Search Results (7)

Multimodal automatic modulation recognition (MAMR) has emerged as a prominent research area. The effective fusion of features from different modalities is crucial for MAMR tasks. An effective multimodal fusion mechanism should maximize the extraction and integration of complementary information. Recently, fusion methods based on cross-modal attention have shown high performance. However, they overlook the differences in information intensity between different modalities, suffering from quadratic complexity. To this end, we propose an efficient Alignment before Fusion Transformer Network (AbFTNet) based on an in-phase quadrature (I/Q) and Fractional Fourier Transform (FRFT). Specifically, we first align and correlate the feature representations of different single modalities to achieve mutual information maximization. The single modality feature representations are obtained using the self-attention mechanism of the Transformer. Then, we design an efficient cross-modal aggregation promoting (CAP) module. By designing the aggregation center, we integrate two modalities to achieve the adaptive complementary learning of modal features. This operation bridges the gap in information intensity between different modalities, enabling fair interaction. To verify the effectiveness of the proposed methods, we conduct experiments on the RML2016.10a dataset. The experimental results show that multimodal fusion features significantly outperform single-modal features in classification accuracy across different signal-to-noise ratios (SNRs). Compared to other methods, AbFTNet achieves an average accuracy of 64.59%, with a 1.36% improvement over the TLDNN method, reaching the state of the art. Full article

Much research has focused on classification within a closed set of emitters, while emitters outside this closed set are misclassified. This paper proposes an open-set recognition model based on prototypical networks and extreme value theory to solve the problem of specific emitter identification in open-set scenes and further improve the recognition accuracy and robustness. Firstly, a one-dimensional convolutional neural network was designed for recognizing I/Q signals, and a squeeze-and-excitation block with an attention mechanism was added to the network to increase the weights of the feature channels with high efficiency. Meanwhile, the recognition was improved by group convolution and channel shuffle. Then, the network was trained with the joint loss function based on prototype learning to complete the separation of intra-class signals and the aggregation of inter-class signals in the feature space. After the training, the Weibull model was fitted for pre-defined classes by incorporating the extreme value theory. Finally, the classification results were obtained according to the known classes and the Weibull model, effectively completing the open-set recognition. The simulation results showed that the proposed model had a higher recognition performance and robustness compared with other classical models for signals collected from five ZigBee and ten USRP 310 devices. Full article

Gram-negative bacteria are difficult to kill due to their complex cell envelope, including the outer membrane (OM) and cytoplasmic membrane (CM). To monitor the membranolytic action of antimicrobials on Gram-negative bacteria would facilitate the development of effective antimicrobials. In this paper, an aggregation-induced emission luminogen (AIEgen) with microenvironment-sensitive properties was employed to indicate the interaction of antimicrobials with the OM and CM of Gram-negative bacteria. The damaged extent of OM and CM caused by antimicrobials with the change of dosage and incubation time can be visually captured based on the variation of two emission colors of IQ-Cm responding to OM-defective (green) and CM-disruptive bacteria (orange). Meanwhile, the activity assessment of antimicrobials can be easily realized within 1~2 h based on the distinct response of IQ-Cm to live and dead E. coli, which is much faster than the agar plate culture. This probe may shed light on the understanding of the interaction between the membrane-active antimicrobials and cell envelope of Gram-negative bacteria and contribute to the future development of antimicrobials. Full article

At the level of the 50 U.S. states, an interconnected nexus of well-being variables exists. These variables strongly correlate with estimates of state IQ in interesting ways. However, the state IQ estimates are now more than 16 years old, and the state well-being estimates are over 12 years old. Updated state IQ and well-being estimates are therefore needed. Thus, I first created new state IQ estimates by analyzing scores from both the Program for the International Assessment of Adult Competency (for adults), and the National Assessment of Educational Progress (for fourth and eighth grade children) exams. I also created new global well-being scores by analyzing state variables from the following four well-being subdomains: crime, income, health, and education. When validating the nexus, several interesting correlations existed among the variables. For example, state IQ most strongly predicted FICO credit scores, alcohol consumption (directly), income inequality, and state temperature. Interestingly, state IQ derived here also correlated 0.58 with state IQ estimates from over 100 years ago. Global well-being likewise correlated with many old and new variables in the nexus, including a correlation of 0.80 with IQ. In sum, at the level of the U.S. state, a nexus of important, strongly correlated variables exists. These variables comprise well-being, and state IQ is a central node in this network. Full article

In the present paper, we investigate how the general intelligence quotient (IQ) and its subtests changed for students from Russian University from 1991 to 2013. This study of the effect of such factors as gender, department, and year on the IQ response is carried out using the ANOVA model. Given the unevenness of the initial sample by years and departments, and consequently, heterogeneity of variances when divided by the original natural categories, we decided to aggregate the values of explanatory variables to build an adequate model. The paper proposes and investigates an algorithm for joint discretization and grouping, which uses the procedure of partial screening of solutions. It is an intermediate option between the greedy algorithm and exhaustive search. As a goodness function (an optimality criterion), we investigate 26 intermediate options between the AIC and BIC criteria. The BIC turned out to be the most informative and the most acceptable criterion for interpretation, which penalizes the complexity of the model, due to some decrease in accuracy. The resulting partition of the explanatory variables values into categories is used to interpret the modeling results and to arrive at the final conclusions of the data analysis. As a result, it is revealed that the observed features of the IQ dynamics are caused by changes in the education system and the socio-economic status of the family that occurred in Russia during the period of restructuring the society and intensive development of information technologies. Full article

Some studies have shown that silicon dioxide nanoparticles (SiO₂-NPs) can reach different regions of the brain and cause toxicity; however, the consequences of SiO₂-NPs exposure on the diverse brain cell lineages is limited. We aimed to investigate the neurotoxic effects of SiO₂-NP (0–100 µg/mL) on rat astrocyte-rich cultures or neuron-rich cultures using scanning electron microscopy, Attenuated Total Reflection-Fourier Transform Infrared spectroscopy (ATR-FTIR), FTIR microspectroscopy mapping (IQ mapping), and cell viability tests. SiO₂-NPs were amorphous particles and aggregated in saline and culture media. Both astrocytes and neurons treated with SiO₂-NPs showed alterations in cell morphology and changes in the IR spectral regions corresponding to nucleic acids, proteins, and lipids. The analysis by the second derivative revealed a significant decrease in the signal of the amide I (α-helix, parallel β-strand, and random coil) at the concentration of 10 µg/mL in astrocytes but not in neurons. IQ mapping confirmed changes in nucleic acids, proteins, and lipids in astrocytes; cell death was higher in astrocytes than in neurons (10–100 µg/mL). We conclude that astrocytes were more vulnerable than neurons to SiO₂-NPs toxicity. Therefore, the evaluation of human exposure to SiO₂-NPs and possible neurotoxic effects must be followed up. Full article

Prion diseases are fatal neurodegenerative diseases affecting mammals. Prions are misfolded amyloid aggregates of the prion protein (PrP), which form when the alpha helical, soluble form of PrP converts to an aggregation-prone, beta sheet form. Thus, prions originate as protein folding problems. The discovery of yeast prion(s) and the development of a red-/white-colony based assay facilitated safe and high-throughput screening of antiprion compounds. With this assay three antiprion compounds; 6-aminophenanthridine (6AP), guanabenz acetate (GA), and imiquimod (IQ) have been identified. Biochemical and genetic studies reveal that these compounds target ribosomal RNA (rRNA) and inhibit specifically the protein folding activity of the ribosome (PFAR). The domain V of the 23S/25S/28S rRNA of the large ribosomal subunit constitutes the active site for PFAR. 6AP and GA inhibit PFAR by competition with the protein substrates for the common binding sites on the domain V rRNA. PFAR inhibition by these antiprion compounds opens up new possibilities for understanding prion formation, propagation and the role of the ribosome therein. In this review, we summarize and analyze the correlation between PFAR and prion processes using the antiprion compounds as tools. Full article