Graph-Based Sports Rankings

Sports rankings are a widely debated topic among sports fanatics and analysts. Many techniques for systematically generating sports rankings have been explored, ranging from simple win-loss systems to various algorithms. In this report, we discuss the application of graph theory to sports rankings. Using this approach, we were able to outperform existing sports rankings with our new four algorithms. We also reverse-engineered existing rankings to understand the factors that influence them

Application of wireless technologies to forward predict crop yields in the poultry production chain

Average bird weight is the primary measure of crop yield and is the basis for calculating payment for the grower by the wholesaler. Furthermore the profit per bird is very small. Thus very tight control of growing process is essential to ensure average bird weight is maximised. The important factors (air temperature, air humidity, Carbon Dioxide concentration and Ammonia concentration) that affect the intake of feed and water must be kept at their optimum during the progress of the growing cycle. These factors can be influenced by activating burners and opening the vents on walls of the growing house. It then follows that the burning and venting strategy will be influential on the average bird weight of the crop. Currently the burning and venting strategy is based on notional ideal levels and data from wall mounted sensors. This suffers from two fundamental problems; firstly the strategy is determined by ideals that may not be suitable for all growing houses and secondly the data is not measured from the chickens own airspace. Thus the management strategy is based on a model that may not reflect reality and on data that may not reflect reality  The “BOSCA” project addresses these problems by placing wireless environmental sensors into the chickens own airspace. This provides for direct measurement of the air experienced by the chickens and reports the recorded data in near real-time to a cloud based data management system. The sensor data is merged with the data from the growing house weighing scales in the cloud repository so a predictive model of average bird weight from the measured environmental data can be calibrated and validated. Furthermore, a timeshift can be applied to the environmental data during model calibration and validation so the average bird weight can be forward predicted by 72 hours (r2 up to 0.89 with neural networks). This gives the grower advance notice of a deviation from ideal feeding and watering conditions and the likely consequences of failing to take remedial action such as turning on the burners or venting the house

Multi-Spectral Visual Crop Assessment Under Limited Data Constraints

In an era of climate change and global population growth, deep learning based multi-spectral imaging has the potential to significantly assist in production management across a wide range of agricultural and food production domains. A key challenge however in applying state-of-the-art methods is that they, unlike classical hand crafted methods, are usually thought of as being only useful when significant amounts of data are available. In this paper we investigate this hypothesis by examining the performance of state-of-the-art deep learning methods when applied to a restricted data set that is not easily bootstrapped through pre-trained image processing networks. We demonstrate that significant result improvement can be obtained from deep residual networks over a baseline image processing model -- even in the case where data collection is highly expensive and pre-trained networks cannot be easily built upon. Our work also constitutes a useful contribution to understanding the benefit of applying deep image multi-spectral processing techniques to the agri-food domain

TRANSFER LEARNING PERFORMANCE FOR REMOTE PASTURELAND TRAIT ESTIMATION IN REAL-TIME FARM MONITORING

In precision agriculture, having knowledge of pastureland forage biomass and moisture content prior to an ensiling process enables pastoralists to enhance silage production. While traditional trait measurement estimation methods relied on hand-crafted vegetation indices, manual measurements, or even destructive methods, remote sensing technology coupled with state-of-the-art deep learning algorithms can enable estimation using a broader spectrum of data, but generally require large volumes of labelled data, which is lacking in this domain. This work investigates the performance of a range of deep learning algorithms on a small dataset for biomass and moisture estimation that was collected with a compact remote sensing system designed to work in real time. Our results showed that applying transfer learning to Inception ResNet improved minimum mean average percentage error from 45.58% on a basic CNN, to 28.07% on biomass, and from 29.33% to 8.03% on moisture content. From scratch models and models optimised for mobile remote sensing applications (MobileNet) failed to produce the same level of improvement

X-rays Studies of the Solar System

X-ray observatories contribute fundamental advances in Solar System studies by probing Sun-object interactions, developing planet and satellite surface composition maps, probing global magnetospheric dynamics, and tracking astrochemical reactions. Despite these crucial results, the technological limitations of current X-ray instruments hinder the overall scope and impact for broader scientific application of X-ray observations both now and in the coming decade. Implementation of modern advances in X-ray optics will provide improvements in effective area, spatial resolution, and spectral resolution for future instruments. These improvements will usher in a truly transformative era of Solar System science through the study of X-ray emission.Comment: White paper submitted to Astro2020, the Astronomy and Astrophysics Decadal Surve

Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient’s quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP - RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization

Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP - RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization.Comment: 19 pages, 8 tables, 18 figure

Преступления в таможенной сфере: характеристика, выявление, ответственность

Объектом исследования является таможенный орган как средство обеспечения экономической безопасности. Цель работы - выявление проблем, возникающих у таможенных органов, как органов дознания при расследовании преступлений в сфере экономической деятельности и разработка рекомендаций, направленных на повышение эффективности правоохранительной деятельности таможенных органов как органов дознания. В процессе исследования был проведен анализ нормативно-правовой базы, регулирующей деятельность таможенных органов, а также практика расследования уголовных дел. В результате исследования были выявлены проблемы, возникающие у таможенных органов при расследовании преступлений и предложены рекомендации по решению этих проблем.The object of the research is customs authorities as tool of providing of economic safety. The goal of the work is the identification of problems, which customs authorities face while the process of investigation of crimes in economic sphere and developing some recommendations aimed to raise the efficiency of customs authorities’ activity. In the researching process there was an analysis of the regulatory framework, which adjusts the customs authorities’ activity and the practice of investigation of crimes in economic sphere. As a result of the research some problems, which customs authorities face while the process of investigation, were revealed and there are some offers aimed to solve this problems