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1. What is in #P and what is not?

   Ikenmeyer, Christian; Pak, Igor (October 2022, Annual Symposium on Foundations of Computer Science)

   We prove that deciding the vanishing of the character of the symmetric group is C=P-complete. We use this hardness result to prove that the square of the character is not contained in #P, unless the polynomial hierarchy collapses to the second level. This rules out the existence of any (unsigned) combinatorial description for the square of the characters. As a byproduct of our proof we conclude that deciding positivity of the character is PP-complete under many-one reductions, and hence PH-hard under Turing-reductions. 

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2. What I like is what I remember: Memory modulation and preferential choice.

   https://doi.org/10.1037/xge0001034  

   Aka, Ada; Bhatia, Sudeep (October 2021, Journal of Experimental Psychology: General)
3. Ergodic LfD: Learning from what to do and what not to do

   Aleksandra Kalinowska, Ahalya Prabhakar (January 2021, IEEE International Conference on Robotics and Automation)

   null (Ed.)

   With growing access to versatile robotics, it is beneficial for end users to be able to teach robots tasks without needing to code a control policy. One possibility is to teach the robot through successful task executions. However, near-optimal demonstrations of a task can be difficult to provide and even successful demonstrations can fail to capture task aspects key to robust skill replication. Here, we propose a learning from demonstration (LfD) approach that enables learning of robust task definitions without the need for near-optimal demonstrations. We present a novel algorithmic framework for learning task specifications based on the ergodic metric—a measure of information content in motion. Moreover, we make use of negative demonstrations— demonstrations of what not to do—and show that they can help compensate for imperfect demonstrations, reduce the number of demonstrations needed, and highlight crucial task elements improving robot performance. In a proof-of-concept example of cart-pole inversion, we show that negative demonstrations alone can be sufficient to successfully learn and recreate a skill. Through a human subject study with 24 participants, we show that consistently more information about a task can be captured from combined positive and negative (posneg) demonstrations than from the same amount of just positive demonstrations. Finally, we demonstrate our learning approach on simulated tasks of target reaching and table cleaning with a 7-DoF Franka arm. Our results point towards a future with robust, data efficient LfD for novice users. 

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4. What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization

   Belth, Caleb; Zheng, Xinyi; Vreeken, Jilles; Koutra, Danai (January 2020, The Web Conference (WWW))
5. Resources-oriented instruction: What does it mean, and what might it look like?

   https://doi.org/10.1119/10.0009796  

   Robertson, Amy D.; Bauman, Lauren C.; Abraham, Yohannes M.; Hansen, Brynna; Tran, Hung; Goodhew, Lisa M. (July 2022, American Journal of Physics)

   Resources-oriented instruction in physics treats student thinking as sensible and then seeks to connect what students are saying and doing to physics content and practices. This paper uses an illustrative case to make progress toward answering the instructional questions: “What does resources-oriented instruction in physics look like?” and “How can I do it?”. We analyze an interaction between a university TA and a group of four introductory physics students completing a worksheet about mechanical wave propagation. We show some of the ways in which the TA's instructional moves supported students in making conceptual progress, even though several of the students' ideas would not be accepted as correct by many physicists. 

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