scholarly journals Fine-tuning heat stress algorithms to optimise global predictions of mass coral bleaching

2021 ◽  
Author(s):  
Liam Lachs ◽  
John C Bythell ◽  
Holly K East ◽  
Alasdair J Edwards ◽  
Peter J Mumby ◽  
...  
Keyword(s):  
Heat Stress ◽  
Coral Bleaching ◽  
Marine Ecosystem ◽  
Fine Tuning ◽  
Prediction Ability ◽  
Bayesian Hierarchical ◽  
Stress Threshold ◽  
Monitoring And Forecasting ◽  
Sensitivity Specificity ◽  
Fine Tune

Increasingly severe marine heatwaves under climate change threaten the persistence of many marine ecosystems. Mass coral bleaching events, caused by periods of anomalously warm sea surface temperatures (SST), have led to catastrophic levels of coral mortality globally. Remotely monitoring and forecasting such biotic responses to heat stress is key for effective marine ecosystem management. The Degree Heating Week (DHW) metric, designed to monitor coral bleaching risk, reflects the duration and intensity of heat stress events, and is computed by accumulating SST anomalies (HotSpot) relative to a stress threshold over a 12-week moving window. Despite significant improvements in the underlying SST datasets, corresponding revisions of the HotSpot threshold and accumulation window are still lacking. Here, we fine-tune the operational DHW algorithm to optimise coral bleaching predictions using the 5km satellite-based SSTs (CoralTemp v3.1) and a global coral bleaching dataset (37,871 observations, National Oceanic and Atmospheric Administration). After developing 234 test DHW algorithms with different combinations of HotSpot threshold and accumulation window, we compared their bleaching-prediction ability using spatiotemporal Bayesian hierarchical models and sensitivity-specificity analyses. Peak DHW performance was reached using HotSpot thresholds less than or equal to Maximum Monthly Mean SST and accumulation windows of 4 - 8 weeks. This new configuration correctly predicted up to an additional 310 bleaching observations compared to the operational DHW algorithm, an improved hit rate of 7.9 %. Given the detrimental impacts of marine heatwaves across ecosystems, heat stress algorithms could also be fine-tuned for other biological systems, improving scientific accuracy, and enabling ecosystem governance.

scholarly journals Fine-Tuning Heat Stress Algorithms to Optimise Global Predictions of Mass Coral Bleaching

2021 ◽  
Vol 13 (14) ◽  
pp. 2677
Author(s):  
Liam Lachs ◽  
John C Bythell ◽  
Holly K East ◽  
Alasdair J Edwards ◽  
Peter J Mumby ◽  
...  
Keyword(s):  
Heat Stress ◽  
Coral Bleaching ◽  
Marine Ecosystem ◽  
Fine Tuning ◽  
Prediction Ability ◽  
Bayesian Hierarchical ◽  
Stress Threshold ◽  
Monitoring And Forecasting ◽  
Sensitivity Specificity ◽  
Fine Tune

Increasingly intense marine heatwaves threaten the persistence of many marine ecosystems. Heat stress-mediated episodes of mass coral bleaching have led to catastrophic coral mortality globally. Remotely monitoring and forecasting such biotic responses to heat stress is key for effective marine ecosystem management. The Degree Heating Week (DHW) metric, designed to monitor coral bleaching risk, reflects the duration and intensity of heat stress events and is computed by accumulating SST anomalies (HotSpot) relative to a stress threshold over a 12-week moving window. Despite significant improvements in the underlying SST datasets, corresponding revisions of the HotSpot threshold and accumulation window are still lacking. Here, we fine-tune the operational DHW algorithm to optimise coral bleaching predictions using the 5 km satellite-based SSTs (CoralTemp v3.1) and a global coral bleaching dataset (37,871 observations, National Oceanic and Atmospheric Administration). After developing 234 test DHW algorithms with different combinations of the HotSpot threshold and accumulation window, we compared their bleaching prediction ability using spatiotemporal Bayesian hierarchical models and sensitivity–specificity analyses. Peak DHW performance was reached using HotSpot thresholds less than or equal to the maximum of monthly means SST climatology (MMM) and accumulation windows of 4–8 weeks. This new configuration correctly predicted up to an additional 310 bleaching observations globally compared to the operational DHW algorithm, an improved hit rate of 7.9%. Given the detrimental impacts of marine heatwaves across ecosystems, heat stress algorithms could also be fine-tuned for other biological systems, improving scientific accuracy, and enabling ecosystem governance.

Fine-Tuning Electronic Properties of Luminescent Pt(II) Complexes via Vertex-Differentiated Coordination of Sterically Invariant Carborane-Based Ligands

2018 ◽  
Author(s):  
Kent O. Kirlikovali ◽  
Jonathan C. Axtell ◽  
Kierstyn Anderson ◽  
Peter I. Djurovich ◽  
Arnold L. Rheingold ◽  
...  
Keyword(s):  
Coordination Chemistry ◽  
Metal Complexes ◽  
Electronic Properties ◽  
Fine Tuning ◽  
Fine Tune

We report the synthesis of two isomeric Pt(II) complexes ligated by doubly deprotonated 1,1′-bis(<i>o</i>-carborane) (<b>bc</b>). This work provides a potential route to fine-tune the electronic properties of luminescent metal complexes by virtue of vertex-differentiated coordination chemistry of carborane-based ligands.

scholarly journals Fine-tuning of a generative neural network for designing multi-target compounds

2021 ◽  
Author(s):  
Thomas Blaschke ◽  
Jürgen Bajorath
Keyword(s):  
Neural Network ◽  
Small Molecules ◽  
De Novo ◽  
Pharmaceutical Research ◽  
Generative Models ◽  
Fine Tuning ◽  
Data Sets ◽  
Single Target ◽  
Fine Tune ◽  
Target Activity

AbstractExploring the origin of multi-target activity of small molecules and designing new multi-target compounds are highly topical issues in pharmaceutical research. We have investigated the ability of a generative neural network to create multi-target compounds. Data sets of experimentally confirmed multi-target, single-target, and consistently inactive compounds were extracted from public screening data considering positive and negative assay results. These data sets were used to fine-tune the REINVENT generative model via transfer learning to systematically recognize multi-target compounds, distinguish them from single-target or inactive compounds, and construct new multi-target compounds. During fine-tuning, the model showed a clear tendency to increasingly generate multi-target compounds and structural analogs. Our findings indicate that generative models can be adopted for de novo multi-target compound design.

scholarly journals Poseidon: A marine environmental monitoring, forecasting and information system for the Greek seas

2000 ◽  
Vol 1 (1) ◽  
pp. 71 ◽  
Author(s):  
T.H. SOUKISSIAN ◽  
G. CHRONIS
Keyword(s):  
Marine Ecosystem ◽  
Coastal Zone Management ◽  
Aegean Sea ◽  
Sea Transport ◽  
Marine Research ◽  
Zone Management ◽  
Operational Oceanography ◽  
Monitoring And Forecasting ◽  
Forecasting System ◽  
Marine Environmental

The scope of this work is twofold: i) to discuss and analyze some principles, issues and problems related to the development and advancement of Operational Oceanography in Greece and ii) to present a real-time monitoring and forecasting system for the Aegean Sea, which is currently under implementation. Operational Oceanography in Greece has become a necessity today, since it can provide aid to find solutions on problems related to societal, economic, environmental and scientific issues. Most of the Greek coastal regions are under pressure, susceptible to damages due to the increasing tendency of the population to move from the inland to the coast, marine environmental pollution, competitive development of the coastal market sector, etc. Moreover, the complex geomorphology of the coastal areas and the interdependence between natural processes and human activities causes significant alterations in this delicate environment. A rational treatment of these problems can be based on integrated coastal zone management (ICZM). An absolutely necessary means for establishing ICZM is the operation of marine moni- toring systems. Such a system ("POSEIDON system") is under implementation by the National Centre for Marine Research. POSEIDON is a comprehensive marine monitoring and forecasting system, that aims to improve environmental surveillance and facilitate sea transport, rescue and safety of life at sea, fishing and aquaculture, protection of the marine ecosystem, etc. POSEIDON is expected to enhance considerably the capabilities to manage, protect and develop the marine resources of the Greek Seas and to promote Greek Operational Oceanography.

scholarly journals Scanning electron microscope fine tuning using four-bar piezoelectric actuated mechanism

2018 ◽  
Vol 69 (1) ◽  
pp. 24-31
Author(s):  
Khaled S. Hatamleh ◽  
Qais A. Khasawneh ◽  
Adnan Al-Ghasem ◽  
Mohammad A. Jaradat ◽  
Laith Sawaqed ◽  
...  
Keyword(s):  
Electron Microscope ◽  
Scanning Electron Microscope ◽  
Inverse Kinematic ◽  
Fine Tuning ◽  
Electron Microscopes ◽  
Tuning Strategy ◽  
Scanning Electron Microscopes ◽  
Fine Tune ◽  
Kinematic Solution ◽  
Scanning Electron

Abstract Scanning Electron Microscopes are extensively used for accurate micro/nano images exploring. Several strategies have been proposed to fine tune those microscopes in the past few years. This work presents a new fine tuning strategy of a scanning electron microscope sample table using four bar piezoelectric actuated mechanisms. The introduced paper presents an algorithm to find all possible inverse kinematics solutions of the proposed mechanism. In addition, another algorithm is presented to search for the optimal inverse kinematic solution. Both algorithms are used simultaneously by means of a simulation study to fine tune a scanning electron microscope sample table through a pre-specified circular or linear path of motion. Results of the study shows that, proposed algorithms were able to minimize the power required to drive the piezoelectric actuated mechanism by a ratio of 97.5% for all simulated paths of motion when compared to general non-optimized solution.

scholarly journals Target Population of Environments for Wheat Breeding in India: Definition, Prediction and Genetic Gains

2021 ◽  
Vol 12 ◽  
Author(s):  
Leonardo Abdiel Crespo-Herrera ◽  
Jose Crossa ◽  
Julio Huerta-Espino ◽  
Suchismita Mondal ◽  
Govindan Velu ◽  
...  
Keyword(s):  
South Asia ◽  
Wheat Yield ◽  
Target Population ◽  
Genotype By Environment Interaction ◽  
Wheat Breeding ◽  
Correlated Response ◽  
Environment Interaction ◽  
Prediction Ability ◽  
North Eastern ◽  
Fine Tune

In this study, we defined the target population of environments (TPE) for wheat breeding in India, the largest wheat producer in South Asia, and estimated the correlated response to the selection and prediction ability of five selection environments (SEs) in Mexico. We also estimated grain yield (GY) gains in each TPE. Our analysis used meteorological, soil, and GY data from the international Elite Spring Wheat Yield Trials (ESWYT) distributed by the International Maize and Wheat Improvement Center (CIMMYT) from 2001 to 2016. We identified three TPEs: TPE 1, the optimally irrigated Northwestern Plain Zone; TPE 2, the optimally irrigated, heat-stressed North Eastern Plains Zone; and TPE 3, the drought-stressed Central-Peninsular Zone. The correlated response to selection ranged from 0.4 to 0.9 within each TPE. The highest prediction accuracies for GY per TPE were derived using models that included genotype-by-environment interaction and/or meteorological information and their interaction with the lines. The highest prediction accuracies for TPEs 1, 2, and 3 were 0.37, 0.46, and 0.51, respectively, and the respective GY gains were 118, 46, and 123 kg/ha/year. These results can help fine-tune the breeding of elite wheat germplasm with stable yields to reduce farmers’ risk from year-to-year environmental variation in India’s wheat lands, which cover 30 million ha, account for 100 million tons of grain or more each year, and provide food and livelihoods for hundreds of millions of farmers and consumers in South Asia.

A Study of the Influence of Data Complexity and Similarity on Soft Biometrics Classification Performance in a Transfer Learning Scenario

2021 ◽  
Vol 18 (2) ◽  
pp. 56-65
Author(s):  
Marcelo Romero ◽  
◽  
Matheus Gutoski ◽  
Leandro Takeshi Hattori ◽  
Manassés Ribeiro ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Classification Performance ◽  
Fine Tuning ◽  
Similarity Metrics ◽  
Soft Biometrics ◽  
Final Layer ◽  
The One ◽  
Fully Connected ◽  
The Relationship ◽  
Fine Tune

Transfer learning is a paradigm that consists in training and testing classifiers with datasets drawn from distinct distributions. This technique allows to solve a particular problem using a model that was trained for another purpose. In the recent years, this practice has become very popular due to the increase of public available pre-trained models that can be fine-tuned to be applied in different scenarios. However, the relationship between the datasets used for training the model and the test data is usually not addressed, specially where the fine-tuning process is done only for the fully connected layers of a Convolutional Neural Network with pre-trained weights. This work presents a study regarding the relationship between the datasets used in a transfer learning process in terms of the performance achieved by models complexities and similarities. For this purpose, we fine-tune the final layer of Convolutional Neural Networks with pre-trained weights using diverse soft biometrics datasets. An evaluation of the performances of the models, when tested with datasets that are different from the one used for training the model, is presented. Complexity and similarity metrics are also used to perform the evaluation.

scholarly journals OmniPD: One-Step Person Detection in Top-View Omnidirectional Indoor Scenes

2019 ◽  
Vol 5 (1) ◽  
pp. 239-244
Author(s):  
Jingrui Yu ◽  
Roman Seidel ◽  
Gangolf Hirtz
Keyword(s):  
Data Augmentation ◽  
Training Data ◽  
Fine Tuning ◽  
Single Shot ◽  
Augmentation Techniques ◽  
Indoor Scenes ◽  
One Step ◽  
Bounding Boxes ◽  
Omnidirectional Images ◽  
Fine Tune

AbstractWe propose a one-step person detector for topview omnidirectional indoor scenes based on convolutional neural networks (CNNs). While state of the art person detectors reach competitive results on perspective images, missing CNN architectures as well as training data that follows the distortion of omnidirectional images makes current approaches not applicable to our data. The method predicts bounding boxes of multiple persons directly in omnidirectional images without perspective transformation, which reduces overhead of pre- and post-processing and enables realtime performance. The basic idea is to utilize transfer learning to fine-tune CNNs trained on perspective images with data augmentation techniques for detection in omnidirectional images. We fine-tune two variants of Single Shot MultiBox detectors (SSDs). The first one uses Mobilenet v1 FPN as feature extractor (moSSD). The second one uses ResNet50 v1 FPN (resSSD). Both models are pre-trained on Microsoft Common Objects in Context (COCO) dataset. We fine-tune both models on PASCAL VOC07 and VOC12 datasets, specifically on class person. Random 90-degree rotation and random vertical flipping are used for data augmentation in addition to the methods proposed by original SSD. We reach an average precision (AP) of 67.3%with moSSD and 74.9%with resSSD on the evaluation dataset. To enhance the fine-tuning process, we add a subset of HDA Person dataset and a subset of PIROPO database and reduce the number of perspective images to PASCAL VOC07. The AP rises to 83.2% for moSSD and 86.3% for resSSD, respectively. The average inference speed is 28 ms per image for moSSD and 38 ms per image for resSSD using Nvidia Quadro P6000. Our method is applicable to other CNN-based object detectors and can potentially generalize for detecting other objects in omnidirectional images.

Investigation of Solid Solubility, Hardness, and Thermal Properties of Au-Ge-Sb System

2014 ◽  
Vol 1016 ◽  
pp. 336-341
Author(s):  
Kamolchanok Thipayarat ◽  
Ekasit Nisaratanaporn ◽  
Boonrat Lohwongwatana
Keyword(s):  
High Performance ◽  
Alloy System ◽  
Fine Tuning ◽  
Lead Free ◽  
Diagram Method ◽  
Average Value ◽  
Wide Range ◽  
Lead Free Solders ◽  
Hardness Values ◽  
Fine Tune

In recent years, the Au-Ge-Sb system has been studied as a possible alternative alloy for soldering applications [1-4]. The alloy has various fbenefits such as (i) low melting temperature which allows the alloy system to be used as a drop-in solution for high performance lead-free solders, (ii) three distinct phases of different hardness values (100, 150 and 500 HV) which offer the ability to fine tune the composition and microstructure to a wide range of properties, and (iii) limited solute solubility which offers ease of control and fine-tuning of microstructure, mechanical properties and colors. Gold compositions centered around 75wt% gold were modeled and selected using the CALPHAD (CALculation of PHAse Diagram) method. Predictions were later confirmed by experimental results. The alloy solidifies in the range of 242.5-261.7 °C. The overall hardness values were measured and confirmed to be within the volume average value of all the phases combined.

scholarly journals Improving Face Alignment Accuracy on Clinical Populations and its effect on the Video-based Detection of Neurological Diseases

2021 ◽  
Author(s):  
Diego L. Guarin ◽  
Andrea Bandini ◽  
Aidan Dempster ◽  
Henry Wang ◽  
Siavash Rezaei ◽  
...  
Keyword(s):  
Neurological Diseases ◽  
Model Performance ◽  
Fine Tuning ◽  
Facial Features ◽  
Representative Data ◽  
Facial Landmark Localization ◽  
Facial Landmark ◽  
Localization Model ◽  
Fine Tune ◽  
Landmark Localization

Background: Automatic facial landmark localization is an essential component in many computer vision applications, including video-based detection of neurological diseases. Machine learning models for facial landmarks localization are typically trained on faces of healthy individuals, and we found that model performance is inferior when applied to faces of people with neurological diseases. Fine-tuning pre-trained models with representative images improves performance on clinical populations significantly. However, questions related to the characteristics of the database used to fine-tune the model and the clinical impact of the improved model remain. Methods: We employed the Toronto NeuroFace dataset – a dataset consisting videos of Healthy Controls (HC), individuals Post-Stroke, and individuals with Amyotrophic Lateral Sclerosis performing speech and non-speech tasks with thousands of manually annotated frames - to fine-tune a well-known deep learning-based facial landmark localization model. The pre-trained and fine-tuned models were used to extract landmark-based facial features from videos, and the facial features were used to discriminate clinical groups from HC. Results: Fine-tuning a facial landmark localization model with a diverse database that includes HC and individuals with neurological disorders resulted in significantly improved performance for all groups. Our results also showed that fine-tuning the model with representative data greatly improved the ability of the subsequent classifier to classify clinical groups vs. HC from videos. Conclusions: Using a diverse database for model fine-tuning might result in better model performance for HC and clinical groups. We demonstrated that fine-tuning a model for landmark localization with representative data results in improved detection of neurological diseases.

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