Un modèle markovien pour GSAT et WalkSAT résultats préliminaires

National audienceLes algorithmes GSAT et WalkSAT ont un comportement, bien connu expérimentalement, mais relativement peu étudié théoriquement. Nous étudions ici un modèle de GSAT et WalkSAT sous la forme de chaînes de Markov, modèle exact pour la partie gloutonne, approché pour la version avec random restart. Les résultats classiques sur les chaînes de Markov permettent d'en déduire deux nouvelles majorations de l'espérance du temps de calcul de WalkSAT sans random restart, en fonction des valeurs propres de la matrice de transition associée. Nous montrons expérimentalement sur de petites instances que cette borne permet de retrouver le paramétrage optimal observé dans la littérature. Nous donnons ensuite deux résultats sur l'espérance de GSAT ou Walk-SAT avec random restart en fonction du nombre d'itérations avant random restart (entre autres). Même si les résultats restent à approfondir, ce modèle donne une piste vers une étude théorique du paramétrage optimal et, au delà, du comportement de ces algorithmes

Stereotype threat may not impact women's inhibitory control or mathematical performance: Providing support for the null hypothesis

Underpinned by the findings of Jamieson and Harkins (2007; Experiment 3), the current study pits the mere effort motivational account of stereotype threat against a working memory interference account. In Experiment 1, females were primed with a negative self- or group stereotype pertaining to their visuospatial ability and completed an anti-saccade eye-tracking task. In Experiment 2 they were primed with a negative or positive group stereotype and completed an anti-saccade and mental arithmetic task. Findings indicate that stereotype threat did not significantly impair women's inhibitory control (Experiments 1 and 2) or mathematical performance (Experiment 2), with Bayesian analyses providing support for the null hypothesis. These findings are discussed in relation to potential moderating factors of stereotype threat, such as task difficulty and stereotype endorsement, as well as the possibility that effect sizes reported in the stereotype threat literature are inflated due to publication bias

COVID-19, the first pandemic in the post-genomic era.

The scale of the international efforts to sequence SARS-CoV-2 genomes is unprecedented. Early availability of genomes allowed rapid characterisation of the virus, thus kickstarting many highly successful vaccine development programmes. Worldwide genomic resources have provided a good understanding of the pandemic, supported close monitoring of the emergence of viral genomic diversity and pinpointed those sites to prioritise for functional characterisation. Continued genomic surveillance of global viral populations will be crucial to inform the timing of vaccine updates so as to pre-empt the spread of immune escape lineages. While genome sequencing has provided us with an exceptionally powerful tool to monitor the evolution of SARS-CoV-2, there is room for further improvements in particular in the form of less heterogeneous global surveillance and tools to rapidly identify concerning viral lineages

Analyzing Social Construction of Knowledge Online by Employing Interaction Analysis, Learning Analytics, and Social Network Analysis

This article examines research methods for analyzing social construction of knowledge in online discussion forums. We begin with an examination of the Interaction Analysis Model (Gunawardena, Lowe, & Anderson, 1997) and its applicability to analyzing social construction of knowledge. Next, employing a dataset from an online discussion, we demonstrate how interaction analysis can be supplemented by employing other research techniques such as learning analytics and Social Network Analysis that shed light on the social dynamics that support knowledge construction. Learning analytics is the application of quantitative techniques for analyzing large volumes of distributed data ( big data ) in order to discover the factors that contribute to learning (Long & Siemens, 2011, p. 34). Social Network Analysis characterizes the information infrastructure that supports the construction of knowledge in social contexts (Scott, 2012). By combining interaction analysis with learning analytics and Social Network Analysis, we were able to conceptualize the process by which knowledge construction takes place in online platforms

The past, current and future epidemiological dynamic of SARS-CoV-2

SARS-CoV-2, the agent of the COVID-19 pandemic, emerged in late 2019 in China, and rapidly spread throughout the world to reach all continents. As the virus expanded in its novel human host, viral lineages diversified through the accumulation of around two mutations a month on average. Different viral lineages have replaced each other since the start of the pandemic, with the most successful Alpha, Delta and Omicron variants of concern (VoCs) sequentially sweeping through the world to reach high global prevalence. Neither Alpha nor Delta was characterized by strong immune escape, with their success coming mainly from their higher transmissibility. Omicron is far more prone to immune evasion and spread primarily due to its increased ability to (re-)infect hosts with prior immunity. As host immunity reaches high levels globally through vaccination and prior infection, the epidemic is expected to transition from a pandemic regime to an endemic one where seasonality and waning host immunization are anticipated to become the primary forces shaping future SARS-CoV-2 lineage dynamics. In this review, we consider a body of evidence on the origins, host tropism, epidemiology, genomic and immunogenetic evolution of SARS-CoV-2 including an assessment of other coronaviruses infecting humans. Considering what is known so far, we conclude by delineating scenarios for the future dynamic of SARS-CoV-2, ranging from the good—circulation of a fifth endemic ‘common cold’ coronavirus of potentially low virulence, the bad—a situation roughly comparable with seasonal flu, and the ugly—extensive diversification into serotypes with long-term high-level endemicity

A breathing zirconium metal-organic framework with reversible loss of crystallinity by correlated nanodomain formation

The isoreticular analogue of the metal-organic framework UiO-66(Zr), synthesized with the flexible trans-1,4-cyclohexanedicarboxylic acid as linker, shows a peculiar breathing behavior by reversibly losing long-range crystalline order upon evacuation. The underlying flexibility is attributed to a concerted conformational contraction of up to two thirds of the linkers, which breaks the local lattice symmetry. X-ray scattering data are described well by a nanodomain model in which differently oriented tetragonal-type distortions propagate over about 7-10 unit cells

A guide to sampling design for GPS‐based studies of animal societies

GPS-based tracking is widely used for studying wild social animals. Much like traditional observational methods, using GPS devices requires making a number of decisions about sampling that can affect the robustness of a study's conclusions. For example, sampling fewer individuals per group across more distinct social groups may not be sufficient to infer group- or subgroup-level behaviours, while sampling more individuals per group across fewer groups limits the ability to draw conclusions about populations. Here, we provide quantitative recommendations when designing GPS-based tracking studies of animal societies. We focus on the trade-offs between three fundamental axes of sampling effort: (1) sampling coverage—the number and allocation of GPS devices among individuals in one or more social groups; (2) sampling duration—the total amount of time over which devices collect data and (3) sampling frequency—the temporal resolution at which GPS devices record data. We first test GPS tags under field conditions to quantify how these aspects of sampling design can affect both GPS accuracy (error in absolute positional estimates) and GPS precision (error in the estimate relative position of two individuals), demonstrating that GPS error can have profound effects when inferring distances between individuals. We then use data from whole-group tracked vulturine guineafowl Acryllium vulturinum to demonstrate how the trade-off between sampling frequency and sampling duration can impact inferences of social interactions and to quantify how sampling coverage can affect common measures of social behaviour in animal groups, identifying which types of measures are more or less robust to lower coverage of individuals. Finally, we use data-informed simulations to extend insights across groups of different sizes and cohesiveness. Based on our results, we are able to offer a range of recommendations on GPS sampling strategies to address research questions across social organizational scales and social systems—from group movement to social network structure and collective decision-making. Our study provides practical advice for empiricists to navigate their decision-making processes when designing GPS-based field studies of animal social behaviours, and highlights the importance of identifying the optimal deployment decisions for drawing informative and robust conclusions