli atti diplomatici Romani erano molto elaborati (per quanto ci è dato vedere). Infine, molte fonti che sembrano a prima vista incoerenti acquistano senso se analizzate a partire da un'analisi della situazione diplomatica.
Le conclusioni riguardano la strategia geopolitica e diplomatica dei Romani fra IV e III sec. a.C. I Romani usarono la diplomazia come strumento di conquista. Furono sofisticati nel redigere gli atti diplomatici, scegliendo con cura clausole e termini e usandoli per promuovere la presenza romana fra altre popolazioni italiche, ampliando i propri orizzonti diplomatici.
Con la diplomazia, i Romani presero contatti con molte presenze politiche fra i popoli italici e italioti; stipularono paci, passarono di guerra in guerra - provocandone alcune più utili per loro stessi - , conclusero alleanze che risultarono nell'ampliamento dell'esercito romano, colonizzarono territori, tennero d'occhio le potenze che non erano ancora sotto il loro dominio.The main subject is the analysis of Roman diplomatic acts stipulated during the Roman conquest of Italy. These acts are mainly foedera, paces, societates and amicitiae, with their Greek equivalents.
Starting from the systematic analysis of diplomatic acts, I have argued that Roman diplomatic action led to the conquest of Italy as much as military action. Moreover, Roman diplomatic action in the Italian political landscape was different from other powers; subsequently, Roman diplomatic acts (as far as we can notice) were much elaborated. Finally, many sources thought to be incoherent acquire sense if they are read within a diplomatic analysis.
My conclusions concern Roman geopolitical and diplomatic strategy between the IV and III centuries BC. The Romans used diplomacy as a tool of conquest. They were sophisticated in redacting diplomatic acts, carefully choosing clauses and words, and they used them to promote Roman presence among other Italic peoples, widening their diplomatic horizon.
With diplomacy, the Romans took contact with many political presences among Italic and Italiote peoples; they made peace, moving onto other wars; they provoked useful wars for them; they made alliances that provided also military enlargements for the Roman army; they colonized territories; they carefully kept an eye on the powers that were not yet under their dominion
Randomized neural networks for preference learning with physiological data
Full text linkThe paper discusses the use of randomized neural networks to learn a complete ordering between samples of heart-rate variability data by relying solely on partial and subject-dependent information concerning pairwise relations between samples. We confront two approaches, i.e. Extreme Learning Machines and Echo State Networks, assessing the effectiveness in exploiting hand-engineered heart-rate variability features versus using raw beat-to-beat sequential data. Additionally, we introduce a weight sharing architecture and a preference learning error function whose performance is compared with a standard architecture realizing pairwise ranking as a binary-classification task. The models are evaluated on real-world data from a mobile application realizing a guided breathing exercise, using a dataset of over 54K exercising sessions. Results show how a randomized neural model processing information in its raw sequential form can outperform its vectorial counterpart, increasing accuracy in predicting the correct sample ordering by about 20%. Further, the experiments highlight the importance of using weight sharing architectures to learn smooth and generalizable complete orders induced by the preference relation
A Game-theoretical Design Technique for Multi-stage Supply Chains under Uncertainty
No full textWe present a design approach for multi-stage Supply Chains (SCs) that allows selecting candidates and assigning them orders under uncertainty. A bargaining game model in its extensive form (i.e., with a time sequencing of moves) and in a fuzzy setting is proposed. The product quantities that each actor requires from the previous SC stage are determined modelling the real behavior of SC stakeholders, which on the one hand act to maximize their own profit, on the other hand cooperate to maximize the overall efficiency of the SC and minimize production costs and lead times. Assignments are determined taking into account stock levels, uncertain production or warehouse capacities, and customers’ demand. Thus, the method supports the decision making process providing an agile, cooperative, and resource-efficient design of multi-stage SCs under uncertain parameters. A literature SC is used as a test case to evaluate the effectiveness of the technique
ELM Preference Learning for Physiological Data
Full text linkThe work confronts two approaches to realize preference learning using Extreme Learning Machine networks, relaying on limited and subject-dependant information concerning pairwise relations between data samples. We describe an application within the context of assessing the effect of breathing exercises on heart-rate variability, using a dataset
of over 19K exercising sessions. Results highlight the importance of using weight sharing architectures to learn smooth and generalizable complete orders induced by the preference relation
Topographic mapping for quality inspection and intelligent filtering of smart-bracelet data
Full text linkWrist-worn wearable devices equipped with heart activity sensors can provide valuable data that can be used for preventative health. However, hearth activity analysis from these devices suffers from noise introduced by motion artifacts. Methods traditionally used to remove outliers based on motion data can yield to discarding clean data, if some movement was present, and accepting noisy data, i.e., subject was still but the sensor was misplaced. This work shows that self-organizing maps (SOMs) can be used to effectively accept or reject sections of heart data collected from unreliable devices, such as wrist-worn devices. In particular, the proposed SOM-based filter can accept a larger amount of measurements (less false negatives) with an higher overall quality with respect to methods solely based on statistical analysis of motion data. We provide an empirical analysis on real-world wearable data, comprising heart and motion data of users. We show how topographic mapping can help identifying and interpreting patterns in the sensor data and help relating them to an assessment of user state. More importantly, our experimental results show the proposed approach is able to retain almost twice the amount of data while keeping samples with an error that is an order of magnitude lower with respect to a filter based on accelerometric data
DropIn: Making reservoir computing neural networks robust to missing inputs by dropout
Full text linkThe paper presents a novel, principled approach to train recurrent neural networks from the Reservoir Computing family that are robust to missing part of the input features at prediction time. By building on the ensembling properties of Dropout regularization, we propose a methodology, named DropIn, which efficiently trains a neural model as a committee machine of subnetworks, each capable of predicting with a subset of the original input features. We discuss the application of the DropIn methodology in the context of Reservoir Computing models and targeting applications characterized by input sources that are unreliable or prone to be disconnected, such as in pervasive wireless sensor networks and ambient intelligence. We provide an experimental assessment using real-world data from such application domains, showing how the Dropin methodology allows to maintain predictive performances comparable to those of a model without missing features, even when 20%-50% of the inputs are not available
Modeling Mood Polarity and Declaration Occurrence by Neural Temporal Point Processes
No full textNeural point processes provide the flexibility needed to deal with time series of heterogeneous nature within the robust framework of point processes. This aspect is of particular relevance when dealing with real-world data, mixing generative processes characterized by radically different distributions and sampling. This brief discusses a neural point process approach for health and behavioral data, comprising both sparse events coming from user subjective declarations as well as fast-flowing time series from wearable sensors. We propose and empirically validate different neural architectures and we assess the effect of including input sources of different nature. The empirical analysis is built on the top of a challenging original dataset, never published before, and collected as part of a real-world experiment in an uncontrolled setting. Results show the potential of neural point processes both in terms of predicting the next event type as well as in predicting the time to next user interaction
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