Interdisciplinary Investigations in Support of Project DI-MOD

Various concepts from time series analysis are used as the basis for the development of algorithms to assist in the analysis and interpretation of remote sensed imagery. An approach to trend detection that is based upon the fractal analysis of power spectrum estimates is presented. Additionally, research was conducted toward the development of a software architecture to support processing tasks associated with databases housing a variety of data. An algorithmic approach which provides for the automation of the state monitoring process is presented

Proceedings of Monterey Workshop 2001 Engineering Automation for Sofware Intensive System Integration

The 2001 Monterey Workshop on Engineering Automation for Software Intensive System Integration was sponsored by the Office of Naval Research, Air Force Office of Scientific Research, Army Research Office and the Defense Advance Research Projects Agency. It is our pleasure to thank the workshop advisory and sponsors for their vision of a principled engineering solution for software and for their many-year tireless effort in supporting a series of workshops to bring everyone together.This workshop is the 8 in a series of International workshops. The workshop was held in Monterey Beach Hotel, Monterey, California during June 18-22, 2001. The general theme of the workshop has been to present and discuss research works that aims at increasing the practical impact of formal methods for software and systems engineering. The particular focus of this workshop was "Engineering Automation for Software Intensive System Integration". Previous workshops have been focused on issues including, "Real-time & Concurrent Systems", "Software Merging and Slicing", "Software Evolution", "Software Architecture", "Requirements Targeting Software" and "Modeling Software System Structures in a fastly moving scenario".Office of Naval ResearchAir Force Office of Scientific Research Army Research OfficeDefense Advanced Research Projects AgencyApproved for public release, distribution unlimite

DocumentCLIP: Linking Figures and Main Body Text in Reflowed Documents

Vision-language pretraining models have achieved great success in supporting multimedia applications by understanding the alignments between images and text. While existing vision-language pretraining models primarily focus on understanding single image associated with a single piece of text, they often ignore the alignment at the intra-document level, consisting of multiple sentences with multiple images. In this work, we propose DocumentCLIP, a salience-aware contrastive learning framework to enforce vision-language pretraining models to comprehend the interaction between images and longer text within documents. Our model is beneficial for the real-world multimodal document understanding like news article, magazines, product descriptions, which contain linguistically and visually richer content. To the best of our knowledge, we are the first to explore multimodal intra-document links by contrastive learning. In addition, we collect a large Wikipedia dataset for pretraining, which provides various topics and structures. Experiments show DocumentCLIP not only outperforms the state-of-the-art baselines in the supervised setting, but also achieves the best zero-shot performance in the wild after human evaluation. Our code is available at https://github.com/FuxiaoLiu/DocumentCLIP.Comment: 8 pages, 5 figures. In submissio

Parallel and Distributed Execution of Model Management Programs

The engineering process of complex systems involves many stakeholders and development artefacts. Model-Driven Engineering (MDE) is an approach to development which aims to help curtail and better manage this complexity by raising the level of abstraction. In MDE, models are first-class artefacts in the development process. Such models can be used to describe artefacts of arbitrary complexity at various levels of abstraction according to the requirements of their prospective stakeholders. These models come in various sizes and formats and can be thought of more broadly as structured data. Since models are the primary artefacts in MDE, and the goal is to enhance the efficiency of the development process, powerful tools are required to work with such models at an appropriate level of abstraction. Model management tasks – such as querying, validation, comparison, transformation and text generation – are often performed using dedicated languages, with declarative constructs used to improve expressiveness. Despite their semantically constrained nature, the execution engines of these languages rarely capitalize on the optimization opportunities afforded to them. Therefore, working with very large models often leads to poor performance when using MDE tools compared to general-purpose programming languages, which has a detrimental effect on productivity. Given the stagnant single-threaded performance of modern CPUs along with the ubiquity of distributed computing, parallelization of these model management program is a necessity to address some of the scalability concerns surrounding MDE. This thesis demonstrates efficient parallel and distributed execution algorithms for model validation, querying and text generation and evaluates their effectiveness. By fully utilizing the CPUs on 26 hexa-core systems, we were able to improve performance of a complex model validation language by 122x compared to its existing sequential implementation. Up to 11x speedup was achieved with 16 cores for model query and model-to-text transformation tasks

Research and Technology Report: 1997

This volume highlights the most significant results from research and development projects sponsored through NASA's Office of Equal Opportunity Programs, Minority University Research and Education Division, in collaboration with Headquarters Program Offices, during Academic Year 1996-97 and Summer 1996. It includes the work of major multidisciplinary research groups, such as those sponsored under NASA's University Research Centers at Minority Institutions and Institutional Research Awards programs, as well as that of individual principal investigators sponsored under the Faculty Awards for Research or other MUREP programs. It encompasses contributions from 863 students and 388 faculty-level researchers at institutions eligible to compete for MUREP funding, including: Historically Black Colleges and Universities (HBCU), Hispanic-Serving Institutions (HSI), Tribal Colleges and Universities (TCU), and accredited minority colleges or universities with a 50 percent or greater underrepresented minority student enrollment. It stands as a testimony to NASA's response to Executive Orders 12876, 12900, and 13021, which mandate increased Federal support to these classes of institutions. We firmly believe that maintaining America's leadership in aerospace and related areas depends on fully utilizing the talents available at the Nation's minority universities

Studying Media Events through Spatio-Temporal Statistical Analysis

This report is written in the context of the ANR Geomedia and summarises the developement of methods of spatio-temporel statistical analysis of media events (delivrable 3.2).This documents presents on-going work on statistical modelling and statistical inference of the ANR GEOMEDIA corpus, that is a collection of international RSS news feeds. Central to this project, RSS news feeds are viewed as a representation of the information flow in geopolitical space. As such they allow us to study media events of global extent and how they affect international relations. Here we propose hidden Markov models (HMM) as an adequate modelling framework to study the evolution of media events in time. This set of models respect the characteristic properties of the data, such as temporal dependencies and correlations between feeds. Its specific structure corresponds well to our conceptualisation of media attention and media events. We specify the general model structure that we use for modelling an ensemble of RSS news feeds. Finally, we apply the proposed models to a case study dedicated to the analysis of the media attention for the Ebola epidemic which spread through West Africa in 2014.Ce document présente les résultats d'un travail en cours sur la modélisation statistique et l'inférence appliqué au corpus de l'ANR GEOMEDIA qui est une collection des flux RSS internationaux. Au coeur du projet, les flux RSS sont considérés comme un marqueur représentatif des flux d'information dans l'espace géopolitique mondial. En tant que tel, ils nous permettent d'étudier des événements médiatiques globaux et leur impact sur les relations internationales. Dans ce contexte, on émet l'hypothèse que les modèles Markoviens cachés (HMM) constituent un cadre méthodologique adapté pour modéliser et étudier l'évolution des événements médiatiques dans le temps. Ces modèles respectent les propriétés des données, comme les corrélations temporelles et les redondances entre flux. Leur structure caractéristique correspond à notre conceptualisation de l'attention médiatique et des événements médiatiques. Nous spécifions la structure général d'un modèle HMM qui peut être appliqué a la modélisation simultané d'un ensemble des flux RSS. Finalement, on teste l'intérêt des modèles proposés à l'aide d'une étude de cas dédié à l'analyse de l'attention médiatique pour l'épidémie d'Ebola en Afrique de l'Ouest en 2014