Structured lexical similarity via convolution Kernels on dependency trees

A central topic in natural language process-ing is the design of lexical and syntactic fea-tures suitable for the target application. In this paper, we study convolution dependency tree kernels for automatic engineering of syntactic and semantic patterns exploiting lexical simi-larities. We define efficient and powerful ker-nels for measuring the similarity between de-pendency structures, whose surface forms of the lexical nodes are in part or completely dif-ferent. The experiments with such kernels for question classification show an unprecedented results, e.g. 41 % of error reduction of the for-mer state-of-the-art. Additionally, semantic role classification confirms the benefit of se-mantic smoothing for dependency kernels.

How Many Templates for GW Chirp Detection? The Minimal-Match Issue Revisited

In a recent paper dealing with maximum likelihood detection of gravitational wave chirps from coalescing binaries with unknown parameters we introduced an accurate representation of the no-signal cumulative distribution of the supremum of the whole correlator bank. This result can be used to derive a refined estimate of the number of templates yielding the best tradeoff between detector's performance (in terms of lost signals among those potentially detectable) and computational burden.Comment: submitted to Class. Quantum Grav. Typing error in eq. (4.8) fixed; figure replaced in version

Automatic induction of framenet lexical units in Italian

In this paper we investigate the applicability of automatic methods for frame induction to improve the coverage of IFrameNet, a novel lexical resource based on Frame Semantics in Italian. The experimental evaluations show that the adopted methods based on neural word embeddings pave the way for the assisted development of a large scale lexical resource for our language

UNITOR @ DANKMEMES: Combining convolutional models and transformer-based architectures for accurate MEME management

This paper describes the UNITOR system that participated to the “multimoDal Artefacts recogNition Knowledge for MEMES” (DANKMEMES) task within the context of EVALITA 2020. UNITOR implements a neural model which combines a Deep Convolutional Neural Network to encode visual information of input images and a Transformer-based architecture to encode the meaning of the attached texts. UNITOR ranked first in all subtasks, clearly confirming the robustness of the investigated neural architectures and suggesting the beneficial impact of the proposed combination strategy

Effects of plasma treatments of polypropylene adhesive joints used in the automotive industry

Plasma treatment has been used in recent years to activate the surfaces of adhesive substrates and thus as an adhesion promoter between adhesive and substrates. The use of plasma treatments is widely adopted in the automotive industries especially for polymers that present low surface energy, such as polypropylene. In this work, polypropylene substrates used in the automotive industries have been treated with two different techniques: vacuum and atmospheric plasma. Then, polyurethane and methacrylate adhesives have been used to bond single lap joints (SLJs). Typically, these two adhesives cannot bond polypropylene substrates without surface treatments. An experimental plan has been designed to investigate the process parameters that can increase the functional polar groups (FPGs) maximizing the adhesion strength. Besides the types of plasma, two different gas carriers (air and nitrogen) and different treatment times have been investigated. The substrates, treated and not treated, have been assessed through scanning electron microscopy, energy-dispersive X-ray analysis, and Fourier-transform infrared spectroscopy to quantitatively assess the increment of FPGs after the different treatments. The experimental plan shows that the atmospheric plasma can improve the surface of the substrates by using a smaller time. Mechanical tests on SLJs show that methacrylate and polyurethane cannot bond polypropylene substrates without the plasma treatment. On the other hand, the treated substrates can form a strong bonding with the adhesive since all SLJs exhibit a substrate failure. Mechanical tests have been also carried out after three different aging cycles showing that the adopted plasma treatment is not affected by the aging cycles

Surface roughness effect on ultracold neutron interaction with a wall and implications for computer simulations

We review the diffuse scattering and the loss coefficient in ultracold neutron reflection from slightly rough surfaces, report a surprising reduction in loss coefficient due to roughness, and discuss the possibility of transition from quantum treatment to ray optics. The results are used in a computer simulation of neutron storage in a recent neutron lifetime experiment that re-ported a large discrepancy of neutron lifetime with the current particle data value. Our partial re-analysis suggests the possibility of systematic effects that were not included in this publication.Comment: 39 pages, 9 figures; additional calculations include

Comparing league formats with respect to match importance in Belgian football

Recently, most clubs in the highest Belgian football division have become convinced that the format of their league should be changed. Moreover, the TV station that broadcasts the league is pleading for a more attractive competition. However, the clubs have not been able to agree on a new league format, mainly because they have conflicting interests. In this paper, we compare the current league format, and three other formats that have been considered by the Royal Belgian Football Association. We simulate the course of each of these league formats, based on historical match results. We assume that the attractiveness of a format is determined by the importance of its games; our importance measure for a game is based on the number of teams for which this game can be decisive to reach a given goal. Furthermore, we provide an overview of how each league format aligns with the expectations and interests of each type of club

An optimal procedure for stride length estimation using foot-mounted magneto-inertial measurement units

Stride length is often used to quantitatively evaluate human locomotion performance. Stride by stride estimation can be conveniently obtained from the signals recorded using miniaturized inertial sensors attached to the feet and appropriate algorithms for data fusion and integration. To reduce the detrimental drift effect, different algorithmic solutions can be implemented. However, the overall method accuracy is supposed to depend on the optimal selection of the parameters which are required to be set. This study aimed at evaluating the influence of the main parameters involved in well-established methods for stride length estimation. An optimization process was conducted to improve methods' performance and preferable values for the considered parameters according to different walking speed ranges are suggested. A parametric solution is also proposed to target the methods on specific subjects' gait characteristics. The stride length estimates were obtained from straight walking trials of five healthy volunteers and were compared with those obtained from a stereo-photogrammetric system. After parameters tuning, percentage errors for stride length were 1.9%, 2.5% and 2.6% for comfortable, slow, and fast walking conditions, respectively