Bidirectional Heuristic Search Reconsidered

The assessment of bidirectional heuristic search has been incorrect since it was first published more than a quarter of a century ago. For quite a long time, this search strategy did not achieve the expected results, and there was a major misunderstanding about the reasons behind it. Although there is still wide-spread belief that bidirectional heuristic search is afflicted by the problem of search frontiers passing each other, we demonstrate that this conjecture is wrong. Based on this finding, we present both a new generic approach to bidirectional heuristic search and a new approach to dynamically improving heuristic values that is feasible in bidirectional search only. These approaches are put into perspective with both the traditional and more recently proposed approaches in order to facilitate a better overall understanding. Empirical results of experiments with our new approaches show that bidirectional heuristic search can be performed very efficiently and also with limited memory. These results suggest that bidirectional heuristic search appears to be better for solving certain difficult problems than corresponding unidirectional search. This provides some evidence for the usefulness of a search strategy that was long neglected. In summary, we show that bidirectional heuristic search is viable and consequently propose that it be reconsidered.Comment: See http://www.jair.org/ for any accompanying file

Learning a Dilated Residual Network for SAR Image Despeckling

In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is based on dilated convolutions, which can both enlarge the receptive field and maintain the filter size and layer depth with a lightweight structure. In addition, skip connections and residual learning strategy are added to the despeckling model to maintain the image details and reduce the vanishing gradient problem. Compared with the traditional despeckling methods, the proposed method shows superior performance over the state-of-the-art methods on both quantitative and visual assessments, especially for strong speckle noise.Comment: 18 pages, 13 figures, 7 table

Finding The Lazy Programmer's Bugs

Traditionally developers and testers created huge numbers of explicit tests, enumerating interesting cases, perhaps biased by what they believe to be the current boundary conditions of the function being tested. Or at least, they were supposed to. A major step forward was the development of property testing. Property testing requires the user to write a few functional properties that are used to generate tests, and requires an external library or tool to create test data for the tests. As such many thousands of tests can be created for a single property. For the purely functional programming language Haskell there are several such libraries; for example QuickCheck [CH00], SmallCheck and Lazy SmallCheck [RNL08]. Unfortunately, property testing still requires the user to write explicit tests. Fortunately, we note there are already many implicit tests present in programs. Developers may throw assertion errors, or the compiler may silently insert runtime exceptions for incomplete pattern matches. We attempt to automate the testing process using these implicit tests. Our contributions are in four main areas: (1) We have developed algorithms to automatically infer appropriate constructors and functions needed to generate test data without requiring additional programmer work or annotations. (2) To combine the constructors and functions into test expressions we take advantage of Haskell's lazy evaluation semantics by applying the techniques of needed narrowing and lazy instantiation to guide generation. (3) We keep the type of test data at its most general, in order to prevent committing too early to monomorphic types that cause needless wasted tests. (4) We have developed novel ways of creating Haskell case expressions to inspect elements inside returned data structures, in order to discover exceptions that may be hidden by laziness, and to make our test data generation algorithm more expressive. In order to validate our claims, we have implemented these techniques in Irulan, a fully automatic tool for generating systematic black-box unit tests for Haskell library code. We have designed Irulan to generate high coverage test suites and detect common programming errors in the process

Early Learning Innovation Fund Evaluation Final Report

This is a formative evaluation of the Hewlett Foundation's Early Learning Innovation Fund that began in 2011 as part of the Quality Education in Developing Countries (QEDC) initiative.  The Fund has four overarching objectives, which are to: promote promising approaches to improve children's learning; strengthen the capacity of organizations implementing those approaches; strengthen those organizations' networks and ownership; and grow 20 percent of implementing organizations into significant players in the education sector. The Fund's original design was to create a "pipeline" of innovative approaches to improve learning outcomes, with the assumption that donors and partners would adopt the most successful ones. A defining feature of the Fund was that it delivered assistance through two intermediary support organizations (ISOs), rather than providing funds directly to implementing organizations. Through an open solicitation process, the Hewlett Foundation selected Firelight Foundation and TrustAfrica to manage the Fund. Firelight Foundation, based in California, was founded in 1999 with a mission to channel resources to community-based organizations (CBOs) working to improve the lives of vulnerable children and families in Africa. It supports 12 implementing organizations in Tanzania for the Fund. TrustAfrica, based in Dakar, Senegal, is a convener that seeks to strengthen African-led initiatives addressing some of the continent's most difficult challenges. The Fund was its first experience working specifically with early learning and childhood development organizations. Under the Fund, it supported 16 such organizations: one in Mali and five each in Senegal, Uganda and Kenya. At the end of 2014, the Hewlett Foundation commissioned Management Systems International (MSI) to conduct a mid-term evaluation assessing the implementation of the Fund exploring the extent to which it achieved intended outcomes and any factors that had limited or enabled its achievements. It analyzed the support that the ISOs provided to their implementing organizations, with specific focus on monitoring and evaluation (M&E). The evaluation included an audit of the implementing organizations' M&E systems and a review of the feasibility of compiling data collected to support an impact evaluation. Finally, the Foundation and the ISOs hoped that this evaluation would reveal the most promising innovations and inform planning for Phase II of the Fund. The evaluation findings sought to inform the Hewlett Foundation and other donors interested in supporting intermediary grant-makers, early learning innovations and the expansion of innovations. TrustAfrica and Firelight Foundation provided input to the evaluation's scope of work. Mid-term evaluation reports for each ISO provided findings about their management of the Fund's Phase I and recommendations for Phase II. This final evaluation report will inform donors, ISOs and other implementing organizations about the best approaches to support promising early learning innovations and their expansion. The full report outlines findings common across both ISOs' experience and includes recommendations in four key areas: adequate time; appropriate capacity building; advocacy and scaling up; and evaluating and documenting innovations. Overall, both Firelight Foundation and TrustAfrica supported a number of effective innovations working through committed and largely competent implementing organizations. The program's open-ended nature avoided being prescriptive in its approach, but based on the lessons learned in this evaluation and the broader literature, the Hewlett Foundation and other donors could have offered more guidance to ISOs to avoid the need to continually relearn some lessons. For example, over the evaluation period, it became increasingly evident that the current context demands more focused advance planning to measure impact on beneficiaries and other stakeholders and a more concrete approach to promoting and resourcing potential scale-up. The main findings from the evaluation and recommendations are summarized here