Instantaneous Spectral Analysis: Time-Frequency Mapping via Wavelet Matching with Application to Contaminated-Site Characterization by 3D GPR

Spectral decomposition, by which a time series is transformed from the 1D time/amplitude domain to the 2D time/spectrum domain, has become a popular and useful tool in seismic exploration for hydrocarbons. The windowed, or short-time Fourier transform (STFT) was one early approach to computing the time-frequency (t-f) distribution. This method relies on the user selecting a fixed time window, then computing the Fourier spectrum within the time window while sliding the window along the length of the trace. The primary limitation of the STFT is the fixed window which prevents either time localization of high frequency components (if a long window is used) or spectral resolution of the low-frequency components (if a short window is used)

LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient Fine-Tuning of Large Language Models

In addressing the computational and memory demands of fine-tuning Large Language Models(LLMs), we propose LoRA-SP(Streamlined Partial Parameter Adaptation), a novel approach utilizing randomized half-selective parameter freezing within the Low-Rank Adaptation(LoRA)framework. This method efficiently balances pre-trained knowledge retention and adaptability for task-specific optimizations. Through a randomized mechanism, LoRA-SP determines which parameters to update or freeze, significantly reducing computational and memory requirements without compromising model performance. We evaluated LoRA-SP across several benchmark NLP tasks, demonstrating its ability to achieve competitive performance with substantially lower resource consumption compared to traditional full-parameter fine-tuning and other parameter-efficient techniques. LoRA-SP innovative approach not only facilitates the deployment of advanced NLP models in resource-limited settings but also opens new research avenues into effective and efficient model adaptation strategies

Polymorphisms of STAT-6, STAT-4 and IFN-γ genes and the risk of asthma in Chinese population

SummaryBackgroundAsthma is a complex disease resulting from multiple gene–gene and gene–environment interactions. Study on gene–gene interactions could provide insight into the pathophysiologic mechanisms of the disease.ObjectivesWe investigated the single nucleotide polymorphisms and interactions among three different loci in three candidate genes (STAT-6 G2964A, STAT-4 T90089C and IFN-γ T874A) in 95 Chinese asthmatic subjects and 95 matched controls to determine the possible associations with asthma.MethodsGenotyping of the gene polymorphisms was performed by means of PCR-SSCP analysis. Genotype–phenotype associations were examined in dominant and recessive genetic models using logistic regression. The method of multifactor dimensionality reduction was used to analyze gene–gene interactions.ResultsNo statistically significant difference was found in the distribution of the STAT-6 G2964A polymorphisms between asthmatic patients and controls in this case–control study. The STAT-4 T90089C polymorphisms were significantly associated with asthma in the dominant model (p=0.007). As for the IFN-γ T874A, the significant associations were found in both dominant model (p=0.004) and recessive model (p=0.006). A significant gene–gene interaction was found among STAT-6, STAT-4 and IFN-γ on the risk of asthma. In the best 3-locus model, the odds ratio for the high-risk to the low-risk group was 6.9 (95% CI, 3.5–13.7; p<0.0001).ConclusionsOur findings suggest that STAT-4 T90089C and IFN-γ T874A polymorphisms might be the genetic factors for the risk of asthma in the Chinese population. In addition, the significant interactions among STAT-6 G2964A, STAT-4 T90089C and IFN-γ T874A may increase an individual's susceptibility and contribute to the pathogenesis of asthma