Sampling-based optimal kinodynamic planning with motion primitives

This paper proposes a novel sampling-based motion planner, which integrates in RRT* (Rapidly exploring Random Tree star) a database of pre-computed motion primitives to alleviate its computational load and allow for motion planning in a dynamic or partially known environment. The database is built by considering a set of initial and final state pairs in some grid space, and determining for each pair an optimal trajectory that is compatible with the system dynamics and constraints, while minimizing a cost. Nodes are progressively added to the tree {of feasible trajectories in the RRT* by extracting at random a sample in the gridded state space and selecting the best obstacle-free motion primitive in the database that joins it to an existing node. The tree is rewired if some nodes can be reached from the new sampled state through an obstacle-free motion primitive with lower cost. The computationally more intensive part of motion planning is thus moved to the preliminary offline phase of the database construction at the price of some performance degradation due to gridding. Grid resolution can be tuned so as to compromise between (sub)optimality and size of the database. The planner is shown to be asymptotically optimal as the grid resolution goes to zero and the number of sampled states grows to infinity

Weak nodes detection in urban transport systems: Planning for resilience in Singapore

The availability of massive data-sets describing human mobility offers the possibility to design simulation tools to monitor and improve the resilience of transport systems in response to traumatic events such as natural and man-made disasters (e.g. floods terroristic attacks, etc...). In this perspective, we propose ACHILLES, an application to model people's movements in a given transport system mode through a multiplex network representation based on mobility data. ACHILLES is a web-based application which provides an easy-to-use interface to explore the mobility fluxes and the connectivity of every urban zone in a city, as well as to visualize changes in the transport system resulting from the addition or removal of transport modes, urban zones, and single stops. Notably, our application allows the user to assess the overall resilience of the transport network by identifying its weakest node, i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To demonstrate the impact of ACHILLES for humanitarian aid we consider its application to a real-world scenario by exploring human mobility in Singapore in response to flood prevention.Comment: 9 pages, 6 figures, IEEE Data Science and Advanced Analytic

A portable wireless-based architecture for solving minimum digital divide problems.

In today's society digital services have become the key to the success of anyone. Hence, for being competitive it is important that these services are available, employ the latest technology and are low cost. Unfortunately, it often happens that these good intentions do not correspond to reality. In this paper an information system is proposed, targeted at those small realities affected by the digital divide and at those companies that employ out of date, high cost technologies, that provides data and voice services in a unified manner using heterogeneous devices. The system utilizes innovative technologies, in particular wireless technology, to deliver low cost solutions. The distinctive feature is that it does not depend on the network hardware infrastructure and the underlying platform. Furthermore, it deals with the configuration, accounting, security, management, and monitoring aspects while maintaining its flexibility and simplicity of use both for the administrator and end user

Nitrogen and energy partitioning in two genetic groups of pigs fed low-protein diets at 130 kg body weight

The aim was to evaluate the effect of low-protein (LP) or low-amino acid diets on digestibility, energy and nitrogen (N) utilisation in 2 genetic groups (GG) of pigs (129±11 kg BW). Duroc×Large White (A) pigs were chosen to represent a traditional GG for ham production, and Danbred Duroc (D) pigs to represent a GG with fast growing rate and high carcass lean yield. Dietary treatments: a conventional diet (CONV) containing 13.2% CP, and two LP diets, one with LP (10.4%) and low essential AA (LP1), the second with LP (9.7%) and high essential AA (LP2). Compared to CONV, LP2 had the same essential AA content per unit feed, while LP1 the same essential AA content per unit CP. Feed was restricted (DMI=6.8% BW0.75). Four consecutive digestibility/balances periods were conducted with 24 barrows, 12 A and 12 D. Metabolic cages and respiration chambers were used. No significant difference between diets was registered for digestibility. Nitrogen excreted: 41.3, 33.4 and 29.0 g/d (P=0.009), for CONV, LP1 and LP2 diets, respectively. Nitrogen retention was similar between the diets. Heat production (HP) was the lowest for LP diets. There was a tendency (P=0.079) for a lower energy digestibility in D group. The D pigs also had a higher HP and hence a lower retained energy in comparison with the A pigs. In conclusion: it is possible to reduce N excretion using very LP diets and LP-low AA diets; Danbred GG have a higher heat production and a lower energy retention than A pigs

Multi-Fractional Brownian Motion: Estimating the Hurst Exponent via Variational Smoothing with Applications in Finance

Beginning with the basics of the Wiener process, we consider limitations characterizing the “Brownian approach” in analyzing real phenomena. This leads us to first consider the fractional Brownian motion (fBm)—also discussing theWood–Chan fast algorithm to generate sample paths—to then focus on multi-fBm and methods to generate its trajectories. This is heavily linked to the Hurst exponent study, which we link to real data, firstly considering an absolute moment method, allowing us to obtain raw estimates, to then consider variational calculus approaches allowing to smooth it. The latter smoothing tool was tested in accuracy on synthetic data, comparing it with the exponential moving average method. Previous analyses and results were exploited to develop a forecasting procedure applied to the real data of foreign exchange rates from the Forex market

A numerical study of the jerky crack growth in elastoplastic materials with localized plasticity

We present a numerical implementation of a model of quasi-static crack growth in linearly elastic-perfectly plastic materials. We assume that the displacement is antiplane, and that the cracks and the plastic slips are localized on a prescribed path. We provide numerical evidence of the fact that the crack growth is intermittent, with jump characteristics that depend on the material properties.Comment: 13 pages, 7 figure

Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information

The movements of individuals within and among cities influence key aspects of our society, such as the objective and subjective well-being, the diffusion of innovations, the spreading of epidemics, and the quality of the environment. For this reason, there is increasing interest around the challenging problem of flow generation, which consists in generating the flows between a set of geographic locations, given the characteristics of the locations and without any information about the real flows. Existing solutions to flow generation are mainly based on mechanistic approaches, such as the gravity model and the radiation model, which suffer from underfitting and overdispersion, neglect important variables such as land use and the transportation network, and cannot describe non-linear relationships between these variables. In this paper, we propose the Multi-Feature Deep Gravity (MFDG) model as an effective solution to flow generation. On the one hand, the MFDG model exploits a large number of variables (e.g., characteristics of land use and the road network; transport, food, and health facilities) extracted from voluntary geographic information data (OpenStreetMap). On the other hand, our model exploits deep neural networks to describe complex non-linear relationships between those variables. Our experiments, conducted on commuting flows in England, show that the MFDG model achieves a significant increase in the performance (up to 250\% for highly populated areas) than mechanistic models that do not use deep neural networks, or that do not exploit geographic voluntary data. Our work presents a precise definition of the flow generation problem, which is a novel task for the deep learning community working with spatio-temporal data, and proposes a deep neural network model that significantly outperforms current state-of-the-art statistical models