Studies of the development of sentence comprehension strategies in English have indicated that, at first, children tend to rely on pragmatic and semantic strategies, whereas, later on, they rely primarily on word order to determine the basic grammatical relations. However, before making strong conclusions regarding the role of semantics in comprehension, it is necessary to distinguish between (1) extragrammatical knowledge of world events and (2) abstract semantic distinctions that may be an integral part of the parsing process. An earlier study of American and Italian adults indicated that use of such abstract semantic strategies may be a core strategy for parsing in Italian. In the present study, we compared sentence interpretation in American and Italian children between the ages of 2 and 5. From the earliest stages, children showed sensitivity to the relative information value of the various cues in their native language; Italians relied primarily on semantic cues, whereas American children relied on word order. In general, the data did not support claims regarding the existence of universal hypotheses about language structure. There was also evidence that the failure of these young children to make full use of certain interpretive cues resulted from their inability to appreciate the discourse functions of these cues.

Deep Learning (DL) has become a crucial technology for multimedia computing. It o ers a powerful instrument to automatically produce high-level abstractions of complex multimedia data, which can be exploited in a number of applications including object detection and recognition, speech-to-text, media retrieval, multimodal data analysis, and so on. e availability of a ordable large-scale parallel processing architectures, and the sharing of e ective open-source codes implementing the basic learning algorithms, caused a rapid di usion of DL methodologies, bringing a number of new technologies and applications that outperform in most cases traditional machine learning technologies. In recent years, the possibility of implementing DL technologies on mobile devices has a racted signi cant a ention. anks to this technology, portable devices may become smart objects capable of learning and acting. e path towards these exciting future scenarios, however, entangles a number of important research challenges. DL architectures and algorithms are hardly adapted to the storage and computation resources of a mobile device. erefore, there is a need for new generations of mobile processors and chipsets, small footprint learning and inference algorithms, new models of collaborative and distributed processing, and a number of other fundamental building blocks. is survey reports the state of the art in this exciting research area, looking back to the evolution of neural networks, and arriving to the most recent results in terms of methodologies, technologies and applications for mobile environments.

No abstract

No abstract

Abstract. The development of efficient automotive accident management systems requires the design of complex multi-function antennas enabling different wireless services (e.g., localization, voice and data communications, emergency calls, etc...).Starting from different specifications (electrical, mechanical, and aerodynamic), the design of a multifunction antenna must consider, in addition to the usual antenna design requirements, also interference phenomena arising from the integration of different classes of antennas in a compact device. In this framework, the paper describes a methodology based on a stochastic multi-phases optimization approach for the design of an integrated multi-function/multi-band antenna system. Moreover, for an exhaustive assessment, the results of an experimental validation performed on a prototype of the multi-function antenna system are shown and discussed.