Simplistic Universal Protocols for Remotely Preparing Arbitrary Equatorial States

We first put forward a deterministic protocol to realize the remote preparation of arbitrary multi-qubit equatorial states via EPR pairs.A set of useful measurement basis is constructed which plays a key role.The receiver just needs to perform Pauli Z operations to recover the target state.Comparing with the previous protocols,the recovery operation is simplified and expressed by a general formula.As there are no universal protocols for high-dimensional systems,we further generalize to the case of multi-qudit equatorial states by means of Fourier transformation.It is worth mentioning that the proposed schemes can be extended to multi-party controlled remote state preparation.Moreover,we consider the effect of two-type decoherence noises.

Maximum Net Benefit Indicator and Its Applications

Receiver operating characteristics(ROC)curve and the area under the curve(AUC)value are often used to illustrate the diagnostic ability of binary classifiers.However,both ROC and AUC focus on high accuracy in theory,which may not be effective for practical applications.In addition,it is difficult to judge which one is better when the ROC curves are intersect and the AUC values are equal.Decision curve analysis(DCA)methods improve ROC by incorporating accuracy and consequences.However,similar to ROC,DCA requires a quantitative indicator to objectively determine which one is better when DCA curves intersect.A DCA-based statistical indicator named maximum net benefit(MNB)is constructed for evaluating clinical treatment regimens rather than just accuracy as in ROC and AUC.As a simple and effective statistical indicator,the construction process of MNB is given theoretically.Moreover,the MNB can still provide effective identification when the AUC values are equal,which is proved by theory.Furthermore,the feasibility and effectiveness of the proposed MNB are verified by gene selection and classifier performance comparison on actual data.

EEG Feature Learning Model Based on Intrinsic Time-Scale Decomposition and Adaptive Huber Loss

According to the World Health Organization,about 50 million people worldwide suffer from epilepsy.The detection and treatment of epilepsy face great challenges.Electroencephalogram(EEG)is a significant research object widely used in diagnosis and treatment of epilepsy.In this paper,an adaptive feature learning model for EEG signals is proposed,which combines Huber loss function with adaptive weight penalty term.Firstly,each EEG signal is decomposed by intrinsic time-scale decomposition.Secondly,the statistical index values are calculated from the instantaneous amplitude and frequency of every component and fed into the proposed model.Finally,the discriminative features learned by the proposed model are used to detect seizures.Our main innovation is to consider a highly flexible penalization based on Huber loss function,which can set different weights according to the influence of different features on epilepsy detection.Besides,the new model can be solved by proximal alternating direction multiplier method,which can effectively ensure the convergence of the algorithm.The performance of the proposed method is evaluated on three public EEG datasets provided by the Bonn University,Childrens Hospital Boston-Massachusetts Institute of Technology,and Neurological and Sleep Center at Hauz Khas,New Delhi(New Delhi Epilepsy data).The recognition accuracy on these two datasets is 98%and 99.05%,respectively,indicating the application value of the new model.

A Level Set Representation Method for N-Dimensional Convex Shape and Applications

In this work,we present a new method for convex shape representation,which is regardless of the dimension of the concerned objects,using level-set approaches.To the best of our knowledge,the proposed prior is the first one which can work for high dimensional objects.Convexity prior is very useful for object completion in computer vision.It is a very challenging task to represent high dimensional convex objects.In this paper,we first prove that the convexity of the considered object is equivalent to the convexity of the associated signed distance function.Then,the second order condition of convex functions is used to characterize the shape convexity equivalently.We apply this new method to two applications:object segmentation with convexity prior and convex hull problem(especially with outliers).For both applications,the involved problems can be written as a general optimization problem with three constraints.An algorithm based on the alternating direction method of multipliers is presented for the optimization problem.Numerical experiments are conducted to verify the effectiveness of the proposed representation method and algorithm.

A universal protocol for bidirectional controlled teleportation with network coding

We investigate bidirectional teleportation that works in a fair and efficient manner. Two explicit protocols are proposed to realize bidirectional teleportation with a controller. One is a symmetric protocol for two-qubit states. The other is an asymmetric protocol for single-and two-qubit states. We then devise a universal protocol for arbitrary n_(1)-and n_(2)-qubit states via a(2n_(1)+2n_(2)+1)-qubit entangled state, where n_(1)≤n_(2).The receiver only needs to perform the single-qubit recovery operation, which is derived by a general expression. Moreover, a(2n_(1)+1)-bit classical communication cost can be saved within the controller’s broadcast channel by the use of network coding technology.