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In this paper we investigate quark deconfinement in neutrons stars and their mergers, focusing on the effects of higher orders for the phase transition between hadronic and quark matter. The different descriptions we use to describe matter microscopically contain varying particle degrees of freedom, including nucleons, hyperons, Delta baryons, and light and strange quarks. We use tabulated equations of state from the CompOSE database in which the quark deconfinement phase transition is described as being first-order, and then smooth it out by introducing a percolation, replacing the single first-order phase transition with two transitions of second or third order. We then perform binary neutron-star merger simulations using these new equations of st ate, focusing on groups of binaries with the same single-star mass, radius, and tidal deformability, but different equations of state. We go on to discuss differences in their evolution, and the ramifications for interpreting future gravitational wave observations and the potential to learn about dense matter. 

ABSTRACT The evolution of oxygenic photosynthesis in the Cyanobacteria was one of the most transformative events in Earth history, eventually leading to the oxygenation of Earth’s atmosphere. However, it is difficult to understand how the earliest Cyanobacteria functioned or evolved on early Earth in part because we do not understand their ecology, including the environments in which they lived. Here, we use a cutting-edge bioinformatics tool to survey nearly 500,000 metagenomes for relatives of the taxa that likely bookended the evolution of oxygenic photosynthesis to identify the modern environments in which these organisms live. Ancestral state reconstruction suggests that the common ancestors of these organisms lived in terrestrial (soil and/or freshwater) environments. This restricted distribution may have increased the lag between the evolution of oxygenic photosynthesis and the oxygenation of Earth’s atmosphere.IMPORTANCECyanobacteria generate oxygen as part of their metabolism and are responsible for the rise of oxygen in Earth’s atmosphere over two billion years ago. However, we do not know how long this process may have taken. To help constrain how long this process would have taken, it is necessary to understand where the earliest Cyanobacteria may have lived. Here, we use a cutting-edge bioinformatics tool called branch water to examine the environments where modern Cyanobacteria and their relatives live to constrain those inhabited by the earliest Cyanobacteria. We find that these species likely lived in non-marine environments. This indicates that the rise of oxygen may have taken longer than previously believed. 

This paper shares a synthesis of the literature related to the application of a relationships-first approach to high-dosage math tutoring. In the context of our research, high-dosage tutoring is delivered multiple times per week during the school day by paraprofessionals who work with students in historically under-resourced schools. We apply a critical perspective to frame the importance of attending to interpersonal relationships during tutoring. We then explain the core ideas of small group interactions, dialogue, relational interactions, care and belonging and provide a synthesis of these constructs. The literature synthesis presented is intended to be applied to research-based efforts aimed at supporting tutors working to increase their skills for cultivating strong interpersonal relationships and enacting equity oriented pedagogy. 

This paper shares a synthesis of the literature related to the application of a relationships-first approach to high-dosage math tutoring. In the context of our research, high-dosage tutoring is delivered multiple times per week during the school day by paraprofessionals who work with students in historically under-resourced schools. We apply a critical perspective to frame the importance of attending to interpersonal relationships during tutoring. We then explain the core ideas of small group interactions, dialogue, relational interactions, care and belonging and provide a synthesis of these constructs. The literature synthesis presented is intended to be applied to research-based efforts aimed at supporting tutors working to increase their skills for cultivating strong interpersonal relationships and enacting equity oriented pedagogy. 

TalkMoves is an AI assistive tool that provides automated feedback to educators to support their daily teaching practices. While originally designed for classroom math teachers, this tool can be useful in a broader context. The University of Colorado Boulder and Saga Education formed a co-design team tasked with re-contextualizing TalkMoves for coaches of novice math tutors to use in their ongoing professional development. To effectively adapt an existing technology to a new problem space, the co-design team iteratively exchanged ideas of what exactly TalkMoves could achieve, as well as the specific needs of the coaches. Facilitators used strategies such as communal orientation, expansive dreaming, backcasting, and revoicing to promote productive collaboration. Three main goals emerged: maximize opportunities for user agency, center design around goal setting, and integrate the tool into the existing workflow. Any adaptation of an AI tool would benefit from this approach. 

TalkMoves is an AI assistive tool that provides automated feedback to educators to support their daily teaching practices. While originally designed for classroom math teachers, this tool can be useful in a broader context. The University of Colorado Boulder and Saga Education formed a co-design team tasked with re-contextualizing TalkMoves for coaches of novice math tutors to use in their ongoing professional development. To effectively adapt an existing technology to a new problem space, the co-design team iteratively exchanged ideas of what exactly TalkMoves could achieve, as well as the specific needs of the coaches. Facilitators used strategies such as communal orientation, expansive dreaming, backcasting, and revoicing to promote productive collaboration. Three main goals emerged: maximize opportunities for user agency, center design around goal setting, and integrate the tool into the existing workflow. Any adaptation of an AI tool would benefit from this approach. 

Human tutoring interventions play a crucial role in supporting student learning, improving academic performance, and promoting personal growth. This paper focuses on analyzing mathematics tutoring discourse using talk moves—a framework of dialogue acts grounded in Accountable Talk theory. However, scaling the collection, annotation, and analysis of extensive tutoring dialogues to develop machine learning models is a challenging and resource-intensive task. To address this, we present SAGA22, a compact dataset, and explore various modeling strategies, including dialogue context, speaker information, pretraining datasets, and further fine-tuning. By leveraging existing datasets and models designed for classroom teaching, our results demonstrate that supplementary pretraining on classroom data enhances model performance in tutoring settings, particularly when incorporating longer context and speaker information. Additionally, we conduct extensive ablation studies to underscore the challenges in talk move modeling.