Solution Path Algorithm for Twin Multi-class Support Vector Machine

The twin support vector machine and its extensions have made great achievements in dealing with binary classification problems, however, which is faced with some difficulties such as model selection and solving multi-classification problems quickly. This paper is devoted to the fast regularization parameter tuning algorithm for the twin multi-class support vector machine. A new sample dataset division method is adopted and the Lagrangian multipliers are proved to be piecewise linear with respect to the regularization parameters by combining the linear equations and block matrix theory. Eight kinds of events are defined to seek for the starting event and then the solution path algorithm is designed, which greatly reduces the computational cost. In addition, only few points are combined to complete the initialization and Lagrangian multipliers are proved to be 1 as the regularization parameter tends to infinity. Simulation results based on UCI datasets show that the proposed method can achieve good classification performance with reducing the computational cost of grid search method from exponential level to the constant level

Longitudinal compression of macro relativistic electron beam

We presented a novel concept of longitudinal bunch train compression capable of manipulating relativistic electron beam in range of hundreds of meters. This concept has the potential to compress the electron beam with a high ratio and raise its power to an ultrahigh level. The method utilizes the spiral motion of electrons in a uniform magnetic field to fold hundreds-of-meters-long trajectories into a compact set-up. The interval between bunches can be adjusted by modulating their sprial movement. The method is explored both analytically and numerically. Compared to set-up of similar size, such as chicane, this method can compress bunches at distinct larger scales and higher intensities, opening up new possibilities for generating beam with ultra-large energy storage.Comment: 6 pages, 6 figure

Multinomial Regression with Elastic Net Penalty and Its Grouping Effect in Gene Selection

For the multiclass classification problem of microarray data, a new optimization model named multinomial regression with the elastic net penalty was proposed in this paper. By combining the multinomial likeliyhood loss and the multiclass elastic net penalty, the optimization model was constructed, which was proved to encourage a grouping effect in gene selection for multiclass classification

Seismo-ionospheric anomalies in ionospheric TEC and plasma density before the 17 July 2006 M 7.7 south of Java earthquake

Abstract. In this paper, we report significant evidence for preseismic ionospheric anomalies in total electron content (TEC) of the global ionosphere map (GIM) and plasma density appearing on day 2 before the 17 July 2006 M7.7 south of Java earthquake. After distinguishing other anomalies related to the geomagnetic activities, we found a temporal precursor around the epicenter on day 2 before the earthquake (15 July 2006), which agrees well with the spatial variations in latitude–longitude–time (LLT) maps. Meanwhile, the sequences of latitude–time–TEC (LTT) plots reveal that the TECs on epicenter side anomalously decrease and lead to an anomalous asymmetric structure with respect to the magnetic equator in the daytime from day 2 before the earthquake. This anomalous asymmetric structure disappears after the earthquake. To further confirm these anomalies, we studied the plasma data from DEMETER satellite in the earthquake preparation zone (2046.4 km in radius) during the period from day 45 before to day 10 after the earthquake, and also found that the densities of both electron and total ion in the daytime significantly increase on day 2 before the earthquake. Very interestingly, O+ density increases significantly and H+ density decreases, while He+ remains relatively stable. These results indicate that there exists a distinct preseismic signal (preseismic ionospheric anomaly) over the epicenter

Ultrasensitive piezoelectric sensor based on two-dimensional Na2Cl crystals with periodic atom vacancies

Pursuing ultrasensitivity of pressure sensors has been a long-standing goal. Here, we report a piezoelectric sensor that exhibits supreme pressure-sensing performance, including a peak sensitivity up to 3.5*10^6 kPa^-1 in the pressure range of 1-100 mPa and a detection limit of less than 1 mPa, superior to the current state-of-the-art pressure sensors. These properties are attributed to the high percentage of periodic atom vacancies in the two-dimensional Na2Cl crystals formed within multilayered graphene oxide membrane in the sensor, which provides giant polarization with high stability. The sensor can even clearly detect the airflow fluctuations surrounding a flapping butterfly, which have long been the elusive tiny signals in the famous "butterfly effect". The finding represents a step towards next-generation pressure sensors for various precision applications

Multinomial Regression with Elastic Net Penalty and Its Grouping Effect in Gene Selection

For the multiclass classification problem of microarray data, a new optimization model named multinomial regression with the elastic net penalty was proposed in this paper. By combining the multinomial likeliyhood loss and the multiclass elastic net penalty, the optimization model was constructed, which was proved to encourage a grouping effect in gene selection for multiclass classification