Deep Room Recognition Using Inaudible Echos

Recent years have seen the increasing need of location awareness by mobile applications. This paper presents a room-level indoor localization approach based on the measured room's echos in response to a two-millisecond single-tone inaudible chirp emitted by a smartphone's loudspeaker. Different from other acoustics-based room recognition systems that record full-spectrum audio for up to ten seconds, our approach records audio in a narrow inaudible band for 0.1 seconds only to preserve the user's privacy. However, the short-time and narrowband audio signal carries limited information about the room's characteristics, presenting challenges to accurate room recognition. This paper applies deep learning to effectively capture the subtle fingerprints in the rooms' acoustic responses. Our extensive experiments show that a two-layer convolutional neural network fed with the spectrogram of the inaudible echos achieve the best performance, compared with alternative designs using other raw data formats and deep models. Based on this result, we design a RoomRecognize cloud service and its mobile client library that enable the mobile application developers to readily implement the room recognition functionality without resorting to any existing infrastructures and add-on hardware. Extensive evaluation shows that RoomRecognize achieves 99.7%, 97.7%, 99%, and 89% accuracy in differentiating 22 and 50 residential/office rooms, 19 spots in a quiet museum, and 15 spots in a crowded museum, respectively. Compared with the state-of-the-art approaches based on support vector machine, RoomRecognize significantly improves the Pareto frontier of recognition accuracy versus robustness against interfering sounds (e.g., ambient music).Comment: 29 page

Subnational credit ratings : a comparative review

This paper surveys methodological issues in subnational credit ratings and highlights key challenges for developing countries. Subnational borrowing from capital markets has been on the rise owing to fiscal decentralization and demand for infrastructure investments. A prerequisite for accessing capital markets, subnational credit ratings have also emerged as a part of broader reform for fiscal sustainability. They facilitate a more transparent budgetary and financial management system. The global financial crisis makes subnational credit ratings more relevant, as they contribute to fiscal risk evaluations and fiscal adjustment. In addition to subnationals’ own credit strength, the creditworthiness of the sovereign and the intergovernmental fiscal system are among the most critical rating criteria. Implicit and contingent liabilities are integral to the rating process. Indirect debt instruments including off-balance-sheet financing create fiscal risks. The ongoing financial crisis has reinforced the rating focus on the management of liquidity, debt structure, and off-balance-sheet liabilities.Debt Markets,Banks&Banking Reform,,Bankruptcy and Resolution of Financial Distress,Access to Finance

From Efficiency-driven to Innovation-driven Economic Growth: Perspectives from Singapore

The Singapore economy is going through a period of major restructuring. Economic stagnation since the 1997 Asia financial crisis (except for a brief recovery in 1999) has called into question the continued relevance of many fundamental policies that had worked well in the past. In 2002, a high-level Economic Review Committee (ERC) was convened by the government to chart new directions for the economy. A common thread that ran through the committee’s various reports was a call to enhance the economy’s innovative capacity, with the aim of making Singapore an innovation hub in the region.2 The call reflects an increased awareness both within and outside the government of the need to redefine Singapore’s comparative advantage through a new national innovation policy.

China’s Changing Economic Structures and Its Implications for Regional Patterns of Trade Production and Integration

There is tremendous momentum for economic and financial integration in East Asia today. Partly inspired by the formation of the European Union and partly as a response to the 1997/98 Asia financial crisis, many East Asian countries are showing greater commitment to regional economic cooperation. A number of bilateral free trade agreements (FTAs) have either been concluded or are being negotiated.1 At a less formal level, the ASEAN+3 grouping has brought the whole region together in regular consultations over trade, investment, as well as monetary and exchange rate policy matters.

Coupled Deep Learning for Heterogeneous Face Recognition

Heterogeneous face matching is a challenge issue in face recognition due to large domain difference as well as insufficient pairwise images in different modalities during training. This paper proposes a coupled deep learning (CDL) approach for the heterogeneous face matching. CDL seeks a shared feature space in which the heterogeneous face matching problem can be approximately treated as a homogeneous face matching problem. The objective function of CDL mainly includes two parts. The first part contains a trace norm and a block-diagonal prior as relevance constraints, which not only make unpaired images from multiple modalities be clustered and correlated, but also regularize the parameters to alleviate overfitting. An approximate variational formulation is introduced to deal with the difficulties of optimizing low-rank constraint directly. The second part contains a cross modal ranking among triplet domain specific images to maximize the margin for different identities and increase data for a small amount of training samples. Besides, an alternating minimization method is employed to iteratively update the parameters of CDL. Experimental results show that CDL achieves better performance on the challenging CASIA NIR-VIS 2.0 face recognition database, the IIIT-D Sketch database, the CUHK Face Sketch (CUFS), and the CUHK Face Sketch FERET (CUFSF), which significantly outperforms state-of-the-art heterogeneous face recognition methods.Comment: AAAI 201

From efficiency-driven to innovation-driven economic growth : perspectives from Singapore

This paper looks at Singapore's efforts to transform the economic growth base from one that is predominantly efficiency-driven to one that is more innovation-driven. To accelerate the transition process, the government is aggressively investing in"innovation infrastructure"-systems and institutions that make the city a more conducive environment for innovations. The modus operandi, with a distinctive"winner-picking"flavor, mirrors that of its earlier strategic industrial policy in building up the manufacturing sector. It is also in sync with the new urban growth literature which argues that the success of any innovation-driven growth strategy depends on a city's ability to attract a large community of creative individuals in different fields. Innovation infrastructure building requires more than putting in the right systems. It also requires a mindset change at various levels of society. This paper looks at how the government's policy philosophy and practices have evolved over time, and discusses the effectiveness of the government-led, strategic supply-push approach in propelling Singapore onto an innovation-driven growth path. It takes into consideration the city-state's underlying comparative advantages (or disadvantages) and asks how Singapore's existing strength in efficiency infrastructure may give it a first mover advantage in attracting creative talent, how its success may be affected by the small size of the economy, and the various political and social constraints that a small sovereign city-state faces. These issues are explored against the backdrop of the keen competition among the major cities in the region to become an innovation hub.Health Monitoring&Evaluation,Environmental Economics&Policies,ICT Policy and Strategies,Agricultural Research,Banks&Banking Reform