How and why communications industry suppliers get “squeezed out” by outsourcing: cases, impact and the next phases

The communications systems,terminals,and service, industries, have undergone over the past ten years a significant technological internal evolution and external revolution at customer end (such as shifting to IP, wireless 3G and LTE evolutions, new terminals, broadband...). Very little management research has studied their survivability irrespective of changes in demand volumes, due to technological sourcing and outsourcing practices driven by other global industries serving as predators in view of the huge business potential of communications products and services. These other industries include computing software, semiconductor and contract manufacturing industries, many of with roots in emerging countries. This paper analyzes the implications of using in-sourced genuine non-proprietary open communications standards , of the wider use of in-sourced /purchased technologies ,and of outsourced contract manufacturing . The methodology used is equilibrium analyses from case analysis data. They show a trend towards active or passive knowledge leakage. Three specific areas will be mentioned as examples .The paper also shows the processes how eventually those industries in a later cycle bounce back.Communications industry; Communications industry suppliers; Business processes; Intellectual property; Technical competence; Customer bases

Sparse motion bases selection for human motion denoising

Human motion denoising is an indispensable step of data preprocessing for many motion data based applications. In this paper, we propose a data-driven based human motion denoising method that sparsely selects the most correlated subset of motion bases for clean motion reconstruction. Meanwhile, it takes the statistic property of two common noises, i.e., Gaussian noise and outliers, into account in deriving the objective functions. In particular, our method firstly divides each human pose into five partitions termed as poselets to gain a much fine-grained pose representation. Then, these poselets are reorganized into multiple overlapped poselet groups using a lagged window moving across the entire motion sequence to preserve the embedded spatial 13temporal motion patterns. Afterward, five compacted and representative motion dictionaries are constructed in parallel by means of fast K-SVD in the training phase; they are used to remove the noise and outliers from noisy motion sequences in the testing phase by solving !131-minimization problems. Extensive experiments show that our method outperforms its competitors. More importantly, compared with other data-driven based method, our method does not need to specifically choose the training data, it can be more easily applied to real-world applications

A Class of Logistic Functions for Approximating State-Inclusive Koopman Operators

An outstanding challenge in nonlinear systems theory is identification or learning of a given nonlinear system's Koopman operator directly from data or models. Advances in extended dynamic mode decomposition approaches and machine learning methods have enabled data-driven discovery of Koopman operators, for both continuous and discrete-time systems. Since Koopman operators are often infinite-dimensional, they are approximated in practice using finite-dimensional systems. The fidelity and convergence of a given finite-dimensional Koopman approximation is a subject of ongoing research. In this paper we introduce a class of Koopman observable functions that confer an approximate closure property on their corresponding finite-dimensional approximations of the Koopman operator. We derive error bounds for the fidelity of this class of observable functions, as well as identify two key learning parameters which can be used to tune performance. We illustrate our approach on two classical nonlinear system models: the Van Der Pol oscillator and the bistable toggle switch.Comment: 8 page

A Purely Functional Computer Algebra System Embedded in Haskell

We demonstrate how methods in Functional Programming can be used to implement a computer algebra system. As a proof-of-concept, we present the computational-algebra package. It is a computer algebra system implemented as an embedded domain-specific language in Haskell, a purely functional programming language. Utilising methods in functional programming and prominent features of Haskell, this library achieves safety, composability, and correctness at the same time. To demonstrate the advantages of our approach, we have implemented advanced Gr\"{o}bner basis algorithms, such as Faug\`{e}re's $F_4$ and $F_5$, in a composable way.Comment: 16 pages, Accepted to CASC 201

Analysis of California's Three Major Tax Bases

Part of a series that examines California's budget and its relationship to the state's economy. Looks at the growth and volatility of personal income, taxable sales, and the assessed value of property

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

Managing intellectual capital : individual rights and the public interest

Managing intellectual capital and intellectual property is a challenging task, especially for knowledge-based organisations vested with a public interest. Scientific ethics and freedom of information may clash with copyright law or with other intellectual property enactments, thereby engendering conflicts of interest. International law and treaties make for a complex regulatory framework. World-wide advocacy of the open access principle has led to some statutory changes, but its proponents mostly assume that copyright owners will act voluntarily. The implications for knowledge management are elucidated