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1. Multilingual CS Education Pathways: Implications for Vertically-Scaled Assessment

   https://doi.org/10.1145/3478431.3499315  

   Kao, Yvonne ; Weintrop, David ( February 2022 , SIGCSE 2022: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education)

   The expansion of computer science (CS) into K-12 contexts has resulted in a diverse ecosystem of curricula designed for various grade levels, teaching a variety of concepts, and using a wide array of different programming languages and environments. Many students will learn more than one programming language over the course of their studies. There is a growing need for computer science assessment that can measure student learning over time, but the multilingual learning pathways create two challenges for assessment in computer science. First, there are not validated assessments for all of the programming languages used in CS classrooms. Second, it is difficult to measure growth in student understanding over time when students move between programming languages as they progress in their CS education. In this position paper, we argue that the field of computing education research needs to develop methods and tools to better measure students' learning over time and across the different programming languages they learn along the way. In presenting this position, we share data that shows students approach assessment problems differently depending on the programming language, even when the problems are conceptually isomorphic, and discuss some approaches for developing multilingual assessments of student learning over time.
2. Collaborative Reflection “in the flow” of Programming: Designing Effective Collaborative Learning Activities in Advanced Computer Science Contexts

   Sankaranarayanan, S. ( June 2022 , General Proceedings of the 2nd Annual Meeting of the International Society of the Learning Sciences 2022)

   Jun Oshima, Toshio Mochizuki (Ed.)

   Designing activities for maximizing collaborative learning in advanced computer science contexts is of broad interest. While programming exercises remain the dominant form of pedagogy here, prior work showed that collaborative reflection over worked examples is as good or even better for conceptual learning and future programming. This work used a “phased” design, with separate collaborative reflection and programming phases, and varied the time boundary between the two to determine their differential impact. A more effective design, however, could involve collaborative reflection prompted “in the flow” of programming, with benefits similar to self-explanation prompts interleaved into individual problem-solving. While total time-on-task is the same, this “interleaved” design might allow learners to spend a larger proportion of this time on reflection. Thus, this paper compares this novel interleaved approach to the phased design. We determine that interleaving increases the proportion of time available for reflection resulting in performance improvements on future programming.
3. Si-based self-programming neuromorphic integrated circuits for intelligent morphing wings

   https://doi.org/10.1177/00219983221134929  

   Nathan, Dhruva ; Deo, Atharva ; Haughn, Kevin ; Yi, Suin ; Lee, Jungmin ; Gao, Dawei ; Shenoy, Rahul ; Xu, Mingjie ; Tran, Ich C. ; Zheng, Jian-Guo ; et al ( October 2022 , Journal of Composite Materials)

   Unlike artificial intelligent systems based on computers, which must be programmed for specific tasks, the human brain can learn in real-time to create new tactics and adapt to complex, unpredictable environments. Computers embedded in artificial intelligent systems can execute arbitrary inference algorithms capable of outperforming humans at specific tasks. However, without real-time self-programming functionality, they must be preprogrammed by humans and will likely to fail in unpredictable environments beyond their preprogrammed domains. In this work, a Si-based synaptic resistor (synstor) was developed by integrating Al₂O_x/TaO_ymaterials to emulate biological synapses. The synstors were characterized, and their operation mechanism based on the charge stored in the oxygen vacancies in the Al₂O_xmaterial was simulated and analyzed, to understand the inference, learning, and memory functions of the synstors. A self-programming neuromorphic integrated circuit (SNIC) based on synstors was fabricated to execute inference and learning algorithms concurrently in real-time with an energy efficiency more than six-orders of magnitudes higher than those of standard digital computers. The SNIC dynamically modified its algorithm in a real-time learning process to control a morphing wing, thus successfully improving its lift-to-drag force ratio and recovering the wing from stall in complex aerodynamic environments. The synaptic resistor circuits can potentially circumventmore »the fundamental limitations of computers, thus providing a platform analogous to neurobiological network with real-time self-programming functionality for artificial intelligent systems.

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4. Counting and Sampling Orientations on Chordal Graphs

   https://doi.org/10.1007/978-3-030-96731-4_29  

   Bezakova, Ivona ; Sun, Wenbo ( January 2022 , International Conference and Workshops on Algorithms and Computation (WALCOM))

   We study counting problems for several types of orientations of chordal graphs: source-sink-free orientations, sink-free orientations, acyclic orientations, and bipolar orientations, and, for the latter two, we also present linear-time uniform samplers. Counting sink-free, acyclic, or bipolar orientations are known to be #P-complete for general graphs, motivating our study on a restricted, yet well-studied, graph class. Our main focus is source-sink-free orientations, a natural restricted version of sink-free orientations related to strong orientations, which we introduce in this work. These orientations are intriguing, since despite their similarity, currently known FPRAS and sampling techniques (such as Markov chains or sink-popping) that apply to sink-free orientations do not seem to apply to source-sink-free orientations. We present fast polynomialtime algorithms counting these orientations on chordal graphs. Our approach combines dynamic programming with inclusion-exclusion (going two levels deep for source-sink-free orientations and one level for sinkfree orientations) throughout the computation. Dynamic programming counting algorithms can be typically used to produce a uniformly random sample. However, due to the negative terms of the inclusion-exclusion, the typical approach to obtain a polynomial-time sampling algorithm does not apply in our case. Obtaining such an almost uniform sampling algorithm for source-sink-free orientations in chordal graphs remains an openmore »problem. Little is known about counting or sampling of acyclic or bipolar orientations, even on restricted graph classes. We design efficient (linear-time) exact uniform sampling algorithms for these orientations on chordal graphs. These algorithms are a byproduct of our counting algorithms, but unlike in other works that provide dynamic-programming-based samplers, we produce a random orientation without computing the corresponding count, which leads to a faster running time than the counting algorithm (since it avoids manipulation of large integers).« less
5. A Continuous Optimization Approach to Drift Counteraction Optimal Control

   https://doi.org/10.23919/ACC50511.2021.9482647  

   Tang, Sunbochen ; Li, Nan ; Kolmanovsky, Ilya ; Zidek, Robert ( May 2021 , Proceedings of 2021 American Control Conference)

   Drift counteraction optimal control (DCOC) aims at optimizing control to maximize the time (or a yield) until the system trajectory exits a prescribed set, which may represent safety constraints, operating limits, and/or efficiency requirements. To DCOC problems formulated in discrete time, conventional approaches were based on dynamic programming (DP) or mixed-integer programming (MIP), which could become computationally prohibitive for higher-order systems. In this paper, we propose a novel approach to discrete-time DCOC problems based on a nonlinear programming formulation with purely continuous variables. We show that this new continuous optimization-based approach leads to the same exit time as the conventional MIP-based approach, while being computationally more efficient than the latter. This is also illustrated by numerical examples representing the drift counteraction control for an indoor airship.