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1. Kindergarteners’ Strategies and Ideas When Reasoning with Function Tables and Graphs

   López, M; Strachota, S; Brizuela, B M; Gardiner, A M; Blanton, M (June 2025, Proceedings of the 19th International Conference of the Learning Sciences - ICLS 2025)

   Rajala, A; Cortez, A; Hofmann, H; Jornet, A; Lotz-Sisitka, H; Markauskaite, L (Ed.)

   This study investigated the seeds of algebraic thinking that Kindergarten students use when engaging with function tables and graphs. Through interviews with three Kindergarteners, we explored how they reasoned about functional relationships. Our results illustrate how the Kindergarteners used seeds of algebraic thinking when using function tables and graphs to represent and reason about functional relationships. Building on the seeds of algebraic thinking and Knowledge in Pieces frameworks, we categorized these seeds as either strategies (classify, pair, and compare) or ideas (seeds of covariation). Strategy seeds were goal-oriented, and seeds of covariation were elicited without any goal and reflected a broader understanding of change between quantities. 

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2. DB Spinner: making a case for iterative processing in databases

   Sofoklis Floratos, Ahmed Ghazal (April 2021, 2021 37th IEEE International Conference on Data Engineering)

   null (Ed.)

   Relational database management systems (RDBMS) have limited iterative processing support. Recursive queries were added to ANSI SQL, however, their semantics do not allow aggregation functions, which disqualifies their use for several applications, such as PageRank and shortest path computations. Recently, another SQL extension, iterative Common Table Expressions (CTEs), is proposed to enable users to perform general iterative computations on RDBMSs.In this work 1 , we demonstrate how iterative CTEs can be efficiently incorporated into a production RDBMS without major intrusion to the system. We have prototyped our approach on Futurewei's MPPDB, a shared nothing relational parallel database engine. The implementation is based on a functional rewrite that translates iterative CTEs to other existing SQL operators. Thus, query plans of iterative CTEs can be optimized and executed by the engine with minimal modification to the code base. We have also applied several optimizations specifically for iterative CTEs to i) minimize data movement, ii) reuse results that remain constant and iii) push down predicates to avoid unnecessary data processing. We verified our implementation through extensive experimental evaluation using real world datasets and queries. The results show the feasibility of the rewrite approach and the effectiveness of the optimizations, which improve performance by an order of magnitude in some cases. 

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3. Supporting Middle School Students’ Understanding of Time-Series Data With Graph Comparisons

   https://doi.org/10.1080/26939169.2025.2560338  

   Mokros, Jan; Sagrans, Jacob; Noyce, Pendred (September 2025, Journal of Statistics and Data Science Education)

   After participating in an afterschool program where they used the Common Online Data Analysis Platform (CODAP) to study time-series data about infectious diseases, four middle school students were interviewed to determine how they understood features of and trends within these graphs. Our focus was on how students compared graphs. Students were readily able to compare cumulative/total infection rates among two countries with differently sized populations. It was more challenging for them to link a graph of yearly cases to the corresponding graph of cumulative cases. Students offered reasonable interpretations for spikes or steady periods in the graphs. Time-series graphs are accessible for 11- to 14-year-old students, who were able to make comparisons within and between graphs. Students used proportional reasoning for one comparison task, and on the other task, while it was challenging, they were beginning to understand how yearly and cumulative graphs were related. Time-series graphs are ubiquitous and socially relevant: Students should study time-series data more regularly in school, and more research is needed on the progression of sense-making with these graphs. 

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4. Inter‐Network Charge‐Transfer Excited State Formation Within a Two‐fold Catenated Metal–Organic Framework

   https://doi.org/10.1002/chem.202202978  

   Nath, Akashdeep; Chawla, Sakshi; K_De, Arijit; Deria, Pravas; Mandal, Sukhendu (November 2022, Chemistry – A European Journal)

   Abstract Charge‐transfer excited state (CTES) defines the ability to split photon energy into work producing redox equivalents suitable for photocatalysis. Here, we report inter‐net CTES formation within a two‐fold catenated crystalline metal–organic framework (MOF), constructed with two linkers, N,N′‐di(4‐pyridyl)‐1,4,5,8‐naphthalenetetracarboxydiimide (DPNDI) and 2,6‐dicarboxynaphthalene (NDC). The structural flexibility puts two complementary linkers from two nets in a proximal position to interact strongly. Supported by the electrochemical and steady‐state electronic spectroscopic data, this ground‐state interaction facilitates forming CTES that can be populated by direct excitation. We map the dynamics of the CTES which persists over a few nanoseconds and highlight the utilities of such relatively long‐lived CTES as enhanced conductivity of the MOF under light over that measured in dark and as a proof‐of‐the‐principle test, photo‐reduction of methyl viologen under white light. 

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5. The Power of D-hops in Matching Power-Law Graphs

   https://doi.org/10.1145/3460094  

   Yu, Liren; Xu, Jiaming; Lin, Xiaojun (June 2021, Proceedings of the ACM on Measurement and Analysis of Computing Systems)

   null (Ed.)

   This paper studies seeded graph matching for power-law graphs. Assume that two edge-correlated graphs are independently edge-sampled from a common parent graph with a power-law degree distribution. A set of correctly matched vertex-pairs is chosen at random and revealed as initial seeds. Our goal is to use the seeds to recover the remaining latent vertex correspondence between the two graphs. Departing from the existing approaches that focus on the use of high-degree seeds in $$1$$-hop neighborhoods, we develop an efficient algorithm that exploits the low-degree seeds in suitably-defined D-hop neighborhoods. Specifically, we first match a set of vertex-pairs with appropriate degrees (which we refer to as the first slice) based on the number of low-degree seeds in their D-hop neighborhoods. This approach significantly reduces the number of initial seeds needed to trigger a cascading process to match the rest of graphs. Under the Chung-Lu random graph model with n vertices, max degree Θ(√n), and the power-law exponent 2<β<3, we show that as soon as D> 4-β/3-β, by optimally choosing the first slice, with high probability our algorithm can correctly match a constant fraction of the true pairs without any error, provided with only Ω((log n)4-β) initial seeds. Our result achieves an exponential reduction in the seed size requirement, as the best previously known result requires n1/2+ε seeds (for any small constant ε>0). Performance evaluation with synthetic and real data further corroborates the improved performance of our algorithm. 

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