Yingtao Jiang

Background: Developing effective learning strategies is essential for first‐year engineering students due to the rigorous and complex nature of the discipline. During this formative stage, constructivist learning environments (CLEs), self‐efficacy, and mindsets are pivotal in shaping how students engage with challenges and develop adaptive approaches to learning. Purpose/Hypothesis(es): This study examined the relationships among CLE, self‐efficacy, growth and fixed mindsets, and learning strategy adoption. Self‐efficacy was hypothesized to mediate the relationship between CLE and learning strategy adoption. Design/Methods: Survey data from 203 first‐year engineering students at an R1 public university in the southwestern United States were analyzed using structural equation modeling. Learning strategy adoption was the outcome variable, and self‐efficacy was the mediator. Direct, indirect, and mediating relationships were assessed to investigate how these variables relate. Results: CLE and growth mindset were positively correlated with self‐efficacy, which fully mediated the relationship between CLE and learning strategy adoption. Self‐efficacy significantly enhanced the use of learning strategies. Fixed mindset was negatively related to learning strategy adoption directly, whereas growth mindset showed no significant direct relationship. Conclusions: The findings are consistent with self‐efficacy serving as a linking factor between CLE and learning strategy adoption among first‐year engineering students. The results suggest that educators can design learning activities that build students' confidence in engineering tasks and explicitly address fixed mindset beliefs to support strategic learning and early academic success.

We present a Lie-geometric framework for analyzing the trainability of quantum neural networks (QNNs) through the algebraic structure of their generator spaces. We prove that QNNs whose dynamics are restricted to polynomial-dimensional Lie subalgebras of su ( 2 n ) admit a polynomial lower bound on gradient variance, thereby avoiding barren plateaus by construction. In contrast, architectures exploring the full Lie algebra su ( 2 n ) exhibit exponentially vanishing gradient variance, O ( 2 − n ) , due to the exponential growth of the associated unitary orbit dimensions. Our results interpret barren plateaus as a geometric concentration phenomenon arising from high-dimensional Lie-group actions, rather than as an intrinsic limitation of parameterized quantum models. We further illustrate the mechanism using a finite-size collective-spin benchmark and a minimal two-qubit sanity check, both of which are consistent with the theoretical predictions.

Fully Homomorphic Encryption (FHE) is regarded as a promising way to protect data privacy with encrypted computation. Due to high computation overhead, hardware based FHE accelerators were proposed to speed up FHE applications. To support complicated FHE-encrypted neural network applications, multi-chiplet based FHE accelerators were further proposed for scaling up system size, whereas one of the challenges is designing efficient intra- and inter-chiplet interconnection networks to accelerate data transfer. Conventional regular topologies like mesh or Kite either lead to high inter-chiplet transmission latency or excessive power consumption as these topologies assume uniform bandwidth or radix for nodes/links, ignoring the highly irregular distribution of inter-chiplet communication volumes. On the other hand, the problem of generating customized intra- and inter-chiplet interconnection networks has high complexity and previous network-on-chip topology generation works cannot efficiently improve the performance of intra- and inter-chiplet interconnection networks. In this article, the intra- and inter-chiplet interconnection optimization problem is defined, aiming to minimize the execution time of FHE applications under cost and power constraints. To efficiently solve this problem, we propose a bilevel optimization algorithm, which decomposes the problem into three sub-problems: (1) FHE parameters selection, (2) task-to-core mapping, and (3) intra-/inter-chiplet interconnection network topology generation. These sub-problems are then solved iteratively. Experimental results demonstrate that our proposed method reduces execution time by 51.66%, 43.16%, 39.44%, 43.34%, and 27.70% compared with REED and four multi-chiplet based FHE accelerators with mesh, Kite, Butterfly, and Florets as inter-chiplet interconnection networks. Therefore, the proposed method can effectively accelerate FHE applications on large-scale multi-chiplet systems.