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"content": "This paper reviews the first challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The challenge had 2 tracks. Track 1 employed the indoor images (using I-HAZE dataset), while Track 2 outdoor images (using O-HAZE dataset). The hazy images have been captured in presence of real haze, generated by professional haze machines. I-HAZE dataset contains 35 scenes that correspond to indoor domestic environments, with objects with different colors and specularities. O-HAZE contains 45 different outdoor scenes depicting the same visual content recorded in haze-free and hazy conditions, under the same illumination parameters. The dehazing process was learnable through provided pairs of haze-free and hazy train images. Each track had ~120 registered participants and 21 teams competed in the final testing phase. They gauge the state-of-the-art in image dehazing.",
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"affiliation": "The University of Sydney,Sydney,Australia",
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"affiliation": "Dalian University of Technology,Liaoning,China",
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"affiliation": "Tongji University,Shanghai,China",
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"title": "NTIRE 2019 Image Dehazing Challenge Report",
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"abstract": "This paper reviews the second NTIRE challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The training data consists from 55 hazy images (with dense haze generated in an indoor or outdoor environment) and their corresponding ground truth (haze-free) images of the same scene. The dense haze has been produced using a professional haze/fog generator that imitates the real conditions of haze scenes. The evaluation consists from the comparison of the dehazed images with the ground truth images. The dehazing process was learnable through provided pairs of haze-free and hazy train images. There were ~ 270 registered participants and 23 teams competed in the final testing phase. They gauge the state-of-the-art in image dehazing.",
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"content": "This paper reviews the second NTIRE challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The training data consists from 55 hazy images (with dense haze generated in an indoor or outdoor environment) and their corresponding ground truth (haze-free) images of the same scene. The dense haze has been produced using a professional haze/fog generator that imitates the real conditions of haze scenes. The evaluation consists from the comparison of the dehazed images with the ground truth images. The dehazing process was learnable through provided pairs of haze-free and hazy train images. There were ~ 270 registered participants and 23 teams competed in the final testing phase. They gauge the state-of-the-art in image dehazing.",
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"authors": [
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"affiliation": "University Politehnica Timisoara",
"fullName": "Codruta O. Ancuti",
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"abstract": "Feature analysis is always beneficial to the detection of anonymous criminals in digital forensics, including people and activities, where vast amount of features extracted from databases are involved. Not all features extracted are continuous or different, some of them are discrete or have the same value with others. We discovered that using visual analytics to select features for forensic investigations is not only improve the analysis time of selection, but can also deeply and obviously display the slight changes of features and criminals and also the relationship between features and criminals in order to find the target with significant difference with others, and also predict the more active features to be used in the future. Experiments show that visual feature analysis can help to catch the desire results quickly and clearly.",
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"abstract": "Pathologists visually examine cell morphology by observing the biopsy slides under a microscope through different magnifying factors, which is time-consuming and error-prone. In this regard, computer-aided whole slide image (WSI) analysis is necessary to help pathologists reduce time effort, and human error. With the recent advances in deep learning for computer vision, convolutional neural networks (ConvNets) have gained attention in the medical domain and have shown significant progress in whole slide image classification. Existing deep learning approaches work by feeding ConvNet with small patches extracted from WSIs. However, it is unknown how the size of the extracted patches and the magnifying factor of the WSI affect the performance of the ConvNet. Therefore, we construct several datasets by extracting patches from stomach histopathological imagery, varying the size of the patches and the magnifying factor of the WSIs. Densely Connected Convolutional Neural Network (DenseNet) is used to classify dysplasia, malignant, and benign patches. We observe the impact of the patch extraction variables using precision and recall. This study shines a light on why these factors would affect the model performance concerning data representation and provides a guideline for histopathological imagery data extraction methods.",
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"content": "Pathologists visually examine cell morphology by observing the biopsy slides under a microscope through different magnifying factors, which is time-consuming and error-prone. In this regard, computer-aided whole slide image (WSI) analysis is necessary to help pathologists reduce time effort, and human error. With the recent advances in deep learning for computer vision, convolutional neural networks (ConvNets) have gained attention in the medical domain and have shown significant progress in whole slide image classification. Existing deep learning approaches work by feeding ConvNet with small patches extracted from WSIs. However, it is unknown how the size of the extracted patches and the magnifying factor of the WSI affect the performance of the ConvNet. Therefore, we construct several datasets by extracting patches from stomach histopathological imagery, varying the size of the patches and the magnifying factor of the WSIs. Densely Connected Convolutional Neural Network (DenseNet) is used to classify dysplasia, malignant, and benign patches. We observe the impact of the patch extraction variables using precision and recall. This study shines a light on why these factors would affect the model performance concerning data representation and provides a guideline for histopathological imagery data extraction methods.",
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"normalizedAbstract": "Pathologists visually examine cell morphology by observing the biopsy slides under a microscope through different magnifying factors, which is time-consuming and error-prone. In this regard, computer-aided whole slide image (WSI) analysis is necessary to help pathologists reduce time effort, and human error. With the recent advances in deep learning for computer vision, convolutional neural networks (ConvNets) have gained attention in the medical domain and have shown significant progress in whole slide image classification. Existing deep learning approaches work by feeding ConvNet with small patches extracted from WSIs. However, it is unknown how the size of the extracted patches and the magnifying factor of the WSI affect the performance of the ConvNet. Therefore, we construct several datasets by extracting patches from stomach histopathological imagery, varying the size of the patches and the magnifying factor of the WSIs. Densely Connected Convolutional Neural Network (DenseNet) is used to classify dysplasia, malignant, and benign patches. We observe the impact of the patch extraction variables using precision and recall. This study shines a light on why these factors would affect the model performance concerning data representation and provides a guideline for histopathological imagery data extraction methods.",
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"affiliation": "Graduate School of Knowledge Service Engineering KAIST,Daejeon,South Korea",
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"abstract": "Visual microscopic study of diseased tissue by pathologists has been the cornerstone for cancer diagnosis and prognostication for more than a century. Recently, deep learning methods have made significant advances in the analysis and classification of tissue images. However, there has been limited work on the utility of such models in generating histopathology images. These synthetic images have several applications in pathology including utilities in education, proficiency testing, privacy, and data sharing. Recently, diffusion probabilistic models were introduced to generate high quality images. Here, for the first time, we investigate the potential use of such models along with prioritized morphology weighting and color normalization to synthesize high quality histopathology images of brain cancer. Our detailed results show that diffusion probabilistic models are capable of synthesizing a wide range of histopathology images and have superior performance compared to generative adversarial networks.",
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"content": "Visual microscopic study of diseased tissue by pathologists has been the cornerstone for cancer diagnosis and prognostication for more than a century. Recently, deep learning methods have made significant advances in the analysis and classification of tissue images. However, there has been limited work on the utility of such models in generating histopathology images. These synthetic images have several applications in pathology including utilities in education, proficiency testing, privacy, and data sharing. Recently, diffusion probabilistic models were introduced to generate high quality images. Here, for the first time, we investigate the potential use of such models along with prioritized morphology weighting and color normalization to synthesize high quality histopathology images of brain cancer. Our detailed results show that diffusion probabilistic models are capable of synthesizing a wide range of histopathology images and have superior performance compared to generative adversarial networks.",
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"affiliation": "BC Cancer Agency",
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"affiliation": "University of British Columbia",
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"abstract": "The conventional force-directed methods for drawing undirected graphs are based on either vertex-vertex repulsion or vertex-edge repulsion. In this paper, we propose a new force-directed method based on edge-edge repulsion to draw graphs. In our framework, edges are modelled as charged springs, and a final drawing can be generated by adjusting positions of vertices according to spring forces and the repulsive forces, derived from potential fields, among edges. Different from the previous methods, our new framework has the advantage of overcoming the problem of zero angular resolution, guaranteeing the absence of any overlapping of edges incident to the common vertex. Given graph layouts probably generated by classical algorithms as the inputs to our algorithm, experimental results reveal that our approach produces promising drawings (especially for trees and hypercubes) not only preserving the original properties of a high degree of symmetry and uniform edge length, but also preventing zero angular resolution. By allowing vertex-vertex overlapping, our algorithm also results in more symmetrical drawings.",
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"affiliation": "National Taiwan University",
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"abstract": "Multimedia applications usually involve a large number of multimedia objects (texts, images, sounds, etc.). Spatial and temporal relationships among these objects should be efficiently supported and retrieved within a multimedia authoring tool. In this paper, we present several spatial, temporal and spatio-temporal relationships of interest, and propose efficient indexing schemes, based on multidimensional (spatial) data structures, for large multimedia applications that involve thousands of objects. Evaluation models of the proposed schemes are also presented, as well as hints for the selection of the most appropriate one, according to the multimedia author's requirements.",
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"abstract": "With the rate of errors that silently effect an application's state/output expected to increase in future HPC machines, numerous mitigation schemes have been proposed, but little work has been done investigating why these schemes detect some error while other is masked. This paper investigates how silent data corruption (SDC) propagates through a sparse matrix vector multiply (SpMV), a fundamental HPC computation kernel. We discover that analyzing the mathematics of the SpMV limits understanding of SDC propagation. We achieve a more complete understanding by investigating how SDC propagates in a SpMV as it is expressed in machine instructions.",
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"abstract": "As high-performance computing systems scale in size and computational power, the danger of silent errors, i.e., errors that can bypass hardware detection mechanisms and impact application state, grows dramatically. Consequently, applications running on HPC systems need to exhibit resilience to such errors. Previous work has found that, for certain codes, this resilience can come for free, i.e., some applications are naturally resilient, but few studies have shown the code patterns-combinations or sequences of computations-that make an application naturally resilient. In this paper, we present FlipTracker, a framework designed to extract these patterns using fine-grained tracking of error propagation and resilience properties, and we use it to present a set of computation patterns that are responsible for making representative HPC applications naturally resilient to errors. This not only enables a deeper understanding of resilience properties of these codes, but also can guide future application designs towards patterns with natural resilience.",
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"content": "As high-performance computing systems scale in size and computational power, the danger of silent errors, i.e., errors that can bypass hardware detection mechanisms and impact application state, grows dramatically. Consequently, applications running on HPC systems need to exhibit resilience to such errors. Previous work has found that, for certain codes, this resilience can come for free, i.e., some applications are naturally resilient, but few studies have shown the code patterns-combinations or sequences of computations-that make an application naturally resilient. In this paper, we present FlipTracker, a framework designed to extract these patterns using fine-grained tracking of error propagation and resilience properties, and we use it to present a set of computation patterns that are responsible for making representative HPC applications naturally resilient to errors. This not only enables a deeper understanding of resilience properties of these codes, but also can guide future application designs towards patterns with natural resilience.",
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"title": "Mitigating Silent Data Corruptions in HPC Applications across Multiple Program Inputs",
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"abstract": "With the ever-shrinking size of transistors, silent data corruptions (SDCs) are becoming a common yet serious issue in HPC. Selective instruction duplication (SID) is a widely used fault-tolerance technique that can obtain high SDC coverage with low performance overhead. However, existing SID methods are confined to single program input in its assessment, assuming that error resilience of a program remains similar across inputs. Nevertheless, we observe that the assumption cannot always hold, leading to a drastic loss in SDC coverage across different inputs, compromising HPC reliability. We notice that the SDC coverage loss correlates with a small set of instructions - we call them incubative instructions, which reveal elusive error propagation characteristics across multiple inputs. We propose Minpsid, an automated SID framework that automatically identifies and re-prioritizes incubative instructions in a given program to enhance SDC coverage. Evaluation shows Minpsid can effectively mitigate the loss of SDC coverage across multiple inputs.",
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"content": "With the ever-shrinking size of transistors, silent data corruptions (SDCs) are becoming a common yet serious issue in HPC. Selective instruction duplication (SID) is a widely used fault-tolerance technique that can obtain high SDC coverage with low performance overhead. However, existing SID methods are confined to single program input in its assessment, assuming that error resilience of a program remains similar across inputs. Nevertheless, we observe that the assumption cannot always hold, leading to a drastic loss in SDC coverage across different inputs, compromising HPC reliability. We notice that the SDC coverage loss correlates with a small set of instructions - we call them incubative instructions, which reveal elusive error propagation characteristics across multiple inputs. We propose Minpsid, an automated SID framework that automatically identifies and re-prioritizes incubative instructions in a given program to enhance SDC coverage. Evaluation shows Minpsid can effectively mitigate the loss of SDC coverage across multiple inputs.",
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"fullName": "Yafan Huang",
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"fullName": "Shengjian Guo",
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"abstract": "Benefits of local recovery (restarting only a failed process or task) have been previously demonstrated in parallel solvers. Local recovery has a reduced impact on application performance due to masking of failure delays (for message-passing codes) or dynamic load balancing (for asynchronous many-task codes). In this paper, we implement MPI-process-local checkpointing and recovery of data (as an extension of the Fenix library) in combination with an existing method for local detection of silent errors in partial-differential-equation solvers, to show a path for incorporating lightweight silent-error resilience. In addition, we demonstrate how asynchrony introduced by maximizing computation-communication overlap can halt the propagation of delays. For a prototype stencil solver (including an iterative-solver-like variant) with injected memory bit flips, results show greatly reduced overhead under weak scaling compared to global recovery, and high failure-masking efficiency. The approach is expected to be generalizable to other MPI-based solvers.",
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"abstract": "This report aimed at promoting communication among project participants (hospital personnel and medical residents), increasing autonomy in decision-making during surgical planning. The steps taken for neuro navigation on a head model produced by additive manufacturing were analyzed for quality and risk assessment. Danger related to each navigation step, expected event sequence, dangerous situation and damage were pinpointed. User involvement was required from each project participant. After technical description of model preparation (filling and target deployment), the following steps were described: 1. Biomodel fixation, 2. T1-weighted image acquisition, 3. Target planning, 4. Stereotactic procedure, 5. Tomography acquisition, 6. Accuracy verification, 7. Disassembly. Error sources were identified and standardized procedures were established. A series of proposals were listed to assure quality and reproducibility. We concluded that accuracy can be improved as materials and procedures used for stereotaxy are optimized.",
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{
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"content": "This report aimed at promoting communication among project participants (hospital personnel and medical residents), increasing autonomy in decision-making during surgical planning. The steps taken for neuro navigation on a head model produced by additive manufacturing were analyzed for quality and risk assessment. Danger related to each navigation step, expected event sequence, dangerous situation and damage were pinpointed. User involvement was required from each project participant. After technical description of model preparation (filling and target deployment), the following steps were described: 1. Biomodel fixation, 2. T1-weighted image acquisition, 3. Target planning, 4. Stereotactic procedure, 5. Tomography acquisition, 6. Accuracy verification, 7. Disassembly. Error sources were identified and standardized procedures were established. A series of proposals were listed to assure quality and reproducibility. We concluded that accuracy can be improved as materials and procedures used for stereotaxy are optimized.",
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"normalizedAbstract": "This report aimed at promoting communication among project participants (hospital personnel and medical residents), increasing autonomy in decision-making during surgical planning. The steps taken for neuro navigation on a head model produced by additive manufacturing were analyzed for quality and risk assessment. Danger related to each navigation step, expected event sequence, dangerous situation and damage were pinpointed. User involvement was required from each project participant. After technical description of model preparation (filling and target deployment), the following steps were described: 1. Biomodel fixation, 2. T1-weighted image acquisition, 3. Target planning, 4. Stereotactic procedure, 5. Tomography acquisition, 6. Accuracy verification, 7. Disassembly. Error sources were identified and standardized procedures were established. A series of proposals were listed to assure quality and reproducibility. We concluded that accuracy can be improved as materials and procedures used for stereotaxy are optimized.",
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"title": "Output-Sensitive Parallel Algorithm for Polygon Clipping",
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"abstract": "Polygon clipping is one of the complex operations in computational geometry. It is a primitive operation in many fields such as Geographic Information Systems (GIS), Computer Graphics and VLSI CAD. Sequential algorithms for this problem are in abundance in literature but there are very few parallel algorithms solving it in its most general form. We present the first output-sensitive CREW PRAM algorithm, which can perform polygon clipping in O(logn) time using (n + k + k') processors, where n is the number of vertices, k is the number of edge intersections and k' is the additional temporary vertices introduced due to the partitioning of polygons. The current best algorithm by Karinthi, Srinivas, and Almasi [1] does not handle self-intersecting polygons, is not output-sensitive and must employ &odash(n2) processors to achieve O(logn) time. Our algorithm is developed from the first principles and it is superior to [1] in cost. It yields a practical implementation on multicores and demonstrates 30x speedup for real-world dataset. Our algorithm can perform the typical clipping operations including intersection, union, and difference.",
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"content": "Polygon clipping is one of the complex operations in computational geometry. It is a primitive operation in many fields such as Geographic Information Systems (GIS), Computer Graphics and VLSI CAD. Sequential algorithms for this problem are in abundance in literature but there are very few parallel algorithms solving it in its most general form. We present the first output-sensitive CREW PRAM algorithm, which can perform polygon clipping in O(logn) time using (n + k + k') processors, where n is the number of vertices, k is the number of edge intersections and k' is the additional temporary vertices introduced due to the partitioning of polygons. The current best algorithm by Karinthi, Srinivas, and Almasi [1] does not handle self-intersecting polygons, is not output-sensitive and must employ &odash(n2) processors to achieve O(logn) time. Our algorithm is developed from the first principles and it is superior to [1] in cost. It yields a practical implementation on multicores and demonstrates 30x speedup for real-world dataset. Our algorithm can perform the typical clipping operations including intersection, union, and difference.",
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"abstract": "Existing 3D human pose estimators suffer poor generalization performance to new datasets, largely due to the limited diversity of 2D-3D pose pairs in the training data. To address this problem, we present PoseAug, a new auto-augmentation framework that learns to augment the available training poses towards a greater diversity and thus improve generalization of the trained 2D-to-3D pose estimator. Specifically, PoseAug introduces a novel pose augmentor that learns to adjust various geometry factors (e.g., posture, body size, view point and position) of a pose through differentiable operations. With such differentiable capacity, the augmentor can be jointly optimized with the 3D pose estimator and take the estimation error as feedback to generate more diverse and harder poses in an online manner. Moreover, PoseAug introduces a novel part-aware Kinematic Chain Space for evaluating local joint-angle plausibility and develops a discriminative module accordingly to ensure the plausibility of the augmented poses. These elaborate designs enable PoseAug to generate more diverse yet plausible poses than existing offline augmentation methods, and thus yield better generalization of the pose estimator. PoseAug is generic and easy to be applied to various 3D pose estimators. Extensive experiments demonstrate that PoseAug brings clear improvements on both intra-scenario and cross-scenario datasets. Notably, it achieves 88.6% 3D PCK on MPI-INF-3DHP under cross-dataset evaluation setup, improving upon the previous best data augmentation based method [22] by 9.1%. Code can be found at: https://github.com/jfzhang95/PoseAug.",
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"abstract": "Community image and video platforms like FlickR and Youtube offer large image collections from different perspectives. However, the majority of publicly available imagery from online communities lack a reasonable exact location and orientation information, which is important for many geo-spatial applications like object geo-referencing, knowledge transfer or augmented reality. In this work we exploit publicly available drone videos in order to bridge the gap between ground and aerial imagery. We propose a framework for the fast determination of full 6-D georeferenced motion trajectories of online community drone video footage using geo-localized map data. Our method requires the registration of a single video frame from a video sequence in order to exactly geo-reference complete motion trajectories w.r.t. to existing geo-referenced map data. The method relies on SfM and SLAM techniques in combination with a simple, yet efficient appearance and structure matching based on rendered map data (e.g. LiDAR) in order to generate geo-registered 3D feature maps. These maps enable a simple and fast global appearance based geo-registration of visually overlapping community videos and images. We evaluate our method on a large set of community drone videos. Our method produces drift free geo-data overlays at an average speed of 29,7 frames per second with an average positional error of 0,4m. In addition we release a large scale processed LiDAR dataset and geo-registered feature maps as an extension to the converging perspectives dataset. This data may provide visual links from ground based sensors to aerial imagery. Possible applications are numerous and include autonomous navigation, map updating/extension, image and video dehazing, object localisation or augmented reality.",
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"content": "Perhaps surprisingly, all electron microscopy (EM) data collected to date is less than a cubic millimeter – presenting a huge demand in the materials and biological sciences to image at greater speed and lower dosage, while maintaining resolution. Traditional EM imaging based on homogeneous raster scanning severely limits the volume of high-resolution data that can be collected, and presents a fundamental limitation to understanding physical processes such as material deformation and crack propagation.,,,,,, We introduce a multi-resolution data fusion (MDF) method for super-resolution computational EM. Our method combines innovative data acquisition with novel algorithmic techniques to dramatically improve the resolution/ volume/speed trade-off. The key to our approach is to collect the entire sample at low resolution, while simultaneously collecting a small fraction of data at high resolution. The high-resolution measurements are then used to create a material-specific model that is used within the “plug-andplay” framework to dramatically improve resolution of the low-resolution data. We present results using FEI electron microscope data that demonstrate super-resolution factors of 4x-16x, while substantially maintaining high image quality and reducing dosage.",
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"abstract": "Three-dimensional reconstruction of icosahedral viruses using electron microscopy is similar to computed tomography except that the direction of projection is initially unknown. In addition, the projection images are noisy and low contrast. However, three-dimensional reconstructions are possible by using techniques which take advantage of the high degree of symmetry present in such viruses. In principle, these reconstructions should be routine, highly automated, and of high resolution. Yet the average resolution obtained by these methods is significantly lower than the limit of electron microscopy. We summarize the open problems which are necessary to allow routine high resolution three-dimensional reconstruction of icosahedral viruses using electron microscopy.",
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"abstract": "In electron microscope tomography, alignment of tilt series images is a major determinant of resolution in 3D reconstructions. One alignment method uses gold beads deposited on or in the specimen as fiducial markers. We have developed software to semi-automatically align tilt series images. It runs two processes iteratively: (1) Marker picking. In this process, it uses a cross-correlation function to determine the shift between tilt images and predicts marker coordinates. Subsequently it refines them in a local search area, and detects and corrects erroneously picked markers automatically. The coordinates of the picked markers are used to align the images. (2) Image alignment. In this process, it uses a least squares method to estimate image rotation, image shift, and image scale factor.",
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"abstract": "Scanning transmission electron microscopy (STEM) is a powerful technique in high-resolution atomic imaging of materials. Decreasing scanning time and reducing electron beam exposure with an acceptable signal-to-noise ratio are two popular research aspects when applying STEM to beam-sensitive materials. Specifically, partially sampling with fixed electron doses is one of the most important solutions, and then the lost information is restored by computational methods. Following successful applications of deep learning in image in-painting, we have developed an encoder-decoder network to reconstruct STEM images in extremely sparse sampling cases. In our model, we combine both local pixel information from convolution operators and global texture features, by applying specific filter operations on the frequency domain to acquire initial reconstruction and global structure prior. Our method can effectively restore texture structures and be robust in different sampling ratios with Poisson noise. A comprehensive study demonstrates that our method gains about 50% performance enhancement in comparison with the state-of-art methods. Code is available at https://github.com/icthrm/Sparse-Sampling-Reconstruction.",
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"abstract": "Communication between embodied AI agents has received increasing attention in recent years. Despite its use, it is still unclear whether the learned communication is interpretable and grounded in perception. To study the grounding of emergent forms of communication, we first introduce the collaborative multi-object navigation task ‘CoMON.' In this task, an ‘oracle agent' has detailed environment information in the form of a map. It communicates with a ‘navigator agent' that perceives the environment visually and is tasked to find a sequence of goals. To succeed at the task, effective communication is essential. CoMON hence serves as a basis to study different communication mechanisms between heterogeneous agents, that is, agents with different capabilities and roles. We study two common communication mechanisms and analyze their communication patterns through an egocentric and spatial lens. We show that the emergent communication can be grounded to the agent observations and the spatial structure of the 3D environment.",
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