title: Removing the human from trademark law

authors: Roy, Alpana

abstract: This paper envisions a future in which humans begin to entrust interconnected and intelligent devices and machines with the power to make purchasing decisions on their behalf. Artificial Intelligence (AI), together with the Internet of Things (IoT) and blockchain technology, will likely make this possible. What might be the role of trademarks and the law governing their protection in such a future? This paper responds to this question by considering how the use of AI, IoT, and blockchain technology in the retail space will impact the foundational concepts underpinning trademark law. The discussion highlights the difficulty of shifting trademark law away from its human-centric focus, where core doctrines and principles revolve around human interaction and perceptions, towards a system capable of adapting to a future where devices and machines interact with trademarks. Perhaps the time is ripe for legislative innovation in the field of trademarks.

text: Removing the human from trademark law This paper envisions a future in which humans begin to entrust interconnected and intelligent devices and machines with the power to make purchasing decisions on their behalf. Artificial Intelligence (AI), together with the Internet of Things (IoT) and blockchain technology, will likely make this possible. What might be the role of trademarks and the law governing their protection in such a future? This paper responds to this question by considering how the use of AI, IoT, and blockchain technology in the retail space will impact the foundational concepts underpinning trademark law. The discussion highlights the difficulty of shifting trademark law away from its human-centric focus, where core doctrines and principles revolve around human interaction and perceptions, towards a system capable of adapting to a future where devices and machines interact with trademarks. Perhaps the time is ripe for legislative innovation in the field of trademarks.

year: 2024-04-22

title: A master equation approach to nonlinear optics and chemical reactions

authors: McNeil, Kenneth James

abstract: Part 1 of this thesis consists of a theoretical quantum statistical analysis of several nonlinear optical processes. As an introduction a master equation describing the time behaviour of the field in a general irreversible nonlinear interaction between an optical photon field and a number of two level atoms is given. This master equation is then used to calculate the exact time dependent statistical properties of the light fields in certain special processes: two photon emission by atoms, two photon absorption by atoms and Raman scattering of photons by atoms. The steady states of the emission-absorption processes are examined, and found to have interesting statistical properties not found in the field states normally met with. Part 1 is concluded with a brief examination of approximate methods of solutions for the nonlinear processes studied.
Part 2 is a brief study of the nature of concentration fluctuations and correlations in chemically reacting systems, using the stochastic master equation approach. The time dependent fluctuations and spatial correlations are discussed for a space dependent model of a linear chemically reacting system. These results are compared with those obtained when correlations are ignored. The steady state behaviour of a particular nonlinear chemical system is then examined. Deterministic and non space-dependent stochastic analysis show that when certain system parameters are varied, the system’s behaviour is very similar to that of a thermodynamic system which undergoes a second order phase transition. A space-dependent analysis is then given, and this makes the analogy very close indeed.

text: A master equation approach to nonlinear optics and chemical reactions Part 1 of this thesis consists of a theoretical quantum statistical analysis of several nonlinear optical processes. As an introduction a master equation describing the time behaviour of the field in a general irreversible nonlinear interaction between an optical photon field and a number of two level atoms is given. This master equation is then used to calculate the exact time dependent statistical properties of the light fields in certain special processes: two photon emission by atoms, two photon absorption by atoms and Raman scattering of photons by atoms. The steady states of the emission-absorption processes are examined, and found to have interesting statistical properties not found in the field states normally met with. Part 1 is concluded with a brief examination of approximate methods of solutions for the nonlinear processes studied.
Part 2 is a brief study of the nature of concentration fluctuations and correlations in chemically reacting systems, using the stochastic master equation approach. The time dependent fluctuations and spatial correlations are discussed for a space dependent model of a linear chemically reacting system. These results are compared with those obtained when correlations are ignored. The steady state behaviour of a particular nonlinear chemical system is then examined. Deterministic and non space-dependent stochastic analysis show that when certain system parameters are varied, the system’s behaviour is very similar to that of a thermodynamic system which undergoes a second order phase transition. A space-dependent analysis is then given, and this makes the analogy very close indeed.

year: 1976

title: Diacritic Restoration and the Development of a Part-of-Speech Tagset for the Māori Language

authors: Cocks, John

subjects: Māori

abstract: This thesis investigates two fundamental problems in natural language processing: diacritic restoration and part-of-speech tagging. Over the past three decades, statistical approaches to diacritic restoration and part-of-speech tagging have grown in interest as a consequence of the increasing availability of manually annotated training data in major languages such as English and French. However, these approaches are not practical for most minority languages, where appropriate training data is either non-existent or not publically available. Furthermore, before developing a part-of-speech tagging system, a suitable tagset is required for that language. In this thesis, we make the following contributions to bridge this gap:

Firstly, we propose a method for diacritic restoration based on naive Bayes classifiers that act at word-level. Classifications are based on a rich set of features, extracted automatically from training data in the form of diacritically marked text. This method requires no additional resources, which makes it language independent. The algorithm was evaluated on one language, namely Māori, and an accuracy exceeding 99% was observed.

Secondly, we present our work on creating one of the necessary resources for the development of a part-of-speech tagging system in Māori, that of a suitable tagset. The tagset described was developed in accordance with the EAGLES guidelines for morphosyntactic annotation of corpora, and was the result of in-depth analysis of the Māori grammar.

text: Diacritic Restoration and the Development of a Part-of-Speech Tagset for the Māori Language This thesis investigates two fundamental problems in natural language processing: diacritic restoration and part-of-speech tagging. Over the past three decades, statistical approaches to diacritic restoration and part-of-speech tagging have grown in interest as a consequence of the increasing availability of manually annotated training data in major languages such as English and French. However, these approaches are not practical for most minority languages, where appropriate training data is either non-existent or not publically available. Furthermore, before developing a part-of-speech tagging system, a suitable tagset is required for that language. In this thesis, we make the following contributions to bridge this gap:

Firstly, we propose a method for diacritic restoration based on naive Bayes classifiers that act at word-level. Classifications are based on a rich set of features, extracted automatically from training data in the form of diacritically marked text. This method requires no additional resources, which makes it language independent. The algorithm was evaluated on one language, namely Māori, and an accuracy exceeding 99% was observed.

Secondly, we present our work on creating one of the necessary resources for the development of a part-of-speech tagging system in Māori, that of a suitable tagset. The tagset described was developed in accordance with the EAGLES guidelines for morphosyntactic annotation of corpora, and was the result of in-depth analysis of the Māori grammar.

year: 2012