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(Vision Paper) A Vision for Spatio-Causal Situation Awareness, Forecasting, and Planning

ACM TRANSACTIONS ON spatial algorithms AND SYSTEMS 2024年第2期10卷 1-42页

作者： Azad, Fahim Tasneema Candan, K. Selcuk Kapkic, Ahmet Li, Mao-Lin Liu, Huan Mandal, Pratanu Sheth, Paras Arslan, Bilgehan Chowell-Puente, Gerardo Sabo, John Muenich, Rebecca Anton, Javier Redondo Sapino, Maria Luisa Arizona State Univ Tempe AZ 85287 USA Gazi Univ Ankara Turkiye Georgia State Univ Dept Populat Hlth Sci Atlanta GA USA Tulane Univ New Orleans LA USA Univ Arkansas Fayetteville AR USA Univ Torino Turin Piemonte Italy

Successfully tackling many urgent challenges in socio-economically critical domains, such as public health and sustainability, requires a deeper understanding of causal relationships and interactions among a diverse spectrum of spatio-temporally distributed entities. In these applications, the ability to leverage spatio-temporal data to obtain causally based situational awareness and to develop informed forecasts to provide resilience at different scales is critical. While the promise of a causally grounded approach to these challenges is apparent, the core data technologies needed to achieve these are in the early stages and lack a framework to help realize their potential. In this article, we argue that there is an urgent need for a novel paradigm of spatio-causal research built on computational advances in spatio-temporal data and model integration, causal learning and discovery, large scale data- and model-driven simulations, emulations, and forecasting, aswell as spatio-temporal data-driven and model-centric operational recommendations, and effective causally driven visualization and explanation. We thus provide a vision, and a roadmap, for spatio-causal situation awareness, forecasting, and planning.

关键词： spatial algorithms spatial big data causal discovery

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Towards Pareto-optimality with Multi-level Bi-objective Routing: A Summary of Results 17

Towards Pareto-optimality with Multi-level Bi-objective Rout...

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17th International Workshop on Computational Transportation Science

作者： Yang, Mingzhou Zeng, Ruolei Sharma, Arun Sawamura, Shunichi Northrop, William F. Shekhar, Shashi Univ Minnesota Minneapolis MN 55455 USA

ISBN: (纸本)9798400711510

Given an origin, a destination, and a directed graph in which each edge is associated with a pair of non-negative costs, the bi-objective routing problem aims to find the set of all Pareto-optimal paths. This problem is societally important due to several applications, such as route finding that considers both vehicle travel time and energy consumption. The problem is challenging due to the potentially large number of candidate Pareto-optimal paths to be enumerated during the search, making existing compute-on-demand methods inefficient due to their high time complexity. One way forward is the introduction of precomputation algorithms. However, the large size of the Pareto-optimal set makes it infeasible to precompute and store all-pair solutions. In addition, generalizing traditional single-objective hierarchical algorithms to bi-objective cases is non-trivial because of the non-comparability of candidate paths and the need to accommodate multiple Pareto-optimal paths for each node pair. To overcome these limitations, we propose Multi-Level Bi-Objective Routing (MBOR) algorithms using three novel ideas: boundary multigraph representation, Pareto frontier encoding, and two-dimensional cost-interval based pruning. Computational experiments using real road network data demonstrate that the proposed methods significantly outperform baseline methods in terms of online runtime and precomputation time.

关键词： Bi-objective routing Pareto optimality spatial query processing spatial algorithms

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Spatiotemporal Omics-Refining the landscape of precision medicine

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Life Medicine 2022年第2期1卷 84-102页

作者： Jiajun Zhang Jianhua Yin Yang Heng Ken Xie Ao Chen Ido Amit Xiu-wu Bian Xun Xu BGI Research-Southwest BGI Chongqing 401329 China JFL-BGI STOmics Center Jinfeng Laboratory Chongqing 401329 China BGI Research-Shenzhen BGI Shenzhen 518083 China BGI Research-Shenzhen BGI Shenzhen 518083 China Department of Immunology Weizmann Institute of Science Rehovot Israel Department of Pathology the First Affiliated Hospital of University of Science & Technology of China Hefei 230036 China Chongqing Institute of Advanced Pathology Jinfeng Laboratory Chongqing 400038 China

Current streamline of precision medicine uses histomorphological and molecular information to indicate individual phenotypes and genotypes to achieve optimal outcome of treatment. The knowledge of detected mutations and alteration can hardly describe molecular interaction and biological process which can finally be manifested as a disease. With molecular diagnosis revising the modalities of disease, there is a trend in precision medicine to apply multiomic and multidimensional information to decode tumors, regarding heterogeneity, pathogenesis, prognosis, etc. Emerging state-of-art spatiotemporal omics provides a novel vision for in discovering clinicopathogenesis associated findings, some of which show a promising potential to be translated to facilitate clinical practice. Here, we summarize the available spatiotemporal omic technologies and algorithms, highlight the novel scientific findings and explore potential applications in the clinical scenario. Spatiotemporal omics present the ability to provide impetus to rewrite clinical pathology and to answer outstanding clinical questions. This review emphasizes the novel vision of spatiotemporal omics to refine the landscape of precision medicine in the clinic.

关键词： spatiotemporal omics precision medicine pathomechanism of disease spatial algorithms technologies of spatial omics

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Classification of Passes in Football Matches Using Spatiotemporal Data

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ACM TRANSACTIONS ON spatial algorithms AND SYSTEMS 2017年第2期3卷 1–30页

作者： Chawla, Sanjay Estephan, Joel Gudmundsson, Joachim Horton, Michael HBKU Doha Qatar Univ Sydney Sch Informat Technol City Rd Sydney NSW 2006 Australia QCRI Hamad Bin Khalifa Res Complex Doha Qatar

A knowledgeable observer of a game of football (soccer) can make a subjective evaluation of the quality of passes made between players during the game, such as rating them as Good, OK, or Bad. In this article, we consider the problem of producing an automated system to make the same evaluation of passes and present a model to solve this problem. Recently, many professional football leagues have installed object tracking systems in their stadiums that generate high-resolution and high-frequency spatiotemporal trajectories of the players and the ball. Beginning with the thesis that much of the information required to make the pass ratings is available in the trajectory signal, we further postulated that using complex data structures derived from computational geometry would enable domain football knowledge to be included in the model by computing metric variables in a principled and efficient manner. We designed a model that computes a vector of predictor variables for each pass made and uses machine learning techniques to determine a classification function that can accurately rate passes based only on the predictor variable vector. Experimental results show that the learned classification functions can rate passes with 90.2% accuracy. The agreement between the classifier ratings and the ratings made by a human observer is comparable to the agreement between the ratings made by human observers, and suggests that significantly higher accuracy is unlikely to be achieved. Furthermore, we show that the predictor variables computed using methods from computational geometry are among the most important to the learned classifiers.

关键词： Classification supervised learning feature engineering spatial algorithms computational geometry

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Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells

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CANCER CYTOPATHOLOGY 2008年第1期114卷 13-21页

作者： Siddiqi, Anwer M. Li, Hui Faruque, Fazlay Williams, Worth Lai, Kent Hughson, Michael Bigler, Steven Beach, James Johnson, William Univ Mississippi Med Ctr Dept Pathol Jackson MS 39216 USA Univ Mississippi Med Ctr Geog Informat Syst Jackson MS 39216 USA Univ Miami Miller Sch Med Dept Pediat Dr John T Macdonald Fdn Ctr Med Genet Miami FL 33152 USA NASA Stennis Space Ctr Inst Technol Dev Stennis MS USA Univ Mississippi Med Ctr Dept Biomed Stat Jackson MS 39216 USA

BACKGROUND. The objective of the current study was to test the hypothesis that the cytologic diagnosis of cancer cells can be enhanced by the technique of hyperspectral imaging (HSI). METHODS. As a proof of principle, HSI was employed to obtain hyperspectrum from a normal human fibroblast, as well as its telomerase-immortalized and SV40-transformed derivatives. Novel algorithms were developed to differentiate among these cell models based on spectral and spatial differences. Using the same technique with modified algorithms, the authors were able to differentiate among normal and precancerous (low-grade [LG] and high-grade [HG]) cervical cells and squamous cell carcinoma (SCC) on liquid-based Papanicolaou (Pap) test slides. RESULTS. The specificity for identifying normal fibroblast cell type based on spatial and spectral algorithms was 74.2%. The sensitivity for identifying telomerase-immortalized and SV40 -transformed cells was 100% and 90.3%, respectively. The system identified normal cervical cells with a specificity of 95.8%. With regard to LG precancerous cells and HG precancerous cells, the sensitivity was 66.7% and 93.5%, respectively. The sensitivity detected for SCC was 98.6%. CONCLUSIONS. HSI can be utilized in prescreening liquid-based Pap test slides to improve efficiency in Pap test diagnoses with the goal of ultimately reducing the mortality from cervical cancer while reducing health care costs.

关键词： hyperspectral imaging Papanicolaou test cervical cancer cytology dysplasia precancerous computer detection spectral algorithms spatial algorithms

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Camera and visual veiling glare in HDR images

Camera and visual veiling glare in HDR images

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14th Color Imaging Conference

作者： McCann, John J. Rizzi, Alessandro McCann Imaging Belmont MA 02478 USA Univ Milan I-20122 Milan Italy

High-dynamic-range (HDR) images are superior to conventional images. The experiments in this paper measure camera and human responses to calibrated HDR test targets. We calibrated a 4.3-log-unit test target, with minimal and maximal glare from a changeable surround. Glare is an uncontrolled spread of an image-dependent fraction of scene luminance in cameras and in the eye. We use this standard test target to measure the range of luminances that can be captured on a camera's image plane. Further, we measure the appearance of these test luminance patches. We discuss why HDR is better than conventional imaging, despite the fact the reproduction of luminance is inaccurate.

关键词： HDR imaging veiling glare calibration of multiple exposure techniques spatial algorithms Retinex ACE

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Art, science, and appearance in HDR

Art, science, and appearance in HDR

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14th Color Imaging Conference

作者： McCann, John J. McCann Imaging Belmont MA 02478 USA

High-dynamic-range (HDR) image capture and display has become an important engineering topic. The discipline of reproducing scenes with a high range of luminances has a five-century history that includes painting, photography, electronic imaging, and image processing. HDR images are superior to conventional images. There are two fundamental scientific issues that control HDR image capture and reproduction. The first is the range of information that can be measured using different techniques. The second is the range of image information that can be utilized by humans. Optical veiling glare severely limits the range of luminance that can be captured and seen. It is the improved quantization of digital data and the preservation of the scene's spatial information that causes the improvement in quality in HDR reproductions.

关键词： HDR imaging veiling glare tone-scale maps human dynamic range spatial algorithms Retinex ACE

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Veiling glare: the dynamic range limit of HDR images

Veiling glare: the dynamic range limit of HDR images

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Conference on Human Vision and Electronic Imaging XII

作者： McCann, J. J. Rizzi, A. McCann Imaging Belmont MA 02478 USA Univ Milan Dip Tecnol Informaz Milan Italy

ISBN: (纸本)9780819466051

High Dynamic Range (HDR) images are superior to conventional images. However, veiling glare is a physical limit to HDR image acquisition and display. We performed camera calibration experiments using a single test target with 40 luminance patches covering a luminance range of 18,619:1. Veiling glare is a scene-dependent physical limit of the camera and the lens. Multiple exposures cannot accurately reconstruct scene luminances beyond the veiling glare limit. Human observer experiments, using the same targets, showed that image-dependent intraocular scatter changes identical display luminances into different retinal luminances. Vision's contrast mechanism further distorts any correlation of scene luminance and appearance. There must be reasons, other than accurate luminance, that explains the improvement in HDR images. The multiple exposure technique significantly improves digital quantization. The improved quantization allows displays to present better spatial information to humans. When human vision looks at high-dynamic range displays, it processes them using spatial comparisons.

关键词： HDR imaging veiling glare calibration of multiple exposure techniques spatial algorithms Retinex ACE

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Art, science, and appearance in HDR

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JOURNAL OF THE SOCIETY FOR INFORMATION DISPLAY 2007年第9期15卷 709-719页

作者： McCann, John J. McCann Imaging Belmont MA 02478 USA

关键词： HDR imaging veiling glare tone-scale maps human dynamic range spatial algorithms Retinex ACE

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Camera and visual veiling glare in HDR images

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JOURNAL OF THE SOCIETY FOR INFORMATION DISPLAY 2007年第9期15卷 721-730页

作者： McCann, John J. Rizzi, Alessandro McCann Imaging Belmont MA 02478 USA Univ Milan I-20122 Milan Italy

关键词： HDR imaging veiling glare calibration of multiple exposure techniques spatial algorithms Retinex ACE

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