Envelopes of legendre curves in the unit spherical bundle over the unit sphere

In this paper, we introduce a one-parameter family of Legendre curves in the unit spherical bundle over the unit sphere and the curvature. We give the existence and uniqueness theorems for one-parameter families of spherical Legendre curves by using the curvatures. Then we define an envelope for the one-parameter family of Legendre curves in the unit spherical bundle. We also consider the parallel curves and evolutes of one-parameter families of Legendre curves in the unit spherical bundle and their envelopes. Moreover, we give relationships among one-parameter families of Legendre curves in the unit spherical bundle over the unit sphere and one-parameter families of Legendre curves in the unit tangent bundle over the Euclidean plane

Back-action Induced Non-equilibrium Effect in Electron Charge Counting Statistics

We report our study of the real-time charge counting statistics measured by a quantum point contact (QPC) coupled to a single quantum dot (QD) under different back-action strength. By tuning the QD-QPC coupling or QPC bias, we controlled the QPC back-action which drives the QD electrons out of thermal equilibrium. The random telegraph signal (RTS) statistics showed strong and tunable non-thermal-equilibrium saturation effect, which can be quantitatively characterized as a back-action induced tunneling out rate. We found that the QD-QPC coupling and QPC bias voltage played different roles on the back-action strength and cut-off energy.Comment: 4 pages, 4 figures, 1 tabl

Deep learning methods for protein torsion angle prediction

Background: Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. Results: We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Conclusions: Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy

Multi-Perspective Fusion Network for Commonsense Reading Comprehension

Commonsense Reading Comprehension (CRC) is a significantly challenging task, aiming at choosing the right answer for the question referring to a narrative passage, which may require commonsense knowledge inference. Most of the existing approaches only fuse the interaction information of choice, passage, and question in a simple combination manner from a \emph{union} perspective, which lacks the comparison information on a deeper level. Instead, we propose a Multi-Perspective Fusion Network (MPFN), extending the single fusion method with multiple perspectives by introducing the \emph{difference} and \emph{similarity} fusion\deleted{along with the \emph{union}}. More comprehensive and accurate information can be captured through the three types of fusion. We design several groups of experiments on MCScript dataset \cite{Ostermann:LREC18:MCScript} to evaluate the effectiveness of the three types of fusion respectively. From the experimental results, we can conclude that the difference fusion is comparable with union fusion, and the similarity fusion needs to be activated by the union fusion. The experimental result also shows that our MPFN model achieves the state-of-the-art with an accuracy of 83.52\% on the official test set

Self-management education interventions for persons with schizophrenia: a meta-analysis

Although self-management education programs for persons with schizophrenia are being developed and advocated, uncertainty about their overall effectiveness remains. The purpose of this meta-analysis was to examine outcomes of self-management education interventions in persons with schizophrenia. Six electronic databases were searched. Manual searches were conducted of the reference lists of the identified studies and major psychiatric journals. Randomized controlled trials of self-management education interventions aimed at reducing relapse and hospital readmissions, as well as improving symptoms, psychosocial functioning, and adherence to medication treatment were identified. Data were extracted and the quality of included studies were rated by two authors independently. Finally, 13 studies with 1404 patients were included. Self-management education interventions were associated with a significant reduction of relapse events and re-hospitalizations. Patients who received self-management education were more likely to improve adherence to medication and symptoms compared to patients receiving other care. However, a benefit on psychosocial functioning was not confirmed in the current meta-analysis. The study concludes that self-management education intervention is a feasible and effective method for persons with schizophrenia and should be routinely offered to all persons with schizophrenia