Time-varying graph representation learning via higher-order skip-gram with negative sampling

Representation learning models for graphs are a successful family of techniques that project nodes into feature spaces that can be exploited by other machine learning algorithms. Since many real-world networks are inherently dynamic, with interactions among nodes changing over time, these techniques can be defined both for static and for time-varying graphs. Here, we show how the skip-gram embedding approach can be generalized to perform implicit tensor factorization on different tensor representations of time-varying graphs. We show that higher-order skip-gram with negative sampling (HOSGNS) is able to disentangle the role of nodes and time, with a small fraction of the number of parameters needed by other approaches. We empirically evaluate our approach using time-resolved face-to-face proximity data, showing that the learned representations outperform state-of-the-art methods when used to solve downstream tasks such as network reconstruction. Good performance on predicting the outcome of dynamical processes such as disease spreading shows the potential of this method to estimate contagion risk, providing early risk awareness based on contact tracing data. Supplementary information: The online version contains supplementary material available at 10.1140/epjds/s13688-022-00344-8

Explainability Methods for Natural Language Processing: Applications to Sentiment Analysis

Sentiment analysis is the process of classifying natural lan-guage sentences as expressing positive or negative sentiments, and it is a crucial task where the explanation of a prediction might arguably be as necessary as the prediction itself. We analysed di fierent explanation techniques, and we applied them to the classification task of Sentiment Analysis. We explored how attention-based techniques can be exploited to extract meaningful sentiment scores with a lower computational cost than existing XAI methods

Anomaly detection in temporal graph data: An iterative tensor decomposition and masking approach

Sensors and Internet-of-Things scenarios promise a wealth of interaction data that can be naturally represented by means of timevarying graphs. This brings forth new challenges for the identification and removal of temporal graph anomalies that entail complex correlations of topological features and activity patterns. Here we present an anomaly detection approach for temporal graph data based on an iterative tensor decomposition and masking procedure. We test this approach using highresolution social network data from wearable sensors and show that it successfully detects anomalies due to sensor wearing time protocols.published_or_final_versio

Explaining Semantic Reasoning Using Argumentation

Multi-Agent Systems (MAS) are popular because they provide a paradigm that naturally meets the current demand to design and implement distributed intelligent systems. When developing a multi-agent application, it is common to use ontologies to provide the domain-specific knowledge and vocabulary necessary for agents to achieve the system goals. In this paper, we propose an approach in which agents can query semantic reasoners and use the received inferences to build explanations for such reasoning. Also, thanks to an internal representation of inference rules used to build explanations, in the form of argumentation schemes, agents are able to reason and make decisions based on the answers from the semantic reasoner. Furthermore, agents can communicate the built explanation to other agents and humans, using computational or natural language representations of arguments. Our approach paves the way towards multi-agent systems able to provide explanations from the reasoning carried out by semantic reasoners

Impact of birth weight and daily weight gain during suckling on the weight gain of weaning piglets.

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Efeito do nível de energia metabolizável da dieta sobre a composição de carcaça de frangos de corte de três diferentes linhagens.

ABSTRACT : The objective of this study was to evaluate the effect of diet energy level over carcass composition of broilers from three different commercial genetics. A completely randomized design was used, and treatments followed a factorial design with three dietary energy values (low, medium and high) and three genetics (A, B and C). Percentages of fat, protein, water and collagen in the carcass were evaluated at 42 days old. The data was submitted to analysis of variance and compared by Tukey test with 5% of probability. There was no interaction between energy and genetic (P>0,05). The genetics differed by carcass fat and protein values. Low energy diet resulted in higher fat percentage, while high diet energy improved carcass protein and water content. The use of high energy diets allowed for better expression of broilers? genetic potential for muscular tissue deposition. Resumo: Na produção avícola atual, além de ótimo peso de abate e melhor conversão alimentar, tem se buscado qualidade na composição da carcaça, visando atender as exigências do mercado consumidor. A seleção genética para rápido crescimento leva a adoção de novos critérios de manejo e nutrição para maximizar o bem estar animal, produtividade e otimizar custos, bem como gerar produtos de qualidade. Estudos demonstram que o nível energético da dieta é um fator de forte influência sobre a qualidade da carcaça de frangos e que níveis maiores de energia na dieta podem levar a maior deposição de gordura abdominal (Barbosa et al., 2008; Rosa et al., 2007; Meza et al., 2015). O objetivo do estudo foi avaliar o efeito do consumo de energia sobre a composição de carcaça de frangos de corte machos de três diferentes linhagens

Temporal networks of face-to-face human interactions

The ever increasing adoption of mobile technologies and ubiquitous services allows to sense human behavior at unprecedented levels of details and scale. Wearable sensors are opening up a new window on human mobility and proximity at the finest resolution of face-to-face proximity. As a consequence, empirical data describing social and behavioral networks are acquiring a longitudinal dimension that brings forth new challenges for analysis and modeling. Here we review recent work on the representation and analysis of temporal networks of face-to-face human proximity, based on large-scale datasets collected in the context of the SocioPatterns collaboration. We show that the raw behavioral data can be studied at various levels of coarse-graining, which turn out to be complementary to one another, with each level exposing different features of the underlying system. We briefly review a generative model of temporal contact networks that reproduces some statistical observables. Then, we shift our focus from surface statistical features to dynamical processes on empirical temporal networks. We discuss how simple dynamical processes can be used as probes to expose important features of the interaction patterns, such as burstiness and causal constraints. We show that simulating dynamical processes on empirical temporal networks can unveil differences between datasets that would otherwise look statistically similar. Moreover, we argue that, due to the temporal heterogeneity of human dynamics, in order to investigate the temporal properties of spreading processes it may be necessary to abandon the notion of wall-clock time in favour of an intrinsic notion of time for each individual node, defined in terms of its activity level. We conclude highlighting several open research questions raised by the nature of the data at hand.Comment: Chapter of the book "Temporal Networks", Springer, 2013. Series: Understanding Complex Systems. Holme, Petter; Saram\"aki, Jari (Eds.

Organic nitrogen in a typic hapludox fertilized with pig slurry.

The application of pig slurry may have a different effect on nitrogen dynamics in soil compared to mineral fertilization. Thus, the aim of this study was to determine the different forms of organic N in a Latossolo Vermelho distroférrico (Typic Hapludox) and their relationship to N uptake by crops in response to 10 years of annual application of pig slurry and mineral fertilizer. The treatments were application rates of 0, 25, 50, 100, and 200 m3 ha-1 of pig slurry, in addition to mineral fertilizer, organized in a randomized block design with four replications. The N contents were determined in the plant tissue and in the forms of total N and acid hydrolyzed fractions: ammonium-N, hexosamine-N, ?-amino-N, amide-N, and unidentified-N. Annual application of pig slurry or mineral fertilizer increased the total-N content in the 0-10 cm depth layer. The main fractions of organic N in the soil were ?-amino-N when pig slurry was applied and unidentified-N in the case of mineral fertilizers. Pig slurry increased the N fractions considered as labile: ?-amino-N, ammonium-N, and amide-N. The increase in these labile organic N fractions in the soil through pig slurry application allows greater N uptake by the maize and oat crops in a no-tillage system. A aplicação de dejeto suíno pode influenciar na dinâmica do nitrogênio no solo de forma distinta em comparação à adubação mineral. O objetivo deste trabalho foi determinar as diferentes formas de N orgânico em Latossolo Vermelho distroférrico e sua relação com a absorção de N pelas culturas, em resposta a 10 anos de aplicação anual de dejeto suíno e adubo mineral. Os tratamentos foram as doses 0, 25, 50, 100 e 200 m3 ha-1 de dejeto suíno, além da adubação na forma mineral, organizados em blocos casualizados com quatro repetições. Foram determinados os teores de N no tecido vegetal e de N total nas frações hidrolisadas em meio ácido: N-amônio, N-hexosamina, N-?-amino, N-amido e N-não identificado do solo. A aplicação anual de dejeto suíno ou adubo mineral aumentou o teor de N total na camada de 0-10 cm de profundidade. As principais frações do N orgânico no solo foram N-?-amino, quando utilizado dejeto suíno e N-não identificado no caso do adubo mineral. O dejeto suíno aumentou as frações do N consideradas lábeis, N-?-amino, N-amônio e N-amida. Esse aumento das formas lábeis de N orgânico no solo, pela aplic