Infectious diseases are transmitted between human hosts when in close contact over space and time. Recently, an unprecedented amount of spatial and spatiotemporal data have been made available that can be used to improve our understanding of the spread of COVID-19 and other infectious diseases. This understanding will be paramount to prepare for future pandemics through spatial algorithms and systems to collect, capture, curate and analyze complex, multi-scale human movement data to solve problems such as infectious diseases prediction, contact tracing, and risk assessment. In exploring and deepening the conversation around this topic, the five articles included in the second volume of this special issue employ diverse theoretical perspectives, methodologies, and frameworks, including but not limited to close contact modeling, infectious diseases spread prediction, mobility analysis, effective testing and intervention strategies. Rather than focusing on a narrow set of problems, these articles provide a glimpse into the diverse possibilities of leveraging spatial and spatiotemporal data for pandemic preparedness.
{"title":"Introduction to the Special Issue on Understanding the Spread of COVID-19, Part 2","authors":"Andreas Züfle, T. Anderson, Song Gao","doi":"10.1145/3568669","DOIUrl":"https://doi.org/10.1145/3568669","url":null,"abstract":"Infectious diseases are transmitted between human hosts when in close contact over space and time. Recently, an unprecedented amount of spatial and spatiotemporal data have been made available that can be used to improve our understanding of the spread of COVID-19 and other infectious diseases. This understanding will be paramount to prepare for future pandemics through spatial algorithms and systems to collect, capture, curate and analyze complex, multi-scale human movement data to solve problems such as infectious diseases prediction, contact tracing, and risk assessment. In exploring and deepening the conversation around this topic, the five articles included in the second volume of this special issue employ diverse theoretical perspectives, methodologies, and frameworks, including but not limited to close contact modeling, infectious diseases spread prediction, mobility analysis, effective testing and intervention strategies. Rather than focusing on a narrow set of problems, these articles provide a glimpse into the diverse possibilities of leveraging spatial and spatiotemporal data for pandemic preparedness.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49624596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Sinop, Lisa Fawcett, Sreenivas Gollapudi, Kostas Kollias
Generating alternative routes in road networks is an application of significant interest for online navigation systems. A high quality set of diverse alternate routes offers two functionalities - a) support multiple (unknown) preferences that the user may have; and b) robust to changes in network conditions. We formulate a new quantification of the latter in this paper, and propose a novel method to produce alternative routes based on concepts from electrical flows and their decompositions. Our method is fundamentally different from the main techniques that produce alternative routes in road networks, which are the penalty and the plateau methods, with the former providing high quality results but being too slow for practical use and the latter being fast but suffering in terms of quality. We evaluate our method against the penalty and plateau methods, showing that it is as fast as the plateau method while also recovering much of the headroom towards the quality of the penalty method. The metrics we use to evaluate performance include the stretch (the average cost of the routes), the diversity, and the robustness (the connectivity between the origin and destination) of the induced set of routes.
{"title":"Robust Routing Using Electrical Flows","authors":"A. Sinop, Lisa Fawcett, Sreenivas Gollapudi, Kostas Kollias","doi":"10.1145/3567421","DOIUrl":"https://doi.org/10.1145/3567421","url":null,"abstract":"Generating alternative routes in road networks is an application of significant interest for online navigation systems. A high quality set of diverse alternate routes offers two functionalities - a) support multiple (unknown) preferences that the user may have; and b) robust to changes in network conditions. We formulate a new quantification of the latter in this paper, and propose a novel method to produce alternative routes based on concepts from electrical flows and their decompositions. Our method is fundamentally different from the main techniques that produce alternative routes in road networks, which are the penalty and the plateau methods, with the former providing high quality results but being too slow for practical use and the latter being fast but suffering in terms of quality. We evaluate our method against the penalty and plateau methods, showing that it is as fast as the plateau method while also recovering much of the headroom towards the quality of the penalty method. The metrics we use to evaluate performance include the stretch (the average cost of the routes), the diversity, and the robustness (the connectivity between the origin and destination) of the induced set of routes.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78694915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Infectious diseases are transmitted between human hosts when in close contact over space and time. Recently, an unprecedented amount of spatial and spatiotemporal data have been made available that can be used to improve our understanding of the spread of COVID-19 and other infectious diseases. This understanding will be paramount to prepare for future pandemics through spatial algorithms and systems to collect, capture, curate, and analyze complex, multi-scale human movement data to solve problems such as infectious diseases prediction, contact tracing, and risk assessment. In exploring and deepening the conversation around this topic, the eight articles included in the first volume of this special issue employ diverse theoretical perspectives, methodologies, and frameworks, including but not limited to infectious diseases simulation, risk prediction, response policy design, mobility analysis, and case diagnosis. Rather than focusing on a narrow set of problems, these articles provide a glimpse into the diverse possibilities of leveraging spatial and spatiotemporal data for pandemic preparedness.
{"title":"Introduction to the Special Issue on Understanding the Spread of COVID-19, Part 1","authors":"Andreas Züfle, T. Anderson, Song Gao","doi":"10.1145/3568670","DOIUrl":"https://doi.org/10.1145/3568670","url":null,"abstract":"Infectious diseases are transmitted between human hosts when in close contact over space and time. Recently, an unprecedented amount of spatial and spatiotemporal data have been made available that can be used to improve our understanding of the spread of COVID-19 and other infectious diseases. This understanding will be paramount to prepare for future pandemics through spatial algorithms and systems to collect, capture, curate, and analyze complex, multi-scale human movement data to solve problems such as infectious diseases prediction, contact tracing, and risk assessment. In exploring and deepening the conversation around this topic, the eight articles included in the first volume of this special issue employ diverse theoretical perspectives, methodologies, and frameworks, including but not limited to infectious diseases simulation, risk prediction, response policy design, mobility analysis, and case diagnosis. Rather than focusing on a narrow set of problems, these articles provide a glimpse into the diverse possibilities of leveraging spatial and spatiotemporal data for pandemic preparedness.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45640223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To utilize a huge amount of observation data based on real-world events, a data assimilation process is needed to estimate the state of the system behind the observed data. The Kalman filter is a very commonly used technique in data assimilation, but it has a problem in terms of practical use from the viewpoint of processing efficiency and estimating the deterioration in precision when applied to particularly large-scale datasets. In this article, we propose a method that simultaneously addresses these problems and demonstrate its usefulness. The proposed method improves the processing efficiency and suppresses the deterioration in estimation precision by introducing correction processes focusing on the non-negative nature and sparseness of data in wavelet space. We show that the proposed method can accurately estimate population dynamics on the basis of an evaluation done using population data generated from cellular networks. In addition, the possibility of wide area abnormality detection using the proposed method is shown from a situation analysis of when Category 5 typhoon Hagibis made landfall in Japan. The proposed method has been deployed in a commercial service to estimate real-time population dynamics in Japan.
{"title":"Application of Kalman Filter to Large-scale Geospatial Data: Modeling Population Dynamics","authors":"Hiroto Akatsuka, Masayuki Terada","doi":"10.1145/3563692","DOIUrl":"https://doi.org/10.1145/3563692","url":null,"abstract":"To utilize a huge amount of observation data based on real-world events, a data assimilation process is needed to estimate the state of the system behind the observed data. The Kalman filter is a very commonly used technique in data assimilation, but it has a problem in terms of practical use from the viewpoint of processing efficiency and estimating the deterioration in precision when applied to particularly large-scale datasets. In this article, we propose a method that simultaneously addresses these problems and demonstrate its usefulness. The proposed method improves the processing efficiency and suppresses the deterioration in estimation precision by introducing correction processes focusing on the non-negative nature and sparseness of data in wavelet space. We show that the proposed method can accurately estimate population dynamics on the basis of an evaluation done using population data generated from cellular networks. In addition, the possibility of wide area abnormality detection using the proposed method is shown from a situation analysis of when Category 5 typhoon Hagibis made landfall in Japan. The proposed method has been deployed in a commercial service to estimate real-time population dynamics in Japan.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41362053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Odisha is a state in the eastern part of India with a population of 46 million. Annually, a large number of people migrate to financial and industrial centers in other states for their livelihood earning. Bulk of them returned to Odisha during the early stage of national lockdown (March–June 2020) due to the Covid-19 outbreak as their places of work became Covid hotspots while Odisha was much less affected. This triggered the Odisha government to take precautionary measures such as mandatory quarantine of returning migrants, setting up of containment zones, and establishing temporary medical centres (TMC). Moreover, it was necessary for the government to devise a policy that could slow down the spread of Covid-19 in Odisha due to inflow of migrants. Being part of a task-force constituted by government to understand Covid-19 spread dynamics in Odisha, we predicted the number of people who would get infected primarily due to reverse-migration. This helped the government to make timely resource mobilisation. After analyzing reasons behind the rise in infections at various districts with large migrant population, we mapped the prediction problem to Sequential Probability Ratio Test (SPRT) of Abraham Wald. Our predictions were highly accurate when compared with real data that were obtained at a later stage. Two levels of SPRT were carried out over the data provided by the government. Use of SPRT for Covid-19 spread analysis is novel, particularly to predict the number of possible infections much ahead in time due to the sudden inflow of migrants.
{"title":"Effect of Migrant Labourer Inflow on the Early Spread of Covid-19 in Odisha: A Case Study","authors":"S. Behera, D. P. Dogra, M. Satpathy","doi":"10.1145/3558778","DOIUrl":"https://doi.org/10.1145/3558778","url":null,"abstract":"Odisha is a state in the eastern part of India with a population of 46 million. Annually, a large number of people migrate to financial and industrial centers in other states for their livelihood earning. Bulk of them returned to Odisha during the early stage of national lockdown (March–June 2020) due to the Covid-19 outbreak as their places of work became Covid hotspots while Odisha was much less affected. This triggered the Odisha government to take precautionary measures such as mandatory quarantine of returning migrants, setting up of containment zones, and establishing temporary medical centres (TMC). Moreover, it was necessary for the government to devise a policy that could slow down the spread of Covid-19 in Odisha due to inflow of migrants. Being part of a task-force constituted by government to understand Covid-19 spread dynamics in Odisha, we predicted the number of people who would get infected primarily due to reverse-migration. This helped the government to make timely resource mobilisation. After analyzing reasons behind the rise in infections at various districts with large migrant population, we mapped the prediction problem to Sequential Probability Ratio Test (SPRT) of Abraham Wald. Our predictions were highly accurate when compared with real data that were obtained at a later stage. Two levels of SPRT were carried out over the data provided by the government. Use of SPRT for Covid-19 spread analysis is novel, particularly to predict the number of possible infections much ahead in time due to the sudden inflow of migrants.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43817738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fahim Tasneema Azad, Robert W. Dodge, Allen M. Varghese, Jaejin Lee, Giulia Pedrielli, K. Candan, Gerardo Chowell-Puente
COVID-19 outbreak was declared a pandemic by the World Health Organization on March 11, 2020. To minimize casualties and the impact on the economy, various mitigation measures have being employed with the purpose to slow the spread of the infection, such as complete lockdown, social distancing, and random testing. The key contribution of this article is twofold. First, we present a novel extended spatially informed epidemic model, SIRTEM, Spatially Informed Rapid Testing for Epidemic Modeling and Response to COVID-19, that integrates a multi-modal testing strategy considering test accuracies. Our second contribution is an optimization model to provide a cost-effective testing strategy when multiple test types are available. The developed optimization model incorporates realistic spatially based constraints, such as testing capacity and hospital bed limitation as well.
{"title":"SIRTEM: Spatially Informed Rapid Testing for Epidemic Modeling and Response to COVID-19","authors":"Fahim Tasneema Azad, Robert W. Dodge, Allen M. Varghese, Jaejin Lee, Giulia Pedrielli, K. Candan, Gerardo Chowell-Puente","doi":"10.1145/3555310","DOIUrl":"https://doi.org/10.1145/3555310","url":null,"abstract":"COVID-19 outbreak was declared a pandemic by the World Health Organization on March 11, 2020. To minimize casualties and the impact on the economy, various mitigation measures have being employed with the purpose to slow the spread of the infection, such as complete lockdown, social distancing, and random testing. The key contribution of this article is twofold. First, we present a novel extended spatially informed epidemic model, SIRTEM, Spatially Informed Rapid Testing for Epidemic Modeling and Response to COVID-19, that integrates a multi-modal testing strategy considering test accuracies. Our second contribution is an optimization model to provide a cost-effective testing strategy when multiple test types are available. The developed optimization model incorporates realistic spatially based constraints, such as testing capacity and hospital bed limitation as well.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46525692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Spatial optimization problems (SOPs) are characterized by spatial relationships governing the decision variables, objectives, and/or constraint functions. In this article, we focus on a specific type of SOP called spatial partitioning, which is a combinatorial problem due to the presence of discrete spatial units. Exact optimization methods do not scale with the size of the problem, especially within practicable time limits. This motivated us to develop population-based metaheuristics for solving such SOPs. However, the search operators employed by these population-based methods are mostly designed for real-parameter continuous optimization problems. For adapting these methods to SOPs, we apply domain knowledge in designing spatially aware search operators for efficiently searching through the discrete search space while preserving the spatial constraints. To this end, we put forward a simple yet effective algorithm called swarm-based spatial memetic algorithm (SPATIAL) and test it on the school (re)districting problem. Detailed experimental investigations are performed on real-world datasets to evaluate the performance of SPATIAL. Besides, ablation studies are performed to understand the role of the individual components of SPATIAL. Additionally, we discuss how SPATIAL is helpful in the real-life planning process and its applicability to different scenarios and motivate future research directions.
{"title":"Memetic Algorithms for Spatial Partitioning Problems","authors":"Subhodip Biswas, Fanglan Chen, Zhiqian Chen, Chang-Tien Lu, Naren Ramakrishnan","doi":"10.1145/3544779","DOIUrl":"https://doi.org/10.1145/3544779","url":null,"abstract":"Spatial optimization problems (SOPs) are characterized by spatial relationships governing the decision variables, objectives, and/or constraint functions. In this article, we focus on a specific type of SOP called spatial partitioning, which is a combinatorial problem due to the presence of discrete spatial units. Exact optimization methods do not scale with the size of the problem, especially within practicable time limits. This motivated us to develop population-based metaheuristics for solving such SOPs. However, the search operators employed by these population-based methods are mostly designed for real-parameter continuous optimization problems. For adapting these methods to SOPs, we apply domain knowledge in designing spatially aware search operators for efficiently searching through the discrete search space while preserving the spatial constraints. To this end, we put forward a simple yet effective algorithm called swarm-based spatial memetic algorithm (SPATIAL) and test it on the school (re)districting problem. Detailed experimental investigations are performed on real-world datasets to evaluate the performance of SPATIAL. Besides, ablation studies are performed to understand the role of the individual components of SPATIAL. Additionally, we discuss how SPATIAL is helpful in the real-life planning process and its applicability to different scenarios and motivate future research directions.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46180892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Uddin, Mehnaz Tabassum Mahin, Payas Rajan, C. Ravishankar, V. Tsotras
A region ℛ is a dwell region for a moving object O if, given a threshold distance rq and duration τq, every point of ℛ remains within distance rq from O for at least time τq. Points within ℛ are likely to be of interest to O, so identification of dwell regions has applications such as monitoring and surveillance. We first present a logarithmic-time online algorithm to find dwell regions in an incoming stream of object positions. Our method maintains the upper and lower bounds for the radius of the smallest circle enclosing the object positions, thereby greatly reducing the number of trajectory points needed to evaluate the query. It approximates the radius of the smallest circle enclosing a given subtrajectory within an arbitrarily small user-defined factor and is also able to efficiently answer decision queries asking whether or not a dwell region exists. For the offline version of the dwell region problem, we first extend our online approach to develop the ρ-Index, which indexes subtrajectories using query radius ranges. We then refine this approach to obtain the τ-Index, which indexes subtrajectories using both query radius ranges and dwell durations. Our experiments using both real-world and synthetic datasets show that the online approach can scale up to hundreds of thousands of moving objects. For archived trajectories, our indexing approaches speed up queries by many orders of magnitude.
{"title":"Dwell Regions: Generalized Stay Regions for Streaming and Archival Trajectory Data","authors":"R. Uddin, Mehnaz Tabassum Mahin, Payas Rajan, C. Ravishankar, V. Tsotras","doi":"10.1145/3543850","DOIUrl":"https://doi.org/10.1145/3543850","url":null,"abstract":"A region ℛ is a dwell region for a moving object O if, given a threshold distance rq and duration τq, every point of ℛ remains within distance rq from O for at least time τq. Points within ℛ are likely to be of interest to O, so identification of dwell regions has applications such as monitoring and surveillance. We first present a logarithmic-time online algorithm to find dwell regions in an incoming stream of object positions. Our method maintains the upper and lower bounds for the radius of the smallest circle enclosing the object positions, thereby greatly reducing the number of trajectory points needed to evaluate the query. It approximates the radius of the smallest circle enclosing a given subtrajectory within an arbitrarily small user-defined factor and is also able to efficiently answer decision queries asking whether or not a dwell region exists. For the offline version of the dwell region problem, we first extend our online approach to develop the ρ-Index, which indexes subtrajectories using query radius ranges. We then refine this approach to obtain the τ-Index, which indexes subtrajectories using both query radius ranges and dwell durations. Our experiments using both real-world and synthetic datasets show that the online approach can scale up to hundreds of thousands of moving objects. For archived trajectories, our indexing approaches speed up queries by many orders of magnitude.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48485948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rafid Mostafiz, Mohammad Shorif Uddin, K. M. Uddin, Mohammad Motiur Rahman
The rapid spreading of coronavirus (COVID-19) caused severe respiratory infections affecting the lungs. Automatic diagnosis helps to fight against COVID-19 in community outbreaks. Medical imaging technology can reinforce disease monitoring and detection facilities with the advancement of computer vision. Unfortunately, deep learning models are facing starvation of more generalized datasets as the data repositories of COVID-19 are not rich enough to provide significant distinct features. To address the limitation, this article describes the generation of synthetic images of COVID-19 along with other chest infections with distinct features by empirical top entropy-based patch selection approach using the generative adversarial network. After that, a diagnosis is performed through a faster region-based convolutional neural network using 6,406 synthetic as well as 3,933 original chest X-ray images of different chest infections, which also addressed the data imbalance problems and not recumbent to a particular class. The experiment confirms a satisfactory COVID-19 diagnosis accuracy of 99.16% in a multi-class scenario.
{"title":"COVID-19 Along with Other Chest Infection Diagnoses Using Faster R-CNN and Generative Adversarial Network","authors":"Rafid Mostafiz, Mohammad Shorif Uddin, K. M. Uddin, Mohammad Motiur Rahman","doi":"10.1145/3520125","DOIUrl":"https://doi.org/10.1145/3520125","url":null,"abstract":"The rapid spreading of coronavirus (COVID-19) caused severe respiratory infections affecting the lungs. Automatic diagnosis helps to fight against COVID-19 in community outbreaks. Medical imaging technology can reinforce disease monitoring and detection facilities with the advancement of computer vision. Unfortunately, deep learning models are facing starvation of more generalized datasets as the data repositories of COVID-19 are not rich enough to provide significant distinct features. To address the limitation, this article describes the generation of synthetic images of COVID-19 along with other chest infections with distinct features by empirical top entropy-based patch selection approach using the generative adversarial network. After that, a diagnosis is performed through a faster region-based convolutional neural network using 6,406 synthetic as well as 3,933 original chest X-ray images of different chest infections, which also addressed the data imbalance problems and not recumbent to a particular class. The experiment confirms a satisfactory COVID-19 diagnosis accuracy of 99.16% in a multi-class scenario.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47079104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Arge, Aaron Lowe, Svend C. Svendsen, P. Agarwal
An important problem in terrain analysis is modeling how water flows across a terrain creating floods by forming channels and filling depressions. In this article, we study a number of flow-query-related problems: Given a terrain Σ, represented as a triangulated xy-monotone surface with n vertices, and a rain distribution R that may vary over time, determine how much water is flowing over a given vertex or edge as a function of time. We develop internal-memory as well as I/O-efficient algorithms for flow queries. This article contains four main algorithmic results: (i) An internal-memory algorithm for answering terrain-flow queries: Preprocess Σ into a linear-size data structure so given a rain distribution R, the flow-rate functions of all vertices and edges of Σ can be reported quickly. (ii) I/O-efficient algorithms for answering terrain-flow queries. (iii) An internal-memory algorithm for answering vertex-flow queries: Preprocess Σ into a linear-size data structure so given a rain distribution R, the flow-rate function of a vertex under the single-flow direction (SFD) model can be computed quickly. (iv) An efficient algorithm that, given a path 𝖯 in Σ and flow rate along 𝖯, computes the two-dimensional channel along which water flows. Additionally, we implement a version of the terrain-flow query and 2D channel algorithms and examine a number of queries on real terrains.
{"title":"1D and 2D Flow Routing on a Terrain","authors":"L. Arge, Aaron Lowe, Svend C. Svendsen, P. Agarwal","doi":"10.1145/3539660","DOIUrl":"https://doi.org/10.1145/3539660","url":null,"abstract":"An important problem in terrain analysis is modeling how water flows across a terrain creating floods by forming channels and filling depressions. In this article, we study a number of flow-query-related problems: Given a terrain Σ, represented as a triangulated xy-monotone surface with n vertices, and a rain distribution R that may vary over time, determine how much water is flowing over a given vertex or edge as a function of time. We develop internal-memory as well as I/O-efficient algorithms for flow queries. This article contains four main algorithmic results: (i) An internal-memory algorithm for answering terrain-flow queries: Preprocess Σ into a linear-size data structure so given a rain distribution R, the flow-rate functions of all vertices and edges of Σ can be reported quickly. (ii) I/O-efficient algorithms for answering terrain-flow queries. (iii) An internal-memory algorithm for answering vertex-flow queries: Preprocess Σ into a linear-size data structure so given a rain distribution R, the flow-rate function of a vertex under the single-flow direction (SFD) model can be computed quickly. (iv) An efficient algorithm that, given a path 𝖯 in Σ and flow rate along 𝖯, computes the two-dimensional channel along which water flows. Additionally, we implement a version of the terrain-flow query and 2D channel algorithms and examine a number of queries on real terrains.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44475160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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