Presentation for blast algorithm bio-informaticezahid6
Presentation for BLAST algorithm
Publisher Md.Zahid Hasan
Bio-informatics blast is the use of computational tools for the process of acquisition, visualization, analysis and distribution of these datasets obtained by imaging modalities.
A review of two alignment-free methods for sequence comparison. In this presentation two alignment-free methods are studied:
- "Similarity analysis of DNA sequences based on LZ complexity and dynamic programming algorithm" by Guo et al.
- "Alignment-free comparison of genome sequences by a new numerical characterization" by Huang et al.
Feature Selection for Document RankingAndrea Gigli
Feature selection for Machine Learning applied to Document Ranking (aka L2R, LtR, LETOR). Contains empirical results on Yahoo! and Bing public available Web Search Engine data.
A report on designing a model for improving CPU Scheduling by using Machine L...MuskanRath1
Disclaimer: Please let me know in case some of the portions of the article match your research. I would include the link to your research in the description section of my article.
Description:
The main concern of our paper describes that we are proposing a model for a uniprocessor system for improving CPU scheduling. Our model is implemented at low-level language or assembly language and LINUX is used for the implementation of the model as it is an open-source environment and its kernel is editable.
There are several methods to predict the length of the CPU bursts, such as the exponential averaging method, however, these methods may not give accurate or reliable predicted values. In this paper, we will propose a Machine Learning (ML) based on the best approach to estimate the length of the CPU bursts for processes. We will make use of Bayesian Theory for our model as a classifier tool that will decide which process will execute first in the ready queue. The proposed approach aims to select the most significant attributes of the process using feature selection techniques and then predicts the CPU-burst for the process in the grid. Furthermore, applying attribute selection techniques improves the performance in terms of space, time, and estimation.
An Automatic Clustering Technique for Optimal ClustersIJCSEA Journal
This paper proposes a simple, automatic and efficient clustering algorithm, namely, Automatic Merging for Optimal Clusters (AMOC) which aims to generate nearly optimal clusters for the given datasets automatically. The AMOC is an extension to standard k-means with a two phase iterative procedure combining certain validation techniques in order to find optimal clusters with automation of merging of clusters. Experiments on both synthetic and real data have proved that the proposed algorithm finds nearly optimal clustering structures in terms of number of clusters, compactness and separation.
Presentation for blast algorithm bio-informaticezahid6
Presentation for BLAST algorithm
Publisher Md.Zahid Hasan
Bio-informatics blast is the use of computational tools for the process of acquisition, visualization, analysis and distribution of these datasets obtained by imaging modalities.
A review of two alignment-free methods for sequence comparison. In this presentation two alignment-free methods are studied:
- "Similarity analysis of DNA sequences based on LZ complexity and dynamic programming algorithm" by Guo et al.
- "Alignment-free comparison of genome sequences by a new numerical characterization" by Huang et al.
Feature Selection for Document RankingAndrea Gigli
Feature selection for Machine Learning applied to Document Ranking (aka L2R, LtR, LETOR). Contains empirical results on Yahoo! and Bing public available Web Search Engine data.
A report on designing a model for improving CPU Scheduling by using Machine L...MuskanRath1
Disclaimer: Please let me know in case some of the portions of the article match your research. I would include the link to your research in the description section of my article.
Description:
The main concern of our paper describes that we are proposing a model for a uniprocessor system for improving CPU scheduling. Our model is implemented at low-level language or assembly language and LINUX is used for the implementation of the model as it is an open-source environment and its kernel is editable.
There are several methods to predict the length of the CPU bursts, such as the exponential averaging method, however, these methods may not give accurate or reliable predicted values. In this paper, we will propose a Machine Learning (ML) based on the best approach to estimate the length of the CPU bursts for processes. We will make use of Bayesian Theory for our model as a classifier tool that will decide which process will execute first in the ready queue. The proposed approach aims to select the most significant attributes of the process using feature selection techniques and then predicts the CPU-burst for the process in the grid. Furthermore, applying attribute selection techniques improves the performance in terms of space, time, and estimation.
An Automatic Clustering Technique for Optimal ClustersIJCSEA Journal
This paper proposes a simple, automatic and efficient clustering algorithm, namely, Automatic Merging for Optimal Clusters (AMOC) which aims to generate nearly optimal clusters for the given datasets automatically. The AMOC is an extension to standard k-means with a two phase iterative procedure combining certain validation techniques in order to find optimal clusters with automation of merging of clusters. Experiments on both synthetic and real data have proved that the proposed algorithm finds nearly optimal clustering structures in terms of number of clusters, compactness and separation.
Approaches to online quantile estimationData Con LA
Data Con LA 2020
Description
This talk will explore and compare several compact data structures for estimation of quantiles on streams, including a discussion of how they balance accuracy against computational resource efficiency. A new approach providing more flexibility in specifying how computational resources should be expended across the distribution will also be explained. Quantiles (e.g., median, 99th percentile) are fundamental summary statistics of one-dimensional distributions. They are particularly important for SLA-type calculations and characterizing latency distributions, but unlike their simpler counterparts such as the mean and standard deviation, their computation is somewhat more expensive. The increasing importance of stream processing (in observability and other domains) and the impossibility of exact online quantile calculation together motivate the construction of compact data structures for estimation of quantiles on streams. In this talk we will explore and compare several such data structures (e.g., moment-based, KLL sketch, t-digest) with an eye towards how they balance accuracy against resource efficiency, theoretical guarantees, and desirable properties such as mergeability. We will also discuss a recent variation of the t-digest which provides more flexibility in specifying how computational resources should be expended across the distribution. No prior knowledge of the subject is assumed. Some familiarity with the general problem area would be helpful but is not required.
Speaker
Joe Ross, Splunk, Principal Data Scientist
Computational Approaches to Systems BiologyMike Hucka
Presentation given at the Sydney Computational Biologists meetup on 21 August 2013 (http://australianbioinformatics.net/past-events/2013/8/21/computational-approaches-to-systems-biology.html).
Cost Optimized Design Technique for Pseudo-Random Numbers in Cellular Automataijait
In this research work, we have put an emphasis on the cost effective design approach for high quality pseudo-random numbers using one dimensional Cellular Automata (CA) over Maximum Length CA. This work focuses on different complexities e.g., space complexity, time complexity, design complexity and searching complexity for the generation of pseudo-random numbers in CA. The optimization procedure for
these associated complexities is commonly referred as the cost effective generation approach for pseudorandom numbers. The mathematical approach for proposed methodology over the existing maximum length CA emphasizes on better flexibility to fault coverage. The randomness quality of the generated patterns for the proposed methodology has been verified using Diehard Tests which reflects that the randomness quality
achieved for proposed methodology is equal to the quality of randomness of the patterns generated by the maximum length cellular automata. The cost effectiveness results a cheap hardware implementation for the concerned pseudo-random pattern generator. Short version of this paper has been published in [1].
Josh Patterson, Principal at Patterson Consulting: Introduction to Parallel Iterative Machine Learning Algorithms on Hadoop’s NextGeneration YARN Framework
Proteins. 2013 Nov;81(11):1885-99. doi: 10.1002/prot.24330. Epub 2013 Aug 16.
DNABind: A hybrid algorithm for structure-based prediction of DNA-binding residues by combining machine learning- and template-based approaches.
Liu R, Hu J.
IMPLEMENTATION OF MACHINE LEARNING IN E-COMMERCE & BEYONDRabi Das
Presentation for the webinar held on 23rd May 2020, conducted by The IoT Academy for FDP program in collaboration with E&ICT Avademy, IIT Guwahati and delivered by Mr. Shree Kant Das, Growth and Digital Strategy Manager from noon.com.
Approaches to online quantile estimationData Con LA
Data Con LA 2020
Description
This talk will explore and compare several compact data structures for estimation of quantiles on streams, including a discussion of how they balance accuracy against computational resource efficiency. A new approach providing more flexibility in specifying how computational resources should be expended across the distribution will also be explained. Quantiles (e.g., median, 99th percentile) are fundamental summary statistics of one-dimensional distributions. They are particularly important for SLA-type calculations and characterizing latency distributions, but unlike their simpler counterparts such as the mean and standard deviation, their computation is somewhat more expensive. The increasing importance of stream processing (in observability and other domains) and the impossibility of exact online quantile calculation together motivate the construction of compact data structures for estimation of quantiles on streams. In this talk we will explore and compare several such data structures (e.g., moment-based, KLL sketch, t-digest) with an eye towards how they balance accuracy against resource efficiency, theoretical guarantees, and desirable properties such as mergeability. We will also discuss a recent variation of the t-digest which provides more flexibility in specifying how computational resources should be expended across the distribution. No prior knowledge of the subject is assumed. Some familiarity with the general problem area would be helpful but is not required.
Speaker
Joe Ross, Splunk, Principal Data Scientist
Computational Approaches to Systems BiologyMike Hucka
Presentation given at the Sydney Computational Biologists meetup on 21 August 2013 (http://australianbioinformatics.net/past-events/2013/8/21/computational-approaches-to-systems-biology.html).
Cost Optimized Design Technique for Pseudo-Random Numbers in Cellular Automataijait
In this research work, we have put an emphasis on the cost effective design approach for high quality pseudo-random numbers using one dimensional Cellular Automata (CA) over Maximum Length CA. This work focuses on different complexities e.g., space complexity, time complexity, design complexity and searching complexity for the generation of pseudo-random numbers in CA. The optimization procedure for
these associated complexities is commonly referred as the cost effective generation approach for pseudorandom numbers. The mathematical approach for proposed methodology over the existing maximum length CA emphasizes on better flexibility to fault coverage. The randomness quality of the generated patterns for the proposed methodology has been verified using Diehard Tests which reflects that the randomness quality
achieved for proposed methodology is equal to the quality of randomness of the patterns generated by the maximum length cellular automata. The cost effectiveness results a cheap hardware implementation for the concerned pseudo-random pattern generator. Short version of this paper has been published in [1].
Josh Patterson, Principal at Patterson Consulting: Introduction to Parallel Iterative Machine Learning Algorithms on Hadoop’s NextGeneration YARN Framework
Proteins. 2013 Nov;81(11):1885-99. doi: 10.1002/prot.24330. Epub 2013 Aug 16.
DNABind: A hybrid algorithm for structure-based prediction of DNA-binding residues by combining machine learning- and template-based approaches.
Liu R, Hu J.
IMPLEMENTATION OF MACHINE LEARNING IN E-COMMERCE & BEYONDRabi Das
Presentation for the webinar held on 23rd May 2020, conducted by The IoT Academy for FDP program in collaboration with E&ICT Avademy, IIT Guwahati and delivered by Mr. Shree Kant Das, Growth and Digital Strategy Manager from noon.com.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
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AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
Concurrency Control for Parallel Machine Learning
1. Concurrency Control for
Parallel Machine Learning
Dimitris Papailiopoulos
Xinghao Pan, Joseph Gonzalez, Stefanie Jegelka, Tamara Broderick, Dimitris Papailiopoulos, Joseph
Bradley, Michael I. Jordan
8. Correctness
Concurrency
Coordination-free
Serial
High
Low High
Low
Concurrency
Control
Database mechanisms
o Guarantee correctness
o Maximize concurrency
Mutual exclusion
Optimistic CC
9. Model
State
Data
Mutual Exclusion Through
Locking
Processor 1
Processor 2
Introduce locking (scheduling) protocols to prevent
conflicts.
10. Mutual Exclusion Through
Model
State
Data
Processor 1
Processor 2
Locking
✗
Enforce local serialization to avoid conflicts.
11. Optimistic Concurrency Control
Model
State
Data
Processor 1
Processor 2
Allow computation to proceed without blocking.
Kung & Robinson. On optimistic methods for concurrency
control.
12. Optimistic Concurrency Control
Model
State
Data
Invalid Outcome
✗ ✗
Processor 1
Processor 2
Validate potential conflicts.
Kung & Robinson. On optimistic methods for concurrency
control.
13. Optimistic Concurrency Control
Model
State
Data
✗ ✗
Processor 1
Processor 2
Rollback and Redo
Take a compensating action.
Kung & Robinson. On optimistic methods for concurrency
control.
14. Concurrency Control
14
Coordination Free:
Provably fast and correct under key assumptions.
Concurrency Control:
Provably correct and fast under key assumptions.
Systems Ideas to
Improve Efficiency
15. Machine Learning + Concurrency
Clusteri
ng
Online
Facility
Location
Control
(Xinghao Pan et al.)
Submodular
Maximization
Subset selection, diminishing
marginal gains
Max Graph
Cut
Set
Cover
Sensor Placement
Social Network
Influence
Propagation
Document
Summarization
Sports
Football
Word Series
Giants
Cardinals
Politics
Midterm
Obama
Democrat
Tea
Finance
QE
market
interest
Dow
Topic Modelling
Correlation
Clustering
Deduplication
Community
Detection
16. Machine Learning + Concurrency
Clusteri
ng
Online
Facility
Location
Control
(Xinghao Pan et al.)
Submodular
Maximization
Subset selection, diminishing
marginal gains
Max Graph
Cut
Set
Cover
Sensor Placement
Social Network
Influence
Propagation
Document
Summarization
Sports
Football
Word Series
Giants
Cardinals
Politics
Midterm
Obama
Democrat
Tea
Finance
QE
market
interest
Dow
Topic Modelling
Correlation
Clustering
Deduplication
Community
Detection
Serial ML
algorithm
Sequence of
transactions
Identify potential
conflicts
Apply Concurrency
Control
mechanisms
Parallel ML
algorithm
17. Application: Deduplication
Computer Science
Division – University of
California Berkeley CA
University of California at Berkeley
Department of
Physics Stanford
University California
Lawrence Berkeley National
Labs <ref>California</ref>
19. Serial Correlation Clustering
Nir Ailon, Moses Charikar, and Alantha Newman.
Aggregating inconsistent information: ranking and clustering.
Journal of the ACM (JACM), 55(5):23, 2008.
Serially process vertices
20. Serial Correlation Clustering
Nir Ailon, Moses Charikar, and Alantha Newman.
Aggregating inconsistent information: ranking and clustering.
Journal of the ACM (JACM), 55(5):23, 2008.
Serially process vertices
21. Serial Correlation Clustering
Nir Ailon, Moses Charikar, and Alantha Newman.
Aggregating inconsistent information: ranking and clustering.
Journal of the ACM (JACM), 55(5):23, 2008.
Serially process vertices
22. Serial Correlation Clustering
Nir Ailon, Moses Charikar, and Alantha Newman.
Aggregating inconsistent information: ranking and clustering.
Journal of the ACM (JACM), 55(5):23, 2008.
Serially process vertices
23. Serial Correlation Clustering
Nir Ailon, Moses Charikar, and Alantha Newman.
Aggregating inconsistent information: ranking and clustering.
Journal of the ACM (JACM), 55(5):23, 2008.
Serially process vertices
Approximation 3 OPT (in expectation)
25. Concurrency Control Correlation Clustering
(C4) Parallel Correlation Clustering
Cannot Resolve introduce
by
Mutual adjacent Exclusion
cluster
centers
26. Concurrency Control Correlation Clustering
(C4)
Common Resolve neighbor by
must be
assigned Optimistic to Concurrency
earliest center
Control
?
Optimistic Assumption
No other new cluster created
Resolution
Assign common neighbor to earliest cluster
27. Properties of C4
(Concurrency Control Correlation Clustering)
Theorem: C4 is correct.
C4 preserves same guarantees as serial algorithm (3
OPT).
Concurren Correctness
Theorem: C4 has provably small overheads.
cy
= almost linear speedup
Expected #blocked transactions < 2τ |E| / |V|.
τ ≡ diff in parallel cpu’s progress
28. Empirical Validation on Billion Edge
Graphs
Amazon EC2 r3.8xlarge instances
Multicore up to 16 threads
Real and synthetic graphs
100 runs (10 random orderings x 10 runs)
Graph Vertices Edges
IT-2004 Italian web-graph 41 Million 1.14 Billion
Webbase-2001 WebBase crawl 118 Million 1.02 Billion
Erdos-Renyi Synthetic random 100 Million ≈ 1.0 Billion
31. Conclusion
Concurrency Control
for Parallel ML
o Guarantee
correctness
o Maximize
concurrency
Code release in the works!
https://amplab.cs.berkeley.edu/projects/cc
ml/
xinghao@berkeley.edu
Applications
Correlation Clustering
Submodular Maximization
Clustering
Online Facility Location
Feature Modeling
