Can You Provide the Current Trends in HR on People Analytics?

[Excerpt] People analytics is an increasingly hot topic and many companies are working to gain insight through this emerging field. Business leaders are asking how analytics can help drive better decision-making in order to improve business results. Among these questions, turnover prediction and succession planning are two key areas that HR professionals identify as high value. Since there isn’t a one-size- fits-all solution to these questions, we compiled our most noteworthy insights and put forward several steps that an organization should follow in order to create its own internal models

Difference Antenna Selection and Power Allocation for Wireless Cognitive Systems

In this paper, we propose an antenna selection method in a wireless cognitive radio (CR) system, namely difference selection, whereby a single transmit antenna is selected at the secondary transmitter out of $M$ possible antennas such that the weighted difference between the channel gains of the data link and the interference link is maximized. We analyze mutual information and outage probability of the secondary transmission in a CR system with difference antenna selection, and propose a method of optimizing these performance metrics of the secondary data link subject to practical constraints on the peak secondary transmit power and the average interference power as seen by the primary receiver. The optimization is performed over two parameters: the peak secondary transmit power and the difference selection weight $\delta\in [0, 1]$. We show that, difference selection using the optimized parameters determined by the proposed method can be, in many cases of interest, superior to a so called ratio selection method disclosed in the literature, although ratio selection has been shown to be optimal, when impractically, the secondary transmission power constraint is not applied. We address the effects that the constraints have on mutual information and outage probability, and discuss the practical implications of the results.Comment: 29 pages, 9 figures, to be submitted to IEEE Transactions on Communication

China's Integration with the World: Development as a Process of Learning and Industrial Upgrading

The process of development is full of uncertainties, especially if it is a process of transition from a planned economy to a market oriented one. Because of uncertainties and country specificity, development must be a process of learning, selective adaptation, and industrial upgrading. This paper attempts to distill lessons from China's reform and opening up process, and investigate the underlying reasons behind China's success in trade expansion and economic growth. From its beginnings with home-grown and second-best institutions, China has embarked on a long journey of reform, experimentation, and learning by doing. It is moving from a comparative advantage-defying strategy to a comparative advantage-following strategy. The country is catching up quickly through augmenting its factor endowments and upgrading industries; but this has been only partially successful. Although China is facing several difficult challenges -- including rising inequality, an industrial structure that is overly capital and energy intensive, and related environmental degradation -- it is better positioned to tackle them now than it was 30 years ago. This paper reviews the drivers behind China's learning and trade integration and provides both positive and negative lessons for developing countries with diverse natural endowments, especially those in Sub-Saharan Africa.patterns of trade; learning; innovation and growth

Beyond Hartigan Consistency: Merge Distortion Metric for Hierarchical Clustering

Hierarchical clustering is a popular method for analyzing data which associates a tree to a dataset. Hartigan consistency has been used extensively as a framework to analyze such clustering algorithms from a statistical point of view. Still, as we show in the paper, a tree which is Hartigan consistent with a given density can look very different than the correct limit tree. Specifically, Hartigan consistency permits two types of undesirable configurations which we term over-segmentation and improper nesting. Moreover, Hartigan consistency is a limit property and does not directly quantify difference between trees. In this paper we identify two limit properties, separation and minimality, which address both over-segmentation and improper nesting and together imply (but are not implied by) Hartigan consistency. We proceed to introduce a merge distortion metric between hierarchical clusterings and show that convergence in our distance implies both separation and minimality. We also prove that uniform separation and minimality imply convergence in the merge distortion metric. Furthermore, we show that our merge distortion metric is stable under perturbations of the density. Finally, we demonstrate applicability of these concepts by proving convergence results for two clustering algorithms. First, we show convergence (and hence separation and minimality) of the recent robust single linkage algorithm of Chaudhuri and Dasgupta (2010). Second, we provide convergence results on manifolds for topological split tree clustering