Helpful Behavior and the Durability of Collaborative Ties

Long-term collaborations are crucial in many creative domains. Although there is ample research on why people collaborate, our knowledge about what drives some collaborations to persist and others to decay is still emerging. In this paper, we extend theory on third-party effects and collaborative persistence to study this question. We specifically consider the role that a third party’s helpful behavior plays in shaping tie durability. We propose that when third parties facilitate helpfulness among their group, the collaboration is stronger, and it persists even in the third’s absence. In contrast, collaborations with third parties that are nonhelpful are unstable and dissolve in their absence. We use a unique data set comprising scientific collaborations among pairs of research immunologists who lost a third coauthor to unexpected death. Using this quasi-random loss as a source of exogenous variation, we separately identify the effect of third parties’ traditional role as an active agent of collaborative stability and the enduring effect of their helpful behavior—as measured by acknowledgments—on the persistence of the remaining authors’ collaboration. We find support for our hypotheses and find evidence that one mechanism driving our effect is that helpful thirds make their coauthors more helpful.

Increasing 28 mitogenomes of Ephemeroptera, Odonata and Plecoptera support the Chiastomyaria hypothesis with three different outgroup combinations

Background The phylogenetic relationships of Odonata (dragonflies and damselflies) and Ephemeroptera (mayflies) remain unresolved. Different researchers have supported one of three hypotheses (Palaeoptera, Chiastomyaria or Metapterygota) based on data from different morphological characters and molecular markers, sometimes even re-assessing the same transcriptomes or mitochondrial genomes. The appropriate choice of outgroups and more taxon sampling is thought to eliminate artificial phylogenetic relationships and obtain an accurate phylogeny. Hence, in the current study, we sequenced 28 mt genomes from Ephemeroptera, Odonata and Plecoptera to further investigate phylogenetic relationships, the probability of each of the three hypotheses, and to examine mt gene arrangements in these species. We selected three different combinations of outgroups to analyze how outgroup choice affected the phylogenetic relationships of Odonata and Ephemeroptera. Methods Mitochondrial genomes from 28 species of mayflies, dragonflies, damselflies and stoneflies were sequenced. We used Bayesian inference (BI) and Maximum likelihood (ML) analyses for each dataset to reconstruct an accurate phylogeny of these winged insect orders. The effect of outgroup choice was assessed by separate analyses using three outgroups combinations: (a) four bristletails and three silverfish as outgroups, (b) five bristletails and three silverfish as outgroups, or (c) five diplurans as outgroups. Results Among these sequenced mitogenomes we found the gene arrangement IMQM in Heptageniidae (Ephemeroptera), and an inverted and translocated tRNA-Ile between the 12S RNA gene and the control region in Ephemerellidae (Ephemeroptera). The IMQM gene arrangement in Heptageniidae (Ephemeroptera) can be explained via the tandem-duplication and random loss model, and the transposition and inversion of tRNA-Ile genes in Ephemerellidae can be explained through the recombination and tandem duplication-random loss (TDRL) model. Our phylogenetic analysis strongly supported the Chiastomyaria hypothesis in three different outgroup combinations in BI analyses. The results also show that suitable outgroups are very important to determining phylogenetic relationships in the rapid evolution of insects especially among Ephemeroptera and Odonata. The mt genome is a suitable marker to investigate the phylogeny of inter-order and inter-family relationships of insects but outgroup choice is very important for deriving these relationships among winged insects. Hence, we must carefully choose the correct outgroup in order to discuss the relationships of Ephemeroptera and Odonata.

Quantile-Based Estimative VaR Forecast and Dependence Measure: A Simulation Approach

A Value-at-Risk (VaR) forecast may be calculated for the case of a random loss alone and/or of a random loss that depends on another random loss. In both cases, the VaR forecast is obtained by employing its (conditional) probability distribution of loss data, specifically the quantile of loss distribution. In practice, we have an estimative VaR forecast in which the distribution parameter vector is replaced by its estimator. In this paper, the quantile-based estimative VaR forecast for dependent random losses is explored through a simulation approach. It is found that the estimative VaR forecast is more accurate when a copula is employed. Furthermore, the stronger the dependence of a random loss to the target loss, in linear correlation, the larger/smaller the conditional mean/variance. In any dependence measure, generally, stronger and negative dependence gives a higher forecast. When there is a tail dependence, the use of upper and lower tail dependence provides a better forecast instead of the single correlation coefficient.

A Recovery Method of Data Lost in Network Communication

At present, ScanDisk is used to recover the data lost in network communication. But this method is limited in scope, and once the lost data is covered, it’s difficult or impossible to recover it, which results in low recovery degree. Accordingly, a recovery method for lost data in network communication based on RAID6 is proposed. Firstly, according to the mechanism of data loss in network communication, the missing data is divided into three categories: random loss, completely random loss and nonrandom loss, and then according to the results of classification, the recovery problem of the data loss in network communication is converted into the problem of matrix completion, finally, a low-rank decomposition model is proposed, according to the low rank characteristics of the matrix, the lost data in the matrix is recovered, thus the recovery of the lost data in network communication is finished. Experimental results show that the proposed method can easily recover the lost data in network communication with a simple operation, low computing complexity and strong applicability, and can be used as a universal recovery method for data lost in network communication.