Search results for "Database system"

Emergent behaviors and scalability for multi-agent reinforcement learning-based pedestrian models

2017

This paper analyzes the emergent behaviors of pedestrian groups that learn through the multiagent reinforcement learning model developed in our group. Five scenarios studied in the pedestrian model literature, and with different levels of complexity, were simulated in order to analyze the robustness and the scalability of the model. Firstly, a reduced group of agents must learn by interaction with the environment in each scenario. In this phase, each agent learns its own kinematic controller, that will drive it at a simulation time. Secondly, the number of simulated agents is increased, in each scenario where agents have previously learnt, to test the appearance of emergent macroscopic beha…

The Challenge of a Place-and Network-based Approach to Development in Italian Regions

2016

Abstract This paper investigates the role of research networks inside local development processes to increase the competitiveness of underdeveloped territories. This paper, within the scope of local development theory, aims to describe the state of the art on the regional research systems resulting largely from programs co-financed between 2000 and 2013, with which the various regions are preparing to engage in programming for the period 2014-2020. The extent of consistency between the objectives of sectorial specialization set by policies previously or currently implemented and those in the planning phase (S3) is assessed, as is their connection with existing territorial specializations at…

Improved GNSS positioning exploiting a vehicular P2P infrastructure

2010

This paper considers the possibility to exploit external altitude measurements to improve the performance of a Kalman based GNSS receiver. The altitude measurements are provided by means of a peer to peer network, that is supposed to be based on the evolution of the 802.11 standard for the vehicular environment, namely the WAVE (802.11p). The performance of such a system are investigated for different characteristics of the aiding measurement and for a different number and disposals of the aiding peers. The aiding measurement is obtained starting from the altitude measurements that the other peers in the network send to the aided user. The experiments highlight the need for a parameter that…

Enforcing Perceptual Consistency on Generative Adversarial Networks by Using the Normalised Laplacian Pyramid Distance

2019

In recent years there has been a growing interest in image generation through deep learning. While an important part of the evaluation of the generated images usually involves visual inspection, the inclusion of human perception as a factor in the training process is often overlooked. In this paper we propose an alternative perceptual regulariser for image-to-image translation using conditional generative adversarial networks (cGANs). To do so automatically (avoiding visual inspection), we use the Normalised Laplacian Pyramid Distance (NLPD) to measure the perceptual similarity between the generated image and the original image. The NLPD is based on the principle of normalising the value of…

ATLAS operations in the GridKa T1/T2 Cloud

2011

The ATLAS GridKa cloud consists of the GridKa Tier1 centre and 12 Tier2 sites from five countries associated to it. Over the last years a well defined and tested operation model evolved. Several core cloud services need to be operated and closely monitored: distributed data management, involving data replication, deletion and consistency checks; support for ATLAS production activities, which includes Monte Carlo simulation, reprocessing and pilot factory operation; continuous checks of data availability and performance for user analysis; software installation and database setup. Of crucial importance is good communication between sites, operations team and ATLAS as well as efficient cloud l…

Fault Detection, Isolation, andTolerant Control of Vehicles using Soft Computing Methods

2014

Heuristic Method to Improve Systematic Collection of Terminology

2016

In this paper, we propose an experimental tool for analysis and graphical representation of glossaries. The original heuristic algorithms and analysis methods incorporated into the tool appeared to be useful to improve the quality of the glossaries. The tool was used for analysis of ISTQB Standard Glossary of Terms Used in Software Testing. There are instances of problems found in ISTQB glossary related to its consistency, completeness, and correctness described in the paper.

GekkoFS — A Temporary Burst Buffer File System for HPC Applications

2020

Many scientific fields increasingly use high-performance computing (HPC) to process and analyze massive amounts of experimental data while storage systems in today’s HPC environments have to cope with new access patterns. These patterns include many metadata operations, small I/O requests, or randomized file I/O, while general-purpose parallel file systems have been optimized for sequential shared access to large files. Burst buffer file systems create a separate file system that applications can use to store temporary data. They aggregate node-local storage available within the compute nodes or use dedicated SSD clusters and offer a peak bandwidth higher than that of the backend parallel f…

LEXOP: a lexical database providing orthography-phonology statistics for French monosyllabic words.

1999

During the last 20 years, psycholinguistic research has identified many variables that influence reading and spelling processes. We describe a new computerized lexical database, LEXOP, which provides quantitative descriptors about the relations between orthography and phonology for French monosyllabic words. Three main classes of variables are considered: consistency of print-to-sound and sound-to-print associations, frequency of orthography-phonology correspondences, and word neighborhood characteristics.

Robust Neural Machine Translation: Modeling Orthographic and Interpunctual Variation

2020

Neural machine translation systems typically are trained on curated corpora and break when faced with non-standard orthography or punctuation. Resilience to spelling mistakes and typos, however, is crucial as machine translation systems are used to translate texts of informal origins, such as chat conversations, social media posts and web pages. We propose a simple generative noise model to generate adversarial examples of ten different types. We use these to augment machine translation systems’ training data and show that, when tested on noisy data, systems trained using adversarial examples perform almost as well as when translating clean data, while baseline systems’ performance drops by…