IMF BANK-RESTRUCTURING EFFICIENCY OUTCOMES:EVIDENCE FROM EAST ASIA

This paper reports new findings for the first time on bank efficiency over the pre- and post-IMF-restructuring periods for East Asia using the DEA and regression models. Bank closures that followed the IMF interventions are economically justified; but mergers and acquisitions experience short-term efficiency losses. Recapitalization and then re-privatization of bad banks have led to efficiency improvements, but still increased government ownership. Ease of entry has resulted in more foreign bank participation with improved performance; further spurts in improvements, however, may take longer time. These findings advocate bank restructuring during the crisis; but well-designed measures are vital to ensure its success. Bank mergers and acquisitions need to be scrutinized. Privatization, particularly with strategic foreign ownership, of domestic banks which should be further encouraged. To reap the potential benefits of such foreign participation, stronger economic reforms of the host countries should be further pursued.

IMF Bank-Restructuring Efficiency Outcomes: Evidence from East Asia

We report new findings on bank efficiency in East Asian countries for the preand post-IMF restructuring periods. We find that bank efficiency has improved, but only to the pre-IMF intervention level, and that restructured banks are not more efficient than their unrestructured counterparts. Different restructuring measures have different effects. Bank closures are economically justified, but mergers show short-term efficiency losses. Recapitalization and reprivatization of badly performing banks lead to efficiency improvement, but also increase government ownership. Ease of entry that has allowed for more foreign bank participation results in slightly improved performance of badly performing banks.

The Global Challenges of the Knowledge Economy: China and the EU

This paper addresses some of the challenges confronting the European Union and China as they build their knowledge economies, and their on-going and possible future actions to address such challenges. Fifty years after the creation of what became the European Union, we argue that there is an urgent need to develop a new European Lisbon Agenda, preparing the EU for globalization. A new and "outward-looking" Lisbon strategy would focus on three key areas: international trade in services, internationalization of research networking, and access to brains and talent. The paper shows that the success of the Chinese economy over the past three decades can be partially attributed to its ability to absorb globally advanced technology and huge flows of foreign investment, its large pool of knowledge and talent, and its enactment of a policy framework that provides incentives to domestic and foreign firms to innovate - a strategy very much reminiscent of Europe's own internal Lisbon agenda. To move further, China needs to overcome the obstacles of regional disparities, transform its industry and deepen industry-academy linkages, which are also unavoidable tasks for the sustainable development of Europe. We contend that the scope for comparative studies of the EU and China, for mutual learning from each other's experience - even for joint initiatives - is substantial.Knowledge Economy, Industry-University Partnerships, Globalization, Internationalization, Highly Skilled Migration, European Union, China

Improving Online Lane Graph Extraction by Object-Lane Clustering

Autonomous driving requires accurate local scene understanding information. To this end, autonomous agents deploy object detection and online BEV lane graph extraction methods as a part of their perception stack. In this work, we propose an architecture and loss formulation to improve the accuracy of local lane graph estimates by using 3D object detection outputs. The proposed method learns to assign the objects to centerlines by considering the centerlines as cluster centers and the objects as data points to be assigned a probability distribution over the cluster centers. This training scheme ensures direct supervision on the relationship between lanes and objects, thus leading to better performance. The proposed method improves lane graph estimation substantially over state-of-the-art methods. The extensive ablations show that our method can achieve significant performance improvements by using the outputs of existing 3D object detection methods. Since our method uses the detection outputs rather than detection method intermediate representations, a single model of our method can use any detection method at test time.Comment: ICCV 202

2D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentation

As 3D perception problems grow in popularity and the need for large-scale labeled datasets for LiDAR semantic segmentation increase, new methods arise that aim to reduce the necessity for dense annotations by employing weakly-supervised training. However these methods continue to show weak boundary estimation and high false negative rates for small objects and distant sparse regions. We argue that such weaknesses can be compensated by using RGB images which provide a denser representation of the scene. We propose an image-guidance network (IGNet) which builds upon the idea of distilling high level feature information from a domain adapted synthetically trained 2D semantic segmentation network. We further utilize a one-way contrastive learning scheme alongside a novel mixing strategy called FOVMix, to combat the horizontal field-of-view mismatch between the two sensors and enhance the effects of image guidance. IGNet achieves state-of-the-art results for weakly-supervised LiDAR semantic segmentation on ScribbleKITTI, boasting up to 98% relative performance to fully supervised training with only 8% labeled points, while introducing no additional annotation burden or computational/memory cost during inference. Furthermore, we show that our contributions also prove effective for semi-supervised training, where IGNet claims state-of-the-art results on both ScribbleKITTI and SemanticKITTI.Comment: Accepted at WACV 202

Reduction of wind turbine noise annoyance: an operational approach

This paper investigates the relationship between wind turbine noise annoyance, exposure indicators, operational characteristics and environmental variables. A six-month field experiment at an industrial site near a residential area includes regular on-line annoyance reports, continuous 1/3-octave band noise level registrations, periodic sound recordings, data on electricity production per minute and meteorological observations. Here the risk of. high annoyance does not only depend on the angular blade velocity, but also on the wind turbines' nacelle position relative to the location of the dwellings, i.e. the wind direction. This directivity effect can be captured when noise parameters such as the background noise level caused by other sources and a so-called fluctuation-indicator are introduced, the latter calculated from the 1/3-octave band spectra to quantify the periodic part of wind turbine noise. In addition, the calculated turbine's specific emission levels are closely related to the angular blade velocity, and an important parameter to predict the risk of high annoyance. Finally, these results suggest that operational restrictions based on wind direction together with the angular blade velocity might help to reduce noise annoyance while preserving cost-effectiveness