Help4Mood is acceptable to some patients receiving treatment for depression although none used it as regularly as intended. Changes in depression symptoms in individuals who used the system regularly reached potentially meaningful levels.

The condensation of elementary quanta and their macroscopic occupation of the same quantum state, say k = 0 in some reference frame Σ, is the essential ingredient of the degenerate vacuum of present-day elementary particle physics. This represents a sort of 'quantum ether' which characterizes the physically realized form of relativity and could play the role of preferred reference frame in a modern re-formulation of the Lorentzian approach. In spite of this, the so called 'null results' of the classical ether-drift experiments, traditionally interpreted as confirmations of Special Relativity, have so deeply influenced scientific thought as to prevent a critical discussion on the real reasons underlying its alleged supremacy. In this paper, we argue that this traditional null interpretation is far from obvious. In fact, by using Lorentz transformations to connect the Earth's frame to Σ, the small observed effects point to an average Earth's velocity of about 300 km/s, as in most cosmic motions. A common feature is the irregular behaviour of the data. While this has motivated, so far, their standard interpretation as instrumental artifacts, our new re-analysis of the very accurate Joos experiment gives clear indications for the type of Earth's motion associated with the CMB anisotropy and leaves little space for this traditional interpretation. The new explanation requires instead a view of the vacuum as a stochastic medium, similar to a fluid in a turbulent state of motion, in agreement with basic foundational aspects of both quantum physics and relativity. The overall consistency of this picture with the present experiments with vacuum optical resonators and the need for a new generation of dedicated ether-drift experiments are also emphasized.

S uppose you're on the Web and looking at the image in Figure 1. You might ask, "What in the digital world is that?" Information about such digital objects is easy to provide to users who, for example, click on the object in their browsers. But clicking does not automatically provide information that is tailored to the individual user's abilities, interests, or preferences, or that takes account of previous exchanges between the user and the system. For that to happen, the information must be stored in a different, nontextual fashion and turned into a spoken or written message in the user's language when needed. This generation process can take into account the context and the user's linguistic and personal preferences so that it looks to the user as if the object has been stored all along as a personalized information object.Usability expert Jakob Nielsen has long argued that computer systems should speak the user's language. By using personalized information objects, a system can indeed speak the user's language. Moreover, a multilingual system can speak all the users' languages. Such objects will find application in many different arenas. For instance, GPS-equipped alwayson mobile devices will offer location-based services, providing users with information about nearby objects and organizations. These include both commercial and cultural products and providers: sales offers, restaurants, historical artifacts, and tourist attractions. But the attractiveness and utility of location-based services could be greatly enhanced if they trade specifically in personalized information objects. Users expect customized information, and they will expect it to be delivered using whatever presentation method makes the most sense: graphics, text, speech, or all three.We don't need to wait for next-generation mobile devices to explore the possibilities of personalized information objects. The system we have been working on, within the M-PIRO project, has concentrated on virtual and other museum settings. A specific advantage is that museum communication professionals are well versed in the need to tailor an organization's preferred message to their visitors' expertise and information needs. The project has focused on developing language-engineering technology for personalized information objects, specifically on multilingual information delivery.Progress on the project has required working on multilingual natural language generation from a single source, improving speech synthesis for languages other than English, generating spoken and written messages from the same source, authoring the single source through symbolic authoring techniques, and working with adaptive user modeling for personalized information presentation. The result is a stateof-the-art system that speaks the users' languages and offers highly personalized information objects.

ElsevierFuster García, E.; Bresó Guardado, A.; Martínez Miranda, JC.; Rosell-Ferrer, J.; Matheson, C.; García Gómez, JM. (2015) AbstractActigraphy devices have been successfully used as effective tools in the treatment of diseases such as sleep disorders or major depression. Although several efforts have been made in recent years to develop smaller and more portable devices, the features necessary for the continuous monitoring of outpatients require a less intrusive, obstructive and stigmatizing acquisition system. A useful strategy to overcome these limitations is based on adapting the monitoring system to the patient lifestyle and behavior by providing sets of different sensors that can be worn simultaneously or alternatively. This strategy offers to the patient the option of using one device or other according to his/her particular preferences. However this strategy requires a robust multi-sensor fusion methodology capable of taking maximum profit from all of the recorded information. With this aim, this study proposes two actigraphy fusion models including centralized and distributed architectures based on artificial neural networks. These novel fusion methods were tested both on synthetic datasets and real datasets, providing a parametric characterization of the models' behavior, and yielding results based on real case applications. The results obtained using both proposed fusion models exhibit good performance in terms of robustness to signal degradation, as well as a * good behavior in terms of the dependence of signal quality on the number of signals fused. The distributed and centralized fusion methods reduce the mean averaged error of the original signals to 44% and 46% respectively when using simulated datasets. The proposed methods may therefore facilitate a less intrusive and more dependable way of acquiring valuable monitoring information from outpatients.

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