A Growing Self-Organizing Network for Reconstructing Curves and Surfaces

Self-organizing networks such as Neural Gas, Growing Neural Gas and many others have been adopted in actual applications for both dimensionality reduction and manifold learning. Typically, in these applications, the structure of the adapted network yields a good estimate of the topology of the unknown subspace from where the input data points are sampled. The approach presented here takes a different perspective, namely by assuming that the input space is a manifold of known dimension. In return, the new type of growing self-organizing network presented gains the ability to adapt itself in way that may guarantee the effective and stable recovery of the exact topological structure of the input manifold

Explicit excluded volume of cylindrically symmetric convex bodies

We represent explicitly the excluded volume Ve{B1,B2} of two generic cylindrically symmetric, convex rigid bodies, B1 and B2, in terms of a family of shape functionals evaluated separately on B1 and B2. We show that Ve{B1,B2} fails systematically to feature a dipolar component, thus making illusory the assignment of any shape dipole to a tapered body in this class. The method proposed here is applied to cones and validated by a shape-reconstruction algorithm. It is further applied to spheroids (ellipsoids of revolution), for which it shows how some analytic estimates already regarded as classics should indeed be emended

Retraction with face saving: Modelling conversational interaction through dynamic hypermedia

This paper describes RWFS (Retraction With Face Saving), a hypermedia application which models an interview between a lawyer and his client ‐ a lorry driver ‐ facing court charges of reckless driving. At one level RWFS takes the form of a sophisticated game in which different outcomes to the interview are possible according to the learner's degree of skill. At another level, RWFS is designed to encourage the language learner's awareness and understanding of the pragmatic features of conversation. RWFS runs on HyperContext, a hybrid hypertextlexpert system developed in Pavia by two of the authors, Marco Piastra and Roberto Bolognesi, and which supports dynamic hypermedia units. HyperContext's dynamic linking capacity plays a vital role in simulating significant conversational features such as the conditioning of a current move in the conversation by information acquired much earlier in the course of the interview. In this connection, the paper discusses the contribution of RMCI (Re‐usable Model of Conversational Interaction), a re‐usable application‐independent applied model of interaction on which the game is based, and which links a tactical level (the conversation) to a metalevel which provides a move‐by‐move commentary on interactional theory. In its turn, RMCFs metalevel is linked to a strategic level which interprets the structure of the conversation in terms of a pyramid‐like hierarchy of increasingly abstract theoretical concepts

Some Further Evidence about Magnification and Shape in Neural Gas

Neural gas (NG) is a robust vector quantization algorithm with a well-known mathematical model. According to this, the neural gas samples the underlying data distribution following a power law with a magnification exponent that depends on data dimensionality only. The effects of shape in the input data distribution, however, are not entirely covered by the NG model above, due to the technical difficulties involved. The experimental work described here shows that shape is indeed relevant in determining the overall NG behavior; in particular, some experiments reveal richer and complex behaviors induced by shape that cannot be explained by the power law alone. Although a more comprehensive analytical model remains to be defined, the evidence collected in these experiments suggests that the NG algorithm has an interesting potential for detecting complex shapes in noisy datasets

Exogenous Surfactant Treatment in Children with ARDS

Since the Food and Drug Administration (FDA) approved exogenous surfactant in the early 90s for the treatment of neonates with Hyaline Membrane Disease (HMD), many studies have focused on enlarging its indications for others types of lung injuries and for other age groups. Although in the past 20 years no studies have shown clear results about the efficacy of exogenous surfactant treatment in paediatric Acute Respiratory Distress Syndrome (ARDS), many of them were able to point out and better define very important aspects of this treatment like dosage, timing, ways of administration and usage of different types of surfactant (natural and synthetic). In this review we retrace the development of studies looking at the role of exogenous surfactant treatment in paediatric ARDS

Online Fall Detection using Recurrent Neural Networks

Unintentional falls can cause severe injuries and even death, especially if no immediate assistance is given. The aim of Fall Detection Systems (FDSs) is to detect an occurring fall. This information can be used to trigger the necessary assistance in case of injury. This can be done by using either ambient-based sensors, e.g. cameras, or wearable devices. The aim of this work is to study the technical aspects of FDSs based on wearable devices and artificial intelligence techniques, in particular Deep Learning (DL), to implement an effective algorithm for on-line fall detection. The proposed classifier is based on a Recurrent Neural Network (RNN) model with underlying Long Short-Term Memory (LSTM) blocks. The method is tested on the publicly available SisFall dataset, with extended annotation, and compared with the results obtained by the SisFall authors.Comment: 6 pages, ICRA 201