Multi-Label Modality Classification for Figures in Biomedical Literature
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CBMS 2017 presentation on multi-Label modality classification for figures in biomedical literature. Presenting three different multi-label approaches to classify biomedical figures from PubMed Central and MEDIEVAL, a web application where you can easily search and filter by modality PMC figures.
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Big Data and machine learning are increasingly important in biomedical science and clinical practice. Big Data refers to large and complex datasets that are too large for traditional tools to handle. Machine learning involves algorithms that can recognize patterns in data without being explicitly programmed. Some challenges of working with big data and machine learning include issues with data volume, variety, and veracity. However, techniques like distributed analysis, standards, and validation can help address these challenges.
Large scale machine learning challenges for systems biology
Large scale machine learning challenges for systems biology
by dr. Yvan Saeys - Machine Learning and Data Mining group, Bioinformatics and Systems Biology Division, VIB-UGent Department of Plant Systems Biology
Due to technological advances, the amount of biological data, and the pace at which it is generated has increased dramatically during the past decade. To extract new knowledge from these ever increasing data sets, automated techniques such as data mining and machine learning techniques have become standard practice.
In this talk, I will give an overview of large scale machine learning challenges in bioinformatics and systems biology, highlighting the importance of using scalable and robust techniques such as ensemble learning methods implemented on large computing grids.
I will present some of our state-of-the-art tools to solve problems such as biomarker discovery, large scale network inference, and biomedical text mining at PubMed scale.
Machine learning to solve bioinformatics problems
This document summarizes a study that evaluated 13 machine learning algorithms on 165 bioinformatics datasets to provide recommendations for applying ML to bioinformatics problems. The key points are:
1) The algorithms were evaluated based on predictive accuracy, and ensemble and tree-based methods performed best overall.
2) Hyperparameter tuning and model selection improved performance for most algorithms.
3) The results showed problem-dependent variation in best algorithms, but Naive Bayes, linear models, random forests, and SVMs provided good coverage.
4) The study provides a starting point for ML practitioners by recommending 5 algorithms and their parameters.
Ijetr021252
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
Challenges and opportunities for machine learning in biomedical research
1. Machine learning faces challenges in biomedical research due to data heterogeneity, lack of labeled data, and complexity in biological patterns and networks.
2. Combining machine learning and biological network models can help address these challenges by encoding data in biologically meaningful networks and extracting network-based features for prediction.
3. Examples applying this approach to cancer datasets showed that models based on network centrality features outperformed other methods, and deep learning using these features achieved the best prediction performance across multiple neuroblastoma datasets.
The Power of Topology - Colleen Farrelly - WiDS Miami 2018
A lot of data science coverage in the media focuses on big data—storage systems, deep learning, and analyzing data with billions or trillions of observations. However, there’s an equally pressing problem in many industries and smaller companies today: small sample sizes or small subgroups within larger datasets. Machine learning algorithms fail to converge. Statistical methods break down completely. And valuable insight is lost.
However, recent advances in a branch of machine learning called topological data analysis (TDA), along with novel applications of topology to existing statistical methods, have provided a toolset suited to the challenges of small data. These methods have great potential as the field of data science moves from quantity to quality of data. This talk overviews several of TDA’s major tools, as well as their applications to three projects in which traditional methods fail.
Women in Data Science 2018 Slides--Small Samples, Subgroups, and Topology
This document discusses using topological data analysis techniques to address challenges with small sample sizes and distinct subgroups in data. It provides three case studies applying topological tools: 1) exploring profoundly gifted students with small educational samples, 2) identifying risk clusters in auto insurance claims data with subgroups, and 3) validating a psychometric survey using topology over traditional factor analysis. Topological data analysis is presented as a robust solution to problems traditional statistical and machine learning algorithms struggle with for small and complex data.
Evolution of Knowledge Discovery and Management
The document discusses the evolution of knowledge discovery and management over time. It outlines how knowledge discovery has progressed from early efforts using simulated data due to lack of large real-world datasets, to today where there is no shortage of complex real-world problems and data available. Key areas evolving now include automated data analysis, integration of tools and databases, and handling different data types like text and images. While expert systems showed early promise but had limited results, knowledge discovery has led to valuable applications through continued academic and industrial research.
A preliminary survey on optimized multiobjective metaheuristic methods for da...
The present survey provides the state-of-the-art of research, copiously devoted to Evolutionary Approach
(EAs) for clustering exemplified with a diversity of evolutionary computations. The Survey provides a
nomenclature that highlights some aspects that are very important in the context of evolutionary data
clustering. The paper missions the clustering trade-offs branched out with wide-ranging Multi Objective
Evolutionary Approaches (MOEAs) methods. Finally, this study addresses the potential challenges of
MOEA design and data clustering, along with conclusions and recommendations for novice and
researchers by positioning most promising paths of future research.
Theory and Practice of Integrating Machine Learning and Conventional Statisti...
Theory and Practice of Integrating Machine Learning and Conventional Statisti...University of Malaya
The practice of medical decision making is changing rapidly with the development of innovative
computing technologies. The growing interest of data analysis in line with the advancement in data
science raises the question of whether machine learning can be integrated with conventional statistics
in health research. To help address this knowledge gap, this talk focuses on the conceptual
integration between conventional statistics and machine learning, with a direction towards health
research. The similarities and differences between the two are compared using mathematical
concepts and algorithms. The comparison between conventional statistics and machine learning
methods indicates that conventional statistics are the fundamental basis of machine learning, where
the black box algorithms are derived from basic mathematics, but are advanced in terms of
automated analysis, handling big data and providing interactive visualizations. While the nature of
both these methods are different, they are conceptually similar. The evidence produced here
concludes that conventional statistics and machine learning are best to be integrated to develop
automated data analysis tools. Health researchers may explore machine learning as a potential tool to
enhance conventional statistics in data analytics for added reliable validation measures.Real life application of statistics in engineering
Main points of this slide presentation:
1.What is statistics?
2.Application
3.Application of Statistics in Computer Science and Engineering
4.Machine learning’s Relation to statistics
5.Application of Statistics in Data mining
6.Data mining relation with Statistics
7.Outline of Applications
8.Some Outline of Application’s details are given below
Thank you
Clinical Data and AI
Clinical data and trials AI: achieving the best results
Big data and machine learning are evolving rapidly and allowing for multiple measurements and analysis of large datasets. However, cleaning and managing data is still important. We need more open and inclusive research with more shared data while protecting privacy. Regulations need to catch up to advancements in AI. Bringing together domain experts and data scientists using both supervised and unsupervised learning can improve data collection, analysis, and outcomes. However, without domain knowledge, data analysis may be meaningless or dangerous. Standards for data sharing while protecting privacy are needed to advance healthcare AI.
Similar to Multi-Label Modality Classification for Figures in Biomedical Literature (20)
A Framework for Statistical Simulation of Physiological Responses (SSPR).
A Framework for Statistical Simulation of Physiological Responses (SSPR).
Machine Learning in Biology and Why It Doesn't Make Sense - Theo Knijnenburg,...
Machine Learning in Biology and Why It Doesn't Make Sense - Theo Knijnenburg,...
Developing a Replicable Methodology for Automated Identification of Emerging ...
Developing a Replicable Methodology for Automated Identification of Emerging ...
Decision Tree Classifiers to determine the patient’s Post-operative Recovery ...
Decision Tree Classifiers to determine the patient’s Post-operative Recovery ...
Large scale machine learning challenges for systems biology
Large scale machine learning challenges for systems biology
Challenges and opportunities for machine learning in biomedical research
Challenges and opportunities for machine learning in biomedical research
The Power of Topology - Colleen Farrelly - WiDS Miami 2018
The Power of Topology - Colleen Farrelly - WiDS Miami 2018
Women in Data Science 2018 Slides--Small Samples, Subgroups, and Topology
Women in Data Science 2018 Slides--Small Samples, Subgroups, and Topology
A preliminary survey on optimized multiobjective metaheuristic methods for da...
A preliminary survey on optimized multiobjective metaheuristic methods for da...
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Theory and Practice of Integrating Machine Learning and Conventional Statisti...
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原版一模一样【微信：741003700 】【莱斯大学毕业证（rice毕业证）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理莱斯大学毕业证（rice毕业证）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理莱斯大学毕业证（rice毕业证）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理莱斯大学毕业证（rice毕业证）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理莱斯大学毕业证（rice毕业证）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
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Telemetry Solution for Gaming (AWS Summit'24)
Discover the cutting-edge telemetry solution implemented for Alan Wake 2 by Remedy Entertainment in collaboration with AWS. This comprehensive presentation dives into our objectives, detailing how we utilized advanced analytics to drive gameplay improvements and player engagement.
Key highlights include:
Primary Goals: Implementing gameplay and technical telemetry to capture detailed player behavior and game performance data, fostering data-driven decision-making.
Tech Stack: Leveraging AWS services such as EKS for hosting, WAF for security, Karpenter for instance optimization, S3 for data storage, and OpenTelemetry Collector for data collection. EventBridge and Lambda were used for data compression, while Glue ETL and Athena facilitated data transformation and preparation.
Data Utilization: Transforming raw data into actionable insights with technologies like Glue ETL (PySpark scripts), Glue Crawler, and Athena, culminating in detailed visualizations with Tableau.
Achievements: Successfully managing 700 million to 1 billion events per month at a cost-effective rate, with significant savings compared to commercial solutions. This approach has enabled simplified scaling and substantial improvements in game design, reducing player churn through targeted adjustments.
Community Engagement: Enhanced ability to engage with player communities by leveraging precise data insights, despite having a small community management team.
This presentation is an invaluable resource for professionals in game development, data analytics, and cloud computing, offering insights into how telemetry and analytics can revolutionize player experience and game performance optimization.
06-20-2024-AI Camp Meetup-Unstructured Data and Vector Databases
Tech Talk: Unstructured Data and Vector Databases
Speaker: Tim Spann (Zilliz)
Abstract: In this session, I will discuss the unstructured data and the world of vector databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture. Wrapping up with an overview of Milvus.
Introduction
Unstructured data, vector databases, traditional databases, similarity search
Vectors
Where, What, How, Why Vectors? We’ll cover a Vector Database Architecture
Introducing Milvus
What drives Milvus' Emergence as the most widely adopted vector database
Hi Unstructured Data Friends!
I hope this video had all the unstructured data processing, AI and Vector Database demo you needed for now. If not, there’s a ton more linked below.
My source code is available here
https://github.com/tspannhw/
Let me know in the comments if you liked what you saw, how I can improve and what should I show next? Thanks, hope to see you soon at a Meetup in Princeton, Philadelphia, New York City or here in the Youtube Matrix.
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/141-10June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
https://www.meetup.com/unstructured-data-meetup-new-york/events/301383476/?slug=unstructured-data-meetup-new-york&eventId=301383476
https://www.aicamp.ai/event/eventdetails/W2024062014
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本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理渥太华大学毕业证（uottawa毕业证书）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理渥太华大学毕业证（uottawa毕业证书）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理渥太华大学毕业证（uottawa毕业证书）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理渥太华大学毕业证（uottawa毕业证书）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版南昆士兰大学毕业证如何办理
原版一模一样【微信：741003700 】【南昆士兰大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理南昆士兰大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理南昆士兰大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理南昆士兰大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理南昆士兰大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
06-18-2024-Princeton Meetup-Introduction to Milvus
06-18-2024-Princeton Meetup-Introduction to Milvus
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://github.com/milvus-io/milvus
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/142-17June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
Expand LLMs' knowledge by incorporating external data sources into LLMs and your AI applications.
Senior Engineering Sample EM DOE - Sheet1.pdf
Sample Engineering Profiles from Product Companies DOE EM etc
Overview IFM June 2024 Consumer Confidence INDEX Report.pdf
Overview IFM June 2024 Consumer Confidence INDEX Report.pdf
Sid Sigma educational and problem solving power point- Six Sigma.ppt
Sid Sigma educational and problem solving power point
一比一原版悉尼大学毕业证如何办理
原版一模一样【微信：741003700 】【悉尼大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理悉尼大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理悉尼大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理悉尼大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理悉尼大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
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Multi-Label Modality Classification for Figures in Biomedical Literature
- 1. Multi-Label Modality Classification for Figures in Biomedical Literature Athanasios Lagopoulos, PhD Student School of Informatics, Aristotle University of Thessaloniki lathanag@csd.auth.gr Anestis Fachantidis (afa@csd.auth.gr), Grigorios Tsoumakas (greg@csd.auth.gr) 30th International Symposium on Computer-Based Medical Systems - IEEE CBMS 2017
- 2. The Case PubMed Central (PMC): • >4 million figures available • Great source of information for biomedical research, education and clinical decision. Lack of associated meta-data Inaccessible information
- 3. 30 different modalities/categories as proposed by ImageCLEF
- 4. Simple Vs Compound 40% are compound figures (multi-panel format) Simple (60%) Compound (40%)
- 5. The Standard Approach Compound Figure Detection Multi-class Model Simple Figure Compound Subfigures Figure Separation Algorithm Figure Separation is not perfect (~85%) Figure Isolation - Information Loss
- 6. Our Approach: Multi-Label Classification No use of figure separation algorithm Three different multi-label learning approaches: • Simple • Standard • Extended
- 7. Simple multi-label approach Multi-label model Compound Simple Training Prediction
- 8. Standard multi-label approach Compound Figure Detection Simple Compound Multi-class Model Multi-label Model Compound Figure Detection Multi-class Model Multi-label Model Compound Simple PredictionTraining
- 9. Extended multi-label approach Compound Figure Detection Simple Compound Multi-class Model Multi-label Model Compound Figure Detection Multi-class Model Multi-label Model Compound Simple PredictionTraining
- 10. Model Training Feature Extraction from JPEG • BVLC model - Caffe1 • Deep learning (1.2 million images) • 4096 visual features/figure Linear Support Vector Machines (SVMs) • scikit-learn2 • One-vs-Rest transformation (multiple binaries) 1 http://caffe.berkeleyvision.org/ 2 http://scikit-learn.org/
- 11. ImageCLEF 2016 dataset 20.985 Figures 1.568 Compound No simple figures with categories Extracted subfigures as simple Split 40% - 60% (compound – subfigures) in order to resemble the distribution of PMC
- 12. Results Approach F1-Macro F1-Micro F1-Samples Standard 0.3569 0.7786 0.7912 Simple multi-label 0.3139 0.7581 0.7215 Standard multi-label 0.3270 0.7667 0.7726 Extended multi-label 0.3309 0.7666 0.7728 Perfect (100%) figure separation Compound Figure Detection: 88,83% (Balanced Accuracy) Multi-class model: 79,54% (F1-micro)
- 13. The System Web app Weekly updates from PMC Extended multi-label approach Easy search & filtering by modality Build with Apache Solr & AngularJS Available @ atypon.csd.auth.gr/medieval/
- 14. Conclusion No information loss Model redundancy Promising results Web application
- 15. Future Work Backend: • Textual features + Visual features Frontend: • User feedback, crowdsourcing • Active learning
- 16. THANK YOU! intelligence.csd.auth.gr Questions? atypon.csd.auth.gr/medieval Partially funded by A. Lagopoulos, A. Fachantidis, G. Tsoumakas Multi-Label Modality Classification for Figures in Biomedical Literature {lathanag,afa,greg}@csd.auth.gr Atypon Systems Inc.