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Introduction to the Special Issue on Understanding the Spread of COVID-19, Part 1 《认识新冠肺炎疫情传播》特刊简介(上
IF 1.9 Q4 REMOTE SENSING Pub Date : 2022-09-30 DOI: 10.1145/3568670
Andreas Züfle, T. Anderson, Song Gao
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.
传染病在人类宿主之间通过空间和时间的密切接触传播。最近,提供了前所未有的空间和时空数据,可用于提高我们对新冠肺炎和其他传染病传播的理解。这一理解对于通过空间算法和系统为未来的流行病做好准备至关重要,这些算法和系统用于收集、捕获、策划和分析复杂的、多尺度的人类活动数据,以解决传染病预测、接触者追踪和风险评估等问题。在探索和深化围绕这一主题的对话时,本期特刊第一卷中的八篇文章采用了不同的理论视角、方法论和框架,包括但不限于传染病模拟、风险预测、应对政策设计、流动性分析和病例诊断。这些文章没有关注一系列狭隘的问题,而是让我们一窥利用空间和时空数据做好疫情准备的各种可能性。
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引用次数: 1
Application of Kalman Filter to Large-scale Geospatial Data: Modeling Population Dynamics 卡尔曼滤波器在大规模地理空间数据中的应用:人口动力学建模
IF 1.9 Q4 REMOTE SENSING Pub Date : 2022-09-20 DOI: 10.1145/3563692
Hiroto Akatsuka, Masayuki Terada
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.
为了利用基于真实世界事件的大量观测数据,需要进行数据同化过程来估计观测数据背后的系统状态。卡尔曼滤波器是数据同化中非常常用的技术,但当应用于特别大规模的数据集时,从处理效率和估计精度下降的角度来看,它在实际应用方面存在问题。在本文中,我们提出了一种同时解决这些问题并证明其有用性的方法。该方法通过引入关注小波空间中数据的非负性和稀疏性的校正过程,提高了处理效率,抑制了估计精度的下降。我们表明,所提出的方法可以在使用从蜂窝网络生成的种群数据进行评估的基础上准确估计种群动态。此外,通过对5级台风“哈比”登陆日本时的情况分析,表明了使用该方法进行大范围异常检测的可能性。所提出的方法已应用于商业服务中,以估计日本的实时人口动态。
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引用次数: 1
Effect of Migrant Labourer Inflow on the Early Spread of Covid-19 in Odisha: A Case Study 外来劳动力流入对奥里萨邦新冠肺炎早期传播的影响:个案研究
IF 1.9 Q4 REMOTE SENSING Pub Date : 2022-08-23 DOI: 10.1145/3558778
S. Behera, D. P. Dogra, M. Satpathy
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.
奥里萨邦是印度东部的一个邦,人口4600万。每年都有大量的人移民到其他州的金融和工业中心谋生。由于新冠肺炎疫情,他们中的大部分人在全国封锁的早期阶段(2020年3月至6月)返回奥迪沙,因为他们的工作场所成为新冠肺炎热点,而奥迪沙受到的影响要小得多。这促使奥里萨邦政府采取预防措施,如对返回的移民进行强制隔离、设立隔离区和建立临时医疗中心(TMC)。此外,政府有必要制定一项政策,以减缓由于移民流入而导致的新冠肺炎在奥迪沙的传播。作为政府为了解新冠肺炎在奥迪沙的传播动态而成立的工作组的一部分,我们预测了主要由于反向传播而感染的人数。这有助于政府及时调动资源。在分析了流动人口众多的各个地区感染率上升的原因后,我们将预测问题映射到Abraham Wald的序列概率比检验(SPRT)中。与后期获得的真实数据相比,我们的预测非常准确。对政府提供的数据进行了两级SPRT。将SPRT用于新冠肺炎传播分析是一种新颖的方法,特别是预测由于移民的突然流入而可能提前的感染人数。
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引用次数: 2
SIRTEM: Spatially Informed Rapid Testing for Epidemic Modeling and Response to COVID-19 SIRTEM:基于空间信息的流行病建模和COVID-19响应快速测试
IF 1.9 Q4 REMOTE SENSING Pub Date : 2022-08-09 DOI: 10.1145/3555310
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.
2020年3月11日,世界卫生组织宣布新冠肺炎疫情为大流行。为了最大限度地减少伤亡和对经济的影响,已经采取了各种缓解措施来减缓感染的传播,例如完全封锁、保持社交距离和随机检测。这篇文章的主要贡献有两方面。首先,我们提出了一种新的扩展的空间知情流行病模型SIRTEM,用于新冠肺炎流行病建模和响应的空间知情快速测试,该模型集成了考虑测试准确性的多模式测试策略。我们的第二个贡献是一个优化模型,当有多种测试类型可用时,它可以提供一种具有成本效益的测试策略。所开发的优化模型包含了现实的基于空间的约束,如检测能力和病床限制。
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引用次数: 2
Memetic Algorithms for Spatial Partitioning Problems 空间划分问题的模因算法
IF 1.9 Q4 REMOTE SENSING Pub Date : 2022-06-17 DOI: 10.1145/3544779
Subhodip Biswas, Fanglan Chen, Zhiqian Chen, Chang-Tien Lu, Naren Ramakrishnan
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.
空间优化问题(SOP)的特征是控制决策变量、目标和/或约束函数的空间关系。在本文中,我们关注一种称为空间划分的特定类型的SOP,这是一个由于存在离散空间单元而引起的组合问题。精确的优化方法不会随着问题的规模而扩大,尤其是在可行的时间限制内。这促使我们开发基于人群的元启发式方法来解决此类SOP。然而,这些基于群体的方法所使用的搜索算子大多是为实参数连续优化问题设计的。为了使这些方法适应SOP,我们将领域知识应用于设计空间感知搜索算子,以便在保留空间约束的情况下有效地搜索离散搜索空间。为此,我们提出了一种简单而有效的算法,称为基于群的空间模因算法(spatial),并在学校(重新)划分问题上进行了测试。在真实世界的数据集上进行了详细的实验研究,以评估SPATIAL的性能。此外,还进行了消融研究,以了解SPATIAL各个组成部分的作用。此外,我们还讨论了SPATIAL在现实生活中的规划过程中是如何有帮助的,以及它对不同场景的适用性,并激励了未来的研究方向。
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引用次数: 2
Dwell Regions: Generalized Stay Regions for Streaming and Archival Trajectory Data 居住区域:流和存档轨迹数据的广义停留区域
IF 1.9 Q4 REMOTE SENSING Pub Date : 2022-06-13 DOI: 10.1145/3543850
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.
一个地区ℛ 是移动对象O的停留区域,如果给定阈值距离rq和持续时间τqℛ 在距离O的距离rq内保持至少时间τq。内的点ℛ O可能感兴趣,因此驻留区域的识别具有监测和监视等应用。我们首先提出了一种对数时间在线算法,以在物体位置的输入流中找到停留区域。我们的方法保持了包围对象位置的最小圆的半径的上限和下限,从而大大减少了评估查询所需的轨迹点的数量。它近似于在任意小的用户定义因子内包围给定子域的最小圆的半径,并且还能够有效地回答询问是否存在驻留区域的决策查询。对于停留区问题的离线版本,我们首先扩展了我们的在线方法来开发ρ-索引,该索引使用查询半径范围对子表进行索引。然后,我们对这种方法进行了改进,以获得τ-索引,该索引使用查询半径范围和停留持续时间对子表进行索引。我们使用真实世界和合成数据集进行的实验表明,在线方法可以扩展到数十万个移动对象。对于存档的轨迹,我们的索引方法将查询速度提高了许多数量级。
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引用次数: 0
COVID-19 Along with Other Chest Infection Diagnoses Using Faster R-CNN and Generative Adversarial Network 使用更快的R-CNN和世代对抗性网络诊断新冠肺炎和其他胸部感染
IF 1.9 Q4 REMOTE SENSING Pub Date : 2022-06-08 DOI: 10.1145/3520125
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.
冠状病毒(新冠肺炎)的快速传播导致严重的呼吸道感染,影响肺部。自动诊断有助于在社区疫情中抗击新冠肺炎。随着计算机视觉的进步,医学成像技术可以加强疾病监测和检测设施。不幸的是,由于新冠肺炎的数据存储库不够丰富,无法提供显著的不同特征,深度学习模型正面临着更通用的数据集的匮乏。为了解决这一限制,本文描述了通过使用生成对抗性网络的基于经验顶部熵的补丁选择方法生成新冠肺炎以及其他具有不同特征的胸部感染的合成图像。之后,使用6406张不同胸部感染的合成和3933张原始胸部X射线图像,通过更快的基于区域的卷积神经网络进行诊断,这也解决了数据不平衡问题,并且不属于特定类别。该实验证实,在多类情况下,新冠肺炎的诊断准确率为99.16%,令人满意。
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引用次数: 7
1D and 2D Flow Routing on a Terrain 地形上的1D和2D流路由
IF 1.9 Q4 REMOTE SENSING Pub Date : 2022-06-02 DOI: 10.1145/3539660
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.
地形分析中的一个重要问题是模拟水如何通过形成沟渠和填满洼地流过地形,从而产生洪水。在本文中,我们研究了许多与流量查询相关的问题:给定地形Σ,表示为具有n个顶点的三角形xy单调表面,以及可能随时间变化的降雨分布R,确定在给定顶点或边缘上流过多少水作为时间的函数。我们为流查询开发了内存和I/ o高效算法。本文包含四个主要的算法结果:(i)用于回答地形流查询的内存算法:将Σ预处理为线性大小的数据结构,因此给定雨分布R,可以快速报告Σ的所有顶点和边的流量函数。(ii)用于回答地形流查询的I/ o高效算法。(iii)回答顶点流查询的内存算法:将Σ预处理成一个线性大小的数据结构,给定一个雨分布R,可以快速计算出单流向(SFD)模型下顶点的流量函数。(iv)一种有效的算法,给定Σ中的路径𝖯和𝖯的流速,计算水沿其流动的二维通道。此外,我们实现了地形流查询和2D通道算法的一个版本,并在真实地形上检查了许多查询。
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引用次数: 0
Efficient 3D Spatial Queries for Complex Objects. 复杂对象的高效3D空间查询。
IF 1.9 Q4 REMOTE SENSING Pub Date : 2022-06-01 Epub Date: 2022-02-12 DOI: 10.1145/3502221
Dejun Teng, Yanhui Liang, Hoang Vo, Jun Kong, Fusheng Wang

3D spatial data has been generated at an extreme scale from many emerging applications, such as high definition maps for autonomous driving and 3D Human BioMolecular Atlas. In particular, 3D digital pathology provides a revolutionary approach to map human tissues in 3D, which is highly promising for advancing computer-aided diagnosis and understanding diseases through spatial queries and analysis. However, the exponential increase of data at 3D leads to significant I/O, communication, and computational challenges for 3D spatial queries. The complex structures of 3D objects such as bifurcated vessels make it difficult to effectively support 3D spatial queries with traditional methods. In this article, we present our work on building an efficient and scalable spatial query system, iSPEED, for large-scale 3D data with complex structures. iSPEED adopts effective progressive compression for each 3D object with successive levels of detail. Further, iSPEED exploits structural indexing for complex structured objects in distance-based queries. By querying with data represented in successive levels of details and structural indexes, iSPEED provides an option for users to balance between query efficiency and query accuracy. iSPEED builds in-memory indexes and decompresses data on-demand, which has a minimal memory footprint. iSPEED provides a 3D spatial query engine that can be invoked on-demand to run many instances in parallel implemented with, but not limited to, MapReduce. We evaluate iSPEED with three representative queries: 3D spatial joins, 3D nearest neighbor query, and 3D spatial proximity estimation. The extensive experiments demonstrate that iSPEED significantly improves the performance of existing spatial query systems.

3D空间数据已经从许多新兴应用中以极端规模生成,例如用于自动驾驶的高清地图和3D人体生物分子图谱。特别是,3D数字病理学提供了一种革命性的方法来绘制人体组织的3D图,这对于通过空间查询和分析来推进计算机辅助诊断和理解疾病非常有希望。然而,3D数据的指数级增长给3D空间查询带来了巨大的I/O、通信和计算挑战。由于分叉血管等三维物体结构复杂,传统方法难以有效支持三维空间查询。在本文中,我们介绍了我们的工作,建立一个高效和可扩展的空间查询系统,iSPEED,具有复杂结构的大规模三维数据。iSPEED对每个具有连续细节级别的3D对象采用有效的渐进式压缩。此外,iSPEED在基于距离的查询中利用复杂结构化对象的结构索引。通过使用连续的细节级别和结构索引表示的数据进行查询,iSPEED为用户提供了在查询效率和查询准确性之间进行平衡的选项。iSPEED在内存中构建索引并按需解压缩数据,这具有最小的内存占用。iSPEED提供了一个3D空间查询引擎,可以按需调用,以并行运行许多实例,但不限于MapReduce。我们使用三种代表性查询来评估iSPEED: 3D空间连接、3D最近邻查询和3D空间接近估计。大量的实验表明,iSPEED显著提高了现有空间查询系统的性能。
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引用次数: 0
Laser Range Scanners for Enabling Zero-overhead WiFi-based Indoor Localization System 用于实现零开销wifi室内定位系统的激光测距扫描仪
IF 1.9 Q4 REMOTE SENSING Pub Date : 2022-06-01 DOI: 10.1145/3539659
Hamada Rizk, H. Yamaguchi, Maged A. Youssef, T. Higashino
Robust and accurate indoor localization has been the goal of several research efforts over the past decade. Toward achieving this goal, WiFi fingerprinting-based indoor localization systems have been proposed. However, fingerprinting involves significant effort—especially when done at high density—and needs to be repeated with any change in the deployment area. While a number of recent systems have been introduced to reduce the calibration effort, these still trade overhead with accuracy. This article presents LiPhi++, an accurate system for enabling fingerprinting-based indoor localization systems without the associated data collection overhead. This is achieved by leveraging the sensing capability of transportable laser range scanners to automatically label WiFi scans, which can subsequently be used to build (and maintain) a fingerprint database. As part of its design, LiPhi++ leverages this database to train a deep long short-term memory network utilizing the signal strength history from the detected access points. LiPhi++ also has provisions for handling practical deployment issues, including the noisy wireless environment, heterogeneous devices, among others. Evaluation of LiPhi++ using Android phones in two realistic testbeds shows that it can match the performance of manual fingerprinting techniques under the same deployment conditions without the overhead associated with the traditional fingerprinting process. In addition, LiPhi++ improves upon the median localization accuracy obtained from crowdsourcing-based and fingerprinting-based systems by 284% and 418%, respectively, when tested with data collected a few months later.
在过去的十年中,稳健和准确的室内定位一直是一些研究工作的目标。为了实现这一目标,已经提出了基于WiFi指纹的室内定位系统。但是,指纹识别需要大量的工作—特别是在高密度的情况下—并且需要在部署区域发生任何变化时重复进行。虽然最近已经引入了许多系统来减少校准工作,但这些系统仍然以准确性为代价。本文介绍了LiPhi++,这是一个精确的系统,可以实现基于指纹的室内定位系统,而不需要相关的数据收集开销。这是通过利用便携式激光测距扫描仪的传感能力来自动标记WiFi扫描,随后可用于建立(和维护)指纹数据库来实现的。作为其设计的一部分,LiPhi++利用这个数据库来训练一个深度的长短期记忆网络,利用来自检测到的接入点的信号强度历史。LiPhi++还提供了处理实际部署问题的规定,包括嘈杂的无线环境、异构设备等。使用Android手机在两个实际测试平台上对LiPhi++进行的评估表明,在相同的部署条件下,它可以匹配手动指纹识别技术的性能,而不会产生与传统指纹识别过程相关的开销。此外,在使用几个月后收集的数据进行测试时,LiPhi++在基于众包系统和基于指纹系统的定位精度中值基础上分别提高了284%和418%。
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引用次数: 10
期刊
ACM Transactions on Spatial Algorithms and Systems
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