Open and completed research requests

Fourier transformation remains a core primitive in scientific computing, signal processing, and ML systems, but practical performance and accuracy depend heavily on algorithm and hardware choices. This project will build a reproducible benchmark framework to compare modern Fourier transformation implementations across representative workloads (1D/2D/3D, real/complex, varying sizes, and precision modes). We will evaluate both numerical fidelity and system-level efficiency to identify configuration-specific SOTA candidates rather than a one-size-fits-all winner. The expected impact is a decision guide and open benchmark suite that helps researchers and engineers select the best Fourier pipeline for their constraints.

We introduce SymbolicAI, a versatile and modular framework employing a logic-based approach to concept learning and flow management in generative processes. SymbolicAI enables the seamless integration of generative models with a diverse range of solvers by treating large language models (LLMs) as semantic parsers that execute tasks based on both natural and formal language instructions, thus bridging the gap between symbolic reasoning and generative AI. We leverage probabilistic programming principles to tackle complex tasks, and utilize differentiable and classical programming paradigms with their respective strengths. The framework introduces a set of polymorphic, compositional, and self-referential operations for multi-modal data that connects multi-step generative processes and aligns their outputs with user objectives in complex workflows. As a result, we can transition between the capabilities of various foundation models with in-context learning capabilities and specialized, fine-tuned models or solvers proficient in addressing specific problems. Through these operations based on in-context learning our framework enables the creation and evaluation of explainable computational graphs. Finally, we introduce a quality measure and its empirical score for evaluating these computational graphs, and propose a benchmark that compares various state-of-the-art LLMs across a set of complex workflows. We refer to the empirical score as the "Vector Embedding for Relational Trajectory Evaluation through Cross-similarity", or VERTEX score for short. The framework codebase and benchmark are linked below.

Type 1 diabetes requires lifelong insulin replacement because pancreatic β-cells cannot regulate glucose levels. Current insulin therapies deliver fixed pharmacokinetic responses and cannot dynamically adapt to changing blood glucose, which exposes patients to both hypoglycemia and hyperglycemia. A long-standing objective in diabetes research is therefore the development of glucose-responsive insulin (GRI) whose activity increases when glucose levels rise and attenuates when glucose falls.

Estimate achievable X-gate speeds and fidelities for a Yb-171 nuclear qubit using Raman transfer via a long-lived inner-shell J=2 intermediate (with 1 W/cm^2 available), limited by hyperfine splitting versus intermediate lifetime, and report gate-time vs target-infidelity tradeoffs (10^-2-10^-5).

We propose the problem of designing a dynamic, continuously-adaptive activation function that embeds continual-learning inductive biases directly into neuron nonlinearity. The target activation should trade off plasticity versus stability so networks can incrementally integrate concepts from multiple domains without catastrophic forgetting, while remaining scalable (avoiding saturation/dead neurons) and self-stabilizing (preserving bounded mean/variance across layers). Crucially, this mechanism must not rely on predefined task counts, explicit task identities, or brittle masking hacks; instead it should fold related concepts into shared representations and enable layer-wise progressive knowledge integration and reuse so that higher-level abstractions and deductive reasoning can emerge.

Robust dynamic operating envelopes (RDOEs) solve the problem of secure allocation of latent network capacity to flexible distributed energy resources (DER) in unbalanced distribution networks. As the computational complexity of RDOEs is much higher than that of dynamic operating envelopes (DOEs), which disregard uncertainties in network parameters and DER capacity utilisation, existing approaches to computing RDOEs have relied on linearised unbalanced three-phase optimal power flow (UTOPF) models to numerate the network feasible region approximately. The use of linearised models, however, risks producing RDOEs that undermine network integrity due to inherent errors in the approximation. This letter presents a practical sensitivity-filtering technique to simplify RDOE numerical computation based on non-convex UTOPF formulations. The accuracy and efficiency of the proposed approach are demonstrated on RDOE allocation with various fairness metrics by testing on representative Australian distribution networks.