Machine learning emulation of a local-scale UK climate model

Climate change is causing the intensification of rainfall extremes. Precipitation projections with high spatial resolution are important for society to prepare for these changes, e.g. to model flooding impacts. Physics-based simulations for creating such projections are very computationally expensive. This work demonstrates the effectiveness of diffusion models, a form of deep generative models, for generating much more cheaply realistic high resolution rainfall samples for the UK conditioned on data from a low resolution simulation. We show for the first time a machine learning model that is able to produce realistic samples of high-resolution rainfall based on a physical model that resolves atmospheric convection, a key process behind extreme rainfall. By adding self-learnt, location-specific information to low resolution relative vorticity, quantiles and time-mean of the samples match well their counterparts from the high-resolution simulation.

Estimation of binary time-frequency masks from ambient noise

We investigate the retrieval of a binary time-frequency mask from a few observations of filtered white ambient noise. Confirming household wisdom in acoustic modeling, we show that this is possible by inspecting the average spectrogram of ambient noise. Specifically, we show that the lower quantile of the average of $\mathcal{O}(\log(|\Omega|/\varepsilon))$ masked spectrograms is enough to identify a rather general mask $\Omega$ with confidence at least $\varepsilon$, up to shape details concentrated near the boundary of $\Omega$. As an application, the expected measure of the estimation error is dominated by the perimeter of the time-frequency mask. The estimator requires no knowledge of the noise variance, and only a very qualitative profile of the filtering window, but no exact knowledge of it.

Gatekeeper: Safety Critical Control of Nonlinear Systems with Limited Perception in Unknown and Dynamic Environments

This paper presents the Gatekeeper algorithm, a real-time method to guarantee the safety of a robotic system operating in environments that are unknown and dynamic. Given a nominal planner designed to meet mission objectives, Gatekeeper extends the nominal trajectories using backup controllers, and determines a control policy that is certified safe for all future time using the currently available information. We demonstrate the algorithm on a dynamic aerial firefighting mission, and show reduced conservatism relative to existing methods. The algorithm was also demonstrated onboard a quadrotor, where a map of the environment was built online, and the Gatekeeper algorithm prevented a human pilot from flying the quadrotor into obstacles and unknown regions.

RIS-Assisted Receive Quadrature Spatial Modulation with Low-Complexity Greedy Detection

In this paper, we propose a novel reconfigurable intelligent surface (RIS)-assisted wireless communication scheme which uses the concept of spatial modulation, namely RIS-assisted receive quadrature spatial modulation (RIS-RQSM). In the proposed RIS-RQSM system, the information bits are conveyed via both the indices of the two selected receive antennas and the conventional in-phase/quadrature (IQ) modulation. We propose a novel methodology to adjust the phase shifts of the RIS elements in order to maximize the signal-to-noise ratio (SNR) and at the same time to construct two separate PAM symbols at the selected receive antennas, as the in-phase and quadrature components of the desired IQ symbol. An energy-based greedy detector (GD) is implemented at the receiver to efficiently detect the received signal with minimal channel state information (CSI) via the use of an appropriately designed one-tap pre-equalizer. We also derive a closed-form upper bound on the average bit error probability (ABEP) of the proposed RIS-RQSM system. Then, we formulate an optimization problem to minimize the ABEP in order to improve the performance of the system, which allows the GD to act as a near-optimal receiver. Extensive numerical results are provided to demonstrate the error rate performance of the system and to compare with that of a prominent benchmark scheme. The results verify the remarkable superiority of the proposed RIS-RQSM system over the benchmark scheme.

Plant species richness prediction from DESIS hyperspectral data: A comparison study on feature extraction procedures and regression models

The diversity of terrestrial vascular plants plays a key role in maintaining the stability and productivity of ecosystems. Monitoring species compositional diversity across large spatial scales is challenging and time consuming. The advanced spectral and spatial specification of the recently launched DESIS (the DLR Earth Sensing Imaging Spectrometer) instrument provides a unique opportunity to test the potential for monitoring plant species diversity with spaceborne hyperspectral data. This study provides a quantitative assessment on the ability of DESIS hyperspectral data for predicting plant species richness in two different habitat types in southeast Australia. Spectral features were first extracted from the DESIS spectra, then regressed against on-ground estimates of plant species richness, with a two-fold cross validation scheme to assess the predictive performance. We tested and compared the effectiveness of Principal Component Analysis (PCA), Canonical Correlation Analysis (CCA), and Partial Least Squares analysis (PLS) for feature extraction, and Kernel Ridge Regression (KRR), Gaussian Process Regression (GPR), Random Forest Regression (RFR) for species richness prediction. The best prediction results were r=0.76 and RMSE=5.89 for the Southern Tablelands region, and r=0.68 and RMSE=5.95 for the Snowy Mountains region. Relative importance analysis for the DESIS spectral bands showed that the red-edge, red, and blue spectral regions were more important for predicting plant species richness than the green bands and the near-infrared bands beyond red-edge. We also found that the DESIS hyperspectral data performed better than Sentinel-2 multispectral data in the prediction of plant species richness. Our results provide a quantitative reference for future studies exploring the potential of spaceborne hyperspectral data for plant biodiversity mapping.

Estimating Task Completion Times for Network Rollouts using Statistical Models within Partitioning-based Regression Methods

This paper proposes a data and Machine Learning-based forecasting solution for the Telecommunications network-rollout planning problem. Milestone completion-time estimation is crucial to network-rollout planning; accurate estimates enable better crew utilisation and optimised cost of materials and logistics. Using historical data of milestone completion times, a model needs to incorporate domain knowledge, handle noise and yet be interpretable to project managers. This paper proposes partition-based regression models that incorporate data-driven statistical models within each partition, as a solution to the problem. Benchmarking experiments demonstrate that the proposed approach obtains competitive to better performance, at a small fraction of the model complexity of the best alternative approach based on Gradient Boosting. Experiments also demonstrate that the proposed approach is effective for both short and long-range forecasts. The proposed idea is applicable in any context requiring time-series regression with noisy and attributed data.

Annotation by Clicks: A Point-Supervised Contrastive Variance Method for Medical Semantic Segmentation

Medical image segmentation methods typically rely on numerous dense annotated images for model training, which are notoriously expensive and time-consuming to collect. To alleviate this burden, weakly supervised techniques have been exploited to train segmentation models with less expensive annotations. In this paper, we propose a novel point-supervised contrastive variance method (PSCV) for medical image semantic segmentation, which only requires one pixel-point from each organ category to be annotated. The proposed method trains the base segmentation network by using a novel contrastive variance (CV) loss to exploit the unlabeled pixels and a partial cross-entropy loss on the labeled pixels. The CV loss function is designed to exploit the statistical spatial distribution properties of organs in medical images and their variance distribution map representations to enforce discriminative predictions over the unlabeled pixels. Experimental results on two standard medical image datasets demonstrate that the proposed method outperforms the state-of-the-art weakly supervised methods on point-supervised medical image semantic segmentation tasks.

Decentralized Online Regularized Learning Over Random Time-Varying Graphs

We study the decentralized online regularized linear regression algorithm over random time-varying graphs. At each time step, every node runs an online estimation algorithm consisting of an innovation term processing its own new measurement, a consensus term taking a weighted sum of estimations of its own and its neighbors with additive and multiplicative communication noises and a regularization term preventing over-fitting. It is not required that the regression matrices and graphs satisfy special statistical assumptions such as mutual independence, spatio-temporal independence or stationarity. We develop the nonnegative supermartingale inequality of the estimation error, and prove that the estimations of all nodes converge to the unknown true parameter vector almost surely if the algorithm gains, graphs and regression matrices jointly satisfy the sample path spatio-temporal persistence of excitation condition. Especially, this condition holds by choosing appropriate algorithm gains if the graphs are uniformly conditionally jointly connected and conditionally balanced, and the regression models of all nodes are uniformly conditionally spatio-temporally jointly observable, under which the algorithm converges in mean square and almost surely. In addition, we prove that the regret upper bound $\mathcal O(T^{1-\tau}\ln T)$, where $\tau\in (0.5,1)$ is a constant depending on the algorithm gains.

Towards Dynamic Causal Discovery with Rare Events: A Nonparametric Conditional Independence Test

Causal phenomena associated with rare events occur across a wide range of engineering problems, such as risk-sensitive safety analysis, accident analysis and prevention, and extreme value theory. However, current methods for causal discovery are often unable to uncover causal links, between random variables in a dynamic setting, that manifest only when the variables first experience low-probability realizations. To address this issue, we introduce a novel statistical independence test on data collected from time-invariant dynamical systems in which rare but consequential events occur. In particular, we exploit the time-invariance of the underlying data to construct a superimposed dataset of the system state before rare events happen at different timesteps. We then design a conditional independence test on the reorganized data. We provide non-asymptotic sample complexity bounds for the consistency of our method, and validate its performance across various simulated and real-world datasets, including incident data collected from the Caltrans Performance Measurement System (PeMS). Code containing the datasets and experiments is publicly available.

Emergent communication enhances foraging behaviour in evolved swarms controlled by Spiking Neural Networks

Social insects such as ants communicate via pheromones which allows them to coordinate their activity and solve complex tasks as a swarm, e.g. foraging for food. This behaviour was shaped through evolutionary processes. In computational models, self-coordination in swarms has been implemented using probabilistic or action rules to shape the decision of each agent and the collective behaviour. However, manual tuned decision rules may limit the behaviour of the swarm. In this work we investigate the emergence of self-coordination and communication in evolved swarms without defining any rule. We evolve a swarm of agents representing an ant colony. We use a genetic algorithm to optimize a spiking neural network (SNN) which serves as an artificial brain to control the behaviour of each agent. The goal of the colony is to find optimal ways to forage for food in the shortest amount of time. In the evolutionary phase, the ants are able to learn to collaborate by depositing pheromone near food piles and near the nest to guide its cohorts. The pheromone usage is not encoded into the network; instead, this behaviour is established through the optimization procedure. We observe that pheromone-based communication enables the ants to perform better in comparison to colonies where communication did not emerge. We assess the foraging performance by comparing the SNN based model to a rule based system. Our results show that the SNN based model can complete the foraging task more efficiently in a shorter time. Our approach illustrates that even in the absence of pre-defined rules, self coordination via pheromone emerges as a result of the network optimization. This work serves as a proof of concept for the possibility of creating complex applications utilizing SNNs as underlying architectures for multi-agent interactions where communication and self-coordination is desired.