666 resultados para Inspiration


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This research project is based on the Multimodal Corpus of Chinese Court Interpreting (MUCCCI [mutʃɪ]), a small-scale multimodal corpus on the basis of eight authentic court hearings with Chinese-English interpreting in Mainland China. The corpus has approximately 92,500 word tokens in total. Besides the transcription of linguistic and para-linguistic features, utilizing the facial expression classification rules suggested by Black and Yacoob (1995), MUCCCI also includes approximately 1,200 annotations of facial expressions linked to the six basic types of human emotions, namely, anger, disgust, happiness, surprise, sadness, and fear (Black & Yacoob, 1995). This thesis is an example of conducting qualitative analysis on interpreter-mediated courtroom interactions through a multimodal corpus. In particular, miscommunication events (MEs) and the reasons behind them were investigated in detail. During the analysis, although queries were conducted based on non-verbal annotations when searching for MEs, both verbal and non-verbal features were considered indispensable parts contributing to the entire context. This thesis also includes a detailed description of the compilation process of MUCCCI utilizing ELAN, from data collection to transcription, POS tagging and non-verbal annotation. The research aims at assessing the possibility and feasibility of conducting qualitative analysis through a multimodal corpus of court interpreting. The concept of integrating both verbal and non-verbal features to contribute to the entire context is emphasized. The qualitative analysis focusing on MEs can provide an inspiration for improving court interpreters’ performances. All the constraints and difficulties presented can be regarded as a reference for similar research in the future.

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Il lavoro affronta lo statuto penale della pubblica amministrazione, osservandolo attraverso moduli interpretativi costituzionalmente orientati e, segnatamente, quelli conformi ai principi di tassatività, offensività e proporzione. A questi se ne aggiunge un altro, ritenuto criterio immanente al titolo, dato dalla funzione amministrativa, che gioca un ruolo centrale nell’economia offensiva all’interno di tutte le fattispecie penali. L’indagine si divide in tre parti. Nella prima, si intende analizzare l’evoluzione diacronica dei modelli istituzionali di amministrazione, dovuta al mutamento radicale del rapporto tra autorità e individuo: a seguito dell’avvento dello Stato sociale e costituzionale di diritto, all’affermarsi di una concezione orizzontale del suddetto rapporto corrisponde un paradigma di amministrazione civil servant, finalisticamente orientata al soddisfacimento dei beni della vita, trovando copertura costituzionale negli articoli 97, 2 e 3 Cost. Queste vicende rappresentano il punto di partenza per ogni riflessione di sistema su questa branca di parte speciale, non potendosi pretendere di calibrare in maniera corretta l’offensività di un reato o la ragionevolezza di un’incriminazione prescindendo da un confronto con il ruolo dell’amministrazione e dalla funzione che essa svolge. Successivamente, dopo aver chiarito il significato di buon andamento e imparzialità, strumentali alla ‘buona amministrazione’, si vuole trovare un punto di equilibrio tra l’esercizio della discrezionalità, che funge da mezzo rispetto al fine del risultato amministrativo, che è ciò che qualifica oggi il modello vigente di amministrazione ‘di risultato’, e alcune disposizioni incriminatrici che possono intersecarsi con lo svolgimento fisiologico del potere discrezionale. Infine, prendendo spunto anche dal recente corso della giurisprudenza costituzionale, si vuole applicare un metodo ermeneutico costituzionalmente conforme ad una serie di fattispecie del titolo. In questo modo si eviterà di costruire un corpus di reati esangue, che riporta il diritto penale ad una superata concezione sanzionatoria, provocando un “effetto di congelamento” dell’efficienza amministrativa con pregiudizio dei diritti sottostanti.

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Biological systems are complex and highly organized architectures governed by non-covalent interactions responsible for the regulation of essential tasks in all living organisms. These systems are a constant source of inspiration for supramolecular chemists aiming to design multicomponent molecular assemblies able to perform elaborated tasks, thanks to the role and action of the components that constitute them. Artificial supramolecular systems exploit non-covalent interactions to mimic naturally occurring events. In this context, stimuli-responsive supramolecular systems have attracted attention due to the possibility to control macroscopic effects through modifications at the nanoscale. This thesis is divided in three experimental chapters, characterized by a progressive increase in molecular complexity. Initially, the preparation and studies of liposomes functionalized with a photoactive guest such as azobenzene in the bilayer were tackled, in order to evaluate the effect of such photochrome on the vesicle properties. Subsequently, the synthesis and studies of thread-like molecules comprising an azobenzene functionality was reported. Such molecules were conceived to be intercalated in the bilayer membrane of liposomes with the aim to be used as components for photoresponsive transmembrane molecular pumps. Finally, a [3]rotaxane was developed and studied in solution. This system is composed of two crown ether rings interlocked with an axle containing three recognition sites for the macrocycles, i.e. two pH-switchable ammonium stations and a permanent triazolium station. Such molecule was designed to achieve a change in the ratio between the recognition sites and the crown ethers as a consequence of acid-base inputs. This leads to the formation of rotaxanes containing a number of recognition sites respectively larger, equal or lower than the number of interlocked rings and connected by a network of acid-base reactions.

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Nowadays, one of the most ambitious challenges in soft robotics is the development of actuators capable to achieve performance comparable to skeletal muscles. Scientists have been working for decades, inspired by Nature, to mimic both their complex structure and their perfectly balanced features in terms of linear contraction, force-to-weight ratio, scalability and flexibility. The present Thesis, contextualized within the FET open Horizon 2020 project MAGNIFY, aims to develop a new family of innovative flexible actuators in the field of soft-robotics. For the realization of this actuator, a biomimetic approach has been chosen, drawing inspiration from skeletal muscle. Their hierarchical fibrous structure was mimicked employing the electrospinning technique, while the contraction of sarcomeres was designed employing chains of molecular machines, supramolecular systems capable of performing movements useful to execute specific tasks. The first part deals with the design and production of the basic unit of the artificial muscle, the artificial myofibril, consisting in a novel electrospun core-shell nanofiber, with elastomeric shell and electrically conductive core, coupled with a conductive coating, for the realization of which numerous strategies have been investigated. The second part deals instead with the integration of molecular machines (provided by the project partners) inside these artificial myofibrils, preceded by the study of several model molecules, aimed at simulating the presence of these molecular machines during the initial phases of the project. The last part concerns the realization of an electrospun multiscale hierarchical structure, aimed at reproducing the entire muscle morphology and fibrous organization. These research will be joined together in the near future like the pieces of a puzzle, recreating the artificial actuator most similar to biological muscle ever made, composed of millions of artificial myofibrils, electrically activated in which the nano-scale movement of molecular machines will be incrementally amplified to the macro-scale contraction of the artificial muscle.

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Creativity seems mysterious; when we experience a creative spark, it is difficult to explain how we got that idea, and we often recall notions like ``inspiration" and ``intuition" when we try to explain the phenomenon. The fact that we are clueless about how a creative idea manifests itself does not necessarily imply that a scientific explanation cannot exist. We are unaware of how we perform certain tasks, such as biking or language understanding, but we have more and more computational techniques that can replicate and hopefully explain such activities. We should understand that every creative act is a fruit of experience, society, and culture. Nothing comes from nothing. Novel ideas are never utterly new; they stem from representations that are already in mind. Creativity involves establishing new relations between pieces of information we had already: then, the greater the knowledge, the greater the possibility of finding uncommon connections, and the more the potential to be creative. In this vein, a beneficial approach to a better understanding of creativity must include computational or mechanistic accounts of such inner procedures and the formation of the knowledge that enables such connections. That is the aim of Computational Creativity: to develop computational systems for emulating and studying creativity. Hence, this dissertation focuses on these two related research areas: discussing computational mechanisms to generate creative artifacts and describing some implicit cognitive processes that can form the basis for creative thoughts.

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Planning is an important sub-field of artificial intelligence (AI) focusing on letting intelligent agents deliberate on the most adequate course of action to attain their goals. Thanks to the recent boost in the number of critical domains and systems which exploit planning for their internal procedures, there is an increasing need for planning systems to become more transparent and trustworthy. Along this line, planning systems are now required to produce not only plans but also explanations about those plans, or the way they were attained. To address this issue, a new research area is emerging in the AI panorama: eXplainable AI (XAI), within which explainable planning (XAIP) is a pivotal sub-field. As a recent domain, XAIP is far from mature. No consensus has been reached in the literature about what explanations are, how they should be computed, and what they should explain in the first place. Furthermore, existing contributions are mostly theoretical, and software implementations are rarely more than preliminary. To overcome such issues, in this thesis we design an explainable planning framework bridging the gap between theoretical contributions from literature and software implementations. More precisely, taking inspiration from the state of the art, we develop a formal model for XAIP, and the software tool enabling its practical exploitation. Accordingly, the contribution of this thesis is four-folded. First, we review the state of the art of XAIP, supplying an outline of its most significant contributions from the literature. We then generalise the aforementioned contributions into a unified model for XAIP, aimed at supporting model-based contrastive explanations. Next, we design and implement an algorithm-agnostic library for XAIP based on our model. Finally, we validate our library from a technological perspective, via an extensive testing suite. Furthermore, we assess its performance and usability through a set of benchmarks and end-to-end examples.