Renewed commitments in a time of vigilance: sexuality education in the USA*

Over the past 20 years, the USA has seen more than its fair share of controversy with respect to education about sexuality, sex and intimate relationships. Attention has focused on content (abstinence-only vs. comprehensive instruction), delivery (by teachers, parents, health professionals or community educators) and context (within school and beyond). In recognition of this fact, Sex Education invited the development of a virtual special issue comprising a sample of its most impactful papers on these and related topics. The 2016 Presidential election results and recent legislative action in the USA point to the importance of thinking broadly about teaching and learning about sexuality inside and outside of schools and of considering sexuality as it intersects with categories of difference, privilege and penalty, including ability, age, immigration, race, gender and class. This paper, developed as an introduction to the virtual special issue, opens with a discussion of the journal’s contributions to the ongoing discussion of pleasure and desire in sexuality education. From there, we turn to the question of what is possible given the material and ideological conditions of schooling and then to opportunities for teachers and learners outside the conventional classroom. We follow with a discussion of the place of intersectional analyses in sexuality education research, and conclude with some thoughts on sexuality education research at this political moment.     

Relevance-based entity selection for ad hoc retrieval

Recent developments have shown that entity-based models that rely on information from the knowledge graph can improve document retrieval performance. However, given the non-transitive nature of relatedness between entities on the knowledge graph, the use of semantic relatedness measures can lead to topic drift. To address this issue, we propose a relevance-based model for entity selection based on pseudo-relevance feedback, which is then used to systematically expand the input query leading to improved retrieval performance. We perform our experiments on the widely used TREC Web corpora and empirically show that our proposed approach to entity selection significantly improves ad hoc document retrieval compared to strong baselines. More concretely, the contributions of this work are as follows: (1) We introduce a graphical probability model that captures dependencies between entities within the query and documents. (2) We propose an unsupervised entity selection method based on the graphical model for query entity expansion and then for ad hoc retrieval. (3) We thoroughly evaluate our method and compare it with the state-of-the-art keyword and entity based retrieval methods. We demonstrate that the proposed retrieval model shows improved performance over all the other baselines on ClueWeb09B and ClueWeb12B, two widely used Web corpora, on the [email protected], and [email protected] metrics. We also show that the proposed method is most effective on the difficult queries. In addition, We compare our proposed entity selection with a state-of-the-art entity selection technique within the context of ad hoc retrieval using a basic query expansion method and illustrate that it provides more effective retrieval for all expansion weights and different number of expansion entities.     

Mean square consensus of double-integrator multi-agent systems under intermittent control: A stochastic time scale approach

We consider the leader–follower consensus problem for a multi-agent system where information is exchanged only on a non-uniform discrete stochastic time domain. For a second-order multi-agent system subject to intermittent information exchange, we model the tracking error dynamics as a $\mu ?$varying linear system on a discrete stochastic time scale, where μ is the graininess operator. Based on a Lyapunov operator and a positive perturbation operator on the space of symmetric matrices, we derive necessary and sufficient conditions to design a decentralized consensus protocol. This protocol allows us to cast the mean-square exponential consensus problem within the framework of dynamic equations on stochastic time scales. We establish some theoretical results which allow for the computation of the control gain matrix which guarantees the mean-square exponential stability with a given decay rate for the error dynamics. To show the effectiveness of the theoretical results, some simulation and experimental results on multi-robot systems have been performed.     

Design of a fast real-time LPV model predictive control system for semi-active suspension control of a full vehicle

This article is concerned with the control of a Semi-Active suspension system of a 7DOF Full Vehicle model, equipped with four Electro Rheological (ER) dampers, taking into account their incipient dissipativity constraints. Herein, a real-time, fast, advanced control structure is presented within the Model Predictive Control framework for Linear Parameter Varying (LPV) systems. The control algorithm is developed to provide a suitable trade-off between comfort and handling performances of the vehicle in a very limited sampling period ($T_s=5\text{ms}$), in view of a possible realtime implementation on a real vehicle. The control structure is tested and compared to other standard fast control approaches. Full nonlinear realistic simulation results illustrate the overall good operation and behaviour of the proposed control approach.     

Measuring scientific contributions with modified fractional counting

We develop and propose a new counting method at the aggregate level for contributions to scientific publications called modified fractional counting (MFC). We show that, compared to traditional complete-normalized fractional counting, it eliminates the extreme differences in contributions over time that otherwise occur between scientists that mainly publish alone or in small groups and those that publish with large groups of co-authors. As an extra benefit we find that scientists in different areas of research turn out to have comparable average contributions to scientific articles. We test the method on scientists at Norway’s largest universities and find that, at an aggregate level, it indeed supports comparability across different co-authorship practices as well as between areas of research. MFC is thereby useful whenever the research output from institutions with different research profiles are compared, as e.g., in the Leiden Ranking. Finally, as MFC is actually a family of indicators, depending on a sensitivity parameter, it can be adapted to the circumstances.     

Ranking scientific articles based on bibliometric networks with a weighting scheme

As the volume of scientific articles has grown rapidly over the last decades, evaluating their impact becomes critical for tracing valuable and significant research output. Many studies have proposed various ranking methods to estimate the prestige of academic papers using bibliometric methods. However, the weight of the links in bibliometric networks has been rarely considered for article ranking in existing literature. Such incomplete investigation in bibliometric methods could lead to biased ranking results. Therefore, a novel scientific article ranking algorithm, W-Rank, is introduced in this study proposing a weighting scheme. The scheme assigns weight to the links of citation network and authorship network by measuring citation relevance and author contribution. Combining the weighted bibliometric networks and a propagation algorithm, W-Rank is able to obtain article ranking results that are more reasonable than existing PageRank-based methods. Experiments are conducted on both arXiv hep-th and Microsoft Academic Graph datasets to verify the W-Rank and compare it with three renowned article ranking algorithms. Experimental results prove that the proposed weighting scheme assists the W-Rank in obtaining ranking results of higher accuracy and, in certain perspectives, outperforming the other algorithms.     

Authorship analysis of specialized vs diversified research output

The present work investigates the relations between amplitude and type of collaboration (intramural, extramural domestic or international) and output of specialized versus diversified research. By specialized or diversified research, we mean within or beyond the author’s dominant research topic. The field of observation is the scientific production over five years from about 23,500 academics. The analyses are conducted at the aggregate and disciplinary level. The results lead to the conclusion that in general, the output of diversified research is no more frequently the fruit of collaboration than is specialized research. At the level of the particular collaboration types, international collaborations weakly underlie the specialized kind of research output; on the contrary, extramural domestic and intramural collaborations are weakly associated with diversified research. While the weakness of association remains, exceptions are observed at the level of the individual disciplines.     

How does collaboration affect researchers’ positions in co-authorship networks?

Collaboration usually has a positive effect on researchers’ productivity: researchers have become increasingly collaborative, according to recent studies. Numerous studies have focused on enhancing research collaboration by recommendation technology and measuring the influence of researchers. However, few studies have investigated the effect of collaboration on the position of a researcher in the research social network. In this paper, we explore the relationships between collaboration and influence by social analytical methods, which are pertinent to analyzing the network structure and individual traits. We evaluate three aspects of the researchers’ influence: friendship paradox validation, social circle, and structure of a researcher's ego network. Furthermore, the ”six degrees of Bacon number” theory, generalized friendship paradox, and triadic closure theory are introduced to support our analysis. Experimental results show that collaboration can help researchers increase their influence to some extent.