Multi-view Regularized Gaussian Processes

Gaussian processes (GPs) have been proven to be powerful tools in various areas of machine learning. However, there are very few applications of GPs in the scenario of multi-view learning. In this paper, we present a new GP model for multi-view learning. Unlike existing methods, it combines multiple views by regularizing marginal likelihood with the consistency among the posterior distributions of latent functions from different views. Moreover, we give a general point selection scheme for multi-view learning and improve the proposed model by this criterion. Experimental results on multiple real world data sets have verified the effectiveness of the proposed model and witnessed the performance improvement through employing this novel point selection scheme

YOGA’S THERAPEUTIC EFFECT ON PERINATAL DEPRESSION: A SYSTEMATIC REVIEW AND META-ANALYSIS

Introduction: In recent years, the incidence of perinatal depression in female population is very high. Perinatal depression has adverse effects on the physical and mental health of mothers and children. However, according to current researches, Yoga has been considered as an effective exercise that can help pregnant women to regulate their emotions. Thus, this review reports the effectiveness of yoga on perinatal depression. Methods: We reviewed all of the relevant RCT (Randomized Control Trial, RCT) studies published until June 2021 from the major open-access databases. Results: 12 RCTs were selected and included in this study, and the total number of people included in the analysis in the combined study was 594. The level of depression and anxiety of participants was evaluated using detailed and recognized scale. Compared with the control group, the yoga intervention group indicates a statistically significant decrease in depression levels (SMD (Standardised Mean Difference, SMD), -2.31; 95% CI, -3.67 to -0.96; P=0.139) and anxiety (SMD, -4.75; 95% CI, -8.3 to -1.19; P=0.002). In addition, we also conducted a subgroup analysis according to the type of population. The subgroup analysis successfully reduced the level of heterogeneity and the results indicated that the difference in population types in the combined analysis leads to the higher heterogeneity. The SMD value for healthy women is -2.3 (95% CI, -4.83 to 0.23) and for depressed women is -9.02 (95% CI, -11.42 to -6.62). Finally, the meta-analysis results of the self-control group prove that yoga can reduce the depression scores (SMD, 5.23; 95% CI, 1.90 to 8.56; P=0.049) compared with baseline. Conclusions: Yoga can effectively relieve symptoms of depression and anxiety in the perinatal period, which can be used as an auxiliary treatment option clinically

Goal-Conditioned Predictive Coding as an Implicit Planner for Offline Reinforcement Learning

Recent work has demonstrated the effectiveness of formulating decision making as a supervised learning problem on offline-collected trajectories. However, the benefits of performing sequence modeling on trajectory data is not yet clear. In this work we investigate if sequence modeling has the capability to condense trajectories into useful representations that can contribute to policy learning. To achieve this, we adopt a two-stage framework that first summarizes trajectories with sequence modeling techniques, and then employs these representations to learn a policy along with a desired goal. This design allows many existing supervised offline RL methods to be considered as specific instances of our framework. Within this framework, we introduce Goal-Conditioned Predicitve Coding (GCPC), an approach that brings powerful trajectory representations and leads to performant policies. We conduct extensive empirical evaluations on AntMaze, FrankaKitchen and Locomotion environments, and observe that sequence modeling has a significant impact on some decision making tasks. In addition, we demonstrate that GCPC learns a goal-conditioned latent representation about the future, which serves as an "implicit planner", and enables competitive performance on all three benchmarks

El Tratamiento de los Nombres Propios en la Traducción de "Viaje al Oeste": Un Estudio Cuantitativo-Cualitativo

Journey to the West is one of the classical Chinese literatures and the most translated works. This study compares two translations of this book in Spanish. It focuses on translating proper names, which encompass the names of the Dharma, alias, and those of religious deities. The research is framed within the translation of literary studies, and it takes both a quantitative and qualitative approach. We have chosen the software R and AntConc to extract and process the quantitative data. Variables related to lexical diversity are analyzed and the specific translational techniques used by both translations have been compared. The results indicate that both translations have common and different characteristics. In addition, various translation strategies are adopted depending on the characteristics of the names. Transliteration, amplification, literal translation, or a combination of the strategies are the commonly used methods.Viaje al Oeste es uno de los clásicos literarios de China y también una de las obras más traducidas. Este estudio compara dos traducciones de esta obra en español y se enfoca en la traducción de los nombres propios, que abarcan los nombres del Dharma, los alias y los de las deidades religiosas. La investigación se enmarca dentro de la traducción de los estudios literarios y se adopta un método tanto cuantitativo como cualitativo. Se utilizan los programas R y AntConc para extraer y procesar los datos cuantitativos y se analizan las variables relacionadas con la diversidad léxica e indaga las técnicas empleadas concretas. Los resultados señalan que ambas traducciones presentan características comunes y diferentes. Además, según el valor del nombre del personaje, se adoptan diversas estrategias de traducción. En general, se utiliza la transliteración, la amplificación, la traducción literal o una combinación de estas técnicas

BMP Signaling Mediated by BMPR1A in Osteoclasts Negatively Regulates Osteoblast Mineralization Through Suppression of Cx43

Osteoblasts and osteoclasts are well orchestrated through different mechanisms of communication during bone remodeling. Previously, we found that osteoclast‐specific disruption of one of the BMP receptors, Bmpr1a, results in increased osteoblastic bone formation in mice. We hypothesized that BMPR1A signaling in osteoclasts regulates production of either membrane bound proteins or secreted molecules that regulated osteoblast differentiation. In our current study, we co‐cultured wild‐type osteoblasts with either control osteoclasts or osteoclasts lacking BMPR1A signaling activity. We found that loss of Bmpr1a in osteoclasts promoted osteoblast mineralization in vitro. Further, we found that the expression of Cx43/Gja1 in the mutant osteoclasts was increased, which encoded for one of the gap junction proteins connexin 43/gap junction alpha 1. Knockdown of Gja1 in the mutant osteoclasts for Bmpr1a reduced osteoblastic mineralization when co‐cultured. Our findings suggest that GJA1 may be one of the downstream targets of BMPR1A signaling in osteoclasts that mediates osteoclast–osteoblast communication during bone remodeling. J. Cell. Biochem. 118: 605–614, 2017. © 2016 Wiley Periodicals, Inc.Disruption of Bmpr1a in osteoclasts promoted osteoblast mineralization when co‐cultured. Up‐regulation of gap junction Cx43/Gja1 in mutant osteoclasts is responsible for the enhanced osteoblast function.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135668/1/jcb25746_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135668/2/jcb25746.pd

Fast Depth and Inter Mode Prediction for Quality Scalable High Efficiency Video Coding

International audienceThe scalable high efficiency video coding (SHVC) is an extension of high efficiency video coding (HEVC), which introduces multiple layers and inter-layer prediction, thus significantly increases the coding complexity on top of the already complicated HEVC encoder. In inter prediction for quality SHVC, in order to determine the best possible mode at each depth level, a coding tree unit can be recursively split into four depth levels, including merge mode, inter2Nx2N, inter2NxN, interNx2N, interNxN, in-ter2NxnU, inter2NxnD, internLx2N and internRx2N, intra modes and inter-layer reference (ILR) mode. This can obtain the highest coding efficiency, but also result in very high coding complexity. Therefore, it is crucial to improve coding speed while maintaining coding efficiency. In this research, we have proposed a new depth level and inter mode prediction algorithm for quality SHVC. First, the depth level candidates are predicted based on inter-layer correlation, spatial correlation and its correlation degree. Second, for a given depth candidate, we divide mode prediction into square and non-square mode predictions respectively. Third, in the square mode prediction, ILR and merge modes are predicted according to depth correlation, and early terminated whether residual distribution follows a Gaussian distribution. Moreover, ILR mode, merge mode and inter2Nx2N are early terminated based on significant differences in Rate Distortion (RD) costs. Fourth, if the early termination condition cannot be satisfied, non-square modes are further predicted based on significant differences in expected values of residual coefficients. Finally, inter-layer and spatial correlations are combined with residual distribution to examine whether to early terminate depth selection. Experimental results have demonstrated that, on average, the proposed algorithm can achieve a time saving of 71.14%, with a bit rate increase of 1.27%

Research on bearing radiation noise and optimization design based on coupled vibro-acoustic method

For bearings, radiation noise was an important evaluation index for mechanical property, in particularly mute machinery. Environmental pollution caused by bearing noise has always been the focus in bearing industry. In this paper, slippage of the rolling bearing and its own variable stiffness excitation were considered to accomplish the vibration coupling between the bearing and bearing seat as well as the coupling between bearing vibration and noise by means of combination of dynamic model, FEA model and boundary element method. A perfect coupled vibro-acoustic model of the bearing was built, and its results were compared with the experimental results to verify the reliability of the proposed method. Based on the verified simulation model, the improved design was carried out for the low-noise rolling bearings. Finally, in order to further verify the superiority of the proposed method in this paper, the designed rolling bearing was compared with that of the traditional design method. The results showed that the proposed design method was reliable