Visual illusions: An interesting tool to investigate developmental dyslexia and autism spectrum disorder

A visual illusion refers to a percept that is different in some aspect from the physical stimulus. Illusions are a powerful non-invasive tool for understanding the neurobiology of vision, telling us, indirectly, how the brain processes visual stimuli. There are some neurodevelopmental disorders characterized by visual deficits. Surprisingly, just a few studies investigated illusory perception in clinical populations. Our aim is to review the literature supporting a possible role for visual illusions in helping us understand the visual deficits in developmental dyslexia and autism spectrum disorder. Future studies could develop new tools – based on visual illusions – to identify an early risk for neurodevelopmental disorders

SVILUPPO E CONVALIDAZIONE SPERIMENTALE DI MODELLI CFD PER LA CARATTERIZZAZIONE DEL FLUSSO ALL'INTERNO DI SERVOVALVOLE ELETTROIDRAULICHE

La presente tesi, che rappresenta la prima esperienza svolta presso il Dipartimento di Ingegneria Aerospaziale dell’Università di Pisa (DIA), sullo sviluppo di modelli di fluidodinamica computazionale (CFD) per l’analisi del flusso interno a servovalvole elettroidrauliche, si inserisce nell’ambito delle attività di ricerca in corso presso il DIA finalizzate allo studio dei moderni sistemi di controllo del volo Fly-By-Wire con attuazione idraulica. Gli obiettivi delle presente tesi sono da un lato la caratterizzazione sperimentale del flusso idraulico attraverso una servovalvola a quattro vie, dall’altro lo sviluppo e la convalidazione di modelli di simulazione CFD relativi alla servovalvola stessa. In particolare, si sono verificate le potenzialità offerte dal software STAR-CD® (versione 3.150) per la realizzazione di modelli CFD relativi a flussi interni. La prima parte del lavoro riguarda sia lo studio dei modelli teorici forniti in letteratura per l’analisi delle prestazioni idrauliche delle servovalvole a quattro vie, che la progettazione di un banco prova per la caratterizzazione sperimentale del flusso attraverso una servovalvola a quattro vie di impiego industriale. Nella seconda parte del lavoro si è passati invece allo sviluppo di modelli di simulazione numerica e all’implementazione di questi sul software STAR-CD®, per simulare il flusso all’interno della servovalvola oggetto di studio. Infine, a conclusione dell’attività, è stata fatta un’analisi critica del confronto tra i risultati ottenuti dalle simulazioni CFD e i dati ricavati dalle campagne di sperimentazione condotte

Elaborazioni SAR tomografiche di dati in banda P - progetto ESA BIOSAR

IL lavoro tratta di analisi tomografiche effettuate su dati ricavati da una missione sulla Svezia su aree forestali, per verificare se con una lunghezza d'onda appropriata, sia possibile ricavare informazioni sia sullo strato di terreno sottostante la foresta, che sulla foresta stessa

Detection vs. grouping thresholds for elements differing in spacing, size and luminance. An alternative approach towards the psychophysics of Gestalten

AbstractThree experiments were performed to compare thresholds for the detection of non-uniformity in spacing, size and luminance with thresholds for grouping. In the first experiment a row of 12 black equi-spaced dots was used and the spacing after the 3rd, 6th, and 9th dot increased in random steps to determine the threshold at which the observer detected an irregularity in the size of the gaps. Thereafter, spacing in the same locations was increased further to find the threshold at which the observer perceived four groups of three dots each (triplets). In the second experiment, empty circles were used instead of dots and the diameter of the circles in the first and second triplet increased until the difference in size gave rise either to a detection or grouping response. In the third experiment, the dots in the second and fourth triplet were increased in luminance. The aim again was to compare the difference in brightness required for detection or grouping, respectively. Results demonstrate that the threshold for perceiving stimuli as irregularly spaced or dissimilar in size or brightness is much smaller than the threshold for grouping. In order to perceive stimuli as grouped, stimulus differences had to be 5.2 times (for dot spacing), 7.4 times (for size) and 6.6 times (for luminance) larger than for detection. Two control experiments demonstrated that the difference between the two kinds of thresholds persisted even when only two gaps were used instead of three and when gap position was randomized

Friendly Training: Neural Networks Can Adapt Data To Make Learning Easier

In the last decade, motivated by the success of Deep Learning, the scientific community proposed several approaches to make the learning procedure of Neural Networks more effective. When focussing on the way in which the training data are provided to the learning machine, we can distinguish between the classic random selection of stochastic gradient-based optimization and more involved techniques that devise curricula to organize data, and progressively increase the complexity of the training set. In this paper, we propose a novel training procedure named Friendly Training that, differently from the aforementioned approaches, involves altering the training examples in order to help the model to better fulfil its learning criterion. The model is allowed to simplify those examples that are too hard to be classified at a certain stage of the training procedure. The data transformation is controlled by a developmental plan that progressively reduces its impact during training, until it completely vanishes. In a sense, this is the opposite of what is commonly done in order to increase robustness against adversarial examples, i.e., Adversarial Training. Experiments on multiple datasets are provided, showing that Friendly Training yields improvements with respect to informed data sub-selection routines and random selection, especially in deep convolutional architectures. Results suggest that adapting the input data is a feasible way to stabilize learning and improve the generalization skills of the network.Comment: 9 pages, 5 figure

Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified by Other Networks

Amongst a variety of approaches aimed at making the learning procedure of neural networks more effective, the scientifc community developed strategies to order the examples according to their estimated complexity, to distil knowledge from larger networks, or to exploit the principles behind adversarial machine learning. A different idea has been recently proposed, named Friendly Training, which consists in altering the input data by adding an automatically estimated perturbation, with the goal of facilitating the learning process of a neural classifer. The transformation progressively fadesout as long as training proceeds, until it completely vanishes. In this work we revisit and extend this idea, introducing a radically different and novel approach inspired by the effectiveness of neural generators in the context of Adversarial Machine Learning. We propose an auxiliary multi-layer network that is responsible of altering the input data to make them easier to be handled by the classifer at the current stage of the training procedure. The auxiliary network is trained jointly with the neural classifer, thus intrinsically increasing the “depth” of the classifer, and it is expected to spot general regularities in the data alteration process. The effect of the auxiliary network is progressively reduced up to the end of training, when it is fully dropped and the classifer is deployed for applications. We refer to this approach as Neural Friendly Training. An extended experimental procedure involving several datasets and different neural architectures shows that Neural Friendly Training overcomes the originally proposed Friendly Training technique, improving the generalization of the classifer, especially in the case of noisy data

A new methodological framework for within-day dynamic estimation of pollutant emissions in a large congested urban network

This paper presents a new methodological framework to address the problem of estimating pollutant emissions for large congested urban networks in a within-day dynamic context. It consists of three main modules: 1) a module to compute pollutant emissions for general links; 2) a module to compute pollutant emissions for all links approaching a signalized intersection; 3) a module to compute pollutant emissions for all links approaching an unsignalized intersection. A dynamic mesoscopic assignment model is performed to derive the main dynamic input of each one of the modules. All the modules have been tested in a real case study (the district of Eur in the city of Rome, Italy), so confirming the reliability of the developed models and their applicability for the estimation of pollutant emissions

Continual Learning with Pretrained Backbones by Tuning in the Input Space

The intrinsic difficulty in adapting deep learning models to non-stationary environments limits the applicability of neural networks to real-world tasks. This issue is critical in practical supervised learning settings, such as the ones in which a pre-trained model computes projections toward a latent space where different task predictors are sequentially learned over time. As a matter of fact, incrementally fine-tuning the whole model to better adapt to new tasks usually results in catastrophic forgetting, with decreasing performance over the past experiences and losing valuable knowledge from the pre-training stage. In this paper, we propose a novel strategy to make the fine-tuning procedure more effective, by avoiding to update the pre-trained part of the network and learning not only the usual classification head, but also a set of newly-introduced learnable parameters that are responsible for transforming the input data. This process allows the network to effectively leverage the pre-training knowledge and find a good trade-off between plasticity and stability with modest computational efforts, thus especially suitable for on-the-edge settings. Our experiments on four image classification problems in a continual learning setting confirm the quality of the proposed approach when compared to several fine-tuning procedures and to popular continual learning methods