Spiking Neural Networks (SNNs) have emerged as a promising alternative to traditional Deep Neural Networks for low-power computing. However, the effectiveness of SNNs is not solely determined by their performance but also by their energy consumption, prediction speed, and robustness to noise. The recent method Fast \& Deep, along with others, achieves fast and energy-efficient computation by constraining neurons to fire at most once. Known as Time-To-First-Spike (TTFS), this constraint however restricts the capabilities of SNNs in many aspects. In this work, we explore the relationships between performance, energy consumption, speed and stability when using this constraint. More precisely, we highlight the existence of tradeoffs where performance and robustness are gained at the cost of sparsity and prediction latency. To improve these tradeoffs, we propose a relaxed version of Fast \& Deep that allows for multiple spikes per neuron. Our experiments show that relaxing the spike constraint provides higher performance while also benefiting from faster convergence, similar sparsity, comparable prediction latency, and better robustness to noise compared to TTFS SNNs. By highlighting the limitations of TTFS and demonstrating the advantages of unconstrained SNNs we provide valuable insight for the development of effective learning strategies for neuromorphic computing.

An Amazon listing may have spilled the beans early, but today Beats is officially debuting its latest true wireless earbuds. That premature appearance was mostly accurate: the Studio Buds have a familiar design with loads of improvements on the inside. Those upgrades include better battery life, retooled call performance and updated noise cancellation. There's also a new transparent design option that offers a look at all of those internal components. However, they come with a slightly higher price tag at $170, which means the new version isn't quite as good of a deal as the original.

To solve this problem, there are two main classes of computational methods that provide different approaches to approximate π. The first one is Markov chain Monte Carlo (MCMC) methods (Metropolis et al., 1953; Hastings, 1970; Robert and Casella, 1999). For many years, MCMC has been the standard approach for Bayesian analysis because of its theoretical soundness. The method constructs a Markov chain to produce simulation consistent samples from the target distribution π. A general MCMC approach is the Metropolis-Hastings algorithm that generates a Markov chain by first generating a proposed state from a proposal distribution, then using an acceptance rule to decide whether to accept the proposal or stay at the current state (Robert and Casella, 1999).

Trajectory planning in an unsteady flow field is an important problem for intelligent mobile agents, with applications including environmental monitoring and data collection [1-6]. When planning trajectories, many applications aim at achieving certain objectives ranging from reaching a static goal location to maintaining certain connectivity of a multi-agent sensor network for part of or the entire the mission [7, 8]. Optimization and control are often employed in designing the decisionmaking algorithms on-board the mobile agents, enabling offline or real-time trajectory planning to achieve the desired objectives. Intelligent algorithms that leverage the background flow are necessary, since naively using full propulsion while aiming at a target can result in wasteful trajectories and the potential of the vehicle being swept away by large currents at a later time. However, even with on-board algorithms, it is still imperative to carefully choose the deployment locations since the agent's ability to reach certain regions is largely determined by its actuation limits and the background flow dynamics. For example, it might be impossible for two groups of agents that are dominated by close-by, but different flow structures, to rendezvous. Furthermore, tuning the hyperparameters of an on-board control strategy to obtain the best performance is a challenging task. The ability to summarize and visualize the dependence of the control performance on the control hyperparameters may aid in this process.

We study Graph Neural Networks (GNNs)-based embedding techniques for knowledge graph (KG) reasoning. For the first time, we link the path redundancy issue in the state-of-the-art KG reasoning models based on path encoding and message passing to the transformation error in model training, which brings us new theoretical insights into KG reasoning, as well as high efficacy in practice. On the theoretical side, we analyze the entropy of transformation error in KG paths and point out query-specific redundant paths causing entropy increases. These findings guide us to maintain the shortest paths and remove redundant paths for minimized-entropy message passing. To achieve this goal, on the practical side, we propose an efficient Graph Percolation Process motivated by the percolation model in Fluid Mechanics, and design a lightweight GNN-based KG reasoning framework called Graph Percolation Embeddings (GraPE). GraPE outperforms previous state-of-the-art methods in both transductive and inductive reasoning tasks while requiring fewer training parameters and less inference time.

Predictive Maintenance (PdM) methods aim to facilitate the scheduling of maintenance work before equipment failure. In this context, detecting early faults in automated teller machines (ATMs) has become increasingly important since these machines are susceptible to various types of unpredictable failures. ATMs track execution status by generating massive event-log data that collect system messages unrelated to the failure event. Predicting machine failure based on event logs poses additional challenges, mainly in extracting features that might represent sequences of events indicating impending failures. Accordingly, feature learning approaches are currently being used in PdM, where informative features are learned automatically from minimally processed sensor data. However, a gap remains to be seen on how these approaches can be exploited for deriving relevant features from event-log-based data. To fill this gap, we present a predictive model based on a convolutional kernel (MiniROCKET and HYDRA) to extract features from the original event-log data and a linear classifier to classify the sample based on the learned features. The proposed methodology is applied to a significant real-world collected dataset. Experimental results demonstrated how one of the proposed convolutional kernels (i.e. HYDRA) exhibited the best classification performance (accuracy of 0.759 and AUC of 0.693). In addition, statistical analysis revealed that the HYDRA and MiniROCKET models significantly overcome one of the established state-of-the-art approaches in time series classification (InceptionTime), and three non-temporal ML methods from the literature. The predictive model was integrated into a container-based decision support system to support operators in the timely maintenance of ATMs.

Recent work provides promising evidence that Physics-informed neural networks (PINN) can efficiently solve partial differential equations (PDE). However, previous works have failed to provide guarantees on the worst-case residual error of a PINN across the spatio-temporal domain - a measure akin to the tolerance of numerical solvers - focusing instead on point-wise comparisons between their solution and the ones obtained by a solver on a set of inputs. In real-world applications, one cannot consider tests on a finite set of points to be sufficient grounds for deployment, as the performance could be substantially worse on a different set. To alleviate this issue, we establish tolerance-based correctness conditions for PINNs over the entire input domain. To verify the extent to which they hold, we introduce $\partial$-CROWN: a general, efficient and scalable post-training framework to bound PINN residual errors. We demonstrate its effectiveness in obtaining tight certificates by applying it to two classically studied PDEs - Burgers' and Schr\"odinger's equations -, and two more challenging ones with real-world applications - the Allan-Cahn and Diffusion-Sorption equations.

Stein Variational Gradient Descent (SVGD) is a popular particle-based method for Bayesian inference. However, its convergence suffers from the variance collapse, which reduces the accuracy and diversity of the estimation. In this paper, we study the isotropy property of finite particles during the convergence process and show that SVGD of finite particles cannot spread across the entire sample space. Instead, all particles tend to cluster around the particle center within a certain range and we provide an analytical bound for this cluster. To further improve the effectiveness of SVGD for high-dimensional problems, we propose the Augmented Message Passing SVGD (AUMP-SVGD) method, which is a two-stage optimization procedure that does not require sparsity of the target distribution, unlike the MP-SVGD method. Our algorithm achieves satisfactory accuracy and overcomes the variance collapse problem in various benchmark problems.

Forecasting large aftershocks is a challenge of great importance for human security. Today we dispose of statistical predictive models called Epidemic Type Aftershock Sequence (ETAS) tuned on the earthquake catalogue of the past seismicity. This catalogues contains basic information such as the location, the time and the magnitude of an earthquake. However we dispose of much richer data set about the crust dynamics, such as the daily displacement of the ground surface, that is nowadays measured by numerous GPS stations, devices that send their absolute position everyday to sattellites, thus telling us about how the ground deforms. In this study, we propose to forecast the Japanese aftershocks by means of a machine learning study of the GPS data alone. Our results show that this method is very promising and relies on the quality and the quantity of the available data.

This study demonstrates that the soft biological tissues of humans can be used as a type of soft body in physical reservoir computing. Soft biological tissues possess characteristics such as stress-strain nonlinearity and viscoelasticity that satisfy the requirements for physical reservoir computing, including nonlinearity and memory. The aim of this study was to utilize the dynamics of human soft tissues as a physical reservoir for the emulation of nonlinear dynamical systems. To demonstrate this concept, joint angle data during motion in the flexion-extension direction of the wrist joint, and ultrasound images of the muscles associated with that motion, were acquired from human participants. The input to the system was the angle of the wrist joint, while the deformation field within the muscle (obtained from ultrasound images) represented the state of the reservoir. The results indicate that the dynamics of soft tissue have a positive impact on the computational task of emulating nonlinear dynamical systems. This research suggests that the soft tissue of humans can be used as a potential computational resource.