Deep learning and computer vision have experienced significant advancements, particularly in critical applications such as autonomous driving and real-time surveillance, where object detection (OD) plays a pivotal role. Ensuring the accuracy and speed of these systems is paramount to prevent accidents or failures. Recently, latency-based attacks have emerged as a new threat, driven by the essential need for real-time performance in various applications. These attacks target model responsiveness to disrupt system performance without necessarily compromising accuracy. Our preliminary experiments show that introducing just a few bit flips to key parameters in OD models can significantly increase latency, degrading performance. Meanwhile, recent advancements in memory-based attacks, such as Row Hammer [18], demonstrate the ability to conveniently introduce bit flips at desired locations without physical hardware interaction. Based on the observations, we propose a novel attack on OD models that leverages row-hammer to introduce bit-flips via side channels, targeting the non-maximum suppression (NMS) filter and significantly increasing latency. Unlike previous methods that modify input data, our technique ensures efficiency by minimizing bit-flips through critical path exploitation and achieves practical applicability with only a subset of validation data. Experiments across various datasets and models validate our approach, demonstrating latency increases up to 71.6 ms (20.4×) with just 31 bit-flips.

Multi-user Augmented Reality (MuAR) allows multiple users to interact with shared virtual objects, facilitated by exchanging environment information. Current MuAR systems rely on 3D point clouds for real-world analysis, view synchronization, object rendering, and movement tracking. However, the complexity of 3D point clouds leads to significant processing delays, with approximately 80% of overhead in commercial frameworks. This hampers usability and degrades user experience. Our analysis reveals that maintaining the facing side of the real-world scene in a stable environment provides sufficient information for virtual object placement and rendering. To address this, we introduce a lightweight quadtree structure, representing 2D scenes through semantic segmentation and geometry, as an alternative to 3D point clouds. Additionally, we propose a novel correction method to handle potential shifts in virtual object placement during view synchronization among users. Combining all designs, we implement a fast and lightweight MuAR framework namedAR-Lightand test our framework on commercial AR devices. The evaluation results on real-world applications demonstrate that AR-Light can achieve high performance in various real-world scenes while maintaining a comparable virtual object placement accuracy.

Deep learning techniques have been used to enhance the joint inversion process of dc resistivity and seismic travel time data. Specifically, we introduce deep perceptual losses (DPLs) derived from a pretrained edge detection network. These losses play a pivotal role in enforcing structural constraints during the training of encoder-decoder networks for model prediction. Our approach is based on the assumption of structural similarity, meaning that we presume a shared structure between pairs of resistivity and velocity models, without necessitating prior relationships between the property values. We provide an exhaustive exposition of our joint inversion framework, elucidating the network construction and training dataset design. Numerical examples demonstrate our DPL-based method outperforms the conventional separate inversions and cross-gradient-based joint inversion in view of recovered property values and boundaries of the anomalous bodies. Furthermore, our approach retains the traditional workflows of separate inversions, endowing our framework with the flexibility to expand into multiphysics joint inversion scenarios. To illustrate this adaptability, we incorporate induced polarization data into the framework, further validating its efficacy.

In the context of Bayesian inversion for scientific and engineering modeling, Markov chain Monte Carlo (MCMC) sampling strategies have become the benchmark due to their flexibility and robustness in dealing with arbitrary posterior probability density functions (PDFs). However, these algorithms have been shown to be inefficient when sampling from high-dimensional posterior distributions or exhibit multimodality and/or strong parameter correlations. In such contexts, transitional MCMC (TMCMC) provides a more efficient alternative. Despite the recent applicability for Bayesian updating and model selection across a variety of disciplines, TMCMC may require a prohibitive number of tempering stages when the prior pdf is significantly different from the target posterior. Furthermore, the need to start with an initial set of samples from the prior distribution may present a challenge when dealing with implicit priors, e.g., based on feasible regions. Finally, TMCMC cannot be used for inverse problems with improper prior PDFs that represent a lack of prior knowledge on all or a subset of parameters. A generalization of TMCMC is proposed that alleviates such challenges and limitations toward providing a more robust and efficient tempering sampling strategy. We present convergence analysis, proving that the distance between the intermediate distributions and the target posterior distribution monotonically decreases as the algorithm proceeds. We also demonstrate the advantages of the proposed generalization through a series of test problems and an engineering application in the oil and gas industry.

In this abstract, we introduce a deep learning enhanced (DLE) framework for solving multi-physics joint inversion. For the inceptive DLE joint inversion scheme, a well-trained neural network based on structural similarity is used to provide better initials for the separate inversions. The preservation of the separate inversions makes the framework flexible to deal with different sensing configurations, nonconforming discretization, as well as joint inversion of multiple data types. Further, deep perceptual losses (DPL) derived from a pre-trained edge detection network are introduced to enforce structural constraints. Synthetic examples demonstrate improved inversion results from the DLE framework compared to the separate inversions and cross-gradient-based joint inversion. In addition, the DLE framework is simplified to solve 3D joint inversion of the airborne magnetic and gravity gradient data collected from the Decorah area. The inversion results verify the effectiveness and higher efficiency of the DL-based method compared to the cross-gradient-based joint inversion.

We introduce the Safe Offline-to-Online Multi-Agent Decision Transformer (SO2-MADT), an innovative framework that revolutionizes safety considerations in Multi-agent Reinforcement Learning (MARL) through a novel sequence modeling approach. Leveraging the dynamic capabilities inherent in Decision Transformers, our methodology seamlessly incorporates safety protocols as a cornerstone element, ensuring secure operations throughout both the offline pre-training phase and the adaptive online fine-tuning phase. At the core of our framework lie two pivotal innovations: the Safety-To-Go (STG) token, embedding safety at a macro level, and the Agent Prioritization Module (APM), facilitating explicit credit assignment at a micro level. Through extensive testing against the challenging environments of the StarCraft Multi-Agent Challenge (SMAC) and Multi-agent MuJoCo, our SO2-MADT not only excels in offline pre-training but also demonstrates superior performance during online fine-tuning, without any degradation in performance. The implications of our work provide a pathway for deployment in critical real-world applications where safety is paramount and non-negotiable. The code is available at https://github.com/shahaamirbader/SO2-MADT.

The data and power transfer systems for long term underground CO2 sequestration monitoring are normally based on wire-line cable, which will lead to a potential leakage path way through the casing and cement annulus in high-temperature, high-pressure, and hash underground environments. In this paper, a novel wireless power transfer system has been developed for real-time underground CO2 monitoring. The system includes an array of toroidal transceivers winding around the highly conductive casing string for wireless power transfer to deep subsurface. This design helps to maintain well integrity and reduce potential leakage by eliminating the need to perforate the casing or an umbilical in the cement annulus. The metal casing’s amplification effect significantly enhances the wireless power transfer efficiency, which provides a highly conductive power/electric current’s pathway instead of omnidirectional wireless radiation loss in the subsurface. Toroidal transceiver’s design has been optimized to improve the received signal, and our results show significant improvements in wireless power transfer efficiency. Using the optimized design, we can receive 1 to 10 % power transfer efficiency at 800 meters deep using only one toroidal transceiver with 1A current as input. Compared with other wireless antenna designs, such as the helix coil antenna, our system has shown 26,000 times power transfer efficiency improvement. In the end, a lab-scale power transfer system is built, and our experimental measurements support the simulation results.

Seismic data interpolation is a vital technology for improving seismic data density. In recent years, deep learning approaches have demonstrated significant potential in this field, yielding impressive results. Nonetheless, challenges still persist and have not been adequately addressed. First, the lack of reliable labeled training datasets induces concerns about the network’s adaptiveness under supervised learning schemes. Additionally, due to inadequate spatial sampling, aliasing frequently poses considerable difficulties for deep neural networks. In this study, we tackle the issue of aliased seismic data interpolation through self-supervised learning. A novel dip-informed neural network (DINN) is introduced to explicitly integrate local dip information into the neural network and regularize the reconstruction of missing traces. To address the training challenges associated with regularly sampled seismic data interpolation under self-supervised learning schemes, a randomized mix training algorithm is developed. The experimental results along with comparisons to existing methods using both synthetic and field datasets demonstrate the effectiveness and robustness of our approach.

In recent years, researchers have explored the use of sparse black-box video adversarial attacks, which involve selecting keyframes to reduce computational complexity and improve efficiency in generating perturbations. However, the current sparse strategy is not optimized for attack and detection steps, resulting in inaccurate frame selection. Some researchers have used reinforcement learning to train an agent to select keyframes, but this method requires additional training. To address these challenges, we propose a plug-and-play black-box sparse attack algorithm called CLVA based on the coreset concept of active learning. Our algorithm treats a video as a mini-dataset and employs the K-Center-Greedy algorithm to compute the distances between frames. We then select the frame that meets the distance condition as the key frame. We conducted extensive experiments using two attack algorithms on five mainstream recognition models and three video recognition datasets. Our results demonstrate that CLVA significantly accelerates the black-box video attack algorithm while achieving state-of-the-art performance in sparsity, time, and success rate compared to recent sparse attack algorithms. The implementation code of our CLVA method is available athttps://github.com/machineNo6/CLVA.

Accurately localizing 1D signal patterns, such as Gamma-ray well-log depth matching, is crucial in the oilfield service industry as it directly affects the quality of oil and gas exploration. However, traditional methods such as well-log curve analysis and pattern hand-picking matching are labor-intensive and heavily rely on human expertise, leading to inconsistent results. Although attempts have been made to automate this process, challenges such as low computational performance, non-robustness, and non-generalization remain unsolved. To address these challenges, we have developed a data-driven AI system that learns an active signal pattern localization strategy inspired by human attention. Our artificial intelligence system uses an offline reinforcement learning (RL) framework as its central component, which solves a highly abstracted Markov decision process problem via offline training on human-labeled historical data. The RL agent uses top-down reasoning to determine the location of target signal fragments by deforming a bounding window using simple transformation actions. To overcome distribution shifts between logged data and real and ensure generalization, we propose a discrete distributionally robust soft actor-critic RL framework (DRSAC-Discrete) to solve the Markov decision process problem under uncertainty. By exploring unfamiliar environments in a restrictive manner, the DRSAC-Discrete algorithm provides a safe solution that can be used when data is limited during the early stage of this industrial application. We evaluated the reinforcement learning-based localization system on augmented field Gamma-ray well-log datasets, and the results showed promising localization capability. Furthermore, the DRSAC-Discrete algorithm demonstrated relatively robust performance guarantees when facing data shortage.

The simultaneous-source technology for high-density seismic acquisition is a key solution to efficient seismic surveying. It is a cost-effective method when blended subsurface responses are recorded within a short time interval using multiple seismic sources. A following deblending process, however, is needed to separate signals contributed by individual sources. Recent advances in deep learning and its data-driven approach toward feature engineering have led to many new applications for a variety of seismic processing problems. It is still a challenge, though, to collect enough labeled data and avoid model overfitting and poor generalization performance over different datasets with a low resemblance from each other. In this article, we propose a novel self-supervised learning method to solve the deblending problem without labeled training datasets. Using a blind-trace deep neural network and a carefully crafted blending loss function, we demonstrate that the individual source-response pairs can be accurately separated under three different blended-acquisition designs.

Full-waveform inversion (FWI) plays a significant role in producing high-resolution subsurface imaging in seismic prospecting and ground penetrating radar (GPR). However, FWI faces various challenges in practice. For example, the lack of low-frequency information due to acquisition limitations will make the FWI prone to falling to the local minimum. In this project, a deep learning-based approach is proposed to extrapolate the low-frequency data. Specifically, we propose a robust progressive learning (RPL) algorithm that combines physics-guided FWI and data-driven deep learning technology. The proposed method is robust against the choice of the initial model. Experimental results show that our method can achieve high efficiency and accuracy by using a limited amount of training data. The subsurface structures are successfully reconstructed with our extrapolated low-frequency data.

Higher dimensional (i.e., 2-D and 3-D) modeling is indispensable to correctly evaluate the responses of electromagnetic (EM) logging tools in complex formation environments. However, limited by the high computational cost of rigorous modeling, such as the finite-difference method and the finite-element method, the real-time applications in the well logging industry primarily rely on the 1-D forward solver, which would result in erroneous formation evaluation for complex scenarios. As a result, aiming at realizing fast modeling for EM logging tools in complex formations, this letter proposes a general framework assisted by deep neural networks (DNNs). The framework consists of three modules: earth model classification, parameter extraction, and surrogate construction. Separate DNNs are trained and tested for different modules. The accuracy and efficiency of the DNN-assisted fast modeling are validated by several experiments. This study finds that the fast modeling assisted by DNNs is able to calculate the tool responses and reconstruct the subsurface formations in real time.

The low-frequency seismic data provide crucial information for guiding the full-waveform inversion (FWI), especially when strong reflectors exist in the velocity model. However, hardware limitations make it difficult to acquire low-frequency data. To overcome the nonlinearity and ill-posedness caused by the absence of the low-frequency data, we develop an efficient progressive transfer learning algorithm for low-frequency extrapolation. The proposed method combines the FWI, the sparsity-promoted bandwidth-extension (BWE) algorithm, and the physics-guided data-driven deep learning approach. Compared with pure data-driven learning-based methods and the original progressive transfer learning method without BWE, our proposed algorithm shows better generalization ability. By integrating the physics constraints and the BWE algorithm, the performance of our method is less dependent on the quality of the initial training velocity model and the corresponding training set. We propose a logarithmic transformation to rebalance the loss function to overcome the challenge of predicting the weak reflection low-frequency data. To accelerate the algorithm, we propose a learning-based BWE method for initializing the training set and a truncated FWI method to reduce the iterative workflow’s computational cost. Experimental results show that our method achieves both high efficiency and high accuracy. The subsurface structures below the strong reflectors are successfully reconstructed with our extrapolated low-frequency data.

The dielectric effects are significant in certain circumstances in hydrocarbon exploration and production. Neglecting these effects can result in an inaccurate description of subsurface structure in terms of resistivity estimation and boundary location. However, almost all existing research has ignored the dielectric effects in the inversion process for ultradeep electromagnetic logging-while-drilling (LWD) measurements. As a complement, this article aims to investigate the impact of the dielectric constant on the inversion performance. In this study, we present the possible causes of the appearance of large dielectric constant in formations and discuss the influence of dielectric constant on one set of measurements via the sensitivity study. Besides, a 2-D pixel-based inversion algorithm is introduced for simultaneous determination of resistivity and dielectric constant. Also, the gradient of measurements with respect to interested parameters using the adjoint method is provided and validated. In addition, the capability of the inversion approach is demonstrated with several numerical examples. It is found that accounting for the dielectric constant in the inversion can help to improve the interpretation of electromagnetic measurements in some scenarios, thus enhancing the understanding of the subsurface structures.

Reconstruction of seismic data is an important but challenging task in seismic data processing. Different machine-learning-based algorithms have been developed to solve this ill-posed problem and achieved great progress. However, most machine-learning-based methods rely on supervised learning where a good training dataset with many complete shot-gathers are required to train the model. Although the generative model has been used for unsupervised learning and reconstructing signals in a shot-gather, it fails to accurately resolve the fine features, especially when aliasing is the main concern. In addition, multiple shots’ interpolation problems have not been fully investigated by the unsupervised machine-learning-based approaches. In this work, we propose a self-supervised learning method using a blind-trace network and two antialiasing techniques (automatic spectrum suppression and mix-training) for seismic data reconstruction. The method is validated using challenging and realistic scenarios. Test results show that the method can be applied to single-shot or multiple shots’ cases and adapt well to different decimation patterns.

In-pipe communication exists in broad applications such as structural health monitoring, gas and oil exploration, and remote sensing. The widely deployed pipeline infrastructure provides a new medium to realize wireless communications in the harsh environment, especially underwater or underground communications. The high frequency (above 80 kHz) reverberating stress wave in a short range allows piezoelectric transducers to transmit information. This paper investigates stress wave propagation, the reverberating stress wave channel characteristics, and several potential techniques for the reverberating stress wave channel. Based on our experimental and simulation results, single-input-multiple-output orthogonal frequency-division multiplexting (SIMO-OFDM) and uplink single-carrier FDMA are considered the most suitable schemes for reverberating stress wave communications in point-to-point and multi-user scenarios, respectively. Our evaluation results show the spectral efficiency upper bound is 3.18 bit/s/Hz for 1×3 SIMO and the rate-sum capacity for uplink SC-FDMA is 75.18 kb/s with 30 kHz total bandwidth.

The research on scene text recognition (STR) has made remarkable progress in recent years with the development of deep neural networks (DNNs). Recent studies on adversarial attack have verified that a DNN model designed for non-sequential tasks (e.g., classification, segmentation and retrieval) can be easily fooled by adversarial examples. Actually, STR is an application highly related to security issues. However, there are few studies considering the safety and reliability of STR models that make sequential prediction. In this paper, we make the first attempt in attacking the state-of-the-art DNN-based STR models. Specifically, we propose a novel and efficient optimization-based method that can be naturally integrated to different sequential prediction schemes, i.e., connectionist temporal classification (CTC) and attention mechanism. We apply our proposed method to five state-of-the-art STR models with both targeted and untargeted attack modes, the comprehensive results on 7 real-world datasets and 2 synthetic datasets consistently show the vulnerability of these STR models with a significant performance drop. Finally, we also test our attack method on a real-world STR engine of Baidu OCR, which demonstrates the practical potentials of our method.

A large number of recent studies on adversarial attack have verified that a Deep Neural Network (DNN) model designed for non-sequential recognition (NSR) tasks (e.g., classification, detection and segmentation) can be easily fooled by adversarial examples. However, only a few researches pay attention to the adversarial attack on sequential recognition (SR). They either apply the attack methods proposed for NSR to SR by neglecting the sequential dependencies, or focus on attacking specific SR models without considering the generality. In this paper, we study the adversarial attack on the general and popular DNN structure of CNN+RNN, i.e., the combination of convolutional neural network (CNN) and recurrent neural network (RNN), which has been widely used in various SR tasks. We take the scene text recognition (STR) and image captioning (IC) as case study, and derive the objective function for attacking the CNN+RNN based models with targeted and untargeted attack modes, and then developed an optimization-based algorithm to learn adversarial perturbations from the derived gradients of each character (or word) in sequence by incorporating the sequential dependencies. Extensive experiments show that our proposed method can effective fool several state-of-the-arts including four STR models and two IC models with higher successful rate and less time consumption, comparing to three latest attack methods.

Leveraging the mobility of autonomous underwater vehicles (AUVs) to collect and deliver data among different underwater devices enables numerous underwater Internet-of-Things (UW-IoT) applications. However, the most versatile underwater acoustic communications (UACs) may not be suitable in the AUV-assisted UW-IoT scenarios, considering the high cost and high power consumption of acoustic transducers, as well as high error rates of UACs due to the complex underwater acoustic channel conditions. Alternatively, we propose to apply the low-power magnetic induction (MI)-based wireless communications for AUV data dissemination and collection. Due to the mobility of AUVs and the underwater turbulence, MI channels between AUVs and other underwater devices are no longer stable and static, which poses great challenges to establish reliable MI links. To tackle this problem, we investigate the dynamic MI wireless communications in this article. We first mathematically characterize the dynamic MI channel when an AUV approaches its target for data collection. Based on this dynamic channel model, the dynamic communication range and available bandwidth of MI are derived. We also build an MI wireless communication system that can work within a dynamic range. The communication performances are evaluated through numerical simulations as well as underwater experiments.

In this article, a new scheme based on the supervised descent method (SDM) for solving directional electromagnetic logging-while-drilling (LWD) inverse problems is proposed. The SDM provides us a new perspective to combine the classical gradient-based inversion and machine-learning-based inversion schemes. It iteratively learns a set of descent directions in the offline training process, where the training model set is generated in advance according to the prior information, and then updates the models with the learned descent directions as well as data residuals in the prediction stage, resulting in great flexibility to incorporate prior information, the capability of skipping local minima, and accelerated convergence. The generalization ability of the SDM to interrogate new models that are not contained in the training model set is also explored. By utilizing real-time information obtained from the logging process, the learned descent directions can be slightly revised with a higher efficiency to get closer to the true model. In addition, we probe the sensitivity of the SDM by adding different levels of random noise to the measurements. Numerical examples demonstrate that SDM-based inversion can achieve a higher resolution, faster convergence, and higher robustness than conventional schemes such as Occam's inversion.

First break picking is an inevitable process in land seismic data processing, which involves a huge amount of human labor to perform. Even after decades of investigation on the first break picking process, there are still enormous challenges in developing a robust automatic approach. Although many experts proposed techniques to solve the first break picking problems automatically, there are no solid solutions to avoid human labors during the picking process. In the late 20th century, the rise of the artificial intelligence and the advancement of computer hardware have overcome some challenges in first break picking but the level of their success is limited. In this article, we proposed a deep machine learning model to achieve automatic seismic first break picking. Our proposed model can find the underlying factors and determine the first break curve. In addition, the network is capable of updating itself through continuous learning. The system is able to identify labeling anomalies on-site and update the model through active learning. Unfortunately, training the machine learning model on a huge data set that contains unnecessary data points is an inefficient way for both model learning process and human labeling labors. Therefore, training the model with data selected by the experts can highly reduce the training time and the number of data that human has to label. In simulation, we show the advantage of our proposed deep semisupervised neural network, which uses both labeled and unlabeled data sets to achieve higher accuracy compared with the supervised neural networks.

Owing to the complex structure of Chinese characters and the huge number of Chinese characters, it is very challenging and time consuming for artists to design a new font of Chinese characters. Therefore, the generation of Chinese characters and the transformation of font styles have become research hotspots. At present, most of the models on Chinese character transformation cannot generate multiple fonts, and they are not doing well in faking fonts. In this article, the authors propose a novel method of Chinese character fonts transformation and generation based on generative adversarial networks. The authors’ model is able to generate multiple fonts at once through font style‐specifying mechanism and it can generate a new font at the same time if the authors combine the characteristics of existing fonts.

This paper designs a novel geometry-conformal antenna for Magnetic Induction (MI)-based subsea wireless communications for autonomous underwater vehicles (AUV). The designed tri-directional antennas can be wrapped directly on the surface of AUVs, such that the AUVs fluid dynamics are well maintained to ensure power efficiency of the vehicles. In addition, ferrite materials are added between the MI antenna and the metallic body surface of the AUVs to overcome the shielding effect and enhance the MI signal strength. The designed MI communication system is implemented in hardware and the effectiveness of the geometry-conformal MI antenna is demonstrated through COMSOL simulations and lab experiments.

Efficient multiscale electromagnetic simulations require several major challenges that need to be addressed, such as flexible and robust geometric modeling schemes, efficient and stable time-stepping methods, etc. Due to the versatile choices of spatial discretization and temporal integration, discontinuous Galerkin time-domain (DGTD) methods can be very promising in simulating transient multiscale problems. This paper provides a comprehensive review of different DGTD schemes, highlighting the fundamental issues arising in each step of constructing a DGTD system. The issues discussed include the selection of governing equations for transient electromagnetic analysis, different basis functions for spatial discretization, as well as the implementation of different time-stepping schemes. Numerical examples demonstrate the advantages of DGTD for multiscale electromagnetic simulations.