Learning a Local Reference Frame for Point Clouds using Spherical CNNs

Uno dei problemi più importanti della 3D Computer Vision è il cosiddetto surface matching, che consiste nel trovare corrispondenze tra oggetti tridimensionali. Attualmente il problema viene affrontato calcolando delle feature locali e compatte, chiamate descrittori, che devono essere riconosciute e messe in corrispondenza al mutare della posa dell'oggetto nello spazio, e devono quindi essere invarianti rispetto all'orientazione. Il metodo più usato per ottenere questa proprietà consiste nell'utilizzare dei Local Reference Frame (LRF): sistemi di coordinate locali che forniscono un'orientazione canonica alle porzioni di oggetti 3D che vengono usate per calcolare i descrittori. In letteratura esistono diversi modi per calcolare gli LRF, ma fanno tutti uso di algoritmi progettati manualmente. Vi è anche una recente proposta che utilizza reti neurali, tuttavia queste vengono addestrate mediante feature specificamente progettate per lo scopo, il che non permette di sfruttare pienamente i benefici delle moderne strategie di end-to-end learning. Lo scopo di questo lavoro è utilizzare un approccio data-driven per far imparare a una rete neurale il calcolo di un Local Reference Frame a partire da point cloud grezze, producendo quindi il primo esempio di end-to-end learning applicato alla stima di LRF. Per farlo, sfruttiamo una recente innovazione chiamata Spherical Convolutional Neural Networks, le quali generano e processano segnali nello spazio SO(3) e sono quindi naturalmente adatte a rappresentare e stimare orientazioni e LRF. Confrontiamo le prestazioni ottenute con quelle di metodi esistenti su benchmark standard, ottenendo risultati promettenti

El miʿrāǧ de Muḥammad según Baldassarre Loyola Mandes S.J. (1631-1667).Fuentes, controversia y cristianización de una tradición islámica

The article deals with an unknown Latin version of the miʿrāǧ the author has discovered in the Archive of the Pontifical Gregorian University within an booklet written by Baldassarre Loyola Mandes S.J. (1631-1667), a Moroccan Muslim prince converted to Christianity who then joined the Society of Jesus. The aim of the article will be to demonstrate how this Latin miʿrāǧ relied on an Arabic source related to the ḥadīṯ literature. As a method for reaching our aim, we will make a comparative study of the sources of which Baldassare may have had knowledge. We will further show the way Baldassarre tried not only to polemicize with the Islamic tradition, but also the strategies he used for Christianizing it.El artículo versa sobre una versión latina desconocida del miʿrāǧ que el autor ha descubierto en el Archivo de la Pontificia Universidad Gregoriana dentro de un opúsculo escrito por Baldassarre Loyola Mandes S.J. (1631-1667), un príncipe musulmán marroquí convertido al cristianismo que luego entró en la Compañía de Jesús. El objetivo del artículo será demostrar cómo este miʿrāǧ latino se basó en una fuente árabe relacionada con la literatura de ḥadīṯ. Como método para alcanzar nuestro objetivo, haremos un estudio comparativo de las fuentes de las que Baldassare pudo tener conocimiento. Además, mostraremos el modo en que Baldassarre intentó no solo polemizar con la tradición islámica, sino también las estrategias que utilizó para cristianizarla

Spatial Representations in the Entorhino-Hippocampal Circuit

After a general introduction and a brief review of the available experimental data on spatial representations (chapter 2), this thesis is divided into two main parts. The first part, comprising the chapters from 3 to 6, is dedicated to grid cells. In chapter 3 we present and discuss the various models proposed for explaining grid cells formation. In chapter 4 and 5 we study our model of grid cells generation based on adaptation in the case of non-planar environments, namely in the case of a spherical environment and of three-dimensional space. In chapter 6 we propose a variant of the model where the alignment of the grid axes is induced through reciprocal inhibition, and we suggest that that the inhibitory connections obtained during this learning process can be used to implement a continuous attractor in mEC. The second part, comprising chapters from 7 to 10 is instead focused on place cell representations. In chapter 7 we analyze the differences between place cells and grid cells in terms on information content, in chapter 8 we describe the properties of attractor dynamics in our model of the Ca3 net- work, and in the following chapter we study the effects of theta oscillations on network dynamics. Finally, in Chapter 10 we analyze to what extent the learning of a new representation, can preserve the topology and the exact metric of physical space

Analysis of the fluctuations of the tumour/host interface

In a recent analysis of metabolic scaling in solid tumours we found a scaling law that interpolates between the power laws μ∝V and μ∝V2∕3, where μ is the metabolic rate expressed as the glucose absorption rate and V is the tumour volume. The scaling law fits quite well both in vitro and in vivo data, however we also observed marked fluctuations that are associated with the specific biological properties of individual tumours. Here we analyse these fluctuations, in an attempt to find the population-wide distribution of an important parameter (A) which expresses the total extent of the interface between the solid tumour and the non-cancerous environment. Heuristic considerations suggest that the values of the A parameter follow a lognormal distribution, and, allowing for the large uncertainties of the experimental data, our statistical analysis confirms this

Associative Memory Storage and Retrieval: Involvement of Theta Oscillations in Hippocampal Information Processing

Theta oscillations are thought to play a critical role in neuronal information processing, especially in the hippocampal region, where their presence is particularly salient. A detailed description of theta dynamics in this region has revealed not only a consortium of layer-specific theta dipoles, but also within-layer differences in the expression of theta. This complex and articulated arrangement of current flows is reflected in the way neuronal firing is modulated in time. Several models have proposed that these different theta modulators flexibly coordinate hippocampal regions, to support associative memory formation and retrieval. Here, we summarily review different approaches related to this issue and we describe a mechanism, based on experimental and simulation results, for memory retrieval in CA3 involving theta modulation

LHCSR Expression under HSP70/RBCS2 Promoter as a Strategy to Increase Productivity in Microalgae

Microalgae are unicellular photosynthetic organisms considered as potential alternative sources for biomass, biofuels or high value products. However, limited biomass productivity is commonly experienced in their cultivating system despite their high potential. One of the reasons for this limitation is the high thermal dissipation of the light absorbed by the outer layers of the cultures exposed to high light caused by the activation of a photoprotective mechanism called non-photochemical quenching (NPQ). In the model organism for green algae Chlamydomonas reinhardtii, NPQ is triggered by pigment binding proteins called light-harvesting-complexes-stress-related (LHCSRs), which are over-accumulated in high light. It was recently reported that biomass productivity can be increased both in microalgae and higher plants by properly tuning NPQ induction. In this work increased light use efficiency is reported by introducing in C. reinhardtii a LHCSR3 gene under the control of Heat Shock Protein 70/RUBISCO small chain 2 promoter in a npq4 lhcsr1 background, a mutant strain knockout for all LHCSR genes. This complementation strategy leads to a low expression of LHCSR3, causing a strong reduction of NPQ induction but is still capable of protecting from photodamage at high irradiance, resulting in an improved photosynthetic efficiency and higher biomass accumulation

Hippocampal reactivation of random trajectories resembling Brownian Diffusion

Hippocampal activity patterns representing movement trajectories are reactivated in immobility and sleep periods, a process associated with memory recall, consolidation, and decision making. It is thought that only fixed, behaviorally relevant patterns can be reactivated, which are stored across hippocampal synaptic connections. To test whether some generalized rules govern reactivation, we examined trajectory reactivation following non-stereotypical exploration of familiar open-field environments. We found that random trajectories of varying lengths and timescales were reactivated, resembling that of Brownian motion of particles. The animals’ behavioral trajectory did not follow Brownian diffusion demonstrating that the exact behavioral experience is not reactivated. Therefore, hippocampal circuits are able to generate random trajectories of any recently active map by following diffusion dynamics. This ability of hippocampal circuits to generate representations of all behavioral outcome combinations, experienced or not, may underlie a wide variety of hippocampal-dependent cognitive functions such as learning, generalization, and planning