The Enhanced Entity- Relationship (EER) Model

Generalization Hierarchies and Lattices (cont’d.) Multiple inheritance Subclass with more than one superclass If attribute (or relationship) originating in the same superclass inherited more than once via different paths in lattice • Included only once in shared subclass Single inheritance

  Inheritance, Hierarchies

ER & EER to Relational Mapping

relationship into a relational schema you must use one of the four options available. Let C be the super-class, K its primary key and A. 1, A. 2, …, A. n. its remaining attributes and let S. 1, S. 2, …, S. m. be the sub-classes. Chapter 9 11. Option 8A (multiple relation option):

  Relational, Schema, Relational schema

Database System Concepts and Architecture

Title: Elmasri_6e_Ch02.ppt Author: Raj Sunderraman Created Date: 8/23/2011 1:18:19 PM

Embedded SQL - tinman.cs.gsu.edu

3.4 Connecting to Oracle 99 Inthiscodefragment, aparticularzipcodevalueischeckedtoseeifitisalready present in the zipcodes table using the EXEC SQL select into statement.

  Oracle

ER & EER to Relational Mapping

relational database schema; the primary keys are underlined. DEPT_LOCATIONS. DNUMBER DLOCATION. Chapter 9 8. Step 7: For each . n-ary relationship type R, n>2 • Create a new relation S to represent R. • Include as foreign key attributes in the S the primary keys of the relations that represent

  Database, Relational, Relational database

Deep Multi-Scale Convolutional Neural Network for …

we propose a multi-scale convolutional neural network that restores sharp images in an end-to-end manner where blur is caused by various sources. Together, we present multi-scale loss function that mimics conventional coarse-to-ne approaches. Furthermore, we propose a new large-scale dataset that provides pairs of realistic blurry image and the

  Multi, Network, Scale, Neural, Convolutional, Multi scale convolutional neural network for, Multi scale convolutional neural network

Multi-scale Residual Network for Image Super-Resolution

Keywords: Super-resolution · Convolutional neural network · Multi-scale residual network 1 Introduction Image super-resolution (SR), particularly single-image super-resolution (SISR), has attracted more and more attention in academia and industry. SISR aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) image

  Multi, Network, Scale, Neural, Convolutional, Convolutional neural networks

Single-Image Crowd Counting via Multi-Column …

2. Multi-column CNN for Crowd Counting 2.1. Density map based crowd counting To estimate the number of people in a given image via the Convolutional Neural Networks (CNNs), there are two natural configurations. One is a network whose input is the image and the output is the estimated head count. The other

  Multi, Network, Neural, Convolutional, Convolutional neural

A arXiv:1609.02907v4 [cs.LG] 22 Feb 2017

In this section, we provide theoretical motivation for a specific graph-based neural network model f(X;A) that we will use in the rest of this paper. We consider a multi-layer Graph Convolutional Network (GCN) with the following layer-wise propagation rule: H(l+1) = ˙ D~ 1 2 A~D~ 1 2 H(l)W(l) : (2) Here, A~ = A+ I

  Multi, Network, Neural network, Neural, Convolutional, Convolutional networks

Notes on Convolutional Neural Networks - Cogprints

Convolutional neural networks in-volve many more connections than weights; the architecture itself realizes a form of regularization. In addition, a convolutional network automatically provides some degree of translation invariance. This particular kind of neural network assumes that we wish to learn filters, in a data-driven fash-

  Network, Neural network, Neural, Convolutional, Convolutional networks, Convolutional neural

Depth Map Prediction from a Single Image using a Multi …

In this way, the local network can edit the global prediction to incorporate finer-scale details. 3.1.1 Global Coarse-Scale Network The task of the coarse-scale network is to predict the overall depth map structure using a global view of the scene. The upper layers of this network are fully connected, and thus contain the entire image

  Multi, Network, Scale, Scale network

Image Style Transfer Using Convolutional Neural Networks

Generally each layer in the network defines a non-linear filter bank whose complexity increases with the position of the layer in the network. Hence a given input image ~x is encoded in each layer of the Convolutional Neural Network by the filter responses to that image. A layer with Nl dis-tinct filters has Nl feature maps each of size Ml ...

  Network, Styles, Transfer, Neural, Convolutional, Convolutional neural networks, Convolutional neural, Style transfer

Convolutional Sequence to Sequence Learning - arXiv

by the decoder network to yield output element represen-tations that are being fed back into the decoder network g = ( g1;:::;gn). Position embeddings are useful in our architecture since they give our model a sense of which portion of the sequence in the input or output it is currently dealing with ( x5.4). 3.2. Convolutional Block Structure

  Network, Convolutional