Full Orbit Sequences in Affine Spaces via Fractional Jumps and Pseudorandom Number Generation

Let $n$ be a positive integer. In this paper we provide a general theory to produce full orbit sequences in the affine $n$-dimensional space over a finite field. For $n=1$ our construction covers the case of the Inversive Congruential Generators (ICG). In addition, for $n>1$ we show that the sequences produced using our construction are easier to compute than ICG sequences. Furthermore, we prove that they have the same discrepancy bounds as the ones constructed using the ICG.Comment: To appear in Mathematics of Computatio

Achieving Success under Pressure in the Conservation of Intensely Used Coastal Areas

2siUnderstanding how conservation and socioeconomic development can be harmonized in organizational and social- ecological systems is at the core of sustainability science. We present the case of an organization that manage a Mediterranean marine protected area (MPA), the Tavolara-Punta Coda Cavallo MPA, that exhibits high ecological performance under intense pressure from fishing, tourism, and coastal development. This case study illustrates how socioeconomic development and significant conservation benefits can coexist, even in a challenging context. Based on this case study, we present a framework for what elements and interactions have determined the high ecological performance of this MPA, and highlight the key organizational leverages that have enabled ecosystem recovery. In particular, the most critical elements underlying high performance were sufficient leadership and knowledge to identify a conservation vision and to catalyze some key actors in the implementation of this vision. Thus, success was ultimately determined by the ability of the leadership of the MPA to devise and implement an effective strategy, with the support and participation of key actors that were external to the MPA organization. The insights from this case study may be applicable to improving MPA management in other systems with similar characteristics, including high human pressures and the presence of an MPA authority.openMICHELI F.; NICCOLINI F.Micheli, F.; Niccolini, Federic

Graph Mixture Density Networks

We introduce the Graph Mixture Density Networks, a new family of machine learning models that can fit multimodal output distributions conditioned on graphs of arbitrary topology. By combining ideas from mixture models and graph representation learning, we address a broader class of challenging conditional density estimation problems that rely on structured data. In this respect, we evaluate our method on a new benchmark application that leverages random graphs for stochastic epidemic simulations. We show a significant improvement in the likelihood of epidemic outcomes when taking into account both multimodality and structure. The empirical analysis is complemented by two real-world regression tasks showing the effectiveness of our approach in modeling the output prediction uncertainty. Graph Mixture Density Networks open appealing research opportunities in the study of structure-dependent phenomena that exhibit non-trivial conditional output distributions

Theoretically Expressive and Edge-aware Graph Learning

We propose a new Graph Neural Network that combines recent advancements in the field. We give theoretical contributions by proving that the model is strictly more general than the Graph Isomorphism Network and the Gated Graph Neural Network, as it can approximate the same functions and deal with arbitrary edge values. Then, we show how a single node information can flow through the graph unchanged

Modeling Edge Features with Deep Bayesian Graph Networks

We propose an extension of the Contextual Graph Markov Model, a deep and probabilistic machine learning model for graphs, to model the distribution of edge features. Our approach is architectural, as we introduce an additional Bayesian network mapping edge features into discrete states to be used by the original model. In doing so, we are also able to build richer graph representations even in the absence of edge features, which is confirmed by the performance improvements on standard graph classification benchmarks. Moreover, we successfully test our proposal in a graph regression scenario where edge features are of fundamental importance, and we show that the learned edge representation provides substantial performance improvements against the original model on three link prediction tasks. By keeping the computational complexity linear in the number of edges, the proposed model is amenable to large-scale graph processing.Comment: Releasing pre-print version to comply with TAILOR project requirement

Preliminary experimental analysis of Reservoir Computing approach for balance assessment

Evaluation of balance stability in elderly people is of prominent relevance in the field of health monitoring. Recently, the use of Wii Balance Board has been proposed as valid alternative to clinical balance tests, such as the widely used Berg Balance Scale (BBS) test, allowing to measure and analyze static features such as the duration or the speed of assessment of patients' center of pressure. In an innovative way, in this paper we propose to take into consideration the whole temporal information generated by the balance board, analyzing it by means of dynamical neural networks. In particular, using Recurrent Neural Networks implemented according to the Reservoir Computing paradigm, we propose to estimate the BBS score from the temporal data generated by the execution of one simple exercise on the balance board. Preliminary experimental assessments of the proposed approach on a real-world dataset show promising results

Improving the Fault Tolerance of Nanometric PLA Designs

Several alternative building blocks have been proposed to replace planar transistors, among which a prominent spot belongs to nanometric laments such as Silicon NanoWires (SiNWs) and Carbon NanoTubes (CNTs). However, chips leveraging these nanoscale structures are expected to be affected by a large amount of manufacturing faults, way beyond what chip architects have learned to counter. In this paper, we show a design ow, based on software mapping algorithms, to improve the yield of nanometric Programmable Logic Arrays (PLAs). While further improvements to the manufacturing technology will be needed to make these devices fully usable, our ow can signi cantly shrink the gap between current and desired yield levels. Also, our approach does not need post-fabrication functional analysis and mapping, therefore dramatically cutting on veri cation costs. We check PLA yields by means of an accurate analyzer after Monte Carlo fault injection. We show that, compared to a baseline policy of wire replication, we achieve equal or better yields (8% over a set of designs) depending on the underlying defect assumptions

Impulsivity Markers in Parkinsonian Subthalamic Single-Unit Activity

Impulsive-compulsive behaviors are common in Parkinson's disease (PD) patients. However, the basal ganglia dysfunctions associated with high impulsivity have not been fully characterized. The objective of this study was to identify the features associated with impulsive-compulsive behaviors in single neurons of the subthalamic nucleus (STN)