Network Structure, Efficiency, and Performance in WikiProjects

The internet has enabled collaborations at a scale never before possible, but the best practices for organizing such large collaborations are still not clear. Wikipedia is a visible and successful example of such a collaboration which might offer insight into what makes large-scale, decentralized collaborations successful. We analyze the relationship between the structural properties of WikiProject coeditor networks and the performance and efficiency of those projects. We confirm the existence of an overall performance-efficiency trade-off, while observing that some projects are higher than others in both performance and efficiency, suggesting the existence factors correlating positively with both. Namely, we find an association between low-degree coeditor networks and both high performance and high efficiency. We also confirm results seen in previous numerical and small-scale lab studies: higher performance with less skewed node distributions, and higher performance with shorter path lengths. We use agent-based models to explore possible mechanisms for degree-dependent performance and efficiency. We present a novel local-majority learning strategy designed to satisfy properties of real-world collaborations. The local-majority strategy as well as a localized conformity-based strategy both show degree-dependent performance and efficiency, but in opposite directions, suggesting that these factors depend on both network structure and learning strategy. Our results suggest possible benefits to decentralized collaborations made of smaller, more tightly-knit teams, and that these benefits may be modulated by the particular learning strategies in use.Comment: 11 pages, 5 figures, to appear in ICWSM 201

Effectiveness of Generative Artificial Intelligence for Scientific Content Analysis

Generative artificial intelligence (GenAI) in general, and large language models (LLMs) in particular, are highly fashionable. As they have the ability to generate coherent output based on prompts in natural language, they are promoted as tools to free knowledge workers from tedious tasks such as content writing, customer support and routine computer code generation. Unsurprisingly, their application is also attractive to professionals in the research domain, where mundane and laborious tasks, such as literature screening, are commonplace. We evaluate Vertex AI ‘text-bison’, a foundational LLM model, in a real-world academic scenario by replicating parts of a popular systematic review in the information management domain. By comparing the results of a zero-shot LLM-based approach with those of the original study, we gather evidence on the suitability of state-of-the-art general-purpose LLMs for the analysis of scientific content. We show that the LLM-based approach delivers good scientific content analysis performance for a general classification problem (ACC = 0.9), acceptable performance for a domain-specific classification problem (ACC = 0.8) and borderline performance for a text comprehension problem (ACC ≈ 0.69). We conclude that some content analysis tasks with moderate accuracy requirements may be supported by current LLMs. As the technology will evolve rapidly in the foreseeable future, studies on large corpora, where some inaccuracies are tolerable, or workflows that prepare large data sets for human processing, may increasingly benefit from the capabilities of GenAI

Mature dendritic cells use endocytic receptors to capture and present antigens

In response to inflammatory stimuli, dendritic cells (DCs) trigger maturation, a terminal differentiation program required to initiate T lymphocyte responses. A hallmark of maturation is downregulation of endocytosis, widely assumed to restrict the ability of mature DCs to capture and present antigens encountered after the initial stimulus. We found that mature DCs continue to internalize antigens, especially by receptor-mediated endocytosis and phagocytosis. These antigens were transported to lysosomal compartments, loaded onto MHCII, and presented efficiently to T cells, both in vitro and in vivo. Antigens were also presented on MHCI with high efficiency. While mature DCs down-regulate macropinocytosis, they capture antigens via endocytic receptors and, in principle, remain able to initiate immune responses during the course of an infection

Are All Successful Communities Alike? Characterizing and Predicting the Success of Online Communities

The proliferation of online communities has created exciting opportunities to study the mechanisms that explain group success. While a growing body of research investigates community success through a single measure -- typically, the number of members -- we argue that there are multiple ways of measuring success. Here, we present a systematic study to understand the relations between these success definitions and test how well they can be predicted based on community properties and behaviors from the earliest period of a community's lifetime. We identify four success measures that are desirable for most communities: (i) growth in the number of members; (ii) retention of members; (iii) long term survival of the community; and (iv) volume of activities within the community. Surprisingly, we find that our measures do not exhibit very high correlations, suggesting that they capture different types of success. Additionally, we find that different success measures are predicted by different attributes of online communities, suggesting that success can be achieved through different behaviors. Our work sheds light on the basic understanding of what success represents in online communities and what predicts it. Our results suggest that success is multi-faceted and cannot be measured nor predicted by a single measurement. This insight has practical implications for the creation of new online communities and the design of platforms that facilitate such communities.Comment: To appear at The Web Conference 201

Group invariant machine learning by fundamental domain projections

We approach the well-studied problem of supervised group invariant and equivariant machine learning from the point of view of geometric topology. We propose a novel approach using a pre-processing step, which involves projecting the input data into a geometric space which parametrises the orbits of the symmetry group. This new data can then be the input for an arbitrary machine learning model (neural network, random forest, support-vector machine etc). We give an algorithm to compute the geometric projection, which is efficient to implement, and we illustrate our approach on some example machine learning problems (including the well-studied problem of predicting Hodge numbers of CICY matrices), in each case finding an improvement in accuracy versus others in the literature. The geometric topology viewpoint also allows us to give a unified description of so-called intrinsic approaches to group equivariant machine learning, which encompasses many other approaches in the literature.Comment: 21 pages, 4 figure