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This full research paper reports findings from a multitiered intervention focused on developing growth mindset among talented, low-income undergraduate students attending a College of Computing in the northeastern United States. Rooted in theories of intelligence, a growth mindset views intelligence and skills as being developed through persistent practice and learning from mistakes, while a fixed mindset sees skills as set at birth, never evolving, with mistakes becoming insurmountable barriers to success. The program in this study was designed to develop a community of learners with a shared framework for responding to academic challenges, to combat imposter syndrome, and to support persistence in their major and enter the workforce. During their first two years as college students, three undergraduate cohorts (totaling 32 participants) experienced four semesters of growth-mindset faculty mentoring concurrent with a community-building, growth mindset-focused seminar, and in their first year experienced two growth-mindset infused introductory programming courses. To address the research question, “How do talented, financially disadvantaged computing students understand growth and fixed mindsets?”, we report on qualitative data collected each semester, for each cohort. Focus group transcripts and individual written responses were thematically analyzed, drawing from a priori frameworks (social constructivism and self-efficacy in the context of mindset theory) and emergent codes to develop categories. Discussion is presented using frames of self-determination theory and positioning theory. We discuss the impact of these findings on students, implications for growth mindset interventions and provide guidance for using educational and developmental theories in the context of studies of growth mindset. 

International Ocean Discovery Program Expedition 401 recovered 983 m of sediment from Portugal’s southwest margin in the northeast Atlantic Ocean at Site U1609 (37°22.6259′ N, 9°35.9120′ W; 1659.5 m water depth). This site was designed to recover the distal contourites deposited by the Mediterranean Overflow Water contour current from the late Miocene to the Pleistocene. We report semiquantitative elemental results from X-ray fluorescence scanning of sediment cores from Site U1609 (Holes U1609A and U1609B) scanned at a 4–5 cm resolution from ~202 to 509 m core depth below seafloor, Method A, equivalent to ~4.52 to ~7.8 Ma. Raw element intensities (in counts per second) for Al, Si, Ca, Ti, Mn, Fe, Rb, Sr, Zr, and Ba are presented here and correlated with lithofacies variations. We also identify biogenic-terrestrial input proportions and illustrate downcore cyclicity and correlation patterns between terrigenous components (Al, Si, Ti, Mn, and Ba), as well as their anticorrelations with biogenic (Ca and Sr) inputs. The cyclical variations in elemental ratios may help stratigraphic correlation between Holes U1609A and U1609B, astronomical tuning of the spliced record, and sedimentary interpretations of changes to the Mediterranean–Atlantic gateway and the bottom current circulation along the Atlantic margin of Portugal before, during, and after the Messinian Salinity Crisis. 

We consider a decentralized wireless network with several source-destination pairs sharing a limited number of orthogonal frequency bands. Sources learn to adapt their transmissions (specifically, their band selection strategy) over time, in a decentralized manner, without sharing information with each other. Sources can only observe the outcome of their own transmissions (i.e., success or collision), having no prior knowledge of the network size or of the transmission strategy of other sources. The goal of each source is to maximize their own throughput while striving for network-wide fairness. We propose a novel fully decentralized Reinforcement Learning (RL)-based solution that achieves fairness without coordination. The proposed Fair Share RL (FSRL) solution combines: (i) state augmentation with a semi-adaptive time reference; (ii) an architecture that leverages risk control and time difference likelihood; and (iii) a fairnessdriven reward structure. We evaluate FSRL in several network settings. Simulation results suggest that, when we compare FSRL with a common baseline RL algorithm from the literature, FSRL can be up to 89.0% fairer (as measured by Jain’s fairness index) in stringent settings with several sources and a single frequency band, and 48.1% fairer on average.