An Accurate Tax Fraud Classifier with Feature Selection based on Complex Network Node Centrality Measure

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Trajectory Modeling via Random Utility Inverse Reinforcement Learning

We consider the problem of modeling trajectories of drivers in a road network from the perspective of inverse reinforcement learning. Cars are detected by sensors placed on sparsely distributed points on the street network of a city. As rational agents, drivers are trying to maximize some reward function unknown to an external observer. We apply the concept of random utility from econometrics to model the unknown reward function as a function of observed and unobserved features. In contrast to current inverse reinforcement learning approaches, we do not assume that agents act according to a stochastic policy; rather, we assume that agents act according to a deterministic optimal policy and show that randomness in data arises because the exact rewards are not fully observed by an external observer. We introduce the concept of extended state to cope with unobserved features and develop a Markov decision process formulation of drivers decisions. We present theoretical results which guarantee the existence of solutions and show that maximum entropy inverse reinforcement learning is a particular case of our approach. Finally, we illustrate Bayesian inference on model parameters through a case study with real trajectory data from a large city in Brazil.Comment: 31 pages; expanded version, with the addition of proofs not present in the first versio

Enhancing Network Slicing Architectures with Machine Learning, Security, Sustainability and Experimental Networks Integration

Network Slicing (NS) is an essential technique extensively used in 5G networks computing strategies, mobile edge computing, mobile cloud computing, and verticals like the Internet of Vehicles and industrial IoT, among others. NS is foreseen as one of the leading enablers for 6G futuristic and highly demanding applications since it allows the optimization and customization of scarce and disputed resources among dynamic, demanding clients with highly distinct application requirements. Various standardization organizations, like 3GPP's proposal for new generation networks and state-of-the-art 5G/6G research projects, are proposing new NS architectures. However, new NS architectures have to deal with an extensive range of requirements that inherently result in having NS architecture proposals typically fulfilling the needs of specific sets of domains with commonalities. The Slicing Future Internet Infrastructures (SFI2) architecture proposal explores the gap resulting from the diversity of NS architectures target domains by proposing a new NS reference architecture with a defined focus on integrating experimental networks and enhancing the NS architecture with Machine Learning (ML) native optimizations, energy-efficient slicing, and slicing-tailored security functionalities. The SFI2 architectural main contribution includes the utilization of the slice-as-a-service paradigm for end-to-end orchestration of resources across multi-domains and multi-technology experimental networks. In addition, the SFI2 reference architecture instantiations will enhance the multi-domain and multi-technology integrated experimental network deployment with native ML optimization, energy-efficient aware slicing, and slicing-tailored security functionalities for the practical domain.Comment: 10 pages, 11 figure

Nutrição mineral de hortaliças: XXIX. absorção de macronutrientes por quatro cultivares de morangueiro (Fragaria spp.)

The aim of this work was to estimate the differences in growth (dry weight basis), nutrients uptake, fruits yield, total uptake and exportation of nutrients among several strawberry cultivars: Campinas (IAC-2712), Camanducaia (IAC-3530), Monte Alegre (IAC-3113) and SH-2. The experimental was carried out in the Escola Superior de Agricultura "Luiz de Queiroz", in field conditions, in 1975/76. The soil belongs to Terra Roxa Estruturada type, and "Luiz de Queiroz" serie. This soil has been cultivated for more than 25 years. The experimental design was that randomized blocks with four replications and analysed together following the design of split-plot. The soil of the plots were revolved to a deep of 12 cm following application of 10 kg organic matter/m². The fertilizers were applied in the groove and in the same amount for all cultivars: Ammonium sulfate (20% N), 10 g/m; triple superphosphate (20% P(2)0(5)) 10 g/m; Potassium cloride (60% K(2)0), 10 g/m. Therty days after planting, 10 g/plant of ammonium sulfate was applied. After 76 days from planting, the firsa sample was taken. Other samples were taken in equal intervals of 20 days, up to 216 days. When a decrease in fruitproduction was noted. The sample (plants) were divided in stems, leaves and fruits and chemical analysis were made for N, R, K, Ca, Mg e S. The variation on production (dry matter) nutrient uptake' and fruits yield, were obtained from data calculated by adjusted regression equation analysis. The maxima point from these equations were taken to show the total nutrient uptake. From the data obtained, the following conclusions could be drawn. Growth - The dry matter of stems, leaves and fruits were different among the cultivars. The production of dry matter by the stems and fruits were linnear for all cultivars up to 196 and 216 days. The highest productions on dry matter varied between 15 to 25 g and 12 to 20 g/plant. The maximum production of dry matter in the leaves among the cultivars varied between 20 to 30 g at 196 and 173 days respectivelly. The cultivars Campinas (IAC-2712) and Camanducaia (IAC-3530) produced more dry matter than SH-2 cultivar. Nutrient uptake -< The were differences on nutrient content in stems and leaves among cultivars (R, K, Ca, S, B) and in the fruits for N, R, K, Mg, S. The highest absorption of nutrients (days after planting) is shown in Table I. Yield - No significant difference in fruit production was observed among the cultivars. The highest yield among the cultivars showed a variation between 103 to 151 g per plant at the 207 and 207 days,Efetuou-se um estudo para avaliar a absorção e a extração dos macronutrientes nos seguintes cultivares de morangueiro: Campinas (IAC-2712); Camanducaia (IAC-3530) ; Monte Alegre (IAC-3113) e SH-2 em condições de campo. A instalação deu-se em um solo pertencente ao grande grupo Terra Roxa Estruturada, e à série "Luiz de Queiroz" cultivado intensivamente com hortaliças há mais de 25 anos, em Piracicaba-SP. A adubação aplicada foi uniforme para todos os cultivares. São apresentadas as concentrações dos macronutrientes em porcentagem nos seguintes órgãos: caules, folhas e frutos dos cultivares em função da idade (X) em dias. Constatou-se que os cultivares diferem quanto à absorção dos macronutrientes (R, K, Ca e S em relação a caules e folhas, e, N, R, K, Mg e S em relação aos frutos). Constatou-se também que os cultivares extraem totais diferentes de R, K, Ca, Mg e S sendo as extrações de R pelos cultivares menores do que as extrações de Ca e Mg, e no global as de Mg são equivalentes às de S. As quantidades máximas extraídas pelos cultivares para uma população de 150.000 plantas/ha foram : N - 192 kg; R - 24-50 kg; K - 133-244 kg; Ca - 76-116 kg; Mg - 30-34 kg; S - 13-27 kg. - A maior produção de matéria seca tanto nos órgãos como na planta inteira, ocorreu nos cultivares Campinas (IAC-2712) e Camanducaia (IAC-3530) e a menor produção verificou-se no cultivar SH-2. - Os cultivares diferem na absorção dos nutrientes: R, K, Ca, S para caules e folhas. E para frutos, N, R, K, Mg e S. - Os cultivares atingem o máximo da absorção de nutrientes nos órgãos nas seguintes épocas, em dias: - Os cultivares extraem e exportam totais diferentes de R, K, Ca e Mg. - Tanto os macronutrientes são extraídos em quantidades mais elevadas através das folhas e em menor proporção por caules e frutos. - As extrações de N, K e Ca são mais altas que aquelas dos demais macronutrientes. - As extrações de R pelos cultivares são menores que as de Ca e Mg, sendo ainda as extrações de Ca superiores às de Mg, enquanto no global as de Mg são equivalentes às de S. - A extração de macronutrientes verifica-se na ordem decrescente: K, N, Ca, Mg, S e P

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Obeticholic acid for the treatment of non-alcoholic steatohepatitis: interim analysis from a multicentre, randomised, placebo-controlled phase 3 trial

Background Non-alcoholic steatohepatitis (NASH) is a common type of chronic liver disease that can lead to cirrhosis. Obeticholic acid, a farnesoid X receptor agonist, has been shown to improve the histological features of NASH. Here we report results from a planned interim analysis of an ongoing, phase 3 study of obeticholic acid for NASH. Methods In this multicentre, randomised, double-blind, placebo-controlled study, adult patients with definite NASH,non-alcoholic fatty liver disease (NAFLD) activity score of at least 4, and fibrosis stages F2–F3, or F1 with at least oneaccompanying comorbidity, were randomly assigned using an interactive web response system in a 1:1:1 ratio to receive oral placebo, obeticholic acid 10 mg, or obeticholic acid 25 mg daily. Patients were excluded if cirrhosis, other chronic liver disease, elevated alcohol consumption, or confounding conditions were present. The primary endpointsfor the month-18 interim analysis were fibrosis improvement (≥1 stage) with no worsening of NASH, or NASH resolution with no worsening of fibrosis, with the study considered successful if either primary endpoint was met. Primary analyses were done by intention to treat, in patients with fibrosis stage F2–F3 who received at least one dose of treatment and reached, or would have reached, the month 18 visit by the prespecified interim analysis cutoff date. The study also evaluated other histological and biochemical markers of NASH and fibrosis, and safety. This study is ongoing, and registered with ClinicalTrials.gov, NCT02548351, and EudraCT, 20150-025601-6. Findings Between Dec 9, 2015, and Oct 26, 2018, 1968 patients with stage F1–F3 fibrosis were enrolled and received at least one dose of study treatment; 931 patients with stage F2–F3 fibrosis were included in the primary analysis (311 in the placebo group, 312 in the obeticholic acid 10 mg group, and 308 in the obeticholic acid 25 mg group). The fibrosis improvement endpoint was achieved by 37 (12%) patients in the placebo group, 55 (18%) in the obeticholic acid 10 mg group (p=0·045), and 71 (23%) in the obeticholic acid 25 mg group (p=0·0002). The NASH resolution endpoint was not met (25 [8%] patients in the placebo group, 35 [11%] in the obeticholic acid 10 mg group [p=0·18], and 36 [12%] in the obeticholic acid 25 mg group [p=0·13]). In the safety population (1968 patients with fibrosis stages F1–F3), the most common adverse event was pruritus (123 [19%] in the placebo group, 183 [28%] in the obeticholic acid 10 mg group, and 336 [51%] in the obeticholic acid 25 mg group); incidence was generally mild to moderate in severity. The overall safety profile was similar to that in previous studies, and incidence of serious adverse events was similar across treatment groups (75 [11%] patients in the placebo group, 72 [11%] in the obeticholic acid 10 mg group, and 93 [14%] in the obeticholic acid 25 mg group). Interpretation Obeticholic acid 25 mg significantly improved fibrosis and key components of NASH disease activity among patients with NASH. The results from this planned interim analysis show clinically significant histological improvement that is reasonably likely to predict clinical benefit. This study is ongoing to assess clinical outcomes

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost