Spiking neural networks are efficient computation models for low-power environments. Spike-based BP algorithms and ANN-to-SNN (ANN2SNN) conversions are successful techniques for SNN training. Nevertheless, the spike-base BP training is slow and requires large memory costs. Though ANN2NN provides a low-cost way to train SNNs, it requires many inference steps to mimic the well-trained ANN for good performance. In this paper, we propose a SNN-to-ANN (SNN2ANN) framework to train the SNN in a fast and memory-efficient way. The SNN2ANN consists of 2 components: a) a weight sharing architecture between ANN and SNN and b) spiking mapping units. Firstly, the architecture trains the weight-sharing parameters on the ANN branch, resulting in fast training and low memory costs for SNN. Secondly, the spiking mapping units ensure that the activation values of the ANN are the spiking features. As a result, the classification error of the SNN can be optimized by training the ANN branch. Besides, we design an adaptive threshold adjustment (ATA) algorithm to address the noisy spike problem. Experiment results show that our SNN2ANN-based models perform well on the benchmark datasets (CIFAR10, CIFAR100, and Tiny-ImageNet). Moreover, the SNN2ANN can achieve comparable accuracy under 0.625x time steps, 0.377x training time, 0.27x GPU memory costs, and 0.33x spike activities of the Spike-based BP model.

A Hyperspectral image contains much more number of channels as compared to a RGB image, hence containing more information about entities within the image. The convolutional neural network (CNN) and the Multi-Layer Perceptron (MLP) have been proven to be an effective method of image classification. However, they suffer from the issues of long training time and requirement of large amounts of the labeled data, to achieve the expected outcome. These issues become more complex while dealing with hyperspectral images. To decrease the training time and reduce the dependence on large labeled dataset, we propose using the method of transfer learning. The hyperspectral dataset is preprocessed to a lower dimension using PCA, then deep learning models are applied to it for the purpose of classification. The features learned by this model are then used by the transfer learning model to solve a new classification problem on an unseen dataset. A detailed comparison of CNN and multiple MLP architectural models is performed, to determine an optimum architecture that suits best the objective. The results show that the scaling of layers not always leads to increase in accuracy but often leads to overfitting, and also an increase in the training time.The training time is reduced to greater extent by applying the transfer learning approach rather than just approaching the problem by directly training a new model on large datasets, without much affecting the accuracy.

In this paper we extend our previous work on UAV localization based on the magnetic field strength. The method is based on a magnetic flux density distribution in vicinity of two very long, thin and parallel transmission lines. An UAV is equipped with 4 magnetometers, positioned so that obtained measurements give unique solution to an optimization problem used to find relative position and orientation of the UAV with respect to conductors. Several sets of experiments, undertaken on a laboratory setup, confirmed validity of the method for both solutions - analytical and numerical optimization. Obtained results, compared with high precision motion capture system, are within range of standard RTK positioning.

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Artificial Intelligence (AI) and Machine Learning (ML) can be used to tackle crucial issues like climate change and carbon emissions, which could bring humanity one step closer to achieving our sustainability goals. However, increased use of AI and ML technologies can also have an impact on greenhouse gas emissions which means creating a sustainable form of these technologies is key to the wider picture of climate consciousness. Recently, a group of researchers led by Professor Lynn H. Kaack at Berlin's Hertie School published a paper in the journal Nature Climate Change investigating how AI and ML technologies may impact greenhouse gas emissions – both positively and negatively – and what measures can be taken help to align AI/ML policy with climate change goals. The aim of the study is to establish how the emissions from AI/ML activities can be quantified in order to better understand how the increasing use of these technologies is influencing the climate. Climate change should be a key consideration when developing and assessing AI technologies.

This article is a survey on deep learning methods for single and multiple sound source localization. We are particularly interested in sound source localization in indoor/domestic environment, where reverberation and diffuse noise are present. We provide an exhaustive topography of the neural-based localization literature in this context, organized according to several aspects: the neural network architecture, the type of input features, the output strategy (classification or regression), the types of data used for model training and evaluation, and the model training strategy. This way, an interested reader can easily comprehend the vast panorama of the deep learning-based sound source localization methods. Tables summarizing the literature survey are provided at the end of the paper for a quick search of methods with a given set of target characteristics.

Predicting the dynamic behaviors of particles in suspension subject to hydrodynamic interaction (HI) and external drive can be critical for many applications. By harvesting advanced deep learning techniques, the present work introduces a new framework, hydrodynamic interaction graph neural network (HIGNN), for inferring and predicting the particles' dynamics in Stokes suspensions. It overcomes the limitations of traditional approaches in computational efficiency, accuracy, and/or transferability. In particular, by uniting the data structure represented by a graph and the neural networks with learnable parameters, the HIGNN constructs surrogate modeling for the mobility tensor of particles which is the key to predicting the dynamics of particles subject to HI and external forces. To account for the many-body nature of HI, we generalize the state-of-the-art GNN by introducing higher-order connectivity into the graph and the corresponding convolutional operation. For training the HIGNN, we only need the data for a small number of particles in the domain of interest, and hence the training cost can be maintained low. Once constructed, the HIGNN permits fast predictions of the particles' velocities and is transferable to suspensions of different numbers/concentrations of particles in the same domain and to any external forcing. It has the ability to accurately capture both the long-range HI and short-range lubrication effects. We demonstrate the accuracy, efficiency, and transferability of the proposed HIGNN framework in a variety of systems. The requirement on computing resource is minimum: most simulations only require a desktop with one GPU; the simulations for a large suspension of 100,000 particles call for up to 6 GPUs.

The photoelectric factor (PEF) is an important well logging tool to distinguish between different types of reservoir rocks because PEF measurement is sensitive to elements with high atomic number. Furthermore, the ratio of rock minerals could be determined by combining PEF log with other well logs. However, PEF log could be missing in some cases such as in old well logs and wells drilled with barite-based mud. Therefore, developing models for estimating missing PEF log is essential in those circumstances. In this work, we developed various machine learning models to predict PEF values using the following well logs as inputs: bulk density (RHOB), neutron porosity (NPHI), gamma ray (GR), compressional and shear velocity. The predictions of PEF values using adaptive-network-fuzzy inference system (ANFIS) and artificial neural network (ANN) models have errors of about 16% and 14% average absolute percentage error (AAPE) in the testing dataset, respectively. Thus, a different approach was proposed that is based on the concept of automated machine learning. It works by automatically searching for the optimal model type and optimizes its hyperparameters for the dataset under investigation. This approach selected a Gaussian process regression (GPR) model for accurate estimation of PEF values. The developed GPR model decreases the AAPE of the predicted PEF values in the testing dataset to about 10% AAPE. This error could be further decreased to about 2% by modeling the potential noise in the measurements using the GPR model.

Cyber-attacks are rarely out of the headlines. We know state actors, terrorists, and criminals can leverage cyber-means to target the digital infrastructures of our societies. We have also learned that, insofar as our societies grow dependent on digital technologies, they become more vulnerable to cyber-attacks. There is no shortage of examples, ranging from the 2007 attacks against Estonia digital services and 2008 cyber-attack against a nuclear power plant in Georgia to WannaCry and NotPetya, two ransomware attacks that encrypted data and demanded ransom payments, and the ransomware cyber-attack on the US Colonial Pipeline, a US oil pipeline system that provides fuel to South-eastern States. My work focuses mostly on state vs state cyber-attacks.

After a long day of work, you might feel tired or exhilarated. Either way, you are affected by what happened to you in the past. Accelerator magnets are no different. What they went through – or what went through them, like an electric current – affects how they will perform in the future. Without understanding a magnet's past, researchers might need to fully reset them before starting a new experiment, a process that can take 10 or 15 minutes.