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1. “Show Them the Playbook That These Companies Are Using”: Youth Voices about Why Computer Science Education Must Center Discussions of Power, Ethics, and Culturally Responsive Computing

   https://doi.org/10.1145/3660645  

   Ryoo, Jean J; Blunt, Takeria (September 2024, ACM Transactions on Computing Education)

   Culturally responsive computing (CRC), that centers sociopolitical issues and transformational uses of technology, has been described as valuable for increasing engagement with computing, especially for historically underrepresented minoritized students. But what do high school students think? Through a sociocultural lens prioritizing student voices recorded in 56 interviews over a period of 2 years (1–3 years after students’ first experience with computer science (CS) education through Exploring Computer Science or Advanced Placement CS Principles in high school), this study centers the perspectives of 39 primarily low-income, Latine and Black youth from urban California and rural Mississippi public schools to understand what they perceive as the role of technology in our world and what they subsequently desire of their computing education. While none have studied CRC before, the majority responded with CRC ideas about the kind of pedagogy they believe would make for a more meaningful computing learning experience: They see computing as a form of power that impacts both good and bad in the world and want computing educators to prepare them to take on these issues of equity, ethics, social responsibility, and underrepresentation in the field. The students’ perspectives offer important pedagogical insight into how to support deeper engagement with computing in current CS for All initiatives, while also preparing youth for the rapidly evolving and increasingly complex computing landscape that impacts all of our lives. 

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2. Projecting clumped transcriptomes onto single cell atlases to achieve single cell resolution

   https://doi.org/10.1101/2022.04.26.489628  

   Johansen, N.; Quon, G. (April 2022, bioRxiv)

   Multi-modal single cell RNA assays capture RNA content as well as other data modalities, such as spatial cell position or the electrophysiological properties of cells. Compared to dedicated scRNA-seq assays however, they may unintentionally capture RNA from multiple adjacent cells, exhibit lower RNA sequencing depth compared to scRNA-seq, or lack genome-wide RNA measurements. We present scProjection, a method for mapping individual multi-modal RNA measurements to deeply sequenced scRNA-seq atlases to extract cell type-specific, single cell gene expression profiles. We demonstrate several use cases of scProjection, including the identification of spatial motifs from spatial transcriptome assays, distinguishing RNA contributions from neighboring cells in both spatial and multi-modal single cell assays, and imputing expression measurements of un-measured genes from gene markers. scProjection therefore combines the advantages of both multi-modal and scRNA-seq assays to yield precise multi-modal measurements of single cells. 

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3. Scene memories are biased toward high-probability views

   Lin, Feikai; Hafri, Alon; Bonner, Michael F. (August 2022, Journal of experimental psychology)

   Visual scenes are often remembered as if they were observed from a different viewpoint. Some scenes are remembered as farther than they appeared, and others as closer. These memory distortions—also known as boundary extension and contraction—are strikingly consistent for a given scene, but their cause remains unknown. We tested whether these distortions can be explained by an inferential process that adjusts scene memories toward high-probability views, using viewing depth as a test case. We first carried out a large-scale analysis of depth maps of natural indoor scenes to quantify the statistical probability of views in depth. We then assessed human observers’ memory for these scenes at various depths and found that viewpoint judgments were consistently biased toward the modal depth, even when just a few seconds elapsed between viewing and reporting. Thus, scenes closer than the modal depth showed a boundary-extension bias (remembered as farther-away), and scenes farther than the modal depth showed a boundary-contraction bias (remembered as closer). By contrast, scenes at the modal depth did not elicit a consistent bias in either direction. This same pattern of results was observed in a follow-up experiment using tightly controlled stimuli from virtual environments. Together, these findings show that scene memories are biased toward statistically probable views, which may serve to increase the accuracy of noisy or incomplete scene representations. 

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4. Application of Orthogonal Projectors in Power System Dynamics

   https://doi.org/10.1109/NAPS61145.2024.10741727  

   Martinez-Lizana, Sebastian; Zelaya-Arrazabal, Francisco; Pulgar-Painemal, Héctor (October 2024, IEEE)

   This paper explores the application of orthogonal projectors to simplify the analysis of complex power system dynamics. By leveraging modal information, orthogonal projections map high-dimensional dynamics onto a lower-dimensional subspace, enabling projected state variables to effectively depict the system's progression over time. A theoretical framework and its practical application are presented, illustrating how this approach enhances the analysis of high-dimensional dynamics. The method is validated using a two-machine infinite bus system and extended to the New England 39-bus system. Results demonstrate that complex trajectories in an n-dimensional space can be efficiently projected onto a 2D subspace, facilitating the observation of specific dynamics, such as those with an electromechanical nature. 

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5. Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders

   https://doi.org/10.1145/3336191.3371829  

   Yang, Carl; Zhang, Jieyu; Wang, Haonan; Li, Sha; Kim, Myungwan; Walker, Matt; Xiao, Yiou; Han, Jiawei (January 2020, WSDM'20: The Thirteenth ACM International Conference on Web Search and Data Mining)

   While node semantics have been extensively explored in social networks, little research attention has been paid to profile edge semantics, i.e., social relations. Ideal edge semantics should not only show that two users are connected, but also why they know each other and what they share in common. However, relations in social networks are often hard to profile, due to noisy multi-modal signals and limited user-generated ground-truth labels. In this work, we aim to develop a unified and principled framework that can profile user relations as edge semantics in social networks by integrating multi-modal signals in the presence of noisy and incomplete data. Our framework is also flexible towards limited or missing supervision. Specifically, we assume a latent distribution of multiple relations underlying each user link, and learn them with multi-modal graph edge variational autoencoders. We encode the network data with a graph convolutional network, and decode arbitrary signals with multiple reconstruction networks. Extensive experiments and case studies on two public DBLP author networks and two internal LinkedIn member networks demonstrate the superior effectiveness and efficiency of our proposed model. 

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