Local Search Query Augmentation for Improved Online Retrieval Performance

This document describes a query augmentation technique designed to enhance machine learning performance in local search online retrieval. Online retrieval tasks in local search involve retrieving relevant information such as review snippets, images/videos, points of interest, merchant posts, and short videos in response to a user\u27s query. The challenge lies in the need for substantial training data to effectively train machine learning models for these diverse retrieval tasks. This document details a solution that leverages the structure of local search queries and large language models to generate augmented training data, leading to improved retrieval performance

Copula-based statistical modelling of synoptic-scale climate indices for quantifying and managing agricultural risks in australia

Australia is an agricultural nation characterised by one of the most naturally diverse climates in the world, which translates into significant sources of risk for agricultural production and subsequent farm revenues. Extreme climatic events have been significantly affecting large parts of Australia in recent decades, contributing to an increase in the vulnerability of crops, and leading to subsequent higher risk to a large number of agricultural producers. However, attempts at better managing climate related risks in the agricultural sector have confronted many challenges. First, crop insurance products, including classical claim-based and index-based insurance, are among the financial implements that allow exposed individuals to pool resources to spread their risk. The classical claim-based insurance indemnifies according to a claim of crop loss from the insured customer, and so can easily manage idiosyncratic risk, which is the case where the loss occurs independently.Nevertheless, the existence of systemic weather risk (covariate risk), which is the spread of extreme events over locations and times (e.g., droughts and floods), has been identified as the main reason for the failure of private insurance markets, such as the classical multi-peril crop insurance, for agricultural crops. The index-based insurance is appropriate to handle systemic but not idiosyncratic risk. The indemnity payments of the index-based insurance are triggered by a predefined threshold of an index (e.g., rainfall), which is related to such losses. Since the covariate nature of a climatic event, it sanctions the insurers to predict losses and ascertain indemnifications for a huge number of insured customers across a wide geographical area. However, basis risk, which is related to the strength of the relationship between the predefined indices used to estimate the average loss by the insured community and the actual loss of insured assets by an individual, is a major barrier that hinders uptake of the index-based insurance. Clearly, the high basis risk, which is a weak relationship between the index and loss, destroys the willingness of potential customers to purchase this insurance product. Second, the impact of multiple synoptic-scale climate mode indices (e.g., Southern Oscillation Index (SOI) and Indian Ocean Index (IOD)) on precipitation and crop yield is not identical in different spatial locations and at different times or seasons across the Australian continent since the influence of large-scale climate heterogeneous over the different regions. The occurrence, role, and amplitude of synoptic-scale climate modes contributing to the variability of seasonal crop production have shifted in recent decades. These variables generally complicate the climate and crop yield relationship that cannot be captured by traditional modelling and analysis approaches commonly found in published agronomic literature such as linear regression. In addition, the traditional linear analysis is not able to model the nonlinear and asymmetric interdependence between extreme insurance losses, which may occur in the case of systemic risk. Relying on the linear method may lead to the problem that different behaviour may be observed from joint distributions, particularly in the upper and lower regions, with the same correlation coefficient. As a result, the likelihood of extreme insurance losses can be underestimated or overestimated that lead to inaccuracies in the pricing of insurance policies. Another alternative is the use of the multivariate normal distribution, where the joint distribution is uniquely defined using the marginal distributions of variables and their correlation matrix. However, phenomena are not always normally distributed in practice. It is therefore important to develop new, scientifically verified, strategic measures to solve the challenges as mentioned above in order to support mitigating the influences of the climate-related risk in the agricultural sector. Copulas provide an advanced statistical approach to model the joint distribution of multivariate random variables. This technique allows estimating the marginal distributions of individual variables independently with their dependence structures. It is clear that the copula method is superior to the conventional linear regression since it does not require variables have to be normally distributed and their correlation can be either linear or non-linear. This doctoral thesis therefore adopts the advanced copula technique within a statistical modelling framework that aims to model: (1) The compound influence of synoptic-scale climate indices (i.e., SOI and IOD) and climate variables (i.e., precipitation) to develop a probabilistic precipitation forecasting system where the integrated role of different factors that govern precipitation dynamics are considered; (2) The compound influence of synoptic-scale climate indices on wheat yield; (3) The scholastic interdependencies of systemic weather risks where potential adaptation strategies are evaluated accordingly; and (4) The risk-reduction efficiencies of geographical diversifications in wheat farming portfolio optimisation. The study areas are Australia’s agro-ecological (i.e., wheat belt) zones where major seasonal wheat and other cereal crops are grown. The results from the first and second objectives can be used for not only forecasting purposes but also understanding the basis risk in the case of pricing climate index-based insurance products. The third and fourth objectives assess the interactions of drought events across different locations and in different seasons and feasible adaptation tools. The findings of these studies can provide useful information for decision-makers in the agricultural sector. The first study found the significant relationship between SOI, IOD, and precipitation. The results suggest that spring precipitation in Australia, except for the western part, can be probabilistically forecasted three months ahead. It is more interesting that the combination of SOI and IOD as the predictors will improve the performance of the forecast model. Similarly, the second study indicated that the largescale climate indices could provide knowledge of wheat crops up to six months in advance. However, it is noted that the influence of different climate indices varies over locations and times. Furthermore, the findings derived from the third study demonstrated the spatio-temporally stochastic dependence of the drought events. The results also prove that time diversification is potentially more effective in reducing the systemic weather risk compared to spatially diversifying strategy. Finally, the fourth objective revealed that wheat-farming portfolio could be effectively optimised through the geographical diversification. The outcomes of this study will lead to the new application of advanced statistical tools that provide a better understanding of the compound influence of synoptic-scale climatic conditions on seasonal precipitation, and therefore on wheat crops in key regions over the Australian continent. Furthermore, a comprehensive analysis of systemic weather risks performed through advanced copula-statistical models can help improve and develop novel agricultural adaptation strategies in not only the selected study region but also globally, where climate extreme events pose a serious threat to the sustainability and survival of the agricultural industry. Finally, the evaluation of the effectiveness of diversification strategies implemented in this study reveals new evidence on whether the risk pooling methods could potentially mitigate climate risks for the agricultural sector and subsequently, help farmers in prior preparation for uncertain climatic events

Relevance Determination for Multimedia Posts Using a Multimodal Ranker

Many online platforms permit various entities to post multimedia content, e.g., text together with visual content. For example, merchants in online stores, digital maps, etc. can add merchant posts to their account to highlight their merchandise and special offers. Matching text queries provided by users against only the text portion of the multimedia content fails to take into account the visual portion (image) of the merchant post. This disclosure describes the use of dual encoders that allow matching user query embeddings against embeddings obtained from textual content and embeddings obtained from the visual content of the same multimedia post to obtain respective relevance scores. Search quality is improved by incorporating information from different modalities. A multimodal ranker is used to rank merchant posts based on both the text and image relevance scores and on post metadata such as freshness, user reviews for the post, etc. The dual encoders can be trained using human-labeled as well as LLM-generated data

Satellite-based data for agricultural index insurance: a systematic quantitative literature review

Index-based insurance (IBI) is an effective tool for managing climate risk and promoting sustainable development. It provides payouts based on a measurable index. Remote sensing data obtained from satellites, planes, UAVs, or drones can be used to design index-based insurance products. However, the extent to which satellite-based data has been used for different crop types and geographical regions has not been systematically explored. To bridge this gap, a systematic quantitative literature review was conducted to examine the use of satellite-based datasets in designing index-based insurance products. The review analyzed 89 global studies on four major types of crops: cereals, pastures and forages, perennial crops, and others (i.e., vegetables, oilseed crops, fruits, nuts, etc.). The analysis revealed a rising interest of developing index-based insurance solutions utilizing satellite-based data, particularly after 2015. Datasets from land surface Earth observation satellites were utilized in 91 % of studies with satellite-based data, outnumbering those from weather satellites. The Normalized Difference Vegetation Index (NDVI) was the most prominent satellite-retrieved vegetation index, featured in 61.2 % of studies utilizing satellite imagery, revealing its effectiveness at designing and developing IBI for various crops. It has also been found that satellite-based vegetation health indices outperform weather indices and reduce basis risk with higher-spatial-resolution data. Most studies have focused on cereal crops, with fewer studies focusing on perennial crops. Countries in Asia and Africa were the most interested regions. However, research has focused on specific countries and has not been adequately spread across different regions, especially developing countries. The review suggests that satellite-based datasets will become increasingly important in designing crop-index-based insurance products. This is due to their potential to reduce basis risk by providing high resolution with adequately long and consistent datasets for data-sparse environments. The review recommends using high-spatial- and high-temporal-resolution satellite datasets to further assess their capability to reduce basis risk

Investigating and implementing a student vocational education model for educational innovation

The development of each student's awareness serves as the governing principle for high school vocational education programs. This awareness then becomes the driving force behind the progression of the educational process. Career education activities for students are the relationships between the objectives, contents, methods and forms of organization of educational activities that are directly and constantly influenced by the educational environment. Student career education activities are the relationships between these aspects of educational activities as determined by research into the programs, textbooks, systematization and theoretical analysis of these activities. This investigation focuses on the following areas: (1) Developing preschool and high school teachers in the province of Dong Thap to meet the criteria of the new educational program (2) Developing models of applying local educational material for students in the province of Dong Thap. Both of these initiatives are part of the Dong Thap Educational Development Project. Findings: Assess the current state of activities for students in the province of Dong Thap that are related to vocational education between 2018- 2021. Develop a model for carrying out activities for students participating in vocational education in the province of Dong Thap to fulfill educational innovation requirements

Methodology for producing the Drought Monitor

Drought is one of the most severe natural disasters Australia faces, inflicting serious impacts on the agricultural industry. An Australia-wide drought monitor has been developed to provide detailed and timely data regarding drought conditions that will aid producers and policy makers alike. The Drought Monitor development was an integral part of the Northern Australia Climate Program (NACP), a major partnership between Meat & Livestock Australia, the Queensland Government and the University of Southern Queensland. This document explains the methodology used to produce the monthly Drought Monitor

Flash flood-risk areas zoning using integration of decision-making trial and evaluation laboratory, GIS-based analytic network process and satellite-derived information

Assessing areas prone to flash floods is crucial for effective disaster management and mitigation. This study proposes a framework for mapping flood-prone areas by integrating geographic information system (GIS), remote sensing data, and multi-criteria decision-making (MCDM) techniques. The hybrid MCDM model combines the decision-making trial and evaluation laboratory (DEMATEL) with GIS-based analytic network process (ANP) to evaluate flood vulnerability in Golestan province, Iran. Fourteen criteria related to flood potential, including elevation, slope, aspect, vegetation density, soil moisture, flow direction, river distance, rainfall and runoff, flow time, geomorphology, drainage density, soil type, lithology, and land use, were considered. In areas where official data was lacking, a questionnaire was administered to gather information from 15 specialists, experts, and 20 local managers. The relationships between criteria were analyzed using the DEMATEL method, and their weights were determined using the ANP method. Topography was found to have the greatest impact on flood risk, followed by the type of surface and vegetation cover. Hydrographic, soil and geology, climatic also influence flooding in the region. The study identified the northern and central parts of the study area being at higher risk of flooding compared to the southern part. Based on the flood intensity map, 68 villages (50% of all villages in the Qarasu watershed) with a population of approximately 83,595 were identified as at risk of flooding. The proposed GIS-DANP model provides a valuable tool for flood management and decision-making, aiding in risk reduction and minimizing casualties

Pyrolytic Pathway of Wheat Straw Pellet by the Thermogravimetric Analyzer

The study of the thermokinetics of two types of wheat straw pellets, T1 (100% wheat straw) and T2 (70% wheat straw, 10% each of bentonite clay, sawdust, and biochar), under a nitrogen atmosphere (31–800 °C and 5, 10, and 20 °C/min heating rates) using model-free and model-based approaches by TG/DTG data, revealed promising results. While model-free methods were not suitable, model-based reactions, particularly Fn (nth-order phase interfacial) and F2 (second-order) models, effectively described the three-phase consecutive thermal degradation pathway (A→B, C→D, and D→E). The activation energy (Eα) for phases 2 and 3 (Fn model) averaged 136.04 and 358.11 kJ/mol for T1 and 132.86 and 227.10 kJ/mol for T2, respectively. The pre-exponential factor (lnA) varied across heating rates and pellets (T2: 38.244–2.9 × 109 1/s; T1: 1.2 × 102–5.45 × 1014 1/s). Notably, pellets with additives (T2) exhibited a higher degradable fraction due to lower Eα. These findings suggest a promising potential for utilizing wheat straw pellet biomass as a bioenergy feedstock, highlighting the practical implications of this research