Factor Replacement versus Factor Substitution, Mechanization and Asymptotic Harrod Neutrality

This paper views technical change as a labor-saving, but capital-using, mechanization process, whereby capital replaces labor; though within any given technique, factors have a limited ability to substitute one another. This is formalized by reinterpreting the “distribution-parameters” of a low substitution CES aggregate production function as time-varying weights, such that technical change corresponds to a decrease in labor’s weight, along with an increase in capital’s. This “direction” of shift is considered a natural outcome of the fact that ideas are embedded within capital. As capital’s weight tends to one, changes in it become increasingly negligible and balanced-growth is attained. Thus the proposed non-neutral mechanism is asymptotically equivalent to Harrod-neutrality. But during industrialization, when capital grows faster than output, its “dis-augmentation” is still significant; the result being constant factor-shares. This resolves a recent controversy regarding the measurement of TFP growth, specifically in East Asian NICs. The capital-using aspect of factors’ replacement, along with the limited degree of factor substitution, also lead to time-ranked “appropriate-technologies”, which are broadly consistent with under-development; despite the lack of non-convexities.Mechanization, Non-Neutral Technical Change, Dis-Augmentation, CES

Neurokernel: An Open Source Platform for Emulating the Fruit Fly Brain

We have developed an open software platform called Neurokernel for collaborative development of comprehensive models of the brain of the fruit fly Drosophila melanogaster and their execution and testing on multiple Graphics Processing Units (GPUs). Neurokernel provides a programming model that capitalizes upon the structural organization of the fly brain into a fixed number of functional modules to distinguish between these modules’ local information processing capabilities and the connectivity patterns that link them. By defining mandatory communication interfaces that specify how data is transmitted between models of each of these modules regardless of their internal design, Neurokernel explicitly enables multiple researchers to collaboratively model the fruit fly’s entire brain by integration of their independently developed models of its constituent processing units. We demonstrate the power of Neurokernel’s model integration by combining independently developed models of the retina and lamina neuropils in the fly’s visual system and by demonstrating their neuroinformation processing capability. We also illustrate Neurokernel’s ability to take advantage of direct GPU-to-GPU data transfers with benchmarks that demonstrate scaling of Neurokernel’s communication performance both over the number of interface ports exposed by an emulation’s constituent modules and the total number of modules comprised by an emulation

Extracting information from fiction

Information Extraction (IE) based techniques have great potential to enable companies to leverage valuable information embedded in unstructured textual data. Such data could be exploited to help drive sales and to enhance the customer's experience when searching or browsing for products. Extensive research has been performed in the field of IE; however, to date no work has been directly applied to the domain of fiction. The aim of this study is to explore the ability of IE techniques to extract the central characters and their relationships from the full textual content of works of fiction. To begin our investigation, we present a collection of hypotheses outlining our expectations in approaching and resolving these problems. We then outline our data collection process, which resulted in the creation of a Gold Standard containing ordered lists of characters and their relationships for eight classic book texts. For the task of character extraction, we test two rule-based co-reference resolution models, and two ordering techniques. Our best model achieves an average of 100% coverage on the three most important characters and 78.4% across all central characters, compared to a baseline of 73.3% and 57.4% respectively. For the task of relation extraction, we implement rule-based systems to detect the presence and types of relationships between characters. We achieved 73.3% coverage in detecting the top three pairs of characters involved in relationships. The results for inferring relationship types are preliminary. We provide an analysis of relationship mentions in works of fiction and propose a number of approaches for future work

An Open Pipeline for Generating Executable Neural Circuits from Fruit Fly Brain Data

Despite considerable progress in mapping the fly’s connectome and elucidating the patterns of information flow in its brain, the complexity of the fly brain’s structure and the still-incomplete state of knowledge regarding its neural circuitry pose significant challenges beyond satisfying the computational resource requirements of current fly brain models that must be addressed to successfully reverse the information processing capabilities of the fly brain. These include the need to explicitly facilitate collaborative development of brain models by combining the efforts of multiple researchers, and the need to enable programmatic generation of brain models that effectively utilize the burgeoning amount of increasingly detailed publicly available fly connectome data. This thesis presents an open pipeline for modular construction of executable models of the fruit fly brain from incomplete biological brain data that addresses both of the above requirements. This pipeline consists of two major open-source components respectively called Neurokernel and NeuroArch. Neurokernel is a framework for collaborative construction of executable connectome-based fly brain models by integration of independently developed models of different functional units in the brain into a single emulation that can be executed upon multiple Graphics Processing Units (GPUs). Neurokernel enforces a programming model that enables functional unit models that comply with its interface requirements to communicate during execution regardless of their internal design. We demonstrate the power of this programming model by using it to integrate independently developed models of the fly retina and lamina into a single vision processing system. We also show how Neurokernel’s communication performance can scale over multiple GPUs, number of functional units in a brain emulation, and over the number of communication ports exposed by a functional unit model. Although the increasing amount of experimentally obtained biological data regarding the fruit fly brain affords brain modelers a potentially valuable resource for model development, the actual use of this data to construct executable neural circuit models is currently challenging because of the disparate nature of different data sources, the range of storage formats they use, and the limited query features of those formats complicates the process of inferring executable circuit designs from biological data. To overcome these limitations, we created a software package called NeuroArch that defines a data model for concurrent representation of both biological data and model structure and the relationships between them within a single graph database. Coupled with a powerful interface for querying both types of data within the database in a uniform high-level manner, this representation enables construction and dispatching of executable neural circuits to Neurokernel for execution and evaluation. We demonstrate the utility of the NeuroArch/Neurokernel pipeline by using the packages to generate an executable model of the central complex of the fruit fly brain from both published and hypothetical data regarding overlapping neuron arborizations in different regions of the central complex neuropils. We also show how the pipeline empowers circuit model designers to devise computational analogues to biological experiments such as parallel concurrent recording from multiple neurons and emulation of genetic mutations that alter the fly’s neural circuitry

Acoustic emphasis in four year olds

Acoustic emphasis may convey a range of subtle discourse distinctions, yet little is known about how this complex ability develops in children. This paper presents a first investigation of the factors which influence the production of acoustic prominence in young children’s spontaneous speech. In a production experiment, SVO sentences were elicited from 4 year olds who were asked to describe events in a video. Children were found to place more acoustic prominence both on ‘new’ words and on words that were ‘given’ but had shifted to a more accessible position within the discourse. This effect of accessibility concurs with recent studies of adult speech. We conclude that, by age four, children show appropriate, adult-like use of acoustic prominence, suggesting sensitivity to a variety of discourse distinctions