Online Machine Learning for Graph Topology Identification from Multiple Time Series

High dimensional time series data are observed in many complex systems. In networked data, some of the time series are influenced by other time series. Identifying these relations encoded in a graph structure or topology among the time series is of paramount interest in certain applications since the identified structure can provide insights about the underlying system and can assist in inference tasks. In practice, the underlying topology is usually sparse, that is, not all the participating time series in influence each other. The goal of this dissertation pertains to study the problem of sparse topology identification under various settings. Topology identification from time series is a challenging task. The first major challenge in topology identification is that the assumption of static topology does not hold always in practice since most of the practical systems are evolving with time. For instance, in econometrics, social networks, etc., the relations among the time series can change over time. Identifying the topologies of such dynamic networks is a major challenge. The second major challenge is that in most practical scenarios, the data is not available at once - it is coming in a streaming fashion. Hence, batch approaches are either not applicable or they become computationally expensive since a batch algorithm is needed to be run when a new datum becomes available. The third challenge is that the multi-dimensional time series data can contain missing values due faulty sensors, privacy and security reasons, or due to saving energy. We address the aforementioned challenges in this dissertation by proposing online/-batch algorithms to solve the problem of time-varying topology identification. A model based on vector autoregressive (VAR) process is adopted initially. The parameters of the VAR model reveal the topology of the underlying network. First, two online algorithms are proposed for the case of streaming data. Next, using the same VAR model, two online algorithms under the framework of online optimization are presented to track the time-varying topologies. To evaluate the performance of propose online algorithms, we show that both the proposed algorithms incur a sublinear static regret. To characterize the performance theoretically in time-varying scenarios, a bound on the dynamic regret for one of the proposed algorithms (TIRSO) is derived. Next, using a structural equation model (SEM) for topology identification, an online algorithm for tracking time-varying topologies is proposed, and a bound on the dynamic regret is also derived for the proposed algorithm. Moreover, using a non-stationary VAR model, an algorithm for dynamic topology identification and breakpoint detection is also proposed, where the notion of local structural breakpoint is introduced to accommodate the concept of breakpoint where instead of the whole topology, only a few edges vary. Finally, the problem of tracking VAR-based time-varying topologies with missing data is investigated. Online algorithms are proposed where the joint signal and topology estimation is carried out. Dynamic regret analysis is also presented for the proposed algorithm. For all the previously mentioned works, simulation tests about the proposed algorithms are also presented and discussed in this dissertation. The numerical results of the proposed algorithms corroborate with the theoretical analysis presented in this dissertation.publishedVersio

Online Joint Topology Identification and Signal Estimation with Inexact Proximal Online Gradient Descent

Identifying the topology that underlies a set of time series is useful for tasks such as prediction, denoising, and data completion. Vector autoregressive (VAR) model based topologies capture dependencies among time series, and are often inferred from observed spatio-temporal data. When the data are affected by noise and/or missing samples, the tasks of topology identification and signal recovery (reconstruction) have to be performed jointly. Additional challenges arise when i) the underlying topology is time-varying, ii) data become available sequentially, and iii) no delay is tolerated. To overcome these challenges, this paper proposes two online algorithms to estimate the VAR model-based topologies. The proposed algorithms have constant complexity per iteration, which makes them interesting for big data scenarios. They also enjoy complementary merits in terms of complexity and performance. A performance guarantee is derived for one of the algorithms in the form of a dynamic regret bound. Numerical tests are also presented, showcasing the ability of the proposed algorithms to track the time-varying topologies with missing data in an online fashion.Comment: 14 pages including supplementary material, 2 figures, submitted to IEEE Transactions on Signal Processin

Dynamic network identification from non-stationary vector autoregressive time series

Author's accepted manuscript (postprint).© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.acceptedVersio

Duplex PCR assay for the detection of avian adeno virus and chicken anemia virus prevalent in Pakistan

Avian Adeno viruses and Chicken Anemia Viruses cause serious economic losses to the poultry industry of Pakistan each year. Timely and efficient diagnosis of the viruses is needed in order to practice prevention and control strategies. In the first part of this study, we investigated broilers, breeder and Layer stocks for morbidity and mortality rates due to AAV and CAV infections and any co-infections by examining signs and symptoms typical of their infestation or post mortem examination. In the second part of the study, we developed a duplex PCR assay for the detection of AAV and CAV which is capable to simultaneously detect both the viral types prevalent in Pakistan with high sensitivity and 100% specificity

Online Machine Learning for Graph Topology Identification from Multiple Time Series

High dimensional time series data are observed in many complex systems. In networked data, some of the time series are influenced by other time series. Identifying these relations encoded in a graph structure or topology among the time series is of paramount interest in certain applications since the identified structure can provide insights about the underlying system and can assist in inference tasks. In practice, the underlying topology is usually sparse, that is, not all the participating time series in influence each other. The goal of this dissertation pertains to study the problem of sparse topology identification under various settings. Topology identification from time series is a challenging task. The first major challenge in topology identification is that the assumption of static topology does not hold always in practice since most of the practical systems are evolving with time. For instance, in econometrics, social networks, etc., the relations among the time series can change over time. Identifying the topologies of such dynamic networks is a major challenge. The second major challenge is that in most practical scenarios, the data is not available at once - it is coming in a streaming fashion. Hence, batch approaches are either not applicable or they become computationally expensive since a batch algorithm is needed to be run when a new datum becomes available. The third challenge is that the multi-dimensional time series data can contain missing values due faulty sensors, privacy and security reasons, or due to saving energy. We address the aforementioned challenges in this dissertation by proposing online/-batch algorithms to solve the problem of time-varying topology identification. A model based on vector autoregressive (VAR) process is adopted initially. The parameters of the VAR model reveal the topology of the underlying network. First, two online algorithms are proposed for the case of streaming data. Next, using the same VAR model, two online algorithms under the framework of online optimization are presented to track the time-varying topologies. To evaluate the performance of propose online algorithms, we show that both the proposed algorithms incur a sublinear static regret. To characterize the performance theoretically in time-varying scenarios, a bound on the dynamic regret for one of the proposed algorithms (TIRSO) is derived. Next, using a structural equation model (SEM) for topology identification, an online algorithm for tracking time-varying topologies is proposed, and a bound on the dynamic regret is also derived for the proposed algorithm. Moreover, using a non-stationary VAR model, an algorithm for dynamic topology identification and breakpoint detection is also proposed, where the notion of local structural breakpoint is introduced to accommodate the concept of breakpoint where instead of the whole topology, only a few edges vary. Finally, the problem of tracking VAR-based time-varying topologies with missing data is investigated. Online algorithms are proposed where the joint signal and topology estimation is carried out. Dynamic regret analysis is also presented for the proposed algorithm. For all the previously mentioned works, simulation tests about the proposed algorithms are also presented and discussed in this dissertation. The numerical results of the proposed algorithms corroborate with the theoretical analysis presented in this dissertation

BBC as a diasporic mass medium or an agent of public diplomacy: Users' perceptions in Pakistan and Germany

BBC World's Urdu Service is deemed as an important, free and fair mass medium in Pakistan , and is regarded as a good source of connecting Pakistani diaspora living in different parts of the world to their homeland and vice versa. However, shifts in global politics, especially the so called „Global War on Terror' (which is considered to be a Western holy war against Islam) on one hand,and professional and technological developments in the field of mass communication in Pakistan, particularly the proliferation of indigenous private electronic news mass media on the other, have affected credibility of the BBC Urdu Service among users. This paper is based on empirical research in Pakistan and among the Pakistani diaspora in Germany. It investigates how the BBC Urdu Service is used on an everyday basis and at times of crisis. It situates the use of the BBC Urdu services in the wider context of news media consumption of our sample group. Through the use of group discussion, in-depth interview techniques and archival data from BBC we offer a unique analytical perspective on the changing role of the BBCWS (BBC Urdu Service) as an international broadcaster and as an agent of public diplomacy

Reactive buffering window trajectory segmentation: RBW-TS

Abstract Mobility data of a moving object, called trajectory data, are continuously generated by vessel navigation systems, wearable devices, and drones, to name a few. Trajectory data consist of samples that include temporal, spatial, and other descriptive features of object movements. One of the main challenges in trajectory data analysis is to divide trajectory data into meaningful segments based on certain criteria. Most of the available segmentation algorithms are limited to processing data offline, i.e., they cannot segment a stream of trajectory samples. In this work, we propose an approach called Reactive Buffering Window - Trajectory Segmentation (RBW-TS), which partitions trajectory data into segments while receiving a stream of trajectory samples. Another novelty compared to existing work is that the proposed algorithm is based on multidimensional features of trajectories, and it can incorporate as many relevant features of the underlying trajectory as needed. This makes RBW-TS general and applicable to numerous domains by simply selecting trajectory features relevant for segmentation purposes. The proposed online algorithm incurs lower computational and memory requirements. Furthermore, it is robust to noisy samples and outliers. We validate RBW-TS on three use cases: (a) segmenting human-movement trajectories in different modes of transportation, (b) segmenting trajectories generated by vessels in the maritime domain, and (c) segmenting human-movement trajectories in a commercial shopping center. The numerical results detailed in the paper demonstrate that (i) RBW-TS is capable of detecting the true breakpoints of segments in all three usecases while processing a stream of trajectory points; (ii) despite low memory and computational requirements, the performance in terms of the harmonic mean of purity and coverage is comparable to that of state-of-the-art batch and online algorithms; (iii) RBW-TS achieves different levels of accuracy depending on the various internal parameter estimation methods used; and (iv) RBW-TS can tackle real-world trajectory data for segmentation purposes

Dynamic network identification from non-stationary vector autoregressive time series

Author's accepted manuscript (postprint).© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.acceptedVersio

Comparative Analysis of 18-Pulse Autotransformer Rectifier Unit Topologies with Intrinsic Harmonic Current Cancellation

With the evolution of the More Electric Aircraft (MEA) concept, high pulse converters have gained the attention of researchers due to their higher power quality. Among the high pulse converters, 18-pulse autotransformer rectifier unit (ATRU) offers better power quality level with small size, weight and medium complexity. The conventional topologies of autotransformers that require the use of extra elements such as Inter Phase Transformers (IPT) or Zero Sequence Blocking Transformers (ZSBT), adding to the complexity, weight and size of the overall system, are not considered in the analysis. For 18-pulse rectification, only those topologies of autotransformers which have the intrinsic current harmonic cancellation capabilities are presented here for comparison. These topologies offer current harmonic levels within limits specified by IEEE 519 with reduced weight and size as compared to the conventional multi-pulse converters. A comparison of different differential delta/fork configured 18-pulse autotransformer rectifier units is presented so as to come up with the best among available topologies with respect to weight, size and power quality. Experimental prototypes of each topology were designed and their results are displayed along with the simulation results for comparison