Specific emitter identification (SEI) technology is crucial for supporting emergency response and safety alerts, and it plays a significant role in maintaining the stable operation and information security of Internet of Things (IoT) systems. To mitigate the catastrophic forgetting and overfitting in few-shot specific emitter incremental identification, we propose a systematic identification framework called data processing and dynamic subnet (DPDS). This framework consists of a data processing module (DPM) and a dynamic subnet module (DSM), where DSM maintains recognition performance for both new and old tasks by adjusting the submodel of the original model dynamically. In DPM, we preprocess all received signals and perform additional data augmentation for few-shot to obtain more significant data representation and reduce overfitting. These processed signals serve as the inputs for DSM. In DSM, we identify an optimal subnet of the model, ensuring performance retention for old tasks while training new tasks on the remaining parts of the model, thus mitigating catastrophic forgetting. Additionally, when the model struggles to support learning new tasks, we expand the model nodes, single parameters in practical terms, appropriately and learn the importance of each expanded node. This approach enables us to compress the expanded nodes and achieve the optimal model architecture. Finally, we design a new recognition paradigm based on the proposed boundary disorder phenomenon, which indicates the differences in the feature space between data from distinct distribution domains. The experimental results indicate that DPDS significantly outperforms baseline methods and demonstrates superior performance compared to other state-of-the-art algorithms.

Abstract Specific emitter identification technology plays a very important role in spectrum resource management, wireless network security, cognitive radio etc. However, in complex electromagnetic environments, the variability and uncertainty of signals make it very difficult to extract representative feature representations of the signals. At the same time, the feature extraction capability of the recognition model is also a factor that needs to be considered. To address these issues, a wavelet residual neural network model based on attention mechanism is proposed for specific emitter identification. First, multi‐level wavelet decomposition is performed on all received signals to obtain their wavelet detail coefficients at different scales. Next, all the wavelet detail coefficients are used as the feature input for the attention‐based residual network, and perform parallel feature extraction at multi scales. Finally, the feature representation capability of all coefficients are compared, and the model's recognition results based on it are obtained. The recognition rates on the three datasets are 94.7%, 93.21%, and 86.1%, respectively, all of which are superior to recent state‐of‐the‐art algorithms. In addition, through ablation experiment, the validity of each component of the model has been verified.

MOTIVATION: Deciphering the functional roles of cis-regulatory variants is a critical challenge in genome analysis and interpretation. It has been hypothesized that altered transcription factor (TF) binding events are a central mechanism by which cis-regulatory variants impact gene expression levels. However, we lack a computational framework to understand and quantify such mechanistic contributions. RESULTS: We present TF2Exp, a gene-based framework to predict the impact of altered TF-binding events on gene expression levels. Using data from lymphoblastoid cell lines, TF2Exp models were applied successfully to predict the expression levels of 3196 genes. Alterations within DNase I hypersensitive, CTCF-bound and tissue-specific TF-bound regions were the greatest contributing features to the models. TF2Exp models performed as well as models based on common variants, both in cross-validation and external validation. Combining TF alteration and common variant features can further improve model performance. Unlike variant-based models, TF2Exp models have the unique advantage to evaluate the functional impact of variants in linkage disequilibrium and uncommon variants. We find that adding TF-binding events altered only by uncommon variants could increase the number of predictable genes (R2 > 0.05). Taken together, TF2Exp represents a key step towards interpreting the functional roles of cis-regulatory variants in the human genome. AVAILABILITY AND IMPLEMENTATION: The code and model training results are publicly available at https://github.com/wqshi/TF2Exp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide. RESULTS: Based on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES based on supervised deep learning approaches for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, the introduction of deep learning methods enables a significant advance in our knowledge of the genomic locations of cis-regulatory regions. Using models for well-characterized cell lines, we identify key experimental features that contribute to the predictive performance. Applying DECRES, we delineate locations of 300,000 candidate enhancers genome wide (6.8% of the genome, of which 40,000 are supported by bidirectional transcription data), and 26,000 candidate promoters (0.6% of the genome). CONCLUSION: The predicted annotations of cis-regulatory regions will provide broad utility for genome interpretation from functional genomics to clinical applications. The DECRES model demonstrates potentials of deep learning technologies when combined with high-throughput sequencing data, and inspires the development of other advanced neural network models for further improvement of genome annotations.

The constantly widening processor-memory speed gap substantially exacerbates the dependence of program performance on the on-chip memory hierarchy design and data management in chip multiprocessors. However, traditional data management mechanisms take neither the characteristic of asymmetric distribution of on-chip memory accesses nor the property of non-uniform access latency into consideration in large distributed cache. It is difficult to make an intelligent trade-off between the hit rate and the hit latency, which has an important impact on the memory efficiency. To tackle this problem, this paper presents a novel pressure self-adapting dynamic non-uniform cache architecture (PSA-NUCA). By integrating the replica and activity aware pseudo-LRU replacement policy (RAA-LRU), the enhanced first-touch mapping policy based on selective victim retention (FT-SVR), and the pressure aware adaptive replication policy (PA-ARP) into a unified intelligent data management framework, PSA-NUCA alleviates the contradiction between the miss rate and hit latency effectively with concern for both the characteristic of asymmetric distribution of memory access and the property of non-uniform access latency. Simulation results using a full system simulator demonstrate that PSA-NUCA outperforms the baseline shared non-uniform cache architecture by an average of 7.78% for the multi-thread benchmark programs we examined, while the hardware overhead is negligible.