Extracting low-dimensional control variables for movement primitives

Movement primitives (MPs) provide a powerful framework for data driven movement generation that has been successfully applied for learning from demonstrations and robot reinforcement learning. In robotics we often want to solve a multitude of different, but related tasks. As the parameters of the primitives are typically high dimensional, a common practice for the generalization of movement primitives to new tasks is to adapt only a small set of control variables, also called meta parameters, of the primitive. Yet, for most MP representations, the encoding of these control variables is pre-coded in the representation and can not be adapted to the considered tasks. In this paper, we want to learn the encoding of task-specific control variables also from data instead of relying on fixed meta-parameter representations. We use hierarchical Bayesian models (HBMs) to estimate a low dimensional latent variable model for probabilistic movement primitives (ProMPs), which is a recent movement primitive representation. We show on two real robot datasets that ProMPs based on HBMs outperform standard ProMPs in terms of generalization and learning from a small amount of data and also allows for an intuitive analysis of the movement. We also extend our HBM by a mixture model, such that we can model different movement types in the same dataset

Diálogo artístico durante la Edad Media. Arte islámico-arte mudéjar

Fruto de la colaboración entre Casa Árabe y las universidades de Córdoba y Zúrich, se publica este volumen, como resultado del seminario internacional que tuvo lugar en Córdoba en noviembre de 2016. Los artículos reunidos en este volumen, como bien ponen de manifiesto los coordinadores del mismo, nos ofrecen una “visión complementaria” en un contexto de necesaria “colaboración interdisciplinaria entre la arqueología y la historia del arte” llegando a cubrir “gran parte de la compleja realidad poliédrica del arte y la arqueología islámica y mudéjar”

MicroRNA and other non-coding RNAs in Epstein–Barr virus-associated cancers

Simple Summary Epstein-Barr virus (EBV) is associated with a variety of malignancies. In this review, we discuss EBV-encoded microRNAs and ncRNAs and consider how their detection could aid in the diagnosis, prognostication, and monitoring of treatment in patients with EBV-associated malignancies, including classical Hodgkin's lymphoma (cHL), Burkitt lymphoma (BL), diffuse large B-cell lymphoma (DLBCL), nasopharyngeal carcinoma (NPC), and gastric carcinoma (GC). EBV is a direct causative agent in around 1.5% of all cancers. The oncogenic properties of EBV are related to its ability to activate processes needed for cellular proliferation, survival, migration, and immune evasion. The EBV latency program is required for the immortalization of infected B cells and involves the expression of non-coding RNAs (ncRNAs), including viral microRNAs. These ncRNAs have different functions that contribute to virus persistence in the asymptomatic host and to the development of EBV-associated cancers. In this review, we discuss the function and potential clinical utility of EBV microRNAs and other ncRNAs in EBV-associated malignancies. This review is not intended to be comprehensive, but rather to provide examples of the importance of ncRNAs

Design of understeer characteristics through torque vectoring on a lumped-parameter full vehicle model

Active safety systems play a fundamental role in improving stability and safety performance of modern passenger cars. Within this context, Torque vectoring (TV) represents one of the most promising technologies for the improvement of vehicle dynamics performance. This paper proposes a TV-based Direct Yaw Moment Control (DYC) strategy aimed at designing the vehicle understeering behaviour through a software simulation environment based on an efficient Lumped-Parameter Full Vehicle Model (LPFVM). Simulation results show that the vehicle is able to successfully follow a predetermined understeer characteristic