Export Promotion Strategies of the SEACEN Countries

This collaborative research study assesses the export performance of the SEACEN countries; their involvement in designing and implementing export promotion strategies; as well as the problems and issues faced by them. The study also examines empirically the export-led growth hypothesis as well as the major factors in determining export demand for the SEACEN countries. As shown in the study, the empirical tests are more significant in those country cases where the promotional activities have been undertaken earlier and hence, with a good track record of industrialization and higher per capita income.

Mapping, Localization and Path Planning for Image-based Navigation using Visual Features and Map

Building on progress in feature representations for image retrieval, image-based localization has seen a surge of research interest. Image-based localization has the advantage of being inexpensive and efficient, often avoiding the use of 3D metric maps altogether. That said, the need to maintain a large number of reference images as an effective support of localization in a scene, nonetheless calls for them to be organized in a map structure of some kind. The problem of localization often arises as part of a navigation process. We are, therefore, interested in summarizing the reference images as a set of landmarks, which meet the requirements for image-based navigation. A contribution of this paper is to formulate such a set of requirements for the two sub-tasks involved: map construction and self-localization. These requirements are then exploited for compact map representation and accurate self-localization, using the framework of a network flow problem. During this process, we formulate the map construction and self-localization problems as convex quadratic and second-order cone programs, respectively. We evaluate our methods on publicly available indoor and outdoor datasets, where they outperform existing methods significantly.Comment: CVPR 2019, for implementation see https://github.com/janinethom

International Capital Movements in the SEACEN Countries

This study attempts to observe the trends and patterns of capital flow in the SEACEN countries, review the major policy implementation and examine the impact of capital inflows, especially on growth and savings. Among others, this study, using the stationarity test on the time-series data, attempts to further clarify the empirical relationship between savings and investment, together with an assessment of impact analysis as well as the causality test between foreign capital, growth and savings. As shown in the study, the empirical tests are more significant in those country cases which have had macroeconomic stability and hence, with a good track record of economic growth.

Money, Income, Prices and Causality: The Nepalese Case

This paper is aimed at reviewing briefly the monetary system in Nepal and then performing empirical tests conducive to the settlement of the disputes on the direction of causality. Accordingly, the author, re-estimated the demand for money and re-examined the causal relationship between money, income and prices in the case of Nepal. He uses annual time series for different monetary aggregates, nominal and real income and prices covering the period 1963-1992.The data series were tested for their stationarity by using unit roots and cointegration techniques followed by optimum-lag-length test for causality based on Akaike's and Schwarz's information criteria. Based on the tests which he performed, he finds unidirectional causality from narrow and reserve money to income and prices.

Exports, Growth and Causality in the SEACEN Countries

This paper examines the causal relationship between exports and economic growth (and vice versa) for the SEACEN Countries. Empirical tests were performed with and without component effects of exports to economic growth to see which variable influences another. Unit root test and cointegration test were conducted to test the stationarity of the time series to be used, and the Granger Causality Test was performed using Akaike's and Schwarz's optimal lag criteria. It is argued that this paper goes beyond the shortcomings of the previous studies which ignore the stationarity of time series data as well as the optimal lag length in Granger's Causality Test. More importantly, this paper also attempts to distinguish the direct and indirect effects between exports and economic growth. The results suggest little support for the export-promotion hypotheses.

Neural Radiance Fields for Manhattan Scenes with Unknown Manhattan Frame

Novel view synthesis and 3D modeling using implicit neural field representation are shown to be very effective for calibrated multi-view cameras. Such representations are known to benefit from additional geometric and semantic supervision. Most existing methods that exploit additional supervision require dense pixel-wise labels or localized scene priors. These methods cannot benefit from high-level vague scene priors provided in terms of scenes' descriptions. In this work, we aim to leverage the geometric prior of Manhattan scenes to improve the implicit neural radiance field representations. More precisely, we assume that only the knowledge of the indoor scene (under investigation) being Manhattan is known -- with no additional information whatsoever -- with an unknown Manhattan coordinate frame. Such high-level prior is used to self-supervise the surface normals derived explicitly in the implicit neural fields. Our modeling allows us to group the derived normals and exploit their orthogonality constraints for self-supervision. Our exhaustive experiments on datasets of diverse indoor scenes demonstrate the significant benefit of the proposed method over the established baselines

Diffusion-Based Particle-DETR for BEV Perception

The Bird-Eye-View (BEV) is one of the most widely-used scene representations for visual perception in Autonomous Vehicles (AVs) due to its well suited compatibility to downstream tasks. For the enhanced safety of AVs, modeling perception uncertainty in BEV is crucial. Recent diffusion-based methods offer a promising approach to uncertainty modeling for visual perception but fail to effectively detect small objects in the large coverage of the BEV. Such degradation of performance can be attributed primarily to the specific network architectures and the matching strategy used when training. Here, we address this problem by combining the diffusion paradigm with current state-of-the-art 3D object detectors in BEV. We analyze the unique challenges of this approach, which do not exist with deterministic detectors, and present a simple technique based on object query interpolation that allows the model to learn positional dependencies even in the presence of the diffusion noise. Based on this, we present a diffusion-based DETR model for object detection that bears similarities to particle methods. Abundant experimentation on the NuScenes dataset shows equal or better performance for our generative approach, compared to deterministic state-of-the-art methods. Our source code will be made publicly available