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1. Multiple paternity and the number of offspring: A model reveals two major groups of species

   https://doi.org/10.1002/bies.202000247  

   Correia, Hannah E.; Abebe, Ash; Dobson, F. Stephen (January 2021, BioEssays)

   Abstract Parentage analyses via microsatellite markers have revealed multiple paternity within the broods of polytocous species of mammals, reptiles, amphibians, fishes and invertebrates. The widespread phenomenon of multiple paternity may have attending relationships with such evolutionary processes as sexual selection and kin selection. However, just how much multiple paternity should a species exhibit? We developed Bayesian null models of how multiple paternity relates to brood sizes. For each of 114 species with published data on brood sizes and numbers of sires, we compared our null model estimates to published frequencies of multiple paternity. The majority of species fell close to our null model, especially among fish and invertebrate species. Some species, however, had low probabilities of multiple paternity, far from the predictions of the null model, likely due to sexual selection and environmental constraints. We suggest a major division among species’ mating systems between those with close to random mating and high levels of multiple paternity, and those with constraints that produce low levels of multiple paternity. 

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2. One internship, two internships, three internships … more!’: exploring the culture of the multiple internship economy

   https://doi.org/10.1080/13639080.2022.2036713  

   Wolfgram, M. (January 2022, Journal of education and work)

   Internships are increasingly promoted as a high impact practice to improve students’ post-graduation employment outcomes, and educators often encourage students to participate in multiple internships. Yet, there is a lack of research on the sociocultural contexts associated with multiple internship participation. We present findings about the culture of multiple internship participation – drawing on focus groups and one-year follow-up interviews with students at five colleges in the United States, along with interviews with their educators and an analysis of online documents about multiple internship participation. The evidence documents a particular culture of the multiple internship economy, representing multiple internships as a linear, progressive, goal-oriented cultural project to accumulate a marketable self; also described by anthropologists as a neoliberal conception of the self. This cultural conception of a marketable neoliberal self is comprised of signs – such as narratives of multiple internships – that provide evidence of skills and experiences, of persevering through obstacles, and of ‘hustle’ and a ‘do what it takes’ attitude, which can be deployed to navigate competitive gatekeeping encounters such as employment interviews. Based on these findings, we develop a sociocultural theory of multiple internship participation as a project of neoliberal gatekeeping navigation. 

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3. Mitigating the Risks of Supporting Multiple Control Plans in a Production SDN Network: A Use Case

   Bezerra, J.; Ibarra, J.; Schwarz, M.; Freitas, H.; Morgan, H. (January 2017, SBRC/WPEIF 2017)

   The SDN paradigm enables network operators to host multiple control planes in parallel, being an approach to support multiple network services. Supporting multiple control planes over production networks exposes the production environment to potential risks and increases operational complexity. To understand and mitigate these risks, we implemented procedures and tools that resulted in a more reliable network. This paper describes our experience and findings with the support of multiple control planes in a wide-area production network. 

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4. Balancing Fairness and Accuracy in Sentiment Detection using Multiple Black Box Models

   https://doi.org/10.1145/3422841.3423536  

   Almuzaini, Abdulaziz A.; Singh, Vivek K. (October 2020, In Proceedings of the 2nd International Workshop on Fairness, Accountability, Transparency and Ethics in Multimedia)

   null (Ed.)

   Sentiment detection is an important building block for multiple information retrieval tasks such as product recommendation, cyberbullying, fake news and misinformation detection. Unsurprisingly, multiple commercial APIs, each with different levels of accuracy and fairness, are now publicly available for sentiment detection. Users can easily incorporate these APIs in their applications. While combining inputs from multiple modalities or black-box models for increasing accuracy is commonly studied in multimedia computing literature, there has been little work on combining different modalities for increasing fairness of the resulting decision. In this work, we audit multiple commercial sentiment detection APIs for the gender bias in two-actor news headlines settings and report on the level of bias observed. Next, we propose a "Flexible Fair Regression" approach, which ensures satisfactory accuracy and fairness by jointly learning from multiple black-box models. The results pave way for fair yet accurate sentiment detectors for multiple applications. 

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5. Holographic particle localization under multiple scattering

   https://doi.org/doi:10.1117/1.ap.1.3.036003  

   Tahir, W.; Tian, L.; Kamilov, U. (January 2019, SPIE journal)

   We introduce a computational framework that incorporates multiple scattering for large-scale three-dimensional (3-D) particle localization using single-shot in-line holography. Traditional holographic techniques rely on single-scattering models that become inaccurate under high particle densities and large refractive index contrasts. Existing multiple scattering solvers become computationally prohibitive for large-scale problems, which comprise millions of voxels within the scattering volume. Our approach overcomes the computational bottleneck by slicewise computation of multiple scattering under an efficient recursive framework. In the forward model, each recursion estimates the next higher-order multiple scattered field among the object slices. In the inverse model, each order of scattering is recursively estimated by a nonlinear optimization procedure. This nonlinear inverse model is further supplemented by a sparsity promoting procedure that is particularly effective in localizing 3-D distributed particles. We show that our multiple-scattering model leads to significant improvement in the quality of 3-D localization compared to traditional methods based on single scattering approximation. Our experiments demonstrate robust inverse multiple scattering, allowing reconstruction of 100 million voxels from a single 1-megapixel hologram with a sparsity prior. The performance bound of our approach is quantified in simulation and validated experimentally. Our work promises utilization of multiple scattering for versatile large-scale applications. 

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