Black hole growth in the early Universe is self-regulated and largely hidden from view

The formation of the first massive objects in the infant Universe remains impossible to observe directly and yet it sets the stage for the subsequent evolution of galaxies. While some black holes with masses > billion solar masses? have been detected in luminous quasars less than one billion years after the Big Bang, these individual extreme objects have limited utility in constraining the channels of formation of the earliest black holes. The initial conditions of black hole seed properties are quickly erased during the growth process. From deep, optimally stacked, archival X-ray observations, we measure the amount of black hole growth in z=6-8 galaxies (0.7-1 billion years after the Big Bang). Our results imply that black holes grow in tandem with their hosts throughout cosmic history, starting from the earliest times. We find that most copiously accreting black holes at these epochs are buried in significant amounts of gas and dust that absorb most radiation except for the highest energy X-rays. This suggests that black holes grow significantly more than previously thought during these early bursts, and due to obscuration they do not contribute to the re-ionization of the Universe with their ultraviolet emission.Comment: Nature, in pres

Data driven crop disease modeling

The concept of precision farming deals with the creation and use of data from machinery and sensors on and off the field to optimize resources and sustainably intensify food production to keep up with increasing demand. However, in the face of a growing amount of data being collected, smarter data processing and analysis techniques are needed and have prompted the evaluation and incorporation of artificial intelligence (AI) and machine learning (ML) techniques for multiple use cases right from seeding to harvesting. One such use case that has yet to fully gauge the propositions of AI and ML is crop disease prediction. Since multiple biotic and abiotic factors could be responsible for the occurrence of a disease, modeling requires finding suitable data associated with these factors from multiple farms for an extended time frame and developing smarter models able to capture underlying relationships between them.   This thesis presents research conducted to develop data-driven methodologies and optimization approaches for building crop disease models. The objective is realized by breaking down the task into three modules: (i) data collection; (ii) data processing and model building; and finally, (iii) the maintenance of models in production.   The traditional data collection approach for disease modeling is through setting up of trials which is expensive and labor-intensive which prompted the evaluation of other novel and free to access data sources. Therefore, in module one two studies were conducted to assess the suitability of social media platforms and remote sensing products. The results show that social media is not a viable option yet due to limited geo-referenced data and ambiguity in categorizing the discussions. On the other hand, vegetation indices derived from multispectral satellite imagery, despite their high spatial granularity, are an interesting addition to the modeling pipeline.   Moving on to module two, a study was conducted to demonstrate the process of fusing and preparing data from multiple sources with different formats collected in an extended time frame to be used for model building. The study establishes the relevance of using advanced machine learning models such as deep learning in the prediction of crop diseases. The results show that given the appropriate data preparation process at the right data granularity and the use of some smart tricks, neural network-based models hold the potential to outperform widely used models such as XGBoost. Since neural networks offer advantages such as multimodal learning, transfer learning, and automated feature engineering, which are crucial in building scalable models with heterogeneous data and reduced human effort, the observations of this study led to a follow-up study. This study investigates neural network-based algorithms specifically designed for tabular data and compares them against popular tree ensemble-based models. Apart from acting as a comprehensive analysis of the two families of techniques the results showed that although neural network-based models were not able to outperform tree-based models, they achieved comparable results and allowed for the creation of easier and more accurate models for new diseases by application of transfer learning.   Climate change leads to unexpected weather events and modified disease occurrence patterns that cause static models to drift rapidly. Models need to be maintained to ensure they are performing as required. Capturing real-time data and triggering retraining when enough new data has been collected can help maintain models by acting as a feedback loop for model improvement. This was attempted by collecting crowd-sourced data from a disease recognition app, but it was not usable in its current form and required further annotation. Since annotations are expensive and time-consuming, a study for real-life agricultural data retrieval and large-scale annotation flow optimization based on similarity search technique is presented which significantly optimizes the annotation process.   The results derived from these studies are highly relevant for progressing the United Nations Sustainable Development Goal of Zero Hunger. It is also expected to ease farmers' anxiety related to yield loss due to crop diseases and enhance their capability of planning and scheduling management practices by giving them an early warning of disease occurrence. The results have been verified through comparison with traditional crop disease prediction methods and interaction with experienced agronomists working for a major AgTech company

Data driven crop disease modeling

The concept of precision farming deals with the creation and use of data from machinery and sensors on and off the field to optimize resources and sustainably intensify food production to keep up with increasing demand. However, in the face of a growing amount of data being collected, smarter data processing and analysis techniques are needed and have prompted the evaluation and incorporation of artificial intelligence (AI) and machine learning (ML) techniques for multiple use cases right from seeding to harvesting. One such use case that has yet to fully gauge the propositions of AI and ML is crop disease prediction. Since multiple biotic and abiotic factors could be responsible for the occurrence of a disease, modeling requires finding suitable data associated with these factors from multiple farms for an extended time frame and developing smarter models able to capture underlying relationships between them.   This thesis presents research conducted to develop data-driven methodologies and optimization approaches for building crop disease models. The objective is realized by breaking down the task into three modules: (i) data collection; (ii) data processing and model building; and finally, (iii) the maintenance of models in production.   The traditional data collection approach for disease modeling is through setting up of trials which is expensive and labor-intensive which prompted the evaluation of other novel and free to access data sources. Therefore, in module one two studies were conducted to assess the suitability of social media platforms and remote sensing products. The results show that social media is not a viable option yet due to limited geo-referenced data and ambiguity in categorizing the discussions. On the other hand, vegetation indices derived from multispectral satellite imagery, despite their high spatial granularity, are an interesting addition to the modeling pipeline.   Moving on to module two, a study was conducted to demonstrate the process of fusing and preparing data from multiple sources with different formats collected in an extended time frame to be used for model building. The study establishes the relevance of using advanced machine learning models such as deep learning in the prediction of crop diseases. The results show that given the appropriate data preparation process at the right data granularity and the use of some smart tricks, neural network-based models hold the potential to outperform widely used models such as XGBoost. Since neural networks offer advantages such as multimodal learning, transfer learning, and automated feature engineering, which are crucial in building scalable models with heterogeneous data and reduced human effort, the observations of this study led to a follow-up study. This study investigates neural network-based algorithms specifically designed for tabular data and compares them against popular tree ensemble-based models. Apart from acting as a comprehensive analysis of the two families of techniques the results showed that although neural network-based models were not able to outperform tree-based models, they achieved comparable results and allowed for the creation of easier and more accurate models for new diseases by application of transfer learning.   Climate change leads to unexpected weather events and modified disease occurrence patterns that cause static models to drift rapidly. Models need to be maintained to ensure they are performing as required. Capturing real-time data and triggering retraining when enough new data has been collected can help maintain models by acting as a feedback loop for model improvement. This was attempted by collecting crowd-sourced data from a disease recognition app, but it was not usable in its current form and required further annotation. Since annotations are expensive and time-consuming, a study for real-life agricultural data retrieval and large-scale annotation flow optimization based on similarity search technique is presented which significantly optimizes the annotation process.   The results derived from these studies are highly relevant for progressing the United Nations Sustainable Development Goal of Zero Hunger. It is also expected to ease farmers' anxiety related to yield loss due to crop diseases and enhance their capability of planning and scheduling management practices by giving them an early warning of disease occurrence. The results have been verified through comparison with traditional crop disease prediction methods and interaction with experienced agronomists working for a major AgTech company

Digital Crop Health Monitoring by Analyzing Social Media Streams

This paper introduces the idea of using social media streams like Twitter to identify occurrences of crop diseases. Climate change and changes in agriculture practices have contributed to a change in crop disease dynamics leading to an increase in crop damages. Monitoring crop disease occurrences across regions is helpful for farmers to prepare for such adverse situations and make effective use of crop protection products thus ensuring enough produce for the growing population and protection of the environment. We investigate Machine Learning and Natural Language Processing techniques in order to spot agricultural discussions on Twitter; then analyze, categorize, and group them; so they can be used by a stakeholder to identify crop disease incidences, patterns, and trends at the regional scale. Current systems using keyword based search of agricultural diseases do not always yield agriculturally relevant tweets and those that do could talk on a range of sub-topics. Therefore, text classification forms the core component of this work. A two fold classification process is employed, classifying agriculturally relevant tweets from the rest and then performing fine-grained categorization on them. The resulting model for agricultural tweets classification performs with 93% accuracy and the fine grained categorization model that categorizes tweets into 6 categories gives 75% accuracy. A prototype of an interactive web based disease monitoring application is also presented. The location estimation is not always accurate but nonetheless, this work acts as a proof of concept for the introduction of social media as a novel data source in precision farming.ISBN för värdpublikation: 978-1-7281-7031-2</p

Data Fusion and Artificial Neural Networks for Modelling Crop Disease Severity

This paper analyzes the possibility of applying data fusion combined with artificial neural networks (ANN) on a dataset combining hard and soft data for prediction of one of the most devastating crop diseases of winter wheat, i.e., Septoria Tritici (Zymoseptoria tritici). In advanced decision support systems for crop protection choices, disease models form a major component.They reproduce the biophysical processes of disease development and temporal spread as a set of rules or processes to predict disease risk value. However, the adaptation of these rules or processes to incorporate the effects of climate change is complex and requires extensive rework. To remedy this issue, statistical machine learning techniques have been introduced to model disease severity percentage for some diseases.However, the use of artificial neural networks has been limited (mainly to image data) and is unexplored for Septoria Tritici.This paper explores the use of Feed Forward neural networks on fused tabular data for the task of disease severity modelling. First, ten years of trial data ranging from 2008 to 2018 across Europe is used for the creation of the new tabular dataset with a fusion of all important data sources baring impact on disease development: Field-specific data, weather data, crop growth stages, and disease severity observation made by human trial operators (response variable). %Correlation and regression analyses and domain expert knowledge were used for the selection of useful predictor variables from these data sources. Next, two implementation architectures of Feed Forward neural networks on tabular data are employed: a) standard architecture with backpropagation, drop out regularization, and batch normalization and b) advanced architecture with improvements such as cyclic learning rate and cosine annealing.%A comparison of generic two layer feed forward network vs the same with the incorporation of the latest advances to improve the performance of the architecture is presented. The advanced architecture is able to better model the data and make estimations of disease severity with a difference of +-10\% giving a better quantifiable estimate of disease stress. For better outreach to farmers, a technique to incorporate such modelling techniques into the well established Decision Support Systems is also presented.ISBN för värdpublikation: 978-0-578-64709-8, 978-1-7281-6830-2</p

Tree-based ensembles vs neuron-based methods for tabular data - A case study in crop disease forecasting

Machine learning and especially deep learning techniques have led to signif-icant success in the last decade and have been predominantly applied to visualdata, natural language, speech, and audio-related tasks but haven’t found ma-jor prominence in the context of tabular data yet. In agriculture, too, deeplearning models are mostly limited to use cases with image data, while tree-based algorithms continue to be the de facto standard for predictive modelingon tabular data. Therefore, the objective of this study is to present a thoroughinvestigation on these two streams of predictive modeling techniques on tab-ular data against a speed-accuracy-complexity tradeoff, namely neuron-basedmethods (Feed Forward fully connected network, LSTM, TabNet, NODE) andtree-based methods(Random Forest, XGBoost, CatBoost, LightGBM). As acase study, in crop disease modeling, prediction models of Septoria and YellowRust disease severity are presented. The results of the study show that tree-based ensemble methods are slightly better in terms of performance metrics.Still, we argue in favor of neuron-based methods since they offer significantadvantages such as automated feature engineering, multi-modal learning, andtransfer learning. We demonstrate how this provides a launchpad for the adop-tion of artificial learning into everyday business. In a broader context, this workdemonstrates that the effective use of machine learning can play a major rolein helping farmers make informed decisions against threats to food productionand thus ensure food security for mankind

Tree-based ensembles vs neuron-based methods for tabular data - A case study in crop disease forecasting

Machine learning and especially deep learning techniques have led to signif-icant success in the last decade and have been predominantly applied to visualdata, natural language, speech, and audio-related tasks but haven’t found ma-jor prominence in the context of tabular data yet. In agriculture, too, deeplearning models are mostly limited to use cases with image data, while tree-based algorithms continue to be the de facto standard for predictive modelingon tabular data. Therefore, the objective of this study is to present a thoroughinvestigation on these two streams of predictive modeling techniques on tab-ular data against a speed-accuracy-complexity tradeoff, namely neuron-basedmethods (Feed Forward fully connected network, LSTM, TabNet, NODE) andtree-based methods(Random Forest, XGBoost, CatBoost, LightGBM). As acase study, in crop disease modeling, prediction models of Septoria and YellowRust disease severity are presented. The results of the study show that tree-based ensemble methods are slightly better in terms of performance metrics.Still, we argue in favor of neuron-based methods since they offer significantadvantages such as automated feature engineering, multi-modal learning, andtransfer learning. We demonstrate how this provides a launchpad for the adop-tion of artificial learning into everyday business. In a broader context, this workdemonstrates that the effective use of machine learning can play a major rolein helping farmers make informed decisions against threats to food productionand thus ensure food security for mankind

Early Onset Yellow Rust Detection Guided by Remote Sensing Indices

Early warning systems help combat crop diseases and enable sustainable plant protection by optimizing the use of resources. The application of remote sensing to detect plant diseases like wheat stripe rust, commonly known as yellow rust, is based on the presumption that the presence of a disease has a direct link with the photosynthesis capability and physical structure of a plant at both canopy and tissue level. This causes changes to the solar radiation absorption capability and thus alters the reflectance spectrum. In comparison to existing methods and technologies, remote sensing offers access to near real-time information at both the field and the regional scale to build robust disease models. This study shows the capability of multispectral images along with weather, in situ and phenology data to detect the onset of yellow rust disease. Crop details and disease observation data from field trials across the globe spanning four years (2015&ndash;2018) are combined with weather data to model disease severity over time as a value between 0 and 1 with 0 being no disease and 1 being the highest infestation level. Various tree-based ensemble algorithms like CatBoost, Random Forest and XGBoost were experimented with. The XGBoost model performs best with a mean absolute error of 0.1568 and a root mean square error of 0.2081 between the measured disease severity and the predicted disease severity. Being a fast-spreading disease and having caused epidemics in the past, it is important to detect yellow rust disease early so farmers can be warned in advance and favorable management practices can be implemented. Vegetation indices like NDVI, NDRE and NDWI from remote-sensing images were used as auxiliary features along with disease severity predictions over time derived by combining weather, in situ and phenology data. A rule-based approach is presented that uses a combination of both model output and changes in vegetation indices to predict an early disease progression window. Analysis on test trials shows that in 80% of the cases, the predicted progression window was ahead of the first disease observation on the field, offering an opportunity to take timely action that could save yield

Artificial Intelligence Driven Crop Protection Optimization for Sustainable Agriculture

This paper introduces digital farming solutions offered by xarvio™ and how these solutions contribute towards achieving the United Nations Sustainable Development Goals. By leveraging recent advancements in Artificial Intelligence, farmers can apply crop protection more efficiently by targeted usage. Respective modules presented in this paper, namely Spray Timer, Zone Spray, Buffer Zones and Product Recommendation ensure crop protection products are applied at the right time and only where they are needed while also ensuring the right product at the optimal rate. This not only reduces the impact on the environment, but moreover increases the productivity and profitability of the farmer. The impact of our digital solutions is exemplified by real world case studies in two major food production regions: Europe and Brazil. In Europe the use of Artificial Intelligence driven spray timing, variable rate application maps and product recommendation have led to a 30% decrease in fungicide usage on field trial cereal crops and a 72% decrease in tank leftovers reducing environmental pollution. In Brazil the Zone Spray weed maps solution created using Computer Vision techniques resulted in a 61% average savings, cutting back on almost two thirds of herbicide and water consumption. As a result the solutions presented in this paper cater to the UN Sustainable Development Goals of zero hunger and responsible consumption and production.ISBN för värdpublikation: 978-1-7281-7031-2</p

Epidemiology and outcomes of acute kidney injury in critically ill: Experience from a tertiary care center

There is only limited information on the epidemiology and outcomes of acute kidney injury (AKI) in critically ill patients from low- and middle-income countries. This study aims to identify the etiology, short-term outcomes, and determinants of mortality in patients with AKI admitted to multiple medical and surgical Intensive Care Units (ICU's) in a tertiary care center. The study also aims to compare the clinical characteristics and outcomes of community-acquired AKI (CAAKI) and hospital-acquired AKI (HAAKI). A prospective, observational study was done from June 2013 to October 2015. All patients over 18 years with AKI admitted in various medical and surgical ICU's seeking nephrology referral were included. AKI was defined according to KDIGO criteria. The follow-up period was 30 days. A total of 236 patients were recruited from five medical and nine surgical ICU's. Majority (73.3%) were males. About 53.38% patients had CAAKI, whereas 46.61% had HAAKI. The predominant etiologies for AKI were sepsis (22.4%), trauma due to road traffic accidents (21.18%), acute abdomen (perforation, acute pancreatitis, bowel gangrene, intestinal obstruction and cholangitis) (18.64%), and cardiac diseases (10.59%). Sepsis and acute abdomen were the most common causes of CAAKI, whereas trauma and cardiac causes were the predominant causes of HAAKI (P < 0.05). Patients with HAAKI were younger, admitted in surgical units, had lower SOFA scores, lower serum creatinine, lesser need for dialysis, longer hospital stay, and earlier stages of AKI compared to patients with CAAKI (P < 0.05). The 30-day mortality was 52.54%. The mortality was not different between CAAKI and HAAKI (56.3% vs. 48.18%; relative risk = 0.86: 95% confidence interval 0.67–1.1). The mortality was similar across different stages of AKI