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Advanced NVMe Controller Technology for Next Generation Memory Devices
KAIST researchers advanced non-volatile memory express (NVMe) controller technology for next generation information storage devices, and made this new technology named ‘OpenExpress’ freely available to all universities and research institutes around the world to help reduce the research cost in related fields. NVMe is a communication protocol made for high-performance storage devices based on a peripheral component interconnect-express (PCI-E) interface. NVMe has been developed to take the place of the Serial AT Attachment (SATA) protocol, which was developed to process data on hard disk drives (HDDs) and did not perform well in solid state drives (SSDs). Unlike HDDs that use magnetic spinning disks, SSDs use semiconductor memory, allowing the rapid reading and writing of data. SSDs also generate less heat and noise, and are much more compact and lightweight. Since data processing in SSDs using NVMe is up to six times faster than when SATA is used, NVMe has become the standard protocol for ultra-high speed and volume data processing, and is currently used in many flash-based information storage devices. Studies on NVMe continue at both the academic and industrial levels, however, its poor accessibility is a drawback. Major information and communications technology (ICT) companies around the world expend astronomical costs to procure intellectual property (IP) related to hardware NVMe controllers, necessary for the use of NVMe. However, such IP is not publicly disclosed, making it difficult to be used by universities and research institutes for research purposes. Although a small number of U.S. Silicon Valley startups provide parts of their independently developed IP for research, the cost of usage is around 34,000 USD per month. The costs skyrocket even further because each copy of single-use source code purchased for IP modification costs approximately 84,000 USD. In order to address these issues, a group of researchers led by Professor Myoungsoo Jung from the School of Electrical Engineering at KAIST developed a next generation NVMe controller technology that achieved parallel data input/output processing for SSDs in a fully hardware automated form. The researchers presented their work at the 2020 USENIX Annual Technical Conference (USENIX ATC ’20) in July, and released it as an open research framework named ‘OpenExpress.’ This NVMe controller technology developed by Professor Jung’s team comprises a wide range of basic hardware IP and key NVMe IP cores. To examine its actual performance, the team made an NVMe hardware controller prototype using OpenExpress, and designed all logics provided by OpenExpress to operate at high frequency. The field-programmable gate array (FPGA) memory card prototype developed using OpenExpress demonstrated increased input/output data processing capacity per second, supporting up to 7 gigabit per second (GB/s) bandwidth. This makes it suitable for ultra-high speed and volume next generation memory device research. In a test comparing various storage server loads on devices, the team’s FPGA also showed 76% higher bandwidth and 68% lower input/output delay compared to Intel’s new high performance SSD (Optane SSD), which is sufficient for many researchers studying systems employing future memory devices. Depending on user needs, silicon devices can be synthesized as well, which is expected to further enhance performance. The NVMe controller technology of Professor Jung’s team can be freely used and modified under the OpenExpress open-source end-user agreement for non-commercial use by all universities and research institutes. This makes it extremely useful for research on next-generation memory compatible NVMe controllers and software stacks. “With the product of this study being disclosed to the world, universities and research institutes can now use controllers that used to be exclusive for only the world’s biggest companies, at no cost,ˮ said Professor Jung. He went on to stress, “This is a meaningful first step in research of information storage device systems such as high-speed and volume next generation memory.” This work was supported by a grant from MemRay, a company specializing in next generation memory development and distribution. More details about the study can be found at http://camelab.org. Image credit: Professor Myoungsoo Jung, KAIST Image usage restrictions: News organizations may use or redistribute these figures and image, with proper attribution, as part of news coverage of this paper only. Publication: Myoungsoo Jung. (2020). OpenExpress: Fully Hardware Automated Open Research Framework for Future Fast NVMe Devices. Presented in the Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC ’20), Available online at https://www.usenix.org/system/files/atc20-jung.pdf Profile: Myoungsoo Jung, PhD. Associate Professor firstname.lastname@example.org http://camelab.org Computer Architecture and Memory Systems Laboratory School of Electrical Engineering http://kaist.ac.kr Korea Advanced Institute of Science and Technology (KAIST) Daejeon, Republic of Korea (END)
Research on the Million Follower Fallacy Receives the Test of Time Award
Professor Meeyoung Cha’s research investigating the correlation between the number of followers on social media and its influence was re-highlighted after 10 years of publication of the paper. Saying that her research is still as relevant today as the day it was published 10 years ago, the Association for the Advancement of Artificial Intelligence (AAAI) presented Professor Cha from the School of Computing with the Test of Time Award during the 14th International Conference on Web and Social Media (ICWSM) held online June 8 through 11. In her 2010 paper titled ‘Measuring User Influence in Twitter: The Million Follower Fallacy,’ Professor Cha proved that number of followers does not match the influential power. She investigated the data including 54,981,152 user accounts, 1,963,263,821 social links, and 1,755,925,520 Tweets, collected with 50 servers. The research compares and illustrates the limitations of various methods used to measure the influence a user has on a social networking platform. These results provided new insights and interpretations to the influencer selection algorithm used to maximize the advertizing impact on big social networking platforms. The research also looked at how long an influential user was active for, and whether the user could freely cross the borders between fields and be influential on different topics as well. By analyzing cases of who becomes an influencer when new events occur, it was shown that a person could quickly become an influencer using several key tactics, unlike what was previously claimed by the ‘accidental influential theory’. Professor Cha explained, “At the time, data from social networking platforms did not receive much attention in computer science, but I remember those all-nighters I pulled to work on this project, fascinated by the fact that internet data could be used to solve difficult social science problems. I feel so grateful that my research has been endeared for such a long time.” Professor Cha received both her undergraduate and graduate degrees from KAIST, and conducted this research during her postdoctoral course at the Max Planck Institute in Germany. She now also serves as a chief investigator of a data science group at the Institute for Basic Science (IBS). (END)
Unravelling Complex Brain Networks with Automated 3-D Neural Mapping
-Automated 3-D brain imaging data analysis technology offers more reliable and standardized analysis of the spatial organization of complex neural circuits.- KAIST researchers developed a new algorithm for brain imaging data analysis that enables the precise and quantitative mapping of complex neural circuits onto a standardized 3-D reference atlas. Brain imaging data analysis is indispensable in the studies of neuroscience. However, analysis of obtained brain imaging data has been heavily dependent on manual processing, which cannot guarantee the accuracy, consistency, and reliability of the results. Conventional brain imaging data analysis typically begins with finding a 2-D brain atlas image that is visually similar to the experimentally obtained brain image. Then, the region-of-interest (ROI) of the atlas image is matched manually with the obtained image, and the number of labeled neurons in the ROI is counted. Such a visual matching process between experimentally obtained brain images and 2-D brain atlas images has been one of the major sources of error in brain imaging data analysis, as the process is highly subjective, sample-specific, and susceptible to human error. Manual analysis processes for brain images are also laborious, and thus studying the complete 3-D neuronal organization on a whole-brain scale is a formidable task. To address these issues, a KAIST research team led by Professor Se-Bum Paik from the Department of Bio and Brain Engineering developed new brain imaging data analysis software named 'AMaSiNe (Automated 3-D Mapping of Single Neurons)', and introduced the algorithm in the May 26 issue of Cell Reports. AMaSiNe automatically detects the positions of single neurons from multiple brain images, and accurately maps all the data onto a common standard 3-D reference space. The algorithm allows the direct comparison of brain data from different animals by automatically matching similar features from the images, and computing the image similarity score. This feature-based quantitative image-to-image comparison technology improves the accuracy, consistency, and reliability of analysis results using only a small number of brain slice image samples, and helps standardize brain imaging data analyses. Unlike other existing brain imaging data analysis methods, AMaSiNe can also automatically find the alignment conditions from misaligned and distorted brain images, and draw an accurate ROI, without any cumbersome manual validation process. AMaSiNe has been further proved to produce consistent results with brain slice images stained utilizing various methods including DAPI, Nissl, and autofluorescence. The two co-lead authors of this study, Jun Ho Song and Woochul Choi, exploited these benefits of AMaSiNe to investigate the topographic organization of neurons that project to the primary visual area (VISp) in various ROIs, such as the dorsal lateral geniculate nucleus (LGd), which could hardly be addressed without proper calibration and standardization of the brain slice image samples. In collaboration with Professor Seung-Hee Lee's group of the Department of Biological Science, the researchers successfully observed the 3-D topographic neural projections to the VISp from LGd, and also demonstrated that these projections could not be observed when the slicing angle was not properly corrected by AMaSiNe. The results suggest that the precise correction of a slicing angle is essential for the investigation of complex and important brain structures. AMaSiNe is widely applicable in the studies of various brain regions and other experimental conditions. For example, in the research team’s previous study jointly conducted with Professor Yang Dan’s group at UC Berkeley, the algorithm enabled the accurate analysis of the neuronal subsets in the substantia nigra and their projections to the whole brain. Their findings were published in Science on January 24. AMaSiNe is of great interest to many neuroscientists in Korea and abroad, and is being actively used by a number of other research groups at KAIST, MIT, Harvard, Caltech, and UC San Diego. Professor Paik said, “Our new algorithm allows the spatial organization of complex neural circuits to be found in a standardized 3-D reference atlas on a whole-brain scale. This will bring brain imaging data analysis to a new level.” He continued, “More in-depth insights for understanding the function of brain circuits can be achieved by facilitating more reliable and standardized analysis of the spatial organization of neural circuits in various regions of the brain.” This work was supported by KAIST and the National Research Foundation of Korea (NRF). Figure and Image Credit: Professor Se-Bum Paik, KAIST Figure and Image Usage Restrictions: News organizations may use or redistribute these figures and images, with proper attribution, as part of news coverage of this paper only. Publication: Song, J. H., et al. (2020). Precise Mapping of Single Neurons by Calibrated 3D Reconstruction of Brain Slices Reveals Topographic Projection in Mouse Visual Cortex. Cell Reports. Volume 31, 107682. Available online at https://doi.org/10.1016/j.celrep.2020.107682 Profile: Se-Bum Paik Assistant Professor email@example.com http://vs.kaist.ac.kr/ VSNN Laboratory Department of Bio and Brain Engineering Program of Brain and Cognitive Engineering http://kaist.ac.kr Korea Advanced Institute of Science and Technology (KAIST) Daejeon, Republic of Korea (END)
Professor Dongsu Han Named Program Chair for ACM CoNEXT 2020
Professor Dongsu Han from the School of Electrical Engineering has been appointed as the program chair for the 16th Association for Computing Machinery’s International Conference on emerging Networking EXperiments and Technologies (ACM CoNEXT 2020). Professor Han is the first program chair to be appointed from an Asian institution. ACM CoNEXT is hosted by ACM SIGCOMM, ACM's Special Interest Group on Data Communications, which specializes in the field of communication and computer networks. Professor Han will serve as program co-chair along with Professor Anja Feldmann from the Max Planck Institute for Informatics. Together, they have appointed 40 world-leading researchers as program committee members for this conference, including Professor Song Min Kim from KAIST School of Electrical Engineering. Paper submissions for the conference can be made by the end of June, and the event itself is to take place from the 1st to 4th of December. Conference Website: https://conferences2.sigcomm.org/co-next/2020/#!/home (END)
A Global Campaign of ‘Facts before Rumors’ on COVID-19 Launched
- A KAIST data scientist group responds to facts and rumors on COVID-19 for global awareness of the pandemic. - Like the novel coronavirus, rumors have no borders. The world is fighting to contain the pandemic, but we also have to deal with the appalling spread of an infodemic that is as contagious as the virus. This infodemic, a pandemic of false information, is bringing chaos and extreme fear to the general public. Professor Meeyoung Cha’s group at the School of Computing started a global campaign called ‘Facts before Rumors,’ to prevent the spread of false information from crossing borders. She explained, “We saw many rumors that had already been fact-checked long before in China and South Korea now begin to circulate in other countries, sometimes leading to detrimental results. We launched an official campaign, Facts before Rumors, to deliver COVID-19-related facts to countries where the number of cases is now increasing.” She released the first set of facts on March 26 via her Twitter account @nekozzang. Professor Cha, a data scientist who has focused on detecting global fake news, is now part of the COVID-19 AI Task Force at the Global Strategy Institute at KAIST. She is also leading the Data Science Group at the Institute for Basic Science (IBS) as Chief Investigator. Her research group worked in collaboration with the College of Nursing at Ewha Woman’s University to identify 15 claims about COVID-19 that circulated on social networks (SNS) and among the general public. The team fact-checked these claims based on information from the WHO and CDCs of Korea and the US. The research group is now working on translating the list of claims into Portuguese, Spanish, Persian, Chinese, Amharic, Hindi, and Vietnamese. Delivering facts before rumors, the team says, will help contain the disease and prevent any harm caused by misinformation. The pandemic, which spread in China and South Korea before arriving in Europe and the US, is now moving into South America, Africa, and Southeast Asia. “We would like to play a part in preventing the further spread of the disease with the provision of only scientifically vetted, truthful facts,” said the team. For this campaign, Professor Cha’s team investigated more than 200 rumored claims on COVID-19 in China during the early days of the pandemic. These claims spread in different levels: while some were only relevant locally or in larger regions of China, others propagated in Asia and are now spreading to countries that are currently most affected by the disease. For example, the false claim which publicized that ‘Fireworks can help tame the virus in the air’ only spread in China. Other claims such as ‘Eating garlic helps people overcome the disease’ or ‘Gargling with salt water prevents the contraction of the disease,’ spread around the world even after being proved groundless. The team noted, however, that the times at which these claims propagate are different from one country to another. “This opens up an opportunity to debunk rumors in some countries, even before they start to emerge,” said Professor Cha. Kun-Woo Kim, a master’s candidate in the Department of Industrial Design who joined this campaign and designed the Facts before Rumors chart also expressed his hope that this campaign will help reduce the number of victims. He added, “I am very grateful to our scientists who quickly responded to the Fact Check in these challenging times.”
COVID-19 Map Shows How the Global Pandemic Moves
- A School of Computing team facilitated the data from COVID-19 to show the global spread of the virus. - The COVID-19 map made by KAIST data scientists shows where and how the virus is spreading from China, reportedly the epicenter of the disease. Professor Meeyoung Cha from the School of Computing and her group facilitated data based on the number of confirmed cases from January 22 to March 22 to analyze the trends of this global epidemic. The statistics include the number of confirmed cases, recoveries, and deaths across major continents based on the number of confirmed case data during that period. The moving dot on the map strikingly shows how the confirmed cases are moving across the globe. According to their statistics, the centroid of the disease starts from near Wuhan in China and moved to Korea, then through the European region via Italy and Iran. The data is collected by a graduate student from the School of Computing, Geng Sun, who started the process during the time he was quarantined since coming back from his home in China. An undergraduate colleague of Geng's, Gabriel Camilo Lima who made the map, is now working remotely from his home in Brazil since all undergraduate students were required to move out of the dormitory last week. The university closed all undergraduate housing and advised the undergraduate students to go back home in a preventive measure to stop the virus from spreading across the campus. Gabriel said he calculated the centroid of all confirmed cases up to a given day. He explained, “I weighed each coordinate by the number of cases in that region and country and calculated an approximate center of gravity.” “The Earth is round, so the shortest path from Asia to Europe is often through Russia. In early March, the center of gravity of new cases was moving from Asia to Europe. Therefore, the centroid is moving to the west and goes through Russia, even though Russia has not reported many cases,” he added. Professor Cha, who is also responsible for the Data Science Group at the Institute for Basic Science (IBS) as the Chief Investigator, said their group will continue to update the map using public data at https://ds.ibs.re.kr/index.php/covid-19/. (END)
Participation in the 2018 Bio-Digital City Workshop in Paris
(A student make a presentatiion during the Bio-Digital City Workshop in Paris last month.) KAIST students explored ideas for developing future cities during the 2018 Bio-Digital City Workshop held in Paris last month. This international workshop hosted by Cité des Sciences et de l'Industrie was held under the theme “Biomimicry, Digital City and Big Data.” During the workshop from July 10 to July 20, students teamed up with French counterparts to develop innovative urban design ideas. Cité des Sciences et de l'Industrie is the largest science museum in Europe and is operated by Universcience, a specialized institute of science and technology in France. Professor Seongju Chang from the Department of Civil and Environmental Engineering and Professor Jihyun Lee of the Graduate School of Culture Technology Students led the students group. Participants presented their ideas and findings on new urban solutions that combine biomimetic systems and digital technology. Each student group analyzed a special natural ecosystem such as sand dunes, jellyfish communities, or mangrove forests and conducted research to extract algorithms for constructing sustainable urban building complexes based on the results. The extracted algorithm was used to conceive a sustainable building complex forming a part of the urban environment by applying it to the actual Parisian city segment given as the virtual site for the workshop. Students from diverse background in both countries participated in this convergence workshop. KAIST students included Ph.D. candidate Hyung Min Cho, undergraduates Min-Woo Jeong, Seung-Hwan Cha, and Sang-Jun Park from the Department of Civil and Environmental Engineering, undergraduate Kyeong-Keun Seo from the Department of Materials Science and Engineering, JiWhan Jeong (Master’s course) from the Department of Industrial and Systems Engineering, Ph.D. candidate Bo-Yoon Zang from the Graduate School of Culture Technology. They teamed up with French students from diverse backgrounds, including Design/Science, Visual Design, Geography, Computer Science and Humanities and Social Science. This workshop will serve as another opportunity to expand academic and human exchange efforts in the domain of smart and sustainable cities with Europe in the future as the first international cooperation activity of KAIST and the Paris La Villette Science Museum. Professor Seong-Ju Chang who led the research group said, "We will continue to establish a cooperative relationship between KAIST and the European scientific community. This workshop is a good opportunity to demonstrate the competence of KAIST students and their scientific and technological excellence on the international stage.”
Strengthening Industry-Academia Cooperation with LG CNS
On November 20, KAIST signed an MoU with LG CNS for industry-academia partnership in education, research, and business in the fields of AI and Big Data. Rather than simply developing education programs or supporting industry-academia scholarships, both organizations agreed to carry out a joint research project on AI and Big Data that can be applied to practical business. KAIST will collaborate with LG CNS in the fields of smart factories, customer analysis, and supply chain management analysis. Not only will LG CNS offer internships to KAIST students, but it also will support professors and students who propose innovative startup ideas for AI and Big Data. Offering an industry-academia scholarship for graduate students is also being discussed. Together with LG CNS, KAIST will put its efforts into propose projects regarding AI and Big Data in the public sector. Furthermore, KAIST and LG CNS will jointly explore and carry out industry-academia projects that could be practically used in business. Both will carry out the project vigorously through strong cooperation; for instance, LG CNS employees can be assigned to KAIST, if necessary. Also, LG CNS’s AI and Big Data platform, called DAP (Data Analytics & AI Platform) will be used as a data analysis tool during the project and the joint outcomes will be installed in DAP. KAIST professors with expertise in AI deep learning have trained LG CNS employees since the Department of Industrial & Systems Engineering established ‘KAIST AI Academy’ in LG CNS last August. “With KAIST, the best research-centered university in Korea, we will continue to lead in developing the field of AI and Big Data and provide innovative services that create value by connecting them to customer business,” Yong Shub Kim, the CEO of LG CNS, highlighted.
KAIST Professors Sweep the Best Science and Technology Award
(Distinguished Professors Sang Yup Lee (left) and Kyu-Young Whang) Distinguished Professors Sang Yup Lee from the Department of Chemical and Biomolecular Engineering and Kyu-Young Whang of the College of Computing were selected as the winners of the "2017 Korea Best Science and Technology Award" by the Ministry of Science, ICT and Future Planning (MSIP) and the Korea Federation of Science and Technology Societies. The award, which was established in 2003, is the highest honor bestowed to the two most outstanding scientists in Korea annually. This is the first time that KAIST faculty members have swept the award since its founding. Distinguished Professor Lee is renowned for his pioneering studies of system metabolic engineering, which produces useful chemicals by utilizing microorganisms. Professor Lee has developed a number of globally-recognized original technologies such as gasoline production using micro-organisms, a bio-butanol production process, microbes for producing nylon and plastic raw materials, and making native-like spider silk produced in metabolically engineering bacterium which is stronger than steel but finer than human hair. System metabolism engineering was also selected as one of the top 10 promising technologies in the world in 2016 by the World Economic Forum. Selected as one of the world’s top 20 applied bioscientists in 2014 by Nature Biotechnology, he has many ‘first’ titles in his academic and research careers. He was the first Asian to win the James Bailey Award (2016) and Marvin Johnson Award (2012), the first Korean elected to both the US National Academy of Science (NAS) and the National Academy of Engineering (NAE) this year. He is the dean of KAIST institutes, a multi and interdisciplinary research institute at KAIST. He serves as co-chair of the Global Council on Biotechnology and as a member of the Global Future Council on the Fourth Industrial Revolution at the World Economic Forum. Distinguished Professor Whang, the first recipient in the field of computer science in this award, has been recognized for his lifetime achievement and contributions to the development of the software industry and the spreading of information culture. He has taken a pioneering role in presenting novel theories and innovative technologies in the field of database systems such as probabilistic aggregation, multidimensional indexing, query, and database and information retrieval. The Odysseus database management system Professor Hwang developed has been applied in many diverse fields of industry, while promoting the domestic software industry and its technical independence. Professor Hwang is a fellow at the American Computer Society (ACM) and life fellow at IEEE. Professor Whang received the ACM SIGMOD Contributions Award in 2014 for his work promoting database research worldwide, the PAKDD Distinguished Contributions Award in 2014, and the DASFAA Outstanding Contributions Award in 2011 for his contributions to database and data mining research in the Asia-Pacific region. He is also the recipient of the prestigious Korea (presidential) Engineering Award in 2012.
KAIST Researchers Receive the 2016 IEEE William R. Bennett Prize
A research team led by Professors Yung Yi and Song Chong from the Electrical Engineering Department at KAIST has been awarded the 2016 William R. Bennett Prize of the Institute of Electrical and Electronics Engineers (IEEE), which is the most prestigious award in the field of communications network. The IEEE bestows the honor annually and selects winning papers from among those published in the past three years for its quality, originality, scientific citation index, and peer reviews. The IEEE award ceremony will take place on May 24, 2016 at the IEEE International Conference on Communications in Kuala Lumpur, Malaysia. The team members include Dr. Kyoung-Han Lee, a KAIST graduate, who is currently a professor at Ulsan National Institute of Science and Technology (UNIST) in Korea, Dr. Joo-Hyun Lee, a postdoctoral researcher at Ohio State University in the United States, and In-Jong Rhee, a vice president of the Mobile Division at Samsung Electronics. The same KAIST team previously received the award back in 2013, making them the second recipient ever to win the IEEE William R. Bennett Prize twice. Past winners include Professors Robert Gallager of the Massachusetts Institute of Technology (MIT), Sachin Katti of Stanford University, and Ion Stoica of the University of California at Berkeley. The research team received the Bennett award for their work on “Mobile Data Offloading: How Much Can WiFi Deliver?” Their research paper has been cited more than 500 times since its publication in 2013. They proposed an original method to effectively offload the cellular network and maximize the Wi-Fi network usage by analyzing the pattern of individual human mobility in daily life.
Workshop on Techniques in Prediction Analysis for the Industry
There has been growing interest in the value and the application of “big data” in recent years. To meet this interest, a workshop was held to discuss the possibility and the future of prediction analysis, which is the next big step in data mining after big data. On February 25 in COEX, Seoul, the Department of Knowledge Service Engineering at KAIST held the 4th knowledge service workshop on “Techniques in Prediction Analysis for the Industry.” Predication analysis is a technique that can predict the future based on the understanding of the past and the present through analyzing “big data.” If “big data” is fuel in figurative sense, the prediction analysis serves as the engine. The Department seeks to help those companies interested in data mining by introducing fundamentals and some application examples to the executives of companies who are interested in implementation of the technique. The lecture was delivered by six professors from the Department of Knowledge Service Engineering and the Department of Industrial and Systems Engineering at KAIST. Thomas Miller, the author of Modeling Techniques in Predictive Analytics, covered the contents of his book at the event. Professor Moon-Yong Yi, Chair of the Department of Knowledge Service Engineering, said, “This conference will be important to companies that are considering the implementation of the prediction analysis as well as to students who are interested in the field.”
Big Data Reveals the Secret of Classical Music Creation
Professor Juyong Park of the Graduate School of Culture Technology at KAIST and his research team have recently published the result of their study (“Topology and Evolution of the Network of Western Classical Music Composers”) on the dynamics of how classical music is created, stylized, and disseminated in EPJ Data Science online on April 22, 2015. For the press release issued by the journal, please go to the link below: EPJ Data Science, May 6, 2015 “EPJ Data Science Highlight—Big Data Reveals Classical Music Creation Secrets” http://www.epj.org/113-epj-ds/941-epjds-highlight-big-data-reveals-classical-music-creation-secrets Researchers used big-data analysis and modelling technique to examine the complex, undercurrent network of classical music composers, which was constructed from the large volume of compact disc (CD) recordings data collected from an online retailer, ArkivMusic, and a music reference website, AllMusicGuide. The study discovered that the basic characteristics of composers’ network are similar to many real-world networks, including the small-world property, the existence of a giant component, high clustering, and heavy-tailed degree distributions. The research team also found that composers collaborated and influenced each other and that composers’ networks grew over time. The research showed that consumers of classical music CDs tend to listen together to the music of a certain group of different composers, offering a useful tool to understand how the music style and market develops. Based on this, the research team predicted the future of the classical music market would be centered on top composers, while maintaining diversity due to the growing number of new composers. Professor Park said, “In recent years, technology greatly affects the way we consume culture and art. Accordingly, we see more and more artists and institutions try to incorporate technology into their creative process, and this will lead us to larger- and higher-quality data that can allow us to learn more about culture and art. The quantitative methodology we have demonstrated in our research will give us an opportunity to explore the nature of art and literature in novel ways.” The European Physical Journal (EPJ) comprises a series of peer-reviewed journals, eleven in total, which cover physics and related subjects such as The Large Hadron Collider, condensed matter, particles, soft matter, and biological physics. The EPJ Data Science is the latest journal launched by EPJ. Figure: Backbone of the Composer Network The composer-composer network backbone, projected from the CD-composer network, reveals the major component of the network. The node sizes represent the composers’ degrees, and the colors represent their active periods.
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