Genetic Algorithm for Epidemic Mitigation by Removing Relationships

Min-SEIS-Cluster is an optimization problem which aims at minimizing the infection spreading in networks. In this problem, nodes can be susceptible to an infection, exposed to an infection, or infectious. One of the main features of this problem is the fact that nodes have different dynamics when interacting with other nodes from the same community. Thus, the problem is characterized by distinct probabilities of infecting nodes from both the same and from different communities. This paper presents a new genetic algorithm that solves the Min-SEIS-Cluster problem. This genetic algorithm surpassed the current heuristic of this problem significantly, reducing the number of infected nodes during the simulation of the epidemics. The results therefore suggest that our new genetic algorithm is the state-of-the-art heuristic to solve this problem.Comment: GECCO '17 - Proceedings of the Genetic and Evolutionary Computation Conferenc

Identifying relationship patterns inside communities

Community detection is an important problem for Computer and other sciences. Following Agarwal and Kempe one of the most important reasons to make clustering over a network is to identify the function/role of each element in a community. If the communities have hundreds or thousands of elements, it is important to understand the functions of internal elements, but that will require an automatic process. In this context, we propose to develop a model, capable to identify elements with common features in different communities, based on the connection between elements and communities, agreeing with Newman and Girvan model features. (Párrafo extraído del texto a modo de resumen)Sociedad Argentina de Informática e Investigación Operativa (SADIO

Identifying relationship patterns inside communities

Community detection is an important problem for Computer and other sciences. Following Agarwal and Kempe one of the most important reasons to make clustering over a network is to identify the function/role of each element in a community. If the communities have hundreds or thousands of elements, it is important to understand the functions of internal elements, but that will require an automatic process. In this context, we propose to develop a model, capable to identify elements with common features in different communities, based on the connection between elements and communities, agreeing with Newman and Girvan model features. (Párrafo extraído del texto a modo de resumen)Sociedad Argentina de Informática e Investigación Operativa (SADIO

Connectionist modal logic: Representing modalities in neural networks

AbstractModal logics are amongst the most successful applied logical systems. Neural networks were proved to be effective learning systems. In this paper, we propose to combine the strengths of modal logics and neural networks by introducing Connectionist Modal Logics (CML). CML belongs to the domain of neural-symbolic integration, which concerns the application of problem-specific symbolic knowledge within the neurocomputing paradigm. In CML, one may represent, reason or learn modal logics using a neural network. This is achieved by a Modalities Algorithm that translates modal logic programs into neural network ensembles. We show that the translation is sound, i.e. the network ensemble computes a fixed-point meaning of the original modal program, acting as a distributed computational model for modal logic. We also show that the fixed-point computation terminates whenever the modal program is well-behaved. Finally, we validate CML as a computational model for integrated knowledge representation and learning by applying it to a well-known testbed for distributed knowledge representation. This paves the way for a range of applications on integrated knowledge representation and learning, from practical reasoning to evolving multi-agent systems

"A Nova Eletricidade: Aplica\c{c}\~oes, Riscos e Tend\^encias da IA Moderna -- "The New Electricity": Applications, Risks, and Trends in Current AI

The thought-provoking analogy between AI and electricity, made by computer scientist and entrepreneur Andrew Ng, summarizes the deep transformation that recent advances in Artificial Intelligence (AI) have triggered in the world. This chapter presents an overview of the ever-evolving landscape of AI, written in Portuguese. With no intent to exhaust the subject, we explore the AI applications that are redefining sectors of the economy, impacting society and humanity. We analyze the risks that may come along with rapid technological progress and future trends in AI, an area that is on the path to becoming a general-purpose technology, just like electricity, which revolutionized society in the 19th and 20th centuries. A provocativa compara\c{c}\~ao entre IA e eletricidade, feita pelo cientista da computa\c{c}\~ao e empreendedor Andrew Ng, resume a profunda transforma\c{c}\~ao que os recentes avan\c{c}os em Intelig\^encia Artificial (IA) t\^em desencadeado no mundo. Este cap\'itulo apresenta uma vis\~ao geral pela paisagem em constante evolu\c{c}\~ao da IA. Sem pretens\~oes de exaurir o assunto, exploramos as aplica\c{c}\~oes que est\~ao redefinindo setores da economia, impactando a sociedade e a humanidade. Analisamos os riscos que acompanham o r\'apido progresso tecnol\'ogico e as tend\^encias futuras da IA, \'area que trilha o caminho para se tornar uma tecnologia de prop\'osito geral, assim como a eletricidade, que revolucionou a sociedade dos s\'eculos XIX e XX.Comment: In Portugues

Understanding How Microplastics Affect Marine Biota on the Cellular Level Is Important for Assessing Ecosystem Function: A Review

Plastic has become indispensable for human life. When plastic debris is discarded into waterways, these items can interact with organisms. Of particular concern are microscopic plastic particles (microplastics) which are subject to ingestion by several taxa. This review summarizes the results of cutting-edge research about the interactions between a range of aquatic species and microplastics, including effects on biota physiology and secondary ingestion. Uptake pathways via digestive or ventilatory systems are discussed, including (1) the physical penetration of microplastic particles into cellular structures, (2) leaching of chemical additives or adsorbed persistent organic pollutants (POPs), and (3) consequences of bacterial or viral microbiota contamination associated with microplastic ingestion. Following uptake, a number of individual-level effects have been observed, including reduction of feeding activities, reduced growth and reproduction through cellular modifications, and oxidative stress. Microplastic-associated effects on marine biota have become increasingly investigated with growing concerns regarding human health through trophic transfer. We argue that research on the cellular interactions with microplastics provide an understanding of their impact to the organisms’ fitness and, therefore, its ability to sustain their functional role in the ecosystem. The review summarizes information from 236 scientific publications. Of those, only 4.6% extrapolate their research of microplastic intake on individual species to the impact on ecosystem functioning. We emphasize the need for risk evaluation from organismal effects to an ecosystem level to effectively evaluate the effect of microplastic pollution on marine environments. Further studies are encouraged to investigate sublethal effects in the context of environmentally relevant microplastic pollution conditions

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Learning to adapt requirements specifications of evolving systems (NIER Track)

We propose a novel framework for adapting and evolving software requirements models. The framework uses model checking and machine learning techniques for verifying properties and evolving model descriptions. The paper offers two novel contributions and a preliminary evaluation and application of the ideas presented. First, the framework is capable of coping with errors in the specification process so that performance degrades gracefully. Second, the framework can also be used to re-engineer a model from examples only, when an initial model is not available. We provide a preliminary evaluation of our framework by applying it to a Pump System case study, and integrate our prototype tool with the NuSMV model checker. We show how the tool integrates verification and evolution of abstract models, and also how it is capable of re-engineering partial models given examples from an existing system

Towards reasoning about the past in neural-symbolic systems

Reasoning about the past is of fundamental importance in several applications in computer science and artificial intelligence, including reactive systems and planning. In this paper we propose efficient temporal knowledge representation algorithms to reason about and implement past time logical operators in neural-symbolic systems. We do so by extending models of the Connectionist Inductive Learning and Logic Programming System with past operators. This contributes towards integrated learning and reasoning systems considering temporal aspects. We validate the effectiveness of our approach by means of case studies.