title: On ensemble techniques for data stream regression

authors: Gomes, Heitor Murilo

subjects: Science & Technology

abstract: An ensemble of learners tends to exceed the predictive performance of individual learners. This approach has been explored for both batch and online learning. Ensembles methods applied to data stream classification were thoroughly investigated over the years, while their regression counterparts received less attention in comparison. In this work, we discuss and analyze several techniques for generating, aggregating, and updating ensembles of regressors for evolving data streams. We investigate the impact of different strategies for inducing diversity into the ensemble by randomizing the input data (resampling, random subspaces and random patches). On top of that, we devote particular attention to techniques that adapt the ensemble model in response to concept drifts, including adaptive window approaches, fixed periodical resets and randomly determined windows. Extensive empirical experiments show that simple techniques can obtain similar predictive performance to sophisticated algorithms that rely on reactive adaptation (i.e., concept drift detection and recovery).

text: On ensemble techniques for data stream regression An ensemble of learners tends to exceed the predictive performance of individual learners. This approach has been explored for both batch and online learning. Ensembles methods applied to data stream classification were thoroughly investigated over the years, while their regression counterparts received less attention in comparison. In this work, we discuss and analyze several techniques for generating, aggregating, and updating ensembles of regressors for evolving data streams. We investigate the impact of different strategies for inducing diversity into the ensemble by randomizing the input data (resampling, random subspaces and random patches). On top of that, we devote particular attention to techniques that adapt the ensemble model in response to concept drifts, including adaptive window approaches, fixed periodical resets and randomly determined windows. Extensive empirical experiments show that simple techniques can obtain similar predictive performance to sophisticated algorithms that rely on reactive adaptation (i.e., concept drift detection and recovery).

year: 2020

title: Technological development in Therapeutic applications of alternating electric fields: Review

authors: Talele, Sadhana

abstract: A number of bacteria, virus and other unhealthy cells need to be killed for getting rid of them. For more than a century antibiotics have been effectively used for killing bacterial pathogens and chemical drugs against the cancer cells. However, there are bacteria and cancer cells that are drug resistant. This may have to be overcome by other stronger drugs, higher dosage. These can have detrimental side effects. Other non drug methods to aid the effect of these drugs have always been in research. Electrochemotherapy, a method of using electric fields along with the drug to be used topically has been one of the successful approaches. One of the most recent methods of Tumor Treating Frequencies (TTF) for a brain cancer has been FDA approved. This article details the use of TTF. The article also details some other latest research where alternating fields are used as antibacterial agents.

text: Technological development in Therapeutic applications of alternating electric fields: Review A number of bacteria, virus and other unhealthy cells need to be killed for getting rid of them. For more than a century antibiotics have been effectively used for killing bacterial pathogens and chemical drugs against the cancer cells. However, there are bacteria and cancer cells that are drug resistant. This may have to be overcome by other stronger drugs, higher dosage. These can have detrimental side effects. Other non drug methods to aid the effect of these drugs have always been in research. Electrochemotherapy, a method of using electric fields along with the drug to be used topically has been one of the successful approaches. One of the most recent methods of Tumor Treating Frequencies (TTF) for a brain cancer has been FDA approved. This article details the use of TTF. The article also details some other latest research where alternating fields are used as antibacterial agents.

year: 2012

title: Weaving the mat of Māori and Pacific learner success

authors: Tiakiwai, Sarah-Jane

subjects: Supporting learners, Pacific learner success, Māori learner success, higher education

abstract: The mat we are trying to weave at the University of Waikato to address educational outcomes, disparities and inequities in education for Māori and Pacific learners will weave rich data, stories and experience. Our goal is to use data to be better informed about what works for Māori and Pacific learners and what does not. Data is useful in building a more complete picture of what is happening to students, but student voice and previous research that has already captured the voices of Māori and Pacific experts, educators, students, families and communities can tell us more about why we are getting these outcomes. As we harness data to improve our knowledge, we must not forget the stories already in the mat and the stories being woven in the mat, for the stories are the gold.

This report details our work on this data-informed rather than data-driven project. It discusses how improved data and data analytic capability enhanced our understanding of the literature on Māori and Pacific learner success and the experience of Māori and Pacific learners at UoW. Our research and the report, however, also unapologetically privilege case studies through literature and Māori and Pacific student voices. We lay out the various materials – data, literature and voice – that will form our mat with this in mind, weaving them together in order to achieve better outcomes for Māori and Pacific learners at our university. These learners are the why of this mahi. Their success will create a beautiful pattern in the
weave.

text: Weaving the mat of Māori and Pacific learner success The mat we are trying to weave at the University of Waikato to address educational outcomes, disparities and inequities in education for Māori and Pacific learners will weave rich data, stories and experience. Our goal is to use data to be better informed about what works for Māori and Pacific learners and what does not. Data is useful in building a more complete picture of what is happening to students, but student voice and previous research that has already captured the voices of Māori and Pacific experts, educators, students, families and communities can tell us more about why we are getting these outcomes. As we harness data to improve our knowledge, we must not forget the stories already in the mat and the stories being woven in the mat, for the stories are the gold.

This report details our work on this data-informed rather than data-driven project. It discusses how improved data and data analytic capability enhanced our understanding of the literature on Māori and Pacific learner success and the experience of Māori and Pacific learners at UoW. Our research and the report, however, also unapologetically privilege case studies through literature and Māori and Pacific student voices. We lay out the various materials – data, literature and voice – that will form our mat with this in mind, weaving them together in order to achieve better outcomes for Māori and Pacific learners at our university. These learners are the why of this mahi. Their success will create a beautiful pattern in the
weave.

year: 2022-06