Distinctive effects of fear and sadness induction on anger and aggressive behavior

A recent study has reported that the successful implementation of cognitive regulation of emotion depends on higher-level cognitive functions, such as top-down control, which may be impaired in stressful situations. This calls for “cognition free” self-regulatory strategies that do not require top-down control. In contrast to the cognitive regulation of emotion that emphasizes the role of cognition, traditional Chinese philosophy and medicine views the relationship among different types of emotions as promoting or counteracting each other without the involvement of cognition, which provides an insightful perspective for developing “cognition free” regulatory strategies. In this study, we examined two hypotheses regarding the modulation of anger and aggressive behavior: sadness counteracts anger and aggressive behavior, whereas fear promotes anger and aggressive behavior. Participants were first provoked by reading extremely negative feedback on their viewpoints (Study 1) and by watching anger-inducing movie clips (Study 2). Then, these angry participants were assigned to three equivalent groups and viewed sad, fear-inducing, or neutral materials to evoke the corresponding emotions. The results showed that participants displayed a lower level of aggressive behavior when sadness was later induced and a higher level of anger when fear was later induced. These results provide evidence that supports the hypothesis of mutual promotion and counteraction relationships among these types of emotions and imply a “cognition free” approach to regulating anger and aggressive behavior

Correlation between periostin and SNCG and esophageal cancer invasion, infiltration and apoptosis

AbstractObjectiveTo investigate the correlation between periostin and SNCG and esophageal cancer invasion, infiltration and apoptosis.MethodsA total of 78 cases esophageal surgical resection specimens were collected, expression of periostin and SNCG in esophageal cancer were detected. Effect of periostin and SNCG in esophageal carcinoma invasion and infiltration was analyzed.ResultsThe upregulated rate of periostin had significant difference in esophageal cancer tissues (39.74%), adjacent tissues (17.86%) and normal tissues (0.00%); The positive expression rates of SNCG had significant difference in esophageal cancer tissues (61.54%), adjacent tissues (32.14%) and normal tissues (1.96%); The upregulated rate of periostin had a significant correlation with lymph node metastasis, adventitia invasion, TNM stage; The positive expression rates of SNCG had a significant correlation with differentiation degree, lymph node metastasis, adventitia invasion, TNM stage; Apoptosis index of the positive of expression of SNCG of esophageal cancer tissue (4.541±2.267) was significantly lower than that of the negative expression (7.316±2.582) (P<0.05).ConclusionsSNCG may play an important role in invasion, infiltration and apoptosis of esophageal cancer and serve as target spots in the targeted therapy of esophageal cancer

Monte Carlo Hamiltonian: the Linear Potentials

We further study the validity of the Monte Carlo Hamiltonian method. The advantage of the method, in comparison with the standard Monte Carlo Lagrangian approach, is its capability to study the excited states. We consider two quantum mechanical models: a symmetric one $V(x) = |x|/2$; and an asymmetric one $V(x)=\infty$, for $x < 0$ and $V(x)=x$, for $x \ge 0$. The results for the spectrum, wave functions and thermodynamical observables are in agreement with the analytical or Runge-Kutta calculations.Comment: Latex file, 8 figure

Detecting genuine multipartite entanglement via machine learning

In recent years, supervised and semi-supervised machine learning methods such as neural networks, support vector machines (SVM), and semi-supervised support vector machines (S4VM) have been widely used in quantum entanglement and quantum steering verification problems. However, few studies have focused on detecting genuine multipartite entanglement based on machine learning. Here, we investigate supervised and semi-supervised machine learning for detecting genuine multipartite entanglement of three-qubit states. We randomly generate three-qubit density matrices, and train an SVM for the detection of genuine multipartite entangled states. Moreover, we improve the training method of S4VM, which optimizes the grouping of prediction samples and then performs iterative predictions. Through numerical simulation, it is confirmed that this method can significantly improve the prediction accuracy.Comment: 9 pages, 8 figure