Influence of Brand Personality-Marker Attributes on Purchasing Intention: The Role of Emotionality

Marketing researchers employ the Five-Factor Model to describe branded products through attributes used for human personality. Marker attributes used to elicit brand personality dimensions can also influence consumers’ intention to purchase. Two connected studies, carried out on two samples of 91 and 557 subjects, respectively, show that brand personality-marker attributes predict intention to purchase, but only to the extent that such attributes are vivid and, in particular, when they elicit emotional responses (i.e., when they are emotionally interesting). These findings have several implications for people involved in developing strategies for persuasive communication

Traversing the margins of corruption amidst informal economies in Amazonia

This article focuses on local idioms of extra-legal economic activity among indigenous Amazonians in eastern Peru, and its overall argument is that these idioms are part of a broader context in which indigenous people are compelled by a variety of factors to act in a seemingly corrupt manner. I further suggest that within such a context these idioms are not confined to the informal economy but are also used to refer to activities that fall within the formal economy, supporting Hart’s (2009) claim that the informal economy is a way of imagining the orthodox economy. I argue that corruption within Amazonian economies is commonly perceived by non-indigenous people as contrasting with the workings of the orthodox economy without proper consideration of the economic conditions and bureaucratic structures that give rise to it. Lastly, I argue that, here, corruption can contravene bureaucracy by restoring the humanity that Herzfeld (1993) claims bureaucracy rejects through its acts of indifference toward individuals

Data driven theory for knowledge discovery in the exact sciences with applications to thermonuclear fusion

In recent years, the techniques of the exact sciences have been applied to the analysis of increasingly complex and non-linear systems. The related uncertainties and the large amounts of data available have progressively shown the limits of the traditional hypothesis driven methods, based on first principle theories. Therefore, a new approach of data driven theory formulation has been developed. It is based on the manipulation of symbols with genetic computing and it is meant to complement traditional procedures, by exploring large datasets to find the most suitable mathematical models to interpret them. The paper reports on the vast amounts of numerical tests that have shown the potential of the new techniques to provide very useful insights in various studies, ranging from the formulation of scaling laws to the original identification of the most appropriate dimensionless variables to investigate a given system. The application to some of the most complex experiments in physics, in particular thermonuclear plasmas, has proved the capability of the methodology to address real problems, even highly nonlinear and practically important ones such as catastrophic instabilities. The proposed tools are therefore being increasingly used in various fields of science and they constitute a very good set of techniques to bridge the gap between experiments, traditional data analysis and theory formulation

Proximity effect in planar superconducting tunnel junctions containing Nb/NiCu superconductor/ferromagnet bilayers

We present experimental results concerning both the fabrication and characterization of superconducting tunnel junctions containing superconductor/ferromagnet (S/F) bilayers made by niobium (S) and a weak ferromagnetic Ni0.50Cu0.50 alloy. Josephson junctions have been characterized down to T=1.4 K in terms of current-voltage I-V characteristics and Josephson critical current versus magnetic field. By means of a numerical deconvolution of the I-V data the electronic density of states on both sides of the S/F bilayer has been evaluated at low temperatures. Results have been compared with theoretical predictions from a proximity model for S/F bilayers in the dirty limit in the framework of Usadel equations for the S and F layers, respectively. The main physical parameters characterizing the proximity effect in the Nb/NiCu bilayer, such as the coherence length and the exchange field energy of the F metal, and the S/F interface parameters have been also estimated

A Neural-based Algorithm for Landslide Detection at Stromboli Volcano: Preliminary Results.

This study presents a neural-based algorithm for the automatic detection of landslides on Stromboli volcano (Italy). It has been shown that landslides are an important short-term precursor of effusive eruptions of Stromboli. In particular, an increase in the occurrence rate of landslides was observed a few hours before the beginning of the February 2007 effusive eruption. Automating the process of detection of these signals will help analysts and represents a useful tool for the monitoring of the stability of the Sciara del Fuoco flank of Stromboli volcano. A multi-layer perceptron neural network is here applied to continuously discriminate landslides from other signals recorded at Stromboli (e.g., explosion quakes, tremor signals), and its output is used by an automatic system for the detection task. To correctly represent the seismic data, coefficients are extracted from both the frequency domain, using the linear predictive coding technique, and the time domain, using temporal waveform parameterization. The network training and testing was carried out using a dataset of 537 signals, from 267 landslides and 270 records that included explosion quakes and tremor signals. The classification results were 99.5% predictive for the best net performance, and 98.7% when the performance was averaged over the different net configurations. Thus, this detection system was effective when tested on the 2007 effusive eruption period. However, continuing investigations into different time intervals are needed, to further define and optimize the algorithm

Enabling monocular depth perception at the very edge

Depth estimation is crucial in several computer vision applications, and a recent trend aims at inferring such a cue from a single camera through computationally demanding CNNs - precluding their practical deployment in several application contexts characterized by low-power constraints. Purposely, we develop a tiny network tailored to microcontrollers, processing low-resolution images to obtain a coarse depth map of the observed scene. Our solution enables depth perception with minimal power requirements (a few hundreds of mW), accurately enough to pave the way to several high-level applications at-the-edge