Santa Fe Institute

About: Santa Fe Institute is a nonprofit organization based out in Santa Fe, New Mexico, United States. It is known for research contribution in the topics: Population & Complex network. The organization has 558 authors who have published 4558 publications receiving 396015 citations. The organization is also known as: SFI.

Statistical detection of systematic election irregularities

Abstract: Democratic societies are built around the principle of free and fair elections, and that each citizen’s vote should count equally. National elections can be regarded as large-scale social experiments, where people are grouped into usually large numbers of electoral districts and vote according to their preferences. The large number of samples implies statistical consequences for the polling results, which can be used to identify election irregularities. Using a suitable data representation, we find that vote distributions of elections with alleged fraud show a kurtosis substantially exceeding the kurtosis of normal elections, depending on the level of data aggregation. As an example, we show that reported irregularities in recent Russian elections are, indeed, well-explained by systematic ballot stuffing. We develop a parametric model quantifying the extent to which fraudulent mechanisms are present. We formulate a parametric test detecting these statistical properties in election results. Remarkably, this technique produces robust outcomes with respect to the resolution of the data and therefore, allows for cross-country comparisons.

Extensive HLA class I allele promiscuity among viral CTL epitopes

Abstract: Promiscuous binding of T helper epitopes to MHC class II molecules has been well established, but few examples of promiscuous class I-restricted epitopes exist To address the extent of promiscuity of HLA class I peptides, responses to 242 well-defined viral epitopes were tested in 100 subjects regardless of the individuals' HLA type Surprisingly, half of all detected responses were seen in the absence of the originally reported restricting HLA class I allele, and only 3% of epitopes were recognized exclusively in the presence of their original allele Functional assays confirmed the frequent recognition of HLA class I-restricted T cell epitopes on several alternative alleles across HLA class I supertypes and encoded on different class I loci These data have significant implications for the understanding of MHC class I-restricted antigen presentation and vaccine development

Why does metabolic rate scale with body size

Abstract: A long-standing problem has been the origin of quarter-power allometric scaling laws that relate many characteristics of organisms to their body mass1,2 — specifically, whole-organism metabolic rate, B = aMb, where M is body mass, a is a taxon-dependent normalization, and b ≈ 3/4 for animals and plants. Darveau et al.3 propose a multiple-cause model for mammalian metabolic rate as the “sum of multiple contributors”, Bi, which they assume to scale as Bi = , and obtain b ≈ 0.78 for the basal and 0.86 for the maximally active rate, $${\mathop V\limits^{\bullet}}{}_{{\rm O}_2}^{\max}$$ . We argue, however, that this scaling equation is based on technical, theoretical and conceptual errors, including misrepresentations of our published results4,5.

Productivity, prominence, and the effects of academic environment

Abstract: Faculty at prestigious institutions produce more scientific papers, receive more citations and scholarly awards, and are typically trained at more-prestigious institutions than faculty with less prestigious appointments. This imbalance is often attributed to a meritocratic system that sorts individuals into more-prestigious positions according to their reputation, past achievements, and potential for future scholarly impact. Here, we investigate the determinants of scholarly productivity and measure their dependence on past training and current work environments. To distinguish the effects of these environments, we apply a matched-pairs experimental design to career and productivity trajectories of 2,453 early-career faculty at all 205 PhD-granting computer science departments in the United States and Canada, who together account for over 200,000 publications and 7.4 million citations. Our results show that the prestige of faculty’s current work environment, not their training environment, drives their future scientific productivity, while current and past locations drive prominence. Furthermore, the characteristics of a work environment are more predictive of faculty productivity and impact than mechanisms representing preferential selection or retention of more-productive scholars by more-prestigious departments. These results identify an environmental mechanism for cumulative advantage, in which an individual’s past successes are “locked in” via placement into a more prestigious environment, which directly facilitates future success. The scientific productivity of early-career faculty is thus driven by where they work, rather than where they trained for their doctorate, indicating a limited role for doctoral prestige in predicting scientific contributions.

Strengths and weaknesses of weak-strong cluster problems: A detailed overview of state-of-the-art classical heuristics versus quantum approaches

Abstract: To date, a conclusive detection of quantum speedup remains elusive. Recently, a team by Google Inc. [V. S. Denchev et al., Phys. Rev. X 6, 031015 (2016)] proposed a weak-strong cluster model tailored to have tall and narrow energy barriers separating local minima, with the aim to highlight the value of finite-range tunneling. More precisely, results from quantum Monte Carlo simulations as well as the D-Wave 2X quantum annealer scale considerably better than state-of-the-art simulated annealing simulations. Moreover, the D-Wave 2X quantum annealer is $\ensuremath{\sim}{10}^{8}$ times faster than simulated annealing on conventional computer hardware for problems with approximately ${10}^{3}$ variables. Here, an overview of different sequential, nontailored, as well as specialized tailored algorithms on the Google instances is given. We show that the quantum speedup is limited to sequential approaches and study the typical complexity of the benchmark problems using insights from the study of spin glasses.