Stepping Stones to Inductive Synthesis of Low-Level Looping Programs

Inductive program synthesis, from input/output examples, can provide an opportunity to automatically create programs from scratch without presupposing the algorithmic form of the solution. For induction of general programs with loops (as opposed to loop-free programs, or synthesis for domain-specific languages), the state of the art is at the level of introductory programming assignments. Most problems that require algorithmic subtlety, such as fast sorting, have remained out of reach without the benefit of significant problem-specific background knowledge. A key challenge is to identify cues that are available to guide search towards correct looping programs. We present MAKESPEARE, a simple delayed-acceptance hillclimbing method that synthesizes low-level looping programs from input/output examples. During search, delayed acceptance bypasses small gains to identify significantly-improved stepping stone programs that tend to generalize and enable further progress. The method performs well on a set of established benchmarks, and succeeds on the previously unsolved "Collatz Numbers" program synthesis problem. Additional benchmarks include the problem of rapidly sorting integer arrays, in which we observe the emergence of comb sort (a Shell sort variant that is empirically fast). MAKESPEARE has also synthesized a record-setting program on one of the puzzles from the TIS-100 assembly language programming game.Comment: AAAI 201

The social practice of sustainable agriculture under audit discipline: initial insights from the ARGOS project in New Zealand

One of the most interesting recent developments in global agri‐food systems has been the rapid emergence and elaboration of market audit systems claiming environmental qualities or sustainability. In New Zealand, as a strongly export‐oriented, high‐value food producer, these environmental market audit systems have emerged as an important pathway for producers to potentially move towards more sustainable production. There have, however, been only sporadic and fractured attempts to study the emerging social practice of sustainable agriculture – particularly in terms of the emergence of new audit disciplines in farming. The ARGOS project in New Zealand was established in 2003 as a longitudinal matched panel study of over 100 farms and orchards using different market audit systems (e.g., organic, integrated or GLOBALG.A.P.). This article reports on the results of social research into the social practice of sustainable agriculture in farm households within the ARGOS projects between 2003‐2009. Results drawn from multiple social research instruments deployed over six years provide an unparalleled level of empirical data on the social practice of sustainable agriculture under audit disciplines. Using 12 criteria identified in prior literature as contributing a significant social dynamic around sustainable agriculture practices in other contexts, the analysis demonstrated that 9 of these 12 dimensions did demonstrate differences in social practices emerging between (or co‐constituting) organic, integrated, or conventional audit disciplines. These differences clustered into three main areas: 1) social and learning/knowledge networks and expertise, 2) key elements of farmer subjectivity – particularly in relation to subjective positioning towards the environment and nature, and 3) the role and importance of environmental dynamics within farm management practices and systems. The findings of the project provide a strong challenge to some older framings of the social practice of sustainable agriculture: particularly those that rely on paradigm‐driven evaluation of social motivations, strong determinism of sustainable practice driven by coherent farmer identity, or deploying overly categorical interpretations of what it means to be ‘organic’ or ‘conventional’. The complex patterning of the ARGOS data can only be understood if the social practice of organic, integrated or (even more loosely) conventional production is understood as being co‐produced by four dynamics: subjectivity/identity, audit disciplines, industry cultures/structure and time. This reframing of how we might research the social practice of sustainable agriculture opens up important new opportunities for understanding the emergence and impact of new audit disciplines in agriculture

An oscillation-based model for the neuronal basis of attention

We propose a model for the neuronal implementation of selective visual attention based on the temporal structure of neuronal activity. In particular, we set out to explain the electrophysiological data from areas V4 and IT in monkey cortex of Moran and Desimone [(1985)Science, 229, 782–784] using the “temporal tagging” hypothesis of Crick and Koch, 1990a and Crick and Koch, 1990bSeminars in the neurosciences (pp. 1–36)]. Neurons in primary visual cortex respond to visual stimuli with a Poisson distributed spike train with an appropriate, stimulus-dependent mean firing rate. The firing rate of neurons whose receptive fields overlap with the “focus of attention” is modulated with a periodic function in the 40 Hz range, such that their mean firing rate is identical to the mean firing rate of neurons in “non-attended” areas. This modulation is detected by inhibitory interneurons in V4 and is used to suppress the response of V4 cells associated with non-attended visual stimuli. Using very simple single-cell models, we obtain quantitative agreement with Moran and Desimone's (1985) experiments

Unweighted Stochastic Local Search can be Effective for Random CSP Benchmarks

We present ULSA, a novel stochastic local search algorithm for random binary constraint satisfaction problems (CSP). ULSA is many times faster than the prior state of the art on a widely-studied suite of random CSP benchmarks. Unlike the best previous methods for these benchmarks, ULSA is a simple unweighted method that does not require dynamic adaptation of weights or penalties. ULSA obtains new record best solutions satisfying 99 of 100 variables in the challenging frb100-40 benchmark instance

De novo mutations in SMCHD1 cause Bosma arhinia microphthalmia syndrome and abrogate nasal development

Bosma arhinia microphthalmia syndrome (BAMS) is an extremely rare and striking condition characterized by complete absence of the nose with or without ocular defects. We report here that missense mutations in the epigenetic regulator SMCHD1 mapping to the extended ATPase domain of the encoded protein cause BAMS in all 14 cases studied. All mutations were de novo where parental DNA was available. Biochemical tests and in vivo assays in Xenopus laevis embryos suggest that these mutations may behave as gain-of-function alleles. This finding is in contrast to the loss-of-function mutations in SMCHD1 that have been associated with facioscapulohumeral muscular dystrophy (FSHD) type 2. Our results establish SMCHD1 as a key player in nasal development and provide biochemical insight into its enzymatic function that may be exploited for development of therapeutics for FSHD

Automatic semantic and geometric enrichment of CityGML building models using HoG-based template matching

Semantically rich 3D building models give the potential for a wealth of rich geo-spatially-enabled applications such as cultural heritage augmented reality, urban planning, radio network planning and personal navigation. However, the majority of existing building models lack much if any semantic detail. This work demonstrates a novel method for automatically locating subclasses of windows and doors, using computer vision techniques including the histogram of oriented gradient (HoG) template matching, and automatically creating enriched CityGML content for the matched windows and doors. Good results were achieved for class identification with potential for further refinement of subclasses of windows and doors and other architectural features. It is part of a wider project to bring even richer semantic content to 3D geo-spatial building models

Between images and built form: automating the recognition of standardised building components using deep learning

Building on the richness of recent contributions in the field, this paper presents a state-of-the-art CNN analysis method for automating the recognition of standardised building components in modern heritage buildings. At the turn of the twentieth century manufactured building components became widely advertised for specification by architects. Consequently, a form of standardisation across various typologies began to take place. During this era of rapid economic and industrialised growth, many forms of public building were erected. This paper seeks to demonstrate a method for informing the recognition of such elements using deep learning to recognise ‘families’ of elements across a range of buildings in order to retrieve and recognise their technical specifications from the contemporary trade literature. The method is illustrated through the case of Carnegie Public Libraries in the UK, which provides a unique but ubiquitous platform from which to explore the potential for the automated recognition of manufactured standard architectural components. The aim of enhancing this knowledge base is to use the degree to which these were standardised originally as a means to inform and so support their ongoing care but also that of many other contemporary buildings. Although these libraries are numerous, they are maintained at a local level and as such, their shared challenges for maintenance remain unknown to one another. Additionally, this paper presents a methodology to indirectly retrieve useful indicators and semantics, relating to emerging HBIM families, by applying deep learning to a varied range of architectural imagery

Semantic and geometric enrichment of 3D geo-spatial models with captioned photos and labelled illustrations

There are many 3D digital models of buildings with cultural heritage interest, but most of them lack semantic annotation that could be used to inform users of mobile and desktop applications about their origins and architectural features. We describe methods in an ongoing project for enriching 3D models with generic annotation, derived from examples of images of building components and from labelled plans and diagrams, and with object-specific descriptions obtained from photo captions. This is the first stage of research that aims to annotate 3D models with facts extracted from the text of authoritative architectural guides