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This document is a guest editorial for a special issue on computational methods and artificial intelligence applications in low-carbon energy systems. The editorial highlights the urgent need for advanced computing and artificial intelligence in the clean energy transition to improve system reliability, economics, and sustainability. The special issue includes 19 original research articles covering topics such as energy forecasting, situational awareness, multi-energy system dispatch, and power system operation. The articles present state-of-the-art methods and techniques in these areas, including wind power forecasting, demand-side flexibility, fault diagnosis of photovoltaic strings, and energy management strategies. The authors express their gratitude to the participating authors and anonymous reviewers for their contributions to the special section. [Extracted from the article]

This document is a guest editorial for a special issue of the CAAI Transactions on Intelligence Technology journal. The special issue focuses on advances in representation learning for computer vision. The editorial highlights the success of deep learning methods in deriving powerful representations from visual data, but also acknowledges the challenges of conducting representation learning with deep models, especially with large and noisy datasets. The document provides summaries of several research papers included in the special issue, covering topics such as cancellable biometrics, medical image analysis, watermarking for medical images, facial pattern description, multi-biometric strategies, semantic segmentation, image enhancement, image classification, and hyperspectral image super-resolution. The authors express their hope that these papers will enhance readers' understanding of current trends and guide future research in the field. The document also includes brief biographies of the authors. [Extracted from the article]

The proposed network provides a quantitative similarity score for any two given faces and has been applied to large-scale face datasets to identify similar face pairs. This special issue of IET Biometrics, "BIOSIG 2021 Special Issue on Efficient, Reliable, and Privacy-Friendly Biometrics", has as starting point the 2021 edition of the Biometric Special Interest Group (BIOSIG) conference. ACKNOWLEDGEMENTS The Guest Editorial Board would like to thank all of the authors for their contributions to this Special Issue: "BIOSIG 2021 Special Issue on Efficient, Reliable, and Privacy-Friendly Biometrics". [Extracted from the article]

The significant risk associated with fault reactivation often necessitates slip tendency analyses for effective risk assessment. However, such analyses are challenging, particularly in large areas with limited or absent reliable stress measurements and where the cost of extensive geomechanical analyses or simulations is prohibitive. In this paper, we propose a novel approach using a physics‐informed neural network that integrates stress orientation and satellite displacement observations in a top‐down multi‐scale framework to estimate two‐dimensional slip tendency analyses even in regions lacking comprehensive stress data. Our study demonstrates that velocities derived from a continental scale analysis, combined with reliable stress orientation averages, can effectively guide models at smaller scales to generate qualitative slip tendency maps. By offering customizable data selection and stress resolution options, this method presents a robust solution to address data scarcity issues, as exemplified through a case study of the South Australian Eyre Peninsula. Plain Language Summary: Fault reactivation poses significant risks, often requiring slip tendency analyses for thorough risk assessment. Yet, such analyses face challenges, especially in large areas lacking reliable stress measurements or where extensive geomechanical analyses are too costly. Our paper suggests a new method using a physics‐based neural network. This approach combines compressive direction and satellite displacement observations to estimate slip tendencies in two dimensions, even where stress data is lacking. Our study shows that by using displacements from a continental scale analysis and reliable averages of compressive directions, we can guide models to create smaller‐scale maps indicating where faults are more likely to reactivate. This method allows for customizable data selection and stress resolution, offering a strong solution to data scarcity issues. We demonstrate its effectiveness through a case study of South Australia's Eyre Peninsula. Key Points: Physics‐based neural networks allow two‐dimensional slip tendency analyses without prior full‐stress informationA multi‐scale approach provides required displacement constraints when inferring full stresses from global navigation satellite system (GNSS) and stress orientation dataWe present a new application for GNSS data that would welcome more stations, even in seismically stable areas [ABSTRACT FROM AUTHOR]

This paper presents a multi‐input deep learning‐based joint pilot decontamination and symbol detection (SD) technique for 5G massive multiple‐input multiple‐output (MAMIMO) systems. It consists of a fully convolutional neural network (FCNN) that finds the 5G channel coefficients using pre‐known sounding reference signals (SRSs) under pilot contamination and a symbol detector derived from projected gradient descent iterations. The study considers pilot contamination caused by inter‐cell interferences between SRSs during channel estimation (CE). The proposed scheme accepts entire 5G orthogonal frequency modulated (OFDM) data and least square estimates and produces the transmitted OFDM signal. Simulation experiments demonstrated that the proposed technique has better CE and SD performance with reduced trainable parameters. Moreover, it is faster due to the lowest elapsed time during end‐to‐end OFDM symbol detection. This paper proposes a joint pilot decontamination and signal detection for 5G MAMIMO systems. It achieves better detection performance with the lowest number of trainable parameters and memory requirements. It is applicable in 5G OFDM symbol detection and channel estimation under pilot contamination. [ABSTRACT FROM AUTHOR]

Eye health has become a global health concern and attracted broad attention. Over the years, researchers have proposed many state‐of‐the‐art convolutional neural networks (CNNs) to assist ophthalmologists in diagnosing ocular diseases efficiently and precisely. However, most existing methods were dedicated to constructing sophisticated CNNs, inevitably ignoring the trade‐off between performance and model complexity. To alleviate this paradox, this paper proposes a lightweight yet efficient network architecture, mixed‐decomposed convolutional network (MDNet), to recognise ocular diseases. In MDNet, we introduce a novel mixed‐decomposed depthwise convolution method, which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low‐resolution and high‐resolution patterns by using fewer computations and fewer parameters. We conduct extensive experiments on the clinical anterior segment optical coherence tomography (AS‐OCT), LAG, University of California San Diego, and CIFAR‐100 datasets. The results show our MDNet achieves a better trade‐off between the performance and model complexity than efficient CNNs including MobileNets and MixNets. Specifically, our MDNet outperforms MobileNets by 2.5% of accuracy by using 22% fewer parameters and 30% fewer computations on the AS‐OCT dataset. [ABSTRACT FROM AUTHOR]

Deep learning‐based models have become ubiquitous across a wide range of applications, including computer vision, natural language processing, and robotics. Despite their efficacy, one of the significant challenges associated with deep neural network (DNN) models is the potential risk of copyright leakage due to the inherent vulnerability of the entire model architecture and the communication burden of the large models during publishing. So far, it is still challenging for us to safeguard the intellectual property rights of these DNN models while reducing the communication time during model publishing. To this end, this paper introduces a novel approach using knowledge distillation techniques aimed at training a surrogate model to stand in for the original DNN model. To be specific, a knowledge distillation generative adversarial network (KDGAN) model is proposed to train a student model capable of achieving remarkable performance levels while simultaneously safeguarding the copyright integrity of the original large teacher model and improving communication efficiency during model publishing. Herein, comprehensive experiments are conducted to showcase the efficacy of model copyright protection, communication‐efficient model publishing, and the superiority of the proposed KDGAN model over other copyright protection mechanisms. [ABSTRACT FROM AUTHOR]

In recent years, research on infrared and visible image fusion has mainly focused on deep learning‐based approaches, particularly deep neural networks with auto‐encoder architectures. However, these approaches suffer from problems such as insufficient feature extraction capability and inefficient fusion strategies. Therefore, this paper introduces a novel image fusion network to address the limitations of infrared and visible image fusion networks with auto‐encoder architectures. In the designed network, the encoder employs a multi‐branch cascade structure, and these convolution branches with different kernel sizes provide the encoder with an adaptive receptive field to extract multi‐scale features. In addition, the fusion layer incorporates a non‐local attention module that is inspired by the self‐attention mechanism. With its global receptive field, this module is used to build a non‐local attention fusion network, which works together with the l1${l}_1$‐norm spatial fusion strategy to extract, split, filter, and fuse global and local features. Comparative experiments on the TNO and MSRS datasets demonstrate that the proposed method outperforms other state‐of‐the‐art fusion approaches. [ABSTRACT FROM AUTHOR]

With the introduction of more recent deep learning models such as encoder‐decoder, text generation frameworks have gained a lot of popularity. In Natural Language Generation (NLG), controlling the information and style of the output produced is a crucial and challenging task. The purpose of this paper is to develop informative and controllable text using social media language by incorporating topic knowledge into a keyword‐to‐text framework. A novel Topic‐Controllable Key‐to‐Text (TC‐K2T) generator that focuses on the issues of ignoring unordered keywords and utilising subject‐controlled information from previous research is presented. TC‐K2T is built on the framework of conditional language encoders. In order to guide the model to produce an informative and controllable language, the generator first inputs unordered keywords and uses subjects to simulate prior human knowledge. Using an additional probability term, the model increases the likelihood of topic words appearing in the generated text to bias the overall distribution. The proposed TC‐K2T can produce more informative and controllable senescence, outperforming state‐of‐the‐art models, according to empirical research on automatic evaluation metrics and human annotations. [ABSTRACT FROM AUTHOR]

Coral reefs are among the most diverse ecosystems on our planet, and essential to the livelihood of hundreds of millions of people who depend on them for food security, income from tourism and coastal protection. Unfortunately, most coral reefs are existentially threatened by global climate change and local anthropogenic pressures. To better understand the dynamics underlying deterioration of reefs, monitoring at high spatial and temporal resolution is key. However, conventional monitoring methods for quantifying coral cover and species abundance are limited in scale due to the extensive manual labor required. Although computer vision tools have been employed to aid in this process, in particular structure‐from‐motion (SfM) photogrammetry for 3D mapping and deep neural networks for image segmentation, analysis of the data products creates a bottleneck, effectively limiting their scalability.This paper presents a new paradigm for mapping underwater environments from ego‐motion video, unifying 3D mapping systems that use machine learning to adapt to challenging conditions under water, combined with a modern approach for semantic segmentation of images.The method is exemplified on coral reefs in the northern Gulf of Aqaba, Red Sea, demonstrating high‐precision 3D semantic mapping at unprecedented scale with significantly reduced required labor costs: given a trained model, a 100 m video transect acquired within 5 min of diving with a cheap consumer‐grade camera can be fully automatically transformed into a semantic point cloud within 5 min. We demonstrate the spatial accuracy of our method and the semantic segmentation performance (of at least 80% total accuracy), and publish a large dataset of ego‐motion videos from the northern Gulf of Aqaba, along with a dataset of video frames annotated for dense semantic segmentation of benthic classes.Our approach significantly scales up coral reef monitoring by taking a leap towards fully automatic analysis of video transects. The method advances coral reef transects by reducing the labor, equipment, logistics, and computing cost. This can help to inform conservation policies more efficiently. The underlying computational method of learning‐based Structure‐from‐Motion has broad implications for fast low‐cost mapping of underwater environments other than coral reefs. [ABSTRACT FROM AUTHOR]

Iris presentation attack detection (PAD) is still an unsolved problem mainly due to the various spoof attack strategies and poor generalisation on unseen attackers. In this paper, the merits of both light field (LF) imaging and deep learning (DL) are leveraged to combine 2D texture and 3D geometry features for iris liveness detection. By exploring off‐the‐shelf deep features of planar‐oriented and sequence‐oriented deep neural networks (DNNs) on the rendered focal stack, the proposed framework excavates the differences in 3D geometric structure and 2D spatial texture between bona fide and spoofing irises captured by LF cameras. A group of pre‐trained DL models are adopted as feature extractor and the parameters of SVM classifiers are optimised on a limited number of samples. Moreover, two‐branch feature fusion further strengthens the framework's robustness and reliability against severe motion blur, noise, and other degradation factors. The results of comparative experiments indicate that variants of the proposed framework significantly surpass the PAD methods that take 2D planar images or LF focal stack as input, even recent state‐of‐the‐art (SOTA) methods fined‐tuned on the adopted database. Presentation attacks, including printed papers, printed photos, and electronic displays, can be accurately detected without fine‐tuning a bulky CNN. In addition, ablation studies validate the effectiveness of fusing geometric structure and spatial texture features. The results of multi‐class attack detection experiments also verify the good generalisation ability of the proposed framework on unseen presentation attacks. [ABSTRACT FROM AUTHOR]

Text-independent speaker verification (TI-SV) is a crucial task in speaker recognition, as it involves verifying an individual's claimed identity from speech of arbitrary content without any human intervention. The target for TI-SV is to design a discriminative network to learn deep speaker embedding for speaker idiosyncrasy. In this paper, we propose a deep speaker embedding learning approach of a hybrid deep neural network (DNN) for TI-SV in FM broadcasting. Not only acoustic features are utilized, but also phoneme features are introduced as prior knowledge to collectively learn deep speaker embedding. The hybrid DNN consists of a convolutional neural network architecture for generating acoustic features and a multilayer perceptron architecture for extracting phoneme features sequentially, which represent significant pronunciation attributes. The extracted acoustic and phoneme features are concatenated to form deep embedding descriptors for speaker identity. The hybrid DNN demonstrates not only the complementarity between acoustic and phoneme features but also the temporality of phoneme features in a sequence. Our experiments show that the hybrid DNN outperforms existing methods and delivers a remarkable performance in FM broadcasting TI-SV. [ABSTRACT FROM AUTHOR]

Producing clean energy and feeding critical loads in islanding mode are the main reasons for interest in microgrids. Different operation topologies of microgrids make traditional protection schemes inefficient. This paper proposes a multi‐agent protection scheme in which each protection agent can detect different fault events and isolate faulty phases at a fast rate. A unique algorithm is utilized for determining fault location in microgrids and system operators are informed accordingly. Microgrids have various operation modes due to the stochastic behavior of distributed generators and different topologies. Here, a significant number of operating conditions of the studied microgrid are considered. These operation conditions are simulated in the DIgSILENT Power Factory, and different parameters are stored. Raw measured parameters need to be pre‐processed by a signal processing method in MATLAB. Discrete wavelet transform is chosen for this purpose. Deep learning is used as a machine learning technique due to the various operation modes of the microgrid. Deep neural networks are constructed using Python programming language. The proposed scheme ensures high accuracy in fault detection and fault location in the microgrid, as well as fault isolation in different operation conditions. We proposed a multi‐agent algorithm for fault detection in alternating current (AC) microgrids that can diagnose the type, phase, location, and impedance of faults. This scheme performs comprehensive case studies containing possible operation conditions such as lateral branches, high fault impedance, and different types of distributed generation (DG). The proposed scheme has higher efficiency in comparison with other state‐of‐the‐art. [ABSTRACT FROM AUTHOR]

Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated diagnosis of cancer patients. Initially, various DL models for cancer diagnosis are presented. Five major categories of cancers such as breast, lung, liver, brain and cervical cancer are considered. As these categories of cancers have a very high percentage of occurrences with high mortality rate. The comparative analysis of different types of DL models is drawn for the diagnosis of cancer at early stages by considering the latest research articles from 2016 to 2022. After comprehensive comparative analysis, it is found that most of the researchers achieved appreciable accuracy with implementation of the convolutional neural network model. These utilized the pretrained models for automated diagnosis of cancer patients. Various shortcomings with the existing DL‐based automated cancer diagnosis models are also been presented. Finally, future directions are discussed to facilitate further research for automated diagnosis of cancer patients. [ABSTRACT FROM AUTHOR]

Drought is an important meteorological event in China and can cause severe damage to both livelihoods and socio‐ecological systems, but current seasonal prediction skill for drought is far from successful. This study used convolutional neural network (CNN) to build an effective seasonal forecast model for the summer consecutive dry days (CDD) over China. The principal components (PC) of the six leading empirical orthogonal function modes of CDD anomaly were predicted by CNN, using the previous winter precipitation, 2‐m temperature and 500 hPa geopotential height as predictors. These predicted PCs were then projected onto the observed spatial patterns to obtain the final predicted summer CDD anomaly over China. In the independent hindcast period of 2007–2018, the interannual variabilities of first six PCs were significantly predicted by CNN. The spatial patterns of CDD were then skillfully predicted with anomaly correlation coefficient skills generally higher than 0.2. The interannual variability of summer CDD over the middle and lower Yangtze River valley, northwestern China and northern China were significant predicted by our CNN model three months in advance. CNN identified that the previous winter precipitation was the important predictor for PC1–PC3, whereas the previous winter 2‐m temperature and 500 hPa geopotential height were important for the prediction of PC4–PC6. This research provides a new and effective method for the seasonal prediction of summer drought. Plain Language Summary: Drought can cause serious agricultural and ecosystem disasters, so its forecast is valuable for preventing and mitigating related natural disasters and regional socioeconomic sustainability. However, current prediction skill for drought is far from successful since its extreme feature. The gradually emerging deep learning methods offer new possibilities, but how to effectively apply deep learning models in climate prediction with a small sample size remains an open question. In this paper, we build seasonal prediction convolutional neural network model for summer consecutive dry days over China using previous winter predictors. This model achieves significant prediction skill three months in advance. The empirical orthogonal function decomposition is used to reduce the dimensionality of consecutive dry days data in our model. Our research provides a new perspective for drought prediction, and it is expected that such method will be also useful for other seasonal climate prediction problems. Key Points: Convolutional neural network (CNN) skillfully predicts summer consecutive dry days (CDD) over China three months in advanceThe principal components of CDD are predicted by CNN and then projected on the observed spatial patternsPrevious winter 2‐m temperature, geopotential height at 500 hPa and precipitation are the essential predictors in CNN [ABSTRACT FROM AUTHOR]

Since the discovery of the quasicrystal, approximately 100 stable quasicrystals are identified. To date, the existence of quasicrystals is verified using transmission electron microscopy; however, this technique requires significantly more elaboration than rapid and automatic powder X‐ray diffraction. Therefore, to facilitate the search for novel quasicrystals, developing a rapid technique for phase‐identification from powder diffraction patterns is desirable. This paper reports the identification of a new Al–Si–Ru quasicrystal using deep learning technologies from multiphase powder patterns, from which it is difficult to discriminate the presence of quasicrystalline phases even for well‐trained human experts. Deep neural networks trained with artificially generated multiphase powder patterns determine the presence of quasicrystals with an accuracy >92% from actual powder patterns. Specifically, 440 powder patterns are screened using the trained classifier, from which the Al–Si–Ru quasicrystal is identified. This study demonstrates an excellent potential of deep learning to identify an unknown phase of a targeted structure from powder patterns even when existing in a multiphase sample. [ABSTRACT FROM AUTHOR]

Deep learning has recently gained attention in the atmospheric and oceanic sciences for its potential to improve the accuracy of numerical simulations or to reduce computational costs. Super‐resolution is one such technique for high‐resolution inference from low‐resolution data. This paper proposes a new scheme, called four‐dimensional super‐resolution data assimilation (4D‐SRDA). This framework calculates the time evolution of a system from low‐resolution simulations using a physics‐based model, while a trained neural network simultaneously performs data assimilation and spatio‐temporal super‐resolution. The use of low‐resolution simulations without ensemble members reduces the computational cost of obtaining inferences at high spatio‐temporal resolution. In 4D‐SRDA, physics‐based simulations and neural‐network inferences are performed alternately, possibly causing a domain shift, that is, a statistical difference between the training and test data, especially in offline training. Domain shifts can reduce the accuracy of inference. To mitigate this risk, we developed super‐resolution mixup (SR‐mixup)–a data augmentation method for domain generalization. SR‐mixup creates a linear combination of randomly sampled inputs, resulting in synthetic data with a different distribution from the original data. The proposed methods were validated using an idealized barotropic ocean jet with supervised learning. The results suggest that the combination of 4D‐SRDA and SR‐mixup is effective for robust inference cycles. This study highlights the potential of super‐resolution and domain‐generalization techniques, in the field of data assimilation, especially for the integration of physics‐based and data‐driven models. Plain Language Summary: One challenge in the Earth sciences is the simulation of atmospheric and oceanic processes with high accuracy or at high spatio‐temporal resolution. The neural network, a model developed in the field of artificial intelligence, has recently gained attention for its potential to learn the relationship between any variables from a sufficient amount of data. For example, a neural network can be trained to make high‐resolution images from low‐resolution images. This process is called super‐resolution. We propose the incorporation of a neural network for super‐resolution into an atmospheric or ocean model that performs numerical simulations at low resolution. Since the atmospheric or ocean model is of low resolution, the proposed framework is computationally efficient while still allowing for high‐resolution results with the aid of the neural network. Furthermore, to make the inference accurate, observation data are fused with atmospheric or ocean predictions in the neural network. In real‐world applications, it is important to make predictions robust to noise from various factors. Accordingly, we developed a new technique where certain noise is added during the training of the neural network. The approach proposed here can efficiently compute high‐resolution predictions of the atmosphere and ocean without compromising robustness. Key Points: We propose a neural network‐based scheme for data assimilation and spatio‐temporal super‐resolution using physics‐based simulationsA data‐augmentation technique was developed to improve the robustness of inference in the fusion of data‐driven and physics‐based modelsA test with idealized ocean jets showed that the proposed approach efficiently infers high‐resolution results with ensuring the robustness [ABSTRACT FROM AUTHOR]

Deep learning and metaheuristic algorithms have recently increased in various sciences, including financial accounting information systems (FAISs). However, the existence of large datasets has dramatically increased the complexity of these hybrid networks, so to address this shortcoming, this paper aims to develop a quantum‐behaved chimp optimization algorithm (QCHOA) and deep neural network (DNN) for the prediction of the profit based on FAISs. Considering that there is no suitable dataset for the challenge, a novel dataset is developed utilizing the 15 features from the Chinese market dataset to compare more. This work designs QCHOA and five DNN‐based predictors to forecast profit. These algorithms include the universal learning CHOA (ULCHOA), the niching CHOA (NCHOA) as the two best‐modified versions of CHOA, the quantum‐behaved whale optimization algorithm (QWOA), and the quantum‐behaved grey wolf optimizer (QGWO) as the two best quantum‐behaved optimizers as well as classic CHOA. The most effective deep learning‐based predictors for forecasting the profit, ranked from highest to lowest, are DNN‐QCHOA, DNN‐NCHOA, DNN‐QWOA, DNN‐QGWO, DNN‐ULCHOA, DNN‐CHOA, and classic DNN, with corresponding ranking scores of 42, 36, 30, 24, 18, 12, and 6. As a final suggestion for profit prediction, the DNN‐CHOA is shown to be the most accurate model. [ABSTRACT FROM AUTHOR]

Researchers can investigate many aspects of animal ecology through noninvasive photo–identification. Photo–identification is becoming more efficient as matching individuals between photos is increasingly automated. However, the convolutional neural network models that have facilitated this change need many training images to generalize well. As a result, they have often been developed for individual species that meet this threshold. These single‐species methods might underperform, as they ignore potential similarities in identifying characteristics and the photo–identification process among species.In this paper, we introduce a multi‐species photo–identification model based on a state‐of‐the‐art method in human facial recognition, the ArcFace classification head. Our model uses two such heads to jointly classify species and identities, allowing species to share information and parameters within the network. As a demonstration, we trained this model with 50,796 images from 39 catalogues of 24 cetacean species, evaluating its predictive performance on 21,192 test images from the same catalogues. We further evaluated its predictive performance with two external catalogues entirely composed of identities that the model did not see during training.The model achieved a mean average precision (MAP) of 0.869 on the test set. Of these, 10 catalogues representing seven species achieved a MAP score over 0.95. For some species, there was notable variation in performance among catalogues, largely explained by variation in photo quality. Finally, the model appeared to generalize well, with the two external catalogues scoring similarly to their species' counterparts in the larger test set.From our cetacean application, we provide a list of recommendations for potential users of this model, focusing on those with cetacean photo–identification catalogues. For example, users with high quality images of animals identified by dorsal nicks and notches should expect near optimal performance. Users can expect decreasing performance for catalogues with higher proportions of indistinct individuals or poor quality photos. Finally, we note that this model is currently freely available as code in a GitHub repository and as a graphical user interface, with additional functionality for collaborative data management, via Happywhale.com. [ABSTRACT FROM AUTHOR]

The surface texture of asphalt pavement has a significant effect on skid resistance performance. However, its contribution to the performance of skid resistance is non‐homogeneous and subjects to local validity. There are also a few deep learning models that take into account the effective contact texture region. This paper proposes a convolutional neural network model based on the effective contact texture region, containing macro‐ and micro‐scale awareness sub‐modules. In this study, the asphalt mixture with varying gradations was designed to accurately obtain the effective contact texture region. Then, the textures were disentangled into macro‐ and micro‐texture scales by applying the fast Fourier transform and fed into the model for training. Finally, the area of effective contact texture region was calculated, and the effective contact ratio parameter was then proposed using the triangulation algorithm. The results showed that the effective contact texture area of pavement varies by the asphalt mixture type. The effective contact ratio parameter exhibited a significant positive correlation (Pearson correlation coefficient is 0.901, R2= 0.8129) with skid resistance performance and was also influenced by key sieve aggregate content from 2.36 to 4.75 mm. The data of effective contact texture region following disentanglement significantly released the model performance (the relative error dropped to 1.81%). The model exhibited improved precision and performance, which can be utilized as an efficient, non‐contact alternative method for skid resistance analysis. [ABSTRACT FROM AUTHOR]

It is important for construction personnel to observe the dress code, such as the correct wearing of safety helmets and reflective vests is conducive to protecting the workers' lives and safety of construction. A YOLO network‐based detection algorithm is proposed for the construction personnel dress code (YOLO‐CPDC). Firstly, Multi‐Head Self‐Attention (MHSA) is introduced into the backbone network to build a hybrid backbone, called Convolution MHSA Network (CMNet). The CMNet gives the model a global field of view and enhances the detection capability of the model for small and obscured targets. Secondly, an efficient and lightweight convolution module is designed. It is named Ghost Shuffle Attention‐Conv‐BN‐SiLU (GSA‐CBS) and is used in the neck network. The GSANeck network reduces the model size without affecting the performance. Finally, the SIoU is used in the loss function and Soft NMS is used for post‐processing. Experimental results on the self‐constructed dataset show that YOLO‐CPDC algorithm has higher detection accuracy than current methods. YOLO‐CPDC achieves a mAP50 of 93.6%. Compared with the YOLOv5s, the number of parameters of our model is reduced by 18% and the mAP50 is improved by 1.1%. Overall, this research effectively meets the actual demand of dress code detection in construction scenes. [ABSTRACT FROM AUTHOR]

Purpose: The macular ganglion cell layer (mGCL) is a strong potential biomarker of axonal degeneration in multiple sclerosis (MS). For this reason, this study aims to develop a computer‐aided method to facilitate diagnosis and prognosis in MS. Methods: This paper combines a cross‐sectional study of 72 MS patients and 30 healthy control subjects for diagnosis and a 10‐year longitudinal study of the same MS patients for the prediction of disability progression, during which the mGCL was measured using optical coherence tomography (OCT). Deep neural networks were used as an automatic classifier. Results: For MS diagnosis, greatest accuracy (90.3%) was achieved using 17 features as inputs. The neural network architecture comprised the input layer, two hidden layers and the output layer with softmax activation. For the prediction of disability progression 8 years later, accuracy of 81.9% was achieved with a neural network comprising two hidden layers and 400 epochs. Conclusion: We present evidence that by applying deep learning techniques to clinical and mGCL thickness data it is possible to identify MS and predict the course of the disease. This approach potentially constitutes a non‐invasive, low‐cost, easy‐to‐implement and effective method. [ABSTRACT FROM AUTHOR]

City infrastructure agencies routinely implement road projects that address various elements of urban infrastructure. The majority of these projects are short‐term in nature (e.g., utility repair), as they are completed in a few hours within 8:00 a.m. to 5:00 p.m. of a workday. The implementation of these projects during working hours, in spite of the inconvenience imposed on road users, helps the agency avoid extra labor costs associated with nonregular working hours. Careful scheduling of these projects can prevent unduly increased travel delays (road users' interest) while keeping project costs low (the agency's interest). This study presents a bi‐level framework for scheduling short‐term urban road projects that analyzes the implicit tradeoffs between the two stakeholders' interests. The upper‐level model establishes the optimal schedule considering the project characteristics, such as cost and duration. The lower‐level model captures the dynamic user equilibrium conditions that yield the road users' path and departure time choices. The bi‐level model is a mixed‐integer program with nonlinear constraints. Recognizing the relatively low efficiency of traditional solution methods, this paper proposes a deep‐neural‐network‐ensemble‐assisted active learning (DN2EA2L) algorithm and adopts a fixed‐point algorithm for solving the bi‐level model. The numerical experiment uses the Sioux Falls network to demonstrate the efficiency of the DN2EA2L, compared to conventional metaheuristic methods. It is shown that travel time increases due to the project implementation during the peak hours can outweigh the agency's saving in wage costs. Further, it is shown that a significant reduction in the road users' value of time enables the agency to schedule more projects during regular working hours. [ABSTRACT FROM AUTHOR]

Image denoising is one of the hottest topics in image restoration area, it has achieved great progress both in terms of quantity and quality in recent years, especially after the wide and intensive application of deep neural networks. In many deep learning based image denoising models, the performance can greatly benefit from the prepared clean/noisy image pairs used for model training, however, it also limits the application of these models in real denoising scenes. Therefore, more and more researchers tend to develop models that can be learned without image pairs, namely the denoising models that can be well generalised in real‐world denoising tasks. This motivates to make a survey on the recent development of image denoising methods. In this paper, the typical denoising methods from the perspective of model training are reviewed, the reviewed methods are categorised into four classes: the models need clean/noisy image pairs to train, the models trained on multiple noisy images, the models can be learned from a single noisy image, and the visual transformer based models. The denoising results of different denoisers were compared on some public datasets to discover the performance and advantages. The challenges and future directions in image denoising area are also discussed. [ABSTRACT FROM AUTHOR]

Object detection has been in the focus of researchers within varying applications propelled by the recent advances in deep learning and neural networks. Many applications require both detection of class instances as well as a quantification of the spatial coverage of the class instances. While the performance of deep learning approaches for these tasks has been extensively studied there has not been much effort into creating a unified network structure to achieve both goals. The purpose of this paper is to present a regressor to the faster R‐CNN architecture that can help quantify the spatial coverage estimation of some detected object. The goal of the regressor is to provide a reproducible result of the spatial coverage. To demonstrate the developed architecture, an example use‐case of land cover estimation is used. The experiments conducted in this paper show that the network does not sacrifice object detection accuracy, and indicate that the network is able to estimate the spatial coverage of six different types of land. [ABSTRACT FROM AUTHOR]

The die shear test is a feasible and conventional method to characterize the shear strength of die‐attaching layer materials in electronic packaging. A new method for determining cohesive zone model (CZM) parameters using deep neural networks (DNN) and die shear tests is proposed, different from classical fracture framework or lap shear test‐based methods. With the sintered nano‐silver die shear test, the results show that the bilinear CZM inversion results agree well with the experimental results. It is found that the DNN model has high accuracy in predicting and identifying the maximum shear traction strength τmax, separation displacement of the interface δf, and the interface stiffness k1 of CZM parameters for sintered nano‐silver adhesive layer through die shear test load versus displacement curves. The presented DNN‐aided inverse identifying method through the die shear test in this paper could provide an alternative and convenient method for extracting CZM parameters of various kinds of adhesive materials in electronic packaging. Highlights: Die shear tests were used for the inverse identification of CZM parameters.The die shear test P–δ curves were established as the dataset.A DNN‐aided CZM inverse identification method was proposed.The DNN‐aided model can accurately identify the CZM parameters. [ABSTRACT FROM AUTHOR]

In this paper, the authors study the compensation of high‐power amplifier (HPA) non‐linear distortion in the multi‐user (MU) massive multiple‐input multiple‐output (MIMO) systems and focus on uplink transmission, where the base station (BS) uses a large antenna array. First, the authors present a non‐linear distortion iterative cancellation (NDIC) algorithm‐based MMSE and approximate message passing (AMP) at the receiver level, in order to estimate and mitigate jointly a non‐linear distortion and the channel noise. Second, the authors propose a novel distortion cancellation technique based on deep learning. At this level, the authors first introduce a multilayer neural network, trained in the Levenberg–Marquardt algorithm by eliminating HPA non‐linearities on the 'Pre distortion' transmitter and 'Post distortion' receiver side. Next, the authors developed a novel end‐to‐end (E2E) learning approach for the joint transmitter and non‐coherent receiver in the Rayleigh fading channel. The basic idea lies in the use of deep neural networks (DNNs), auto encoder (AE) for unknown channels, where DNNs are applied to perform several functions and modules existing in the transmission chain. The simulation results demonstrate the strong potential of the proposed approach E2E in terms of improving the link quality and symbol error rate (SER) compared to other compensation techniques presented in this work. [ABSTRACT FROM AUTHOR]

In this paper, a deep learning integrated reinforcement learning (DLIRL) algorithm is proposed for comprehending intelligent beamsteering in Beyond Fifth Generation (B5G) networks. The smart base station in B5G networks aims to steer the beam towards appropriate user equipment based on the acquaintance of isotropic transmissions. The foremost methodology is to optimize beam direction through reinforcement learning that delivers significant improvement in signal to noise ratio (SNR). This includes alternate path finding during path obstruction and steering the beam appropriately between the smart base station and user equipment. The DLIRL is realized through supervised learning with deep neural networks and deep Q‐learning schemes. The proposed algorithm comprises of an online learning phase for training the weights and a working phase for carrying out the prediction. Results confirm that the performance of the B5G system is improved considerably as compared to its counterparts with a spectral efficiency of 11 bps/Hz at SNR = 10 dB for a bit error rate performance of 10−5. As compared to reinforced learning and deep neural network with a deviation of ±3o and ±5°, respectively, the DLIRL beamforming displays a deviation of ±2o. Moreover, the DLIRL can track the user equipment and steer the beam in its direction with an accuracy of 92%. [ABSTRACT FROM AUTHOR]

Deep learning has been recently used in safety‐critical cyber‐physical systems (CPS) such as the smart grid. The security assessment of such learning‐based methods within CPS algorithms, however, is still an open problem. Despite existing research on adversarial attacks against deep learning models, only few works are concerned about safety‐critical energy CPS, especially the state estimation routine. This paper investigates security issues of neural network based state estimation in the smart grid. Specifically, the problem of adversarial attacks against neural network based state estimation is analysed and an efficient adversarial attack method is proposed. To thwart this attack, two defense methods based on protection and adversarial training, respectively, are proposed further. The experiments demonstrate that the proposed attack method poses a major threat to neural network based state estimation models. In addition, our results present that defense methods can improve the ability of neural network models to defend against such adversarial attacks. [ABSTRACT FROM AUTHOR]

Cognitive Radio (CR) with other advancements such as the Internet of things and machine learning has recently emerged as the main involved technique to use spectrum in an efficient manner. It can access the spectrum in a fully dynamic way and exploit the unused spectrum resources without creating any harm to cognitive users. In this paper, the authors develop a CR access strategy founded on the implementation of an efficient Deep Multi‐user Reinforcement Learning algorithm based on a combination of a Deep neural network, Q‐learning, and cooperative multi‐agent systems. The proposed approach consists of two stages: the user choice algorithm to set up an agent's activation order, and the frequency choice method to select the optimal channel on the appropriate bandwidth. Reasonable implementation is proposed, and the obtained results demonstrate that the authors' approach can improve wireless communication for all CR terminals. It shows satisfactory performances in terms of user satisfaction degree and the number of used channels and can keep the channel allocation plan always in the appropriate state. [ABSTRACT FROM AUTHOR]

The Madden‐Julian oscillation (MJO) is the dominant source of sub‐seasonal variability in the tropics. It consists of an Eastward moving region of enhanced convection coupled to changes in zonal winds. It is not possible to predict the precise evolution of the MJO, so sub‐seasonal forecasts are generally probabilistic. We present a deep convolutional neural network (CNN) that produces skilful state‐dependent probabilistic MJO forecasts. Importantly, the CNN's forecast uncertainty varies depending on the instantaneous predictability of the MJO. The CNN accounts for intrinsic chaotic uncertainty by predicting the standard deviation about the mean, and model uncertainty using Monte‐Carlo dropout. Interpretation of the CNN mean forecasts highlights known MJO mechanisms, providing confidence in the model. Interpretation of forecast uncertainty indicates mechanisms governing MJO predictability. In particular, we find an initially stronger MJO signal is associated with more uncertainty, and that MJO predictability is affected by the state of the Walker Circulation. Plain Language Summary: The Madden‐Julian oscillation (MJO) is an important tropical climate phenomenon. It consists of enhanced convective thunderstorms and anomalous winds that propagate eastward along the Equator for a few weeks. The MJO is difficult to predict and exhibits great variability. This means that forecasts are often probabilistic. However, current models have difficulty in correctly predicting the uncertainty in the forecast based on the current conditions. In this paper, we propose a model using neural networks capable of making reliable probabilistic forecasts. We interpret the behavior of the algorithm to verify its consistency with the known physical mechanisms of the MJO and to highlight new physical conditions that affect MJO prediction uncertainty. Key Points: A deep convolutional neural network (CNN) is used to produce probabilistic forecasts of the Madden‐Julian oscillation (MJO)The forecasts provide well‐calibrated state‐dependent estimates of forecast uncertaintyThe CNN forecasts are used to probe sources of predictability for the MJO [ABSTRACT FROM AUTHOR]

Text regression is an important task in natural language processing (NLP), which aims to predict continuous numerical values associated with text. Previous work focused on linear text regression requiring manual feature selection for financial prediction. Recently, non‐linear text regression through neural network models has become a trend. However, most models rely only on convolutional neural networks (CNN) and suffer from insufficient interpretability. In this paper, we propose a deep neural network model named EM‐CBA for text regression and further interpret the model. The proposed model is powered by word EMbedding, CNN, Bidirectional long short‐term memory (Bi‐LSTM) and Attention mechanism. The proposed EM‐CBA takes financial report texts as input and predicts a financial metric named return on assets (ROA). We conduct comprehensive experiments on a dataset about the reports of enterprises. Experimental results show that the proposed model provides more accurate predictions of enterprises' metrics than previous convolutional neural network models and other classical models. The validity of each module of the model is also verified. Finally, we demonstrate a way of performing analysis in words change and results errors to intuitively interpret the effect of different text inputs on the model. The analysis demonstrates that the model is able to use information about sentiment words to analyse their associated contexts to revise the predictions. [ABSTRACT FROM AUTHOR]

In this paper, the geometry factors of mode I and mode II (YI and YII) and the normalized T‐stress (T*) of the centrally cracked Brazilian disk specimen are predicted using a deep learning approach. Three deep neural networks are developed to model the relationship between the crack angle (α) and the ratio of half the crack length to the radius (a/R) as input variables and each of YI, YII, and T* as output variables. Three independent databases consisting of 174, 174, and 117 data points are prepared for YI, YII, and T*, respectively, from the previous works to train, generalize, and validate the deep neural networks. Finally, sensitivity analysis of YI, YII, and T* to α and a/R is conducted, and mathematical models are obtained using the partial dependence plots, which can be used in the optimization of stress intensity factors and T‐stress of the cracked Brazilian disk specimens with different dimensions. Highlights: Three analytical databases are prepared for YI, YII, and T*.Three independent DNN models are developed for predicting YI, YII, and T* of the BD specimen.The sensitivity of YI, YII, and T* to the α and a/R are evaluated using PDP analysis.The mathematical relationship between α and a/R, and YI, YII, and T* are obtained. [ABSTRACT FROM AUTHOR]

The research paper presents the heterogeneous computing model for analysis & restoration of human walking deformity and posture instability. Gait‐related walking activities are very important for the analysis of postural instability, repairment of gait abnormality, diagnosis of cognitive declination, enhance the cognitive ability of human‐centered humanoid robot system, and many clinical diagnoses, for example, Parkinson, pathological gait, freezing of gait, etc. at an early stage. For experiment analysis, 10 different lower limb activities are being considered of healthy and crouch walking subjects. A total of 25 healthy and 10 crouch walk subjects are considered for experiment purposes of different age groups, sex, and mental status. To achieve this objective the pattern of 10 different rehabilitation activities are captured using RGB‐Depth (RGB‐D) camera and classified using heterogeneous deep learning models. Different deep learning models Convolutional Neural Network (CNN) and CNN‐LSTM (CNN‐Long Short Term Memory) are used for the classification of these rehabilitation exercises. The RGB‐D data is obtained using a Microsoft Kinect v2 sensor on a 100 Hz sampling frequency. Experimental results have shown significant activity recognition accuracy with 96% and 98% for CNN and CNN‐LSTM models respectively. [ABSTRACT FROM AUTHOR]

Unsupervised segmentation is a significant pre‐processing task in various computer visualization processes. However, recently used unsupervised segmentation methods are very sensitive to few of the parameters like number of segmentation or numerous training and more inference complication. Neurodegenerative disorders namely Parkinson's and Alzheimer's, add up to main aspects to longer disability and has grown to be a more serious concept in the developed nations. At present, there does not exist efficient therapies. Earlier diagnosis along with avoiding misdiagnosis significantly assists in ensuring a good quality for patient's life. Hence, adopting computer‐aided‐diagnosis tools provides clinical assistance. This paper focus on detecting neurodegenerative disease from CT brain images by segmentation through region splitting based segmentation with deep unsupervised neural networks (RSS‐DUNN).Auto encoder deep neural network is constructed with reconstruction process of input image, which further improves the accuracy. The performance of the proposed model is evaluated by comparing it with two standard methods in terms of accuracy, precision, recall, Jaccard similarity index (JSI) and dice similarity coefficient (DSC). As a result, it achieves 70.4% of accuracy, 70.8% of precision, 75.8% of recall, 73.6% of JSI and 81% of DSC. [ABSTRACT FROM AUTHOR]

Malignancies and diseases of various genetic origins can be diagnosed and classified with microarray data. There are many obstacles to overcome due to the large size of the gene and the small number of samples in the microarray. A combination strategy for gene expression in a variety of diseases is described in this paper, consisting of two steps: identifying the most effective genes via soft ensembling and classifying them with a novel deep neural network. The feature selection approach combines three strategies to select wrapper genes and rank them according to the k‐nearest neighbour algorithm, resulting in a very generalisable model with low error levels. Using soft ensembling, the most effective subsets of genes were identified from three microarray datasets of diffuse large cell lymphoma, leukaemia, and prostate cancer. A stacked deep neural network was used to classify all three datasets, achieving an average accuracy of 97.51%, 99.6%, and 96.34%, respectively. In addition, two previously unreported datasets from small, round blue cell tumors (SRBCTs)and multiple sclerosis‐related brain tissue lesions were examined to show the generalisability of the model method. [ABSTRACT FROM AUTHOR]

Vector Quantization (VQ) is a clustering problem in the fields of signal processing, source coding, information theory etc. Taking advantage of recent advances in the field of deep neural networks, this paper investigates the performance between VQ clustering problems and deep neural networks. A k‐means‐based deep network architecture for VQ is presented to solve clustering problems. By applying the deep learning implementation of convergence optimization, a clustering neural network (algorithm) for the purpose of VQ is proposed. In practice, the proposed network quantifies the vectors over a set of stacked neural layers, overcoming the exponential complexity problem of VQ methods by trainable parameters. Experiments show that the work can improve the results without human intervention, and outperforms traditional clustering methods modified for VQ. [ABSTRACT FROM AUTHOR]

The recent concept of contextual information in error correcting code (ECC) can significantly improve the capacity of the blind recognition of codes with deep learning (DL) approaches. However, the fundamental challenges of existing DL‐based methods are inflexible structure and limited kernel size which bring great difficulties to exploit the characteristics of contextual information in ECC. To handle this problem, in this paper, a state‐of‐the‐art framework for natural language processing (NLP), bidirectional encoder representation from transformers (BERT), is utilized in ECC classification scenarios. To strengthen the effectiveness of contextual information, the BERT model is improved by weighted relative positional encoding and error bit embedding. The proposed approach achieves higher classification accuracy than the methods based on Gauss‐Jordan elimination and traditional deep learning schemes. Further simulation results show that the classification accuracy is affected by block length and the employment of weighted relative positional encoding and error bit embedding to a large extent. [ABSTRACT FROM AUTHOR]

This paper proposes a Gaussian Distance Intersection over Union (GDIoU) loss function‐based YOLOv4 deep learning network to solve the problem of slow speed and low accuracy insulator location in power facilities health inspection. In the scheme, A GDIoU loss function is designed to accelerate the convergence speed of the YOLOv4 deep learning network; at the same time, the GDIoU loss is added as one part of the network propagation loss, and the insulator's location accuracy is accordingly improved. Moreover, a re‐location scheme for tilt insulators correction is proposed to enhance the location accuracy of the insulators in different spatial angle states. Large amounts of field insulator images were gathered as training and testing samples to evaluate the performance of the proposed scheme. The experimental results have demonstrated that the GDIoU‐based YOLOv4 deep learning network combined with the tilt correction scheme can improve the insulator location speed by three times compared with the peer schemes, and the average precision is increased by 7.37% compared with the naive YOLOv4 network. The performance of the proposed scheme meets the requirement of online insulator location adequately. [ABSTRACT FROM AUTHOR]

This paper aims to detect road regions based on a two‐scaled deep neural network. The information from different scales is helpful to boost the performance of deep learning models, and it is also a widely used strategy in various computer vision applications. In the two‐scaled model, skip‐architecture and fully convolutional layers are used to fuse the low‐level details and high‐level semantic information. It enables to detect the road areas by multi‐scale feature maps from different reception fields. To avoid the redundancy of scale information and the loss of features caused by the pooling layer, the feature maps before the first pooling layer are adopted in our model. By the convolutional kernels, our model can balance the information of two scales automatically. The loss function is also improved, in which the intersection over union (IoU) term is taken into account to guide the model to learn more features on the whole road regions. Comprehensive experiments on three benchmark datasets demonstrate that this approach can reach state‐of‐the‐art performance. [ABSTRACT FROM AUTHOR]

Parkinson's disease is a degenerative disease that leads to brain disorder and nonfunctioning of different body parts. Deep learning tools like artificial neural network (ANN), convolution neural network (CNN), regression Analysis (RA), and so on, has been considered to a great extent in recent days. Several data sets based on the motor and nonmotor symptoms are applied to different classifier for correct identification of Parkinson's patient from healthy people. In this paper, hybridization of two deep learning tools such as, RA and ANN are done for effective diagnosis of the disease by probability estimation. The communal merits of individual approaches of the existing approaches are realized in this context for accurate probability estimation. Data preprocessing and probability estimation of preprocessed data is done in RA. The second existing approach is used to identify the PD patient by comparing with a predefined threshold value of a neuron. The estimation is performed on the data set of speech recognition, iron content, and pulse rate among a group of people. The proposed approach is compared with the existing approaches like, SVM and k‐NN classifier. The computed result reveals the superiority of the proposed algorithm with 93.46% accuracy. [ABSTRACT FROM AUTHOR]

Multispectral remote sensing images are a very important data source, but its acquisition process is often affected by haze weather and other factors, resulting in the decline of image quality, blurred details and poor visual effect, which seriously affect their application and interpretation. To reduce the impact of haze on multispectral remote sensing image and improve the image clarity and the use value, a new haze removal method based on rolling deep learning and Retinex theory (RDLRT) was proposed here. It uses the rolling deep learning theory to realize the preliminary removal of image haze, and then calculates the peak signal‐to‐noise ratio (PSNR) value of the processed image, which is used to judge whether the obtained image is a stage effect image (SEI). Then, on the one hand, the SEI image is processed for colour saturation and brightness; on the other hand, the Retinex theory is used to further filter the haze of the SEI image. Finally, the results of the two processing are fused, and it is used as the restored image after removing the haze. A series of validation experiments were carried out with true multispectral remote sensing images, outdoor colour images and different methods, and good experimental results were obtained. The purpose of removing image haze and improving image quality is achieved. Through the comparison of these experiments, it is concluded that the RDLRT algorithm proposed in this paper can not only effectively remove haze in images, but also maintain good details and colour of the image, which has some popularization values for image pre‐processing and restoration. [ABSTRACT FROM AUTHOR]

Automatic generation of calligraphy fonts has attracted broad attention of researchers. However, previous font generation research mainly focused on the known font style imitation based on image to image translation. For poor interpretability, it is hard for deep learning to create new fonts with various font styles and features according to human understanding. To address this issue, the font fusion network based on generative adversarial networks (GANs) and disentangled representation learning is proposed in this paper to generate brand new fonts. It separates font into two understandable disentangled features: stroke style and skeleton shape. According to personal preferences, various new fonts with multiple styles can be generated by fusing the stroke style and skeleton shape of different fonts. First, this task improves the interpretability of deep learning, and is more challenging than simply imitating font styles. Second, considering the robustness of the network, a fuzzy supervised learning skill is proposed to enhance the stability of the fusion of two fonts with considerable discrepancy. Finally, instead of retraining, the authors' trained model can be quickly transferred to other font fusion samples. It improves the efficiency of the model. Qualitative and quantitative results demonstrate that the proposed method is capable of efficiently and stably generating the new font images with multiple styles. The source code and the implementation details of our model are available at https://github.com/Qinmengxi/Fontfusion. [ABSTRACT FROM AUTHOR]

The sudden rise in the ability of machine learning methodology, such as deep neural networks, to identify and predict with great accuracy instances of malignant cell growth from radiological images has led prominent developers of this technology, such as Geoffrey Hinton, to hold the view that "[...] we should stop training radiologists." Similar views exist in other contexts regarding the replacement of humans with artificial intelligence (AI) technologies. The assumption in these kinds of views is that deep neural networks are better than human radiologists in that they are more accurate, less costly, and have more predictive power than their human counterparts. In this paper, I argue that these considerations, even if true, are simply inadequate as reasons for us to allocate the kind of trust suggested by Hinton and others to these sorts of artifacts. In particular, I show that if the same considerations were true of something other than an AI device, say a pigeon, we would not have sufficient reason to trust them in the same way as suggested of deep neural networks in a medical setting. If this is the case then these considerations are also insufficient to trust AI enough to replace radiologists. Furthermore, I argue that the reliability of AI methodologies such as deep neural networks—which are at the center of this argument—is something that has not yet been established, and doing so faces fundamental challenges. Because of these challenges, it is not possible to ascribe the level of reliability expected from the deployment of a medical device. So, not only are the reasons cited in favor of the deployment of AI technologies in medical settings not sufficient/adequate even if they are true, but knowing whether they are true or not faces non‐trivial epistemic challenges. If this is so, then we have no good reasons to advocate replacing radiologists with AI methodologies such as deep neural networks. [ABSTRACT FROM AUTHOR]

Although NEXRAD radars have proven to be an effective tool for detecting airborne animals, detecting biological phenomena in radar images often involves a manual, time‐ consuming data‐extraction process. This paper focuses on applying machine learning to automatically find radar data that snapshots large aggregations of birds (specifically Purple Martins and Tree Swallows) as they depart en masse from roosting sites. These aggregations are evident in radar images as rings of elevated reflectivity that appear early in the morning as birds depart from roost sites. Our goal was to develop an algorithm that could determine whether an individual radar image contained at least one Purple Martin or Tree Swallow roost. We use a dataset of known roost locations to train three machine learning algorithms that employed (1) a traditional Artificial Neural Network (ANN), (2) a sophisticated preexisting Convolutional Neural Network (CNN) called Inception‐v3, and (3) a shallow CNN built from scratch. The resulting programs were all effective at finding bird roosts, with both the shallow CNN and the Inception‐v3 network making correct determinations about 90 per cent of the time with an AUC above.9. To the best of our knowledge, this study is the first to apply neural networks in the analysis of bird roosts in radar imagery, and these analytical tools offer new avenues of research into the ecology and behavior of flying animals, with practical applications to wind farm placement, air traffic administration and wildlife conservation. The NEXRAD radar network offers a tremendous archive of continental‐scale data and has the potential to capture entire vertebrate populations. We apply existing machine learning models to a new dataset which constitutes a valuable approach to extracting information from this archive. Although NEXRAD radars have proven to be an effective tool for detecting airborne animals, detecting biological phenomena in radar images often involves a manual, time‐consuming data‐extraction process. This paper focuses on applying convolutional neural networks to automatically locate large aggregations of birds (specifically Purple Martins and Tree Swallows) as they depart en masse from roosting sites. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to improve the resilience of data center cooling system and reduce the energy consumption in data center by using the Tilted Active Tile (TATs). We deploy multiple TATs in cold aisle environment then analyze the thermal performance of racks. TATs also help to improve the reliability in case of cooling failure by increased ride through time. We observed an extended ride‐through time for data centers deployed with Tilted Active Tiles (TATs) when the CRAC blower failed. Proposed approach can maintain cooling system temperature for up to 10 minutes. We also introduced ANN based thermal models to predict the thermal performance of TATs, To train the ANN models, we collect the rack inlet and CRAC outlet temperature from Data Center. We also fixed blank panel in empty U part of racks and then analyzed that the thermal performance of racks are better and temperature distribution at bottom, middle and top of the rack were same under 30°angle. The airflow of TATs and passive tile influence on surrounding racks. That's why we analyzed the disturbance of TATs on surrounding rack and observed that the TATs at 30°angle has positiveimpact on the surrounding racks. [ABSTRACT FROM AUTHOR]

Hyperspectral Imaging (HSI) has grown to be one of the most crucial optical imaging modalities with applications in numerous industries. The non‐invasive nature of HSI has led to widening its horizon to the medical domain, especially in areas like early diagnosis of various diseases. HSI combines both imaging and spectroscopy properties, thereby exploiting spectral and spatial dimensions of images captured, providing quick and accurate interpretation of data. The current study aims to give an exhaustive overview of HSI's applications pertaining to the medical industry for fast detection of diseases and aiding in surgical procedures. The survey focuses on hyperspectral imaging combined with various approaches ‐ machine learning, deep learning, genetic algorithms, and anomaly detection for the treatment of disorders. In addition, the survey highlights accompanying pre‐processing approaches, performance metrics, inferences, and future prospects of HSI in the medical domain. The current study can gauge computer vision specialists, researchers in machine and deep learning domain, doctors, and scientists by giving them a platform for improving existing treatment methods for the betterment of society. [ABSTRACT FROM AUTHOR]

This proof‐of‐concept study couples machine learning and physical modeling paradigms to develop a computationally efficient simulator‐emulator framework for generating super‐resolution (<250 m) urban climate information, that is required by many sectors. To this end, a regional climate model/simulator is applied over the city of Montreal, for the summers of 2019 and 2020, at 2.5 km (LR) and 250 m (HR) resolutions, which are used to train and validate the proposed super‐resolution deep learning (DL) model/emulator. The DL model uses an efficient sub‐pixel convolution layer to generate HR information from LR data, with adversarial training applied to improve physical consistency. The DL model reduces temperature errors significantly over urbanized areas present in the LR simulation, while also demonstrating considerable skill in capturing the magnitude and location of heat stress indicators. These results portray the value of the innovative simulator‐emulator framework, that can be extended to other seasons/periods, variables and regions. Plain Language Summary: One of the major barriers in undertaking super‐resolution (<250 m) urban climate simulations to generate climate and climate change information at high spatial and temporal resolutions, as required by many sectors, is their high computational cost. New approaches are therefore required to overcome this barrier. This paper makes use of the unique opportunity to couple machine learning and physical modeling to develop a computationally efficient simulator‐emulator framework to generate super‐resolution climate information. The trained deep neural network model generates high‐resolution urban climate data from low‐resolution (LR) inputs, considering also the urban morphology fields and inter‐variable relationships to improve output realism. The developed framework, applied to urban heat‐related variables, demonstrates the high potential of this approach as it captures well the magnitude and location of heat stress indicators. The generic nature of the developed framework makes it even more promising as it can be applied to other climate variables, periods and regions. Key Points: Innovative simulator‐emulator framework proposed to generate super‐resolution climate informationNew framework applied at city‐scale demonstrates considerable skill for urban heat‐related variablesThe generic nature of the framework enables adaptability to other climate variables and regions [ABSTRACT FROM AUTHOR]

Strong convection nowcasting has been gaining importance in operational weather forecasting. Recently, deep learning methods have been used to meet the increasing requirement for precise and timely nowcasting. One of the promising deep learning models is the convolutional gated recurrent unit (ConvGRU), which has been proven to perform better than traditional methods in strong convection nowcasting. Despite its encouraging performance, ConvGRU tends to produce blurry radar echo images and fails to model radar echo intensities that have multi‐modal and skewed distributions. To overcome these disadvantages, we tested the structural similarity (SSIM) and multiscale structural similarity (MS‐SSIM) indexes as loss functions. The SSIM and MS‐SSIM loss functions are composed of luminance, contrast, and structure and provide more information about the intensity, grade, and shape of the radar echo, which can reduce blurring. Due to multi‐layer downscaling, MS‐SSIM extracted more radar echo characteristics, and its extrapolation was the most realistic and accurate among all of the loss function schemes. Only the MS‐SSIM scheme successfully predicted strong radar echoes after 2 h, especially those at the rainstorm level. Plain Language Summary: The Convolutional Recurrent Neural Network model has been considered a pioneering work in the field of nowcasting. However, this model tends to predict blurred radar echo images and fails to model the radar echo intensity that has a multi‐modal and skewed distribution. In this paper, we adopt two measures of structural similarity (SSIM and MS‐SSIM) as loss functions to reduce the blurring problem caused by the mean square error (MSE) loss function for precipitation nowcasting. We have shown that the MS‐SSIM scheme is more efficient in capturing the spatiotemporal correlations, and has the best prediction performance, especially for heavier precipitation. Even for rotating moving radar echoes, the MS‐SSIM scheme has the best forecast performance Key Points: Convolutional gated recurrent unit model was selected to solve the problem of strong convective nowcastingUsing the structural similarity index and multiscale structural similarity (MS‐SSIM) index as a loss function can effectively reduce the blurring problem of extrapolation resultsOnly the MS‐SSIM scheme successfully predicted the strong radar echo after 2 h, especially for the radar echo at the rainstorm level [ABSTRACT FROM AUTHOR]

To reduce passenger waiting time and driver search friction, ride‐hailing companies need to accurately forecast spatio‐temporal demand and supply‐demand gap. However, due to spatio‐temporal dependencies pertaining to demand and supply‐demand gap in a ride‐hailing system, making accurate forecasts for both demand and supply‐demand gap is a difficult task. Furthermore, due to confidentiality and privacy issues, ride‐hailing data are sometimes released to the researchers by removing spatial adjacency information of the zones, which hinders the detection of spatio‐temporal dependencies. To that end, a novel spatio‐temporal deep learning architecture is proposed in this paper for forecasting demand and supply‐demand gap in a ride‐hailing system with anonymised spatial adjacency information, which integrates feature importance layer with a spatio‐temporal deep learning architecture containing 1D convolutional neural network (CNN) and zone‐distributed independently recurrent neural network (IndRNN). The developed architecture is tested with real‐world datasets of Didi Chuxing, which shows that the models based on the proposed architecture can outperform conventional time‐series models (e.g. ARIMA) and machine learning models (e.g. gradient boosting machine, distributed random forest, generalized linear model, artificial neural network). Additionally, the feature importance layer provides an interpretation of the model by revealing the contribution of the input features utilized in prediction. [ABSTRACT FROM AUTHOR]