Abstract
We investigate the learning of a flexible humanoid robot kick controller, i.e., the controller should be applicable for multiple contexts, such as different kick distances, initial robot position with respect to the ball or both. Current approaches typically tune or optimise the parameters of the biped kick controller for a single context, such as a kick with longest distance or a kick with a specific distance. Hence our research question is that, how can we obtain a flexible kick controller that controls the robot (near) optimally for a continuous range of kick distances? The goal is to find a parametric function that given a desired kick distance, outputs the (near) optimal controller parameters. We achieve the desired flexibility of the controller by applying a contextual policy search method. With such a contextual policy search algorithm, we can generalize the robot kick controller for different distances, where the desired distance is described by a real-valued vector. We will also show that the optimal parameters of the kick controller is a non-linear function of the desired distances and a linear function will fail to properly generalize the kick controller over desired kick distances. © 2017, Springer International Publishing AG.

Abstract
Bone repair is a specialized type of wound repair controlled by complex multi-factorial events. The nervous system is recognized as one of the key regulators of bone mass, thereby suggesting a role for neuronal pathways in bone homeostasis. However, in the context of bone injury and repair, little is known on the interplay between the nervous system and bone. Here, we addressed the neuropeptide Y (NPY) neuronal arm during the initial stages of bone repair encompassing the inflammatory response and ossification phases in femoral-defect mouse model. Spatial and temporal analysis of transcriptional and protein levels of NPY and its receptors, Y1R and Y2R, reported to be involved in bone homeostasis, was performed in bone, dorsal root ganglia (DRG) and hypothalamus after femoral injury. The results showed that NPY system activity is increased in a time- and space-dependent manner during bone repair. Y1R expression was trigged in both bone and DRG throughout the inflammatory phase, while a Y2R response was restricted to the hypothalamus and at a later stage, during the ossification step. Our results provide new insights into the involvement of NPY neuronal pathways in bone repair.

Abstract
A multi-wavelengths analysis for pulse waveform extraction using laser speckle is conducted. The proposed system consists of three coherent light sources (532 nm, 635 nm, 850 nm). A bench-test composed of a moving skin-like phantom (silicone membrane) is used to compare the results obtained from different wavelengths. The system is able to identify a skin-like phantom vibration frequency, within physiological values, with a minimum error of 0.5 mHz for the 635 nm and 850 nm wavelengths and a minimum error of 1.3 mHz for the 532 nm light wavelength using a FFT-based algorithm. The phantom velocity profile is estimated with an error ranging from 27% to 9% using a bidimensional correlation coefficient-based algorithm. An in vivo trial is also conducted, using the 532 nm and 635 nm laser sources. The 850 nm light source has not been able to extract the pulse waveform. The heart rate is identified with a minimum error of 0.48 beats per minute for the 532 nm light source and a minimal error of 1.15 beats per minute for the 635 nm light source. Our work reveals that a laser speckle-based system with a 532 nm wavelength is able to give arterial pulse waveform with better results than those given with a 635 nm laser.

Abstract
In Project TEMPUS “Enhancement of Russian Creative Education: new Master Programme in Digital Arts in line with EU standards” (2014-2016) the Russian students had the opportunity to study in EU Universities for one semester. The Universidade Aberta, in Portugal, didn’t have a master degree in Digital Arts so a pilot programme had to be created: a new postgraduation in Digital Art Practice. This new curriculum, using blearning (based on online and face to face activities) with transdisciplinary methods, aims a practice oriented training on digital art. It started with a deep understanding of Lisbon, the relationship between people, cultural and artistic spaces and their environments. This knowledge inspired the students to produce and to create an artistic artefact presented in exhibition to an audience. With this postgraduation new possibilities started for reflection about global challenges for education in the millennium.

Abstract
We study the problem of learning lexicographic preferences on multiattribute domains, and propose Rankdom Forests as a compact way to express preferences in learning to rank scenarios. We start generalizing Conditional Lexicographic Preference Trees by introducing multiple kernels in order to handle non-categorical attributes. Then, we define a learning strategy for inferring lexicographic rankers from partial pairwise comparisons between options. Finally, a Lexicographic Ensemble is introduced to handle multiple weak partial rankers, being Rankdom Forests one of these ensembles. We tested the performance of the proposed method using several datasets and obtained competitive results when compared with other lexicographic rankers.

Abstract
Content-based image retrieval (CBIR) is a search technology that could aid medical diagnosis by retrieving and presenting earlier reported cases that are related to the one being diagnosed. To retrieve relevant cases, CBIR systems depend on supervised learning to map low-level image contents to high-level diagnostic concepts. However, the annotation by medical doctors for training and evaluation purposes is a difficult and time-consuming task, which restricts the supervised learning phase to specific CBIR problems of well-defined clinical applications. This paper proposes a new technique that automatically learns the similarity between the several exams from textual distances extracted from radiology reports, thereby successfully reducing the number of annotations needed. Our method first infers the relation between patients by using information retrieval techniques to determine the textual distances between patient radiology reports. These distances are subsequently used to supervise a metric learning algorithm, that transforms the image space accordingly to textual distances. CBIR systems with different image descriptions and different levels of medical annotations were evaluated, with and without supervision from textual distances, using a database of computer tomography scans of patients with interstitial lung diseases. The proposed method consistently improves CBIR mean average precision, with improvements that can reach 38%, and more marked gains for small annotation sets. Given the overall availability of radiology reports in picture archiving and communication systems, the proposed approach can be broadly applied to CBIR systems in different medical problems, and may facilitate the introduction of CBIR in clinical practice.