Security and VO management capabilities in a large-scale Grid operating system

This paper presents a number of security and VO management capabilities in a large-scale distributed Grid operating system. The capabilities formed the basis of the design and implementation of a number of security and VO management services in the system. The main aim of the paper is to provide some idea of the various functionality cases that need to be considered when designing similar large-scale systems in the future

Modelling the McGurk effect

Abstract. The current study investigates the McGurk effect by modelling it with neural networks. The simulations are designed to test the two main theories about the moment at which the auditory-visual integration happens. To further analyze the causes behind the McGurk illusion, the neural network that best models the effect is used to simulate the influence of language and the frequency of phonemes on auditory-visual speech perception, using two phonetic distribution from English and Japanese, with different empirical results in the McGurk effect

Supervised Learning in Multilayer Spiking Neural Networks

The current article introduces a supervised learning algorithm for multilayer spiking neural networks. The algorithm presented here overcomes some limitations of existing learning algorithms as it can be applied to neurons firing multiple spikes and it can in principle be applied to any linearisable neuron model. The algorithm is applied successfully to various benchmarks, such as the XOR problem and the Iris data set, as well as complex classifications problems. The simulations also show the flexibility of this supervised learning algorithm which permits different encodings of the spike timing patterns, including precise spike trains encoding.Comment: 38 pages, 4 figure

New methodological aspects in rehabilitation after proximal humerus fracture

Proximal humerus fracture ranks third in the elderly after femoral neck fractures and distal radius fractures, and seventh in adults, and the risk of occurrence is related to advancing age. In this study we aimed to analyze the efficacy of a 24-weeks physical therapy programme based on a particular methodology consisting of the reprogramming of the specific proprioceptive neuromuscular facil-itation techniques added to the classical physical therapy and by introducing modern interactive therapies and technologies: Capacitive Resistive Electric Transference, Instrument Assisted Soft Tissue Mobilization, kinesiological tapes and PRAMA system, compared with classical physical therapy. Our study included 26 patients, aged between 18 and 55 years, with proximal humerus fracture, who complete the 24-weeks rehabilitation programme. We assessed pain, shoulder range of motion, muscle strength and the ability to perform activities of daily living. The statistical analysis was performed using IBM SPSS and Excel 2021. The results showed statistically significant im-provement in all shoulder motion, increased muscle strength, decreased pain, and a better ability to perform daily activities. The physical therapy programme based on the proposed particular methodology has proven to be more effective than classical physical therapy, both regarding the improvement of the movement parameters compared to the physiological values, as well as the symmetry of both shoulders

Mobile Mechatronic/Robotic Orthotic Devices to Assist–Rehabilitate Neuromotor Impairments in the Upper Limb: A Systematic and Synthetic Review

This paper overviews the state-of-the-art in upper limb robot-supported approaches, focusing on advancements in the related mechatronic devices for the patients' rehabilitation and/or assistance. Dedicated to the technical, comprehensively methodological and global effectiveness and improvement in this inter-disciplinary field of research, it includes information beyond the therapy administrated in clinical settings-but with no diminished safety requirements. Our systematic review, based on PRISMA guidelines, searched articles published between January 2001 and November 2017 from the following databases: Cochrane, Medline/PubMed, PMC, Elsevier, PEDro, and ISI Web of Knowledge/Science. Then we have applied a new innovative PEDro-inspired technique to classify the relevant articles. The article focuses on the main indications, current technologies, categories of intervention and outcome assessment modalities. It includes also, in tabular form, the main characteristics of the most relevant mobile (wearable and/or portable) mechatronic/robotic orthoses/exoskeletons prototype devices used to assist-rehabilitate neuromotor impairments in the upper limb

Supervised Learning in Multilayer Spiking Neural Networks.

In this thesis, a new supervised learning algorithm for multilayer spiking neural networks is presented. Gradient descent learning algorithms have led traditional neural networks with multiple layers to be one of the most powerful and flexible computational models derived from artificial neural networks. However, more recent experimental evidence suggests that biological neural systems use the exact time of single action potentials to encode information. These findings have led to a new way of simulating neural networks based on temporal encoding with single spikes. Analytical demonstrations show that these types of neural networks are computationally more powerful than networks of rate neurons. Conversely, the existing learning algorithms no longer apply to spiking neural networks. Supervised learning algorithms based on gradient descent, such as SpikeProp and its extensions, have been developed for spiking neural networks with multiple layers, but these are limited to a specific model of neurons, with only the first spike being considered. Another learning algorithm, ReSuMe, for single layer networks is based on spike-timing dependent plasticity (STDP) and uses the computational power of multiple spikes; moreover, this algorithm is not limited to a specific neuron model. The algorithm presented here is based on the gradient descent method, while making use of STDP and can be applied to networks with multiple layers. Furthermore, the algorithm is not limited to neurons firing single spikes or specific neuron models. Results on classic benchmarks, such as the XOR problem and the Iris data set, show that the algorithm is capable of non-linear transformations. Complex classification tasks have also been applied with fast convergence times. The results of the simulations show that the new learning rule is as efficient as SpikeProp while having all the advantages of STDP. The supervised learning algorithm for spiking neurons is compared with the back-propagation algorithm for rate neurons by modelling an audio-visual perceptual illusion, the McGurk effect

Supervised learning in multilayer spiking neural networks

In this thesis, a new supervised learning algorithm for multilayer spik- ing neural networks is presented. Gradient descent learning algo- rithms have led traditional neural networks with multiple layers to be one of the most powerful and flexible computational models derived from artificial neural networks. However, more recent experimental evidence suggests that biological neural systems use the exact time of single action potentials to encode information. These findings have led to a new way of simulating neural networks based on temporal en- coding with single spikes. Analytical demonstrations show that these types of neural networks are computationally more powerful than net- works of rate neurons. Conversely, the existing learning algorithms no longer apply to spik- ing neural networks. Supervised learning algorithms based on gradient descent, such as SpikeProp and its extensions, have been developed for spiking neural networks with multiple layers, but these ate limited to a specific model of neurons, with only the first spike being consid- ered. Another learning algorithm, ReSuMe, for single layer networks is based on spike-timing dependent plasticity ~STDP) and uses the computational power of multiple spikes; moreover, this algorithm is not limited to a specific neuron model. The algorithm presented here is based on the gradient descent method, while making use of STDP and can be applied to networks with multi- ple layers. Furthermore, the algorithm is not limited to neurons firing single spikes or specific neuron models. Results on classic benchmarks, such as the XOR problem and the Iris data set, show that the algo- rithm is capable of non-linear transformations. Complex classification tasks have also been applied with fast convergence times. The results of the simulations show that the new learning rule is as efficient as SpikeProp while having all the advantages of STDP. The supervised learning algorithm for spiking neurons is compared with the back- propagation algorithm for rate neurons by modelling an audio-visual perceptual illusion, the McGurk effect.EThOS - Electronic Theses Online ServiceGBUnited Kingdo