Asca: less audio data is more insightful

Audio recognition in specialized areas such as birdsong and submarine acoustics faces challenges in large-scale pre-training due to the limitations in available samples imposed by sampling environments and specificity requirements. While the Transformer model excels in audio recognition, its dependence on vast amounts of data becomes restrictive in resource-limited settings. Addressing this, we introduce the Audio Spectrogram Convolution Attention (ASCA) based on CoAtNet, integrating a Transformer-convolution hybrid architecture, novel network design, and attention techniques, further augmented with data enhancement and regularization strategies. On the BirdCLEF2023 and AudioSet(Balanced), ASCA achieved accuracies of 81.2% and 35.1%, respectively, significantly outperforming competing methods. The unique structure of our model enriches output, enabling generalization across various audio detection tasks. Our code can be found at https://github.com/LeeCiang/ASCA.Comment: 6 pages,3 figure

Soulstyler: Using Large Language Model to Guide Image Style Transfer for Target Object

Image style transfer occupies an important place in both computer graphics and computer vision. However, most current methods require reference to stylized images and cannot individually stylize specific objects. To overcome this limitation, we propose the "Soulstyler" framework, which allows users to guide the stylization of specific objects in an image through simple textual descriptions. We introduce a large language model to parse the text and identify stylization goals and specific styles. Combined with a CLIP-based semantic visual embedding encoder, the model understands and matches text and image content. We also introduce a novel localized text-image block matching loss that ensures that style transfer is performed only on specified target objects, while non-target regions remain in their original style. Experimental results demonstrate that our model is able to accurately perform style transfer on target objects according to textual descriptions without affecting the style of background regions. Our code will be available at https://github.com/yisuanwang/Soulstyler.Comment: 5 pages,3 figures,ICASSP202

Spectrum Migration Approach Based on Pre-decision Aid and Interval Mamdani Fuzzy Inference in Cognitive Radio Networks

This study intends to improve the QoS of SUs and CRNs performance. A novel spectrum migration approach based on pre-decision aid and interval Mamdani fuzzy inference is presented. we first define spectrum migration factors as spectrum characteristic metrics for spectrum migration decision. In addition, we use predecision aid to reduce system complexity and improve spectrum migration efficiency. To shorten spectrum migration decision time and seek the optimal spectrum holes, interval Mamdani fuzzy inference is put forward. Finally, simulation results show the proposed approach can inhibit the upward trend of service retransmission probability and average migration times effectively, and improve the effective utilization of CRNs spectrum resource significantly

Integrated 16S rRNA sequencing and nontargeted metabolomics analysis to reveal the mechanisms of Yu-Ye Tang on type 2 diabetes mellitus rats

IntroductionYu–Ye Tang (YYT) is a classical formula widely used in treatment of type 2 diabetes mellitus (T2DM). However, the specific mechanism of YYT in treating T2DM is not clear.MethodsThe aim of this study was to investigate the therapeutic effect of YYT on T2DM by establishing a rat model of T2DM. The mechanism of action of YYT was also explored through investigating gut microbiota and serum metabolites.ResultsThe results indicated YYT had significant therapeutic effects on T2DM. Moreover, YYT could increase the abundance of Lactobacillus, Candidatus_Saccharimonas, UCG-005, Bacteroides and Blautia while decrease the abundance of and Allobaculum and Desulfovibrio in gut microbiota of T2DM rats. Nontargeted metabolomics analysis showed YYT treatment could regulate arachidonic acid metabolism, alanine, aspartate and glutamate metabolism, arginine and proline metabolism, glycerophospholipid metabolism, pentose and glucuronate interconversions, phenylalanine metabolism, steroid hormone biosynthesis, terpenoid backbone biosynthesis, tryptophan metabolism, and tyrosine metabolism in T2DM rats.DiscussionIn conclusion, our research showed that YYT has a wide range of therapeutic effects on T2DM rats, including antioxidative and anti-inflammatory effects. Furthermore, YYT corrected the altered gut microbiota and serum metabolites in T2DM rats. This study suggests that YYT may have a therapeutic impact on T2DM by regulating gut microbiota and modulating tryptophan and glycerophospholipid metabolism, which are potential key pathways in treating T2DM

TPpred-LE: therapeutic peptide function prediction based on label embedding

Abstract Background Therapeutic peptides play an essential role in human physiology, treatment paradigms and bio-pharmacy. Several computational methods have been developed to identify the functions of therapeutic peptides based on binary classification and multi-label classification. However, these methods fail to explicitly exploit the relationship information among different functions, preventing the further improvement of the prediction performance. Besides, with the development of peptide detection technology, peptide functions will be more comprehensively discovered. Therefore, it is necessary to explore computational methods for detecting therapeutic peptide functions with limited labeled data. Results In this study, a novel method called TPpred-LE based on Transformer framework was proposed for predicting therapeutic peptide multiple functions, which can explicitly extract the function correlation information by using label embedding methodology and exploit the specificity information based on function-specific classifiers. Besides, we incorporated the multi-label classifier retraining approach (MCRT) into TPpred-LE to detect the new therapeutic functions with limited labeled data. Experimental results demonstrate that TPpred-LE outperforms the other state-of-the-art methods, and TPpred-LE with MCRT is robust for the limited labeled data. Conclusions In summary, TPpred-LE is a function-specific classifier for accurate therapeutic peptide function prediction, demonstrating the importance of the relationship information for therapeutic peptide function prediction. MCRT is a simple but effective strategy to detect functions with limited labeled data

An Optimization Technique of the 3D Indoor Map Data Based on an Improved Octree Structure

The construction and retrieval of indoor maps are important for indoor positioning and navigation. It is necessary to ensure a good user experience while meeting real-time requirements. Unlike outdoor maps, indoor space is limited, and the relationship between indoor objects is complex which would result in an uneven indoor data distribution and close relationship between the data. A data storage model based on the octree scene segmentation structure was proposed in this paper initially. The traditional octree structure data storage model has been improved so that the data could be backtracked. The proposed method will solve the problem of partition lines within the range of the object data and improve the overall storage efficiency. Moreover, a data retrieval algorithm based on octree storage structure was proposed. The algorithm adopts the idea of “searching for a point, points around the searched point are within the searching range.” Combined with the octree neighbor retrieval methods, the closure constraints are added. Experimental results show that using the improved octree storage structure, the retrieval cost is 1/8 of R-tree. However, by using the neighbor retrieval, it improved the search efficiency by about 27% on average. After adding the closure constraint, the retrieval efficiency increases by 25% on average

Application of Empirical Orthogonal Function Interpolation to Reconstruct Hourly Fine Particulate Matter Concentration Data in Tianjin, China

Fine particulate matter with diameters less than 2.5 μm (PM2.5) concentration monitoring is closely related to public health, outdoor activities, environmental protection, and other fields. However, the incomplete PM2.5 observation records provided by ground-based PM2.5 concentration monitoring stations pose a challenge to the study of PM2.5 propagation and evolution model. Consequently, PM2.5 concentration data imputation has been widely studied. Based on empirical orthogonal function (EOF), a new spatiotemporal interpolation method, EOF interpolation (EOFI) is introduced in this paper, and then, EOFI is applied to reconstruct the hourly PM2.5 concentration records of two stations in the first half of the year. The main steps of EOFI here are to firstly decompose the spatiotemporal data matrix of the original observation site into mutually orthogonal temporal and spatial modes with EOF method. Secondly, the spatial mode of the missing data station is estimated by inverse distance weighting interpolation of the spatial mode of the observation sites. After that, the records of the missing data station can be reconstructed by multiplying the estimated spatial mode and the corresponding temporal mode. The optimal mode number for EOFI is determined by minimizing the root mean square error (RMSE) between reconstructed records and corresponding valid records. Finally, six evaluation indices (mean absolute error (MAE), RMSE, correlation coefficient (Corr), deviation rate bias, Nash–Sutcliffe efficiency (NSE), and index of agreement (IA)) are calculated. The results show that EOFI performs better than the other three interpolation methods, namely, inverse distance weight interpolation, thin plate spline, and surface spline interpolation. The EOFI has the advantages of less computation, less parameter selection, and ease of implementation, it is an alternative method when the number of observation stations is rare, and the proportion of missing value at some stations is large. Moreover, it can also be applied to other spatiotemporal variables interpolation and imputation

Application of Fuzzy Comprehensive Evaluation in Cognitive Networks for Optimal Network Selection

Network selection mechanisms play a vital role in ensuring quality of service (QoS)in the cognitive networks environment. In this paper we develop a network selection scheme for an integrated UMTS/WLAN system to guarantee mobile users being always best connected. We focus on the optimization of resource utilization, while ensuring acceptable QoS provision to the end users. The proposed scheme comprises three parts. First one is to detect the availability of access networks by following cognitive network architecture under the cross-layer design paradigm. While the second is to apply fuzzy comprehensive evaluation to decide the relative weights of evaluative criteria set according to network condition and service applications. Thirdly, a novel approach using quantum genetic algorithm to adjust the weights of fuzzy comprehensive evaluation is adopted. Extensive computer based simulation experiments were carried out. Simulation results show that this method gives better performance in throughput, delay and average packet loss, hence proves that this method is superior to the traditional methods of link capacity and network capacity

Application of Fuzzy Comprehensive Evaluation in Cognitive Networks for Optimal Network Selection

Abstract: Network selection mechanisms play a vital role in ensuring quality of service (QoS)in the cognitive networks environment. In this paper we develop a network selection scheme for an integrated UMTS/WLAN system to guarantee mobile users being always best connected. We focus on the optimization of resource utilization, while ensuring acceptable QoS provision to the end users. The proposed scheme comprises three parts. First one is to detect the availability of access networks by following cognitive network architecture under the cross-layer design paradigm. While the second is to apply fuzzy comprehensive evaluation to decide the relative weights of evaluative criteria set according to network condition and service applications. Thirdly, a novel approach using quantum genetic algorithm to adjust the weights of fuzzy comprehensive evaluation is adopted. Extensive computer based simulation experiments were carried out. Simulation results show that this method gives better performance in throughput, delay and average packet loss, hence proves that this method is superior to the traditional methods of link capacity and network capacity