Abstract
Human Activity Recognition has been primarily investigated as a machine learning classification task forcing it to handle with two main limitations. First, it must assume that the testing data has an equal distribution with the training sample. However, the inherent structure of an activity recognition systems is fertile in distribution changes over time, for instance, a specific person can perform physical activities differently from others, and even sensors are prone to misfunction. Secondly, to model the pattern of activities carried out by each user, a significant amount of data is needed. This is impractical especially in the actual era of Big Data with effortless access to public repositories. In order to deal with these problems, this paper investigates the use of Transfer Learning, specifically Unsupervised Domain Adaptation, within human activity recognition systems. The yielded experiment results reveal a useful transfer of knowledge and more importantly the convenience of transfer learning within human activity recognition. Apart from the delineated experiments, our work also contributes to the field of transfer learning in general through an exhaustive survey on transfer learning for human activity recognition based on wearables. © 2018, Springer Nature Switzerland AG.

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One of the main advantages of Prolog is its potential for the implicit exploitation of parallelism and, as a high-level language, Prolog is also often used as a means to explicitly control concurrent tasks. Tabling is a powerful implementation technique that overcomes some limitations of traditional Prolog systems in dealing with recursion and redundant subcomputations. Given these advantages, the question that arises is if tabling has also the potential for the exploitation of concurrency/parallelism. On one hand, tabling still exploits a search space as traditional Prolog but, on the other hand, the concurrent model of tabling is necessarily far more complex, since it also introduces concurrency on the access to the tables. In this paper, we summarize Yap's main contributions to concurrent tabled evaluation and we describe the design and implementation challenges of several alternative table space designs for implicit and explicit concurrent tabled evaluation that represent different tradeoffs between concurrency and memory usage. We also motivate for the advantages of using fixed-size and lock freedata structures, elaborate on the key role that the engine's memory allocator plays on such environments, and discuss how Yap's mode-directed tabling support can be extended to concurrent evaluation. Finally, we present our future perspectives toward an efficient and novel concurrent framework which integrates both implicit and explicit concurrent tabled evaluation in a single Prolog engine.

Abstract
Skeletal muscle dispersion and optical clearing (OC) kinetics were studied experimentally to prove the existence of the refractive index (RI) matching mechanism of OC. Sample thickness and collimated transmittance spectra were measured during treatments with glucose (40%) and ethylene glycol (EG; 99%) solutions and used to obtain the time dependence of the RI of tissue fluids based on the proposed theoretical model. Calculated results demonstrated an increase of RI of tissue fluids and consequently proved the occurrence of the RI matching mechanism. The RI increase was observed for the wavelength range between 400 and 1000 nm and for the 2 probing molecules explored. We found that for 30 min treatment with 40% glucose and 99% EG, RI of sarcoplasm plus interstitial fluid was increased at 800 nm from 1.328 to 1.348 and from 1.328 to 1.369, respectively.