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.     

Recommending multimedia visiting paths in cultural heritage applications

The valorization and promotion of worldwide Cultural Heritage by the adoption of Information and Communication Technologies represent nowadays some of the most important research issues with a large variety of potential applications. This challenge is particularly perceived in the Italian scenario, where the artistic patrimony is one of the most diverse and rich of the world, able to attract millions of visitors every year to monuments, archaeological sites and museums. In this paper, we present a general recommendation framework able to uniformly manage heterogeneous multimedia data coming from several web repositories and to provide context-aware recommendation techniques supporting intelligent multimedia services for the users—i.e. dynamic visiting paths for a given environment. Specific applications of our system within the cultural heritage domain are proposed by means of real case studies in the mobile environment related both to an outdoor and indoor scenario, together with some results on user’s satisfaction and system accuracy.     

Conceptual aspects of geometric quantum computation

Geometric quantum computation is the idea that geometric phases can be used to implement quantum gates, i.e., the basic elements of the Boolean network that forms a quantum computer. Although originally thought to be limited to adiabatic evolution, controlled by slowly changing parameters, this form of quantum computation can as well be realized at high speed by using nonadiabatic schemes. Recent advances in quantum gate technology have allowed for experimental demonstrations of different types of geometric gates in adiabatic and nonadiabatic evolution. Here, we address some conceptual issues that arise in the realizations of geometric gates. We examine the appearance of dynamical phases in quantum evolution and point out that not all dynamical phases need to be compensated for in geometric quantum computation. We delineate the relation between Abelian and non-Abelian geometric gates and find an explicit physical example where the two types of gates coincide. We identify differences and similarities between adiabatic and nonadiabatic realizations of quantum computation based on non-Abelian geometric phases.     

Stabilizability of switched linear systems does not imply the existence of convex Lyapunov functions

Counterexamples are given which show that a linear switched system (with controlled switching) that can be stabilized by means of a suitable switching law does not necessarily admit a convex Lyapunov function. Both continuous- and discrete-time cases are considered. This fact contributes in focusing the difficulties encountered so far in the theory of stabilization of switched system. In particular the result is in contrast with the case of uncontrolled switching in which it is known that if a system is stable under arbitrary switching then admits a polyhedral norm as a Lyapunov function.     

Test Technology Newsletter

Journal of Electronic Testing -     

Construction of Asymmetrical Hexameric Biomimetic Motors with Continuous Single‐Directional Motion by Sequential Coordination

The significance of bionanomotors in nanotechnology is analogous to mechanical motors in daily life. Here the principle and approach for designing and constructing biomimetic nanomotors with continuous single‐directional motion are reported. This bionanomotor is composed of a dodecameric protein channel, a six‐pRNA ring, and an ATPase hexamer. Based on recent elucidations of the one‐way revolving mechanisms of the phi29 double‐stranded DNA (dsDNA) motor, various RNA and protein elements are designed and tested by single‐molecule imaging and biochemical assays, with which the motor with active components has been constructed. The motor motion direction is controlled by three operation elements: (1) Asymmetrical ATPase with ATP‐interacting domains for alternative DNA binding/pushing regulated by an arginine finger in a sequential action manner. The arginine finger bridges two adjacent ATPase subunits into a non‐covalent dimer, resulting in an asymmetrical hexameric complex containing one dimer and four monomers. (2) The dsDNA translocation channel as a one‐way valve. (3) The hexameric pRNA ring geared with left‐/right‐handed loops. Assessments of these constructs reveal that one inactive subunit of pRNA/ATPase is sufficient to completely block motor function (defined as K = 1), implying that these components work sequentially based on the principle of binomial distribution and Yang Hui's triangle.     

Proteomic profile of differentially expressed plasma proteins from dystrophic mice and following suberoylanilide hydroxamic acid treatment

Purpose: Histone Deacetylase Inhibitors (DI) ameliorates dystrophic muscle regeneration restoring muscular strength in the mdx mouse model of Duchenne muscular dystrophy (DMD). The further development of these compounds as drugs for DMD treatment is currently hampered by the lack of knowledge about DIs effect in large dystrophic animal models and that of suitable biomarkers to monitor their efficacy. Experimental design: In this study we applied proteomic analysis to identify differentially expressed proteins present in plasma samples from mdx mice treated with the Suberoylanilide hydroxamic acid (SAHA) and relative normal controls (WT). Results: Several differentially expressed proteins were identified between untreated wild type and mdx mice. Among these, fibrinogen, epidermal growth factor 2 receptor, major urinary protein and glutathione peroxidase 3 (GPX3) were constitutively up‐regulated in mdx, while complement C3, complement C6, gelsolin, leukaemia inhibitory factor receptor (LIFr), and alpha 2 macroglobulin were down‐regulated compared to WT mice. SAHA determined the normalization of LIFr and GPX3 protein level while apoliprotein E was de novo up‐regulated in comparison to vehicle‐treated mdx mice. Conclusions and clinical relevance: Collectively, these data unravel potential serological disease biomarkers of mdx that could be useful to monitor muscular dystrophy response to DI treatment.     

A study of co-occurrence based local features for camera model identification

Camera model identification has great relevance for many forensic applications, and is receiving growing attention in the literature. Virtually all techniques rely on the traces left in the image by the long sequence of in-camera processes which are specific of each model. They differ in the prior assumptions, if any, and in how such evidence is gathered in expressive features. In this work we study a class of blind features, based on the analysis of the image residuals of all color bands. They are extracted locally, based on co-occurrence matrices of selected neighbors, and then used to train a classifier. A number of experiments are carried out on the well-known Dresden Image Database. Besides the full-knowledge case, where all models of interest are known in advance, other scenarios with more limited knowledge and partially corrupted images are also investigated. Experimental results show these features to provide a state-of-the-art performance.     

Multi-scale stacked sequential learning

Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring labels exhibit some kind of relationship. The paper main contribution is two-fold: first, we generalize the stacked sequential learning, highlighting the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions. We tested the method on two tasks: text lines classification and image pixel classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning as well as state-of-the-art conditional random fields.