Challenges of Public Housing in a Democratic Nigeria: a Case Study of the Presidential Mandate Housing Scheme

This study examined the challenges of public housing in a democratic Nigeria using the Presidential Mandate Housing Scheme as a case study. Data were derived from purposively selected members of staff of public institutions charged with the responsibility of implementing this scheme in urban areas of Southern Nigeria through interview enquiries and participant observation. These were analyzed using content analysis. The result shows that the scheme was implemented in very few States in Southern part of Nigeria with miniscule number of housing units constructed in those States. Poor programme conception and planning, funding inadequacies and the dearth of preferred building materials were identified as the key challenges that led to the failure of this scheme. The paper argues that despite the return of democratic rule in 1999 and subsequent adoption of the New National Housing and Urban Development Policy in 2002, low organizational capacity of public housing agencies, the lack of collaborations between these agencies and private sector organizations and the none availability of reliable local building materials constitute serious impediments to smooth and successful implementation of public housing programmes in Nigeria. It therefore suggests that the prospects of public housing in democratic Nigeria are contingent upon addressing these challenge

FinBook: literary content as digital commodity

This short essay explains the significance of the FinBook intervention, and invites the reader to participate. We have associated each chapter within this book with a financial robot (FinBot), and created a market whereby book content will be traded with financial securities. As human labour increasingly consists of unstable and uncertain work practices and as algorithms replace people on the virtual trading floors of the worlds markets, we see members of society taking advantage of FinBots to invest and make extra funds. Bots of all kinds are making financial decisions for us, searching online on our behalf to help us invest, to consume products and services. Our contribution to this compilation is to turn the collection of chapters in this book into a dynamic investment portfolio, and thereby play out what might happen to the process of buying and consuming literature in the not-so-distant future. By attaching identities (through QR codes) to each chapter, we create a market in which the chapter can ‘perform’. Our FinBots will trade based on features extracted from the authors’ words in this book: the political, ethical and cultural values embedded in the work, and the extent to which the FinBots share authors’ concerns; and the performance of chapters amongst those human and non-human actors that make up the market, and readership. In short, the FinBook model turns our work and the work of our co-authors into an investment portfolio, mediated by the market and the attention of readers. By creating a digital economy specifically around the content of online texts, our chapter and the FinBook platform aims to challenge the reader to consider how their personal values align them with individual articles, and how these become contested as they perform different value judgements about the financial performance of each chapter and the book as a whole. At the same time, by introducing ‘autonomous’ trading bots, we also explore the different ‘network’ affordances that differ between paper based books that’s scarcity is developed through analogue form, and digital forms of books whose uniqueness is reached through encryption. We thereby speak to wider questions about the conditions of an aggressive market in which algorithms subject cultural and intellectual items – books – to economic parameters, and the increasing ubiquity of data bots as actors in our social, political, economic and cultural lives. We understand that our marketization of literature may be an uncomfortable juxtaposition against the conventionally-imagined way a book is created, enjoyed and shared: it is intended to be

SON/RRM Functionality for mobility load balancing in LTE networks

Implementation and Analysis of a Mobility Load Balancing Algorithm base on the adjustment of mobility parameters. This functionality of Self-Optimization belongs to the proposed solution fon Self-Organizing Networks from the 3GPP for LTE Networks.Implementación y Análisis de un algoritmo Balanceador de carga basado en el cambio de los parámetros de movilidad. Esta funcionalidad de auto-optimización pertenece a las soluciones aconsejadas en redes auto-organizadas del 3GPP para redes LTE.Navarro Suria, S. (2013). SON/RRM Functionality for mobility load balancing in LTE networks. http://hdl.handle.net/10251/29044.Archivo delegad

Advances in Measuring the Apparent Optical Properties (AOPs) of Optically Complex Waters

This report documents new technology used to measure the apparent optical properties (AOPs) of optically complex waters. The principal objective is to be prepared for the launch of next-generation ocean color satellites with the most capable commercial off-the-shelf (COTS) instrumentation. An enhanced COTS radiometer was the starting point for designing and testing the new sensors. The follow-on steps were to apply the lessons learned towards a new in-water profiler based on a kite-shaped backplane for mounting the light sensors. The next level of sophistication involved evaluating new radiometers emerging from a development activity based on so-called microradiometers. The exploitation of microradiometers resulted in an in-water profiling system, which includes a sensor networking capability to control ancillary sensors like a shadowband or global positioning system (GPS) device. A principal advantage of microradiometers is their flexibility in producing, interconnecting, and maintaining instruments. The full problem set for collecting sea-truth data--whether in coastal waters or the open ocean-- involves other aspects of data collection that were improved for instruments measuring both AOPs and inherent optical properties (IOPs), if the uncertainty budget is to be minimized. New capabilities associated with deploying solar references were developed as well as a compact solution for recovering in-water instrument systems from small boats

Framework for transfer learning: Maximization of quadratic mutual information to create discriminative subspaces

In the area of pattern recognition and computer vision, Transfer learning has become an emerging topic in recent years. It is motivated by the mechanism of human vision system that is capable of accumulating previous knowledge or experience to unveil a novel domain. Learning an effective model to solve a classification or recognition task in a new domain (dataset) requires sufficient data with ground truth information. Visual data are being generated in an enormous amount every moment with the advance of photo capturing devices. Most of these data remain unannotated. Manually collecting and annotating training data by human intervention is expensive and hence the learned model may suffer from performance bottleneck because of poor generalization and label scarcity. Also an existing trained model may become outdated if the distribution of training data differs from the distribution where the model is tested. Traditional machine learning methods generally assume that training and test data are sampled from the same distribution. This assumption is often challenged in real life scenario. Therefore, adapting an existing model or utilizing the knowledge of a label-rich domain becomes inevitable to overcome the issue of continuous evolving data distribution and the lack of label information in a novel domain. In other words, a knowledge transfer process is developed with a goal to minimize the distribution divergence between domains such that a classifier trained using source dataset can also generalize over target domain. In this thesis, we propose a novel framework for transfer learning by creating a common subspace based on maximization of non-parametric quadratic mutual information (QMI) between data and corresponding class labels. We extend the prior work of QMI in the context of knowledge transfer by introducing soft class assignment and instance weighting for data across domains. The proposed approach learns a class discriminative subspace by leveraging soft-labeling. Also by employing a suitable weighting scheme, the method identifies samples with underlying shared similarity across domains in order to maximize their impact on subspace learning. Variants of the proposed framework, parameter sensitivity, extensive experiments using benchmark datasets and also performance comparison with recent competitive methods are provided to prove the efficacy of our novel framework

Graph-based Facial Affect Analysis: A Review of Methods, Applications and Challenges

Facial affect analysis (FAA) using visual signals is important in human-computer interaction. Early methods focus on extracting appearance and geometry features associated with human affects, while ignoring the latent semantic information among individual facial changes, leading to limited performance and generalization. Recent work attempts to establish a graph-based representation to model these semantic relationships and develop frameworks to leverage them for various FAA tasks. In this paper, we provide a comprehensive review of graph-based FAA, including the evolution of algorithms and their applications. First, the FAA background knowledge is introduced, especially on the role of the graph. We then discuss approaches that are widely used for graph-based affective representation in literature and show a trend towards graph construction. For the relational reasoning in graph-based FAA, existing studies are categorized according to their usage of traditional methods or deep models, with a special emphasis on the latest graph neural networks. Performance comparisons of the state-of-the-art graph-based FAA methods are also summarized. Finally, we discuss the challenges and potential directions. As far as we know, this is the first survey of graph-based FAA methods. Our findings can serve as a reference for future research in this field.Comment: 20 pages, 12 figures, 5 table

Deciphering the genetic architecture of native resistance and tolerance to western corn rootworm larval feeding

Plants can exploit complex suites of biochemical, morphological, and physiological mechanisms to defend against herbivory. This research expands that body of knowledge by investigating mechanisms of defense in maize (Zea mays) against one of its most economically important pests, the western corn rootworm (Diabrotica virgifera virgifera, WCR). Natural variation for resistance and tolerance to WCR larval herbivory has been previously reported; however, characterization of the underlying genetic architecture has remained elusive. The results from three separate studies are presented that confirm heritable variation exists for WCR resistance that is both experimentally tractable and reproducible. The findings highlight that both genetic and environmental components contribute to the observed variation and interactions exist between rootworm population dynamics and root phenology. Using F2, BC1, and DH populations capturing natural variation for three native resistance traits, we demonstrate that discrete regions on chromosomes 2, 3, 5, and 7 are consistently associated with a resistance phenotype. QTL co-localized across analysis populations that were evaluated in different locations and years. Among 21 QTL fixed in the DH population, between 46% and 56% of the variation was explained for three resistance traits. The alleles were found to act robustly by reducing node-injury and increasing root biomass, which was confirmed in hybrid testcrosses. In a separate study, we identified particular physiological and genetic mechanisms of response to WCR root herbivory and revealed evidence of genetic overcompensation. A QTL on c3 (bin 3.05) was localized to a 2.8 cM region and was associated with increased growth rate under high herbivory. The sps2 gene involved in regulating source-sink transition fell precisely within the QTL interval, and is a possible candidate in the herbivory stress response. These results advance our current understanding of host-plant defense and also provide a route for applied maize improvement by providing a genetic framework for native resistance that can be exploited to reduce larval feeding damage by WCRs