647 research outputs found
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
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
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, 782â784] using the âtemporal taggingâ hypothesis of Crick and Koch, 1990a and Crick and Koch, 1990bSeminars in the neurosciences (pp. 1â36)]. 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 âfocus of attentionâ 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 ânon-attendedâ 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
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 agriâfood systems has been the rapid
emergence and elaboration of market audit systems claiming environmental qualities or
sustainability. In New Zealand, as a strongly exportâoriented, highâvalue 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 â 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 2003â2009. 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 coâconstituting) 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 â 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 paradigmâdriven 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 âorganicâ or âconventionalâ. 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 coâproduced 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
Exploring the Stability Limits of Actin and its Suprastructures
AbstractActin is the main component of the microfilament system in eukaryotic cells and can be found in distinct morphological states. Global (G)-actin is able to assemble into highly organized, supramolecular cellular structures known as filamentous (F)-actin and bundled (B)-actin. To evaluate the structure and stability of G-, F-, and B-actin over a wide range of temperatures and pressures, we used Fourier transform infrared spectroscopy in combination with differential scanning and pressure perturbation calorimetry, small-angle x-ray scattering, laser confocal scanning microscopy, and transmission electron microscopy. Our analysis was designed to provide new (to our knowledge) insights into the stabilizing forces of actin self-assembly and to reveal the stability of the actin polymorphs, including in conditions encountered in extreme environments. In addition, we sought to explain the limited pressure stability of actin self-assembly observed in vivo. G-actin is not only the least temperature-stable but also the least pressure-stable actin species. Under abyssal conditions, where temperatures as low as 1â4°C and pressures up to 1 kbar are reached, G-actin is hardly stable. However, the supramolecular assemblies of actin are stable enough to withstand the extreme conditions usually encountered on Earth. Beyond âŒ3â4 kbar, filamentous structures disassemble, and beyond âŒ4 kbar, complete dissociation of F-actin structures is observed. Between âŒ1 and 2 kbar, some disordering of actin assemblies commences, in agreement with in vivo observations. The limited pressure stability of the monomeric building block seems to be responsible for the suppression of actin assembly in the kbar pressure range
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 âfamiliesâ 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
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