Electroencephalography (EEG) signals are susceptible to numerous sources of interference; therefore, it is important to employ appropriate EEG denoising techniques. This study investigates common EEG denoising techniques, aiming to identify the optimal strategy for contemporary EEG signal processing. Using the publicly available EEGDenoiseNet dataset, eight techniques are used, namely EEGIFNet (a Dual-Branch Interactive Fusion Network), EEGDnet (a 2D-Transfomer-based architecture), EEGDfus(a Dual-Branch Fusion Network with Transfomer attention), GCTNet(a Generative Adversarial Network based model), EEGDiR(a Retentive Network based model), 2×3R-CNN, CNN, and RNN-LSTM. These techniques are compared on the basis of the root mean square error (RMSE) and signal-to-noise ratio (SNR). Experimental results indicate that the “EEGDfus” model achieved the highest performance metrics with an RMSE of 20.1 and an SNR of 10.3 dB. The “GCTNet” model has the best balance with performance and efficiency. These findings highlight the potential of the interactive Fusion-CNN Network to enhance the precision of denoising, particularly in cases of mixed interference from various artifacts. Our code is available at https://github.com/AIPMLab/EEG-Denoising-Comparation/.

Modern large-scale graph processing faces a critical challenge: conventional adjacency lists incur excessive L3 cache misses due to irregular memory access. We introduce GraphTwin, a hybrid graph representation system combining: (1) Cache-optimized k -bit vectors (termed GT-vectors, 64 bits per vertex), where each bit indicates vertex membership in a precomputed independent set; and (2) Memory-resident adjacency lists for exact verification of queries unresolved by GT-vectors. This dual-component design enables 95% of negative edge queries, which are dominant in sparse graphs, to be resolved in 1 CPU cycle via in-cache bitwise-AND operations, reducing latency from 54ns (adjacency list) to 18ns per query. Unresolved queries delegate to adjacency lists, guaranteeing zero false positives/negatives. Crucially, GT-vectors scales linearly with vertex count ( k|V| bits ), decoupling the space overhead from edge density and minimizing cache dependency. For example, GT-vectors for a graph with |V|=10 7 vertices occupy 80MB, fitting entirely within modern CPU caches (e.g., AMD Ryzen 7 9800X3D's 96MB L3). We formalize the GT-vectors construction as an NP-hard and submodular optimization problem and introduce GTWICE, a linear-time heuristic algorithm that iteratively extracts diversified maximal independent sets to maximize non-edge coverage. Experiments on 15 graphs show that GraphTwin reduces L3 cache misses by 63% on average, achieves 6.2× speedup for edge queries and accelerates triangle counting and set inclusion by 1.7× and 5.4×, respectively. By optimizing cache residency and accelerating foundational primitives, GraphTwin addresses cache inefficiencies in graph processing, enabling fast graph queries without sacrificing exactness.

Computational cell type deconvolution on bulk transcriptomics data can reveal cell type proportion heterogeneity across samples. One critical factor for accurate deconvolution is the reference signature matrix for different cell types. Compared with inferring reference signature matrices from cell lines, rapidly accumulating single-cell RNA-sequencing (scRNA-seq) data provide a richer and less biased resource. However, deriving cell type signature from scRNA-seq data is challenging due to high biological and technical noises. In this article, we introduce a novel Bayesian framework, tranSig, to improve signature matrix inference from scRNA-seq by leveraging shared cell type-specific expression patterns across different tissues and studies. Our simulations show that tranSig is robust to the number of signature genes and tissues specified in the model. Applications of tranSig to bulk RNA sequencing data from peripheral blood, bronchoalveolar lavage and aorta demonstrate its accuracy and power to characterize biological heterogeneity across groups. In summary, tranSig offers an accurate and robust approach to defining gene expression signatures of different cell types, facilitating improved in silico cell type deconvolutions.