Changle Zhou

Emotions play an important role in human cognition. With the development of affective computing, there is an increasing number of studies on building emotional machines. However, today's machines are far from having emotional feelings, which are the very nature of emotions. To address this issue, we propose a quantum circuit model of emotions and feelings based on quantum theory. In this model, an emotion is always accompanied by a feeling, and their simultaneity is modeled by quantum entanglement. In addition, mixed emotions are modeled as quantum superposition states and the generation of a specific emotion is regarded as the result of quantum collapse by measurement which can be seen as cognitive activities. Experimental results on the IBM quantum simulation platform demonstrate the feasibility of the proposed model, suggesting that quantum computing is a promising way to create genuine emotional machines.

As an important part of power grid emergency management, power communication fault emergency repair scheduling can provide decision support for emergency management. Therefore, based on the current situation of emergency repair resource allocation of power communication, a multi-point fault emergency repair scheduling algorithm considering multi group cooperation is proposed, and a joint optimization model of power communication fault emergency repair considering resource allocation and multi team cooperation mechanism is established. The blackboard information processing mode is designed, which is composed of initial layer, adaptive measure optimization layer and emergency repair plan optimization layer. It can quickly give the emergency repair resource grouping scheme and emergency repair plan after multiple failures of power communication, so as to reduce the economic loss of power failure. Simulation results show the effectiveness of the proposed method.

Compound words in psycholinguistics pose a significant challenge for researchers as their meaning involves more than the sum of their parts. The role of semantic relations in this process is crucial, and studies have reported a phenomenon known as relation priming. It suggests that previously encountered relations enhance the processing of subsequent words with the same relation. Notably, this priming effect is limited to cases where there is morpheme repetition between the priming and target words. In the present study, 33 samples from the target group were selected, and the within-subject design of 3 morphemes (modifier-shared, head-shared, non-repeated) × 2 relations (relation-same, relation-different) was adopted to explore whether the relation priming effect would occur without morpheme repetition and its time course. Significant relation priming effects were found in both behavioral and electrophysiological experimental results. These findings indicating relation priming can occur independently of morpheme repetition, and it has been activated at a very early stage (about 200 ms). As the word processing progresses, this activation gradually strengthens, indicating that the relation role is slowly increasing in the process of compound word recognition. It may first be used as context information to help determine the constituent morphemes’ meaning. After the meaning access of the constituent morphemes, they begin to play a role in the semantic composition process. This study uses electrophysiological technology to precisely describe the representation of relation and its time course for the first time, which gives us a deeper understanding of the relation priming process, and at the same time, sheds light on the meaning construction process of compounds.

Background. The prevalence of diabetes increases year by year, posing a severe threat to human health. Current treatments are difficult to prevent the progression of diabetes and its complications. It is imperative to carry out individualized treatment of diabetes, but current diagnostic methods are difficult to specify an individualized treatment plan. Objective. Clarify the distribution law of tongue features of the diabetic population, and provide the diagnostic basis for individualized treatment of traditional Chinese medicine (TCM) in the treatment of diabetes. Methods. We use the TFDA-1 tongue diagnosis instrument to collect tongue images of people with diabetes and accurately calculate the color features, texture features, and tongue coating ratio features through the Tongue Diagnosis Analysis System (TDAS). Then, we used K-means and Self-organizing Maps (SOM) networks to analyze the distribution of tongue features in diabetic people. Statistical analysis of TDAS features was used to identify differences between clusters. Results. The silhouette coefficient of the K-means clustering result is 0.194, and the silhouette coefficient of the SOM clustering result is 0.127. SOM Cluster 3 and Cluster 4 are derived from K-means Cluster 1, and the intersections account for (76.7% 97.5%) and (22.3% and 70.4%), respectively. K-means Cluster 2 and SOM Cluster 1 are highly overlapping, and the intersection accounts for the ratios of 66.9% and 95.0%. K-means Cluster 3 and SOM Cluster 2 are highly overlaid, and the intersection ratio is 94.1% and 82.1%. For the clustering results of K-means, TB-a and TC-a of Cluster 3 are the highest ( ), TB-a of Cluster 2 is the lowest ( ), and TB-a of Cluster 1 is between Cluster 2 and Cluster 3 ( ). Cluster 1 has the highest TB-b and TC-b ( ), Cluster 2 has the lowest TB-b and TC-b ( ), and TB-b and TC-b of Cluster 3 are between Cluster 1 and Cluster 2 ( ). Cluster 1 has the highest TB-ASM and TC-ASM ( ), Cluster 3 has the lowest TB-ASM and TC-ASM ( ), and TB-ASM and TC-ASM of Cluster 2 are between the Cluster 1 and Cluster 3 ( ). CON, ENT, and MEAN show the opposite trend. Cluster 2 had the highest Per-all ( ). SOM divides K-means Cluster 1 into two categories. There is almost no difference in texture features between Cluster 3 and Cluster 4 in the SOM clustering results. Cluster 3’s TB-L, TC-L, and Per-all are lower than Cluster 4 ( ), Cluster 3’s TB-a, TC-a, TB-b, TC-b, and Per-part are higher than Cluster 4 ( ). Conclusions. The precise tongue image features calculated by TDAS are the basis for characterizing the disease state of diabetic people. Unsupervised learning technology combined with statistical analysis is an important means to discover subtle changes in the tongue features of diabetic people. The machine vision analysis method based on unsupervised machine learning technology realizes the classification of the diabetic population based on fine tongue features. It provides a diagnostic basis for the designated diabetes TCM treatment plan.

Event-related potentials (ERPs) can reflect the high-level thinking activities of the brain. In ERP analysis, the superposition and averaging method is often used to estimate ERPs. However, the single-trial ERP estimation can provide researchers with more information on cognitive activities. In recent years, more and more researchers try to find an effective method to extract single-trial ERPs, because most of the existing methods have poor generalization ability or suffer from strong assumptions about the characteristics of ERPs, resulting in unsatisfactory results under the condition of a very low signal-to-noise ratio. In this paper, an EEG classification-based method for single-trial ERP detection and estimation was proposed. This study used a linear generated EEG model containing templates of ERP local descriptors which include amplitude and latency, and this model can avoid the invalid assumption about ERPs taken by other methods. The purpose of this method is not to recover the whole ERP waveform but to model the amplitude and latency of ERP components. This method afterwards examined the three machine learning models including logistic regression, neural network, and support vector machine in the EEG signal classification for ERP detection and selected the best performed MLPNN model for detection. To get the utmost out of information produced in the classification process, this study also used extra information to propose a new optimization model, with which outperformed detection results were obtained. Performance of the proposed method is evaluated on simulated N170 and real P50 data sets, and the results show that the model is more effective than the Woody filter and the SingleTrialEM algorithm. These results are also consistent with the conclusion of sensory gating, which demonstrated good generalization ability.

It is humankind’s unique wisdom to compose a limited number of words together through specific rules to convey endless information. Researchers have found that this composition process also plays a vital role in the comprehension of compounds. The specific manifestation is relation priming; that is, the previously used relation will promote subsequent word processing using the same relation. This priming phenomenon is bound to morpheme repetition (modifier or head). This study combines a self-paced priming paradigm with electrophysiological technology to explore whether relation priming will occur without sharing morphemes and its time course. We found that relation priming can occur independently of morpheme-repetition, which shows an independent representation of relation information. And it has been activated at a very early stage (about 200ms). As the word processing progresses, this activation gradually strengthens, indicating that the relation’s role is slowly increasing in the process of compound word recognition. It may first be used as a kind of context information to help determine the constituent morphemes’ meaning. After the meaning access of the constituent morphemes, they begin to play a role in the semantic composition process. This study uses electrophysiological technology to precisely describe the representation of relation and its time course for the first time. Which gives us a deeper understanding of the relation priming process, and at the same time, shed light on the meaning construction process of compounds.

The ability of robots to write Chinese strokes, which is recognized as a sophisticated task, involves complicated kinematic control algorithms. The conventional approaches for robotic writing of Chinese strokes often suffer from limited font generation methods, which limits the ability of robots to perform high-quality writing. This article instead proposes a developmental evolutionary learning framework that enables a robot to learn to write fundamental Chinese strokes. The framework first considers the learning process of robotic writing as an evolutionary easy-to-difficult procedure. Then, a developmental learning mechanism called “Lift-constraint, act and saturate” that stems from developmental robotics is used to determine how the robot learns tasks ranging from simple to difficult by building on the learning results from the easy tasks. The developmental constraints, which include altitude adjustments, number of mutation points, and stroke trajectory points, determine the learning complexity of robot writing. The developmental algorithm divides the evolutionary procedure into three developmental learning stages. In each stage, the stroke trajectory points gradually increase, while the number of mutation points and adjustment altitudes gradually decrease, allowing the learning difficulties involved in these three stages to be categorized as easy, medium, and difficult. Our robot starts with an easy learning task and then gradually progresses to the medium and difficult tasks. Under various developmental constraint setups in each stage, the robot applies an evolutionary algorithm to handle the basic shapes of the Chinese strokes and eventually acquires the ability to write with good quality. The experimental results demonstrate that the proposed framework allows a calligraphic robot to gradually learn to write five fundamental Chinese strokes and also reveals a developmental pattern similar to that of humans. Compared to an evolutionary algorithm without the developmental mechanism, the proposed framework achieves good writing quality more rapidly.