The use of autonomous vehicles for chemical source localisation is a key enabling tool for disaster response teams to safely and efficiently deal with chemical emergencies. Whilst much work has been performed on source localisation using autonomous systems, most previous works have assumed an open environment or employed simplistic obstacle avoidance, separate to the estimation procedure. In this paper, we explore the coupling of the path planning task for both source term estimation and obstacle avoidance in a holistic framework. The proposed system intelligently produces potential gas sampling locations based on the current estimation of the wind field and the local map. Then a tree search is performed to generate paths toward the estimated source location that traverse around any obstacles and still allow for exploration of potentially superior sampling locations. The proposed informed tree planning algorithm is then tested against the Entrotaxis technique in a series of high fidelity simulations. The proposed system is found to reduce source position error far more efficiently than Entrotaxis in a feature rich environment, whilst also exhibiting vastly more consistent and robust results.

Memristors are an electronic device whose resistance depends on the voltage history that has been applied to its two terminals. Despite its clear advantage as a computational element, a suitable transport model is lacking for the special class of interface-based memristors. Here, we adapt the widely-used Yakopcic compact model by including transport equations relevant to interface-type memristors. This model is able to reproduce the qualitative behaviour measured upon Nb-doped SrTiO$_3$ memristive devices. Our analysis demonstrates a direct correlation between the devices' characteristic parameters and those of our model. The model can clearly identify the charge transport mechanism in different resistive states thus facilitating evaluation of the relevant parameters pertaining to resistive switching in interface-based memristors. One clear application of our study is its ability to inform the design and fabrication of related memristive devices.

An artificial intelligence known as'Dragonfly' has been used by researchers to design more efficient batteries. Scientists at Carnegie Mellon have used the tool to design better electrolytes for lithium-ion batteries, which would allow the batteries to charge faster. An electrolyte moves ions – atoms that have been charged by either gaining or losing an electron – between the two electrodes in a battery. Lithium ions are created at the negative electrode, the anode, and flow to the cathode where they gain electrons. When a battery charges, the ions move back to the anode.

The Eufy Edge Security System aims to fix two of the most common problems with wireless security cameras: false alerts and having to charge them. To avoid creating a constant stream of false alerts and eating up storage space, Eufy's engineers have added their proprietary BionicMind self-learning AI to the HomeBase 3, which acts as a data hub for the two included EufyCam 3 cameras. As for having to take the cameras down to charge them, the company has added integrated solar panels at the top of each camera. Another thing that sets Eufy's security cameras apart from other devices on our list of the best outdoor security cameras is that there are no subscription fees whatsoever. Instead, the company's customers can store and access recorded videos for free using the HomeBase 3's built-in storage.

Fotech has launched two next-generation Helios DAS systems at the International Pipeline Expo in Calgary, Canada, between 27 and 29 September 2022. The new Helios DAS TL4 (single-channel) and the Helios DAS TX4 (dual-channel) interrogators deliver lower false alarm rates and enhanced monitoring and incident detection. They incorporate new machine learning capabilities, which allows a faster, cost effective and more systematic deployment of solutions in long linear assets such, as pipelines and perimeters. Pedro Barbosa, Senior Product Manager at Fotech, says, "The new Helios DAS TL4 and Helios DAS TX4 interrogators take monitoring of pipelines, critical infrastructure and perimeters to the next level. The machine learning that is built into them means they deliver exceptional accuracy with a much-reduced false alarm rate. As a result, users have extremely high confidence in alarms, and don't waste precious time or resource investigating false alarms."

A new technical paper titled "A Thermal Machine Learning Solver For Chip Simulation" was published by researchers at Ansys. Abstract "Thermal analysis provides deeper insights into electronic chips' behavior under different temperature scenarios and enables faster design exploration. However, obtaining detailed and accurate thermal profile on chip is very time-consuming using FEM or CFD. Therefore, there is an urgent need for speeding up the on-chip thermal solution to address various system scenarios. In this paper, we propose a thermal machine-learning (ML) solver to speed-up thermal simulations of chips. The thermal ML-Solver is an extension of the recent novel approach, CoAEMLSim (Composable Autoencoder Machine Learning Simulator) with modifications to the solution algorithm to handle constant and distributed HTC. The proposed method is validated against commercial solvers, such as Ansys MAPDL, as well as a latest ML baseline, UNet, under different scenarios to demonstrate its enhanced accuracy, scalability, and generalizability."

They range from Empirical performance tuning, also known as autotuning, is multiuser, high-speed storage systems such as burst buffers [2], a hot topic in software optimization nowadays, and a promising [3], [4], to transient, application-specific services providing approach for HPC storage service tuning. In this approach, processing capabilities such as in situ analysis [5], [6], [7]. the user exposes the tunable parameters and defines the range These systems aim to improve I/O and storage performance of values that each parameter can take; a search method by moving away from file-based interfaces and from the is then used to explore the parameter space by executing POSIX semantics, instead providing specific interfaces and optimizations different parameter configurations on the target platform. The that can be tailored to individual applications. An challenge for HPC storage services autotuning stems from example of such a distributed storage service is HEPnOS [8], the complexity of the workflow and the search space. First, an in-memory object store for high-energy physics (HEP) several tunable parameters can be interdependent, requiring an applications developed by Argonne National Laboratory and execution of the complete workflow on the target platform for FermiLab.

Power systems are prone to a variety of events (e.g. line trips and generation loss) and real-time identification of such events is crucial in terms of situational awareness, reliability, and security. Using measurements from multiple synchrophasors, i.e., phasor measurement units (PMUs), we propose to identify events by extracting features based on modal dynamics. We combine such traditional physics-based feature extraction methods with machine learning to distinguish different event types. Including all measurement channels at each PMU allows exploiting diverse features but also requires learning classification models over a high-dimensional space. To address this issue, various feature selection methods are implemented to choose the best subset of features. Using the obtained subset of features, we investigate the performance of two well-known classification models, namely, logistic regression (LR) and support vector machines (SVM) to identify generation loss and line trip events in two datasets. The first dataset is obtained from simulated generation loss and line trip events in the Texas 2000-bus synthetic grid. The second is a proprietary dataset with labeled events obtained from a large utility in the USA involving measurements from nearly 500 PMUs. Our results indicate that the proposed framework is promising for identifying the two types of events.

In data-driven modeling of spatiotemporal phenomena careful consideration often needs to be made in capturing the dynamics of the high wavenumbers. This problem becomes especially challenging when the system of interest exhibits shocks or chaotic dynamics. We present a data-driven modeling method that accurately captures shocks and chaotic dynamics by proposing a novel architecture, stabilized neural ordinary differential equation (ODE). In our proposed architecture, we learn the right-hand-side (RHS) of an ODE by adding the outputs of two NN together where one learns a linear term and the other a nonlinear term. Specifically, we implement this by training a sparse linear convolutional NN to learn the linear term and a dense fully-connected nonlinear NN to learn the nonlinear term. This is in contrast with the standard neural ODE which involves training only a single NN for learning the RHS. We apply this setup to the viscous Burgers equation, which exhibits shocked behavior, and show better short-time tracking and prediction of the energy spectrum at high wavenumbers than a standard neural ODE. We also find that the stabilized neural ODE models are much more robust to noisy initial conditions than the standard neural ODE approach. We also apply this method to chaotic trajectories of the Kuramoto-Sivashinsky equation. In this case, stabilized neural ODEs keep long-time trajectories on the attractor, and are highly robust to noisy initial conditions, while standard neural ODEs fail at achieving either of these results. We conclude by demonstrating how stabilizing neural ODEs provide a natural extension for use in reduced-order modeling by projecting the dynamics onto the eigenvectors of the learned linear term.

Large-scale self-supervised pre-training Transformer architecture have significantly boosted the performance for various tasks in natural language processing (NLP) and computer vision (CV). However, there is a lack of researches on processing multivariate time-series by pre-trained Transformer, and especially, current study on masking time-series for self-supervised learning is still a gap. Different from language and image processing, the information density of time-series increases the difficulty of research. The challenge goes further with the invalidity of the previous patch embedding and mask methods. In this paper, according to the data characteristics of multivariate time-series, a patch embedding method is proposed, and we present an self-supervised pre-training approach based on Masked Autoencoders (MAE), called MTSMAE, which can improve the performance significantly over supervised learning without pre-training. Evaluating our method on several common multivariate time-series datasets from different fields and with different characteristics, experiment results demonstrate that the performance of our method is significantly better than the best method currently available.