LLM-based solvers have emerged as a promising means of automating problem modeling and solving. However, they remain unreliable and often depend on iterative repair loops that result in significant latency. We introduce OptiHive, a framework that enhances any solver-generation pipeline to produce higher-quality solvers from natural-language descriptions of optimization problems. OptiHive uses a single batched generation to produce diverse components (solvers, problem instances, and validation tests) and filters out erroneous components to ensure fully interpretable outputs. Accounting for the imperfection of the generated components, we employ a statistical model to infer their true performance, enabling principled uncertainty quantification and solver selection. On tasks ranging from traditional optimization problems to challenging variants of the Multi-Depot Vehicle Routing Problem, OptiHive significantly outperforms baselines, increasing the optimality rate from 5% to 92% on the most complex problems.

This supplementary document is organized as follows. This covariance function can be equally presented as a spectral density function, as discussed in the main paper (Wiener-Khinchin theorem). From the Fourier-duality, the spectral density function of Eq. (11) can be recovered by the Fourier Starting from Eq. (13), we can now do the spectral factorization by manipulating the G ( i ω), (17) which is the form we use in the main paper. Following Eq. (17), we can collect the transfer function of the corresponding stable part (see discussion From the empirical results in Figure 1 it is clear that the Matérn activations of form Eq. (20) approach Thus we provide the following high-level proof. The effect of the decay envelope is clearly visible when moving along the diagonal.

Text-to-image diffusion models, while proficient at generating high-fidelity images, often suffer from limited output diversity, hindering their application in exploratory and ideation tasks. Existing prompt optimization techniques typically target aesthetic fitness or are ill-suited to the creative visual domain. To address this shortcoming, we introduce WANDER, a novelty search-based approach to generating diverse sets of images from a single input prompt. WANDER operates directly on natural language prompts, employing a Large Language Model (LLM) for semantic evolution of diverse sets of images, and using CLIP embeddings to quantify novelty. We additionally apply emitters to guide the search into distinct regions of the prompt space, and demonstrate that they boost the diversity of the generated images. Empirical evaluations using FLUX-DEV for generation and GPT-4o-mini for mutation demonstrate that WANDER significantly outperforms existing evolutionary prompt optimization baselines in diversity metrics. Ablation studies confirm the efficacy of emitters.

This paper introduces a method for designing spatially intelligent robot swarm behaviors to localize concealed radio emitters. We use differential evolution to generate geometric patrol routes that localize unknown signals independently of emitter parameters, a key challenge in electromagnetic surveillance. Patrol shape and antenna type are shown to influence information gain, which in turn determines the effective triangulation coverage. We simulate a four-robot swarm across eight configurations, assigning pre-generated patrol routes based on a specified patrol shape and sensing capability (antenna type: omnidirectional or directional). An emitter is placed within the map for each trial, with randomized position, transmission power and frequency. Results show that omnidirectional localization success rates are driven primarily by source location rather than signal properties, with failures occurring most often when sources are placed in peripheral areas of the map. Directional antennas are able to overcome this limitation due to their higher gain and directivity, with an average detection success rate of 98.75% compared to 80.25% for omnidirectional. Average localization errors range from 1.01-1.30 m for directional sensing and 1.67-1.90 m for omnidirectional sensing; while directional sensing also benefits from shorter patrol edges. These results demonstrate that a swarm's ability to predict electromagnetic phenomena is directly dependent on its physical interaction with the environment. Consequently, spatial intelligence, realized here through optimized patrol routes and antenna selection, is a critical design consideration for effective robotic surveillance.

Fingerprinting Radio Frequency (RF) emitters typically involves finding unique emitter characteristics that are featured in their transmitted signals. These fingerprints are nuanced but sufficiently detailed, motivating the pursuit of methods that can successfully extract them. The most granular downstream task is known as Specific Emitter Identification (SEI), which requires a well informed RF fingerprinting (RFF) approach for it to be successful. RFF and SEI have a long history, with numerous application areas in defence and civilian contexts such as signal intelligence, electronic surveillance, physical-layer authentication of wireless communication devices, to name a few. RFF methods also support many other downstream tasks such as Emitter Data Association (EDA) and RF Emitter Clustering (RFEC) and are applicable to a range of transmission types. In recent years, data-driven approaches have become popular in the RFF domain due to their ability to automatically learn intricate fingerprints from raw data. These methods generally deliver superior performance when compared to traditional techniques. The more traditional approaches are often labour-intensive, inflexible and only applicable to a particular emitter type or transmission scheme. Therefore, we consider data-driven Machine Learning (ML)-enabled RFF. In particular, we propose a generic framework for ML-enabled RFF which is inclusive of several popular downstream tasks such as SEI, EDA and RFEC. Each task is formulated as a RF fingerprint-dependent task. A variety of use cases using real RF datasets are presented here to demonstrate the framework for a range of tasks and application areas, such as spaceborne surveillance, signal intelligence and countering drones.

Data fusion--the process of combining observational and exp erimental data--can enable the identification of causal effects that would otherwise rem ain non-identifiable. Although identification algorithms have been developed for specific s cenarios, do-calculus remains the only general-purpose tool for causal data fusion, particul arly when variables are present in some data sources but not others. However, approaches based on do-calculus may encounter computational challenges as the number of variables increa ses and the causal graph grows in complexity. Consequently, there exists a need to reduce t he size of such models while preserving the essential features. For this purpose, we pro pose pruning (removing unnecessary variables) and clustering (combining variables) as pr eprocessing operations for causal data fusion. We generalize earlier results on a single data s ource and derive conditions for applying pruning and clustering in the case of multiple data sources. We give sufficient conditions for inferring the identifiability or non-identi fiability of a causal effect in a larger graph based on a smaller graph and show how to obtain the corre sponding identifying functional for identifiable causal effects. Examples from ep idemiology and social science demonstrate the use of the results.

--When receiving radar pulses it is common for a recorded pulse train to contain pulses from many different emitters. The radar pulse deinterleaving problem is the task of separating out these pulses by the emitter from which they originated. Notably, the number of emitters in any particular recorded pulse train is considered unknown. In this paper, we define the problem and present metrics that can be used to measure model performance. We propose a metric learning approach to this problem using a transformer trained with the triplet loss on synthetic data. This model achieves strong results in comparison with other deep learning models with an adjusted mutual information score of 0.882. Radar pulse deinterleaving aims to separate out a train of radar pulses by the emitters from which they originated. We want to transform a single interleaved pulse train into many smaller deinterleaved pulse trains where each train contains all the pulses from a single emitter and only pulses from that emitter.

Deep neural network models can be used to learn complex dynamics from data and reconstruct sparse or noisy signals, thereby accelerating and augmenting experimental measurements. Evaluating the quantum optical properties of solid-state single-photon emitters is a time-consuming task that typically requires interferometric photon correlation experiments, such as Photon correlation Fourier spectroscopy (PCFS) which measures time-resolved single emitter lineshapes. Here, we demonstrate a latent neural ordinary differential equation model that can forecast a complete and noise-free PCFS experiment from a small subset of noisy correlation functions. By encoding measured photon correlations into an initial value problem, the NODE can be propagated to an arbitrary number of interferometer delay times. We demonstrate this with 10 noisy photon correlation functions that are used to extrapolate an entire de-noised interferograms of up to 200 stage positions, enabling up to a 20-fold speedup in experimental acquisition time from $\sim$3 hours to 10 minutes. Our work presents a new approach to greatly accelerate the experimental characterization of novel quantum emitter materials using deep learning.

One of the most useful applications of intelligent aerial robots sometimes called Unmanned Aerial Vehicles (UAV) in Australia is known to be in bushfire monitoring and prediction operations. A swarm of autonomous drones/UAVs programmed to work in real-time observing the fire parameters using their onboard sensors would be valuable in reducing the life-threatening impact of that fire. However autonomous UAVs face serious challenges in their positioning and navigation in critical bushfire conditions such as remoteness and severe weather conditions where GPS signals could also be unreliable. This paper tackles one of the most important factors in autonomous UAV navigation, namely Initial Positioning sometimes called Localisation. The solution provided by this paper will enable a team of autonomous UAVs to establish a relative position to their base of operation to be able to commence a team search and reconnaissance in a bushfire-affected area and find their way back to their base without the help of GPS signals.