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• A novel physics-informed shallow convolutional neural network is proposed. • The solving of the nonlinear physical operator equation is implemented. • The physical information is reconstructed from some noisy observations. • The effectiveness of current development is illustrated through extensive cases. • The speed acceleration of current development is significantly improved. We introduce a novel methodology for solving nonlinear partial differential equation (PDE) on regular or irregular domains using physics-informed ConvNet, which we call the PICN. The network structure consists of three parts: 1) a convolutional neural network for physical field generation, 2) a pre-trained convolutional layer corresponding to the finite-difference filters to estimate differential fields of the generated physical field, and 3) an interpolation network for loss analysis in irregular geometry domains. From a CNN perspective, the physical field is generated by a deconvolution layer and a convolution layer. Unlike the standard Physics-informed Neural Network (PINN) approach, the convolutions corresponding to the finite-difference filters estimate the spatial gradients forming the physical operator and then construct the PDE residual in a PINN-like loss function. The total loss function involving boundary conditions and the physical constraints in irregular geometry domains can be calculated from an efficient linear interpolation network. The theoretical analysis of PICN convergence is performed on a simplified case for solving a one-dimensional physical field, and several examples of nonlinear PDE of solutions with multifrequency characteristics are executed. The theory and examples confirm the effective learning capability of PICN for the physical field solution with high-frequency components, compared to the standard PINN. A series of numerical cases are performed to validate the current PICN, including the solving (and estimation) of nonlinear physical operator equations and recovering physical information from noisy observations. First, the ability of PICN to solve nonlinear PDE has been verified by executing three nonlinear problems including ODE with sine nonlinearity, PDE involving nonlinear sine-square operators, and Schrödinger equation. The proposed PICN has been assessed by solving some nonlinear PDE on irregular domains such as star-shaped domain, bird-like domain, and starfish domain. Moreover, PICN is applied to identify the thermal diffusivity parameters in an anisotropic heat transfer problem from noisy data, and a denoising display of the temperature field from strong noisy data with standard deviations ranging from 0.1 to 0.4. The numerical results demonstrate the high accuracy approximation and fast convergence performance of PICN. The potential advantage in approximating complex physical field with multi-frequency components indicates that PICN may become an alternative efficient neural network solver in physics-informed machine learning. This paper is adapted from the work originally posted on arXiv.com by the same authors (arXiv:2201.10967, Jan 26, 2022). The data and code accompanying this paper are publicly available at https://github.com/zengzhi2015/PICN. [ABSTRACT FROM AUTHOR]

Few-Shot Class-Incremental Learning (FSCIL)presents an extension of the Class Incremental Learning (CIL)problem where a model is faced with the problem of data scarcity while addressing the Catastrophic Forgetting (CF)problem. This problem remains an open problem because all recent works are built upon the Convolutional Neural Networks (CNNs)performing sub-optimally compared to the transformer approaches. Our paper presents Robust Transformer Approach (ROBUSTA)built upon the Compact Convolutional Transformer (CCT). The issue of overfitting due to few samples is overcome with the notion of the stochastic classifier, where the classifier's weights are sampled from a distribution with mean and variance vectors, thus increasing the likelihood of correct classifications, and the batch-norm layer to stabilize the training process. The issue of CFis dealt with the idea of delta parameters, small task-specific trainable parameters while keeping the backbone networks frozen. A non-parametric approach is developed to infer the delta parameters for the model's predictions. The prototype rectification approach is applied to avoid biased prototype calculations due to the issue of data scarcity. The advantage of ROBUSTAis demonstrated through a series of experiments in the benchmark problems where it is capable of outperforming prior arts with big margins without any data augmentation protocols. [ABSTRACT FROM AUTHOR]

Adversarial Training has proved to be an effective training paradigm to enforce robustness against adversarial examples in modern neural network architectures. Despite many efforts, explanations of the foundational principles underpinning the effectiveness of Adversarial Training are limited and far from being widely accepted by the Deep Learning community. Moreover, very few research works investigated the limitations of robust Convolutional Neural Networks beyond the well-known accuracy drop on natural images. In this paper, we describe surprising properties of these models, shedding light on mechanisms through which robustness against adversarial attacks is implemented. We also highlight limitations and failure modes that were not discussed in prior works. Through extensive analyses on a wide range of architectures and datasets, we empirically demonstrate that adversarially-trained Convolutional Neural Networks do not exploit efficiently the model capacity and that the simplicity biases induced by Adversarial Training may lead to undesired behaviors. [ABSTRACT FROM AUTHOR]

• First introduced PD control theory for optimizing learning rate in CNN models. • Designed of innovative center selection and elite guidance strategies. • Developed an efficient enhanced battle royale algorithm(EBRO). • Proposed a new EBRO-ICNN model for air quality prediction. PM 2.5 concentration predictions can provide air pollution control, management, and early warning. However, the PM 2.5 data with high-dimensionality, complexity, and dynamics pose a great challenge to achieve optimal prediction results. Convolutional Neural Networks (CNNs) has unique advantages in processing complex data and contributes to state-of-the-art performances. However, designing the architecture and selecting the learning rate for CNN are time-consuming and requires prior knowledge. Evolutionary algorithms, with the advantages of global convergence, ergodicity, robustness and adaptability, are the most commonly used methods to design the optimal framework for CNNs. Therefore, to improve the predictive performance of CNNs, this paper proposes an improved CNN method (EBRO-ICNN) which employs the enhanced battle royale optimization (EBRO) algorithm and proportional-derivative (PD) control. Firstly, the EBRO algorithm with multistrategy collaborative optimization is introduced, and validated by CEC2017 benchmark functions, which demonstrates EBRO strong global exploration capability, fast convergence speed, and low time complexity. Next, PD control is applied to adjust the learning rate of CNN (ICNN) dynamically, which improves the efficiency and stability of the network training process. At the same time, the ICNN model is optimized using the EBRO algorithm, which can reduce the human interference, generate the optimal framework automatically and enhance the prediction accuracy effectively. Finally, a private air quality dataset and two public datasets are utilized to evaluate the performance of the EBRO-ICNN model, considering 3 error evaluation metrics and 7 prediction comparison models. The experimental results demonstrate that the EBRO-ICNN model exhibits great accuracy and stability. [ABSTRACT FROM AUTHOR]

• Equity value and debt maturity make this portfolio more complete by using KMV model. • The objectives of the proposed portfolio include order moments and default distance. • Improved NSGA-III with chaos map is developed to solve five-objective optimization. • Static penalty is transformed the constrained optimization to unconstrained problem. • CNN-BiLSTM is firstly used to predict trend when selecting stocks. Portfolio management is a critical aspect of investment strategies, with the goal to balance the low-risk and high-return investments. Despite this, existing portfolios frequently overlook the integration of stock selection outcomes and underutilize data from listed companies, leading to suboptimal portfolio performance. Addressing these shortcomings, this paper introduces a hybrid system involving stock selection and portfolio optimization. In stock selection, the system employs a combination of convolutional neural network and bi-directional recurrent neural network to predict stock trends. This approach enables the identification of stocks likely to appreciate in value, setting the stage for their inclusion in the subsequent optimization process. For portfolio optimization, the study formulates a five-objective optimization problem that incorporates mean, variance, skewness, kurtosis, and distance-to-default as key considerations. To solve the many-objective constrained optimization problem, an advanced strategy employing a static penalty function and an improved Non-dominated Sorting Genetic Algorithm III (NSGA-III) based on tent chaotic mapping is utilized. The efficacy of the proposed hybrid system is rigorously tested through three sets of ablation experiments alongside two discussions focused on its robustness and computational efficiency. The findings from these investigations reveal that the hybrid system outperforms traditional approaches, reducing risks and improving returns for investors. [ABSTRACT FROM AUTHOR]

Palmprint recognition techniques have been widely applied in security authentication. As a typical image processing method, convolutional neural network (CNN) has been applied to extract features from palmprint images. However, most existing CNN methods cannot meet the demand in recognition speed due to their high computational complexity. This paper proposes a simple yet effective network (SYEnet) for palmprint recognition, which is a lightweight neural network. SYEnet is composed of four learnable feature extraction networks, which can extract discriminative information from 9 patches. In addition, a dedicated network for palmprint ROI extraction (PREnet) is designed. PREnet extracts ROI from the original image, generating ROI images of consistent dimensions, which can be adapted to complex environments without limitations of lighting, gesture, image quality, and resolution. Moreover, a new contactless palmprint database is collected to validate our method. The database contains 12,800 images from 400 palms and provides data supplementation for current research on palmprint recognition. The proposed SYEnet is evaluated on eight real-world palmprint databases, in comparison to other related methods. Experimental results prove that our method is more efficient and has higher recognition accuracy. [ABSTRACT FROM AUTHOR]

Temporal Knowledge Graphs (TKGs) model time-dependent facts as relations between entities at specific timestamps, making them well-suited for real-world scenarios. However, TKGs are susceptible to incompleteness, necessitating Temporal Knowledge Graph Completion (TKGC) to predict missing facts. Prior methods often struggle to effectively handle two critical properties of TKGs, time-variability and time-stability, simultaneously, which hinders their performance. In this paper, we propose Space Adaptation Network (SANe), a novel approach for TKGC. SANe adapts facts at different timestamps to distinct latent spaces, effectively addressing time-variability. Our model introduces Parameter Generation Network to produce separate neural networks for each snapshot, which are then encoded into different latent spaces. A dynamic convolutional neural network processes entities and relations, utilizing different learned parameters generated by parameter generation network with respect to timestamps. By handling different temporal snapshots separately, TKGC is transformed into static KGC, enabling the modeling of time-variability. Dynamic convolutional neural network efficiently learns collective knowledge over large periods and supplements more specific knowledge gradually in smaller periods, facilitating time-stability. To strike a balance between learning time-variability and time-stability, we introduce a time-aware parameter generator to produce parameters hierarchically based on year, month, and day timestamps. Long-term knowledge is effectively shared across adjacent snapshots within the same year or month, while short-term knowledge within a day is preserved in specific parameters. However, in unbalanced TKGs, where many facts occur in small intervals, the large number of parameters generated by time-aware parameter generator may remain underutilized. To address this, we propose Adaptive Parameter Generation with a partition tree, ensuring parameter load balancing while maintaining time-stability. We conduct extensive experiments on five benchmark datasets, demonstrating the superiority of SANe over existing methods for TKGC, achieving state-of-the-art performance. Our contributions include pioneering TKGC from the perspective of space adaptation, achieving a balance between time-variability and time-stability through latent space overlap constraints, and substantiating the effectiveness of our model through comprehensive experiments on rich temporal datasets. [ABSTRACT FROM AUTHOR]

In this paper, a novel Extended Logistic Chaotic Map (ELCM) with two control parameters is proposed. Overcoming the major drawback of the standard Logistic and other existing chaotic maps, the ELCM not only has infinite chaotic range as well as good ergodicity, but also has a simple structure just like the Logistic map, which greatly facilitates its practical implementation and becomes very suitable for today's real-time applications. Moreover, a new color image encryption scheme based on chaos concept, DNA (Deoxyribonucleic Acid) encoding and Convolutional Neural Network (CNN) is also presented. To perform a chaotic scrambling, the ELCM is however used at different stages of the encryption process. In addition, a pre-trained AlexNet CNN is used to generate a public key. After XORing with the secret key, the latter is used, on the one hand, to generate the initials values and the control parameters of the ELCM. On the other hand, it will be split into two keys in order to generate a random grayscale image, which will then be XORed with the three components (i.e., R, G and B) of the color plaintext image. Afterward, a permutation operation, DNA encoding, diffusion operation as well as bit reversion operation are then applied to the R, G and B components. The performance evaluation demonstrates that the ELCM not only has an infinite chaotic parameter range, but also exhibits a high chaotic complexity. Besides, experimental results as well as security analysis confirm that with NPCR of 99.6287%, the designed color image encryption scheme achieves an average entropy of 7.9975 and a near zero correlation (-0.0027). Furthermore, the proposed encryption scheme is in fact of higher security level in comparison to other schemes recently presented in the literature, thus making it highly suitable for today's real-time applications. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) provides a platform for humans to communicate using Electroencephalogram (EEG) signals by converting them into commands that can be used by the output device to perform the desired tasks. This paper focuses on the identification of vowels from EEG signals. First, a dataset of EEG signals has been created for the identification of vowels by collecting data using a 14-channel EEG device Emotiv- epoc+ from 16 subjects. Then, a deep learning-based model is proposed using a multi-headed Convolutional Neural Network for feature extraction and classification of imagined speech of vowels. Butterworth lowpass and bandpass filter of order five are implemented for denoising and sub-banding of the EEG signals which are further pre-processed using Hilbert Huang Transform. The model has achieved an average accuracy of 97.67% with a five-fold cross-validation technique using all six sub-bands of the EEG signals. The model has achieved an average precision and recall of 95.54% and 95.11% respectively. The proposed model is statistically tested using the Mann-Whitney U test and paired t-test with a p-value less than 0.05. [ABSTRACT FROM AUTHOR]

Recently, deep learning methods have achieved excellent performance in single image super-resolution (SISR), but most existing methods suffer from heavy computational costs and memory storage. To address this problem, some lightweight SR methods have been proposed, among which convolutional neural network (CNN) with attention mechanisms has received increasing attention. However, the existing CNN-based lightweight network and attention mechanism still have redundancy in feature extraction and enhancement. This paper presents Efficient Self-calibrated and Hierarchical Refinement Network (ESHR) for Lightweight Super-Resolution that includes three effective designs. First, we propose a more effective feature extraction basic module called Self-calibrated Residual Block (SRB), which can achieve superior performance when replacing other common convolution modules such as traditional convolution, BSConv and DSConv. Second, we propose a more lightweight spatial attention mechanism called Squeeze-Excitation Spatial Attention (SESA), which can interact and enhance features more effectively in spatial dimensions to improve the representation ability of the model. Third, we design an Efficient Hierarchical Refinement Block (EHRB) to integrate and utilize multi-level features, which can achieve better performance than single-level refinement without increasing the number of parameters. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art CNN-based methods quantitatively and qualitatively. [ABSTRACT FROM AUTHOR]

Sugar crystal growth is a non-linear process dependent on the complex interaction of several variables. This causes the development of first-principal models of the process to be exceedingly difficult and often inaccurate. Louisiana Sugar Refining has partnered with SysCAD to develop a machine learning model of crystal growth to incorporate into the digital twin infrastructure being developed at LSR. The collaboration has successfully developed a supervised training machine learning model based on Deep Feed Forward (DFF) and Convolutional Neural Network (ConvNet) models to simulate the crystal growth in vacuum pans. Initially, a proof-of-concept model showed promising results using both DFF and ConvNet architectures, which lead to the expansion of these models to include the entire white and recovery boiling schemes. In this paper we present the concept of using neural network dynamic models, their analysis, and results of predicted particle size distribution parameters compared to real field observations. These fully optimized models will be implemented as standard unit operation models in SysCAD to enhance the larger process simulation model used by LSR to monitor and predict process conditions in real-time. This machine learning technique will also be expanded to develop a concept model for other non-linear phenomena in the sugar industry including colour. [ABSTRACT FROM AUTHOR]

This paper develops a nonlinear architecture to control different fixed-wing aircraft. This architecture has inner and outer loops. The inner loops, designed based on the convolutional neural networks, control the internal dynamics of the aircraft, and the outer loops, which use linear controllers, are designed to control the kinematic states, which are the same for all aircraft. So, the inner loops are designed to have adaptation mechanisms based on the convolutional neural networks to control the internal dynamics of different aircraft. The networks are trained offline based on a generated database to avoid time-consuming online procedures. The database is created by simulating simple linear training models. Then, the input-output data of these training models are preprocessed and mapped to an appropriate form to be fed to convolutional neural networks. After that, an appropriate network structure is selected, and the networks are trained based on the mapped database. These trained networks, along with linear controllers in a cascade form, are applied to control nonlinear simulations of 15 different fixed-wing aircraft. Then, the performance of the represented controller is compared to two adaptive controllers. The promising results show that the controller can sufficiently be utilized in controlling fixed-wing aircraft, even in slow or sudden changes in aircraft dynamics. [ABSTRACT FROM AUTHOR]

The brain neurons' electrical activities represented by Electroencephalogram (EEG) signals are the most common data for diagnosing Epilepsy seizure, which is considered a chronic nervous disorder that cannot be controlled medically using surgical operation or medications with more than 40 % of Epilepsy seizure case. With the progress and development of artificial intelligence and deep learning techniques, it becomes possible to detect these seizures over the observation of the non-stationary-dynamic EEG signals, which contain important information about the mental state of patients. This paper provides a concerted deep machine learning model consisting of two simultaneous techniques detecting the activity of epileptic seizures using EEG signals. The time-frequency image of EEG waves and EEG raw waves are used as input components for the convolution neural network (CNN) and recurrent neural network (RNN) with long- and short-term memory (LSTM). Two processing signal methods have been used, Short-Time Fourier Transform (STFT) and Continuous Wavelet Transformation (CWT), have been used for generating spectrogram and scalogram images with sizes of 77 × 75 and 32 × 32, respectively. The experimental results showed a detection accuracy of 99.57 %, 99.57 % using CWT Scalograms, and 99.26 %, 97.12 % using STFT spectrograms as CNN input for the Bonn University dataset and the CHB-MIT dataset, respectively. Thus, the proposed models provide the ability to detect epileptic seizures with high success compared to previous studies. [ABSTRACT FROM AUTHOR]

The 12-lead electrocardiogram (ECG) is a reliable diagnostic tool for detecting and treating severe cardiovascular conditions like arrhythmia and heart attack. Deep neural networks (DNNs) have achieved higher accuracy in recent years than traditional ECG signal classification task methods. Convolutional neural network (CNN) and Transformer are the two mainstream architectures of DNN, respectively good at extracting local and global features from input data. This paper proposes the multi-scale convolutional Transformer network (MCTnet), an efficient combination of Transformer encoder and CNN for ECG signal classification. MCTnet utilizes the advantages of CNN and self-attention mechanisms to capture potential features in ECG signal accurately. The dual-branch Transformer encoder extracts different-scale feature representations, enabling the capture of both local and global information. Additionally, an information bottleneck method eliminates redundant information and enhances task-relevant information in the learned representations. To evaluate the performance of MCTnet, comprehensive experiments are conducted on three commonly used ECG datasets. The results demonstrate that MCTnet outperforms current deep learning-based models, highlighting its effectiveness in ECG signal classification. It also shows that the performance of the model can be effectively improved by utilizing multi-scale representation learning and information bottleneck. [ABSTRACT FROM AUTHOR]

Providing interpretability of deep-learning models to non-experts, while fundamental for a responsible real-world usage, is challenging. Attribution maps from xAI techniques, such as Integrated Gradients, are a typical example of a visualization technique containing a high level of information, but with difficult interpretation. In this paper, we propose two methods, Maximum Activation Groups Extraction (MAGE) and Multiscale Interpretable Visualization (Ms-IV), to explain the model's decision, enhancing global interpretability. MAGE finds, for a given CNN, combinations of features which, globally, form a semantic meaning, that we call concepts. We group these similar feature patterns by clustering in "concepts", that we visualize through Ms-IV. This last method is inspired by Occlusion and Sensitivity analysis (incorporating causality) and uses a novel metric, called Class-aware Order Correlation (CAOC), to globally evaluate the most important image regions according to the model's decision space. We compare our approach to xAI methods such as LIME and Integrated Gradients. Experimental results evince the Ms-IV higher localization and faithfulness values. Finally, qualitative evaluation of combined MAGE and Ms-IV demonstrates humans' ability to agree, based on the visualization, with the decision of clusters' concepts; and, to detect, among a given set of networks, the existence of bias. • We introduce Maximum Activation Groups Extraction (MAGE) to represent feature maps. • The representations are based on activation patterns localization in multiple images. • We introduce Class-aware Order Correlation (CAOC), to determine occlusion impacts. • We introduce Multiscale Interpretable Visualization (Ms-IV) highlighting image parts. • CAOC , used with Ms-IV, considers the relation of dataset images according to the model. [ABSTRACT FROM AUTHOR]

Filter pruning has been successfully applied in convolutional neural networks (CNN) to reduce model redundancy and speed up inference. Current pruning algorithms require setting a target value for a pruned model, which is not conducive to the rapid implementation in practical application. Firstly, for a target model, it may be necessary to pay attention to multiple performances at the same time, but it is hard to keep the best trade-off among indicators. Secondly, it is time-consuming to find a suitable model by pruning in a trial-and-error way. Therefore, this paper attempts to combine the most concerning indicators into a search target, rather than producing a model that only contains some pre-defined constraint. Specifically, the multi-object differential evolution algorithm is introduced into the search process, which is used to obtain a set of sparse structures that have taken all useful indicators into account. Then select proper sub-network to retrain from search results according to actual requirements. Experiments on multiple advanced CNN networks show that our algorithm can compress different indicators while keeping comparable or better performance than prior works. On ImageNet, pruning based on ResNet50 reduces FLOPs by 60.4%, while the accuracy of top-1 and top-5 only loses 1.09% and 0.57%, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes low-biased probabilistic multipliers and applies the proposed designs to the inference stage of convolutional neural networks (CNNs). Highly inaccurate compressors in the probabilistic multiplier make the error distribution unbalanced and produce significant relative errors. We describe design rules for applying different compressors to the probabilistic multiplier. Besides, the proposed design rules enhance the error characteristics by determining the input values of each compressor, suppressing the significant relative error. Notably, motivated by the design rules, we propose a novel inaccurate 4:2 compressor suitable for our new design. The design rules and proposed 4:2 compressor are adopted in 8-bit probabilistic multipliers. The error analysis shows that the new designs can enhance error characteristics without increasing hardware costs. Furthermore, the proposed approximate multipliers are applied to the convolutional layers of CNNs. We describe the fine-tuning scheme to reduce the retraining time of approximation-aware training. The experiments show that proposed designs can produce acceptable classification results compared with those using the floating-point and 8-bit exact multiplications on CIFAR and ImageNet datasets. [ABSTRACT FROM AUTHOR]

Turing dynamics mainly focuses on non-homogeneous self-adaptive spatial patterns of reaction diffusion systems in a continuous space. If defining the diffusion environment as network structure, then network patterns are available under Turing instability conditions. In allusion to parameter estimation for network Turing-instable systems, this paper proposes a new recognition method using artificial neural networks. When diffusion occurs on quadrilateral lattice networks, a deep convolutional neural network (CNN) is built to invert the unknown parameters. The results on training set and validation set indicate that the CNN model exhibits great robustness without overfitting and gradient explosion. Meanwhile, the prediction performance on two test sets is excellent, with average relative errors of the estimators ending up at 0.68% and 1.04%, respectively. When diffusion occurs on non-regular networks, a spatial domain graph convolutional network (GCN) is established, which is slightly less robust than the CNN. The average relative error of the estimators on both test sets ends up between 1.1% and 2.8%, which is still in the ideal range. • The parameter estimation method for network Turing systems is investigated. • A 14-layer deep neural network is designed for systems on lattice networks. • A 6-layer graph neural network is designed for systems on irregular networks. [ABSTRACT FROM AUTHOR]

Network pruning has been a hot topic in recent years, and many popular pruning methods rely on network design expertise. However, the pruning process usually involves manual intervention and can be difficult for users who lack prior knowledge. Automatic pruning using evolutionary algorithms shows great promise, but it must address the challenge of performing time-consuming model evaluations and searching through a large solution space. Dataset distillation is a technique that compresses the original dataset to decrease the cost of fine-tuning models. In this paper, we explore the potential of using the distilled dataset to exhibit a similar role as the real dataset in network pruning, and proposed the evolutionary pruning framework using distilled dataset. Specifically, the network pruning pipeline is carried out on the distilled dataset to significantly reduce the model evaluation cost, and the number of filters in the convolutional layer is directly coded to narrow the search space. In addition, a tailored evolutionary algorithm is proposed that takes the form of constrained optimization to search the most suitable pruned network. The experiments conducted on VGG16, VGG19, ResNet56, and ResNet110 demonstrate that the proposed method reduces at least 41.56% of the flops and achieves competitive results with little compromising accuracy. • Distillation dataset is first applied to network pruning. • A constrained evolutionary algorithm with double-balanced multi-branch is proposed. • Deep convolutional neural network pruning is performed using the constrained evolutionary algorithm. [ABSTRACT FROM AUTHOR]

Currently, weapon detection through video surveillance has been extensively studied using deep learning techniques by researchers. However, limited attention has been given to the detection of weapons in night time or dark scenarios. This paper aims to address this gap by proposing a novel approach for weapon detection specifically modified for low-light conditions. The authors demonstrate accurate and robust detection of weapons in challenging nighttime environments by modifying the deep learning model YOLOv7. The YOLOv7-DarkVision model has been developed by combining a brightening algorithm with the advanced image processing techniques and architecture of YOLOv7. A dataset of 15,367 images were collected for training of the model, along with five dark videos from various sources for performance evaluation. The derived detection model, which has a precision score of 95.50% and an F1-Score of 93.41%, performs absolutely well as a weapon detector. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals contains a major role in examining the behavior of brain activity. Moreover, these signals are contaminated with artifacts, which may affect the EEG signal analysis. The EEG performs as the low spatial resolution in the unimodal analysis. Owing to the highly time-consumption procedure, the removal of artifact techniques cannot be applied in practical applications. In existing works, automatic artifact removal techniques are ineffective for eliminating the artifacts in the EEG signals. Thus, the core of this concept is to design a low complexity aware, intelligent artifacts removal model using deep learning. This paper attempts to introduce a 1-Dimensional-Convolutional Neural Network with a Weighted Deep Feature (1D-CNN-WDF), which acts as a filter to remove the unnecessary noise in EEG signals. The WDF extraction in 1D-CNN-WDF is developed by combining the Coyote Optimization Algorithm (COA), and Bird Swarm Algorithm (BSA) named Hybrid Bird-COA (HB-COA). The same hybrid algorithm is used for the tuning of model parameters in 1D-CNN-WDF. The analysis results confirm that the recommended method is more enriched than recently introduced methods when considering the improved Signal to Noise Ratio (SNR), less Mean Squared Error (MSE), and reduced computational complexity, which shows that the suggested method is attained the superior reconstruction quality. [ABSTRACT FROM AUTHOR]

Near-field multiple-input multiple-output (MIMO) radar imaging systems have recently gained significant attention. These systems generally reconstruct the three-dimensional (3D) complex-valued reflectivity distribution of the scene using sparse measurements. Consequently, imaging quality highly relies on the image reconstruction approach. Existing analytical reconstruction approaches suffer from either high computational cost or low image quality. In this paper, we develop novel non-iterative deep learning-based reconstruction methods for real-time near-field MIMO imaging. The goal is to achieve high image quality with low computational cost at compressive settings. The developed approaches have two stages. In the first approach, physics-based initial stage performs adjoint operation to back-project the measurements to the image-space, and deep neural network (DNN)-based second stage converts the 3D backprojected measurements to a magnitude-only reflectivity image. Since scene reflectivities often have random phase, DNN processes directly the magnitude of the adjoint result. As DNN, 3D U-Net is used to jointly exploit range and cross-range correlations. To comparatively evaluate the significance of exploiting physics in a learning-based approach, two additional approaches that replace the physics-based first stage with fully connected layers are also developed as purely learning-based methods. The performance is also analyzed by changing the DNN architecture for the second stage to include complex-valued processing (instead of magnitude-only processing), 2D convolution kernels (instead of 3D), and ResNet architecture (instead of U-Net). Moreover, we develop a synthesizer to generate large-scale dataset for training the neural networks with 3D extended targets. We illustrate the performance through experimental data and extensive simulations. The results show the effectiveness of the developed physics-based learned reconstruction approach compared to commonly used approaches in terms of both runtime and image quality at highly compressive settings. Our source codes and dataset are made available at https://github.com/METU-SPACE-Lab/Efficient-Learned-3D-Near-Field-MIMO-Imaging upon publication to advance research in this field. • 3D image reconstruction methods are developed for near-field MIMO radar imaging using deep learning-based direct inversion and 3D CNNs. • A synthesizer to generate 3D scenes with extended targets is developed to obtain large data for training the neural networks. • Resolution analysis is performed for compressive imaging settings with sparse arrays. • Performance is comprehensively evaluated with simulated and experimental data, using both magnitude-only and complex-valued processing. • The results show the effectiveness of the developed physics-based learned reconstruction method in terms of image quality and runtime. [ABSTRACT FROM AUTHOR]

Machine learning has emerged as a promising paradigm to study the quantum dissipative dynamics of open quantum systems. To facilitate the use of our recently published ML-based approaches for quantum dissipative dynamics, here we present an open-source Python package MLQD (https://github.com/Arif-PhyChem/MLQD), which currently supports the three ML-based quantum dynamics approaches: (1) the recursive dynamics with kernel ridge regression (KRR) method, (2) the non-recursive artificial-intelligence-based quantum dynamics (AIQD) approach and (3) the blazingly fast one-shot trajectory learning (OSTL) approach, where both AIQD and OSTL use the convolutional neural networks (CNN). This paper describes the features of the MLQD package, the technical details, optimization of hyperparameters, visualization of results, and the demonstration of the MLQD 's applicability for two widely studied systems, namely the spin-boson model and the Fenna–Matthews–Olson (FMO) complex. To make MLQD more user-friendly and accessible, we have made it available on the Python Package Index (PyPi) platform and it can be installed via ▪. In addition, it is also available on the XACS cloud computing platform (https://XACScloud.com) via the interface to the MLatom package (http://MLatom.com). Program Title: MLQD CPC Library link to program files: https://doi.org/10.17632/yxp37csy5x.1 Developer's repository link: https://github.com/Arif-PhyChem/MLQD Code Ocean capsule: https://codeocean.com/capsule/5563143/tree Licensing provisions: Apache Software License 2.0 Programming language: Python 3.0 Supplementary material: Jupyter Notebook-based tutorials External routines/libraries: Tensorflow, Scikit-learn, Hyperopt, Matplotlib, MLatom Nature of problem: Fast propagation of quantum dissipative dynamics with machine learning approaches. Solution method: We have developed MLQD as a comprehensive framework that streamlines and supports the implementation of our recently published machine learning-based approaches for efficient propagation of quantum dissipative dynamics. This framework encompasses: (1) the recursive dynamics with kernel ridge regression (KRR) method, as well as the non-recursive approaches utilizing convolutional neural networks (CNN), namely (2) artificial intelligence-based quantum dynamics (AIQD), and (3) one-shot trajectory learning (OSTL). Additional comments including restrictions and unusual features: 1. Users can train a machine learning (ML) model following one of the ML-based approaches: KRR, AIQD and OSTL. 2. Users have the option to propagate dynamics with the existing trained ML models. 3. MLQD also provides the transformation of trajectories into the training data. 4. MLQD also supports hyperparameter optimization using MLATOM's grid search functionality for KRR and Bayesian methods with Tree-structured Parzen Estimator (TPE) for CNN models via the HYPEROPT package. 5. MLQD also facilitates the visualization of results via auto-plotting. 6. MLQD is designed to be user-friendly and easily accessible, with availability on the XACS cloud computing platform (https://XACScloud.com) via the interface to the MLATOM package (http://MLatom.com). In addition, it is also available as a pip package which makes it easy to install. Future outlook: MLQD will be extended to more realistic systems along with the incorporation of other machine learning-based approaches as well as the traditional quantum dynamics methods. [ABSTRACT FROM AUTHOR]