75 research outputs found
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
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