Recommender Systems with Characterized Social Regularization

Social recommendation, which utilizes social relations to enhance recommender systems, has been gaining increasing attention recently with the rapid development of online social network. Existing social recommendation methods are based on the fact that users preference or decision is influenced by their social friends' behaviors. However, they assume that the influences of social relation are always the same, which violates the fact that users are likely to share preference on diverse products with different friends. In this paper, we present a novel CSR (short for Characterized Social Regularization) model by designing a universal regularization term for modeling variable social influence. Our proposed model can be applied to both explicit and implicit iteration. Extensive experiments on a real-world dataset demonstrate that CSR significantly outperforms state-of-the-art social recommendation methods.Comment: to appear in CIKM 201

Effect of combined application of sevoflurane and remifentanil on laparoscopic surgery, postoperative recovery time and stress response

Purpose: To investigate the effect of application of sevoflurane and remifentanil on laparoscopic surgery, and its effect on patients’ postoperative recovery time and stress response. Methods: Ninety patients undergoing laparoscopic surgery in Zhongshan City People's Hospital, Guangdong Province, China were selected and randomly divided into propofol group (PG) and sevoflurane group (SG), with 45 patients in each group. Patients in PG were anesthetized with combination of propofol and remifentanil, while those in SG received combination of sevoflurane and remifentanil. Patients’ heart rate (HR), stroke volume (SV) and mean arterial pressure (MAP) were tested before anesthesia induction (T1), after intubation (T2), 15 min after pneumoperitoneum (T3), and after extubation (T4), in order to evaluate the stability of vital signs in the patients. Results: At T2, T3, and T4, HR, SV, and MAP were more stable in SG than in PG (p < 0.05). At T3 and T4, the levels of ET-1, noradrenaline (NE) and cortisol (Cor) were significantly lower in SG than in PG (p < 0.05). Furthermore, postoperative recovery time, spontaneous breathing time, time taken to open the eyes under command, and orientation recovery time were shorter in SG than in PG (p < 0.05). After awakening, SG had significantly higher Ramsay score than PG (p < 0.05). Conclusion: The combined use of sevoflurane and remifentanil for anesthesia in patients undergoing laparoscopic surgery results in stable vital signs, facilitates recovery after surgery, improve quality of recovery, and reduce stress response. Therefore, the combination anesthesia merits further mechanistic and large-scale investigation before clinical application

Physics-informed neural network methods based on Miura transformations and discovery of new localized wave solutions

We put forth two physics-informed neural network (PINN) schemes based on Miura transformations and the novelty of this research is the incorporation of Miura transformation constraints into neural networks to solve nonlinear PDEs. The most noteworthy advantage of our method is that we can simply exploit the initial-boundary data of a solution of a certain nonlinear equation to obtain the data-driven solution of another evolution equation with the aid of PINNs and during the process, the Miura transformation plays an indispensable role of a bridge between solutions of two separate equations. It is tailored to the inverse process of the Miura transformation and can overcome the difficulties in solving solutions based on the implicit expression. Moreover, two schemes are applied to perform abundant computational experiments to effectively reproduce dynamic behaviors of solutions for the well-known KdV equation and mKdV equation. Significantly, new data-driven solutions are successfully simulated and one of the most important results is the discovery of a new localized wave solution: kink-bell type solution of the defocusing mKdV equation and it has not been previously observed and reported to our knowledge. It provides a possibility for new types of numerical solutions by fully leveraging the many-to-one relationship between solutions before and after Miura transformations. Performance comparisons in different cases as well as advantages and disadvantages analysis of two schemes are also discussed. On the basis of the performance of two schemes and no free lunch theorem, they both have their own merits and thus more appropriate one should be chosen according to specific cases

The improved backward compatible physics-informed neural networks for reducing error accumulation and applications in data-driven higher-order rogue waves

Due to the dynamic characteristics of instantaneity and steepness, employing domain decomposition techniques for simulating rogue wave solutions is highly appropriate. Wherein, the backward compatible PINN (bc-PINN) is a temporally sequential scheme to solve PDEs over successive time segments while satisfying all previously obtained solutions. In this work, we propose improvements to the original bc-PINN algorithm in two aspects based on the characteristics of error propagation. One is to modify the loss term for ensuring backward compatibility by selecting the earliest learned solution for each sub-domain as pseudo reference solution. The other is to adopt the concatenation of solutions obtained from individual subnetworks as the final form of the predicted solution. The improved backward compatible PINN (Ibc-PINN) is applied to study data-driven higher-order rogue waves for the nonlinear Schr\"{o}dinger (NLS) equation and the AB system to demonstrate the effectiveness and advantages. Transfer learning and initial condition guided learning (ICGL) techniques are also utilized to accelerate the training. Moreover, the error analysis is conducted on each sub-domain and it turns out that the slowdown of Ibc-PINN in error accumulation speed can yield greater advantages in accuracy. In short, numerical results fully indicate that Ibc-PINN significantly outperforms bc-PINN in terms of accuracy and stability without sacrificing efficiency

Direct Observation of Long-Term Durability of Superconductivity in YBa2_2Cu3_3O7_7-Ag2_2O Composites

We report direct observation of long-term durability of superconductivity of several YBa$_2$Cu$_3$O$_7$-Ag$_2$O composites that were first prepared and studied almost 14 years ago [J. J. Lin {\it et al}., Jpn. J. Appl. Phys. {\bf 29}, 497 (1990)]. Remeasurements performed recently on both resistances and magnetizations indicate a sharp critical transition temperature at 91 K. We also find that such long-term environmental stability of high-temperature superconductivity can only be achieved in YBa$_2$Cu$_3$O$_7$ with Ag$_2$O addition, but not with pure Ag addition.Comment: to be published in Jpn. J. Appl. Phy