In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

The original edition of this book summarized more than a decade of work on communications flow in science and engineering organizations, showing how human and organizational systems could be restructured to bring about improved productivity and better person-to-person contact. While many studies have been done since then, few of them invalidate the general conclusions and recommendations Allen offers. In a new preface he points out new developments, noting areas that need some modification, elaboration, or extension, and directing readers to the appropriate journal articles where the findings, are reported. The first three chapters provide an overview of the communication system in technology, present the author's research methods, and describe differences in the career paths and goals of engineers and scientists that cause special problems for organizations. The book then discusses how technological information is acquired by the R & D organization, shows how critical technical communication within the laboratory is for R & D performance, and originates the idea of the \"gatekeeper,\" the person who links his or her organization to the world at large. Concluding chapters take up the influence of formal and informal organization and of architecture and office layouts on communication. Many of these ideas have been successfully incorporated by architects and managers in the design of new R & D facilities and complexes. Suggested Citation Handle: RePEc:mtp:titles:0262510278 Download reference as HTML Thomas J. Allen, 1984. \"Managing the Flow of Technology: Technology Transfer and the Dissemination of Technological Information Within the R&D Organization,\" MIT Press Books, The MIT Press, edition 1, volume 1, number 0262510278, November. More services and features MyIDEAS Follow serials, authors, keywords & more New papers by email Subscribe to new additions to RePEc Author registration Public profiles for Economics researchers MPRA Upload your paper to be listed on RePEc and IDEAS EconAcademics Blog aggregator for economics research Plagiarism Cases of plagiarism in Economics Rankings Various rankings of research in Economics & related fields RePEc Genealogy Who was a student of whom, using RePEc RePEc Biblio Curated articles & papers on various economics topics Job market papers RePEc working paper series dedicated to the job market Fantasy league Pretend you are at the helm of an economics department Services from the StL Fed Data, research, apps & more from the St. Louis Fed IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.

/pdf/managing-the-flow-of-technology-technology-transfer-and-the-27luzgsrys.pdf

Managing the Flow of Technology: Technology Transfer and the Dissemination of Technological Information Within the R&D Organization (Book Review)

This paper introduces a review article on indoor localization techniques and technologies. The paper starts with current localization systems and summarizes comparisons between these systems in terms of accuracy, cost, advantages, and disadvantages. Also, the paper presents different detection techniques and compare them in terms of accuracy and cost. Finally, localization methods and algorithms, including angle of arrival (AOA), time of arrival (TOA), and recived signal strength (RSS) are introduced. The study contains concepts, requirements, and specifications for each category of methods presents pros and cons for investigated methods, and conducts comparisons between them.

/pdf/a-review-of-indoor-localization-techniques-and-wireless-99jk5etowd.pdf

A Review of Indoor Localization Techniques and Wireless Technologies

Navigation systems help users access unfamiliar environments. Current technological advancements enable users to encapsulate these systems in handheld devices, which effectively increases the popularity of navigation systems and the number of users. In indoor environments, lack of Global Positioning System (GPS) signals and line of sight with orbiting satellites makes navigation more challenging compared to outdoor environments. Radio frequency (RF) signals, computer vision, and sensor-based solutions are more suitable for tracking the users in indoor environments. This article provides a comprehensive summary of evolution in indoor navigation and indoor positioning technologies. In particular, the paper reviews different computer vision-based indoor navigation and positioning systems along with indoor scene recognition methods that can aid the indoor navigation. Navigation and positioning systems that utilize pedestrian dead reckoning (PDR) methods and various communication technologies, such as Wi-Fi, Radio Frequency Identification (RFID) visible light, Bluetooth and ultra-wide band (UWB), are detailed as well. Moreover, this article investigates and contrasts the different navigation systems in each category. Various evaluation criteria for indoor navigation systems are proposed in this work. The article concludes with a brief insight into future directions in indoor positioning and navigation systems.

/pdf/indoor-positioning-and-wayfinding-systems-a-survey-49mq36uclc.pdf

Indoor positioning and wayfinding systems: a survey

As a new way to design, deploy, and manage network services, network functions virtualization (NFV) decouples the network functions, from one or more physical network infrastructures and black boxes so they can run in software. It therefore comes as no surprise that NFV originated from service providers, who were looking to improve the deployment of new network services to support their revenue and growth objectives. Within the NFV ecosystem, high availability, and low latency are one of the key quality of service (QoS) benefits that service providers can expect from the 5G Cloud and the NFV networks to make delay-critical services such as remote surgery a reality. Therefore, network services should be placed, chained, and routed through the network considering users/tenants stringent QoS and service-level agreement requirements. To this end, routing and placement optimization plays a major role in improving network performance and the overall network cost. In this paper, we study the problem of virtual network functions (VNFs) placement and routing across the physical hosts to minimize overall latency defined as the queuing delay within the edge clouds and in network links. In that respect, this paper takes a holistic view by considering not only VNFs chaining and placement problem but also considering the flows routing aspect since these two problems are inter-related and have a major impact on network latency.

/pdf/virtual-network-functions-routing-and-placement-for-edge-4gvma2gfxz.pdf

Virtual Network Functions Routing and Placement for Edge Cloud Latency Minimization

As a key technology that is widely adopted in location-based services (LBS), indoor localization has received considerable attention in both research and industrial areas. Despite the huge efforts made for localization using smartphone inertial sensors, its performance is still unsatisfactory in large open areas, such as halls, supermarkets, and museums, due to accumulated errors arising from the uncertainty of users’ mobility and fluctuations of magnetic field. Regarding that, this paper presents iBILL, an indoor localization approach that jointly uses iBeacon and inertial sensors in large open areas. With users’ real-time locations estimated by inertial sensors through an improved particle filter, we revise the algorithm of augmented particle filter to cope with fluctuations of magnetic field. When users enter vicinity of iBeacon devices clusters, their locations are accurately determined based on received signal strength of iBeacon devices, and accumulated errors can, therefore, be corrected. Proposed by Apple Inc. for developing LBS market, iBeacon is a type of Bluetooth low energy, and we characterize both the advantages and limitations of localization when it is utilized. Moreover, with the help of iBeacon devices, we also provide solutions of two localization problems that have long remained tough due to the increasingly large computational overhead and arbitrarily placed smartphones. Through extensive experiments in the library on our campus, we demonstrate that iBILL exhibits 90% errors within 3.5 m in large open areas.

iBILL: Using iBeacon and Inertial Sensors for Accurate Indoor Localization in Large Open Areas

This paper studies the problem of optimizing multicast energy consumption in delay-constrained mobile wireless networks, where information from the source needs to be delivered to all the $k$ destinations within an imposed delay constraint Most existing works simply focus on deriving transmission schemes with the minimum transmitting energy, overlooking the energy consumption at the receiver side Therefore, in this paper, we propose ConMap, a novel and general framework for efficient transmission scheme design that jointly optimizes both the transmitting and receiving energy In doing so, we formulate our problem of designing minimum energy transmission scheme, called DeMEM, as a combinatorial optimization one, and prove that the approximation ratio of any polynomial time algorithm for DeMEM cannot be better than $({1}/{4})\ln k$  Aiming to provide more efficient approximation schemes, the proposed ConMap first converts DeMEM into an equivalent directed Steiner tree problem through creating auxiliary graph gadgets to capture energy consumption, then maps the computed tree back into a transmission scheme The advantages of ConMap are threefolded: 1) Generality– ConMap exhibits strong applicability to a wide range of energy models; 2) Flexibility– Any algorithm designed for the problem of directed Steiner tree can be embedded into our ConMap framework to achieve different performance guarantees and complexities; 3) Efficiency– ConMap preserves the approximation ratio of the embedded Steiner tree algorithm, to which only slight overhead will be incurred The three features are then empirically validated, with ConMap also yielding near-optimal transmission schemes compared to a brute-force exact algorithm To our best knowledge, this is the first work that jointly considers both the transmitting and receiving energy in the design of multicast transmission schemes in mobile wireless networks

Joint Optimization of Multicast Energy in Delay-Constrained Mobile Wireless Networks

Location-aware viral marketing is crucial in modern commercial applications for attracting customers to certain points of interests. Prior works are mainly based on formulating it into a location-aware influence maximization problem in Geo-social Networks (GSNs), where $K$ initial seed individuals are selected in hope of maximizing the number of final influenced users. In this paper, we present the first look into the group-level location promotion, which can potentially enhance its performance, with the phenomenon that users belonging to the same geo-community share similar moving preferences. We propose GLP, a new and novel framework of group-level location promotion by virtue of geo-communities, each of which is treated as a group in GSNs. Aiming to attract more users to designated locations, GLP firstly carries out user grouping through an iterative learning approach based on information extraction from massive check-ins records. The advantage of GLP is three-folded: i) by aggregating movements of group members, GLP significantly avoids the sparsity and sporadicity of individual check-ins, and thus obtains more reliable mobility models; ii) by generalizing a new group-level social graph, GLP can exponentially reduce the computational complexity of seed nodes selection that is algorithmically executed by a greedy algorithm; iii) in comparison with prior individual-level cases, GLP is theoretically demonstrated to drastically increase influence spread under the same given budget. Extensive experiments on real datasets demonstrate that the GLP outperforms four baselines, with notably up to 10 times larger influence spread and 100 times faster seed selection over two individual-level cases, meanwhile verifying the impact of group numbers in final influence spread.

GLP: A Novel Framework for Group-Level Location Promotion in Geo-Social Networks

The cascading of sensitive information such as private contents and rumors is a severe issue in online social networks. One approach for limiting the cascading of sensitive information is constraining the diffusion among social network users. However, the diffusion constraining measures limit the diffusion of non-sensitive information diffusion as well, resulting in the bad user experiences. To tackle this issue, in this paper, we study the problem of how to minimize the sensitive information diffusion while preserve the diffusion of non-sensitive information, and formulate it as a constrained minimization problem where we characterize the intention of preserving non-sensitive information diffusion as the constraint. We study the problem of interest over the fully-known network with known diffusion abilities of all users and the semi-known network where diffusion abilities of partial users remain unknown in advance. By modeling the sensitive information diffusion size as the reward of a bandit, we utilize the bandit framework to jointly design the solutions with polynomial complexity in the both scenarios. Moreover, the unknown diffusion abilities over the semi-known network induce it difficult to quantify the information diffusion size in algorithm design. For this issue, we propose to learn the unknown diffusion abilities from the diffusion process in real time and then adaptively conduct the diffusion constraining measures based on the learned diffusion abilities, relying on the bandit framework. Extensive experiments on real and synthetic datasets demonstrate that our solutions can effectively constrain the sensitive information diffusion, and enjoy a 40 percent less diffusion loss of non-sensitive information comparing with four baseline algorithms.

Adaptive Diffusion of Sensitive Information in Online Social Networks

Influence diffusion in social networks has been intensively studied over last two decades. Most prior arts assume that the underlying network structure is static, remaining fixed during the influence diffusion process. However, many real networks are growing over time in terms of both the number of users and the relations in between. This motivates us to make the first study on influence diffusion over such evolving networks, where we extend the classical Influence Maximization (IM) problem to accomodate network evolution, i.e., a fairly general joining in behavior of new users. The goal of evolving IM problem studied in this paper is to select, at a given time, a few influential nodes (seeds) to initiate a diffusion with maximum spread over the network evolved after a certain time period. To tackle the both unknown network topology and influence probability arisen from the network evolution, our solution is to propose an Uppber Confidence Bound (UCB) based learning method to learn, on a multi-round basis, the unknown influence probabilities from previous diffusion feedbacks, and perform seed selection for diffusion maximization accordingly in each round. Our influence learning method provably returns a sub-linear regret bound of influence diffusion size to time. Meanwhile, empirical results performed under real evolving network datasets unravel the effect of network evolution on the influence diffusion, and demonstrate that the proposed solution significantly outperforms the non-evolving counterparts on maximizing the influence diffusion size over evolving social networks.

Xudong Wu

Papers

iBILL: Using iBeacon and Inertial Sensors for Accurate Indoor Localization in Large Open Areas

Joint Optimization of Multicast Energy in Delay-Constrained Mobile Wireless Networks

GLP: A Novel Framework for Group-Level Location Promotion in Geo-Social Networks

Adaptive Diffusion of Sensitive Information in Online Social Networks

Maximizing Influence Diffusion over Evolving Social Networks