More Like this

1. Reading to write

   https://doi.org/10.1111/1740-9713.01469  

   Nolan, Deborah; Stoudt, Sara (December 2020, Significance)

   null (Ed.)
2. SMART-WRITE: Adaptive Learning-Based Write Energy Optimization for Phase Change Memory

   https://doi.org/10.1109/CCWC62904.2025.10903957  

   Desai, Mahek; Quinn, Rowena; Asadinia, Marjan (January 2025, IEEE)
3. Write-Optimized Skip Lists

   https://doi.org/10.1145/3034786.3056117  

   Bender, Michael; Farach-Colton, Martin; Johnson, Rob; Mauras, Simon; Mayer, Tyler; Phillips, Cynthia; Xu, Helen (January 2017, PODS '17 Proceedings of the 36th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems)

   The skip list is an elegant dictionary data structure that is com- monly deployed in RAM. A skip list with N elements supports searches, inserts, and deletes in O(logN) operations with high probability (w.h.p.) and range queries returning K elements in O(log N + K) operations w.h.p. A seemingly natural way to generalize the skip list to external memory with block size B is to “promote” with probability 1/B, rather than 1/2. However, there are practical and theoretical obsta- cles to getting the skip list to retain its efficient performance, space bounds, and high-probability guarantees. We give an external-memory skip list that achieves write- optimized bounds. That is, for 0 < ε < 1, range queries take O(logBε N + K/B) I/Os w.h.p. and insertions and deletions take O((logBε N)/B1−ε) amortized I/Os w.h.p. Our write-optimized skip list inherits the virtue of simplicity from RAM skip lists. Moreover, it matches or beats the asymptotic bounds of prior write-optimized data structures such as Bε trees or LSM trees. These data structures are deployed in high-performance databases and file systems. 

   more » « less
4. Learn With Martian: A Tool For Creating Assignments That Can Write And Re-Write Themselves

   https://doi.org/10.18653/v1/2023.eacl-demo.30  

   Upadhyay, Shriyash; Callison-burch, Chris; Ginsberg, Etan (May 2023, Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations)

   In this paper, we propose Learn, a unified, easy-to-use tool to apply question generation and selection in classrooms. The tool lets instructors and TAs create assignments that can write and re-write themselves. Given existing course materials, for example a reference textbook, Learn can generate questions, select the highest quality questions, show the questions to students, adapt question difficulty to student knowledge, and generate new questions based on how effectively old questions help students learn. The modular, composable nature of the tools for handling each sub-task allow instructors to use only the parts of the tool necessary to the course, allowing for integration in a large number of courses with varied teaching styles. We also report on the adoption of the tool in classes at the University of Pennsylvania with over 1000 students. Learn is publicly released at https://learn.withmartian.com. 

   more » « less
5. Optimized pulsed write schemes improve linearity and write speed for low-power organic neuromorphic devices

   Scott T Keene, Armantas Melianas (May 2018, Journal of physics. D, Applied physics)