This work analyzes the propagation the highly transmissible COVID-19 variant Omicron across Spain via simulation by using EpiGraph. EpiGraph is an agent-based parallel simulator that reproduces the COVID-19 propagation over wide areas. In this work we consider a population of 19,574,086 individuals of the 63 most populated cities of Spain, for the time interval between May 15th 2021 and March 6th 2022. The main variants existing at the start of the simulation were the Alpha and Delta, with prevalence of 4% and 96%. Then, during the second half of November 2021, the Omicron variant appears in Spain. Due to the higher transmission of this new variant - about 2 times larger than Delta, it quickly spreads through all the cities and becomes the dominant strain in the country. In this work we analyze the propagation of this variant under different mobility restrictions and patient zero scenarios. We first define a baseline scenario which reproduces the existing conditions of the COVID-19 propagation in Spain for our period of study. We then consider alternative scenarios for different starting locations of the propagation. Finally, for each one of these scenarios, we evaluate different transportation intensities - i.e. movement of individuals between the cities. The main conclusion is that, independently of the initial location of the Omicron variant and the existing transportation conditions, the Omicron variant spreads through all the country in a short time interval. The work presented in this paper also implements and evaluates a power monitoring and optimization system aimed at reducing the energy consumption of such massive simulations as the ones performed in EpiGraph.

This work analyzes the propagation of Omicron, a high transmissible COVID-19 variant, across Spain by means of EpiGraph. EpiGraph is an agent-based parallel simulator that reproduces the COVID-19 propagation over wide areas. In this work we consider a population of 19,574,086 individuals related to the 63 most populated cities of Spain for the time interval comprised between May 15th of 2021 and March 6th of 2022. The main variants existing at the start of the simulation were the Alpha and Delta, with a a 4% and 96% prevalence of the existing infections, respectively. Then, during the second mid of November of 2021 the Delta variant appears in Spain. Given to the higher transmission of this new variant -about 2 times larger than Delta-, it quickly spreads through all the cities and becomes the dominant strain of the country. In this work we analyze the propagation of this variant under multiple conditions. First, we define a baseline scenario, that reproduces the existing conditions of the COVID-19 propagation in Spain for this period. Then, we consider alternative scenarios, in which different locations of the initial spread of Omicron variant are considered. Finally, for each one of these scenarios, we evaluate different transportation intensities -i.e. movement of individuals between the cities-. The main conclusion of this work is that, independently of the initial location of the Omicron variant, and the existing transportation conditions, the Omicron variant spreads though all the country in a short time interval.

We present ten simple rules that support converting a legacy vocabulary-a list of terms available in a print-based glossary or in a table not accessible using web standards-into a FAIR vocabulary. Various pathways may be followed to publish the FAIR vocabulary, but we emphasise particularly the goal of providing a globally unique resolvable identifier for each term or concept. A standard representation of the concept should be returned when the individual web identifier is resolved, using SKOS or OWL serialised in an RDF-based representation for machine-interchange and in a web-page for human consumption. Guidelines for vocabulary and term metadata are provided, as well as development and maintenance considerations. The rules are arranged as a stepwise recipe for creating a FAIR vocabulary based on the legacy vocabulary. By following these rules you can achieve the outcome of converting a legacy vocabulary into a standalone FAIR vocabulary, which can be used for unambiguous data annotation. In turn, this increases data interoperability and enables data integration.

A set of constraints is unsatisfiable if there is no solution that satisfies these constraints. To analyse unsatisfiable problems, the user needs to understand where inconsistencies come from and how they can be repaired. Minimal unsatisfiable cores and correction sets are important subsets of constraints that enable such analysis. In this work, we propose a new algorithm for extracting minimal unsatisfiable cores and correction sets simultaneously. Building on top of the relaxation and strengthening framework, we introduce novel techniques for extracting these sets. Our new solver significantly outperforms several state of the art algorithms on common benchmarks when it comes to extracting correction sets and compares favorably on core extraction.

The work we present in this paper focuses on understanding the propagation of flu-like infectious outbreaks between geographically distant regions due to the movement of people outside their base location. Our approach incorporates geographic location and a transportation model into our existing region-based, closed-world EpiGraph simulator to model a more realistic movement of the virus between different geographic areas. This paper describes the MPI-based implementation of this simulator, including several optimization techniques such as a novel approach for mapping processes onto available processing elements based on the temporal distribution of process loads. We present an extensive evaluation of EpiGraph in terms of its ability to simulate large-scale scenarios, as well as from a performance perspective.

This paper introduces an approach to modeling and simulating the propagation of flu-like infectious diseases over large, widely spread urban areas connected by transportation networks. We incorporate geographic location and a transportation model into our region-based, closed-world EpiGraph simulator to realistically model the movement of the virus between different geographic regions. The resulting simulator can assist in understanding how outbreaks propagate between far apart regions due to the movement of people outside their base location. This paper describes the MPI-based implementation of EpiGraph and its performance evaluation when simulating large-scale scenarios. We evaluate the simulator both on a distributed memory system and on a shared memory system.

This article presents FUSE, an approach for modeling and implementing embedded software components which starts from a main-stream programming language and brings some of the key concepts of Statecharts as first-class elements within this language. Our approach provides a unified programming environment which not only preserves some of the advantages of Statecharts' formal foundation but also directly supports features of object-orientation and strong typing. By specifying Statecharts directly in FUSE we eliminate the out-of-synch between the model and the generated code and we allow the tuning and debugging to be done within the same programming model. This article describes the main language constructs of FUSE and presents its semantics by translation into the Java programming language. We conclude by discussing extensions to the base language which enable the efficient static checking of program properties.

The goal of this work is to identify famous tenors from commercial recordings. Our approach is based on training expressive singer-specific models and using them to classify new musical fragments interpreted by singers that perform arias from the training set. In this paper we focus on expressive timing variations and build the models by applying machine learning techniques to a body of data consisting of high-level descriptors extracted from audio recordings. The experimental results show evidence that performers can be automatically identified at a rate significantly better than random choice.

Statecharts are probably the most popular mechanism for behavior modeling of embedded system components. Modeling a component involves using a mainstream language for features that statecharts cannot express: detailed behavior of conditions and actions, object-orientation and distributed computing features. Debugging is done at the level of the generated native code. Rather than treating statecharts as a separate programming model from the native programming model, we extend a (Java-like) language with support for key concepts of statecharts: (1) explicit states, (2) asynchronous events, and (3) conditional execution. This paper presents ESP*, a language that supports statecharts and a set of other advanced programming concepts to make programming embedded systems easier. The paper also shows how to translate statecharts to ESP*.

This paper describes a novel approach to high-level synthesis of complex pipelined circuits, including pipelined circuits with feedback. This approach combines a high-level, modular specification language with an efficient implementation. In our system, the designer specifies the circuit as a set of independent modules connected by conceptually unbounded queues. Our synthesis algorithm automatically transforms this modular, asynchronous specification into a tightly coupled, fully synchronous implementation in synthesizable Verilog.