title: Analyzing and repairing concept drift adaptation in data stream classification

authors: Halstead, Ben

subjects: computer science

abstract: Data collected over time often exhibit changes in distribution, or concept drift, caused by changes in factors relevant to the classification task, e.g. weather conditions. Incorporating all relevant factors into the model may be able to capture these changes, however, this is usually not practical. Data stream based methods, which instead explicitly detect concept drift, have been shown to retain performance under unknown changing conditions. These methods adapt to concept drift by training a model to classify each distinct data distribution. However, we hypothesize that existing methods do not robustly handle real-world tasks, leading to adaptation errors where context is misidentified. Adaptation errors may cause a system to use a model which does not fit the current data, reducing performance. We propose a novel repair algorithm to identify and correct errors in concept drift adaptation. Evaluation on synthetic data shows that our proposed AiRStream system has higher performance than baseline methods, while is also better at capturing the dynamics of the stream. Evaluation on an air quality inference task shows AiRStream provides increased real-world performance compared to eight baseline methods. A case study shows that AiRStream is able to build a robust model of environmental conditions over this task, allowing the adaptions made to concept drift to be analysed and related to changes in weather. We discovered a strong predictive link between the adaptions made by AiRStream and changes in meteorological conditions.

text: Analyzing and repairing concept drift adaptation in data stream classification Data collected over time often exhibit changes in distribution, or concept drift, caused by changes in factors relevant to the classification task, e.g. weather conditions. Incorporating all relevant factors into the model may be able to capture these changes, however, this is usually not practical. Data stream based methods, which instead explicitly detect concept drift, have been shown to retain performance under unknown changing conditions. These methods adapt to concept drift by training a model to classify each distinct data distribution. However, we hypothesize that existing methods do not robustly handle real-world tasks, leading to adaptation errors where context is misidentified. Adaptation errors may cause a system to use a model which does not fit the current data, reducing performance. We propose a novel repair algorithm to identify and correct errors in concept drift adaptation. Evaluation on synthetic data shows that our proposed AiRStream system has higher performance than baseline methods, while is also better at capturing the dynamics of the stream. Evaluation on an air quality inference task shows AiRStream provides increased real-world performance compared to eight baseline methods. A case study shows that AiRStream is able to build a robust model of environmental conditions over this task, allowing the adaptions made to concept drift to be analysed and related to changes in weather. We discovered a strong predictive link between the adaptions made by AiRStream and changes in meteorological conditions.

year: 2021

title: Screening of Ophiostoma species for removal of eucalyptus extractives

authors: Su, Yu-Chang

subjects: Eucalyptus camadulensis

abstract: Ophiostoma species have been demonstrated to metabolize wood extractives and be useful to the pulp and paper industry. In order to have new isolates for the Asian market, Eucalyptus camaldulensis logs were harvested from forest sites in central Taiwan and 28 strains of the Ophiostoma genus were isolated from them. These strains were subsequently inoculated onto Eucalyptus wood chips to evaluate their effects on weight losses of wood and the amounts of acetone extractives degraded. At the same time, Gas Chromatography-Mass Spectroscopy (GC-MS) analysis was conducted and by using calibration curves and a database of GC-MS mass spectra, changes in lipophilic compounds were analyzed. Fatty acids, hydrocarbons, sterol compounds, sterol esters, and triglycerides were significantly reduced after two weeks’ inoculation by the fungal strains. The results show that six of the strains were capable of reducing the lipophilic fractions by more than 60% in a two-week treatment. DNA of the most effective strains were analyzed and found to be a variant of Ophiostoma querci.

text: Screening of Ophiostoma species for removal of eucalyptus extractives Ophiostoma species have been demonstrated to metabolize wood extractives and be useful to the pulp and paper industry. In order to have new isolates for the Asian market, Eucalyptus camaldulensis logs were harvested from forest sites in central Taiwan and 28 strains of the Ophiostoma genus were isolated from them. These strains were subsequently inoculated onto Eucalyptus wood chips to evaluate their effects on weight losses of wood and the amounts of acetone extractives degraded. At the same time, Gas Chromatography-Mass Spectroscopy (GC-MS) analysis was conducted and by using calibration curves and a database of GC-MS mass spectra, changes in lipophilic compounds were analyzed. Fatty acids, hydrocarbons, sterol compounds, sterol esters, and triglycerides were significantly reduced after two weeks’ inoculation by the fungal strains. The results show that six of the strains were capable of reducing the lipophilic fractions by more than 60% in a two-week treatment. DNA of the most effective strains were analyzed and found to be a variant of Ophiostoma querci.

year: 2011

title: Intellectual Property, Mātauranga Māori

authors: Sterling, Rogena

abstract: This literature review has been conducted to consider the various national legislation and international agreements that comprise New Zealand’s Intellectual Property Rights (IP) regime. It will evaluate if and how such legislation and agreements protect and enable Māori IP rights and interests with respect to Māori data, genomic data and mātauranga Māori. The review also identifies some mechanisms that might also enhance Māori control of these types of data. The Westminster approach of legislation in New Zealand and its approach to IP protection based on Copyrights, Patents and Trade Marks are juxtaposed against traditional Maori approaches of communally held ancestral knowledge (mātauranga) passed down through generations (whanaungatanga) based on guardianship and protection (kaitiakitanga) and the self-determination of use of such knowledge (rangatiratanga). Attempting to align tikanga concepts to the Westmionster model of law is challenging as the two share completely different notions of ownership and responsibility. Expectations of protection, to prevent misappropriation and commercialisation by non- Māori of mātauranga Māori and Māori data, extend beyond the parameters of existing IP law, creates a similar disjunct. Genomic Research generates data, some of which can be protected by IP, however researchers working with genetic/genomic data from taonga species have often failed to acknowledge the non-IP interests of Maori. As a result, Maori have taken it upon themselves to advocate for their rights to data through Māori data sovereignty discourse as well as create guidelines for culturally appropriate genomic research with explicit references to data security and management (e.g. Te Mata Ira, Te Nohonga Kaitiaki). Other extra-legal options, such as Biocultural Labelling to alert users where particular data has Māori rights and/or interests, are emerging to maintain create durable provenance data and connect next users of data with the responsible Indigenous communities. Though the intellectual property regime in New Zealand may provide some protections, there are still significant areas where the legal system does not provide sufficient protections for Māori data, taonga species and mātauranga. The UN Declaration on the Rights of Indigenous Peoples provides a framework of international support for Indigenous rights, but local government and other home-grown mechanisms are important to enable Māori governance of data. Local approaches Māori Data Sovereignty and Māori Data Governance afford Maori the opportunity to be directly involved as kaitiaki of their mātauranga and assert rangatiratanga over data and its use.

text: Intellectual Property, Mātauranga Māori This literature review has been conducted to consider the various national legislation and international agreements that comprise New Zealand’s Intellectual Property Rights (IP) regime. It will evaluate if and how such legislation and agreements protect and enable Māori IP rights and interests with respect to Māori data, genomic data and mātauranga Māori. The review also identifies some mechanisms that might also enhance Māori control of these types of data. The Westminster approach of legislation in New Zealand and its approach to IP protection based on Copyrights, Patents and Trade Marks are juxtaposed against traditional Maori approaches of communally held ancestral knowledge (mātauranga) passed down through generations (whanaungatanga) based on guardianship and protection (kaitiakitanga) and the self-determination of use of such knowledge (rangatiratanga). Attempting to align tikanga concepts to the Westmionster model of law is challenging as the two share completely different notions of ownership and responsibility. Expectations of protection, to prevent misappropriation and commercialisation by non- Māori of mātauranga Māori and Māori data, extend beyond the parameters of existing IP law, creates a similar disjunct. Genomic Research generates data, some of which can be protected by IP, however researchers working with genetic/genomic data from taonga species have often failed to acknowledge the non-IP interests of Maori. As a result, Maori have taken it upon themselves to advocate for their rights to data through Māori data sovereignty discourse as well as create guidelines for culturally appropriate genomic research with explicit references to data security and management (e.g. Te Mata Ira, Te Nohonga Kaitiaki). Other extra-legal options, such as Biocultural Labelling to alert users where particular data has Māori rights and/or interests, are emerging to maintain create durable provenance data and connect next users of data with the responsible Indigenous communities. Though the intellectual property regime in New Zealand may provide some protections, there are still significant areas where the legal system does not provide sufficient protections for Māori data, taonga species and mātauranga. The UN Declaration on the Rights of Indigenous Peoples provides a framework of international support for Indigenous rights, but local government and other home-grown mechanisms are important to enable Māori governance of data. Local approaches Māori Data Sovereignty and Māori Data Governance afford Maori the opportunity to be directly involved as kaitiaki of their mātauranga and assert rangatiratanga over data and its use.

year: 2021