Innovative Asia: Advancing the Knowledge-Based Economy - Highlights of the Forthcoming ADB Study Report

[Excerpt] The development of knowledge-based economies (KBEs) is both an imperative and an opportunity for developing Asia. It is an imperative to sustain high rates of growth in the future and an opportunity whereby emerging economies can draw from beneficial trending developments that may allow them to move faster to advance in global value chains and in position in world markets. Over the last quarter of a century, driven mostly by cheap labor, developing countries in Asia have seen unprecedented growth rates and contributions to the global economy. Sustaining Asia’s growth trajectory, however, requires developing economies to seek different approaches to economic growth and progress, especially if they aspire to move from the middle-income to the high-income level. KBE is an important platform that can enable them to sustain growth and even accelerate it. It is time for Asia to consolidate and accelerate its pace of growth. Asia is positioned in a unique moment in history with many advantages that can serve as a boost: to name a couple, an expanding middle of the pyramid—Asia is likely to hold 50% of the global middle class and 40% of the global consumer market by 2020; and the growing importance of intra-regional trade within Asia, increasing from 54% in 2001 to 58% in 2011. Many developing economies are well placed to assimilate frontier technologies into their manufacturing environment

A Framework for Integrating Transportation Into Smart Cities

In recent years, economic, environmental, and political forces have quickly given rise to “Smart Cities” -- an array of strategies that can transform transportation in cities. Using a multi-method approach to research and develop a framework for smart cities, this study provides a framework that can be employed to: Understand what a smart city is and how to replicate smart city successes; The role of pilot projects, metrics, and evaluations to test, implement, and replicate strategies; and Understand the role of shared micromobility, big data, and other key issues impacting communities. This research provides recommendations for policy and professional practice as it relates to integrating transportation into smart cities

An Attention-based Collaboration Framework for Multi-View Network Representation Learning

Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations. During the voting process, an attention mechanism is introduced, which enables each node to focus on the most informative views. Experimental results on real-world networks show that the proposed approach outperforms existing state-of-the-art approaches for network representation learning with a single view and other competitive approaches with multiple views.Comment: CIKM 201

Investing in Knowledge: Insights on the Funding Landscape for Research on Inequality Among Young People in the United States

This report maps the current funding environment for research on understanding and addressing social and economic inequality among young people in the U.S. To situate the existing funding landscape, I begin with a broad articulation of what is meant by inequality. Next, I characterize the structure of the funding landscape before turning to a description of three primary approaches used by funders to support research on inequality among young people in the U.S. These characterizations emerged from a set of informant interviews with social science researchers and foundation and government funders and a document scan of funder's websites. The report concludes with a brief discussion of potential strategies funding organizations could use to improve efforts to understand and address inequality among young people in the U.S

Resources, Capabilities, and Routines in Public Organization

States, state agencies, multilateral agencies, and other non-market actors are relatively under-studied in the strategic entrepreneurship literature. While important contributions examining public decision makers have been made within the agency-theoretic and transaction-cost traditions, there is little research that builds on resource-based, dynamic capabilities, and behavioral approaches to organizations. Yet public organizations can be usefully characterized as stocks of physical, organizational, and human resources; they interact with other organizations in pursuing a type of competitive advantage; they can possess excess capacity, and may grow and diversify in part according to Penrosean (dynamic) capabilities and behavioral logic. Public organizations may be managed as stewards of resources, capabilities, and routines. This paper shows how resource-based, (dynamic) capabilities, and behavioral approaches shed light on the nature and governance of public organizations and suggests a research agenda for public entrepreneurship that reflects insights gained from applying strategic management theory to public organization.

The future of Earth observation in hydrology

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smart-phones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems