VP-Fronting in Sardinian: a structural paradox

This paper investigates the phenomena of Inversion and VP-fronting in Sardinian in examples like Dormende sunt sos pitzinnos ?sleeping are the children?. It is argued that the postverbal subject in these constructions cannot occupy the same position as the subject in general cases of Inversion, but raises to a higher position within the clause. This raising operation yields sharply ungrammatical sentences if VP fronting does not apply. However, these can be excluded by postulating general conditions (distinct from the Agree operation) on the structural relations which must hold at spell-out between overt heads and the elements which they license. It is argued that these conditions, along with further provisions which are necessary to accommodate the position of heavy subjects in Inversion constructions, may play a role in facilitating processing

Prolific domains and the left periphery

The left periphery has enjoyed extensive study over the past years, especially drawn against the framework of Rizzi (1997). It is argued that in this part of the clause, relations are licensed that have direct impact on discourse interpretation and information structure, such as topic, focus, clause type, and the like. I take this line of research up and argue in favour of a split CP on the basis of strictly left-peripheral phenomena across languages. But I also want to link the relation of articulated clause structure, syntactic derivations, and information structure. In particular, I outline the basics of a model of syntactic derivation that makes explicit reference to the interpretive interfaces in a cyclic, dynamic manner. I suggest a return to older stages of generative grammar, at least in spirit, by proposing that clausal derivation stretches over three important areas which I call prolific domains: the part of the clause which licenses argument/thematic relations (V- or θ-domain), the part that licenses agreement/grammatica1 relations (T- or ϕ-domain), and the part that licenses discourse/information-relevant relations (C- or ω-domain). It is thus a rather broad and conceptual notion of "adding" and "omitting" that I am concerned with here, namely licensing of material to relate to information structure, and the desire to find an answer to the question which elements might be added or omitted across languages to establish such links

A Survey of Physical Layer Security Techniques for 5G Wireless Networks and Challenges Ahead

Physical layer security which safeguards data confidentiality based on the information-theoretic approaches has received significant research interest recently. The key idea behind physical layer security is to utilize the intrinsic randomness of the transmission channel to guarantee the security in physical layer. The evolution towards 5G wireless communications poses new challenges for physical layer security research. This paper provides a latest survey of the physical layer security research on various promising 5G technologies, including physical layer security coding, massive multiple-input multiple-output, millimeter wave communications, heterogeneous networks, non-orthogonal multiple access, full duplex technology, etc. Technical challenges which remain unresolved at the time of writing are summarized and the future trends of physical layer security in 5G and beyond are discussed.Comment: To appear in IEEE Journal on Selected Areas in Communication

Fast and Chaotic Fiber-Based Nonlinear Polarization Scrambler

International audienceWe report a simple and efficient all-optical polarization scrambler based on the nonlinear interaction in an optical fiber between a signal beam and its backward replica which is generated and amplified by a reflective loop. When the amplification factor exceeds a certain threshold, the system exhibits a chaotic regime in which the evolution of the output polarization state of the signal becomes temporally chaotic and scrambled all over the surface of the Poincaré sphere. We numerically derive some design rules for the scrambling performances of our device which are well confirmed by the experimental results. The polarization scrambler has been successfully tested on a 10-Gbit/s On/Off Keying Telecom signal, reaching scrambling speeds up to 500-krad/s, as well as in a wavelength division multiplexing configuration. A different configuration based on a following cascade of polarization scramblers is also discussed numerically, which leads to an increase of the scrambling performances

A novel feature-scrambling approach reveals the capacity of convolutional neural networks to learn spatial relations

Convolutional neural networks (CNNs) are one of the most successful computer vision systems to solve object recognition. Furthermore, CNNs have major applications in understanding the nature of visual representations in the human brain. Yet it remains poorly understood how CNNs actually make their decisions, what the nature of their internal representations is, and how their recognition strategies differ from humans. Specifically, there is a major debate about the question of whether CNNs primarily rely on surface regularities of objects, or whether they are capable of exploiting the spatial arrangement of features, similar to humans. Here, we develop a novel feature-scrambling approach to explicitly test whether CNNs use the spatial arrangement of features (i.e. object parts) to classify objects. We combine this approach with a systematic manipulation of effective receptive field sizes of CNNs as well as minimal recognizable configurations (MIRCs) analysis. In contrast to much previous literature, we provide evidence that CNNs are in fact capable of using relatively long-range spatial relationships for object classification. Moreover, the extent to which CNNs use spatial relationships depends heavily on the dataset, e.g. texture vs. sketch. In fact, CNNs even use different strategies for different classes within heterogeneous datasets (ImageNet), suggesting CNNs have a continuous spectrum of classification strategies. Finally, we show that CNNs learn the spatial arrangement of features only up to an intermediate level of granularity, which suggests that intermediate rather than global shape features provide the optimal trade-off between sensitivity and specificity in object classification. These results provide novel insights into the nature of CNN representations and the extent to which they rely on the spatial arrangement of features for object classification