Phase 1 IUCRC Rutgers-New Brunswick: Center for Accelerated Real Time Analytics (CARTA)

第一阶段 IUCRC 罗格斯-新不伦瑞克：加速实时分析中心 (CARTA)

• 批准号: 1747778
• 负责人: Dimitris Metaxas
• 金额: $ 75万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1747778&HistoricalAwards=false
• 关键词: Phase; IUCRC; Rutgers; New; Brunswick

Nearly every research field and industry sector is struggling with extracting useful information from massive and dynamic data in a timely way. Developing tools and technologies in this realm of real-time and accelerated analytics contributes to promoting the progress of science and to advancing the national prosperity and welfare. Success in this realm hinges on balancing fundamental research, technological know-how, and commercial market intelligence. To address this challenge, this project joins industry members with academic centers to conduct multidisciplinary science and research towards extracting value from massive and moving data and enabling better decision making of complex, dynamic data.The Center of Accelerated Real Time Analytics (CARTA) project explores the ways in which relatively-high-risk fundamental developments can be leveraged to help organizations that have longer-term, more complex analytic needs. The focus of CARTA is on horizontal foundational technologies that would create an infrastructure capable of powering applications of national significance. In this context, at Rutgers-New Brunswick (RU-NB), the CARTA/RU-NB site will enable new application domains through innovative machine learning, statistical, modeling methods and technologies for accelerated and real-time analytics. Having these technologies and tools will be key in achieving the goals of the overall CARTA center. The broader impact of the work of the CARTA center will be in addressing the future advanced, real-time analytics needs of the industry and society. The techniques developed by CARTA can be applied across industry sectors, including national security, healthcare, manufacturing, energy, and business intelligence. The fundamental research done at CARTA will be translated into technology developments, delivering practical solutions to hard problems. The ultimate success of this paradigm shift by the analytics industry will rest on the ability of CARTA universities to prepare experts to take advantage of the science and technologies to solve a variety of real-life applications. CARTA research may involve sensitive academic and industrial data along with public domain data. This data and resulting research outputs will be maintained using appropriate best practices for each type of data for a period of three years after the closing of CARTA. A central repository, suitably tagged for appropriate referencing and documentation, will be set up at https://carta.umbc.edu for maintaining the acquired and generated data from Center projects. Access to all models and project results will be stored on line and made available for downloading in near real time to respond to approved user requests.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

几乎每个研究领域和行业都在努力从大量动态数据中及时提取有用的信息。开发实时和加速分析领域的工具和技术有助于促进科学进步，促进国家繁荣和福利。在这一领域的成功取决于平衡基础研究，技术诀窍和商业市场情报。为了应对这一挑战，该项目将行业成员与学术中心联合起来，开展多学科科学和研究，从海量和移动数据中提取价值，并对复杂的动态数据进行更好的决策。加速真实的时间分析中心（CARTA）项目探索了利用相对高风险的基本面发展来帮助组织进行长期、更复杂的分析需求。CARTA的重点是横向基础技术，这些技术将创建能够为国家重要应用提供动力的基础设施。在这种情况下，在Rutgers-New Brunswick（RU-NB），CARTA/RU-NB网站将通过创新的机器学习、统计、建模方法和技术来实现新的应用领域，以实现加速和实时分析。拥有这些技术和工具将是实现整个CARTA中心目标的关键。CARTA中心工作的更广泛影响将是满足行业和社会未来先进的实时分析需求。CARTA开发的技术可以应用于各个行业，包括国家安全、医疗保健、制造业、能源和商业智能。在CARTA所做的基础研究将转化为技术开发，为难题提供实用的解决方案。分析行业这种范式转变的最终成功将取决于CARTA大学是否有能力让专家们准备好利用科学和技术来解决各种现实生活中的应用。CARTA研究可能涉及敏感的学术和工业数据以及公共领域数据沿着。 这些数据和由此产生的研究成果将在CARTA结束后的三年内使用每类数据的适当最佳做法予以保留。将在https://carta.umbc.edu上建立一个中央存储库，适当标记以进行适当的参考和记录，以维护从中心项目中获取和生成的数据。访问所有的模型和项目结果将被存储在网上，并提供下载在近真实的时间，以回应批准的用户requests.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

项目成果

Interactive Architectural Design with Diverse Solution Exploration

• DOI: 10.1109/tvcg.2019.2938961
• 发表时间: 2021-01-01
• 期刊: IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
• 影响因子: 5.2
• 作者: Berseth, Glen;Haworth, Brandon;Faloutsos, Petros
• 通讯作者: Faloutsos, Petros

Generation of crowd arrival and destination locations/times in complex transit facilities

生成复杂交通设施中的人群到达和目的地位置/时间

• DOI: 10.1007/s00371-019-01761-z
• 发表时间: 2019
• 期刊: The Visual Computer
• 作者: Ricks, Brian;Dobson, Andrew;Krontiris, Athanasios;Bekris, Kostas;Kapadia, Mubbasir;Roberts, Fred
• 通讯作者: Roberts, Fred

The Importance of Multimodal Emotion Conditioning and Affect Consistency for Embodied Conversational Agents

• DOI: 10.1145/3581641.3584045
• 发表时间: 2023-03
• 期刊: Proceedings of the 28th International Conference on Intelligent User Interfaces
• 作者: Che-Jui Chang;Samuel S. Sohn;Sen Zhang;R. Jayashankar;Muhammad Usman;M. Kapadia

Knowledge As Priors: Cross-Modal Knowledge Generalization for Datasets Without Superior Knowledge

• DOI: 10.1109/cvpr42600.2020.00656
• 发表时间: 2020-04
• 期刊: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
• 作者: Long Zhao;Xi Peng;Yuxiao Chen;M. Kapadia;Dimitris N. Metaxas

Towards Image-to-Video Translation: A Structure-Aware Approach via Multi-stage Generative Adversarial Networks

• DOI: 10.1007/s11263-020-01328-9
• 发表时间: 2020-04
• 期刊: International Journal of Computer Vision
• 影响因子: 19.5
• 作者: Long Zhao;Xi Peng;Yu Tian;M. Kapadia;Dimitris N. Metaxas