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Item type: Publication , Access status: Metadata only , Hierarchical Sparse Prior-Based Channel Estimation for Multi-User Massive MIMO-OFDM Systems Using Unitary AMP(2026) Yang, Jing; Yang, Menghan; Song, Yi; Yang, NanAccurate uplink channel estimation is a cornerstone for achieving spatial multiplexing gains in multi-user massive multiple-input multiple-output (MIMO) systems under practical pilot constraints. In this paper, we propose a novel Bayesian inference framework-based unitary approximate message passing (BIF-UAMP) algorithm tailored to the TR 38.901 channel specification. Specifically, a hierarchical prior channel model is formulated in the antenna-delay domain, which jointly exploits the physical support patterns and numerical amplitude variations via a delay-tap-specific Bernoulli-Gaussian first-order Markov chain (BG-MC). Driven by this structured prior, the UAMP rule is sequentially integrated into the three-layer Bayesian architecture to efficiently process the deterministic sensing matrix, effectively mitigating the numerical degradation triggered by ill-conditioned measurements. Simulation results demonstrate that the proposed BIF-UAMP algorithm achieves superior channel estimation accuracy compared with state-of-the-art schemes, while consistently approaching the theoretical Cramér-Rao bound.Item type: Publication , Access status: Metadata only , Whatever Happened to the Albanese Government's Wellbeing Agenda?(2025-06-26) Sollis, Kate; Drake, Nicholas; Campbell, PaulItem type: Publication , Access status: Metadata only , Affordance Analyses of AI Safety Policies: A Proof-of-Concept using OpenAI's Preparedness Framework(Association for Computing Machinery (ACM), 2026-06-25) Coggins, Sam; Saeri, Alexander K.; Daniell, Katherine A.; Henne, Kathryn; Ruster, Lorenn P.; Liu, Jessie; Davis, Jenny L.Prominent AI companies are producing risk management frameworks as a type of voluntary self-regulation. As interventions, these policies purport to establish risk thresholds and safety procedures for the development and deployment of highly capable AI. Understanding which AI risks are covered and what actions are allowed, refused, demanded, encouraged, or discouraged by these risk management policies is a foundational step in assessing how they might govern the development and deployment of AI systems in practice. To investigate how such policies are operationalised, we introduce a transferable AI policy analysis method based on the Mechanisms & Conditions model of affordances (M&C) and the MIT AI Risk Repository. We illustrate the utility of this method by applying it to OpenAI's "Preparedness Framework Version 2"(April 2025). We find that OpenAI's safety policy requests evaluation of a small minority of AI risks, encourages deployment of systems with "Medium"capabilities for unintentionally enabling "severe harm"(which OpenAI defines as > 1000 deaths or > $100B in damages), and allows OpenAI's CEO to deploy even more dangerous capabilities. These findings suggest that effective mitigation of AI risks requires more robust governance interventions beyond current industry self-regulation - of which there are well-established models in other domains. In addition, we illustrate how our affordance analysis provides a replicable method for evaluating what AI policies permit versus what they claim. Applied broadly, our AI policy analysis method will help clarify what is needed to mitigate AI-enabled harms and to facilitate trustworthy and socially beneficial AI systems.Item type: Publication , Access status: Metadata only , Strengthening field epidemiology capacity in Canada: a mixed-methods evaluation of the Canadian field epidemiology program(2026) Flint, James A.; Marcynuk, Pasha; Bilandzic, Anja; White, Jennifer; Housen, Tambri; Durrheim, David N.; Kirk, Martyn D.; Laberge, KathleenBackground – Field epidemiology training programs (FETPs) play a critical role in strengthening public health capacity for surveillance, outbreak investigation, and evidence-based decision making. The Canadian Field Epidemiology Program (CFEP), established in 1975, was the first program outside of the United States. This study presents findings from a mixed-methods evaluation examining program outputs, outcomes, and impacts. Methods – Canadian field epidemiology program was evaluated using a theory-based mixed-methods study design. An online survey was administered to all CFEP graduates completing the program from 2018 to 2023. Semi-structured interviews were conducted with graduates, placement site supervisors, and current managers. Quantitative and qualitative data were analyzed and integrated through triangulation. Results – The online survey was completed by 24 (69%) CFEP graduates and interviews were conducted with 22 graduates, 15 placement site supervisors, and 13 current managers. Graduates developed and applied technical and non-technical skills across all six CFEP competency domains. Most trainees (91%, n = 20) evaluated, enhanced, or implemented a surveillance system; after graduation, 84% (n = 16) continued to be involved in surveillance activities. Trainees investigated 145 outbreak investigations, while graduates investigated 48 outbreaks. Trainees and graduates introduced innovations in data management and analysis to support evidence-based decision making. Most graduates (75%, n = 18) were employed as epidemiologists after CFEP, primarily at the federal level, with a majority (54%, n = 13) progressing to supervisory or management roles. Placement site supervisors and current managers emphasized the contributions of CFEP graduates to strengthening disease surveillance and outbreak response systems, improving data analysis processes, and sharing field epidemiology skills with the broader workforce. Trainees were noted for introducing new analytical tools and approaches, including the use of R and advanced data visualization. Discussion – CFEP made a substantial contribution to Canada’s public health workforce by developing skilled field epidemiologists who applied their skills to strengthen disease surveillance, outbreak response and evidence-based decision making. Expanding mobilization opportunities, formalizing alumni networks, and providing ongoing professional development opportunities would further strengthen field epidemiology competencies and amplify program impact.Item type: Publication , Access status: Metadata only , Nepal’s vulnerability to Nipah virus transmission: an urgent call for preparedness(2026) Lama, Bhupendra; Yadav, Uday Narayan; Syangtan, Gopiram; Bhandari, Alaka; Binod, G. C.; Rai, Mukesh; Kirk, Martyn D.; Rayamajhee, BinodThe confirmation of two Nipah virus (NiV) infections among healthcare workers in West Bengal, India, in December 2025 highlights the ongoing regional threat of this highly contagious zoonotic pathogen and the risk of nosocomial transmission before clinical suspicion. Increasing reports of human-to-human transmission in South Asia call for stronger preparedness efforts in neighboring countries. Nepal is particularly vulnerable due to its open border with India, high population mobility, ecological suitability for Petropus bat reservoirs, mixed livestock farming, limited surveillance, and diagnostic capacity. The absence of reported cases likely reflects underdetection rather than the absence of risk. Current gaps include weak infection prevention and control, limited laboratory readiness, minimal wildlife and livestock surveillance, and inadequate cross-border coordination. We propose priority actions based on the One Health approach, including bat roost mapping, cross-border information sharing, targeted community risk communication, strengthening tertiary hospitals and laboratory capacity, enhanced veterinary surveillance, and the development of a national outbreak preparedness and response plan for future potential NiV or other emerging pathogen outbreaks.