Optimal Linear Network Coding When 3 Nodes Communicate Over Broadcast Erasure Channels with ACK

This work considers the following scenario: Three nodes {1, 2, 3} would like to communicate with each other by sending packets through unreliable wireless medium.We consider the most general unicast traffic demands. Namely, there are six co-existing unicast flows with rates (R1--\u3e2,R1--\u3e3,R2--\u3e1,R2--\u3e3, R3--\u3e1,R3--\u3e2). When a node broadcasts a packet, a random subset of the other two nodes will receive the packet. After each transmission, causal ACKnowledgment is sent so that all nodes know whether the other nodes have received the packet or not. Such a setting has many unique features. For example, each node, say node 1, can assume many different roles: Being the transmitter of the information R1--\u3e2 and R1--\u3e3; being the receiver of the information R2--\u3e1 and R3--\u3e1; and being the relay for the information R2--\u3e3 and R3--\u3e2. This fully captures the fundamental behaviors of 3-node network communications. Allowing network coding (NC) to capitalize the diversity gain (i.e., overhearing packets transmitted by other nodes), this work characterizes the 6-dimensional linear network coding (LNC) capacity of the above erasure network. The results show that for any channel parameters, the LNC capacity can be achieved by a simple strategy that involves only a few LNC choices

A Study on the Role of Information Systems in Organizational Growth: A Longitudinal Case Study

The purpose of this paper is to present an integrated framework which can explain how the role of information systems evolves in organizations. To develop the framework, two critical dimensions, each of which is classified further into three categories, are selected to explain the role of information systems in organizational growth: the purpose of information processing, the scope of information processing. As these are considered to be major dimensions underpinning much research regarding the role of information systems in organizations, the framework proposed in this paper could serve to integrate much existing research, while stimulating future research aimed at verifying its applicability

The Role of IT in a Healthy Business Ecosystem: An Exploratory Study of the Korean Capital Market from a Keystone Company\u27s Perspective

The business environmental structure is constantly being shaped by customer\u27s desires and market dynamics. A large number of loosely interconnected participants make up a business ecosystem, and offers a customized and complete set of products and services. In a business ecosystem, the competitiveness of a company is influenced by its own capability and its interrelated partners\u27 capabilities. Therefore, a company should enhance not only its competitiveness but also related companies\u27 capabilities. To do this, a company has to find its place in the business ecosystem to make a complete business strategy using IT. This paper provides business ecosystem strategies and academic guidelines from the perspective of a single company. Firstly, a business ecosystem perspective is conceptualized, and the importance of a keystone company\u27s role in the ecosystem is examined. Then, using a case study method, focus is put on the keystone\u27s IT capabilities that pave the way for a healthy business ecosystem. This paper explores the Korean Capital Market as an example of a business ecosystem and finds the role of IT, particularly from the perspective of a keystone company. Through this study, it is confirmed that the keystone company\u27s IT capabilities play an important role in the healthy business ecosystem. This paper provides guidelines to practitioners in keystone companies and gives ideas to IT researchers

DiagrammerGPT: Generating Open-Domain, Open-Platform Diagrams via LLM Planning

Text-to-image (T2I) generation has seen significant growth over the past few years. Despite this, there has been little work on generating diagrams with T2I models. A diagram is a symbolic/schematic representation that explains information using structurally rich and spatially complex visualizations (e.g., a dense combination of related objects, text labels, directional arrows, connection lines, etc.). Existing state-of-the-art T2I models often fail at diagram generation because they lack fine-grained object layout control when many objects are densely connected via complex relations such as arrows/lines and also often fail to render comprehensible text labels. To address this gap, we present DiagrammerGPT, a novel two-stage text-to-diagram generation framework that leverages the layout guidance capabilities of LLMs (e.g., GPT-4) to generate more accurate open-domain, open-platform diagrams. In the first stage, we use LLMs to generate and iteratively refine 'diagram plans' (in a planner-auditor feedback loop) which describe all the entities (objects and text labels), their relationships (arrows or lines), and their bounding box layouts. In the second stage, we use a diagram generator, DiagramGLIGEN, and a text label rendering module to generate diagrams following the diagram plans. To benchmark the text-to-diagram generation task, we introduce AI2D-Caption, a densely annotated diagram dataset built on top of the AI2D dataset. We show quantitatively and qualitatively that our DiagrammerGPT framework produces more accurate diagrams, outperforming existing T2I models. We also provide comprehensive analysis including open-domain diagram generation, vector graphic diagram generation in different platforms, human-in-the-loop diagram plan editing, and multimodal planner/auditor LLMs (e.g., GPT-4Vision). We hope our work can inspire further research on diagram generation via T2I models and LLMs.Comment: Project page: https://diagrammerGPT.github.io

VideoDirectorGPT: Consistent Multi-scene Video Generation via LLM-Guided Planning

Although recent text-to-video (T2V) generation methods have seen significant advancements, most of these works focus on producing short video clips of a single event with a single background (i.e., single-scene videos). Meanwhile, recent large language models (LLMs) have demonstrated their capability in generating layouts and programs to control downstream visual modules such as image generation models. This raises an important question: can we leverage the knowledge embedded in these LLMs for temporally consistent long video generation? In this paper, we propose VideoDirectorGPT, a novel framework for consistent multi-scene video generation that uses the knowledge of LLMs for video content planning and grounded video generation. Specifically, given a single text prompt, we first ask our video planner LLM (GPT-4) to expand it into a 'video plan', which involves generating the scene descriptions, the entities with their respective layouts, the background for each scene, and consistency groupings of the entities and backgrounds. Next, guided by this output from the video planner, our video generator, Layout2Vid, has explicit control over spatial layouts and can maintain temporal consistency of entities/backgrounds across scenes, while only trained with image-level annotations. Our experiments demonstrate that VideoDirectorGPT framework substantially improves layout and movement control in both single- and multi-scene video generation and can generate multi-scene videos with visual consistency across scenes, while achieving competitive performance with SOTAs in open-domain single-scene T2V generation. We also demonstrate that our framework can dynamically control the strength for layout guidance and can also generate videos with user-provided images. We hope our framework can inspire future work on better integrating the planning ability of LLMs into consistent long video generation.Comment: Project page: https://videodirectorgpt.github.i

The United States - Korea Free Trade Agreement: Path to Common Economic Prosperity or False Promise?

The U.S.-Korea Free Trade Agreement, currently awaiting ratification in the legislatures of both countries, is known to be the most significant bilateral trade agreement for the United States since the conclusion of the North America Free Trade Agreement (NAFTA) in 1993 and for Korea since the initiation of the FTA drive in 2003. Both governments have promoted the U.S.-Korea FTA as the trade agreement that will enhance trade between the two countries and promote economic prosperity. The article critically reviews the inherent features of the U.S.-Korea FTA and examines whether the FTA is expected to promote the promised economic prosperity for both countries. The article also discusses prospects and impacts of the FTA on creating even larger free trade agreements between East Asia and North America and between East Asia and Europe

Contrastive Vicinal Space for Unsupervised Domain Adaptation

Recent unsupervised domain adaptation methods have utilized vicinal space between the source and target domains. However, the equilibrium collapse of labels, a problem where the source labels are dominant over the target labels in the predictions of vicinal instances, has never been addressed. In this paper, we propose an instance-wise minimax strategy that minimizes the entropy of high uncertainty instances in the vicinal space to tackle the stated problem. We divide the vicinal space into two subspaces through the solution of the minimax problem: contrastive space and consensus space. In the contrastive space, inter-domain discrepancy is mitigated by constraining instances to have contrastive views and labels, and the consensus space reduces the confusion between intra-domain categories. The effectiveness of our method is demonstrated on public benchmarks, including Office-31, Office-Home, and VisDA-C, achieving state-of-the-art performances. We further show that our method outperforms the current state-of-the-art methods on PACS, which indicates that our instance-wise approach works well for multi-source domain adaptation as well. Code is available at https://github.com/NaJaeMin92/CoVi.Comment: 10 pages, 7 figures, 5 table