Poverty and community: understanding culture and politics in poor places

This lack of participation, low trust and failure to invest in community wide institutions allows corrupt politics to emerge in poor inner cities and rural communities, and then that bad politics in turn becomes an obstacle to change and development. Those in charge see schools and local government as sources of patronage jobs and political power rather than as public institutions to serve the common good. Politics and political forces become part of the problem instead of part of the solution. Only investment and organizing can turn the poor community around and provide real opportunity for low income residents to succeed

Multiple Instance Learning: A Survey of Problem Characteristics and Applications

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research

The Global Media and Information Literacy Week: Moving Towards MIL Cities

The Global Media and Information Literacy Week commemorates the progress in achieving “MIL for all” by aggregating various MIL-related local and international events and actions across different disciplines around the world.The MIL Global Week 2018, 24 to 31 October, was marked by the United Nations Educational, Scientific and Cultural Organization in collaboration with various organizations including the UN Alliance of Civilizations, the Global Alliance for Partnership on MIL, the International Federation of Library Associations, the International Association of School Libraries, and the UNESCO-UNAOC University Cooperation Programme on Media and Information Literacy and Intercultural Dialogue

Causation of Late Quaternary Rapid-increase Radiocarbon Anomalies

Brief (less than 100 years) rapid-increase anomalies in the Earth's atmospheric radiocarbon production have previously been attributed to either gamma photon radiation from supernovae or to cosmic ray particle radiation from exceptionally large solar flares. Analysis of distances and ages of nearby supernovae remnants, the probable gamma emissions, the predicted Earth incident radiation, and the terrestrial radiocarbon record indicates that supernova causation may be the case. Supernovae include Type Ia white dwarf explosions, Type Ib, c, and II core collapse events, and some types of gamma burst objects. All generate significant pulses of atmospheric radiocarbon depending on distances. Surveys of supernova remnants offer a nearly complete accounting for the past 50,000 years. There are 18 events less than or at 1.4 kilo-parsec distance, and brief radiocarbon anomalies with appropriate sizes occurred for each of the closest events. In calendar years before 1950, these are: Vela, 22 per mil del 14C at 12,760; S165, 20 per mil at 7431; Vela Junior, 13 per mil at 2765; HB9, 9 per mil at 5372; Boomerang, 11 per mil at 10,255; and Cygnus Loop (per mil change not calculated) at 14,722. Although uncertainties remain large, the agreements of prediction to observation support a possible causal connection

What Determines the Growth Ambition of Dutch Early-Stage Entrepreneurs?

This paper investigates the determinants of the ambition to grow among Dutch early-stage entrepreneurs (nascents and young business owners). We use Adult Population Survey data of the Global Entrepreneurship Monitor (GEM) for the Netherlands. Merging cross-sectional data of the years 2002 to 2007, we arrive at a sample of 409 nascents and 336 young business owners. Growth ambition is measured by asking the respondent which statement fits him or her best: (1) I want my company to be as large as possible, or (2) I want a size I can manage myself or with a few key employees. We find that nascent entrepreneurs and young business owners are equally likely to strive after business growth. For nascent entrepreneurs we find that fear of failure and entrepreneurial self-efficacy are important factors explaining growth ambition. Starting a business because of perceiving and exploiting a business opportunity (as opposed to starting a business out of necessity) is an important driver of growth ambition for both nascents and young business owners, although it is more important for nascents.

A Conditional Random Field for Multiple-Instance Learning

We present MI-CRF, a conditional random field (CRF) model for multiple instance learning (MIL). MI-CRF models bags as nodes in a CRF with instances as their states. It combines discriminative unary instance classifiers and pairwise dissimilarity measures. We show that both forces improve the classification performance. Unlike other approaches, MI-CRF considers all bags jointly during training as well as during testing. This makes it possible to classify test bags in an imputation setup. The parameters of MI-CRF are learned using constraint generation. Furthermore, we show that MI-CRF can incorporate previous MIL algorithms to improve on their results. MI-CRF obtains competitive results on five standard MIL datasets. 1