Network data appear in a number of applications, such as online social networks and biological networks, and there is growing interest in both developing models for networks as well as studying the properties of such data. Since individual network datasets continue to grow in size, it is necessary to develop models that accurately represent the real-life scaling properties of networks. One behavior of interest is having a power law in the degree distribution. However, other types of power laws that have been observed empirically and considered for applications such as clustering and feature allocation models have not been studied as frequently in models for graph data. In this paper, we enumerate desirable asymptotic behavior that may be of interest for modeling graph data, including sparsity and several types of power laws. We outline a general framework for graph generative models using completely random measures; by contrast to the pioneering work of Caron and Fox (2015), we consider instantiating more of the existing atoms of the random measure as the dataset size increases rather than adding new atoms to the measure. We see that these two models can be complementary; they respectively yield interpretations as (1) time passing among existing members of a network and (2) new individuals joining a network. We detail a particular instance of this framework and show simulated results that suggest this model exhibits some desirable asymptotic power-law behavior.

When HP Labs research scientist Lei Liu was a child in XianYang, China, he read a newspaper article detailing how HP originated in a garage in Palo Alto. "That inspired me," he recalls. "Silicon Valley was clearly somewhere where you could have a dream, incubate it, and see it come true." Today, Lei is living that dream as a member of HP's Print and 3D Lab. After studying for his B.S. and M.S. in computer science at the Beijing University of Posts and Telecommunications, he moved to Michigan State University where he received his Ph.D. in Computer Science and Engineering, focusing on data mining and machine learning.

As retailers and brands predict and plan for the way consumers will shop in the future, artificial intelligence (AI) is high on the business development strategy for 2016 and beyond. Promising significant benefits for both retailers and consumers, AI is already around us and used everyday within shopping and payments. Businesses are embracing the benefits of the technology and progress within AI is accelerating at pace, with big things expected for the near, and distant, future. AI can process'big data' far more efficiently than humans and can recognise speech, images, text, patterns of online behaviour – for example to detect fraud – as well as appropriate advertisements for upselling. Smart machines and technology can turn data into customer insights and enhance service provisions, bringing the digital experience closer to the in-store interaction for consumers.

Ishiguro does not expect the average household to buy a Geminoid -- in part because of the 100,000 price tag -- but he already has some orders from researchers. He does expect his smaller CommU communicative robots to make their way inside many households within the next couple of years. Like Amazon's Echo -- but much cuter -- these chatty robots use voice recognition technology and artificial intelligence to simulate conversation. An example of where they can be useful is in tutoring, said Ishiguro. Many Japanese learners struggle with speaking English because they do not get enough practice.

Adaptive Filter for Automatic Identification of Multiple Faults in a Noisy OTDR Profile Jean Pierre von der Weid, Mario H. Souto, Joaquim D. Garcia, and Gustavo C. Amaral November 7, 2018 Abstract We present a novel methodology able to distinguish meaningful level shifts from typical signal fluctuations. A two-stage regularization filtering can accurately identify the location of the significant level-shifts with an efficient parameter-free algorithm. The developed methodology demands low computational effort and can easily be embedded in a dedicated processing unit. Our case studies compare the new methodology with current available ones and show that it is the most adequate technique for fast detection of multiple unknown level-shifts in a noisy OTDR profile. 1 Introduction The central problem in fiber monitoring is the detection of small faults or losses most commonly performed by inspecting the trace of an Optical Time Domain Reflectometer (OTDR) [1]. These faults appear as small level shifts in a slowly varying backscattered optical power, eventually masked by the detector noise. Averaging over many OTDR shots is usually required to get access to the information needed. However, measurement time is of paramount importance in network monitoring, so that signal processing and filtering is a fundamental tool to improve time and sensitivity of the overall process. Moreover, in the case of wavelength multiplexed optical networks (WDM-PON) the problem is still worse because coherent backscattered power fluctuations (CRN) cannot be averaged out by summing up many OTDR shots [2].

Kozlenko, M.I. (2012), "Frequency resource using of the spread spectrum signals forming in the distributed computer and telecommunication systems", Kozlenko, M.I. (2012), "Time complexity of the variable entropy spread spectrum signals digital demodulation", To obtain the interference immunity of the data exchange by spread spectrum signals with variable entropy of the telemetric information data exchange with autonomous mobile robots. The results have been obtained by the theoretical investigations and have been confirmed by the modeling experiments. The interference immunity in form of dependence of bit error probability on normalized signal/noise ratio of the data exchange by spread spectrum signals with variable entropy has been obtained. It has been proved that the interference immunity fa ctor (needed normalized signal/noise ratio) is at least 2 dB better under condition of equal time complexity as compared with correlation processing methods of orthogonal signals. For the first time the interference immunity in form of dependence of bit error probability on normalized signal/noise ratio of the data exchange by spread spectrum signals with variable entropy has been obtained.

Many statistical methods for network data parameterize the edge-probability by attributing latent traits to the vertices such as block structure and assume exchangeability in the sense of the Aldous-Hoover representation theorem. Empirical studies of networks indicate that many real-world networks have a power-law distribution of the vertices which in turn implies the number of edges scale slower than quadratically in the number of vertices. These assumptions are fundamentally irreconcilable as the Aldous-Hoover theorem implies quadratic scaling of the number of edges. Recently Caron and Fox (2014) proposed the use of a different notion of exchangeability due to Kallenberg (2009) and obtained a network model which admits power-law behaviour while retaining desirable statistical properties, however this model does not capture latent vertex traits such as block-structure. In this work we re-introduce the use of block-structure for network models obeying Kallenberg's notion of exchangeability and thereby obtain a model which admits the inference of block-structure and edge inhomogeneity. We derive a simple expression for the likelihood and an efficient sampling method. The obtained model is not significantly more difficult to implement than existing approaches to block-modelling and performs well on real network datasets.

For the last few years, the amount of data has significantly increased in the companies. It is the reason why data analysis methods have to evolve to meet new demands. In this article, we introduce a practical analysis of a large database from a telecommunication operator. The problem is to segment a territory and characterize the retrieved areas owing to their inhabitant behavior in terms of mobile telephony. We have call detail records collected during five months in France. We propose a two stages analysis. The first one aims at grouping source antennas which originating calls are similarly distributed on target antennas and conversely for target antenna w.r.t. source antenna. A geographic projection of the data is used to display the results on a map of France. The second stage discretizes the time into periods between which we note changes in distributions of calls emerging from the clusters of source antennas. This enables an analysis of temporal changes of inhabitants behavior in every area of the country.

Multi-cell cooperative processing with limited backhaul traffic is studied for cellular uplinks. Aiming at reduced backhaul overhead, a sparsity-regularized multi-cell receive-filter design problem is formulated. Both unstructured distributed cooperation as well as clustered cooperation, in which base station groups are formed for tight cooperation, are considered. Dynamic clustered cooperation, where the sparse equalizer and the cooperation clusters are jointly determined, is solved via alternating minimization based on spectral clustering and group-sparse regression. Furthermore, decentralized implementations of both unstructured and clustered cooperation schemes are developed for scalability, robustness and computational efficiency. Extensive numerical tests verify the efficacy of the proposed methods.

Telecommunications operators (telcos) traditional sources of income, voice and SMS, are shrinking due to customers using over-the-top (OTT) applications such as WhatsApp or Viber. In this challenging environment it is critical for telcos to maintain or grow their market share, by providing users with as good an experience as possible on their network. But the task of extracting customer insights from the vast amounts of data collected by telcos is growing in complexity and scale everey day. How can we measure and predict the quality of a user's experience on a telco network in real-time? That is the problem that we address in this paper. We present an approach to capture, in (near) real-time, the mobile customer experience in order to assess which conditions lead the user to place a call to a telco's customer care center. To this end, we follow a supervised learning approach for prediction and train our 'Restricted Random Forest' model using, as a proxy for bad experience, the observed customer transactions in the telco data feed before the user places a call to a customer care center. We evaluate our approach using a rich dataset provided by a major African telecommunication's company and a novel big data architecture for both the training and scoring of predictive models. Our empirical study shows our solution to be effective at predicting user experience by inferring if a customer will place a call based on his current context. These promising results open new possibilities for improved customer service, which will help telcos to reduce churn rates and improve customer experience, both factors that directly impact their revenue growth.