1,285 research outputs found
Flight Measurements of the Longitudinal Stability, Stalling, and Lift Characteristics of an Airplane Having a 35 Degrees Sweptback Wing Without Slots and with 40-percent-span Slots and a Comparison with Wind-tunnel Data
Flight Measurements of the Stability, Control, and Stalling Characteristics of an Airplane Having a 35 Degree Sweptback Wing Without Slots and with 80-percent-span Slots and a Comparison with Wind-tunnel Data
Flight Measurements of the Lateral and Directional Stability and Control Characteristics of an Airplane Having a 35 Degree Sweptback Wing 40-percent-span Slots and a Comparison with Wind-tunnel Data
New views of the spherical Bouguer gravity anomaly
This paper presents a number of new concepts concerning the gravity anomaly. First, it identifies a distinct difference between a surface (2-D) gravity anomaly (the difference between actual gravity on one surface and normal gravity on another surface) and a solid (3-D) gravity anomaly defined in the fundamental gravimetric equation. Second, it introduces the 'no topography' gravity anomaly (which turns out to be the complete spherical Bouguer anomaly) as a means to generate a quantity that is smooth, thus suitable for gridding, and harmonic, thus suitable for downward continuation. It is understood that the possibility of downward continuing a smooth gravity anomaly would simplify the task of computing an accurate geoid. It is also shown that the planar Bouguer anomaly is not harmonic, and thus cannot be downward continued
The Last Whole XGP Font Catalog
This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-75-C-0643.MIT Artificial Intelligence Laboratory
Department of Defense Advanced Research Projects Agenc
Entity Recommendation for Everyday Digital Tasks
Recommender systems can support everyday digital tasks by retrieving and recommending useful information contextually. This is becoming increasingly relevant in services and operating systems. Previous research often focuses on specific recommendation tasks with data captured from interactions with an individual application. The quality of recommendations is also often evaluated addressing only computational measures of accuracy, without investigating the usefulness of recommendations in realistic tasks. The aim of this work is to synthesize the research in this area through a novel approach by (1) demonstrating comprehensive digital activity monitoring, (2) introducing entity-based computing and interaction, and (3) investigating the previously overlooked usefulness of entity recommendations and their actual impact on user behavior in real tasks. The methodology exploits context from screen frames recorded every 2 seconds to recommend information entities related to the current task. We embodied this methodology in an interactive system and investigated the relevance and influence of the recommended entities in a study with participants resuming their real-world tasks after a 14-day monitoring phase. Results show that the recommendations allowed participants to find more relevant entities than in a control without the system. In addition, the recommended entities were also used in the actual tasks. In the discussion, we reflect on a research agenda for entity recommendation in context, revisiting comprehensive monitoring to include the physical world, considering entities as actionable recommendations, capturing drifting intent and routines, and considering explainability and transparency of recommendations, ethics, and ownership of data
EntityBot: Supporting everyday digital tasks with entity recommendations
Everyday digital tasks can highly benefit from systems that recommend the right information to use at the right time. However, existing solutions typically support only specific applications and tasks. In this demo, we showcase EntityBot, a system that captures context across application boundaries and recommends information entities related to the current task. The user's digital activity is continuously monitored by capturing all content on the computer screen using optical character recognition. This includes all applications and services being used and specific to individuals' computer usages such as instant messaging, emailing, web browsing, and word processing. A linear model is then applied to detect the user's task context to retrieve entities such as applications, documents, contact information, and several keywords determining the task. The system has been evaluated with real-world tasks, demonstrating that the recommendation had an impact on the tasks and led to high user satisfaction
Reclaiming the political : emancipation and critique in security studies
The critical security studies literature has been marked by a shared commitment towards the politicization of security – that is, the analysis of its assumptions, implications and the practices through which it is (re)produced. In recent years, however, politicization has been accompanied by a tendency to conceive security as connected with a logic of exclusion, totalization and even violence. This has resulted in an imbalanced politicization that weakens critique. Seeking to tackle this situation, the present article engages with contributions that have advanced emancipatory versions of security. Starting with, but going beyond, the so-called Aberystwyth School of security studies, the argument reconsiders the meaning of security as emancipation by making the case for a systematic engagement with the notions of reality and power. This revised version of security as emancipation strengthens critique by addressing political dimensions that have been underplayed in the critical security literature
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