Planting date, storage and gibberellic acid affect dormancy of Zantedeschia Spreng. hybrids : a thesis presented in partial fulfilment of the requirements for the degree of Masters in Applied Science, Massey University, Palmerston North, New Zealand

To match the supply of Zantedeschia cut flowers and tubers to the demands of the international market, crops have to be timed to a schedule, which requires control of the growth cycle and, in particular, dormancy. In order to improve the predictability and accuracy of timing of Zantedeschia, the effect of different planting seasons and two dormancy-breaking treatments were tested on cultivars 'Black Magic' and 'Treasure', which were known to have a contrasting level of dormancy. Tissue-cultured plants were ex-flasked in July and November 1999, and grown for 180 days in a heated glasshouse (first cycle). Between 120 and 180 days of growth, plants were harvested at 15 days intervals, and tubers cured. Subsequently, tubers were stored for 0 or 3 weeks (10 ± 1°C; 70-80% RH) and dipped in 100 mg.L -1 gibberellic acid plus surfactant or water plus surfactant, prior to planting for dormancy assessment (second cycle). Growing the plants with four months difference in planting date did not cause major alteration in the occurrence of dormancy. Dormancy was brought forward by up to 10 days after the November date of ex-flask, but this was most likely to be due to higher temperatures during that period. In contrast, depth of dormancy varied between cultivars, with 'Black Magic' taking in average 16 days longer to emerge than 'Treasure'. Storage partially released bud dormancy of the tubers. It increased emergence to over 80% regardless of the time of harvest in the first cycle and cultivar, but reduced time to emergence mostly after harvests at 180 days. Furthermore, following storage, time to emergence was reduced to over 50 and 30 days for 'Black Magic' and 'Treasure', respectively, which exceeded the commercially acceptable period to emerge. Gibberellic acid also broke bud dormancy, improving emergence to over 80%, and reduced time to emergence to between 29 and 57 days, irrespective of the time of harvest in the first cycle and cultivar. The effectiveness of gibberellic acid at any time following harvest during the first cycle, may imply that dormancy of Zantedeschia is not as deep as in temperate woody plants. Cessation of leaf emergence in the first cycle was found not to be directly related to the occurrence of dormancy. Degree-days, on the other hand, presented a possible alternative to predict this process. It was estimated that deepest dormancy of 'Black Magic' occurred between 2614 and 2732 °C-days after planting, while deepest dormancy of 'Treasure' occurred between 2681 and 2839 °C-days after planting. The present study presents storage and gibberellic acid as possible options to control dormancy, and the use of degree-days to predict the occurrence of this process. Further research is necessary to develop these options as commercially applicable practices, and to further clarify the process of dormancy in Zantedeschia

Economic Integration in East Asia: Trends, Prospects, and a Possible Roadmap

This paper, which is a revised version of the ADB Working Paper on Regional Economic Integration No. 2, reviews trends in East Asian regionalism in the areas of trade and investment, money and finance, and infrastructure. It finds that trade and, to a lesser extent, financial integration is starting to increase in the region. It also finds that business cycles are starting to be more synchronized, enhancing the case for further monetary integration among these countries. The paper also outlines a roadmap for East Asian integration.

Bias adjustment of satellite-based precipitation estimation using gauge observations: A case study in Chile

Satellite-based precipitation estimates (SPEs) are promising alternative precipitation data for climatic and hydrological applications, especially for regions where ground-based observations are limited. However, existing satellite-based rainfall estimations are subject to systematic biases. This study aims to adjust the biases in the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) rainfall data over Chile, using gauge observations as reference. A novel bias adjustment framework, termed QM-GW, is proposed based on the nonparametric quantile mapping approach and a Gaussian weighting interpolation scheme. The PERSIANN-CCS precipitation estimates (daily, 0.04°×0.04°) over Chile are adjusted for the period of 2009–2014. The historical data (satellite and gauge) for 2009–2013 are used to calibrate the methodology; nonparametric cumulative distribution functions of satellite and gauge observations are estimated at every 1°×1° box region. One year (2014) of gauge data was used for validation. The results show that the biases of the PERSIANN-CCS precipitation data are effectively reduced. The spatial patterns of adjusted satellite rainfall show high consistency to the gauge observations, with reduced root-mean-square errors and mean biases. The systematic biases of the PERSIANN-CCS precipitation time series, at both monthly and daily scales, are removed. The extended validation also verifies that the proposed approach can be applied to adjust SPEs into the future, without further need for ground-based measurements. This study serves as a valuable reference for the bias adjustment of existing SPEs using gauge observations worldwide

Economic Integration in East Asia: Trends, Prospects, and a Possible Roadmap

This paper reviews trends in East Asian regionalism in the areas of trade and investment, money and finance, and infrastructure. It presents various measures of trade and financial integration. An important finding of the paper is that increasing trade and financial integration in the region is now starting to lead to a synchronization of business cycles in a selected group of countries, further enhancing the case for monetary integration among these countries. The paper also outlines a roadmap for East Asian integration.ASEAN/East Asian economic cooperation and integration; business cycle synchronization; free trade agreements; policy coordination

Neuron-level fuzzy memoization in RNNs

The final publication is available at ACM via http://dx.doi.org/10.1145/3352460.3358309Recurrent Neural Networks (RNNs) are a key technology for applications such as automatic speech recognition or machine translation. Unlike conventional feed-forward DNNs, RNNs remember past information to improve the accuracy of future predictions and, therefore, they are very effective for sequence processing problems. For each application run, each recurrent layer is executed many times for processing a potentially large sequence of inputs (words, images, audio frames, etc.). In this paper, we make the observation that the output of a neuron exhibits small changes in consecutive invocations. We exploit this property to build a neuron-level fuzzy memoization scheme, which dynamically caches the output of each neuron and reuses it whenever it is predicted that the current output will be similar to a previously computed result, avoiding in this way the output computations. The main challenge in this scheme is determining whether the new neuron's output for the current input in the sequence will be similar to a recently computed result. To this end, we extend the recurrent layer with a much simpler Bitwise Neural Network (BNN), and show that the BNN and RNN outputs are highly correlated: if two BNN outputs are very similar, the corresponding outputs in the original RNN layer are likely to exhibit negligible changes. The BNN provides a low-cost and effective mechanism for deciding when fuzzy memoization can be applied with a small impact on accuracy. We evaluate our memoization scheme on top of a state-of-the-art accelerator for RNNs, for a variety of different neural networks from multiple application domains. We show that our technique avoids more than 24.2% of computations, resulting in 18.5% energy savings and 1.35x speedup on average.Peer ReviewedPostprint (author's final draft

How significant is the impact of irrigation on the local hydroclimate in Californias Central Valley? Comparison of model results with ground and remote-sensing data

The effect of irrigation on regional climate has been studied over the years. However, in most studies, the model was usually set at coarse resolution, and the soil moisture was set to field capacity at each time step. We reinvestigated this issue over the Central Valley of California's agricultural area by: (1) using the regional climate model at different resolutions down to the finest resolution of 4 km for the most inner domain, covering California's Central Valley, the central coast, the Sierra Nevada Mountains, and water; (2) using a more realistic irrigation scheme in the model through the use of different allowable soil water depletion configurations; and (3) evaluating the simulated results against satellite and in situ observations available through the California Irrigation Management Information System (CIMIS). The simulation results with fine model resolution and with the more realistic irrigation scheme indicate that the surface meteorological fields are noticeably improved when compared with observations from the CIMIS network and Moderate Resolution Imaging Spectroradiometer data. Our results also indicate that irrigation has significant impacts on local meteorological fields by decreasing temperature by 3°-7°C and increasing relative humidity by 9-20%, depending on model resolutions and allowable soil water depletion configurations. More significantly, our results using the improved model show that the effects of irrigation on weather and climate do not extend very far into nonirrigated regions. Copyright 2011 by the American Geophysical Union

Neural Natural Language Inference Models Enhanced with External Knowledge

Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.Comment: Accepted by ACL 201