Valley symmetry breaking in bilayer graphene: a test to the minimal model

Physical properties reflecting valley asymmetry of Landau levels in a biased bilayer graphene under magnetic field are discussed. Within the $4-$band continuum model with Hartree-corrected self-consistent gap and finite damping factor we predict the appearance of anomalous steps in quantized Hall conductivity due to the degeneracy lifting of Landau levels. Moreover, the valley symmetry breaking effect appears as a non-semiclassical de Haas-van Alphen effect where the reduction of the oscillation period to half cannot be accounted for through quasi-classical quantization of the orbits in reciprocal space, still valley degenerate.Comment: 4 pages, 3 figure

A Topic-Agnostic Approach for Identifying Fake News Pages

Fake news and misinformation have been increasingly used to manipulate popular opinion and influence political processes. To better understand fake news, how they are propagated, and how to counter their effect, it is necessary to first identify them. Recently, approaches have been proposed to automatically classify articles as fake based on their content. An important challenge for these approaches comes from the dynamic nature of news: as new political events are covered, topics and discourse constantly change and thus, a classifier trained using content from articles published at a given time is likely to become ineffective in the future. To address this challenge, we propose a topic-agnostic (TAG) classification strategy that uses linguistic and web-markup features to identify fake news pages. We report experimental results using multiple data sets which show that our approach attains high accuracy in the identification of fake news, even as topics evolve over time.Comment: Accepted for publication in the Companion Proceedings of the 2019 World Wide Web Conference (WWW'19 Companion). Presented in the 2019 International Workshop on Misinformation, Computational Fact-Checking and Credible Web (MisinfoWorkshop2019). 6 page

A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks

Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption

Tissue- and stage-specific Wnt target gene expression is controlled subsequent to β-catenin recruitment to cis-regulatory modules

Acknowledgements We thank Saartje Hontelez (Radboud University, Nijmegen), Sylvie Janssens and Kris Vleminckx (Vlaams Instituut voor Biotechnologie, Universiteit Gent) and Shelby Blythe (Princeton University) for advice on ChIP experiments; Caroline Hill (CRUK, LRI) for discussion on BMP signalling; Juan Larraín (Pontificia Universitad Católica de Chile) and Susan Fairley (European Bioinformatics Institute) for advice on RNA-seq experiments; Yvonne Turnbull (IMSARU, University of Aberdeen) for technical assistance; Alasdair MacKenzie (University of Aberdeen) for discussion and suggestions on the manuscript; Hajime Ogino (Nagahama Institute of Bio-Science and Technology) and Atsushi Suzuki (Hiroshima University) for plasmids; Pierre McCrea (University of Texas MD Anderson Cancer Center) for anti-Xenopus β-catenin antibody; The Genome Analysis Centre (TGAC, BBSRC, Norwich) for high-throughput sequencing; and Xenbase (http://www.xenbase.org) for reference database access. Funding This work was supported by the Biotechnology and Biological Sciences Research Council [BB/I003746/1 to S.H., BB/M001695/1 to S.H. and Y.N.]. Deposited in PMC for immediate release.Peer reviewedPublisher PD

Sexuality in pregnancy: are Brazilian physicians prepared to conduct these questions?

Universidade Federal de São Paulo (UNIFESP) Departamento de ObstetríciaUNIFESP, Depto. de ObstetríciaSciEL

Estimativa de demanda potencial de matrículas em ensino superior usando dados públicos e múltiplos modelos de regressão

Este artigo apresenta uma proposta de aplicação de múltiplos modelos de regressão (ensembles) para prever a demanda potencial por vagas de ensino superior no ensino público brasileiro. Foram utilizadas variáveis socioeconômicas e educacionais disponibilizadas por MEC, INEP e IBGE para construir modelos de regressão que prevêem a quantidade atual de alunos matriculados em cada município brasileiro. Em seguida, pode-se comparar a quantidade de alunos prevista pelos modelos com a quantidade real; a diferença entre esses valores é interpretada como indicador da demanda potencial de cada município. Este trabalho em andamento (i) reforça as possibilidades de exploração de grandes volumes de dados públicos por modelos de aprendizado de máquina que geram indicadores que ajudam a aprimorar procedimentos e processos de gestão pública no Brasil, além de (ii) chamar a atenção para o fato de que métodos de aprendizado por ensembles são úteis também em tarefas de regressão, embora a literatura seja fortemente enviesada para tarefas de classificação, e (iii) ressaltar a utilidade de modelos de regressão para aplicações em que se está interessado na informação contida no erro da predição, e não somente na predição em si

Adaptive foraging for simulated and real robotic swarms: The dynamical response threshold approach

Developing self-organised swarm systems capable of adapting to environmental changes as well as to dynamic situations is a complex challenge. An efficient labour division model, with the ability to regulate the distribution of work among swarm robots, is an important element of this kind of system. This paper extends the popular response threshold model and proposes a new adaptive response threshold model (ARTM). Experiments were carried out in simulation and in real-robot scenarios with the aim of studying the performance of this new adaptive model. Results presented in this paper verify that the extended approach improves on the adaptability of previous systems. For example, by reducing collision duration among robots in foraging missions, our approach helps small swarms of robots to adapt more efficiently to changing environments, thus increasing their self-sustainability (survival rate). Finally, we propose a minimal version of ARTM, which is derived from the conclusions drawn through real-robot and simulation results

IDENTIFICANDO BOLHAS ESPECULATIVAS RACIONAIS NO IBOVESPA (PÓS-PLANO REAL), A PARTIR DE REGIMES MARKOVIANOS DE CONVERSÃO

The present article intend to verify the presence of speculative rational bubbles, starting from the identification of switching regime of the returns generation process in the brazilian market exchange, BOVESPA, for the Plano Real period (July of 1994 to March of 2004). In order to achieve this end, it was used of the model of markovian switching regime that allows to verify the nonlinear structure of the data and it is relation to the conditional mean and conditional variance. As result the dynamics of the data generation process, the returns can be described as function of two regimes ("bull markets" and "bear markets"). These cycles, however, they could be decomposed in other cycles, initial and final phases of the growth cycle ("bull") and decrease ("bear"). This decomposition was shown more coherent with the concept of speculative bubble, in which there is a nonlinear relationship between the price and their foundations.

Can off-training physical behaviors influence recovery in athletes? A sccoping review

Recently, the attention on recovery in sport increased enormously although there is lack of scientific evidence on the role of lifestyle in terms of movement [i.e., physical behaviors (PBs)], apart from sleep. Few studies assessed physical activity (PA) and sedentary behavior (SB) in athletes. The aims of this scoping review were to answer to the following scientific questions: (1) How active/inactive are competitive athletes out of training? (2) Do off-training PBs affect recovery, performance, and health? (3) What strategies can be implemented to improve recovery using off-training PBs, apart from sleep? From 1,116 potentially relevant articles, nine were eligible for inclusion in this review. The main issues identified were related to the heterogeneity concerning the types of sports, age category, gender, competitive level, sample size, and instruments/devices adopted, the paucity of studies investigating the effects of PBs while awake on recovery, and the lack of experimental designs manipulating PBs while awake to accelerate recovery. Furthermore, PA and SB domains were rarely investigated, while no research articles focused on the combined effect of 24-h PBs. Eight out of nine studies measured PA, seven SB, and two included sleep. Three studies included training practice into PA measurement by the means of accelerometry. Overall, almost the totality of the athletes achieved recommended PA levels although they sustained prolonged SB. In conclusion, more descriptive researches are needed in different athletic populations and settings. Furthermore, experimental designs aimed at investigating the effects of PBs manipulation on recovery and the putative mechanisms are encouraged

Aerobic Fitness Evaluation during Walking Tests Identifies the Maximal Lactate Steady State

Objective. The aim of this study was to verify the possibility of lactate minimum (LM) determination during a walking test and the validity of such LM protocol on predicting the maximal lactate steady-state (MLSS) intensity. Design. Eleven healthy subjects (24.2 ± 4.5 yr; 74.3 ± 7.7 kg; 176.9 ± 4.1 cm) performed LM tests on a treadmill, consisting of walking at 5.5 km · h−1 and with 20–22% of inclination until voluntary exhaustion to induce metabolic acidosis. After 7 minutes of recovery the participants performed an incremental test starting at 7% incline with increments of 2% at each 3 minutes until exhaustion. A polynomial modeling approach (LMp) and a visual inspection (LMv) were used to identify the LM as the exercise intensity associated to the lowest [bLac] during the test. Participants also underwent to 2–4 constant intensity tests of 30 minutes to determine the MLSS intensity. Results. There were no differences among LMv (12.6 ± 1.7%), LMp (13.1 ± 1.5%), and MLSS (13.6 ± 2.1%) and the Bland and Altman plots evidenced acceptable agreement between them. Conclusion. It was possible to identify the LM during walking tests with intensity imposed by treadmill inclination, and it seemed to be valid on identifying the exercise intensity associated to the MLSS