Our previous results on the N-soliton interaction in the adiabatic approximation have been extended. It is shown that the complex Toda chain (CTC) model is an universal one in the sense that it describes the N-soliton train interactions for all NLEE from the NLS hierarchy. We derive the perturbed CTC system and show that the small perturbations affect only the center of mass motion and the glob...

Train scheduling is a complex and time consuming task of vital importance in many countries. To schedule trains more accurately, efficiently and robustly than permitted by current techniques a novel job shop approach is proposed and implemented. Unique characteristics of train scheduling are firstly incorporated into the disjunctive graph representation of the solution. Constructive algorithms ...

Spike train distance measures serve two purposes: to measure neuronal firing reliability, and to provide a metric with which spike trains can be classified. We introduce a novel spike train distance based on the Lempel-Ziv complexity that does not require the choice of arbitrary analysis parameters, is easy to implement, and computationally cheap. We determine firing reliability in vivo by calc...

We present a method to estimate the neuronal firing rate from single-trial spike trains. The method, based on convolution of the spike train with a fixed kernel function, is calibrated by means of simulated spike trains for a representative selection of realistic dynamic rate functions. We derive rules for the optimized use and performance of the kernel method, specifically with respect to an e...

A GA-based train dispatching is presented in this paper. A model for the train dispatching on the lines with double tracks is established at first, the model can optimize train dispatching by adjusting the orders and times of trains’ departure from stations, and then the efficiency of the method is demonstrated by simulation at Guangzhou to Shenzhen high-speed railway.

Abstract—Neural networks have become an increasingly popular solution for network intrusion detection systems (NIDS). Their capability of learning complex patterns and behaviors make them a suitable solution for differentiating between normal traffic and network attacks. However, a drawback of neural networks is the amount of resources needed to train them. Many network gateways and routers dev...

In this work we resolve the long-outstanding problem of how to effectively train recurrent neural networks (RNNs) on complex and difficult sequence modeling problems which may contain long-term data dependencies. Utilizing recent advances in the Hessian-free optimization approach (Martens, 2010), together with a novel damping scheme, we successfully train RNNs on two sets of challenging problem...

This paper considers a Train Shunting problem which occurs in cargo train organizations: We have a locomotive travelling along a track segment and a collection of n cars, where each car has a source and a target. Whenever the train passes the source of a car, it needs to be added to the train, and on the target, the respective car needs to be removed. Any such operation at the end of the train ...