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Longevity of the Multiplex CytoBead CeliAK Immunoassay to Assess Anti-tTG IgA for Coeliac disease Verification.
Experimental results show that the proposed methods consume less additional computational cost and result in a significant increase in the performance of MI-based BCI systems.Tensors are increasingly encountered in prediction problems. #link# We extend previous results for high-dimensional least-squares convex tensor regression to classification problems with a hinge loss and establish its asymptotic statistical properties. Based on a general convex decomposable penalty, the rate depends on both the intrinsic dimension and the Rademacher complexity of the class of linear functions of tensor predictors.Ultrasound haptics is a contactless haptic technology that enables novel mid-air interactions with rich multisensory feedback. This paper surveys recent advances in ultrasound haptic technology. We discuss the fundamentals of this haptic technology, how a variety of perceptible sensations are rendered, and how it is currently being used to enable novel interaction techniques. We summarize its strengths, weaknesses, and potential applications across various domains. We conclude with our perspective on key directions for this promising haptic technology.Biological nonassociative learning is one of the simplest forms of unsupervised learning in animals and can be categorized into habituation and sensitization according to mechanism. This paper proposes a memristive circuit that is based on nonassociative learning and can adapt to repeated inputs, reduce power consumption (habituation), and be sensitive to harmful inputs (sensitization). The circuit includes 1) synapse module, 2) neuron module, 3) feedback module. The first module mainly consists of memristors representing synapse weights that vary with corresponding inputs. Memristance is automatically reduced when a harmful stimulus is input, and climbs at the input interval according to the feedback input when repeated stimuli are input. The second module produces spiking voltage when the total input is above the given threshold. The third module can provide feedback voltage according to the frequency and quantity of input stimuli. Simulation results show that the proposed circuit can generate output signals with biological nonassociative learning characteristics, with varying amplitudes depending on the characteristics of input signals. When the frequency and quantity of the input stimuli are high, the degree of habituation and sensitization intensifies. Danusertib ic50 proposed circuit has good robustness; can reduce the influence of noise, circuit parasitics and circuit aging during nonassociative learning; and simulate the afterimages caused by visual fatigue for application in automatic exposure compensation.Bowel sounds (BSs), typically generated by the intestinal peristalses, are a significant physiological indicator of the digestive system's health condition. In this study, a wearable BS monitoring system is presented for long-term BS monitoring. The system features a wearable BS sensor that can record BSs for days long and transmit them wirelessly in real-time. With the system, a total of 20 subjects' BS data under the hospital environment were collected. Each subject is recorded for 24 hours. Through manual screening and annotation, from every subject's BS data, 400 segments were extracted, in which half are BS event-contained segments. Thus, a BS dataset that contains 20 × 400 sound segments is formed. Afterwards, CNNs are introduced for BS segment recognition. Specifically, this study proposes a novel CNN design method that makes it possible to transfer the popular CNN modules in image recognition into the BS segmentation domain. Experimental results show that in holdout evaluation with corrected labels, the designed CNN model achieves a moderate accuracy of 91.8% and the highest sensitivity of 97.0% compared with the similar works. In cross validation with noisy labels, the designed CNN delivers the best generability. By using a CNN visualizing technique-class activation maps, it is found that the designed CNN has learned the effective features of BS events. Finally, the proposed CNN design method is scalable to different sizes of datasets.Symmetries express the invariance of a system towards sets of mathematical transformations. In more practical terms, symmetries greatly reduce or simplify the computational efforts required to evaluate relevant properties of a system. In this paper, two methods are proposed to implement spin symmetries which simplify the analysis of the spreading of diseases in an agent-based epidemic model. We perform a set of simulations to measure the efficiency gains compared to traditional methods. Our findings show symmetry-based algorithms improve the performance of the Monte Carlo simulation and the exact Markov process.Affective computing is one of the key technologies to achieve advanced brain-machine interfacing. It is increasingly concerning research orientation in the field of artificial intelligence. Emotion recognition is closely related to affective computing. Although emotion recognition based on electroencephalogram (EEG) has attracted more and more attention at home and abroad, subject-independent emotion recognition still faces enormous challenges. We proposed a subject-independent emotion recognition algorithm based on dynamic empirical convolutional neural network (DECNN) in view of the challenges. Combining the advantages of empirical mode decomposition (EMD) and differential entropy (DE), we proposed a dynamic differential entropy (DDE) algorithm to extract the features of EEG signals. After that, the extracted DDE features were classified by convolutional neural networks (CNN). Finally, the proposed algorithm is verified on SJTU Emotion EEG Dataset (SEED). In addition, we discuss the brain area closely related to emotion and design the best profile of electrode placements to reduce the calculation and complexity. Experimental results show that the accuracy of this algorithm is 3.53% higher than that of the state-of-the-art emotion recognition methods. What's more, we studied the key electrodes for EEG emotion recognition, which is of guiding significance for the development of wearable EEG devices.
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