Rapid urbanization, employment crisis and poverty in African LDCs:A new development strategy and aid policy

Rapid urbanization is a fact of live even in the least developed countries (LDCs) where the lion’s share of the population presently lives in rural areas and will continue to do so for decades to come. At the turn of the millennium 75% of the LDCs’ population still lived in rural areas and 71% of the LDCs’ labor force was involved in agriculture. But even though the largest share of their population lives in rural areas and directly or indirectly derives their livelihoods from agriculture, a rapidly increasing share of the population migrates to urban centers in search for employment opportunities outside agriculture in industrial enterprises or the services sector. The main purpose of this paper is to examine the causes and consequences -- in particular, the policy implications -- of the ongoing urbanization in the African LDCs. It is found that the employment opportunities in either rural or the urban sector are not growing adequately. This paper attempts to analyze the emerging trends and patterns of urbanization in the African LDCs within a dynamic dual-dual framework with a strong emphasis on rural-urban migration and the informal sectors. The analysis pinpoints, among other things, the need to build up productive capacities in order to create adequate employment and incomes for the rapidly growing population---particularly in the urban areas. The development of productive capacities, which is a precondition for the creation of productive employment opportunities, is a central element of viable poverty reduction strategy for Bangladesh as well. Without significant poverty reduction it is impossible to think of viable urbanization on the basis of sustainable development criteria in this group of very African countries. The donors, especially the OECD/ DAC countries, should provide the necessary financial backing for such a sustainable and equitable development strategy for Africa. It is necessary to reverse the trends in aid, and to provide a much larger share of aid for productive sector development, including the development of rural and urban areas, and the development of agricultural and non-agricultural sectors in line with the perspective of the dual-dual model. Although urban centers mostly host non-agricultural industries, sustainable urbanization also strongly depends on what happens in the agricultural sectors. Productive employment opportunities in rural areas are important in order to combat an unsustainable migration from rural areas to urban centers, and productive employment opportunities in urban centers are essential to absorb the rapidly increasing labor force in the non-agricultural sector.Urbanization, Africa, LDCs, Dual-Dual Model, Informal Sector, Poverty, Employment, Capabilities

Room-temperature superparamagnetism due to giant magnetic anisotropy in MoS_{S} defected single-layer MoS2_{2}

Room-temperature superparamagnetism due to a large magnetic anisotropy energy (MAE) of a single atom magnet has always been a prerequisite for nanoscale magnetic devices. Realization of two dimensional (2D) materials such as single-layer (SL) MoS$_{2}$, has provided new platforms for exploring magnetic effects, which is important for both fundamental research and for industrial applications. Here, we use density functional theory (DFT) to show that the antisite defect (Mo$_{S}$) in SL MoS$_{2}$ is magnetic in nature with a magnetic moment of $\mu$ of $\sim$ 2$\mu_{B}$ and, remarkably, exhibits an exceptionally large atomic scale MAE$=\varepsilon_{\parallel}-\varepsilon_{\perp}$ of $\sim$500 meV. Our calculations reveal that this giant anisotropy is the joint effect of strong crystal field and significant spin-orbit coupling (SOC). In addition, the magnetic moment $\mu$ can be tuned between 1$\mu_{B}$ and 3$\mu_{B}$ by varying the Fermi energy $\varepsilon_{F}$, which can be achieved either by changing the gate voltage or by chemical doping. We also show that MAE can be raised to $\sim$1 eV with n-type doping of the MoS$_{2}$:Mo$_{S}$ sample. Our systematic investigations deepen our understanding of spin-related phenomena in SL MoS$_{2}$ and could provide a route to nanoscale spintronic devices.Comment: 7 pages, 7 figure

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

Two-dimensional Fermionic Hong-Ou-Mandel Interference with Weyl Fermions

We propose a two-dimensional Hong-Ou-Mandel (HOM) type interference experiment for Weyl fermions in graphene and 3D topological insulators. Since Weyl fermions exhibit linear dispersion, similar to photons in vacuum, they can be used to obtain the HOM interference intensity pattern as a function of the delay time between two Weyl fermions. We show that while the Coulomb interaction leads to a significant change in the angle dependence of the tunneling of two identical Weyl fermions incident from opposite sides of a potential barrier, it does not affect the HOM interference pattern, in contrast to previous expectations. We apply our formalism to develop a Weyl fermion beam-splitter (BS) for controlling the transmission and reflection coefficients. We calculate the resulting time-resolved correlation function for two identical Weyl fermions scattering off the BS.Comment: 4 pages, 3 figure

Confidence Propagation through CNNs for Guided Sparse Depth Regression

Generally, convolutional neural networks (CNNs) process data on a regular grid, e.g. data generated by ordinary cameras. Designing CNNs for sparse and irregularly spaced input data is still an open research problem with numerous applications in autonomous driving, robotics, and surveillance. In this paper, we propose an algebraically-constrained normalized convolution layer for CNNs with highly sparse input that has a smaller number of network parameters compared to related work. We propose novel strategies for determining the confidence from the convolution operation and propagating it to consecutive layers. We also propose an objective function that simultaneously minimizes the data error while maximizing the output confidence. To integrate structural information, we also investigate fusion strategies to combine depth and RGB information in our normalized convolution network framework. In addition, we introduce the use of output confidence as an auxiliary information to improve the results. The capabilities of our normalized convolution network framework are demonstrated for the problem of scene depth completion. Comprehensive experiments are performed on the KITTI-Depth and the NYU-Depth-v2 datasets. The results clearly demonstrate that the proposed approach achieves superior performance while requiring only about 1-5% of the number of parameters compared to the state-of-the-art methods.Comment: 14 pages, 14 Figure

Propagating Confidences through CNNs for Sparse Data Regression

In most computer vision applications, convolutional neural networks (CNNs) operate on dense image data generated by ordinary cameras. Designing CNNs for sparse and irregularly spaced input data is still an open problem with numerous applications in autonomous driving, robotics, and surveillance. To tackle this challenging problem, we introduce an algebraically-constrained convolution layer for CNNs with sparse input and demonstrate its capabilities for the scene depth completion task. We propose novel strategies for determining the confidence from the convolution operation and propagating it to consecutive layers. Furthermore, we propose an objective function that simultaneously minimizes the data error while maximizing the output confidence. Comprehensive experiments are performed on the KITTI depth benchmark and the results clearly demonstrate that the proposed approach achieves superior performance while requiring three times fewer parameters than the state-of-the-art methods. Moreover, our approach produces a continuous pixel-wise confidence map enabling information fusion, state inference, and decision support.Comment: To appear in the British Machine Vision Conference (BMVC2018

Optical signatures of states bound to vacancy defects in monolayer MoS2_2

We show that pristine MoS$_2$ single layer (SL) exhibits two bandgaps $E_{g\parallel}=1.9$ eV and $E_{g\perp}=3.2$ eV for the optical in-plane and out-of-plane susceptibilities $\chi_\parallel$ and $\chi_\perp$, respectively. In particular, we show that odd states bound to vacancy defects (VDs) lead to resonances in $\chi_\perp$ inside $E_{g\perp}$ in MoS$_2$ SL with VDs. We use density functional theory, the tight-binding model, and the Dirac equation to study MoS$_2$ SL with three types of VDs: (i) Mo-vacancy, (ii) S$_2$-vacancy, and (iii) 3$\times$MoS$_2$ quantum antidot. The resulting optical spectra identify and characterize the VDs.Comment: 5 pages, 5 figure