RTSim: A cycle-accurate simulator for racetrack memories

Racetrack memories (RTMs) have drawn considerable attention from computer architects of late. Owing to the ultra-high capacity and comparable access latency to SRAM, RTMs are promising candidates to revolutionize the memory subsystem. In order to evaluate their performance and suitability at various levels in the memory hierarchy, it is crucial to have RTM-specific simulation tools that accurately model their behavior and enable exhaustive design space exploration. To this end, we propose RTSim, an open source cycle-accurate memory simulator that enables performance evaluation of the domain-wall-based racetrack memories. The skyrmions-based RTMs can also be modeled with RTSim because they are architecturally similar to domain-wall-based RTMs. RTSim is developed in collaboration with physicists and computer scientists. It accurately models RTM-specific shift operations, access ports management and the sequence of memory commands beside handling the routine read/write operations. RTSim is built on top of NVMain2.0, offering larger design space for exploration

A quantitative perspective on ethics in large team science

The gradual crowding out of singleton and small team science by large team endeavors is challenging key features of research culture. It is therefore important for the future of scientific practice to reflect upon the individual scientist's ethical responsibilities within teams. To facilitate this reflection we show labor force trends in the US revealing a skewed growth in academic ranks and increased levels of competition for promotion within the system; we analyze teaming trends across disciplines and national borders demonstrating why it is becoming difficult to distribute credit and to avoid conflicts of interest; and we use more than a century of Nobel prize data to show how science is outgrowing its old institutions of singleton awards. Of particular concern within the large team environment is the weakening of the mentor-mentee relation, which undermines the cultivation of virtue ethics across scientific generations. These trends and emerging organizational complexities call for a universal set of behavioral norms that transcend team heterogeneity and hierarchy. To this end, our expository analysis provides a survey of ethical issues in team settings to inform science ethics education and science policy.Comment: 13 pages, 5 figures, 1 table. Keywords: team ethics; team management; team evaluation; science of scienc

Predicting Scientific Success Based on Coauthorship Networks

We address the question to what extent the success of scientific articles is due to social influence. Analyzing a data set of over 100000 publications from the field of Computer Science, we study how centrality in the coauthorship network differs between authors who have highly cited papers and those who do not. We further show that a machine learning classifier, based only on coauthorship network centrality measures at time of publication, is able to predict with high precision whether an article will be highly cited five years after publication. By this we provide quantitative insight into the social dimension of scientific publishing - challenging the perception of citations as an objective, socially unbiased measure of scientific success.Comment: 21 pages, 2 figures, incl. Supplementary Materia

Detecting rich-club ordering in complex networks

Uncovering the hidden regularities and organizational principles of networks arising in physical systems ranging from the molecular level to the scale of large communication infrastructures is the key issue for the understanding of their fabric and dynamical properties [1-5]. The ``rich-club'' phenomenon refers to the tendency of nodes with high centrality, the dominant elements of the system, to form tightly interconnected communities and it is one of the crucial properties accounting for the formation of dominant communities in both computer and social sciences [4-8]. Here we provide the analytical expression and the correct null models which allow for a quantitative discussion of the rich-club phenomenon. The presented analysis enables the measurement of the rich-club ordering and its relation with the function and dynamics of networks in examples drawn from the biological, social and technological domains.Comment: 1 table, 3 figure

What went wrong with: "The Interaction of Neutrons With 7Be: "Lack of Standard Nuclear Physics Solution to the "Primordial 7Li Problem"", by M. Gai [arXiv:1812.09914v1]?

We comment here on results of the project aimed at measuring the 7Be(n,x) reactions at SARAF, Israel, in 2016, posted by M. Gai in [arXiv:1812.09914v1] without the knowledge of parts of the collaboration and against the explicit veto of the collaborators and the administration of the Paul Scherrer Institut, Switzerland. We address both the experimental shortcomings and the drawbacks in project conduction. M. Gais preprint is labeled as "on behalf of the SARAF Israel-US-Switzerland Collaboration", the author list is given as a reference to another unpublished contribution (cited as [27]) to the NPA8 conference in June 2017 in Catania). However, M. Gai did never have the right to report on unpublished proprietary data of the entire collaboration, and he was not authorized to act "on behalf of the collaboration". The contribution is declared as "accepted for publication", but in fact was retracted during the refereeing process. After several careful data evaluations, we have to state that the results of these measurements are not trustworthy and neither the given experimental data basis nor the corresponding data analysis can be improved further. Therefore, we requested to retract the posting immediately [arXiv:1904.03023]. We have to emphasize that, in our opinion, arXiv is not the appropriate platform for handling frictions in a collaboration. These problems should have been solved internally before publishing. Unfortunately, with his single-handed posting against the explicit disagreement of parts of the collaboration, M. Gai did not leave another possibility. With the present article, we expressed all our concerns and objections and we consider herewith the public discussion of this issue as closed.Comment: arXiv admin note: This version has been removed by arXiv administrators due to copyright infringemen

Prediction of scientific collaborations through multiplex interaction networks

Link prediction algorithms can help to understand the structure and dynamics of scientific collaborations and the evolution of Science. However, available algorithms based on similarity between nodes of collaboration networks are bounded by the limited amount of links present in these networks. In this work, we reduce the latter intrinsic limitation by generalizing the Adamic-Adar method to multiplex networks composed by an arbitrary number of layers, that encode diverse forms of scientific interactions. We show that the new metric outperforms other single-layered, similarity-based scores and that scientific credit, represented by citations, and common interests, measured by the usage of common keywords, can be predictive of new collaborations. Our work paves the way for a deeper understanding of the dynamics driving scientific collaborations, and provides a new algorithm for link prediction in multiplex networks that can be applied to a plethora of systems

Collaboration in sensor network research: an in-depth longitudinal analysis of assortative mixing patterns

Many investigations of scientific collaboration are based on statistical analyses of large networks constructed from bibliographic repositories. These investigations often rely on a wealth of bibliographic data, but very little or no other information about the individuals in the network, and thus, fail to illustrate the broader social and academic landscape in which collaboration takes place. In this article, we perform an in-depth longitudinal analysis of a relatively small network of scientific collaboration (N = 291) constructed from the bibliographic record of a research center involved in the development and application of sensor network and wireless technologies. We perform a preliminary analysis of selected structural properties of the network, computing its range, configuration and topology. We then support our preliminary statistical analysis with an in-depth temporal investigation of the assortative mixing of selected node characteristics, unveiling the researchers' propensity to collaborate preferentially with others with a similar academic profile. Our qualitative analysis of mixing patterns offers clues as to the nature of the scientific community being modeled in relation to its organizational, disciplinary, institutional, and international arrangements of collaboration.Comment: Scientometrics (In press