Detecting Majorana fermions by use of superconductor-quantum Hall liquid junctions

The point contact tunnel junctions between a one-dimensional topological superconductor and single-channel quantum Hall (QH) liquids are investigated theoretically with bosonization technology and renormalization group methods. For the $\nu=1$ integer QH liquid, the universal low-energy tunneling transport is governed by the perfect Andreev reflection fixed point with quantized zero-bias conductance $G(0)=2e^{2}/h$, which can serve as a definitive fingerprint of the existence of a Majorana fermion. For the $\nu =1/m$ Laughlin fractional QH liquids, its transport is governed by the perfect normal reflection fixed point with vanishing zero-bias conductance and bias-dependent conductance $G(V) \sim V^{m-2}$. Our setup is within reach of present experimental techniques.Comment: 6 pages, 1 figure, Added references,Corrected typo

Line graphs as social networks

The line graphs are clustered and assortative. They share these topological features with some social networks. We argue that this similarity reveals the cliquey character of the social networks. In the model proposed here, a social network is the line graph of an initial network of families, communities, interest groups, school classes and small companies. These groups play the role of nodes, and individuals are represented by links between these nodes. The picture is supported by the data on the LiveJournal network of about 8 x 10^6 people. In particular, sharp maxima of the observed data of the degree dependence of the clustering coefficient C(k) are associated with cliques in the social network.Comment: 11 pages, 4 figure

Theories for influencer identification in complex networks

In social and biological systems, the structural heterogeneity of interaction networks gives rise to the emergence of a small set of influential nodes, or influencers, in a series of dynamical processes. Although much smaller than the entire network, these influencers were observed to be able to shape the collective dynamics of large populations in different contexts. As such, the successful identification of influencers should have profound implications in various real-world spreading dynamics such as viral marketing, epidemic outbreaks and cascading failure. In this chapter, we first summarize the centrality-based approach in finding single influencers in complex networks, and then discuss the more complicated problem of locating multiple influencers from a collective point of view. Progress rooted in collective influence theory, belief-propagation and computer science will be presented. Finally, we present some applications of influencer identification in diverse real-world systems, including online social platforms, scientific publication, brain networks and socioeconomic systems.Comment: 24 pages, 6 figure

World citation and collaboration networks: uncovering the role of geography in science

Modern information and communication technologies, especially the Internet, have diminished the role of spatial distances and territorial boundaries on the access and transmissibility of information. This has enabled scientists for closer collaboration and internationalization. Nevertheless, geography remains an important factor affecting the dynamics of science. Here we present a systematic analysis of citation and collaboration networks between cities and countries, by assigning papers to the geographic locations of their authors' affiliations. The citation flows as well as the collaboration strengths between cities decrease with the distance between them and follow gravity laws. In addition, the total research impact of a country grows linearly with the amount of national funding for research & development. However, the average impact reveals a peculiar threshold effect: the scientific output of a country may reach an impact larger than the world average only if the country invests more than about 100,000 USD per researcher annually.Comment: Published version. 9 pages, 5 figures + Appendix, The world citation and collaboration networks at both city and country level are available at http://becs.aalto.fi/~rajkp/datasets.htm

Individualization as driving force of clustering phenomena in humans

One of the most intriguing dynamics in biological systems is the emergence of clustering, the self-organization into separated agglomerations of individuals. Several theories have been developed to explain clustering in, for instance, multi-cellular organisms, ant colonies, bee hives, flocks of birds, schools of fish, and animal herds. A persistent puzzle, however, is clustering of opinions in human populations. The puzzle is particularly pressing if opinions vary continuously, such as the degree to which citizens are in favor of or against a vaccination program. Existing opinion formation models suggest that "monoculture" is unavoidable in the long run, unless subsets of the population are perfectly separated from each other. Yet, social diversity is a robust empirical phenomenon, although perfect separation is hardly possible in an increasingly connected world. Considering randomness did not overcome the theoretical shortcomings so far. Small perturbations of individual opinions trigger social influence cascades that inevitably lead to monoculture, while larger noise disrupts opinion clusters and results in rampant individualism without any social structure. Our solution of the puzzle builds on recent empirical research, combining the integrative tendencies of social influence with the disintegrative effects of individualization. A key element of the new computational model is an adaptive kind of noise. We conduct simulation experiments to demonstrate that with this kind of noise, a third phase besides individualism and monoculture becomes possible, characterized by the formation of metastable clusters with diversity between and consensus within clusters. When clusters are small, individualization tendencies are too weak to prohibit a fusion of clusters. When clusters grow too large, however, individualization increases in strength, which promotes their splitting.Comment: 12 pages, 4 figure

Computational fact checking from knowledge networks

Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation

Geographic constraints on social network groups

Social groups are fundamental building blocks of human societies. While our social interactions have always been constrained by geography, it has been impossible, due to practical difficulties, to evaluate the nature of this restriction on social group structure. We construct a social network of individuals whose most frequent geographical locations are also known. We also classify the individuals into groups according to a community detection algorithm. We study the variation of geographical span for social groups of varying sizes, and explore the relationship between topological positions and geographic positions of their members. We find that small social groups are geographically very tight, but become much more clumped when the group size exceeds about 30 members. Also, we find no correlation between the topological positions and geographic positions of individuals within network communities. These results suggest that spreading processes face distinct structural and spatial constraints.Comment: 10 pages, 5 figure

Piecing together the puzzle of pictorial representation: How jigsaw puzzles index metacognitive development

Jigsaw puzzles are ubiquitous developmental toys in Western societies, used here to examine the development of metarepresentation. For jigsaw puzzles this entails understanding that individual pieces, when assembled, produce a picture. In Experiment 1, 3- to 5-year-olds (N = 117) completed jigsaw puzzles that were normal, had no picture, or comprised noninterlocking rectangular pieces. Pictorial puzzle completion was associated with mental and graphical metarepresentational task performance. Guide pictures of completed pictorial puzzles were not useful. In Experiment 2, 3- to 4-year-olds (N = 52) completed a simplified task, to choose the correct final piece. Guide-use associated with age and specifically graphical metarepresentation performance. We conclude that the pragmatically natural measure of jigsaw puzzle completion ability demonstrates general and pictorial metarepresentational development at 4 years

Temporal rainfall trend analysis in different agro-ecological regions of southern Africa

Rainfall is a major driver of food production in rainfed smallholder farming systems. This study was conducted to assess linear trends in (i) different daily rainfall amounts (<5, 5–10, 11–20, 21–40 and >40 mm∙day-1), and (ii) monthly and seasonal rainfall amounts. Drought was determined using the rainfall variability index. Daily rainfall data were derived from 18 meteorological stations in southern Africa. Daily rainfall was dominated by <5 mm∙day-1 followed by 5–10 mm∙day -1. Three locations experienced increasing linear trends of <5 mm∙day-1 amounts and two others in sub-humid region had increases in the >40 mm day -1 category. Semi-arid location experienced increasing trends in <5 and 5–10 mm∙day-1 events. A significant linear trend in seasonal rainfall occurred at two locations with decreasing rainfall (1.24 and 3 mm∙season-1). A 3 mm∙season-1 decrease in seasonal rainfall was experienced under semi-arid conditions. There were no apparent linear trends in monthly and seasonal rainfall at 15 of the 18 locations studied. Drought frequencies varied with location and were 50% or higher during the November–March growing season. Rainfall trends were location and agro-ecology specific, but most of the locations studied did not experience significant changes between the 1900s and 2000s

Trade-off strategies between growth and defense of spring ephemeral plants in early spring

IntroductionSpring ephemeral plants represent a unique ecological category of herbaceous plants, characterized by early blooming and vivid flowers with significant ornamental value. Understanding the adaptive strategies of spring ephemerals is crucial for the introduction and cultivation of early spring plants, as well as for optimizing light energy utilization and nutrient cycling within ecosystems.MethodsWe evaluated 26 functional traits across four spring ephemerals and four spring non-ephemeral plants along an elevation gradient. By establishing a plant functional trait network, we examined the adaptation strategies of early spring plants at different elevations and compared the differences in adaptation strategies between two types of plants.ResultsSpring ephemerals exhibited higher concentrations of carbon and nitrogen, lower concentrations of carbohydrates, higher edge density and modularity in trait networks, and stronger linkages between defense traits. Plants at higher elevations demonstrated higher leaf dry matter content and leaf total flavonoid concentration, and lower nitrogen concentration, influenced by temperature, precipitation, and soil nutrients.DiscussionThese results demonstrated that spring ephemerals have a strong nutrient uptake capacity, and adopt resource competition strategies to rapidly accumulate nutrients and reproduce. The plants at higher elevations adopt more conservative strategies, with trait networks showing increased modularity, edge density, and closer correlations among traits to enhance resource utilization. This study provides new insights into the adaptive strategies of spring ephemerals by demonstrating how plants allocate resources for growth and defense through the regulation of trait variation and correlations among traits