A robust modulation classification method using convolutional neural networks

Automatic modulation classification (AMC) is a core technique in noncooperative communication systems. In particular, feature-based (FB) AMC algorithms have been widely studied. Current FB AMC methods are commonly designed for a limited set of modulation and lack of generalization ability; to tackle this challenge, a robust AMC method using convolutional neural networks (CNN) is proposed in this paper. In total, 15 different modulation types are considered. The proposed method can classify the received signal directly without feature extracion, and it can automatically learn features from the received signals. The features learned by the CNN are presented and analyzed. The robust features of the received signals in a specific SNR range are studied. The accuracy of classification using CNN is shown to be remarkable, particularly for low SNRs. The generalization ability of robust features is also proven to be excellent using the support vector machine (SVM). Finally, to help us better understand the process of feature learning, some outputs of intermediate layers of the CNN are visualized

A mixed-methods feasibility study protocol to assess the communication behaviours within the dental health professional-parent-child triad in a general dental practice setting

Background: The promotion of twice yearly application of fluoride varnish (FVA) to the teeth of pre-school children in the dental practice is one component of Scotland's child oral health improvement programme (Childsmile). Nevertheless, evidence shows that application rates of FVA are variable and below optimal levels. The reasons are complex, with many contextual factors influencing activity. However, we propose that one possible reason may be related to the communication challenges when interacting with younger children. Therefore, the primary aim of the study is to assess the feasibility of conducting a video observational study in primary dental care. The secondary aim is to assess the communication behaviours of dental professionals and those of the parents to predict child cooperation when receiving FVA using this video observational study design. Methods: Approximately 50 eligible pairs of parents and child patients aged between 2 years and 5 years from general dental practices will be recruited to participate in the study. The consecutive mixed-method study will consist of two parts. The first part will be cross-sectional observations of the dental health professional-child-parent communication during dental appointments conducted in the general dental practice setting, using video recording. The second part will be a post-observation, semi-structured interview with parents and dental health professionals respectively. This will be implemented to explore their views on the acceptability and feasibility of being observed using video cameras during treatment provision. Discussion: The mixed-methods study will allow for directly observing the communication behaviours in the clinical setting and uncovering the views of participating dental health professionals and parents. Therefore, the study will enable us to [i] explore new ways to study the nature of triadic interaction of dental health professional-child-parent, [ii] identify dental health professionals' effective communication behaviours that promote child patient and parent's experience of using preventive dental service and [iii] to assess the feasibility of the study through uncovering the views of dental health professionals and parents

Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection

This paper presents our Facial Action Units (AUs) recognition submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence; (ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation; and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition

An Analysis of the Interactions between Adjustment Factors of Saturation Flow Rates at Signalized Intersections

n insufficient functional relationship between adjustment factors and saturation flow rate (SFR) in the U.S. Highway Capacity Manual (HCM) method increases an additional prediction bias. The error of SFR predictions can reach 8%&ndash 10%. To solve this problem, this paper proposes a comprehensive adjusted method that considers the effects of interactions between factors. Based on the data from 35 through lanes in Beijing and 25 shared through and left-turn lanes in Washington, DC, the interactions between lane width and percentage of heavy vehicles and proportion of left-turning vehicles were analyzed. Two comprehensive adjustment factor models were established and tested. The mean absolute percentage error (MAPE) of model 1 (considering the interaction between lane width and percentage of heavy vehicles) was 4.89% smaller than the MAPE of Chinese National Standard method (Standard Number is GB50647) at 13.64%. The MAPE of model 2 (considering the interaction between lane width and proportion of left-turning vehicles was 33.16% smaller than the MAPE of HCM method at 14.56%. This method could improve the accuracy of SFR prediction, provide support for traffic operation measures, alleviate the traffic congestion, and improve sustainable development of cities. Document type: Articl

Prolonged drought regulates the silage quality of maize (Zea mays L.): Alterations in fermentation microecology

Prolonged drought stress caused by global warming poses a tremendous challenge to silage production of maize. Drought during maize growth and development resulted in altered micro-environment for silage fermentation. How fermentation of silage maize responds to moisture scales remains uncharted territory. In this research, Maize water control trials were conducted and the silage quality and microbial community of drought-affected maize were determined. The results showed that drought stress significantly reduced the dry matter but increased root-to-shoot ratio, soluble sugar and malonaldehyde content in maize. Before fermentation, the crude protein, crude ash and acid detergent fiber contents were significantly increased but the ether extract content was decreased under drought. The crude protein and acid detergent fiber were significantly decreased in the drought affected group after fermentation. Furthermore, water stress at maize maturity stage greatly reduced the number of total bacteria in silage fermentation but increased the proportion of the lactobacillus and lactic acid content of silage. Drought stress alters the microbial ecosystem of the fermentation process and reconstitutes the diversity of the bacterial community and its metabolites. This study provides a theoretical basis for the study of changes in silage fermentation as affected by abiotic stresses

XNET: A Real-Time Unified Secure Inference Framework Using Homomorphic Encryption

Homomorphic Encryption (HE) presents a promising solution to securing neural networks for Machine Learning as a Service (MLaaS). Despite its potential, the real-time applicability of current HE-based solutions remains a challenge, and the diversity in network structures often results in inefficient implementations and maintenance. To address these issues, we introduce a unified and compact network structure for real-time inference in convolutional neural networks based on HE. We further propose several optimization strategies, including an innovative compression and encoding technique and rearrangement in the pixel encoding sequence, enabling a highly efficient batched computation and reducing the demand for time-consuming HE operations. To further expedite computation, we propose a GPU acceleration engine to leverage the massive thread-level parallelism to speed up computations. We test our framework with the MNIST, Fashion-MNIST, and CIFAR-10 datasets, demonstrating accuracies of 99.14%, 90.8%, and 61.09%, respectively. Furthermore, our framework maintains a steady processing speed of 0.46 seconds on a single-thread CPU, and a brisk 31.862 milliseconds on an A100 GPU for all datasets. This represents an enhancement in speed more than 3000 times compared to pervious work, paving the way for future explorations in the realm of secure and real-time machine learning applications

Causal relationship between human blood metabolites and risk of ischemic stroke: a Mendelian randomization study

Background: Ischemic stroke (IS) is a major cause of death and disability worldwide. Previous studies have reported associations between metabolic disorders and IS. However, evidence regarding the causal relationship between blood metabolites and IS lacking.Methods: A two-sample Mendelian randomization analysis (MR) was used to assess the causal relationship between 1,400 serum metabolites and IS. The inverse variance-weighted (IVW) method was employed to estimate the causal effect between exposure and outcome. Additionally, MR-Egger regression, weighted median, simple mode, and weighted mode approaches were employed as supplementary comprehensive evaluations of the causal effects between blood metabolites and IS. Tests for pleiotropy and heterogeneity were conducted.Results: After rigorous selection, 23 known and 5 unknown metabolites were identified to be associated with IS. Among the 23 known metabolites, 13 showed significant causal effects with IS based on 2 MR methods, including 5-acetylamino-6-formylamino-3-methyluracil, 1-ribosyl-imidazoleacetate, Behenoylcarnitine (C22), N-acetyltyrosine, and N-acetylputrescine to (N (1) + N (8))-acetate,these five metabolites were positively associated with increased IS risk. Xanthurenate, Glycosyl-N-tricosanoyl-sphingadienine, Orotate, Bilirubin (E,E), Bilirubin degradation product, C17H18N2O, Bilirubin (Z,Z) to androsterone glucuronide, Bilirubin (Z,Z) to etiocholanolone glucuronide, Biliverdin, and Uridine to pseudouridine ratio were associated with decreased IS risk.Conclusion: Among 1,400 blood metabolites, this study identified 23 known metabolites that are significantly associated with IS risk, with 13 being more prominent. The integration of genomics and metabolomics provides important insights for the screening and prevention of IS

Instability Mechanism of Osimertinib in Plasma and a Solving Strategy in the Pharmacokinetics Study

Osimertinib is a third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) and a star medication used to treat non-small-cell lung carcinomas (NSCLCs). It has caused broad public concern that osimertinib has relatively low stability in plasma. We explored why osimertinib and its primary metabolites AZ-5104 and AZ-7550 are unstable in rat plasma. Our results suggested that it is the main reason inducing their unstable phenomenon that the Michael addition reaction was putatively produced between the Michael acceptor of osimertinib and the cysteine in the plasma matrix. Consequently, we identified a method to stabilize osimertinib and its metabolite contents in plasma. The assay was observed to enhance the stability of osimertinib, AZ-5104, and AZ-7550 significantly. The validated method was subsequently applied to perform the pharmacokinetic study for osimertinib in rats with the newly established, elegant, and optimized ultra-performance liquid chromatography–tandem mass spectrometer (UPLC-MS/MS) strategy. The assay was assessed for accuracy, precision, matrix effects, recovery, and stability. This study can help understand the pharmacological effects of osimertinib and promote a solution for the similar problem of other Michael acceptor-contained third-generation EGFR-TKI