Apollo experience report: Development of guidance targeting techniques for the command module and launch vehicle

The development of the guidance targeting techniques for the Apollo command module and launch vehicle is discussed for four types of maneuvers: (1) translunar injection, (2) translunar midcourse, (3) lunar orbit insertion, and (4) return to earth. The development of real-time targeting programs for these maneuvers and the targeting procedures represented are discussed. The material is intended to convey historically the development of the targeting techniques required to meet the defined target objectives and to illustrate the solutions to problems encountered during that development

PlaNet - Photo Geolocation with Convolutional Neural Networks

Is it possible to build a system to determine the location where a photo was taken using just its pixels? In general, the problem seems exceptionally difficult: it is trivial to construct situations where no location can be inferred. Yet images often contain informative cues such as landmarks, weather patterns, vegetation, road markings, and architectural details, which in combination may allow one to determine an approximate location and occasionally an exact location. Websites such as GeoGuessr and View from your Window suggest that humans are relatively good at integrating these cues to geolocate images, especially en-masse. In computer vision, the photo geolocation problem is usually approached using image retrieval methods. In contrast, we pose the problem as one of classification by subdividing the surface of the earth into thousands of multi-scale geographic cells, and train a deep network using millions of geotagged images. While previous approaches only recognize landmarks or perform approximate matching using global image descriptors, our model is able to use and integrate multiple visible cues. We show that the resulting model, called PlaNet, outperforms previous approaches and even attains superhuman levels of accuracy in some cases. Moreover, we extend our model to photo albums by combining it with a long short-term memory (LSTM) architecture. By learning to exploit temporal coherence to geolocate uncertain photos, we demonstrate that this model achieves a 50% performance improvement over the single-image model

Comparison of high versus low frequency cerebral physiology for cerebrovascular reactivity assessment in traumatic brain injury: a multi-center pilot study

Current accepted cerebrovascular reactivity indices suffer from the need of high frequency data capture and export for post-acquisition processing. The role for minute-by-minute data in cerebrovascular reactivity monitoring remains uncertain. The goal was to explore the statistical time-series relationships between intra-cranial pressure (ICP), mean arterial pressure (MAP) and pressure reactivity index (PRx) using both 10-s and minute data update frequency in TBI. Prospective data from 31 patients from 3 centers with moderate/severe TBI and high-frequency archived physiology were reviewed. Both 10-s by 10-s and minute-by-minute mean values were derived for ICP and MAP for each patient. Similarly, PRx was derived using 30 consecutive 10-s data points, updated every minute. While long-PRx (L-PRx) was derived via similar methodology using minute-by-minute data, with L-PRx derived using various window lengths (5, 10, 20, 30, 40, and 60 min; denoted L-PRx_5, etc.). Time-series autoregressive integrative moving average (ARIMA) and vector autoregressive integrative moving average (VARIMA) models were created to analyze the relationship of these parameters over time. ARIMA modelling, Granger causality testing and VARIMA impulse response function (IRF) plotting demonstrated that similar information is carried in minute mean ICP and MAP data, compared to 10-s mean slow-wave ICP and MAP data. Shorter window L-PRx variants, such as L-PRx_5, appear to have a similar ARIMA structure, have a linear association with PRx and display moderate-to-strong correlations (r ~ 0.700, p Peer reviewe

Backpropagated Gradient Representations for Anomaly Detection

Learning representations that clearly distinguish between normal and abnormal data is key to the success of anomaly detection. Most of existing anomaly detection algorithms use activation representations from forward propagation while not exploiting gradients from backpropagation to characterize data. Gradients capture model updates required to represent data. Anomalies require more drastic model updates to fully represent them compared to normal data. Hence, we propose the utilization of backpropagated gradients as representations to characterize model behavior on anomalies and, consequently, detect such anomalies. We show that the proposed method using gradient-based representations achieves state-of-the-art anomaly detection performance in benchmark image recognition datasets. Also, we highlight the computational efficiency and the simplicity of the proposed method in comparison with other state-of-the-art methods relying on adversarial networks or autoregressive models, which require at least 27 times more model parameters than the proposed method.Comment: European Conference on Computer Vision (ECCV) 202

Transition from ion-coupled to electron-only reconnection: Basic physics and implications for plasma turbulence

Using kinetic particle-in-cell (PIC) simulations, we simulate reconnection conditions appropriate for the magnetosheath and solar wind, i.e., plasma beta (ratio of gas pressure to magnetic pressure) greater than 1 and low magnetic shear (strong guide field). Changing the simulation domain size, we find that the ion response varies greatly. For reconnecting regions with scales comparable to the ion Larmor radius, the ions do not respond to the reconnection dynamics leading to ''electron-only'' reconnection with very large quasi-steady reconnection rates. The transition to more traditional ''ion-coupled'' reconnection is gradual as the reconnection domain size increases, with the ions becoming frozen-in in the exhaust when the magnetic island width in the normal direction reaches many ion inertial lengths. During this transition, the quasi-steady reconnection rate decreases until the ions are fully coupled, ultimately reaching an asymptotic value. The scaling of the ion outflow velocity with exhaust width during this electron-only to ion-coupled transition is found to be consistent with a theoretical model of a newly reconnected field line. In order to have a fully frozen-in ion exhaust with ion flows comparable to the reconnection Alfv\'en speed, an exhaust width of at least several ion inertial lengths is needed. In turbulent systems with reconnection occurring between magnetic bubbles associated with fluctuations, using geometric arguments we estimate that fully ion-coupled reconnection requires magnetic bubble length scales of at least several tens of ion inertial lengths

An Exploratory Analysis of the Latent Structure of Process Data via Action Sequence Autoencoder

Computer simulations have become a popular tool of assessing complex skills such as problem-solving skills. Log files of computer-based items record the entire human-computer interactive processes for each respondent. The response processes are very diverse, noisy, and of nonstandard formats. Few generic methods have been developed for exploiting the information contained in process data. In this article, we propose a method to extract latent variables from process data. The method utilizes a sequence-to-sequence autoencoder to compress response processes into standard numerical vectors. It does not require prior knowledge of the specific items and human-computers interaction patterns. The proposed method is applied to both simulated and real process data to demonstrate that the resulting latent variables extract useful information from the response processes.Comment: 28 pages, 13 figure

TBI lesion segmentation in head CT: impact of preprocessing and data augmentation

Automatic segmentation of lesions in head CT provides keyinformation for patient management, prognosis and disease monitoring.Despite its clinical importance, method development has mostly focusedon multi-parametric MRI. Analysis of the brain in CT is challengingdue to limited soft tissue contrast and its mono-modal nature. We studythe under-explored problem of fine-grained CT segmentation of multiplelesion types (core, blood, oedema) in traumatic brain injury (TBI). Weobserve that preprocessing and data augmentation choices greatly impactthe segmentation accuracy of a neural network, yet these factors arerarely thoroughly assessed in prior work. We design an empirical studythat extensively evaluates the impact of different data preprocessing andaugmentation methods. We show that these choices can have an impactof up to 18% DSC. We conclude that resampling to isotropic resolutionyields improved performance, skull-stripping can be replaced by using theright intensity window, and affine-to-atlas registration is not necessaryif we use sufficient spatial augmentation. Since both skull-stripping andaffine-to-atlas registration are susceptible to failure, we recommend theiralternatives to be used in practice. We believe this is the first work toreport results for fine-grained multi-class segmentation of TBI in CT. Ourfindings may inform further research in this under-explored yet clinicallyimportant task of automatic head CT lesion segmentation

Rewriting a Deep Generative Model

A deep generative model such as a GAN learns to model a rich set of semantic and physical rules about the target distribution, but up to now, it has been obscure how such rules are encoded in the network, or how a rule could be changed. In this paper, we introduce a new problem setting: manipulation of specific rules encoded by a deep generative model. To address the problem, we propose a formulation in which the desired rule is changed by manipulating a layer of a deep network as a linear associative memory. We derive an algorithm for modifying one entry of the associative memory, and we demonstrate that several interesting structural rules can be located and modified within the layers of state-of-the-art generative models. We present a user interface to enable users to interactively change the rules of a generative model to achieve desired effects, and we show several proof-of-concept applications. Finally, results on multiple datasets demonstrate the advantage of our method against standard fine-tuning methods and edit transfer algorithms.Comment: ECCV 2020 (oral). Code at https://github.com/davidbau/rewriting. For videos and demos see https://rewriting.csail.mit.edu

Healthy environments from a broad perspective : an overview of research performed at the unit Building Physics and Systems of Eindhoven University of Technology

The design and realization of a healthy indoor environment is a challenge that is investigated from different perspectives at the unit Building Physics and Systems (BPS; Faculty of Architecture, Building and Planning) of Eindhoven University of Technology. Performance requirements (for instance, with respect to air quality, thermal comfort and lighting) and performance based assessment methods are the point-of-departure, focusing at computational techniques supporting the design process. Different specific application fields such as dwellings, offices, schools, but also, operating theatres, churches, musea and multifunctional stadiums, underline the applied approach that is part of the research within the unit. In the design of healthy environments, the performance based design assessment is crucial in arriving at innovative design solutions and optimized indoor and outdoor environments. In this assessment computational support tools and experimental verification play an important role. However, assessing the right indicators in an objective way, applying the correct tools and correct application of these tools is not yet well established. Alongside, developments are still ongoing. The work performed in the unit by the different researchers relates to the research questions that can be derived from this notice. The paper gives an introduction to the Unit BPS and presents a brief overview of recent and ongoing research. An extensive list of references is provided for further reading and supports the conclusion that healthy environments can and should be addressed from a wide angle