We present Trout (Two-ROUnd Threshold), the first distributed two-round ECDSA signing protocol for arbitrary thresholds. Trout has constant upload bandwidth per-party and processing time linear in the amount of participants. Moreover, Trout achieves the Identifiable Abort (IA) property, which means that if the protocol cannot terminate due to a failure, parties can attribute the failure to a specific party. We achieve this without a trusted setup.

Abstract The rapid integration of artificial intelligence (AI) in higher education has led to significant gaps in policy frameworks across universities worldwide. This study analyzes AI policies at 343 leading universities in Australia, Canada, China, the U.K., and the U.S., selected from the top 1500 institutions globally, as ranked by Times Higher Education for excellence in education across 2024/2025. Using a comparative analysis approach, we examined how these institutions govern the use of AI tools in teaching and learning contexts. Data were gathered from publicly available policy documents, student handbooks, and faculty guidelines, and analyzed using qualitative thematic coding supported by descriptive statistics. Our findings reveal diverse approaches, ranging from outright prohibitions to policies granting instructor's discretion, with notable regional differences influenced by cultural and regulatory factors. Quantitatively, for example, nearly half of universities adopted discretionary policies, while fewer than one in five issued outright bans. This study fills a gap in the literature by providing the first cross-regional analysis of AI policies in higher education, highlighting the absence of a universal framework. These insights offer valuable guidance for institutions to develop flexible, adaptive AI governance models that reflect their unique needs and values.

A longstanding goal of neuromorphic computing is to develop novel computer architectures with a brain-like energy footprint. Still, key biological components such as dendrites are often overlooked. Instead, more compact scalable neuron models are favored for neuromorphic implementations. Here we present a novel neuroscience-inspired multiplicative circuit that uses shunting inhibition as the multiplicative mechanism in a CMOS dendrite circuit. We demonstrate an experimental implementation of our model on a 350-nm process field programmable analog array (FPAA) integrated circuit (IC), that exhibits shunting inhibition as a multiplicative process in neuromorphic circuits.