Innovation in Services - Theoretical Approach

The aim of this article is to present the evolution of theoretical studies on service innovation. The author also attempts to put these different approaches to service innovation into order and to indicate the possible forms of service innovation that emerge from these researches. In further part of the article the issue of the availability of statistical data and its relevance to the possible forms of service innovation, as well as some changes that has been implemented recently in order to improve this relevance, are discussed.Celem artykułu jest przedstawienie ewolucji teoretycznych badań nad innowacjami w usługach. Autorka podejmuje również próbę uporządkowania różnych podejść do kwestii innowacji usługowych oraz wskazać możliwe formy tych innowacji, wyłaniające się z analizowanych badań. W dalszej części artykułu, podejmowana jest kwestia dostępności danych statystycznych oraz ich adekwatności, jeśli chodzi o możliwość zastosowania do analizy różnych form innowacji usługowych. Omawiane są również wprowadzone ostatnio zmiany, mające na celu poprawę adekwatności tych danych

Anytime Hierarchical Clustering

We propose a new anytime hierarchical clustering method that iteratively transforms an arbitrary initial hierarchy on the configuration of measurements along a sequence of trees we prove for a fixed data set must terminate in a chain of nested partitions that satisfies a natural homogeneity requirement. Each recursive step re-edits the tree so as to improve a local measure of cluster homogeneity that is compatible with a number of commonly used (e.g., single, average, complete) linkage functions. As an alternative to the standard batch algorithms, we present numerical evidence to suggest that appropriate adaptations of this method can yield decentralized, scalable algorithms suitable for distributed/parallel computation of clustering hierarchies and online tracking of clustering trees applicable to large, dynamically changing databases and anomaly detection.Comment: 13 pages, 6 figures, 5 tables, in preparation for submission to a conferenc

Patterns of Discovery

From a given directed weighted network of knowledge links between technology fields, the paper develops a multisector dynamic model of incremental innovation and R&D activity in these fields. The model is focused on the equilibrium share distribution of these variables, which is proved to be locally stable, with reference to a simple low dimensional case. Simulation methods suggest that local, and also global, stability extend to any model dimension. It is also shown how different network structures map to different asymptotic share distributions. Using the NBER patents and patent citation data files, the analytical framework is then used to analyse some general features of the pattern of knowledge creation and transfer in the period 1975-1999. From a descriptive viewpoint, the changes in the share distribution of innovation activity predicted by the model match reasonably well the actual changes in the perioddirected weighted network, knowledge spillovers, share distribution, incremental innovation and R&D dynamics, local stability, simulation, patents and patent citations

Joint on-manifold self-calibration of odometry model and sensor extrinsics using pre-integration

© 2019 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.This paper describes a self-calibration procedure that jointly estimates the extrinsic parameters of an exteroceptive sensor able to observe ego-motion, and the intrinsic parameters of an odometry motion model, consisting of wheel radii and wheel separation. We use iterative nonlinear onmanifold optimization with a graphical representation of the state, and resort to an adaptation of the pre-integration theory, initially developed for the IMU motion sensor, to be applied to the differential drive motion model. For this, we describe the construction of a pre-integrated factor for the differential drive motion model, which includes the motion increment, its covariance, and a first-order approximation of its dependence with the calibration parameters. As the calibration parameters change at each solver iteration, this allows a posteriori factor correction without the need of re-integrating the motion data. We validate our proposal in simulations and on a real robot and show the convergence of the calibration towards the true values of the parameters. It is then tested online in simulation and is shown to accommodate to variations in the calibration parameters when the vehicle is subject to physical changes such as loading and unloading a freight.Peer ReviewedPostprint (author's final draft