Good year, bad year: changing strategies, changing networks? A two-year study on seed acquisition in northern Cameroon

Analysis of seed exchange networks at a single point in time may reify sporadic relations into apparently fixed and long-lasting ones. In northern Cameroon, where environment is not only strongly seasonal but also shows unpredictable interannual variation, farmers’ social networks are flexible from year to year. When adjusting their strategies, Tupuri farmers do not systematically solicit the same partners to acquire the desired propagules. Seed acquisitions documented during a single cropping season may thus not accurately reflect the underlying larger social network that can be mobilized at the local level. To test this hypothesis, we documented, at the outset of two cropping seasons (2010 and 2011), the relationships through which seeds were acquired by the members of 16 households in a Tupuri community. In 2011, farmers faced sudden failure of the rains and had to solicit distant relatives, highlighting their ability to quickly trigger specific social relations to acquire necessary seeding material. Observing the same set of individuals during two successive years and the seed sources they solicited in each year enabled us to discriminate repeated relations from sporadic ones. Although farmers did not acquire seeds from the same individuals from one year to the next, they relied on quite similar relational categories of people. However, the worse weather conditions during the second year led to (1) a shift from red sorghum seeds to pearl millet seeds, (2) a geographical extension of the network, and (3) an increased participation of women in seed acquisitions. In critical situations, women mobilized their own kin almost exclusively. We suggest that studying the seed acquisition network over a single year provides a misrepresentation of the underlying social network. Depending on the difficulties farmers face, they may occasionally call on relationships that transcend the local relationships used each year.

Comments on: Static and dynamic source locations in undirected networks

SCOPUS: ed.j

A Recommender System based on Idiotypic Artificial Immune Networks

The immune system is a complex biological system with a highly distributed, adaptive and self-organising nature. This paper presents an Artificial Immune System (AIS) that exploits some of these characteristics and is applied to the task of film recommendation by Collaborative Filtering (CF). Natural evolution and in particular the immune system have not been designed for classical optimisation. However, for this problem, we are not interested in finding a single optimum. Rather we intend to identify a sub-set of good matches on which recommendations can be based. It is our hypothesis that an AIS built on two central aspects of the biological immune system will be an ideal candidate to achieve this: Antigen-antibody interaction for matching and idiotypic antibody-antibody interaction for diversity. Computational results are presented in support of this conjecture and compared to those found by other CF techniques.

IVIS: Search and visualization on heterogeneous information networks

We build a system to support search and visualization on heterogeneous information networks. We first build our system on a specialized heterogeneous information network: DBLP. The system aims to facilitate people, especially computer science researchers, toward a better understanding and user experience about academic information networks. Then we extend our system to the Web. Our results are much more intuitive and knowledgeable than the simple top-k blue links from traditional search engines, and bring more meaningful structural results with correlated entities. We also investigate the ranking algorithm, and we show that the personalized PageRank and proposed Hetero-personalized PageRank outperform the TF-IDF ranking or mixture of TF-IDF and authority ranking. Our work opens several directions for future research.

On the application of contextual IoT service discovery in Information Centric Networks

The continuous flow of technological developments in communications and electronic industries has led to the growing expansion of the Internet of Things (IoT). By leveraging the capabilities of smart networked devices and integrating them into existing industrial, leisure and communication applications, the IoT is expected to positively impact both economy and society, reducing the gap between the physical and digital worlds. Therefore, several efforts have been dedicated to the development of networking solutions addressing the diversity of challenges associated with such a vision. In this context, the integration of Information Centric Networking (ICN) concepts into the core of IoT is a research area gaining momentum and involving both research and industry actors. The massive amount of heterogeneous devices, as well as the data they produce, is a significant challenge for a wide-scale adoption of the IoT. In this paper we propose a service discovery mechanism, based on Named Data Networking (NDN), that leverages the use of a semantic matching mechanism for achieving a flexible discovery process. The development of appropriate service discovery mechanisms enriched with semantic capabilities for understanding and processing context information is a key feature for turning raw data into useful knowledge and ensuring the interoperability among different devices and applications. We assessed the performance of our solution through the implementation and deployment of a proof-of-concept prototype. Obtained results illustrate the potential of integrating semantic and ICN mechanisms to enable a flexible service discovery in IoT scenarios.

Undersampled Critical Branching Processes on Small-World and Random Networks Fail to Reproduce the Statistics of Spike Avalanches

The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent t~3=2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.

A Recommender System based on Idiotypic Artificial Immune Networks

The immune system is a complex biological system with a highly distributed, adaptive and self-organising nature. This paper presents an Artificial Immune System (AIS) that exploits some of these characteristics and is applied to the task of film recommendation by Collaborative Filtering (CF). Natural evolution and in particular the immune system have not been designed for classical optimisation. However, for this problem, we are not interested in finding a single optimum. Rather we intend to identify a sub-set of good matches on which recommendations can be based. It is our hypothesis that an AIS built on two central aspects of the biological immune system will be an ideal candidate to achieve this: Antigen-antibody interaction for matching and idiotypic antibody-antibody interaction for diversity. Computational results are presented in support of this conjecture and compared to those found by other CF techniques.

A Recommender System based on Idiotypic Artificial Immune Networks

The immune system is a complex biological system with a highly distributed, adaptive and self-organising nature. This paper presents an Artificial Immune System (AIS) that exploits some of these characteristics and is applied to the task of film recommendation by Collaborative Filtering (CF). Natural evolution and in particular the immune system have not been designed for classical optimisation. However, for this problem, we are not interested in finding a single optimum. Rather we intend to identify a sub-set of good matches on which recommendations can be based. It is our hypothesis that an AIS built on two central aspects of the biological immune system will be an ideal candidate to achieve this: Antigen-antibody interaction for matching and idiotypic antibody-antibody interaction for diversity. Computational results are presented in support of this conjecture and compared to those found by other CF techniques.

The Effect of Architecture and Shear Stress on Endothelialization of 3D Printed Vascular Networks

Despite significant progress in the field of tissue engineering within the last decade, a number of unsolved problems still remain. One of the most relevant issues is the lack of proper vascularization that limits the size of engineered tissues to smaller than clinically relevant dimensions. In particular, the growth of engineered tissue in vitro within bioreactors is plagued with this challenge. Specifically, the tubular perfusion system bioreactor has been used for large scale bone constructs; however these engineered constructs lack inherent vasculature and quickly develop a hypoxic core, where no nutrient exchange can occur, thus leading to cell death. Through the use of 3D printed vascular templates in conjunction with a tubular perfusion system bioreactor, we attempt to create an endothelial cell monolayer on 3D scaffolds that could potentially serve as the foundation of inherent vasculature within these engineered bone grafts.

Reasoning on the Risks of Dynamic Manufacturing Networks through Cognitive Mapping

Part 17: Risk Analysis

Mathematical Programming Models for Influence Maximization on Social Networks

In this dissertation, we apply mathematical programming techniques (i.e., integer programming and polyhedral combinatorics) to develop exact approaches for influence maximization on social networks. We study four combinatorial optimization problems that deal with maximizing influence at minimum cost over a social network. To our knowl- edge, all previous work to date involving influence maximization problems has focused on heuristics and approximation. We start with the following viral marketing problem that has attracted a significant amount of interest from the computer science literature. Given a social network, find a target set of customers to seed with a product. Then, a cascade will be caused by these initial adopters and other people start to adopt this product due to the influence they re- ceive from earlier adopters. The idea is to find the minimum cost that results in the entire network adopting the product. We first study a problem called the Weighted Target Set Selection (WTSS) Prob- lem. In the WTSS problem, the diffusion can take place over as many time periods as needed and a free product is given out to the individuals in the target set. Restricting the number of time periods that the diffusion takes place over to be one, we obtain a problem called the Positive Influence Dominating Set (PIDS) problem. Next, incorporating partial incentives, we consider a problem called the Least Cost Influence Problem (LCIP). The fourth problem studied is the One Time Period Least Cost Influence Problem (1TPLCIP) which is identical to the LCIP except that we restrict the number of time periods that the diffusion takes place over to be one. We apply a common research paradigm to each of these four problems. First, we work on special graphs: trees and cycles. Based on the insights we obtain from special graphs, we develop efficient methods for general graphs. On trees, first, we propose a polynomial time algorithm. More importantly, we present a tight and compact extended formulation. We also project the extended formulation onto the space of the natural vari- ables that gives the polytope on trees. Next, building upon the result for trees---we derive the polytope on cycles for the WTSS problem; as well as a polynomial time algorithm on cycles. This leads to our contribution on general graphs. For the WTSS problem and the LCIP, using the observation that the influence propagation network must be a directed acyclic graph (DAG), the strong formulation for trees can be embedded into a formulation on general graphs. We use this to design and implement a branch-and-cut approach for the WTSS problem and the LCIP. In our computational study, we are able to obtain high quality solutions for random graph instances with up to 10,000 nodes and 20,000 edges (40,000 arcs) within a reasonable amount of time.

Development of a lab-on-a-chip device for rapid nanotoxicity assessment in vitro

Increasing useof nanomaterials in consumer products and biomedical applications creates the possibilities of intentional/unintentional exposure to humans and the environment. Beyond the physiological limit, the nanomaterialexposure to humans can induce toxicity. It is difficult to define toxicity of nanoparticles on humans as it varies by nanomaterialcomposition, size, surface properties and the target organ/cell line. Traditional tests for nanomaterialtoxicity assessment are mostly based on bulk-colorimetric assays. In many studies, nanomaterials have found to interfere with assay-dye to produce false results and usually require several hours or days to collect results. Therefore, there is a clear need for alternative tools that can provide accurate, rapid, and sensitive measure of initial nanomaterialscreening. Recent advancement in single cell studies has suggested discovering cell properties not found earlier in traditional bulk assays. A complex phenomenon, like nanotoxicity, may become clearer when studied at the single cell level, including with small colonies of cells. Advances in lab-on-a-chip techniques have played a significant role in drug discoveries and biosensor applications, however, rarely explored for nanomaterialtoxicity assessment. We presented such cell-integrated chip-based approach that provided quantitative and rapid response of cellhealth, through electrochemical measurements. Moreover, the novel design of the device presented in this study was capable of capturing and analyzing the cells at a single cell and small cell-population level. We examined the change in exocytosis (i.e. neurotransmitterrelease) properties of a single PC12 cell, when exposed to CuOand TiO2 nanoparticles. We found both nanomaterials to interfere with the cell exocytosis function. We also studied the whole-cell response of a single-cell and a small cell-population simultaneously in real-time for the first time. The presented study can be a reference to the future research in the direction of nanotoxicity assessment to develop miniature, simple, and cost-effective tool for fast, quantitative measurements at high throughput level. The designed lab-on-a-chip device and measurement techniques utilized in the present work can be applied for the assessment of othernanoparticles' toxicity, as well.^

Avoided losses on LV networks as a result of microgeneration

In the scope of the discussions about microgeneration (and microgrids), the avoided electrical losses are often pointed out as an important value to be credited to those entities. Therefore, methods to assess the impact of microgeneration on losses must be developed in order to support the definition of a suitable regulatory framework for the economic integration of microgeneration on distribution networks. This paper presents an analytical method to quantify the value of avoided losses that microgeneration may produce on LV networks. Intervals of expected avoided losses are used to account for the variation of avoided losses due to the number, size and location of microgenerators, as well as for the kind of load distribution on LV networks.

On the Bleeding Edge : Debloating Internet Access Networks

As ever more devices are connected to the internet, and applications turn ever more interactive, it becomes more important that the network can be counted on to respond reliably and without unnecessary delay. However, this is far from always the case today, as there can be many potential sources of unnecessary delay. In this thesis we focus on one of them: Excess queueing delay in network routers along the path, also known as bufferbloat. We focus on the home network, and treat the issue in three stages. We examine latency variation and queueing delay on the public internet and show that significant excess delay is often present. Then, we evaluate several modern AQM algorithms and packet schedulers in a residential setting, and show that modern AQMs can almost entirely eliminate bufferbloat and extra queueing latency for wired connections, but that they are not as effective for WiFi links. Finally, we go on to design and implement a solution for bufferbloat at the WiFi link, and also design a workable scheduler-based solution for realising airtime fairness in WiFi. Also included in this thesis is a description of Flent, a measurement tool used to perform most of the experiments in the other papers, and also used widely in the bufferbloat community.

State-narco networks and the ‘War on Drugs’ in post-transition Bolivia, with special reference to 1989-1993

This thesis examines the development of state-narco networks in post-transition Bolivia. Mainstream discourses of drugs tend to undertheorise such relationships, holding illicit economies, weak states and violence as synergistic phenomena. Such assumptions fail to capture the nuanced relations that emerge between the state and the drug trade in different contexts, their underlying logics and diverse effects. As an understudied case, Bolivia offers novel insights into these dynamics. Bolivian military authoritarian governments (1964-1982), for example, integrated drug rents into clientelistic systems of governance, helping to establish factional coalitions and reinforce regime authority. Following democratic transition in 1982 and the escalation of US counterdrug efforts, these stable modes of exchange between the state and the coca-cocaine economy fragmented. Bolivia, though, continued to experience lower levels of drug-related violence than its Andean neighbours, and sustained democratisation despite being a major drug producer. Focusing on the introduction of the Andean Initiative (1989-1993), I explore state-narco interactions during this period of flux: from authoritarianism to (formal) democracy, and from Cold War to Drug War. As such, the thesis transcends the conventional analyses of the drugs literature and orthodox readings of Latin American narco-violence, providing insights into the relationship between illicit economies and democratic transition, the regional role of the US, and the (unintended) consequences of drug policy interventions. I utilise a mixed methods approach to offer discrete perspectives on the object of study. Drawing on documentary and secondary sources, I argue that state-narco networks were interwoven with Bolivia’s post-transition political settlement. Uneven democratisation ensured pockets of informalism, as clientelistic and authoritarian practices continued. This included police and military autonomy, and tolerance of drug corruption within both institutions. Non-enforcement of democratic norms of accountability and transparency was linked to the maintenance of fragile political equilibrium. Interviews with key US and Bolivian elite actors also revealed differing interpretations of state-narco interactions. These exposed competing agendas, and were folded into alternative paradigms and narratives of the ‘war on drugs’. The extension of US Drug War goals and the targeting of ‘corrupt’ local power structures, clashed with local ambivalence towards the drug trade, opposition to destabilising, ‘Colombianised’ policies and the claimed ‘democratising mission’ of the Bolivian government. In contrasting these US and Bolivian accounts, the thesis shows how real and perceived state-narco webs were understood and navigated by different actors in distinct ways. ‘Drug corruption’ held significance beyond simple economic transaction or institutional failure. Contestation around state-narco interactions was enmeshed in US-Bolivian relations of power and control.