Shot noise suppression in quasi one-dimensional Field Effect Transistors

We present a novel method for the evaluation of shot noise in quasi one-dimensional field-effect transistors, such as those based on carbon nanotubes and silicon nanowires. The method is derived by using a statistical approach within the second quantization formalism and allows to include both the effects of Pauli exclusion and Coulomb repulsion among charge carriers. In this way it extends Landauer-Buttiker approach by explicitly including the effect of Coulomb repulsion on noise. We implement the method through the self-consistent solution of the 3D Poisson and transport equations within the NEGF framework and a Monte Carlo procedure for populating injected electron states. We show that the combined effect of Pauli and Coulomb interactions reduces shot noise in strong inversion down to 23 % of the full shot noise for a gate overdrive of 0.4 V, and that neglecting the effect of Coulomb repulsion would lead to an overestimation of noise up to 180 %.Comment: Changed content, 7 pages,5 figure

Enhanced shot noise in carbon nanotube field-effect transistors

We predict shot noise enhancement in defect-free carbon nanotube field-effect transistors through a numerical investigation based on the self-consistent solution of the Poisson and Schrodinger equations within the non-equilibrium Green functions formalism, and on a Monte Carlo approach to reproduce injection statistics. Noise enhancement is due to the correlation between trapping of holes from the drain into quasi-bound states in the channel and thermionic injection of electrons from the source, and can lead to an appreciable Fano factor of 1.22 at room temperature.Comment: 4 pages, 4 figure

Motion Invariance in Visual Environments

The puzzle of computer vision might find new challenging solutions when we realize that most successful methods are working at image level, which is remarkably more difficult than processing directly visual streams, just as happens in nature. In this paper, we claim that their processing naturally leads to formulate the motion invariance principle, which enables the construction of a new theory of visual learning based on convolutional features. The theory addresses a number of intriguing questions that arise in natural vision, and offers a well-posed computational scheme for the discovery of convolutional filters over the retina. They are driven by the Euler-Lagrange differential equations derived from the principle of least cognitive action, that parallels laws of mechanics. Unlike traditional convolutional networks, which need massive supervision, the proposed theory offers a truly new scenario in which feature learning takes place by unsupervised processing of video signals. An experimental report of the theory is presented where we show that features extracted under motion invariance yield an improvement that can be assessed by measuring information-based indexes.Comment: arXiv admin note: substantial text overlap with arXiv:1801.0711

Is family farming educational? The Australian experience

The Australian rural landscape has been changing throughout history since the first European settlement. The progressive expansion of agriculture in the past centuries is responsible for its modification and diversification. Family farming has a relevant role in the Australian agriculture and food production, however in the last decades it has been facing a consistent decline, primarily because of economic and climatic reasons. This paper aims to retrace the historical development of agriculture in Australia and to analyse the current situation of family farming, by reporting the tendencies and the changed features, the educational and social aspects, and the interaction with the rural landscape. According to our research it emerged that family farming has been one of the major keys of the agricultural sector development in Australia and was deeply affected through history by internal and external factors such as globalization, neoliberalism, immigration and climatic conditions. Nowadays family farming is pivotal in the interface connection between modern societies and rural environment. In fact it is also becoming an important component of national tourism, with the birth and development of agrotourisms and holiday farms which in the past years have accounted for a considerable percentage of visits both from international and national people

Backprop Diffusion is Biologically Plausible

The Backpropagation algorithm relies on the abstraction of using a neural model that gets rid of the notion of time, since the input is mapped instantaneously to the output. In this paper, we claim that this abstraction of ignoring time, along with the abrupt input changes that occur when feeding the training set, are in fact the reasons why, in some papers, Backprop biological plausibility is regarded as an arguable issue. We show that as soon as a deep feedforward network operates with neurons with time-delayed response, the backprop weight update turns out to be the basic equation of a biologically plausible diffusion process based on forward-backward waves. We also show that such a process very well approximates the gradient for inputs that are not too fast with respect to the depth of the network. These remarks somewhat disclose the diffusion process behind the backprop equation and leads us to interpret the corresponding algorithm as a degeneration of a more general diffusion process that takes place also in neural networks with cyclic connections.Comment: 9 pages, 3 figures. arXiv admin note: text overlap with arXiv:1907.0510