Whole-body vibration exposure in sport: four relevant cases

This study investigates the whole-body vibration exposure in kite surfing, alpine skiing, snowboarding and cycling. The vibration exposure was experimentally evaluated following the ISO 2631 guidelines. Results evidenced that the most critical axis is the vertical one. The weighted vibration levels are always larger than 2.5 m/s² and the vibration dose values are larger than 25 m/s^1.75. The exposure limit values of the EU directive are reached after 8–37 min depending on the sport. The vibration magnitude is influenced by the athletes’ speed, by their skill level and sometimes by the equipment. The large vibration values suggest that the practice of sport activities may be a confounding factor in the aetiology of vibration-related diseases.     

Texture Mapping Real‐World Objects with Hydrographics

In the digital world, assigning arbitrary colors to an object is a simple operation thanks to texture mapping. However, in the real world, the same basic function of applying colors onto an object is far from trivial. One can specify colors during the fabrication process using a color 3D printer, but this does not apply to already existing objects. Paint and decals can be used during post‐fabrication, but they are challenging to apply on complex shapes. In this paper, we develop a method to enable texture mapping of physical objects, that is, we allow one to map an arbitrary color image onto a three‐dimensional object. Our approach builds upon hydrographics, a technique to transfer pigments printed on a sheet of polymer onto curved surfaces. We first describe a setup that makes the traditional water transfer printing process more accurate and consistent across prints. We then simulate the transfer process using a specialized parameterization to estimate the mapping between the planar color map and the object surface. We demonstrate that our approach enables the application of detailed color maps onto complex shapes such as 3D models of faces and anatomical casts.     

Mocking Ads through Mobile Web Services

The need for creativity is ubiquitous, and mobile devices connected to Web services can help us. Linguistic creativity is widely used in advertisements to surprise us, to get our attention, and to stick concepts in our memory. However, creativity can also be used as a defense. When we walk in the street, we are overwhelmed by messages that try to get our attention with any persuasive device at hand. As messages get ever more aggressive, often our basic cognitive defenses—trying not to perceive those messages—are not sufficient. One advanced defensive technique is based on transforming the perceived message into something different (for instance, making use of irony or hyperbole) from what was originally meant in the message. In this article, we describe an implemented application for smartphones, which creatively modifies the linguistic expression in a virtual copy of a poster encountered on the street. The mobile system is inspired by the subvertising practice of countercultural art.     

Being a Serial Transnational Activist

Transnational activism endures as a political practice turning a mirror onto the world's powerbrokers. We analyse a variety of transnational activism best characterized as serial by virtue of an observed systematic time and border‐spanning commitment to protest communication. Following statistical disambiguation of a dataset of 2.5 million unique Twitter users, we identified a subset of exceptionally prolific communicators and interviewed 21 of them. We show that a noted prominence in networked communication of otherwise unremarkable Twitter users may be an upshot of purposive strategies intended to publicize, support or help orchestrate collective action. Accordingly, we propose the term “engagement compass” to address the relationship between activists' life‐patterns and their personal investment in protest over time.     

A cubic regularization algorithm for unconstrained optimization using line search and nonmonotone techniques

In recent years, cubic regularization algorithms for unconstrained optimization have been defined as alternatives to trust-region and line search schemes. These regularization techniques are based on the strategy of computing an (approximate) global minimizer of a cubic overestimator of the objective function. In this work we focus on the adaptive regularization algorithm using cubics (ARC) proposed in Cartis et al. [Adaptive cubic regularisation methods for unconstrained optimization. Part I: motivation, convergence and numerical results, Mathematical Programming A 127 (2011), pp. 245–295]. Our purpose is to design a modified version of ARC in order to improve the computational efficiency preserving global convergence properties. The basic idea is to suitably combine a Goldstein-type line search and a nonmonotone accepting criterion with the aim of advantageously exploiting the possible good descent properties of the trial step computed as (approximate) minimizer of the cubic model. Global convergence properties of the proposed nonmonotone ARC algorithm are proved. Numerical experiments are performed and the obtained results clearly show satisfactory performance of the new algorithm when compared to the basic ARC algorithm.     

A multimodal feature learning approach for sentiment analysis of social network multimedia

In this paper we investigate the use of a multimodal feature learning approach, using neural network based models such as Skip-gram and Denoising Autoencoders, to address sentiment analysis of micro-blogging content, such as Twitter short messages, that are composed by a short text and, possibly, an image. The approach used in this work is motivated by the recent advances in: i) training language models based on neural networks that have proved to be extremely efficient when dealing with web-scale text corpora, and have shown very good performances when dealing with syntactic and semantic word similarities; ii) unsupervised learning, with neural networks, of robust visual features, that are recoverable from partial observations that may be due to occlusions or noisy and heavily modified images. We propose a novel architecture that incorporates these neural networks, testing it on several standard Twitter datasets, and showing that the approach is efficient and obtains good classification results.     

Comparing and experimenting machine learning techniques for code smell detection

Several code smell detection tools have been developed providing different results, because smells can be subjectively interpreted, and hence detected, in different ways. In this paper, we perform the largest experiment of applying machine learning algorithms to code smells to the best of our knowledge. We experiment 16 different machine-learning algorithms on four code smells (Data Class, Large Class, Feature Envy, Long Method) and 74 software systems, with 1986 manually validated code smell samples. We found that all algorithms achieved high performances in the cross-validation data set, yet the highest performances were obtained by J48 and Random Forest, while the worst performance were achieved by support vector machines. However, the lower prevalence of code smells, i.e., imbalanced data, in the entire data set caused varying performances that need to be addressed in the future studies. We conclude that the application of machine learning to the detection of these code smells can provide high accuracy (>96 %), and only a hundred training examples are needed to reach at least 95 % accuracy.     

Maximum density of quantum information in a scalable CMOS implementation of the hybrid qubit architecture

Scalability from single-qubit operations to multi-qubit circuits for quantum information processing requires architecture-specific implementations. Semiconductor hybrid qubit architecture is a suitable candidate to realize large-scale quantum information processing, as it combines a universal set of logic gates with fast and all-electrical manipulation of qubits. We propose an implementation of hybrid qubits, based on Si metal-oxide-semiconductor (MOS) quantum dots, compatible with the CMOS industrial technological standards. We discuss the realization of multi-qubit circuits capable of fault-tolerant computation and quantum error correction, by evaluating the time and space resources needed for their implementation. As a result, the maximum density of quantum information is extracted from a circuit including eight logical qubits encoded by the [[7, 1, 3]] Steane code. The corresponding surface density of logical qubits is 2.6 Mqubit/cm\(^2\).