Dark matter and stable bound states of primordial black holes

We present three reasons for the formation of gravitational bound states of primordial black holes,called holeums,in the early universe.Using Newtonian gravity and nonrelativistic quantum mechanics we find a purely quantum mechanical mass-dependant exclusion property for the nonoverlap of the constituent black holes in a holeum.This ensures that the holeum occupies space just like ordinary matter.A holeum emits only gravitational radiation whose spectrum is an exact analogue of that of a hydrogen atom. A part of this spectrum lies in the region accessible to the detectors being built.The holeums would form haloes around the galaxies and would be an important component of the dark matter in the universe today.They may also be the constituents of the invisible domain walls in the universe.Comment: 13 pages,2tables,for wider circulation,PD

A Survey of Model Used for Web User’s Browsing Behavior Prediction

The motivation behind the work is that the prediction of web user’s browsing behavior while serving the Internet, reduces the user’s browsing access time and avoids the visit of unnecessary pages to ease network traffic. Various models such as fuzzy interference models, support vector machines (SVMs), artificial neural networks (ANNs), association rule mining (ARM), k-nearest neighbor(kNN) Markov model, Kth order Markov model, all-Kth Markov model and modified Markov model were proposed to handle Web page prediction problem. Many times, the combination of two or more models were used to achieve higher prediction accuracy. This research work introduces the Support Vector Machines for web page prediction. The advantages of using support vector machines is that it offers most robust and accurate classification due to their generalized properties with its solid theoretical foundation and proven effectiveness. Web contains enormous amount of data and web data increases exponentially but the training time for Support vector machine is very large. That is, SVM’s suffer from a widely recognized scalability problem in both memory requirement and computation time when the input dataset is too large. To address this, I aimed at training the Support vector machine model in MapReduce programming model of Hadoop framework, since the MapReduce programming model has the ability to rapidly process large amount of data in parallel. MapReduce works in tandem with Hadoop Distributed File System (HDFS). So proposed approach will solve the scalability problem of present SVM algorithm. Keywords:Web Page Prediction, Support Vector Machines, Hadoop, MapReduce, HDFS

Spectral analysis of molecular resonances in erbium isotopes: Are they close to semi-Poisson?

We perform a thorough analysis of the spectral statistics of experimental molecular resonances, of bosonic erbium $^{166}$Er and $^{168}$Er isotopes, produced as a function of magnetic field($B$) by Frisch et al. [Nature 507, (2014) 475], utilizing some recently derived surmises which interpolate between Poisson and GOE and without unfolding. Supplementing this with an analysis using unfolded spectrum, it is shown that the resonances are close to semi-Poisson distribution. There is an earlier claim of missing resonances by Molina et al. [Phys. Rev. E 92, (2015) 042906]. These two interpretations can be tested by more precise measurements in future experiments.Comment: 7 pages, 6 figure