ROBUST DEPENDENCE MEASURE FOR DETECTING ASSOCIATIONS IN LARGE DATA SET

In this paper, we proposed a new statistical dependency measure for two random vectors based on copula, called copula dependency coefficient （CDC）. The CDC is proved to be robust to outliers and easy to be implemented. Especially, it is powerful and applicable to high-dimensional problems. All these properties make CDC practically important in related applications. Both experimental and application results show that CDC is a good robust dependence measure for association detecting.

Decision Bayes Criteria for Optimal Classifier Based on Probabilistic Measures

This paper addresses the high dimension sample problem in discriminate analysis under nonparametric and supervised assumptions. Since there is a kind of equivalence between the probabilistic dependence measure and the Bayes classification error probability, we propose to use an iterative algorithm to optimize the dimension reduction for classification with a probabilistic approach to achieve the Bayes classifier. The estimated probabilities of different errors encountered along the different phases of the system are realized by the Kernel estimate which is adjusted in a means of the smoothing parameter. Experiment results suggest that the proposed approach performs well.

A time-dependent measuring system for welding deformation

In this paper the establishment and application of a time dependent measuring system for welding deformation are presented which is established with high quality sensors shielded from strong welding interference. By using this system, vertical and horizontal displacements of the high temperature area are surveyed at the same time. And this system is also used for monitoring and controlling the deformation of real welded structures.

Success and Incoherence of Orthodox Quantum Mechanics

Orthodox quantum mechanics is a highly successful theory despite its serious conceptual flaws. It renounces realism, implies a kind of action-at-a-distance and is incompatible with determinism. Orthodox quantum mechanics states that Schr&oumldinger’s equation (a deterministic law) governs spontaneous processes while measurement processes are ruled by probability laws. It is well established that time dependent perturbation theory must be used for solving problems involving time. In order to account for spontaneous processes, this last theory makes use of laws valid only when measurements are performed. This incoherence seems absent from the literature.

Is the kinematics of special relativity incomplete?

A thorough analysis of composite inertial motion (relativistic sum) within the framework of special relativity leads to the conclusion that every translational motion must be the symmetrically composite relativistic sum of a finite number of quanta of velocity. It is shown that the resulting spacetime geometry is Gaussian and the four-vector calculus to have its roots in the complex-number algebra. Furthermore, this results in superluminality of signals travelling at or nearly at the canonical velocity of light between rest frames even if resting to each other.

Measurement on Spot Size Dependence of Dense WDM Dielectric Multilayer Filters

We present the spot size dependence of dielectric multilayer filters for use in dense WDM systems. We found large dependences of filter performances on the spot size and the incident angle of input light, which should be important for miniaturizing multi-channel add/drop filters.

Consistency of kernel density estimators for causal processes

Using the blocking techniques and m-dependent methods,the asymptotic behavior of kernel density estimators for a class of stationary processes,which includes some nonlinear time series models,is investigated.First,the pointwise and uniformly weak convergence rates of the deviation of kernel density estimator with respect to its mean(and the true density function)are derived.Secondly,the corresponding strong convergence rates are investigated.It is showed,under mild conditions on the kernel functions and bandwidths,that the optimal rates for the i.i.d.density models are also optimal for these processes.

This work was supported in part by US Department of Energy Los Alamos National Laboratory contract 47145 and UT-Battelle LLC contract 4000159447 program manager Laura Biven.

There is a growing trend of applying machine learning methods to medical datasets in order to predict patients’future status.Although some of these methods achieve high performance,challenges still exist in comparing and evaluating different models through their interpretable information.Such analytics can help clinicians improve evidence-based medical decision making.In this work,we develop a visual analytics system that compares multiple models’prediction criteria and evaluates their consistency.With our system,users can generate knowledge on different models’inner criteria and how confidently we can rely on each model’s prediction for a certain patient.Through a case study of a publicly available clinical dataset,we demonstrate the effectiveness of our visual analytics system to assist clinicians and researchers in comparing and quantitatively evaluating different machine learning methods.