title: Reliability of a 2-Bout exercise test on a Wattbike cycle ergometer

authors: Driller, Matthew W.

subjects: power output

abstract: Purpose: To determine the intraday and interday reliability of a 2 × 4-min performance test on a cycle ergometer (Wattbike) separated by 30 min of passive recovery (2 × 4MMP). Methods: Twelve highly trained cyclists (mean ± SD; age = 20 ± 2 y, predicted VO2max = 59.0 ± 3.6 mL · kg–1 · min–1) completed six 2 × 4MMP cycling tests on a Wattbike ergometer separated by 7 d. Mean power was measured to determine intraday (test 1 [T1] to test 2 [T2]) and interday reliability (weeks 1–6) over the repeated trials. Results: The mean intraday reliabilities of the 2 × 4MMP test, as expressed by the typical error of measurement (TEM, W) and coefficient of variation (CV, %) over the 6 wk, were 10.0 W (95% confidence limits [CL] 8.2–11.8), and 2.6% (95%CL 2.1–3.1), respectively. The mean interday reliability TEM and CV for T1 over the 6 wk were 10.4 W (95%CL 8.7–13.3) and 2.7% (95%CL 2.3–3.5), respectively, and 11.7 W (95%CL 9.8–15.1) and 3.0% (95%CL 2.5–3.9) for T2. Conclusion: The testing protocol performed on a Wattbike cycle ergometer in the current study is reproducible in highly trained cyclists. The high intraday and interday reliability make it a reliable method for monitoring cycling performance and for investigating factors that affect performance in cycling events.

text: Reliability of a 2-Bout exercise test on a Wattbike cycle ergometer Purpose: To determine the intraday and interday reliability of a 2 × 4-min performance test on a cycle ergometer (Wattbike) separated by 30 min of passive recovery (2 × 4MMP). Methods: Twelve highly trained cyclists (mean ± SD; age = 20 ± 2 y, predicted VO2max = 59.0 ± 3.6 mL · kg–1 · min–1) completed six 2 × 4MMP cycling tests on a Wattbike ergometer separated by 7 d. Mean power was measured to determine intraday (test 1 [T1] to test 2 [T2]) and interday reliability (weeks 1–6) over the repeated trials. Results: The mean intraday reliabilities of the 2 × 4MMP test, as expressed by the typical error of measurement (TEM, W) and coefficient of variation (CV, %) over the 6 wk, were 10.0 W (95% confidence limits [CL] 8.2–11.8), and 2.6% (95%CL 2.1–3.1), respectively. The mean interday reliability TEM and CV for T1 over the 6 wk were 10.4 W (95%CL 8.7–13.3) and 2.7% (95%CL 2.3–3.5), respectively, and 11.7 W (95%CL 9.8–15.1) and 3.0% (95%CL 2.5–3.9) for T2. Conclusion: The testing protocol performed on a Wattbike cycle ergometer in the current study is reproducible in highly trained cyclists. The high intraday and interday reliability make it a reliable method for monitoring cycling performance and for investigating factors that affect performance in cycling events.

year: 2014

title: A two-level learning method for generalized multi-instance problems

authors: Weidmann, Nils

subjects: computer science

abstract: In traditional multi-instance (MI) learning, a single positive instance in a bag produces a positive class label. Hence, the learner knows how the bags class label depends on the labels of the instances in the bag and can explicitly use this information to solve the learning task. In this paper we investigate a generalized view of the MI problem where this simple assumption no longer holds. We assume that an interaction between instances in a bag determines the class label. Our two-level learning method for this type of problem transforms an MI bag into a single meta-instance that can be learned by a standard propositional method. The meta-instance indicates which regions in the instance space are covered by instances of the bag. Results on both artificial and real-world data show that this two-level classification approach is well suited for generalized MI problems.

text: A two-level learning method for generalized multi-instance problems In traditional multi-instance (MI) learning, a single positive instance in a bag produces a positive class label. Hence, the learner knows how the bags class label depends on the labels of the instances in the bag and can explicitly use this information to solve the learning task. In this paper we investigate a generalized view of the MI problem where this simple assumption no longer holds. We assume that an interaction between instances in a bag determines the class label. Our two-level learning method for this type of problem transforms an MI bag into a single meta-instance that can be learned by a standard propositional method. The meta-instance indicates which regions in the instance space are covered by instances of the bag. Results on both artificial and real-world data show that this two-level classification approach is well suited for generalized MI problems.

year: 2003

title: Te Whānau Mārama

authors: Tuahine, Hōhepa

subjects: Whānau mārama

abstract: Ko te rā, ko te marama, ko te whetū, ko te auahitūroa, ko te kōtiritiri anō i tapaina e te Māori ko te Whānau Mārama.  Ko te whānau nei ka heke i a Tangotango rāua ko Wainui, he tamariki nā Ranginui rāua ko Papatūānuku, he atua. I whakaako motuhaketia ngā āhuatanga katoa o te Whānau Mārama ki te whare kōkōrangi.  Ko ana whakapapa, ko ana karakia, ko ana waiata, ko ana whakataukī maha.  Ko ngā kai mārō o taua whare wānanga e hoki ana ki te orokotīmatanga mai o Ranginui rāua ko Papatūānuku, heke iho mai ki te tangata me te ao tūroa e mōhiotia ana i tēnei rangi.  He tipua ngā tohunga o te whare kōkōrangi ki te whakamāori i ngā nekehanga me ngā āhuatanga rere kē o te Whānau Mārama nei.  Ka nui ngā tohutohu a te Whānau Mārama e tōnui ai te noho a te Māori ki te whenua.  Heoi anō, kei te āhua tonu o tā te whetū nekehanga i te rangi, kei te āhua tonu o tōna hikohiko mai, otirā kei te āhua tonu o tā te Whānau Mārama taunekeneke atu, taunekeneke mai ki a rātau anō.  Katoa ngā āhuatanga o te Whānau Mārama he mea hei ārahi i te Māori, me te aha, ko ngā mātauranga o te whare nei hei kāpehu ki te tohunga whakatere waka, hei tohu nō Tū ki te ope tauā, hei maramataka ki te ihu oneone, hei kai mārō ki te tauira, hei aha noa atu.  Ko tā tēnei tuhinga, he tūhura i ngā kōrero mō te Whānau Mārama me tana whare wānanga.  Ka titiro ki ngā kōrero kua whakamoanahia i roto i ngā tau maha me te whakamārama i te whakamahia o tēnei mātauranga e kui mā, e koro mā i te wā i a rātau.

text: Te Whānau Mārama Ko te rā, ko te marama, ko te whetū, ko te auahitūroa, ko te kōtiritiri anō i tapaina e te Māori ko te Whānau Mārama.  Ko te whānau nei ka heke i a Tangotango rāua ko Wainui, he tamariki nā Ranginui rāua ko Papatūānuku, he atua. I whakaako motuhaketia ngā āhuatanga katoa o te Whānau Mārama ki te whare kōkōrangi.  Ko ana whakapapa, ko ana karakia, ko ana waiata, ko ana whakataukī maha.  Ko ngā kai mārō o taua whare wānanga e hoki ana ki te orokotīmatanga mai o Ranginui rāua ko Papatūānuku, heke iho mai ki te tangata me te ao tūroa e mōhiotia ana i tēnei rangi.  He tipua ngā tohunga o te whare kōkōrangi ki te whakamāori i ngā nekehanga me ngā āhuatanga rere kē o te Whānau Mārama nei.  Ka nui ngā tohutohu a te Whānau Mārama e tōnui ai te noho a te Māori ki te whenua.  Heoi anō, kei te āhua tonu o tā te whetū nekehanga i te rangi, kei te āhua tonu o tōna hikohiko mai, otirā kei te āhua tonu o tā te Whānau Mārama taunekeneke atu, taunekeneke mai ki a rātau anō.  Katoa ngā āhuatanga o te Whānau Mārama he mea hei ārahi i te Māori, me te aha, ko ngā mātauranga o te whare nei hei kāpehu ki te tohunga whakatere waka, hei tohu nō Tū ki te ope tauā, hei maramataka ki te ihu oneone, hei kai mārō ki te tauira, hei aha noa atu.  Ko tā tēnei tuhinga, he tūhura i ngā kōrero mō te Whānau Mārama me tana whare wānanga.  Ka titiro ki ngā kōrero kua whakamoanahia i roto i ngā tau maha me te whakamārama i te whakamahia o tēnei mātauranga e kui mā, e koro mā i te wā i a rātau.

year: 2016