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1. Recent Innovations in Drilling in Ice

   Albert, M; Slawny, K; Boeckmann, G; Gibson, C; Johnson, J; Makinson, K; Rix, J (December 2021, CRC Press)

   Bar-Cohen, Y; Zacny, K (Ed.)

   The need for scientific ice drilling in glaciers and ice sheets has been driven by many fields of science, including drilling ice cores for evidence of past environment and paleoclimate information, and drilling access holes through the ice to gather data relevant to glacial dynamics, history of glacier extent, sediment sampling, and discovery of ecosystems within and beneath the ice. Many nations have contributed to drilling technologies relevant to each of these fields, and developments in any one nation often build on prior designs from other nations. A description of the very early polar ice coring endeavors in Greenland and Antarctica is provided in Langway (2008). Ice drilling and coring technologies that were developed before 2008 are well described in Bentley et al (2009), including a wide array of ice coring drills, drills designed to create holes in ice only, and autonomous instruments that melt their way through ice. The text by [Talalay 2016] provides a review of mechanical ice drilling technology that includes design, parameters and performance of an assortment of tools and drills for making holes in snow, firn and ice. Described in detail are direct-push drilling, hand- and power-driven portable drills, percussion drills, conventional machine-driven rotary drill rigs, flexible drill-stem drill rigs, cable-suspended electromechanical auger drills, cable-suspended electromechanical drills with bottom-hole circulation, and drilling challenges and perspective for future development. In this chapter our goal is to describe new ice drilling and coring technologies that have been designed, built, and used in the field in the most recent decade. Some of these technologies are improvements on prior drills, while other technologies such as a replicate ice coring drill, geologic drilling underneath many meters of glacial ice, and the rapid access isotope drill are the first of their kind. There are many additional ice drilling and sampling designs currently in the design or development stage that are not included in this chapter; rather our goal in this chapter is to describe proven ice drilling technologies that have been developed since 2009. 

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2. he Brain and Language in Autism In: Language in Autism, edited by

   Larson, C; Eigsti, IM (August 2025, Wiley Blackwell)

   Schaeffer, J; Novogrodsky, R; Perovic, A; Prévost, P; Tuller, L (Ed.)

   Alongside the behavioral features of autism, this neurodevelopmental disorder is characterized by important differences in the neural circuitry underlying language processing. Regarding brain structure, most neurotypical individuals have larger left-hemisphere volumes of brain regions that are important for language, compared to the same regions in the right hemisphere (the right half of the brain). This asymmetry is due to neural specialization of left hemisphere regions for the purpose of language functions. In contrast, the brains of autistic individuals seem to be more symmetrical, suggesting that language difficulties are associated with reduced left hemisphere specialization for language in the brain. The activity of brain regions involved in language also differs in autism. Examining brain activity reveals nuanced and important differences in the processes underlying language production and comprehension in neurotypical and autistic individuals, even when their language behavior appears similar. 

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3. Negotiating Tensions in Collaborations and its Role in Disciplinary Engagement in Science

   Krishnan, H.; Jaber, L. J.; Schellinger, J.; Southerland, S. A. (January 2022, Annual meeting program American Educational Research Association)

   While recent reforms in science education envision engaging students in doing science as a way to learn science, less is known about how such an engagement can take hold in the classroom. In an effort to address this gap, this study examines the dynamics of students’ disciplinary engagement in small groups in a middle school science classroom. Using multimodal discourse analysis, we conducted a comparative case analysis of three groups of students to examine the dynamics of their engagement along conceptual, epistemological, social, and affective dimensions. Through this analysis, our findings highlight tensions that emerge along these dimensions and the ways in which students negotiate these tensions in ways that support or hinder disciplinary engagement. 

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4. Gaps in Information Access in Social Networks

   https://doi.org/10.1145/3308558.3313680  

   Fish, Benjamin; Bashardoust, Ashkan; Boyd, Danah; Friedler, Sorelle; Scheidegger, Carlos; Venkatasubramanian, Suresh (January 2019, The Web Conference (WWW))

   The study of influence maximization in social networks has largely ignored disparate effects these algorithms might have on the individuals contained in the social network. Individuals may place a high value on receiving information, e.g. job openings or advertisements for loans. While well-connected individuals at the center of the network are likely to receive the information that is being distributed through the network, poorly connected individuals are systematically less likely to receive the information, producing a gap in access to the information between individuals. In this work, we study how best to spread information in a social network while minimizing this access gap. We propose to use the maximin social welfare function as an objective function, where we maximize the minimum probability of receiving the information under an intervention. We prove that in this setting this welfare function constrains the access gap whereas maximizing the expected number of nodes reached does not. We also investigate the difficulties of using the maximin, and present hardness results and analysis for standard greedy strategies. Finally, we investigate practical ways of optimizing for the maximin, and give empirical evidence that a simple greedy-based strategy works well in practice. 

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5. Uncertainty Quantification in Inverse Models in Hydrology

   Chatterjee, Somya Sharma; Ghosh, Rahul; Renganathan, Arvind; Li, Xiang; Chatterjee, Snigdhansu; Nieber, John; Duffy, Christopher; Kumar, Vipin (August 2023, ACM)

   In hydrology, modeling streamflow remains a challenging task due to the limited availability of basin characteristics information such as soil geology and geomorphology. These characteristics may be noisy due to measurement errors or may be missing altogether. To overcome this challenge, we propose a knowledge-guided, probabilistic inverse modeling method for recovering physical characteristics from streamflow and weather data, which are more readily available. We compare our framework with state-of-the-art inverse models for estimating river basin characteristics. We also show that these estimates offer improvement in streamflow modeling as opposed to using the original basin characteristic values. Our inverse model offers a 3% improvement in R2 for the inverse model (basin characteristic estimation) and 6% for the forward model (streamflow prediction). Our framework also offers improved explainability since it can quantify uncertainty in both the inverse and the forward model. Uncertainty quantification plays a pivotal role in improving the explainability of machine learning models by providing additional insights into the reliability and limitations of model predictions. In our analysis, we assess the quality of the uncertainty estimates. Compared to baseline uncertainty quantification methods, our framework offers a 10% improvement in the dispersion of epistemic uncertainty and a 13% improvement in coverage rate. This information can help stakeholders understand the level of uncertainty associated with the predictions and provide a more comprehensive view of the potential outcomes. 

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