We prove that except possibly for small exceptional sets, discrete(cid:173) time analog neural nets are globally observable, i.e. all their cor(cid:173) rupted pseudo-orbits on computer simulations actually reflect the true dynamical behavior of the network. Locally finite discrete (boolean) neural networks are observable without exception.

A training method based on a form of continuous spatially distributed optical error back-propagation is presented for an all optical network composed of nondiscrete neurons and weighted interconnections. The all optical network is feed-forward and is composed of thin layers of a Kerr(cid:173) type self focusing/defocusing nonlinear optical material. The training method is derived from a Lagrangian formulation of the constrained minimization of the network error at the output. This leads to a formulation that describes training as a calculation of the distributed error of the optical signal at the output which is then reflected back through the device to assign a spatially distributed error to the internal layers. This error is then used to modify the internal weighting values.

Random errors and insufficiencies in databases limit the perfor(cid:173) mance of any classifier trained from and applied to the database. In this paper we propose a method to estimate the limiting perfor(cid:173) mance of classifiers imposed by the database. We demonstrate this technique on the task of predicting failure in telecommunication paths.

This paper describes a neural network based controller for allocating capacity in a telecommunications network. This system was proposed in order to overcome a "real time" response constraint. Two basic architectures are evaluated: 1) a feedforward network-heuristic and; 2) a feedforward network-recurrent network. These architectures are compared against a linear programming (LP) optimiser as a benchmark. This LP optimiser was also used as a teacher to label the data samples for the feedforward neural network training algorithm.

In cellular telephone systems, an important problem is to dynami(cid:173) cally allocate the communication resource (channels) so as to max(cid:173) imize service in a stochastic caller environment. This problem is naturally formulated as a dynamic programming problem and we use a reinforcement learning (RL) method to find dynamic channel allocation policies that are better than previous heuristic solutions. The policies obtained perform well for a broad variety of call traf(cid:173) fic patterns. In cellular communication systems, an important problem is to allocate the com(cid:173) munication resource (bandwidth) so as to maximize the service provided to a set of mobile callers whose demand for service changes stochastically. A given geograph(cid:173) ical area is divided into mutually disjoint cells, and each cell serves the calls that are within its boundaries (see Figure 1a).

This paper examines the application of reinforcement learning to a telecommunications networking problem . The problem requires that rev(cid:173) enue be maximized while simultaneously meeting a quality of service constraint that forbids entry into certain states. We present a general solution to this multi-criteria problem that is able to earn significantly higher revenues than alternatives.

When designing a two-alternative classifier, one ordinarily aims to maximize the classifier's ability to discriminate between members of the two classes. We describe a situation in a real-world business application of machine-learning prediction in which an additional constraint is placed on the nature of the solu- tion: that the classifier achieve a specified correct acceptance or correct rejection rate (i.e., that it achieve a fixed accuracy on members of one class or the other). Our domain is predicting churn in the telecommunications industry. Churn refers to customers who switch from one service provider to another. We pro- pose four algorithms for training a classifier subject to this domain constraint, and present results showing that each algorithm yields a reliable improvement in performance.

Qualcomm is one of America's leading players in the wireless space. That being said, they are also wrestling with several weak links in their otherwise strong chain. Some of their key wireless sectors like chip sets and smartphones have weakened. I believe that is why Qualcomm is trying to refocus their efforts on new segments for growth to keep investors excited. That's why when we pull the camera back, we see Qualcomm searching for new areas of growth in recent years.

Chinese tech giants Alibaba and Huawei are reportedly ready to satisfy local demand for generative AI chatbots in coming weeks. Since the release of OpenAI's ChatGPT, Chinese users have been eager to get their hands on the technology. Cities like Beijing have pledged to assist developers, while academia and private industry alike have made progress. A university-developed ChatGPT analog crashed within a mere four hours under the weight of a crushing traffic surge. Meanwhile, private industry versions like Baidu's ERNIE have had their own troubles – including managing censorship and botching some requests.

The increasing prevalence of network data in a vast variety of fields and the need to extract useful information out of them have spurred fast developments in related models and algorithms. Among the various learning tasks with network data, community detection, the discovery of node clusters or "communities," has arguably received the most attention in the scientific community. In many real-world applications, the network data often come with additional information in the form of node or edge covariates that should ideally be leveraged for inference. In this paper, we add to a limited literature on community detection for networks with covariates by proposing a Bayesian stochastic block model with a covariate-dependent random partition prior. Under our prior, the covariates are explicitly expressed in specifying the prior distribution on the cluster membership. Our model has the flexibility of modeling uncertainties of all the parameter estimates including the community membership. Importantly, and unlike the majority of existing methods, our model has the ability to learn the number of the communities via posterior inference without having to assume it to be known. Our model can be applied to community detection in both dense and sparse networks, with both categorical and continuous covariates, and our MCMC algorithm is very efficient with good mixing properties. We demonstrate the superior performance of our model over existing models in a comprehensive simulation study and an application to two real datasets.