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Double Patterning
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Multiple patterning is a class of technologies for manufacturing integrated circuits (ICs), developed for photolithography to enhance the feature density. The simplest case of multiple patterning is double patterning, where a conventional lithography process is enhanced to produce double the expected number of features. The resolution of a photoresist pattern is believed to blur at around 45 nm half-pitch. For the semiconductor industry, therefore, double patterning was introduced for the 32 nm half-pitch node and below. This presentation gives us an insight of why multiple patterning is an important to give us a better resolution below 32nm.
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Transportation networks, such as streets, railroads or metro systems, constitute primary elements in cartography for reckoning and navigation. In recent years, they have become an increasingly important part of 3D virtual environments for the interactive analysis and communication of complex hierarchical information, for example in routing, logistics optimization, and disaster management. A variety of rendering techniques have been proposed that deal with integrating transportation networks within these environments, but have so far neglected the many challenges of an interactive design process to adapt their spatial and thematic granularity (i.e., level-of-detail and level-of-abstraction) according to a user's context. This paper presents an efficient real-time rendering technique for the view-dependent rendering of geometrically complex transportation networks within 3D virtual environments. Our technique is based on distance fields using deferred texturing that shifts the design process to the shading stage for real-time stylization. We demonstrate and discuss our approach by means of street networks using cartographic design principles for context-aware stylization, including view-dependent scaling for clutter reduction, contour-lining to provide figure-ground, handling of street crossings via shading-based blending, and task-dependent colorization. Finally, we present potential usage scenarios and applications together with a performance evaluation of our implementation.
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Double Patterning
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Multiple patterning is a class of technologies for manufacturing integrated circuits (ICs), developed for photolithography to enhance the feature density. The simplest case of multiple patterning is double patterning, where a conventional lithography process is enhanced to produce double the expected number of features. The resolution of a photoresist pattern is believed to blur at around 45 nm half-pitch. For the semiconductor industry, therefore, double patterning was introduced for the 32 nm half-pitch node and below. This presentation gives us an insight of why multiple patterning is an important to give us a better resolution below 32nm.
Double patterning for 32nm and beyond
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Transportation networks, such as streets, railroads or metro systems, constitute primary elements in cartography for reckoning and navigation. In recent years, they have become an increasingly important part of 3D virtual environments for the interactive analysis and communication of complex hierarchical information, for example in routing, logistics optimization, and disaster management. A variety of rendering techniques have been proposed that deal with integrating transportation networks within these environments, but have so far neglected the many challenges of an interactive design process to adapt their spatial and thematic granularity (i.e., level-of-detail and level-of-abstraction) according to a user's context. This paper presents an efficient real-time rendering technique for the view-dependent rendering of geometrically complex transportation networks within 3D virtual environments. Our technique is based on distance fields using deferred texturing that shifts the design process to the shading stage for real-time stylization. We demonstrate and discuss our approach by means of street networks using cartographic design principles for context-aware stylization, including view-dependent scaling for clutter reduction, contour-lining to provide figure-ground, handling of street crossings via shading-based blending, and task-dependent colorization. Finally, we present potential usage scenarios and applications together with a performance evaluation of our implementation.
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Graph neural networks have been widely used on modeling graph data, achieving impressive results on node classification and link prediction tasks. Yet, obtaining an accurate representation for a graph further requires a pooling function that maps a set of node representations into a compact form. A simple sum or average over all node representations considers all node features equally without consideration of their task relevance, and any structural dependencies among them. Recently proposed hierarchical graph pooling methods, on the other hand, may yield the same representation for two different graphs that are distinguished by the Weisfeiler-Lehman test, as they suboptimally preserve information from the node features. To tackle these limitations of existing graph pooling methods, we first formulate the graph pooling problem as a multiset encoding problem with auxiliary information about the graph structure, and propose a Graph Multiset Transformer (GMT) which is a multi-head attention based global pooling layer that captures the interaction between nodes according to their structural dependencies. We show that GMT satisfies both injectiveness and permutation invariance, such that it is at most as powerful as the Weisfeiler-Lehman graph isomorphism test. Moreover, our methods can be easily extended to the previous node clustering approaches for hierarchical graph pooling. Our experimental results show that GMT significantly outperforms state-of-the-art graph pooling methods on graph classification benchmarks with high memory and time efficiency, and obtains even larger performance gain on graph reconstruction and generation tasks.
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Graph neural networks have recently achieved remarkable success in representing graph-structured data, with rapid progress in both the node embedding and graph pooling methods. Yet, they mostly focus on capturing information from the nodes considering their connectivity, and not much work has been done in representing the edges, which are essential components of a graph. However, for tasks such as graph reconstruction and generation, as well as graph classification tasks for which the edges are important for discrimination, accurately representing edges of a given graph is crucial to the success of the graph representation learning. To this end, we propose a novel edge representation learning framework based on Dual Hypergraph Transformation (DHT), which transforms the edges of a graph into the nodes of a hypergraph. This dual hypergraph construction allows us to apply message-passing techniques for node representations to edges. After obtaining edge representations from the hypergraphs, we then cluster or drop edges to obtain holistic graph-level edge representations. We validate our edge representation learning method with hypergraphs on diverse graph datasets for graph representation and generation performance, on which our method largely outperforms existing graph representation learning methods. Moreover, our edge representation learning and pooling method also largely outperforms state-of-theart graph pooling methods on graph classification, not only because of its accurate edge representation learning, but also due to its lossless compression of the nodes and removal of irrelevant edges for effective message-passing. Code is available at https://github.com/harryjo97/EHGNN.
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Many practical graph problems, such as knowledge graph construction and drug-drug interaction prediction, require to handle multi-relational graphs. However, handling real-world multi-relational graphs with Graph Neural Networks (GNNs) is often challenging due to their evolving nature, as new entities (nodes) can emerge over time. Moreover, newly emerged entities often have few links, which makes the learning even more difficult. Motivated by this challenge, we introduce a realistic problem of few-shot out-of-graph link prediction, where we not only predict the links between the seen and unseen nodes as in a conventional out-of-knowledge link prediction task but also between the unseen nodes, with only few edges per node. We tackle this problem with a novel transductive meta-learning framework which we refer to as Graph Extrapolation Networks (GEN). GEN meta-learns both the node embedding network for inductive inference (seen-to-unseen) and the link prediction network for transductive inference (unseen-to-unseen). For transductive link prediction, we further propose a stochastic embedding layer to model uncertainty in the link prediction between unseen entities. We validate our model on multiple benchmark datasets for knowledge graph completion and drug-drug interaction prediction. The results show that our model significantly outperforms relevant baselines for out-of-graph link prediction tasks.
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Graph neural networks have been widely used on modeling graph data, achieving impressive results on node classification and link prediction tasks. Yet, obtaining an accurate representation for a graph further requires a pooling function that maps a set of node representations into a compact form. A simple sum or average over all node representations considers all node features equally without consideration of their task relevance, and any structural dependencies among them. Recently proposed hierarchical graph pooling methods, on the other hand, may yield the same representation for two different graphs that are distinguished by the Weisfeiler-Lehman test, as they suboptimally preserve information from the node features. To tackle these limitations of existing graph pooling methods, we first formulate the graph pooling problem as a multiset encoding problem with auxiliary information about the graph structure, and propose a Graph Multiset Transformer (GMT) which is a multi-head attention based global pooling layer that captures the interaction between nodes according to their structural dependencies. We show that GMT satisfies both injectiveness and permutation invariance, such that it is at most as powerful as the Weisfeiler-Lehman graph isomorphism test. Moreover, our methods can be easily extended to the previous node clustering approaches for hierarchical graph pooling. Our experimental results show that GMT significantly outperforms state-of-the-art graph pooling methods on graph classification benchmarks with high memory and time efficiency, and obtains even larger performance gain on graph reconstruction and generation tasks.
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Graph neural networks have recently achieved remarkable success in representing graph-structured data, with rapid progress in both the node embedding and graph pooling methods. Yet, they mostly focus on capturing information from the nodes considering their connectivity, and not much work has been done in representing the edges, which are essential components of a graph. However, for tasks such as graph reconstruction and generation, as well as graph classification tasks for which the edges are important for discrimination, accurately representing edges of a given graph is crucial to the success of the graph representation learning. To this end, we propose a novel edge representation learning framework based on Dual Hypergraph Transformation (DHT), which transforms the edges of a graph into the nodes of a hypergraph. This dual hypergraph construction allows us to apply message-passing techniques for node representations to edges. After obtaining edge representations from the hypergraphs, we then cluster or drop edges to obtain holistic graph-level edge representations. We validate our edge representation learning method with hypergraphs on diverse graph datasets for graph representation and generation performance, on which our method largely outperforms existing graph representation learning methods. Moreover, our edge representation learning and pooling method also largely outperforms state-of-theart graph pooling methods on graph classification, not only because of its accurate edge representation learning, but also due to its lossless compression of the nodes and removal of irrelevant edges for effective message-passing. Code is available at https://github.com/harryjo97/EHGNN.
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Histogram Equalization(Image Processing Presentation)
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Many practical graph problems, such as knowledge graph construction and drug-drug interaction prediction, require to handle multi-relational graphs. However, handling real-world multi-relational graphs with Graph Neural Networks (GNNs) is often challenging due to their evolving nature, as new entities (nodes) can emerge over time. Moreover, newly emerged entities often have few links, which makes the learning even more difficult. Motivated by this challenge, we introduce a realistic problem of few-shot out-of-graph link prediction, where we not only predict the links between the seen and unseen nodes as in a conventional out-of-knowledge link prediction task but also between the unseen nodes, with only few edges per node. We tackle this problem with a novel transductive meta-learning framework which we refer to as Graph Extrapolation Networks (GEN). GEN meta-learns both the node embedding network for inductive inference (seen-to-unseen) and the link prediction network for transductive inference (unseen-to-unseen). For transductive link prediction, we further propose a stochastic embedding layer to model uncertainty in the link prediction between unseen entities. We validate our model on multiple benchmark datasets for knowledge graph completion and drug-drug interaction prediction. The results show that our model significantly outperforms relevant baselines for out-of-graph link prediction tasks.
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Histogram equalization is a nonlinear technique for adjusting the contrast of an image using its histogram. It increases the brightness of a gray scale image which is different from the mean brightness of the original image. There are various types of Histogram equalization techniques like Histogram Equalization, Contrast Limited Adaptive Histogram Equalization, Brightness Preserving Bi Histogram Equalization, Dualistic Sub Image Histogram Equalization, Minimum Mean Brightness Error Bi Histogram Equalization, Recursive Mean Separate Histogram Equalization and Recursive Sub Image Histogram Equalization. In this paper, the histogram equalization approach of gray-level images is extended for colour images. The acquired image is converted into HSV (Hue, Saturation, Value). The image is then decomposed into two parts by using exposure threshold and then equalized them independently Over enhancement is also controlled in this method by using clipping threshold. For measuring the performance of the enhanced image, entropy and contrast are calculated.
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06 spatial filtering DIP
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This talk covers changes in CryENGINE 3 technology during 2012, with DX11 related topics such as moving to deferred rendering while maintaining backward compatibility on a multiplatform engine, massive vegetation rendering, MSAA support and how to deal with its common visual artifacts, among other topics.
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Image segmentation is a computer vision task that involves dividing an image into multiple segments or regions, where each segment corresponds to a distinct object, region, or feature within the image. The goal of image segmentation is to simplify and analyze an image by partitioning it into meaningful and semantically relevant parts. This is a crucial step in various applications, including object recognition, medical imaging, autonomous driving, and more. Key points about image segmentation: Semantic Segmentation: This type of segmentation assigns each pixel in an image to a specific class, essentially labeling each pixel with the object or region it belongs to. It's commonly used for object detection and scene understanding. Instance Segmentation: Here, individual instances of objects are separated and labeled separately. This is especially useful when multiple objects of the same class are present in the image. Boundary Detection: Some segmentation methods focus on identifying the boundaries that separate different objects or regions in an image. Methods: Image segmentation can be achieved through various techniques, including traditional methods like thresholding, clustering, and region growing, as well as more advanced techniques involving deep learning, such as using convolutional neural networks (CNNs) and fully convolutional networks (FCNs). Challenges: Image segmentation can be challenging due to variations in lighting, color, texture, and object shape. Overlapping objects and unclear boundaries further complicate the task. Applications: Image segmentation is used in diverse fields. For example, in medical imaging, it helps identify organs or abnormalities. In autonomous vehicles, it aids in identifying pedestrians, other vehicles, and obstacles. Evaluation: Measuring the accuracy of segmentation methods can be complex. Metrics like Intersection over Union (IoU) and Dice coefficient are often used to compare segmented results to ground truth. Data Annotation: Creating ground truth annotations for segmentation can be labor-intensive, as each pixel must be labeled. This has led to the development of datasets and tools to facilitate annotation. Semantic Segmentation Networks: Deep learning architectures like U-Net, Mask R-CNN, and Deeplab have significantly improved the accuracy of image segmentation by effectively learning complex patterns and features. Image segmentation plays a fundamental role in understanding and processing images, enabling computers to "see" and interpret visual information in ways that mimic human perception. Image segmentation is a computer vision task that involves dividing an image into meaningful and distinct segments or regions. The goal is to partition an image into segments that represent different objects or areas of interest within the image. Image segmentation plays a crucial role in various applications, such as object detection, medical imaging, autonomous vehicles, and more.
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Graph coloring is an important concept in graph theory. It is a special kind of problem in which we have assign colors to certain elements of the graph along with certain constraints. Suppose we are given K colors, we have to color the vertices in such a way that no two adjacent vertices of the graph have the same color, this is known as vertex coloring, similarly we have edge coloring and face coloring. The coloring problem has a huge number of applications in modern computer science such as making schedule of time table , Sudoku, Bipartite graphs , Map coloring, data mining, networking. In this paper we are going to focus on certain applications like Final exam timetabling, Aircraft Scheduling, guarding an art gallery.
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Lec00 generalized network flows
Lec03 parametric problems
Lec03 parametric problems
Double patterning (4/20 update)
Double patterning (4/20 update)
Double Patterning
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Double Patterning Wai-Shing
Luk
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TBUF_X16, Layer 11
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SDFFRS_X2 Layer 9,
11
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Random, 4K rectangles
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fft_all.gds, 320K polygons
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SPQR-Tree virtual edge
skeleton
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Example
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Notas del editor
the 820 million transistors of an Intel Core 2 Extreme chip can process nearly 72 billion instructions per second
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