A general framework for predicting the optimal computing configuration for climate-driven ecological forecasting models: Scott Farley's Masters Thesis Defense
This document summarizes Scott Farley's master's thesis presentation on developing a framework to predict optimal computing configurations for ecological forecasting models under climate change. The presentation discusses species distribution modeling and biodiversity informatics, describes challenges posed by big biodiversity data, and proposes using computational performance models to identify the hardware configuration that maximizes model accuracy while minimizing time and costs. The goal is to efficiently run ecological forecasting models on flexible cloud computing resources.
We have proposed a new form of growth rate for population ecology. Generally, the growth rate is
dependent on the size of the population at that particular epoch. We have introduced an alternative
time-dependent form of growth rate. This form satisfies essential conditions to represent population
growth and can be an alternative form for growth models to analyze population dynamics. We have
employed the generalized Richards model as a guideline to compare our results. Further, we have
applied our model in the case of epidemics. To check the efficacy of our model, we have verified the
2003 SARS data. This model has estimated the final epidemic size with good accuracy. Thereafter,
we intend to describe the present COVID-2019 pandemic. We have performed our analysis with
data for Italy and Germany. Following, we have tried to predict the number of COVID-19
cases and the turning point for the USA and India.
Machine learning is permeating nearly every industry – from retail and financial services to entertainment and transportation. And, while it's been slow to make its way into healthcare, machine learning stands to transform this space, too… positioning us to better diagnose, predict outcomes, provide follow-up care, and tailor treatments.
In this webinar, PointClear Solutions' Michael Atkins discusses the current state of machine learning in healthcare and what we can expect in the near future:
• What is machine learning and how is it being used today?
• What are some of the risks and obstacles we face in implementing this new technology?
• Looking into the future, what role will machine learning play in transforming healthcare?
• How can my company prepare for machine learning?
Using the Open Science Data Cloud for Data Science ResearchRobert Grossman
The Open Science Data Cloud is a petabyte scale science cloud for managing, analyzing, and sharing large datasets. We give an overview of the Open Science Data Cloud and how it can be used for data science research.
We have proposed a new form of growth rate for population ecology. Generally, the growth rate is
dependent on the size of the population at that particular epoch. We have introduced an alternative
time-dependent form of growth rate. This form satisfies essential conditions to represent population
growth and can be an alternative form for growth models to analyze population dynamics. We have
employed the generalized Richards model as a guideline to compare our results. Further, we have
applied our model in the case of epidemics. To check the efficacy of our model, we have verified the
2003 SARS data. This model has estimated the final epidemic size with good accuracy. Thereafter,
we intend to describe the present COVID-2019 pandemic. We have performed our analysis with
data for Italy and Germany. Following, we have tried to predict the number of COVID-19
cases and the turning point for the USA and India.
Machine learning is permeating nearly every industry – from retail and financial services to entertainment and transportation. And, while it's been slow to make its way into healthcare, machine learning stands to transform this space, too… positioning us to better diagnose, predict outcomes, provide follow-up care, and tailor treatments.
In this webinar, PointClear Solutions' Michael Atkins discusses the current state of machine learning in healthcare and what we can expect in the near future:
• What is machine learning and how is it being used today?
• What are some of the risks and obstacles we face in implementing this new technology?
• Looking into the future, what role will machine learning play in transforming healthcare?
• How can my company prepare for machine learning?
Using the Open Science Data Cloud for Data Science ResearchRobert Grossman
The Open Science Data Cloud is a petabyte scale science cloud for managing, analyzing, and sharing large datasets. We give an overview of the Open Science Data Cloud and how it can be used for data science research.
Propagating Data Policies - A User StudyEnrico Daga
When publishing data, data licences are used to specify the actions that are permitted or prohibited, and the duties that target data consumers must comply with. However, in com- plex environments such as a smart city data portal, multiple data sources are constantly being combined, processed and redistributed. In such a scenario, deciding which policies ap- ply to the output of a process based on the licences attached to its input data is a difficult, knowledge-intensive task. In this paper, we evaluate how automatic reasoning upon se- mantic representations of policies and of data flows could support decision making on policy propagation. We report on the results of a user study designed to assess both the accuracy and the utility of such a policy-propagation tool, in comparison to a manual approach.
Adversarial Analytics - 2013 Strata & Hadoop World TalkRobert Grossman
This is a talk I gave at the Strata Conference and Hadoop World in New York City on October 28, 2013. It describes predictive modeling in the context of modeling an adversary's behavior.
Prediction of heart disease using classification mining technique on sparkdbpublications
This paper identifies the increasing health care data which is being accumulated digitally every day. The healthcare industry is becoming very data intensive. Worldwide digital healthcare data is estimated to be equal to 500 petabytes (1015 bytes), and is expected to reach 25 exabytes (1018 bytes) in 2020 [6].In this paper, heart disease is one such disease selected among variety of disease in healthcare. The purpose of this work is to predict the diagnosis of heart disease with reduced number of attributes. Each dataset stored in HDFS is classified based on attributes. This prediction solution using random forest on apache spark gives massive opportunity for health care analysts to deploy this solution on ever changing, scalable big data landscape for insightful decision making.
Short presentation for a special lecture on Medicine Graduation Course in Hospital de Clínicas (https://www.hcpa.edu.br/), as part of a one-day special discipline on Machine Learning and Healthcare. The goal was introducing the importance of Deep Learning for Healthcare as well as showing some of the recent impact.
Modern, large scale data analysis typically involves the use of massive data stored on different computers that do not share the same file system. Computing complex statistical quantities, such as those that characterize spatial or temporal statistical dependence, requires information that crosses the boundaries imposed by this partitioning of the data. To leverage the information in these distributed data sets, analysts are faced with a trade-off between various costs (e.g., computational, transmission, and even the cost building an appropriate data system infrastructure) and inferential uncertainties (bias, variance, etc.) in the estimates produced by the analysis. In this talk we introduce a framework for quantifying this trade-off by optimizing over both statistical and data system design aspects of the problem. We illustrate with a simple example, and discuss how it may be extended to more complex settings.
Multipleregression covidmobility and Covid-19 policy recommendationKan Yuenyong
Multiple Regression Analysis and Covid-19 policy is the contemporary agenda. It demonstrates how to use Python to do data wrangler, to use R to do statistical analysis, and is enable to publish in standard academic journal. The model will explain whether lockdown policy is relevant to control Covid-19 outbreak? It cinc
Principle Component Analysis Based on Optimal Centroid Selection Model for Su...ijtsrd
Clustering a large sparse and large scale data is an open research in the data mining. To discover the significant information through clustering algorithm stands inadequate as most of the data finds to be non actionable. Existing clustering technique is not feasible to time varying data in high dimensional space. Hence Subspace clustering will be answerable to problems in the clustering through incorporation of domain knowledge and parameter sensitive prediction. Sensitiveness of the data is also predicted through thresholding mechanism. The problems of usability and usefulness in 3D subspace clustering are very important issue in subspace clustering. . The Solutions is highly helpful benefit for police departments and law enforcement organisations to better understand stock issues and provide insights that will enable them to track activities, predict the likelihood. Also determining the correct dimension is inconsistent and challenging issue in subspace clustering .In this thesis, we propose Centroid based Subspace Forecasting Framework by constraints is proposed, i.e. must link and must not link with domain knowledge. Unsupervised Subspace clustering algorithm with inbuilt process like inconsistent constraints correlating to dimensions has been resolved through singular value decomposition. Principle component analysis is been used in which condition has been explored to estimate the strength of actionable to be particular attributes and utilizing the domain knowledge to refinement and validating the optimal centroids dynamically. An experimental result proves that proposed framework outperforms other competition subspace clustering technique in terms of efficiency, Fmeasure, parameter insensitiveness and accuracy. G. Raj Kamal | A. Deepika | D. Pavithra | J. Mohammed Nadeem | V. Prasath Kumar "Principle Component Analysis Based on Optimal Centroid Selection Model for SubSpace Clustering Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31374.pdf Paper Url :https://www.ijtsrd.com/computer-science/data-miining/31374/principle-component-analysis-based-on-optimal-centroid-selection-model-for-subspace-clustering-model/g-raj-kamal
Clustering technology has been applied in numerous applications. It can enhance the performance
of information retrieval systems, it can also group Internet users to help improve the click-through rate of
on-line advertising, etc. Over the past few decades, a great many data clustering algorithms have been
developed, including K-Means, DBSCAN, Bi-Clustering and Spectral clustering, etc. In recent years, two
new data clustering algorithms have been proposed, which are affinity propagation (AP, 2007) and density
peak based clustering (DP, 2014). In this work, we empirically compare the performance of these two latest
data clustering algorithms with state-of-the-art, using 6 external and 2 internal clustering validation metrics.
Our experimental results on 16 public datasets show that, the two latest clustering algorithms, AP and DP,
do not always outperform DBSCAN. Therefore, to find the best clustering algorithm for a specific dataset, all
of AP, DP and DBSCAN should be considered. Moreover, we find that the comparison of different clustering
algorithms is closely related to the clustering evaluation metrics adopted. For instance, when using the
Silhouette clustering validation metric, the overall performance of K-Means is as good as AP and DP. This
work has important reference values for researchers and engineers who need to select appropriate clustering
algorithms for their specific applications.
In this session we will explore how Google's Cloud services (CloudML, Vision, Genomics API) can be used to process genomic and phenotypic data and solve problems in healthcare and agriculture.
Propagating Data Policies - A User StudyEnrico Daga
When publishing data, data licences are used to specify the actions that are permitted or prohibited, and the duties that target data consumers must comply with. However, in com- plex environments such as a smart city data portal, multiple data sources are constantly being combined, processed and redistributed. In such a scenario, deciding which policies ap- ply to the output of a process based on the licences attached to its input data is a difficult, knowledge-intensive task. In this paper, we evaluate how automatic reasoning upon se- mantic representations of policies and of data flows could support decision making on policy propagation. We report on the results of a user study designed to assess both the accuracy and the utility of such a policy-propagation tool, in comparison to a manual approach.
Adversarial Analytics - 2013 Strata & Hadoop World TalkRobert Grossman
This is a talk I gave at the Strata Conference and Hadoop World in New York City on October 28, 2013. It describes predictive modeling in the context of modeling an adversary's behavior.
Prediction of heart disease using classification mining technique on sparkdbpublications
This paper identifies the increasing health care data which is being accumulated digitally every day. The healthcare industry is becoming very data intensive. Worldwide digital healthcare data is estimated to be equal to 500 petabytes (1015 bytes), and is expected to reach 25 exabytes (1018 bytes) in 2020 [6].In this paper, heart disease is one such disease selected among variety of disease in healthcare. The purpose of this work is to predict the diagnosis of heart disease with reduced number of attributes. Each dataset stored in HDFS is classified based on attributes. This prediction solution using random forest on apache spark gives massive opportunity for health care analysts to deploy this solution on ever changing, scalable big data landscape for insightful decision making.
Short presentation for a special lecture on Medicine Graduation Course in Hospital de Clínicas (https://www.hcpa.edu.br/), as part of a one-day special discipline on Machine Learning and Healthcare. The goal was introducing the importance of Deep Learning for Healthcare as well as showing some of the recent impact.
Modern, large scale data analysis typically involves the use of massive data stored on different computers that do not share the same file system. Computing complex statistical quantities, such as those that characterize spatial or temporal statistical dependence, requires information that crosses the boundaries imposed by this partitioning of the data. To leverage the information in these distributed data sets, analysts are faced with a trade-off between various costs (e.g., computational, transmission, and even the cost building an appropriate data system infrastructure) and inferential uncertainties (bias, variance, etc.) in the estimates produced by the analysis. In this talk we introduce a framework for quantifying this trade-off by optimizing over both statistical and data system design aspects of the problem. We illustrate with a simple example, and discuss how it may be extended to more complex settings.
Multipleregression covidmobility and Covid-19 policy recommendationKan Yuenyong
Multiple Regression Analysis and Covid-19 policy is the contemporary agenda. It demonstrates how to use Python to do data wrangler, to use R to do statistical analysis, and is enable to publish in standard academic journal. The model will explain whether lockdown policy is relevant to control Covid-19 outbreak? It cinc
Principle Component Analysis Based on Optimal Centroid Selection Model for Su...ijtsrd
Clustering a large sparse and large scale data is an open research in the data mining. To discover the significant information through clustering algorithm stands inadequate as most of the data finds to be non actionable. Existing clustering technique is not feasible to time varying data in high dimensional space. Hence Subspace clustering will be answerable to problems in the clustering through incorporation of domain knowledge and parameter sensitive prediction. Sensitiveness of the data is also predicted through thresholding mechanism. The problems of usability and usefulness in 3D subspace clustering are very important issue in subspace clustering. . The Solutions is highly helpful benefit for police departments and law enforcement organisations to better understand stock issues and provide insights that will enable them to track activities, predict the likelihood. Also determining the correct dimension is inconsistent and challenging issue in subspace clustering .In this thesis, we propose Centroid based Subspace Forecasting Framework by constraints is proposed, i.e. must link and must not link with domain knowledge. Unsupervised Subspace clustering algorithm with inbuilt process like inconsistent constraints correlating to dimensions has been resolved through singular value decomposition. Principle component analysis is been used in which condition has been explored to estimate the strength of actionable to be particular attributes and utilizing the domain knowledge to refinement and validating the optimal centroids dynamically. An experimental result proves that proposed framework outperforms other competition subspace clustering technique in terms of efficiency, Fmeasure, parameter insensitiveness and accuracy. G. Raj Kamal | A. Deepika | D. Pavithra | J. Mohammed Nadeem | V. Prasath Kumar "Principle Component Analysis Based on Optimal Centroid Selection Model for SubSpace Clustering Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31374.pdf Paper Url :https://www.ijtsrd.com/computer-science/data-miining/31374/principle-component-analysis-based-on-optimal-centroid-selection-model-for-subspace-clustering-model/g-raj-kamal
Clustering technology has been applied in numerous applications. It can enhance the performance
of information retrieval systems, it can also group Internet users to help improve the click-through rate of
on-line advertising, etc. Over the past few decades, a great many data clustering algorithms have been
developed, including K-Means, DBSCAN, Bi-Clustering and Spectral clustering, etc. In recent years, two
new data clustering algorithms have been proposed, which are affinity propagation (AP, 2007) and density
peak based clustering (DP, 2014). In this work, we empirically compare the performance of these two latest
data clustering algorithms with state-of-the-art, using 6 external and 2 internal clustering validation metrics.
Our experimental results on 16 public datasets show that, the two latest clustering algorithms, AP and DP,
do not always outperform DBSCAN. Therefore, to find the best clustering algorithm for a specific dataset, all
of AP, DP and DBSCAN should be considered. Moreover, we find that the comparison of different clustering
algorithms is closely related to the clustering evaluation metrics adopted. For instance, when using the
Silhouette clustering validation metric, the overall performance of K-Means is as good as AP and DP. This
work has important reference values for researchers and engineers who need to select appropriate clustering
algorithms for their specific applications.
In this session we will explore how Google's Cloud services (CloudML, Vision, Genomics API) can be used to process genomic and phenotypic data and solve problems in healthcare and agriculture.
20170315 Cloud Accelerated Genomics - Tel Aviv / Phoenix
Similar to A general framework for predicting the optimal computing configuration for climate-driven ecological forecasting models: Scott Farley's Masters Thesis Defense
Accelerating Insight - Smart Data Lake Customer Success StoriesCambridge Semantics
At Gartner Data & Analytics Summit 2017 Alok Prasad, President, was joined by Peter Horowitz of PricewaterhouseCoopers in presenting a session on how Cambridge Semantics' in-memory, massively parallel, semantic graph-based platform delivers an accelerating edge to data-driven organizations, while maintaining trust with security and governance.
The importance of model fairness and interpretability in AI systemsFrancesca Lazzeri, PhD
Machine learning model fairness and interpretability are critical for data scientists, researchers and developers to explain their models and understand the value and accuracy of their findings. Interpretability is also important to debug machine learning models and make informed decisions about how to improve them.
In this session, Francesca will go over a few methods and tools that enable you to "unpack” machine learning models, gain insights into how and why they produce specific results, assess your AI systems fairness and mitigate any observed fairness issues.
Using open-source fairness and interpretability packages, attendees will learn how to:
- Explain model prediction by generating feature importance values for the entire model and/or individual data points.
- Achieve model interpretability on real-world datasets at scale, during training and inference.
- Use an interactive visualization dashboard to discover patterns in data and explanations at training time.
- Leverage additional interactive visualizations to assess which groups of users might be negatively impacted by a model and compare multiple models in terms of their fairness and performance.
Entity matching and entity resolution are becoming more important disciplines in data management over time, based on increasing number of data sources that should be addressed in economy that is undergoing digital transformation process, growing data volumes and increasing requirements related to data privacy. Data matching process is also called record linkage, entity matching or entity resolution in some published works. For long time research about the process was focused on matching entities from same dataset (i.e. deduplication) or from two datasets. Different algorithms used for matching different types of attributes were described in the literature, developed and implemented in data matching and data cleansing platforms. Entity resolution is element of larger entity integration process that include data acquisition, data profiling, data cleansing, schema alignment, data matching and data merge (fusion).
We can use motivating example of global pharmaceutical company with offices in more than 60 countries worldwide that migrated customer data from various legacy systems in different countries to new common CRM system in the cloud. Migration was phased by regions and countries, with new sources and data incrementally added and merged with data already migrated in previous phases. Entity integration in such case require deep understanding of data architectures, data content and each step of the process. Even with such deep understanding, design and implementation of the solution require many iterations in development process that consume human resources, time and financial resources. Reducing the number of iterations by automating and optimizing steps in the process can save vast amount of resources. There is a lot of available literature addressing any of the steps in the process, proposing different options for improvement of results or processing optimization, but the whole process still require a lot of human work and subject matter specific knowledge and many iterations to produce results that will have high F-measure (both high precision and recall). Most of the algorithms used in the various steps of the process are Human in the loop (HITL) algorithms that require human interaction. Human is always part of the simulation and consequently influences the outcome.
This paper is a part of the work in progress aimed to define conceptual framework that will try to automate and optimize some steps of entity integration process and try to reduce requirements for human influence in the process. In this paper focus will be on conceptual process definition, recommended data architecture and use of existing open source solutions for entity integration process automation and optimization.
Coping with Data Variety in the Big Data Era: The Semantic Computing ApproachAndre Freitas
Big Data is based on the vision of providing users and applications with a more complete picture of the reality supported and mediated by data. This vision comes with the inherent price of data variety, i.e. data which is semantically heterogeneous, poorly structured, complex and with data quality issues. Despite the hype on technologies targeting data volume and velocity, solutions for coping with data variety remain fragmented and with limited adoption. In this talk we will focus on emerging data management approaches, supported by semantic technologies, to cope with data variety. We will provide a broad overview of semantic computing approaches and how they can be applied to data management challenges within organizations today. This talk will allow the audience to have a glimpse into the next-generation, Big Data-driven information systems.
This lecture presented at Remote Sensing, Uncertainty Quantification and a Theory of Data Systems Workshop - Cahill Center, California Institute of Technology
A brief tutorial on Big Data and its applications to healthcare. The discussion is centered around technical aspects related to this method of computing rather than concrete examples of its use in medical practice.
Distributed Scalable Systems Short OverviewRNeches
Closing description of work in the Distributed Scalable Systems Division just prior to reorganization as the Collaborative Systems component of the merged Computational Systems and Technology Division.
This talk presents areas of investigation underway at the Rensselaer Institute for Data Exploration and Applications. First presented at Flipkart, Bangalore India, 3/2015.
Similar to A general framework for predicting the optimal computing configuration for climate-driven ecological forecasting models: Scott Farley's Masters Thesis Defense (20)
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
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insect taxonomy importance systematics and classification
A general framework for predicting the optimal computing configuration for climate-driven ecological forecasting models: Scott Farley's Masters Thesis Defense
1. A general framework for predicting the optimal
computing configurations for climate driven
ecological forecasting models
Scott Farley
Department of Geography
University of Wisconsin – Madison
Master of Science
Cartography & GIS
Public Talk
April 17, 2017
6. Given flexible computing resources and
massive data stores, what is the most efficient
computing hardware on which to run
ecological forecasting models?
Cloud
Computing
Species
Distribution
Modeling
Biodiversity
Informatics
7. Motivation
Figure adapted from: Urban, M. C. (2015). Accelerating extinction risk from climate change. Science, 348(6234), 571-573.
1 in 6
species is likely to go extinct due to
climate change.
8. Motivation
Figure adapted from: Dickson et al. (2014). Towards a global map of natural capital: key ecosystem assets. United National Environmental Programme. 1-33.
$125 trillion/year
Global ecosystem services are valued at
12. Biodiversity Informatics Community efforts to manage,
archive, analyze, distribute,
and interpret primary data
regarding life
Neotoma Paleoecology
Database
13. Paleobiodiversity Data
Figure adapted from: Plumber, B (2014). There have been five mass extinctions in Earth’s history. Now we’re facing a sixth. The Washington Post.
https://www.washingtonpost.com/news/wonk/wp/2014/02/11/there-have-been-five-mass-extinctions-in-earths-history-now-were-facing-a-sixth
14. Paleobiodiversity Data
Figure adapted from: Booth, R. K., Brewer, S., Blaauw, M., Minckley, T. A., & Jackson, S. T. (2012). Decomposing the mid‐Holocene Tsuga decline in eastern North America. Ecology, 93(8), 1841-1852.
15. Recent Growth in Biodiversity Databases
Added 65.8
million records in
2015
Date of Record
MillionsofRecords
100
200
300
400
500
1500 1700 1900
16. Recent Growth in Biodiversity Databases
Neotoma
Paleoecology
Database
Added 65.8
million records in
2015
Added 1.5 million
fossil records since
2010
MillionsofRecords
Date of Record Date of Accession
MillionsofRecords
100
200
300
400
500
2.5
3
3.5
2
1500 1700 1900 2010 2015
17. The Four V’s of Big Data
Volume
Size of the data
✓
18. The Four V’s of Big Data
Volume
Veracity
Size of the data
Uncertain
ty of the
data
✓
✓
19. The Four V’s of Big Data
Volume Variety
Veracity
Size of the data
Uncertain
ty of the
data
Heterogeneity
of the data and
complexity of
interrelationshi
ps
✓
✓
✓
20. The Four V’s of Big Data
Volume Variety
Veracity
Size of the data
Uncertain
ty of the
data
Heterogeneity
of the data and
complexity of
interrelationshi
ps
✓
✓
✓
21. The Four V’s of Big Data
Volume Variety
Veracity Velocity
Size of the data
Uncertain
ty of the
data
Sensitivity of
data analysis
to time
Heterogeneity
of the data and
complexity of
interrelationshi
ps
✓
✓
✓
22. The Four V’s of Big Data
Volume Variety
Veracity Velocity
Size of the data
Uncertain
ty of the
data
Sensitivity of
data analysis
to time
Heterogeneity
of the data and
complexity of
interrelationshi
ps
✓
✓
✓
Biodiversity data does not typically
require real-time analysis
✗
24. Inductive Learning
Predict future response values (Y) given a set of potential covariates (X).
Training Examples
X and Y both known
Build Model
Estimate functional relationship
from x and y by minimizing loss
criteria (y-ŷ)
25. Inductive Learning
Predict future response values (Y) given a set of potential covariates (X).
Training Examples
X and Y both known
Build Model
Future Cases
Only X is known
Estimate new Y’s (ŷ)
from X
Use
approximate
d functional
relationship
in prediction
26. Species Distribution Modeling
Training
Exampl
es
Environment
al Covariates
SDM Algorithm
Predicted Future
Distribution
Predicted Future
Environment
Predict future distribution of species from observations of current (or fossil)
distribution and environmental/climatic covariates.
Figure source: https://www.unil.ch/idyst/en/home/menuinst/research-topics/geoinformatics-and-spatial-m/predictive-biogeography/advancing-the-science-of-eco.html
27. Types of SDM
Model Driven
Fit a parametric statistical
model to a dataset
Make assumptions about
form of the functional
relationship between
inputs and output
• Linear Regression
• Generalized linear
models
• Logistic regression
28. Types of SDM
Model Driven
Fit a parametric statistical
model to a dataset
Make assumptions about
form of the functional
relationship between
inputs and output
• Linear Regression
• Generalized linear
models
• Logistic regression
Data Driven
Estimate relationship
between inputs and
outputs from the data
• Regression trees
• Artificial neural
networks
• MaxEnt
High sensitivity to small
changes in input data
29. Types of SDM
Model Driven
Fit a parametric statistical
model to a dataset
Make assumptions about
form of the functional
relationship between
inputs and output
• Linear Regression
• Generalized linear
models
• Logistic regression
Data Driven Bayesian
Estimate the relationship
between inputs and
outputs as a probability
distribution using prior
knowledge and new data
Formally account for
model uncertainty
• Gaussian random fields
• Community full joint
distribution modeling
Estimate relationship
between inputs and
outputs from the data
• Regression trees
• Artificial neural
networks
• MaxEnt
High sensitivity to small
changes in input data
30. Algorithms in Contemporary SDM literature
Bayesian Data
Driven
Model
Driven
Other
120
100
80
60
40
20
Frequency
In 100 randomly sampled SDM papers…
31. Enables convenient and on-demand access to configurable
computing resources
Rapid provisioning and release with minimal management effort
Recent growth supported by federal agencies and public cloud
providers
Cloud Computing
32. For each SDM, there exists an
optimal data-hardware
configuration that:
1. Maximizes SDM accuracy
2. Balances the tradeoff between
performance and expense by
jointly minimizing the time and
cost of modeling
Hypothesis
33. For each SDM, there exists an
optimal data-hardware
configuration that:
1. Maximizes SDM accuracy
2. Balances the tradeoff between
performance and expense by
jointly minimizing the time and
cost of modeling
Hypothesis Data
Training Examples
Number of Covariates
Hardware
CPU Cores
Memory
SDMs
Random Forests
Boosted Regression Trees
Generalized Additive Models
Adaptive Regression Splines
34. 1. Build large empirical dataset of SDM accuracy and
runtime
Methods
35. 1. Build large empirical
dataset of SDM
accuracy and
runtime
2. Build model of
computing cost
Methods
36. 1. Build large empirical dataset of SDM accuracy and
runtime
2. Build model of computing cost
3. Build two computational performance models (CPM):
– Accuracy (AUC): Expected accuracy using given data
– Runtime (seconds): Expected execution time on given data-
hardware configuration
Methods
37. 1. Build large empirical dataset of SDM accuracy and
runtime
2. Build model of computing cost
3. Build two computational performance models
4. Use CPMs to identify optimal data-hardware
configuration
Methods
38. Bayesian Additive Regression Trees
Additive tree inductive learning model in a Bayesian framework
Probability density of SDM execution time or accuracy under
given input conditions
Performance and Accuracy Modeling Framework
40. Runtime CPM Drivers For each predictor, build a CPM without
that predictor and compare its skill to
the skill of the full model.
Generalized Additive Models
Boosted Regression Trees
Adaptive Regression Splines
Random Forests
41. Runtime CPM Drivers
Random Forests can
execute in parallel
For each predictor, build a CPM without
that predictor and compare its skill to
the skill of the full model.
Generalized Additive Models
Boosted Regression Trees
Adaptive Regression Splines
Random Forests
44. Choosing the Optimal Hardware for an SDM
Hardware
Data
Accuracy
CPM
Performance
CPM
Time
Cost
Uncertainty
Predictors CPMs Optimization
Optimal
Configuration
Result
45. Choosing the Optimal Hardware for an SDM
1. Identify data configuration of training
examples and covariates that will maximize
accuracy
46. Choosing the Optimal Hardware for an SDM
2. Predict execution time
of that configuration on
different hardware
configurations
Number of CPU Cores
Memory(GB)
Unique
Hardware
Configuratio
47. Choosing the Optimal Hardware for an SDM
3. Hierarchical clustering
- Time
- Cost
- Posterior SD (spread)
Dissimilarity
Unique
Hardware
Configuratio
n
48. Choosing the Optimal Hardware for an SDM
4. Calculate cluster
mean distance from
origin
Choose the cluster
closest to the origin
Optimal:
No Time
No Cost
No Uncertainty
49. Optimal Configuration for Each SDM
CPU Cores
Memory(GB)
Generalized Additive Models
Boosted Regression Trees
Adaptive Regression Splines
Random Forests
50. MARS: An Unresolved Quandary
Multivariate
Adaptive
Regression Splines
No incremental preference
for higher memory.
CPU Cores
Memory(GB)
52. Recommendations
Redevelop models at the code-
infrastructure interface to leverage
high performance computing
technologies
Prioritize model efficiency along
with ecological realism in future
development
Cloud computing offers the ability
to run models on the right
resources, not just the convenient
ones
Date of Record
MillionsofRecords
100
200
300
400
500
1500 1700 1900
Promote extensions of this
framework
54. Bayesian Additive Tree Model Structure and Priors
1. Node Depth Prior: P(Tt) ~ α(1+d)−β where α ∈ (0, 1) and β ∈ [0, ∞]
2. Leaf-Value Prior: P(Mt | Tt) ~ μl ~ N(μμ / m, σμ
2 )
μμ, is picked to be the range center, (ymin + ymax)/2
σμ
2 is empirically chosen so that the range center plus or minus k = 2 variances
cover 95% of the provided response values in the training set
3. Error Variance Prior: σ2 ∼ InvGamma(ν/2, νλ/2)
λ is determined from the data so that there is a q = 90% a priori chance (by
default) that the BART model will improve upon the RMSE from an ordinary least
squares regression.
55. Bayesian Additive Tree Model Structure and Priors
5. Response likelihood: mean of response in leaf in given MCMC iteration with
variance: yl ∼ N(μl, σ2)
6. Hyperparameters: α, β, k, ν and q
α: 0.95
β: 2
k: 2
ν: 3
q: 90%
R Package: bartMachine
Citation: Adam Kapelner, Justin Bleich (2016). bartMachine: Machine Learning with
Bayesian Additive Regression Trees. Journal of Statistical Software, 70(4), 1-40. doi:
10.18637/jss.v070.i04
56. MARS Remedial Measures
• Resample so each configuration has n=1 observations
– Completely covering parameter space
– Need to reduce the influence imbalanced design
– No qualitative change in results
• Future steps
– Recollect dataset using a balanced design with multiple replicates
57. Master
Compute
Node
Central Database
A. Which configurations have experiments that are not marked as
COMPLETED?
B. Return next configuration with experiments not
marked as COMPLETE.
C. Create instance group with specified vCPU
and memory
[GET /nextconfig]
[json]
[gcloud create]
1. Configure and build virtual instances
2. Run simulations and report results
3. Manage virtual infrastructure
Central Database
A. Select random experiment within machine’ s
computing capabilities.
B. Return experiment specification.
C. Report
time and
accuracy
measures to
the database
[json]
Compute
Node RScript
TimeSDM
Function
Load variables
Fit BRT Model
Predict to 2100
Evaluate Accuracy
Total
Time
Fit Time
Predict Time
Accuracy
Time
Central Database
Master
Compute
Node
A. What percent of the experiments in this configuration have been
completed?
A. Destroy instances, instance
group, and instance template
B. Return
percentage
completion.
[GET configstatus/cores/memory]
[json]
Poll every 30-seconds
if percent == 100
[gcloud delete]
Distributed
Computing for SDM:
A semi-automated
workflow
58. Clustering Specifications
• Axes: Run time, run cost, run time prediction standard deviation
• Distance Metric: Euclidean
• Linkage: Complete
• Splitting rule: Silhouette (Rousseeuw, 1987): Maximize between
cluster variance, minimize within cluster variance
• Initial scale and center to reduce effect of axes with different
dimensions.
• Clustering package: base R (function hclust)
60. Ecological Informatics
The scope of the journal takes into account the data-intensive nature of ecology, the
precious information content of ecological data, the growing capacity of computational
technology to leverage complex data as well as the critical need for informing sustainable
management in spite of global environmental and climate change.
The nature of the journal is interdisciplinary at the crossover between ecology and
informatics.
The journal invites papers on:
• novel concepts and tools for monitoring, acquisition, management, analysis and
synthesis of ecological data, including genomic and paleo-ecological data,
• understanding ecosystem functioning and evolution, and
• informing decisions on environmental issues like sustainability, climate change and
biodiversity.
Impact Factor: 1.683
61. Environmental Modeling and Software
Impact Factor: 4.207
The aim is to improve our capacity to represent, understand, predict or manage the
behaviour of environmental systems at all practical scales, and to communicate those
improvements to a wide scientific and professional audience.
• Generic and pervasive frameworks, techniques and issues - including system
identification theory and practice, model conception, model integration, model and/or
software evaluation, sensitivity and uncertainty assessment, visualization, scale and
regionalization issues.
• Artificial Intelligence (AI) techniques and systems, such as knowledge-based systems /
expert systems, case-based reasoning systems, data mining, multi-agent systems,
Bayesian networks, artificial neural networks, fuzzy logic, or knowledge elicitation and
knowledge acquisition methods.
• Decision support systems and environmental information systems- implementation and
use of environmental data and models to support all phases and aspects of decision
making, in particular supporting group and participatory decision making processes.
Intelligent Environmental Decision Support Systems can include qualitative, quantitative,
mathematical, statistical, AI models and meta-models.
62. Computers and Geosciences
Impact Factor: 2.474
Publications should apply modern computer science paradigms, whether
computational or informatics-based, to address problems in the geosciences.
• Computational/informatics elements may include: computational methods; algorithms;
data structure; database retrieval; information retrieval; data processing; artificial
intelligence; computer graphics; computer visualization; programming languages;
parallel systems; distributed systems; the World-Wide Web; social media; and software
engineering.
• Geoscientific topics of interest include: mineralogy; petrology; geochemistry;
geomorphology; paleontology; stratigraphy; structural geology; sedimentology;
hydrogeology; oceanography; atmospheric sciences; climatology; meteorology;
geophysics; geomatics; remote sensing; geodesy; hydrology; and glaciology.
63. Computers, Environment and Urban Systems
Impact Factor: 2.092
innovative computer-based research on urban systems, systems of cities, and built and
natural environments , that privileges the geospatial perspective.
Applied and theoretical contributions demonstrate the scope of computer-based analysis
fostering a better understanding of urban systems, the synergistic relationships between
built and natural environments, their spatial scope and their dynamics.
Contributions emphasizing the development and enhancement of computer-based
technologies for the analysis and modeling, policy formulation, planning, and
management of environmental and urban systems that enhance sustainable futures are
especially sought. The journal also encourages research on the modalities through which
information and other computer-based technologies mold environmental and urban
systems.
64. Applied Artificial Intelligence
Impact Factor: 0.540
addresses concerns in applied research and applications of artificial intelligence (AI).
Articles highlight advances in uses of AI systems for solving tasks in management,
industry, engineering, administration, and education; evaluations of existing AI systems
and tools, emphasizing comparative studies and user experiences; and the economic,
social, and cultural impacts of AI. Papers on key applications, highlighting methods, time
schedules, person-months needed, and other relevant material are welcome.
65. Species Distribution Models
“Species distribution models (SDMs) are numerical
tools that combine observations of species
occurrence or abundance with environmental
estimates…to predict distributions across
landscapes.”
Elith and Leathwick (2009)
Environmental
Covariates
Training
Examples
66. Species Distribution Models
“Species distribution models (SDMs) are numerical
tools that combine observations of species
occurrence or abundance with environmental
estimates…to predict distributions across
landscapes.”
Elith and Leathwick (2009)
67. Species Distribution Models
“Species distribution models (SDMs) are numerical
tools that combine observations of species
occurrence or abundance with environmental
estimates…to predict distributions across
landscapes.”
Elith and Leathwick (2009)
21st Century Climate Scenario
v
Probability of Presence in 2100
0 20 40 60
Predict
Model
70. What is a regression tree?
Regression trees rely on recursive binary partitioning of predictor space into a set of hyperrectangles in order to
approximate some unknown function f.
Editor's Notes
SDMs are most often run on laptops/lab desktops for multiuse with little attention paid to the optimal strategy for SDMs in particular.
Cloud computing offers a convenient way easily to provision and release configurable resources
Gives users to opportunity to get the correct tool for the job by renting space on virtual machines rather than purchasing hardware.
Led by pushes from major federal funding agencies like NSF. “Cloud first” strategy. NSF -> $20 million dollars to cloud computing. NASA lots of research. OMB 25 point plan to reduce barriers to entry to cloud computing. Amazon EC2 now hosts terrabytes of scientific data on its public clouds.
For each SDM, there exists an optimal data/hardware configuration that:
Maximizes classification accuracy
Balances tradeoffs between performance and cost by jointly minimizing cost ($) and time (hr)
Hypotheses:
That an optimal exists
That accuracy will depend only on data
That execution time will depend on hardware and data
That Random forests will see performance gains on multicores, while other SDMs will not, since RF is able to execute in parallel.
For each SDM, there exists an optimal data/hardware configuration that:
Maximizes classification accuracy
Balances tradeoffs between performance and cost by jointly minimizing cost ($) and time (hr)
Hypotheses:
That an optimal exists
That accuracy will depend only on data
That execution time will depend on hardware and data
That Random forests will see performance gains on multicores, while other SDMs will not, since RF is able to execute in parallel.
Gathered empirical data on SDM runtime and accuracy for ~30,000 SDM simulation experiments. For each experiment, evaluated classification accuracy and measured runtime. Approximately evenly split over all four models. Ran on the Google Cloud infrastructure.
Used the pricing scheme of the GCE. Use this to build deterministic link between time of computation and cost of computation.
Build empirical dataset of SDM accuracy and runtime.
Build model of computing cost
Build component predictive models
Accuracy
Runtime
Use model to predict runtime and accuracy on many algorithm-hardware configurations.
Choose optimal configuration
Build empirical dataset of SDM accuracy and runtime.
Build model of computing cost
Build component predictive models
Accuracy
Runtime
Use model to predict runtime and accuracy on many algorithm-hardware configurations.
Choose optimal configuration
Yield probability density of runtime and accuracy under different configurations.
Amount of data used to fit the models is most important
Drivers vary amongst models
Number of cores is important for random forests
Hardware is not influential for GAM, GBM
Weird MARS memory affinity
Amount of data used to fit the models is most important
Drivers vary amongst models
Number of cores is important for random forests
Hardware is not influential for GAM, GBM
Weird MARS memory affinity
Node depth prior enforces shallow trees
Leaf-Value prior provides regularization so that single trees do not dominate
Error variance prior provides additional insurance against overfitting.
Node depth prior enforces shallow trees
Leaf-Value prior provides regularization so that single trees do not dominate
Error variance prior provides additional insurance against overfitting.
Species Distribution Models (SDMs) are a class of statistical models that quantify the relationships between a species and its environmental range determinants
Supervised machine-learning/statistical models
(Austin, 2007) has posited that a solid foundation of ecological theory is essential to the correct prediction and interpretation of species distribution models. He notes that the ecological underpinnings of the statistics are, perhaps, more important the statistical method itself.
(Elith & Leathwick, 2009a) further suggest that additional improvements in species distribution modeling will come from the incorporation of additional, ecological relevant information in the statistical model itself, and the covariates used to fit it. Indeed, “further advances in SDM are more likely to come from better integration of theory, concepts, and practice than from improved methods per se” (Elith & Leathwick, 2009a).
People are doing many hundreds or thousands of species under many different warming scenarios.
Species Distribution Models (SDMs) are a class of statistical models that quantify the relationships between a species and its environmental range determinants
Supervised machine-learning/statistical models
(Austin, 2007) has posited that a solid foundation of ecological theory is essential to the correct prediction and interpretation of species distribution models. He notes that the ecological underpinnings of the statistics are, perhaps, more important the statistical method itself.
(Elith & Leathwick, 2009a) further suggest that additional improvements in species distribution modeling will come from the incorporation of additional, ecological relevant information in the statistical model itself, and the covariates used to fit it. Indeed, “further advances in SDM are more likely to come from better integration of theory, concepts, and practice than from improved methods per se” (Elith & Leathwick, 2009a).
People are doing many hundreds or thousands of species under many different warming scenarios.
Species Distribution Models (SDMs) are a class of statistical models that quantify the relationships between a species and its environmental range determinants
Supervised machine-learning/statistical models
(Austin, 2007) has posited that a solid foundation of ecological theory is essential to the correct prediction and interpretation of species distribution models. He notes that the ecological underpinnings of the statistics are, perhaps, more important the statistical method itself.
(Elith & Leathwick, 2009a) further suggest that additional improvements in species distribution modeling will come from the incorporation of additional, ecological relevant information in the statistical model itself, and the covariates used to fit it. Indeed, “further advances in SDM are more likely to come from better integration of theory, concepts, and practice than from improved methods per se” (Elith & Leathwick, 2009a).
People are doing many hundreds or thousands of species under many different warming scenarios.