Assertion-based verification (ABV) is a key approach to checking whether a logic design complies with its architectural specifications. Existing assertion generation methods based on design specifications typically produce only top-level assertions, overlooking verification needs on the implementation details in the modules at the micro-architectural level, where design errors occur more frequently. To address this limitation, we present AssertMiner, a module-level assertion generation framework that leverages static information generated from abstract syntax tree (AST) to assist LLMs in mining assertions. Specifically, it performs AST-based structural extraction to derive the module call graph, I/O table, and dataflow graph, guiding the LLM to generate module-level specifications and mine module-level assertions. Our evaluation demonstrates that AssertMiner outperforms existing methods such as AssertLLM and Spec2Assertion in generating high-quality assertions for modules. When integrated with these methods, AssertMiner can enhance the structural coverage and significantly improve the error detection capability, enabling a more comprehensive and efficient verification process.

Large language models (LLMs) hold promise for automating integrated circuit (IC) engineering using register transfer level (RTL) hardware description languages (HDLs) like Verilog. However, challenges remain in ensuring the quality of Verilog generation. Complex designs often fail in a single generation due to the lack of targeted decoupling strategies, and evaluating the correctness of decoupled sub-tasks remains difficult. While the chain-of-thought (CoT) method is commonly used to improve LLM reasoning, it has been largely ineffective in automating IC design workflows, requiring manual intervention. The key issue is controlling CoT reasoning direction and step granularity, which do not align with expert RTL design knowledge. This paper introduces VeriBToT, a specialized LLM reasoning paradigm for automated Verilog generation. By integrating Top-down and design-for-verification (DFV) approaches, VeriBToT achieves self-decoupling and self-verification of intermediate steps, constructing a Backtrack Tree of Thought with formal operators. Compared to traditional CoT paradigms, our approach enhances Verilog generation while optimizing token costs through flexible modularity, hierarchy, and reusability.

LLMs can generate SystemVerilog assertions (SVAs) from natural language specs, but single-pass outputs often lack functional coverage due to limited IC design understanding. We propose CoverAssert, an iterative framework that clusters semantic and AST-based structural features of assertions, maps them to specifications, and uses functional coverage feedback to guide LLMs in prioritizing uncovered points. Experiments on four open-source designs show that integrating CoverAssert with AssertLLM and Spec2Assertion improves average improvements of 9.57% in branch coverage, 9.64% in statement coverage, and 15.69% in toggle coverage.

Assertion-based verification (ABV) serves as a crucial technique for ensuring that register-transfer level (RTL) designs adhere to their specifications. While Large Language Model (LLM) aided assertion generation approaches have recently achieved remarkable progress, existing methods are still unable to effectively identify the relationship from the behavioral interactions of signals across different layers, which leads to the insufficiency of the generated assertions. To address this issue, we propose AssertGen, an assertion generation framework that automatically generates SystemVerilog assertions (SVA). AssertGen first extracts verification objectives from specifications using a chain-of-thought (CoT) reasoning strategy, then bridges corresponding signals between these objectives and the RTL code to construct a cross-layer signal chain, and finally generates SVAs based on the LLM. Experimental results demonstrate that AssertGen outperforms the existing state-of-the-art methods across several key metrics, such as pass rate of formal property verification (FPV), cone of influence (COI), proof core and mutation testing coverage.

Seismic image segmentation is important in geological interpretation. In recent years, numerous studies have leveraged texture features to analyze seismic images. However, traditional texture feature extraction methods are computationally intensive and cannot be updated through back-propagation. To address these challenges, we propose a model named Lightweight Segmentation Network based on Co-occurring Matrix (LSCM-Net). The overall architecture of LSCMNet employs an asymmetric encoder-decoder structure. The encoder mainly consists of a lightweight bottleneck that integrates the Parametric Co-occurrence Matrix model based on the Convolutional Neural Network (CNN) for Segmentation (S-PCMCNN) module, along with channel shuffle and split for feature fusion, enhancing the model representational capacity. The pyramid decoder encompasses a spatial attention mechanism. This design significantly reduces the parameters while maintaining accuracy in seismic image segmentation. In the application of igneous rocks, an ablation experiment was conducted to validate the effectiveness of the S-PCMCNN module. Moreover, compared with other classical segmentation models, LSCMNet demonstrates superior segmentation accuracy in few-shot scenarios while having fewer parameters and floating point operations (FLOPs).

Ground-penetrating radar (GPR) inversion typically relies on iterative methods, which often involve high computational complexity and challenges in noise handling. These limitations affect the robustness and generalization of traditional approaches. To address these issues, we propose an innovative inversion method using diffusion models (DGPRI-Net) tailored for GPR. Diffusion models inherently capture signal characteristics through progressive noise addition and subtraction, reducing noise impact and enhancing robustness. This approach effectively overcomes the noise management weaknesses of conventional methods. In the reverse generation process, we use the UNet++ network architecture, enhanced with vision transformer (ViT) structures and a simple parameter-free attention module (SimAM). This combination improves multiscale feature extraction and contextual understanding, increasing robustness and enabling high-precision permittivity models. To further evaluate the robustness of the model, we prepared three dedicated test sets: one with added noise, one without low-frequency signals, and one with 30% of the columns missing. Comparative experiments with synthetic data showed exceptional inversion accuracy, superior noise management, and enhanced robustness and generalization. We also validated performance with metrics such as structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and mean squared error (mse). Applied to measured data, our method continued to yield impressive results, confirming its practical value and effectiveness. This study highlights the potential of diffusion models in advancing GPR inversion applications.

Although deep learning techniques for Ground Penetrating Radar (GPR) inversion offer significant advantages, such as high accuracy and computational efficiency, they still face challenges related to limited robustness and generalization. Using envelope radar data in inversion helps mitigate some of these issues. This data emphasizes amplitude characteristics, reducing the impact of high-frequency noise and phase-related problems on the inversion results. In this paper, we propose a GPR inversion method based on a conditional generative adversarial network (cGAN), incorporating envelope radar data as conditional input for both the generator and discriminator. We also use double normalization to adjust the discriminator’s convergence speed, ensuring the adversarial relationship is maintained, which enhances model robustness without sacrificing inversion accuracy. For the loss function, we introduce a hybrid of Mean Squared Error (MSE) and Multi-Scale Structural Similarity Index Measure (MS-SSIM) loss, guiding the generator to produce results closer to the true model. To evaluate the inversion performance of our proposed method, we designed three synthetic data experiments: a comparison experiments with varying degrees of low-frequency component depletion, a comparison experiments with different noise levels and a comparison experiments with varying central frequencies. Results show high resolution, clear inversion boundaries, and well-defined anomalous structures, demonstrating high inversion accuracy and robustness. Furthermore, our method shows superior performance and practical value with real-world data.

Fault simulation is a fundamental component in the design for testability (DFT) processes, especially in automatic test pattern generation (ATPG). Various approaches have been proposed to enhance the efficiency of fault simulation on multi-core systems. However, these approaches have not taken full consideration of the intrinsic characteristics of deterministic patterns. Deterministic patterns are generated by ATPG and are predominantly employed in practical applications rather than random patterns. In this paper, we introduce DDP-Fsim, a fast and scalable fault simulator on multi-core systems. DDP-Fsim capitalizes on the distinctive nature of deterministic patterns, wherein a small subset of patterns can effectively detect the majority of faults. Initially, DDP-Fsim parallels in fault dimension by dynamically scheduling fanout-free regions (FFR) to handle easy-to-detect faults. Subsequently, it parallels in pattern dimension by dynamically scheduling patterns to address the remaining hard-to-detect faults. Experiments demonstrate that on a 24-core system, DDP-Fsim is 10× faster than the commercial tools for full-scan circuits and deterministic patterns. Additionally, DDP-Fsim with 24 cores achieves an average speed-up of 16× compared to its single-core execution, while the commercial tools with 24 cores achieves only 3×-6× speed-up than their single-core execution. This indicates the significantly superior scalability for DDP-Fsim.

To address the issue of how to maximize renewable power utilization, a dual power supply strategy for green base station is proposed in this article. The strategy consists of Grid-Connection Depth (GCD) model and Battery Power Sharing (BPS) model, which reduce the dependence on the grid and take advantage of idle power. The both developed models are optimized with the constant assumption method. The optimal power transfer variables are obtained to facilitate the strategy for maximizing renewable power utilization. Finally, the simulation results demonstrate that the proposed strategy has higher practical value compared with other power supply strategies.

Financial reports serve as crucial resources for investors and researchers, providing analysts’ assessments of stocks that play a vital role in stock market applications. However, detecting analysts’ opinions and sentiments in financial reports is challenging. First, the formal and professional language used in these reports makes it difficult for previous methods to comprehend domain-specific knowledge. Second, financial reports often adopt lengthy and elaborate expressions to convey rich semantics, which exposes the existing methods to contextual information loss, especially on long-term dependencies. To address these problems, we propose a summary-enhanced hierarchical framework (SEHF), which leverages summary information to enhance financial report sentiment analysis. Our framework incorporates financial bidirectional and auto-regressive transformer (FinBART), equipped with extended position encoding to summarize lengthy report articles and capture long-range interactions. To mitigate information loss, we initially divide each report into segments and then propose the hierarchical analyst sentiment representation network (ASRN), which utilizes financial bidirectional encoder representation from transformer (FinBERT), bidirectional long short-term memory (BiLSTM)-Attention, and dendrite (DD) network to fuse information in the generated summary and report segments. Notably, FinBART and FinBERT are pretrained on large-scale financial corpora to effectively understand professional expressions. Furthermore, we construct a new dataset large-scale Chinese financial report (LCFR) for the lack of supervised datasets. Experimental results on LCFR and a benchmark dataset show that SEHF significantly outperforms state-of-the-art (SOTA) baselines, and the ablation study highlights the effectiveness of aggregating sentiment information in the summary and report segments.

The field acquired seismic data are often irregular, which affects the accuracy of subsequent processing algorithms. We develop a framework based on a dual-attention-based wavelet integrated convolutional neural network (DAWCNN) constrained via stochastic structural similarity (S3IM) for reconstruction of seismic data with regularly as well as irregularly missing traces. The proposed method utilizes discrete wavelet transform (DWT) and inverse wavelet transform (IWT) to preserve the valid information. It also leverages skip connections based on the group multiaxis Hadamard product attention (GHPA) mechanism and spatial attention (SA) mechanism to perform the fusion of more critical and refined multiscale features and subband feature recalibration, respectively. Additionally, a hybrid loss function is designed, which reduces the pixel differences through mean square error (MSE) loss and the differences in local structures and stochastic nonlocal structures via S3IM loss. We evaluate the proposed method on synthetic and field data. The numerical experiments demonstrate the effective and superior reconstruction capability of the proposed method, which outperforms four traditional and deep learning (DL)-based benchmark algorithms. The proposed method can also perform reconstruction and denoising simultaneously.

Quality factor (Q) estimation is critical for the processing of nonstationary seismic data and is an important indicator of oil and gas. Traditional methods for Q value estimation require the identification of the top and bottom of each constant Q layer, which can be challenging in the processing of field seismic data. Deep-learning (DL)-based Q inversion methods leverage the powerful nonlinear fitting capabilities of deep network to automatically obtain interval Q estimates directly from the input seismic data. However, these methods possess so-called “black box” characteristics and lack interpretability, thereby limiting their practical application. To address these issues, this study proposes a physics-driven neural network (PDNN) that integrates physical knowledge with deep neural networks, embedding the frequency-shift method for Q value calculation into the computational layers of the network. Our approach uses nonstationary seismic signals and their corresponding logarithmic time-frequency amplitude spectrum (LTFAS) as input. The neural network decouples the dynamic wavelets and reflection coefficients to obtain the LTFAS of dynamic wavelets. Furthermore, a network layer is designed based on the frequency-shift method to generate the interval Q curve. Experiments on both synthetic and field data demonstrate that the neural network constrained by physical knowledge can alleviate the instability in interval Q calculations, yielding more stable Q estimates. Additionally, this approach enhances the interpretability and generalization capabilities of DL methods, offering significant practical value.

Inversion of seismic data, particularly full waveform inversion (FWI), allows for high-resolution subsurface velocity estimation. However, the inversion of subsurface velocities using only seismic data typically involves severe non-uniqueness. Electromagnetic exploration, due to its broad detection range and low cost, can effectively complement seismic exploration. Although electromagnetic data give a lower resolution resistivity information, they are sensitive to subsurface anomalies. Hence, the joint inversion of electromagnetic and seismic data effectively integrates the complementary information in both data sets to reduce the inversion non-uniqueness to improve accuracy and reliability of the inversion results. Nevertheless, current joint inversion techniques are confronted with issues such as the complexity of objective function design, challenges in achieving convergence, and insufficient coupling between seismic and electromagnetic data. To address these challenges, we propose a Seismic-Electromagnetic joint Inversion Network (SEMI Net) based on joint learning. Our approach leverages the powerful nonlinear fitting capabilities of neural networks for efficient multi-objective optimization. Moreover, we establish coupling between seismic and electromagnetic data across multiple sampling scales. Harnessing the frequency band complementarity of seismic and electromagnetic data, i.e. the low-frequency of the electromagnetic data and the mid-to-high frequency of the seismic data, we obtain high-resolution resistivity and velocity models by SEMI Net. Results on synthetic data and the Overthrust model demonstrate the effectiveness of our approach.

Seismic stratigraphic interpretation plays an important role in geophysics and geoscience. Recently, deep learning has been widely applied to seismic stratigraphic interpretation. These deep learning-based stratigraphic interpretation methods have shown greater potential than traditional methods. Despite the promising results achieved by deep learning-based methods, it is still necessary to enhance their generalization capabilities and the reasonability of stratigraphic interpretation. Therefore, we propose a semi-supervised deep learning-based method to improve the accuracy and reasonability of interpretation results. First, we quantitatively describe the correlation of adjacent seismic data using the dynamic time warping algorithm. The correlation of all seismic data can be regarded as the spatial structure of seismic data. The interpretation results of seismic data should conform to such spatial structure. Then, we train a deep learning model to interpret seismic stratigraphic units under the constraints of seismic spatial structure. The performance of the proposed seismic stratigraphic interpretation method is evaluated on the Netherlands F3 data. We build two scenarios to interpret the stratum: 1D scenario for the one seismic profile and 2D scenario for the whole seismic volume. The results on the field data demonstrate that the proposed method has better generalization ability and the interpretation results are more reasonable. Therefore, the proposed method can be a useful tool for seismic stratigraphic interpretation.

The difficulty of 3D magnetic inversion is to use 2D magnetic anomaly data to obtain 3D magnetic susceptibility structure. The contribution of the underground medium to the magnetic anomaly decreases rapidly with the increase of the depth, which leads to the rapid attenuation of the inversion resolution with the depth. In this paper, artificial intelligence (AI) technology is applied to 3D magnetic inversion to predict the susceptibility model corresponding to magnetic anomaly. The inversion network built in this paper uses the method of down-sampling in the encoder to increase the receptive field and realize the feature extraction of magnetic anomaly data. In the decoder, attention fusion modules are added to fuse feature maps from different sources. Finally, we added a 3D refiner behind the decoder. The 3D refiner converts the 2D feature map from the decoder into 3D data. Based on the typical complex medium theory, this paper constructs a diverse sample set of complex 3D susceptibility models. The inversion experiment of synthetic data verifies the feasibility and versatility of the proposed network. Compared with the other methods, the distribution of susceptibility prediction obtained by our method is more accurate and more reliable in determining the magnetic body boundary. In the field example of Jinchuan Copper-nickel sulfide deposit in China, the network constructed in this paper can achieve high-precision 3D underground susceptibility imaging in this area. The susceptibility distribution is in good agreement with the borehole data and the proved deposit distribution.

Magnetotelluric (MT) inversion constitutes a pivotal research domain within the purview of electromagnetic data interpretation, characterized by its inherent nonlinearity and illposed problem. Traditional MT inversion algorithms often require introducing an initial model as a prior constraint, and then drawing the electrical distribution of the structure based on the observed data, which has limitations such as low computational efficiency and high computational costs. This paper proposes an efficient and high-quality MT intelligent joint inversion method based on artificial intelligence (AI) control strategy to address the issues in MT inversion problems. Capitalizing on the strong nonlinear fitting capabilities of convolutional neural networks (CNNs), the closed-loop network composed of forward and inversion subnetworks is constructed to enable the closed-loop network to train in the absence of labels, thereby solving the restrictive problem of the small number of label samples faced by MT inversion. Simultaneously, the reciprocal constraint between forward and inversion subnetworks can suppress inversion multiplicity, leading to improved inversion accuracy. In addition, the uncertainty in inversion can be further reduced by mutual constraints between apparent resistivity and phase data. Finally, this paper tests and verifies the effectiveness of the closed-loop network using synthetic and measured data. The results demonstrate that the closed-loop network significantly enhances the depth resolution of inversion and elevates the reliability of inversion results. Moreover, the closed-loop network can also effectively predict the apparent resistivity and phase response data that are close to those simulated via the finite element method.

Known for its great potential for determining subsurface properties quantitatively, full-waveform inversion (FWI) is a hot topic in the field of exploration seismology. The success of FWI depends significantly on the accuracy of the starting model. Given that both the migration and velocity profiles originate from the same geological structure, the two should be morphologically consistent. Starting from the velocity-reflector depth tradeoff, we propose a deep learning approach with a new training paradigm for building a good starting model. A velocity model and the corresponding migration image are used to form two-channel inputs, and the generative adversarial network (GAN) is trained to minimize the difference between the output and the true velocity model. After the training, the velocity generator network becomes a plug-in component to enhance the FWI performance. The network can be well generalized to unseen data by training with only the synthetic data. We perform extensive experiments on our test dataset, the Marmousi model, the salt velocity model, and field data to demonstrate the effectiveness of our method. Besides, we briefly give an explanation of why our model produces such outputs in this article, making the proposed method more controllable and credible.

We discuss the problem of accountability when multiple parties cooperate towards an end result, such as multiple companies in a supply chain or departments of a government service under different authorities. In cases where a fully trusted central point does not exist, it is difficult to obtain a trusted audit trail of a workflow when each individual participant is unaccountable to all others. We propose AudiWFlow, an auditing architecture that makes participants accountable for their contributions in a distributed workflow. Our scheme provides confidentiality in most cases, collusion detection, and availability of evidence after the workflow terminates. AudiWFlow is based on verifiable secret sharing and real-time peer-to-peer verification of records; it further supports multiple levels of assurance to meet a desired trade-off between the availability of evidence and the overhead resulting from the auditing approach. We propose and evaluate two implementation approaches for AudiWFlow. The first one is fully distributed except for a central auxiliary point that, nevertheless, needs only a low level of trust. The second one is based on smart contracts running on a public blockchain, which is able to remove the need for any central point but requires integration with a blockchain.

Traditional convolutional neural network classification models often only focus on the most distinguishing feature regions of the image and ignore the weaker feature regions. However, the image position distribution of plant diseases is very uneven. If we use convolutional neural network for plant disease recognition, there will be insufficient feature response, which will cause recognition errors. Aiming at such problems, we have designed a deep complementary feature classification network. First, the network uses DeepLabv3+ and Conditional Random Field (CRF) to generate disease part detection frames in a weakly supervised manner and combines semantic segmentation to extract disease object instances. Then we designed Complementary Feature Part Generation Models. Finally, it uses a bidirectional Gated Recurrent Unit (Bi-GRU) to perform the classification and recognition of the complementary features described above. We performed experiments on the PlantVillage dataset. The experimental results show that the proposed network recognition accuracy is 99.21%, which is 4.2% higher than the baseline model xception-65 used. We also performed experiments on the grape disease data set that we created. The accuracy of the proposed network recognition is 93.46%, which is 7.2% higher than the baseline model xception-65. In addition, compared with the better algorithms for plant disease identification in recent years, the accuracy performance has also been improved.

When the traditional heterogeneous multi-core scheduling algorithm performs tasks with high resource density, a large amount of idle time often occurs on the processor core. Therefore, based on the environment of heterogeneous multi-core processors, this paper studies the static heuristic table scheduling algorithm, and proposes an optimization approach for the problem of single priority assignment and too simple task assignment. We design optimization in the static heuristic scheduling algorithm list generation phase and task allocation phase, and propose a hybrid task allocation method with three strategies to improve the standby time utilization of processor core. Then, DVFS technology is used to optimize the scheduling results, so that the task can run with lower energy consumption without increasing makespan. Finally, the new algorithm is compared with three traditional scheduling algorithms through design experiments, and it is proved that the new algorithm has better performance when executing more tasks.

Nuclei segmentation occupies an important position in medical image analysis, which helps to predict and diagnose diseases. With the further research of deep learning, the task of nuclei segmentation has been automated. However, most existing methods require a great deal of manually marked full masks for training, which is time-consuming and labor-intensive, and can only be done by professional personnel. For the purpose of reducing the cost of labeling, we propose a weakly supervised method using generative adversarial training for segmentation of nucleus. In the case of no boundary, but only the centroid of the nucleus, the proposed method segmented the nucleus region with blurred boundaries. We first use the generative adversarial network(GAN) to generate the likelihood map of the nuclear centroid, then use Guided Backpropagation to visualize the pixels that contributes to the detection of the centroid of each nucleus, and finally obtain the segmentation mask of the nucleus by graph-cut. In addition, for the purpose of training the network better, we performed stain normalization on each pathological image. We have verified the proposed method on a multi-organ nuclei dataset. The final experiment results show that our advanced method achieves better segmentation performance than other weakly supervised methods, and can even reach the level of full supervision.

We tackle the problem of sharing eHealth data across different jurisdictions. As a general rule, and due to the sensitive nature of the information, different national regulations impose severe limits on what can be exchanged, even in case of emergencies. Furthermore, different systems in different jurisdictions do not communicate. We propose BRUE as a scheme that allows eHealth data to be securely exchanged, with the data subject always in the position of mediation. We combine several technologies, namely, Blockchain, OAuth and User-Managed Access, and concept Receipts, to achieve our aim.

The broadcasting industry has recently begun to adopt statistical multiplexing based network platform in their workflow to support professional live audio/video (AV) transmission instead of the Time Division Multiplexing (TDM) based system. These audio-over-packet switched systems require a carefully designed and managed network to ensure key quality measures of the real-time (RT) media, such as low jitter and low latency. Often the best effort traffic or different types of media are still physically or logically segregated from these dedicated systems, or require large redundant links. The proposed Flexilink architecture is an alternative that combines both circuit switched and best effort features. However, there is no research evaluation that shows the actual performance of this proposed architecture. In this paper, we give a simulation based study and critical evaluation of the performance of the Flexilink network. The simulation results show that Flexilink has a better and more stable RT performance when compared with both Ethernet and priority queueing networks, especially when given a burst of traffic and/or multiple RT traffic sources. In addition, Unlike other networking protocols, jitter in Flexilink is below the audible threshold.