title: Modelling a blockchain for smart contract verification using DeepSEA

authors: Britten, Daniel

subjects: blockchain

abstract: To create trustworthy programs, the 'gold standard' is specifications at a high-enough level to clearly correspond to the informal specifications, and also a refinement proof linking these high-level specifications down to, in our case, executable bytecode. The DeepSEA system demonstrates how this can be done, in the context of smart contracts on the Ethereum blockchain. A key component of this is the model of the blockchain on which the smart contracts reside. When doing proofs in DeepSEA, it is critical to have such a model, which allows for the writing of specifications at a high-level clearly corresponding to informal specifications. A candidate model for doing so and its usefulness for carrying out proofs is discussed in this paper.

text: Modelling a blockchain for smart contract verification using DeepSEA To create trustworthy programs, the 'gold standard' is specifications at a high-enough level to clearly correspond to the informal specifications, and also a refinement proof linking these high-level specifications down to, in our case, executable bytecode. The DeepSEA system demonstrates how this can be done, in the context of smart contracts on the Ethereum blockchain. A key component of this is the model of the blockchain on which the smart contracts reside. When doing proofs in DeepSEA, it is critical to have such a model, which allows for the writing of specifications at a high-level clearly corresponding to informal specifications. A candidate model for doing so and its usefulness for carrying out proofs is discussed in this paper.

year: 2022

title: Detection of differences in soil carbon and nitrogen stocks between paired dairy and drystock pastures

authors: Norris, Timothy

abstract: Soil is the largest terrestrial store of carbon (C) with some 2000 Pg to a depth of 1 m compared to 500 Pg in the atmosphere. Maximizing storage of C in soil is not only important for reducing atmospheric CO2 concentrations but also for maintaining soil quality. Recent research has shown that land use management is a key factor in determining the storage of C in pastoral systems. Barnett et al. (2014, AEE 185:34-40) used a paired pit approach to sample 25 adjacent dairy and drystock pastures to a fixed depth of 0.6 m and showed that soils under drystock sites had about 8.6 t.ha-1 more C in the top soil than adjacent dairy sites (P<0.05). However, there was no significant difference between land uses when C was accumulated to 0.6 m.

The main objective of this research was to test a potentially more accurate method for estimating differences in C stocks between sites sampled by Barnett et al. (2014), with a second objective being to better understand the effect of dairy and drystock grazed pastures on soil C and N stocks. A third objective was to investigate the effect of dairy and drystock managed pastures on earthworm abundance and biomass. 

A synthesis of recent literature showed that measuring differences in soil C stocks is difficult, given the high variability of soil C over small spatial scales. However, careful consideration to sampling methodology and statistical analysis can greatly improve the detection of differences in soil C stocks.   

Twenty three paired dairy and drystock sites were sampled to a depth of 0.6 m by taking 5 soil cores from each of two plots (5x5 m) within a paddock of each land use and soil C/N and soil mass were determined. Seventeen of the paired dairy and drystock farms were sampled from 3 points in each paddock between August and November 2013 for earthworms. Samples were sorted and earthworms were classified to species level.

To a depth of ~60 cm (C stocks adjusted for equivalent soil mass), drystock sites had 1.6 t ha-1 more C than dairy sites but this was not significant. However, when soil layers were analysed separately, drystock sites contained more C (4.1 ± 2.1 t C ha-1) in the top 10 cm (P=0.06) and dairy farms had significantly more C (3.7 ± 1.7 t C ha-1) in the 25-60 cm layer (P=0.04). The difference in the relative distribution of soil C in dairy and drystock sites may be due to the greater size and concentration of dairy urine patches which can solubilise C in the top-soil and redeposit dissolved C lower in the profile. 

When comparing whole-profile C stocks between dairy and drystock sites, the two-plot coring approach would have been able to detect a true difference of 9.3 t C ha-1, had it occurred, compared to 13.6 t C ha-1 for the pit approach (P<0.05). For the purpose of providing information for future sampling, power analysis was also conducted and revealed that with 23 paired sites, the pit approach could detect a significant difference (P<0.05) of 16 t C ha-1 with 66% certainty. In contrast, the coring approach could detect the same difference of 16 t C ha-1 with 90% certainty. These results supported the literature synthesis which demonstrated that sampling methodologies that include spatial variability of soil C can greatly improve the detection of differences. Furthermore, the coring approach reduced cost and increased efficiency compared to the single-pit approach.  

Earthworm abundance and biomass were not significantly different between dairy and drystock farms despite the significantly higher grazing intensity and top soil bulk density of dairy sites. Total earthworm abundance and biomass averaged 193 ± 30 ind m-2 and 77 ± 12 g m-2 for dairy farms compared to 188 ± 26 ind m-2 and 75 ± 13 g m-2 for drystock farms. These results suggested that for Allophanic Soils in the Waikato Region, the effects of varying grazing management on earthworm abundance and biomass is negligible.

text: Detection of differences in soil carbon and nitrogen stocks between paired dairy and drystock pastures Soil is the largest terrestrial store of carbon (C) with some 2000 Pg to a depth of 1 m compared to 500 Pg in the atmosphere. Maximizing storage of C in soil is not only important for reducing atmospheric CO2 concentrations but also for maintaining soil quality. Recent research has shown that land use management is a key factor in determining the storage of C in pastoral systems. Barnett et al. (2014, AEE 185:34-40) used a paired pit approach to sample 25 adjacent dairy and drystock pastures to a fixed depth of 0.6 m and showed that soils under drystock sites had about 8.6 t.ha-1 more C in the top soil than adjacent dairy sites (P<0.05). However, there was no significant difference between land uses when C was accumulated to 0.6 m.

The main objective of this research was to test a potentially more accurate method for estimating differences in C stocks between sites sampled by Barnett et al. (2014), with a second objective being to better understand the effect of dairy and drystock grazed pastures on soil C and N stocks. A third objective was to investigate the effect of dairy and drystock managed pastures on earthworm abundance and biomass. 

A synthesis of recent literature showed that measuring differences in soil C stocks is difficult, given the high variability of soil C over small spatial scales. However, careful consideration to sampling methodology and statistical analysis can greatly improve the detection of differences in soil C stocks.   

Twenty three paired dairy and drystock sites were sampled to a depth of 0.6 m by taking 5 soil cores from each of two plots (5x5 m) within a paddock of each land use and soil C/N and soil mass were determined. Seventeen of the paired dairy and drystock farms were sampled from 3 points in each paddock between August and November 2013 for earthworms. Samples were sorted and earthworms were classified to species level.

To a depth of ~60 cm (C stocks adjusted for equivalent soil mass), drystock sites had 1.6 t ha-1 more C than dairy sites but this was not significant. However, when soil layers were analysed separately, drystock sites contained more C (4.1 ± 2.1 t C ha-1) in the top 10 cm (P=0.06) and dairy farms had significantly more C (3.7 ± 1.7 t C ha-1) in the 25-60 cm layer (P=0.04). The difference in the relative distribution of soil C in dairy and drystock sites may be due to the greater size and concentration of dairy urine patches which can solubilise C in the top-soil and redeposit dissolved C lower in the profile. 

When comparing whole-profile C stocks between dairy and drystock sites, the two-plot coring approach would have been able to detect a true difference of 9.3 t C ha-1, had it occurred, compared to 13.6 t C ha-1 for the pit approach (P<0.05). For the purpose of providing information for future sampling, power analysis was also conducted and revealed that with 23 paired sites, the pit approach could detect a significant difference (P<0.05) of 16 t C ha-1 with 66% certainty. In contrast, the coring approach could detect the same difference of 16 t C ha-1 with 90% certainty. These results supported the literature synthesis which demonstrated that sampling methodologies that include spatial variability of soil C can greatly improve the detection of differences. Furthermore, the coring approach reduced cost and increased efficiency compared to the single-pit approach.  

Earthworm abundance and biomass were not significantly different between dairy and drystock farms despite the significantly higher grazing intensity and top soil bulk density of dairy sites. Total earthworm abundance and biomass averaged 193 ± 30 ind m-2 and 77 ± 12 g m-2 for dairy farms compared to 188 ± 26 ind m-2 and 75 ± 13 g m-2 for drystock farms. These results suggested that for Allophanic Soils in the Waikato Region, the effects of varying grazing management on earthworm abundance and biomass is negligible.

year: 2014

title: Machine translation for te reo Māori

authors: Keegan, Te Taka Adrian Gregory

abstract: What is Machine Translation? Machine Translation (MT) is a process where computer software is used to translate texts from one natural (or spoken) language to another. Early research centred on two distinct approaches: Rules Based Machine Translation (RBMT) and Statistical Machine Translation (SMT). In simple terms RBMT makes use of large sets of linguistic rules that define languages whereas SMT uses statistical techniques to build language models from large language corpora. Increases in computing power and the amount of language corpora available has meant that SMT had become the preferred option with recent advances in neural networks also being applied to improve the accuracy of SMT. For commercial reasons, this is an area of research that has generated a lot of interest and funding support from some major international computer companies, including Google¹,Microsoft² and Facebook³.
Why is Machine Translation important for te reo Māori? A te reo Māori purist may argue that it is not important to focus activities on having a machine undertake translations for te reo Māori; if people want to understand te reo Māori then they should put in the effort to learn the language. This line of reasoning is difficult to argue with. But, from a Māori language activist perspective the value of MT is not so much in the translation of te reo Māori to (say) English, but rather the translation of English to te reo Māori. If this translation can be done efficiently, with low costs, it will assist in the proliferation of te reo Māori into new contexts, new environments and will assist its normalisation in New Zealand's society.
At this time, two of the major international companies, Google and Microsoft, have invested significantly in MT for te reo Māori. This paper summarises their endeavours and reports on the quality of translations they have been able to generate.

text: Machine translation for te reo Māori What is Machine Translation? Machine Translation (MT) is a process where computer software is used to translate texts from one natural (or spoken) language to another. Early research centred on two distinct approaches: Rules Based Machine Translation (RBMT) and Statistical Machine Translation (SMT). In simple terms RBMT makes use of large sets of linguistic rules that define languages whereas SMT uses statistical techniques to build language models from large language corpora. Increases in computing power and the amount of language corpora available has meant that SMT had become the preferred option with recent advances in neural networks also being applied to improve the accuracy of SMT. For commercial reasons, this is an area of research that has generated a lot of interest and funding support from some major international computer companies, including Google¹,Microsoft² and Facebook³.
Why is Machine Translation important for te reo Māori? A te reo Māori purist may argue that it is not important to focus activities on having a machine undertake translations for te reo Māori; if people want to understand te reo Māori then they should put in the effort to learn the language. This line of reasoning is difficult to argue with. But, from a Māori language activist perspective the value of MT is not so much in the translation of te reo Māori to (say) English, but rather the translation of English to te reo Māori. If this translation can be done efficiently, with low costs, it will assist in the proliferation of te reo Māori into new contexts, new environments and will assist its normalisation in New Zealand's society.
At this time, two of the major international companies, Google and Microsoft, have invested significantly in MT for te reo Māori. This paper summarises their endeavours and reports on the quality of translations they have been able to generate.

year: 2017