Advances in computer and communications technology have deeply affected the way we communicate. Social media emerged as a major means of human communication. However, limitation such is lack non-verbal stimuli, which sometimes hinders understanding message, particular associated emotional content. In an effort to compensate for this, people started use emoticons, are combinations keyboard characters that resemble facial expressions, more recently their evolution: emojis, namely, small...

In this paper, the task-related fMRI problem is treated in its matrix factorization form, focusing on Dictionary Learning (DL) approach. The proposed method allows incorporation of a priori knowledge that associated with both experimental design and available brain atlases. Moreover, it can cope efficiently uncertainties modeling hemodynamic response function. addition, bypasses one major drawbacks DL methods; namely, selection sparsity-related regularization parameters. Under formulation,...

Predicting the remaining useful life (RUL) of ball bearings plays an important role in predictive maintenance. A common definition RUL is time until a bearing no longer functional, which we denote as event, and many data-driven methods have been proposed to predict RUL. However, few studies addressed problem censored data, where this event interest not observed, simply ignoring these observations can lead overestimation failure risk. In paper, propose probabilistic estimation using survival...

Predicting the remaining useful life (RUL) of ball bearings plays an important role in predictive maintenance. A common definition RUL is time until a bearing no longer functional, which we denote as event, and many data-driven methods have been proposed to predict RUL. However, few studies addressed problem censored data, where this event interest not observed, simply ignoring these observations can lead overestimation failure risk. In paper, propose probabilistic estimation using survival...

Our ability to share memories constitutes a social foundation of our world. When exposed another person's memory, individuals can mentally reconstruct the events described, even if they were not present during related events. However, extent which neuronal connectivity patterns elicited by mental reconstruction an event mirror those in brains who experienced original remains unclear. Through two independent fMRI experiments, we explore Functional Connectivity (FC) at different timescales...

Las entidades públicas deben contar con un proceso que permita detectar a tiempo cualquier desviación afecte el alcance de sus objetivos, lo cual se conoce administrativamente como control interno. En este contexto, la investigación realizada tuvo objetivo analizar algunas propuestas teóricas sobre concepto interno, finalidad conocer cómo ha sido enfocado dicho constructo en ámbito gestión pública, periodos recientes; esta manera desarrolló estudio documental basado revisión literatura...

When working with task-related fMRI data, one of the most crucial parts data analysis consists determining a proper estimate BOLD response. The following document presents lite model for Hemodynamic Response Function HRF. Between other advances, proposed present less number parameters compared to similar HRF alternative, which reduces its optimization complexity and facilitates potential applications.

In this communication, we present a new contribution to radar-target identification via the extinction-pulse (E-pulse) method. New E-pulses are built using exponential β-splines, which include in their construction free parameters that adjusted obtain more robust systems. The adjusting process is done by searching for maximize discrimination capability of system absence noise. Simulation results justify approach and show obtained maximization improve with classical scheme.

In this paper, we introduce a novel methodology for the analysis of task-related fMRI data. particular, propose an alternative way constructing design matrix, based on newly suggested Information-Assisted Dictionary Learning (IADL) method. This technique offers enhanced potential, within conventional GLM framework, (a) to efficiently cope with uncertainties in modeling hemodynamic response function, (b) accommodate unmodeled brain-induced sources, beyond ones, as well potential interfering...

Extracting information from fMRI data constitutes a broad active area of research. Current techniques still present several limitations; some ignore relevant aspects regarding the brain functioning or lack interpretability. In an effort to overcome such limitations, we introduce extension sparse matrix factorization approach multilinear decomposition. The proposed model is built upon natural justifiable assumptions and better accommodates behavior. Tests on realistic synthetic as well real...

In this paper, we propose a novel unsupervised learning method to learn the brain dynamics using deep architecture named residual D-net. As it is often case in medical research, contrast typical tasks, size of resting-state functional Magnetic Resonance Image (rs-fMRI) datasets for training limited. Thus, available data should be very efficiently used complex patterns underneath connectivity dynamics. To address issue, use connections alleviate complexity through recurrent multi-scale...

In this paper, the task-related fMRI problem is treated in its matrix factorization formulation, focused on Dictionary Learning (DL) approach. The new method allows incorporation of a priori knowledge associated both with experimental design as well available brain Atlases. Moreover, proposed can efficiently cope uncertainties related to HRF modeling. addition, bypasses one major drawbacks that are DL methods; is, selection sparsity-related regularization parameters. our an alternative...