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In many real-world routing problems, decision makers must optimise over sparse graphs such as transportation networks with non-metric costs on the edges that do not obey the triangle inequality. Motivated by finding a sufficiently long running route in a city that minimises the air pollution exposure of the runner, we study the Prize-collecting Travelling Salesperson Problem (Pc-TSP) on sparse graphs with non-metric costs. Given an undirected graph with a cost function on the edges and a prize function on the vertices, the goal of Pc-TSP is to find a tour rooted at the origin that minimises the total cost such that the total prize is at least some quota. First, we introduce heuristics designed for sparse graphs with non-metric cost functions where previous work dealt with either a complete graph or a metric cost function. Next, we develop a branch & cut algorithm that employs a new cut we call the disjoint-paths cost-cover (DPCC) cut. Empirical experiments on two datasets show that our heuristics can produce a feasible solution with less cost than a state-of-the-art heuristic from the literature. On datasets with non-metric cost functions, DPCC is found to solve more instances to optimality than the baseline cutting algorithm we compare against., Comment: Accepted to ATT'24: Workshop Agents in Traffic and Transportation

Decision making under uncertainty is challenging since the data-generating process (DGP) is often unknown. Bayesian inference proceeds by estimating the DGP through posterior beliefs about the model's parameters. However, minimising the expected risk under these posterior beliefs can lead to sub-optimal decisions due to model uncertainty or limited, noisy observations. To address this, we introduce Distributionally Robust Optimisation with Bayesian Ambiguity Sets (DRO-BAS) which hedges against uncertainty in the model by optimising the worst-case risk over a posterior-informed ambiguity set. We show that our method admits a closed-form dual representation for many exponential family members and showcase its improved out-of-sample robustness against existing Bayesian DRO methodology in the Newsvendor problem., Comment: 13 pages, 3 figures. Under review

We present a numerical method for the analysis of mutual coupling effects in large, dense and irregular arrays with identical antennas. Building on the Method of Moments (MoM), our technique employs a Macro Basis Function (MBF) approach for rapid direct inversion of the MoM impedance matrix. To expedite the reduced matrix filling, we propose an extension of the Steepest-Descent Multipole expansion which remains numerically stable and efficient across a wide bandwidth. This broadband multipole-based approach is well suited to quasi-planar problems and requires only the pre-computation of each MBF's complex patterns, resulting in low antenna-dependent pre-processing costs. The method also supports arrays with arbitrarily rotated antennas at low additional cost. A simulation of all embedded element patterns of irregular arrays of 256 complex log-periodic antennas completes in just 10 minutes per frequency point on a current laptop, with an additional minute per new layout.

We study the problem whether regular polygons and cycle graphs can be identified by the magnitude or the discrete Riesz energy. We give a positive answer in some cases and a negative answer in other cases by giving a way to construxt non-isometric spaces with the same magnitude or the discrete Riesz energy., Comment: 16 pages, 16 figures

Mixed effects models are among the most commonly used statistical methods for the exploration of multispecies data. In recent years, also Joint Species Distribution Models and Generalized Linear Latent Variale Models have gained in popularity when the goal is to incorporate residual covariation between species that cannot be explained due to measured environmental covariates. Few software implementations of such models exist that can additionally incorporate phylogenetic information, and those that exist tend to utilize Markov chain Monte Carlo methods for estimation, so that model fitting takes a long time. In this article we develop new methods for quickly and flexibly fitting phylogenetic mixed models, potentially incorporating residual covariation between species using latent variables, with the possibility to estimate the strength of phylogenetic structuring in species responses per environmental covariate, and while incorporating correlation between different covariate effects. By combining Variational approximations with a reduced rank matrix normal covariance structure, Nearest Neighbours Gaussian Processes, and parallel computation, phylogenetic mixed models can be fitted much more quickly than the current state-of-the-art. Two simulation studies demonstrate that the proposed combination of approximations is not only fast, but also enjoys high accuracy. Finally, we demonstrate use of the method with a real world dataset of wood-decaying fungi.

The spin-statistics theorem is generalized to include quantum entanglement. Specifically, within the context of spin entanglement, we prove that isotropic spin-correlated (ISC) states must occur in pairs. This pairing process can be composed of parallel or anti-parallel states. Consequently, the article proposes using ISC states as a unifying principle to explain better Bose-Einstein condensates, the theory of superconductivity, and molecular and atomic orbitals, all of which involve a pairing process. The theoretical framework is established in sections 1 and 2. The other qualitative sections focus primarily on the experimental evidence to support the theory., Comment: 25 pages

This article discusses the challenges and opportunities of managing group delay dispersion (GDD) and its relation to the performance standards of future sixth-generation (6G) wireless communication systems utilizing terahertz frequency waves. The unique susceptibilities of 6G systems to GDD are described, along with a quantitative description of the sources of GDD, including multipath, rough surface scattering, intelligent reflecting surfaces, and propagation through the atmosphere. An experimental case-study is presented that confirms previous models quantifying the impact of atmospheric GDD. Several GDD manipulation strategies are presented illustrating their hindered effectiveness in the 6G context. Conversely, some benefits of leveraging GDD to enhance 6G systems, such as improved security and simplified hardware, are also discussed. Finally, a perspective on using photonic GDD control devices is provided, revealing quantitative benefits that may unburden existing equalization schemes. The article argues that GDD will uniquely and significantly impact some 6G systems, but that its careful consideration along with new mitigation strategies, including photonic devices, will help optimize system performance. The conclusion provides a perspective to guide future research in this area., Comment: 7 pages, 4 figures, 2 tables. This work has been submitted to the IEEE for possible publication

Channel turbulence presents a formidable obstacle for free-space optical (FSO) communication. Anticipation of turbulence levels is highly important for mitigating disruptions. We study the application of machine learning (ML) to FSO data streams to rapidly predict channel turbulence levels with no additional sensing hardware. An optical bit stream was transmitted through a controlled channel in the lab under six distinct turbulence levels, and the efficacy of using ML to classify turbulence levels was examined. ML-based turbulence level classification was found to be >98% accurate with multiple ML training parameters, but highly dependent upon the timescale of changes between turbulence levels., Comment: 5 pages, 4 figures, 1 table. Submitted to IEEE letters

Radio frequency (RF) communications offer reliable but low data rates and energy-inefficient satellite links, while free-space optical (FSO) promises high bandwidth but struggles with disturbances imposed by atmospheric effects. A hybrid RF/FSO architecture aims to achieve optimal reliability along with high data rates for space communications. Accurate prediction of dynamic ground-to-satellite FSO link availability is critical for routing decisions in low-earth orbit constellations. In this paper, we propose a system leveraging ubiquitous RF links to proactively forecast FSO link degradation prior to signal drops below threshold levels. This enables pre-calculation of rerouting to maximally maintain high data rate FSO links throughout the duration of weather effects. We implement a supervised learning model to anticipate FSO attenuation based on the analysis of RF patterns. Through the simulation of a dense lower earth orbit (LEO) satellite constellation, we demonstrate the efficacy of our approach in a simulated satellite network, highlighting the balance between predictive accuracy and prediction duration. An emulated cloud attenuation model is proposed which provides insight into the temporal profiles of RF signals and their correlation to FSO channel dynamics. Our investigation sheds light on the trade-offs between prediction horizon and accuracy arising from RF beacon proximity, achieving a prediction accuracy of 86\% with 16 RF beacons., Comment: This work has been submitted to the IEEE for possible publication

Quantum photonics promises significant advances in secure communications, metrology, sensing and information processing/computation. Single photon sources are fundamental to this endeavor. However, the lack of high quality single photon sources remains a significant obstacle. We present here a new paradigm for the control of single photon emitters (SPEs) and single photon purity by integrating monolayer WS$_2$ with the organic ferroelectric polymer poly(vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)). We demonstrate that the ferroelectric domains in the P(VDF-TrFE) film control the purity of single photon emission from the adjacent WS$_2$. By switching the ferroelectric polarization, we reversibly tune the single photon purity between the semi-classical and quantum light regimes, with single photon purities as high as 94%. This provides another avenue for modulating and encoding quantum photonic information, complementing more complex approaches. This novel multidimensional heterostructure introduces a new avenue for control of quantum emitters by combining the nonvolatile ferroic properties of a ferroelectric with the radiative properties of the zero-dimensional atomic scale emitters embedded in the two-dimensional WS$_2$ semiconductor monolayer.

Minimising cycle time without inducing quality defects is a major challenge in the injection moulding (IM). Design of Experiment methods (DoE) have been widely studied for optimisation of the IM, however existing methods have limitations, including the need for a large number of experiments and a pre-determined search space. Bayesian adaptive design of experiment (ADoE) is an iterative process where the results of the previous experiments are used to make an informed selection for the next design. In this study, for the first time, an experimental ADoE approach, based on Bayesian optimisation, was developed in injection moulding using process and sensor data to optimise the quality and cycle time in real-time. A novel approach for the real-time characterisation of post-production shrinkage was introduced, utilising in-mould sensor data on temperature differential during part cooling. This characterisation approach was verified by post-production metrology results. A single and multi-objective optimisation of the cycle time and temperature differential in an injection moulded component is proposed. The multi-objective optimisation techniques, composite desirability function and Nondominated Sorting Genetic Algorithm (NSGA-II) using Response Surface Methodology (RSM) model, are compared with the real-time novel ADoE approach. ADoE achieved almost a 50% reduction in the number of experiments required for the single optimisation of temperature differential, and an almost 30% decrease for the optimisation of temperature differential and cycle time together compared to composite desirability function and NSGA-II. Also, the optimal settings identified by ADoE for multiobjective optimisation were similar to the selected Pareto optimal solution found by the NSGA-II., Comment: 19 pages, 12 figures

The Cosmic Dawn was marked by the formation of the first stars, and preceded the Epoch of Reionization (EoR), when the Universe underwent a fundamental transformation caused by the radiation from these first stars and galaxies. Interferometric 21-cm experiments aim to probe redshifted neutral hydrogen signals from these periods, constraining the conditions of the early Universe. The SKA-Low instrument of the Square Kilometre Array (SKA) is envisaged to be the largest and most sensitive radio telescope at metre and centimetre wavelengths. The latest Aperture Array Verification Systems feature 7-m coaxial transmission lines connecting the low noise amplifiers to optical transmitters at the front of the analogue-receiving chain. An impedance mismatch between these components results in a partially reflected electromagnetic signal, introducing chromatic aberrations into the instrument bandpass. This causes power from the foreground signals to appear at higher delays, potentially contaminating the 'EoR window', a region in which the 21-cm signal should be detectable. We present an end-to-end simulation pipeline for SKA-Low using a composite sky model combining radio foregrounds from the Galactic and Extragalactic All-Sky MWA (GLEAM) Survey, Haslam $408\,$MHz, and a 1.5-cGpc 21-cm brightness temperature cube generated with the 21cmSPACE simulator. We derive a model for the scattering parameters of a coaxial transmission line in terms of its specifications and bulk material properties. Assuming identical cables of length $\leq 15.0\,$m with impedance mismatch $\leq 10\, \Omega$, the reflection is confined below the EoR window. However, we demonstrate that even a 0.1 per cent length tolerance introduces contamination with an absolute fractional difference of $\sim 10$ per cent across all accessible k-modes., Comment: 17 pages, 12 figures

Cubic perovskite-structure ABO$_3$ and A$_{1-x}$A$^{\prime}$$_x$BO$_3$-type oxides have been investigated extensively while their hexagonal-structure versions have received minimal attention, even though they are multiferroic and can form heterostructures with the manifold hexagonal two-dimensional materials. Hexagonal ferrites of the form RFeO$_3$, where R is yttrium or a rare-earth element such as Lu, Yb, etc., feature coupled ferroelectricity (FE) and weak-ferromagnetism (wFM), exhibiting linear magnetoelectricity. Their only drawback is weak ferromagnetism. In this paper, we employ density-functional-theory (DFT) calculations on hexagonal LuFeO$_3$ ($h$-LFO), targeting its magnetic ordering by electron doping,anticipating spin-disproportionation of the Fe sublattices. Indeed, we show that spin-disproportionation in heavily-electron-doped versions Lu$_{1-x}$Hf$_x$FeO$_3$ ($h$-LHFO), especially for x=1/3 and 1/2, leads to robust out-of-plane collinear ferrimagnetism that is stable at room temperature. Furthermore, the robust ferroelectricity of $h$-LFO persists via a Jahn-Teller metal-to-insulator transition. Finally, we construct a $h$-LHFO/$h$-2D heterostructure, where $h$-2D stands for the FE/FM monolayer MnSTe, and demonstrate strong magnetoelectric coupling, namely manipulation of magnetic skyrmions in MnSTe by an external electric field through the $h$-LHFO polarization, opening up a new realm for magnetoelectric applications.

1. Temporal trends in species distributions are necessary for monitoring changes in biodiversity, which aids policymakers and conservationists in making informed decisions. Dynamic species distribution models are often fitted to ecological time series data using Markov Chain Monte Carlo algorithms to produce these temporal trends. However, the fitted models can be time-consuming to produce and run, making it inefficient to refit them as new observations become available. 2. We propose an algorithm that updates model parameters and the latent state distribution (e.g. true occupancy) using the saved information from a previously fitted model. This algorithm capitalises on the strength of importance sampling to generate new posterior samples of interest by updating the model output. The algorithm was validated with simulation studies on linear Gaussian state space models and occupancy models, and we applied the framework to Crested Tits in Switzerland and Yellow Meadow Ants in the UK. 3. We found that models updated with the proposed algorithm captured the true model parameters and latent state values as good as the models refitted to the expanded dataset. Moreover, the updated models were much faster to run and preserved the trajectory of the derived quantities. 4. The proposed approach serves as an alternative to conventional methods for updating state-space models (SSMs), and it is most beneficial when the fitted SSMs have a long run time. Overall, we provide a Monte Carlo algorithm to efficiently update complex models, a key issue in developing biodiversity models and indicators.

We give sufficient conditions for a finite metric space to be determined by the magnitude function. In particular, a generic finite metric space such that the distances between the points are rationally independent is determined by the asymptotic behavior of the magnitude function., Comment: 20 pages, 4 figures

In this study, we present a deep learning-based approach for time-series respiration data classification. The dataset contains regular breathing patterns as well as various forms of abnormal breathing, obtained through non-contact incoherent light-wave sensing (LWS) technology. Given the one-dimensional (1D) nature of the data, we employed a 1D convolutional neural network (1D-CNN) for classification purposes. Genetic algorithm was employed to optimize the 1D-CNN architecture to maximize classification accuracy. Addressing the computational complexity associated with training the 1D-CNN across multiple generations, we implemented transfer learning from a pre-trained model. This approach significantly reduced the computational time required for training, thereby enhancing the efficiency of the optimization process. This study contributes valuable insights into the potential applications of deep learning methodologies for enhancing respiratory anomaly detection through precise and efficient respiration classification., Comment: 7 pages, 8 figures, accepted in International Conference on Fuzzy Systems, Soft Computing, and Explainable AI (NAFIPS2024)

This work is focused on the experimental characterization of traction-separation laws for cohesive modeling of mode-II interlaminar fracture in composites. For the experimental investigation, damage progression in end-notched flexure specimens under three-point bending was captured using microscopic and macroscopic Digital Image Correlation (DIC) techniques. The specimens were geometrically scaled with three scaling levels for a size effect study. The fracture energy of the material was analyzed based on both linear elastic fracture mechanics and Bazant's type-II size effect law for comparison. For damage modeling, traction-separation laws were built from the experimental data, and the fracture process zones of the specimens were modeled using cohesive interactions. Difficulties in characterizing traction-separation laws merely relying on load-displacement curves (i.e., global behaviors) were demonstrated and discussed through extensive simulations. To address this issue, through-thickness deformation analysis and progressive DIC methods were proposed and implemented in this work. The observations and proposed methods herein will contribute to characterizing a single traction-separation law for a given composite material by capturing global and local fracture behaviors on any geometric scale.

Learned object detection methods based on fusion of LiDAR and camera data require labeled training samples, but niche applications, such as warehouse robotics or automated infrastructure, require semantic classes not available in large existing datasets. Therefore, to facilitate the rapid creation of multimodal object detection datasets and alleviate the burden of human labeling, we propose a novel automated annotation pipeline. Our method uses an indoor positioning system (IPS) to produce accurate detection labels for both point clouds and images and eliminates manual annotation entirely. In an experiment, the system annotates objects of interest 261.8 times faster than a human baseline and speeds up end-to-end dataset creation by 61.5%., Comment: Contains eight pages and 10 figures. A version of this document was accepted to IEEE IRC 2023

The quantity and types of biodiversity data being collected have increased in recent years. If we are to model and monitor biodiversity effectively, we need to respect how different data sets were collected, and effectively integrate these data types together. The framework that has emerged to do this is based on a point process formulation, with individuals as points and their distribution as a realisation of a random field. We describe this formulation and how the process model for the actual distribution is linked to the data that is collected through observation models. The observation models describe the data collection process and its uncertainties and biases. We provide an example of using these methods to model species of Norwegian freshwater fish, which shows how integrated models can be adapted to the data we can collect. We summarise the modelling issues that arise and the approaches that could be taken to solve them.

Contribution: Case study of a rural-focused educational program with two components: 1) introducing high school students and teachers to Smart and Connected Technologies (SCTs) that can be used to solve local problems; 2) engaging the local community in supporting local technology-driven entrepreneurship. Background: Rural communities typically lag behind in terms of participation in the digital economy, and use of technology in general. Yet they often have the most to gain, due to high rates of self-employment and lower private-sector job opportunities. Research Questions: Can a broadly-scoped rural technology education program lead to improvements in 1) student and teacher SCT awareness, 2) SCT skills, 3) aspirations for future SCT use directed toward entrepreneurship and overall community wellbeing? Methodology: Our multidisciplinary team used a mixed-methods approach to engage a rural high school robotics team as well as the local community. Over the course of one year, students took part in hands-on-training with SCTs ("PETE-Kits" and associated curriculum) and brainstormed entrepreneurial projects via ideation events. Community members were involved at the beginning and end of the project, including judging a "shark-tank" style event where student business ideas using SCT were presented. Findings: Results from student pre / post activity assessments suggest that the program was effective at increasing comfort with technology and combining technical skills with entrepreneurial opportunities. Post surveys from community members, including teachers, demonstrated clear support for the program and an appreciation of how SCTs / digital skills could benefit the local economy and wellbeing.

In this study, we present a non-contact respiratory anomaly detection method using incoherent light-wave signals reflected from the chest of a mechanical robot that can breathe like human beings. In comparison to existing radar and camera-based sensing systems for vitals monitoring, this technology uses only a low-cost ubiquitous infrared light source and sensor. This light-wave sensing system recognizes different breathing anomalies from the variations of light intensity reflected from the chest of the robot within a 0.5m-1.5m range with an average classification accuracy of up to 96.6% using machine learning., Comment: 1 page poster paper, 1 figure, 2 tables, accepted and presented in 23rd Wireless Telecommunications Symposium 2024. Symposium proceedings link: https://wtsconference.org/documents/WTS%202024%20-%20Program.pdf . Full version at 2311.01367v1

An experimental testbed has been constructed to assess the capabilities of Light-Wave Sensing, a promising new vitals monitoring approach. A Light-Wave Sensing apparatus utilizes infrared radiation to contactlessly monitor the subtle respiratory motions of a subject from meters away. A respiration-simulating robot was programmed to produce controllable, humanlike chest displacement patterns for accuracy analysis. Estimation of respiration rate within tenths of a breath per minute has been demonstrated with the testbed, establishing the tenability of the method for use in commercial non-contact respiration monitoring equipment, and setting practical expectations on the usable range of this sensing modality. An analytical model is then presented to guide hardware selection, and used to derive the absolute range limitations of Light-Wave Sensing., Comment: 12 pages, 11 figures (except photos of authors)

Vibrations in materials and nanostructures at sufficiently high temperatures result in anharmonic atomic displacements, which leads to new phenomena such as thermal expansion and multiphonon scattering processes, with a profound impact on temperature-dependent material properties including thermal conductivity, phonon lifetimes, nonradiative electronic transitions, and phase transitions. Nanoscale momentum-resolved vibrational spectroscopy, which has recently become possible on monochromated scanning-transmission-electron microscopes, is a unique method to probe the underpinnings of these phenomena. Here we report momentum-resolved vibrational spectroscopy in hexagonal boron nitride at temperatures of 300, 800, and 1300 K across three Brillouin zones (BZs) that reveals temperature-dependent phonon energy shifts and demonstrates the presence of strong Umklapp processes. Density-functional-theory calculations of temperature-dependent phonon self-energies reproduce the observed energy shifts and identify the contributing mechanisms., Comment: 21 pages, 4 figures, 2 tables, 3 supplemental figures, 3 supplemental tables

The Los Alamos Neutron Science Center (LANSCE) accelerator at Los Alamos National Laboratory (LANL) has been in service for over 50 years. Efforts to update and modernize crucial systems, many of which are origi-nal, are ongoing. This paper reports on the refurbishment of the Low-Level Radio Frequency (LLRF) Resonance Control Water System (RCWS) for the half-mile long Cavity-Coupled LINAC (CCL). The RCWS controls the resonance frequency of the cavities by controlling the temperature of the cooling water delivered to each of 44 accelerator modules. Of the 44 modules making up the CCL, 20 now have an upgraded RCWS. Upgrading in-cludes removing the old hardware and installing new components including: water pumps, mix tanks, valves, temperature switches, flow switches and plumbing. This paper describes the design of the new system, material and component selection, installation, and technical challenges., Comment: Talk presented at LLRF Workshop 2023 (LLRF2023, arXiv: 2310.03199)