Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images

Histopathological characterization of colorectal polyps is an important principle for determining the risk of colorectal cancer and future rates of surveillance for patients. This characterization is time-intensive, requires years of specialized training, and suffers from significant inter-observer and intra-observer variability. In this work, we built an automatic image-understanding method that can accurately classify different types of colorectal polyps in whole-slide histology images to help pathologists with histopathological characterization and diagnosis of colorectal polyps. The proposed image-understanding method is based on deep-learning techniques, which rely on numerous levels of abstraction for data representation and have shown state-of-the-art results for various image analysis tasks. Our image-understanding method covers all five polyp types (hyperplastic polyp, sessile serrated polyp, traditional serrated adenoma, tubular adenoma, and tubulovillous/villous adenoma) that are included in the US multi-society task force guidelines for colorectal cancer risk assessment and surveillance, and encompasses the most common occurrences of colorectal polyps. Our evaluation on 239 independent test samples shows our proposed method can identify the types of colorectal polyps in whole-slide images with a high efficacy (accuracy: 93.0%, precision: 89.7%, recall: 88.3%, F1 score: 88.8%). The presented method in this paper can reduce the cognitive burden on pathologists and improve their accuracy and efficiency in histopathological characterization of colorectal polyps, and in subsequent risk assessment and follow-up recommendations

Environmental impact analysis with the airspace concept evaluation system

The National Aeronautics and Space Administration (NASA) Ames Research Center has developed the Airspace Concept Evaluation System (ACES), which is a fast-time simulation tool for evaluating Air Traffic Management (ATM) systems. This paper describes linking a capability to ACES which can analyze the environmental impact of proposed future ATM systems. This provides the ability to quickly evaluate metrics associated with environmental impacts of aviation for inclusion in multi-dimensional cost-benefit analysis of concepts for evolution of the National Airspace System (NAS) over the next several decades. The methodology used here may be summarized as follows: 1) Standard Federal Aviation Administration (FAA) noise and emissions-inventory models, the Noise Impact Routing System (NIRS) and the Emissions and Dispersion Modeling System (EDMS), respectively, are linked to ACES simulation outputs; 2) appropriate modifications are made to ACES outputs to incorporate all information needed by the environmental models (e.g., specific airframe and engine data); 3) noise and emissions calculations are performed for all traffic and airports in the study area for each of several scenarios, as simulated by ACES; and 4) impacts of future scenarios are compared to the current NAS baseline scenario. This paper also provides the results of initial end-to-end, proof-of-concept runs of the integrated ACES and environmental-modeling capability. These preliminary results demonstrate that if no growth is likely to be impeded by significant environmental impacts that could negatively affect communities throughout the nation

SuNeRF: Validation of a 3D Global Reconstruction of the Solar Corona Using Simulated EUV Images

Extreme Ultraviolet (EUV) light emitted by the Sun impacts satellite operations and communications and affects the habitability of planets. Currently, EUV-observing instruments are constrained to viewing the Sun from its equator (i.e., ecliptic), limiting our ability to forecast EUV emission for other viewpoints (e.g. solar poles), and to generalize our knowledge of the Sun-Earth system to other host stars. In this work, we adapt Neural Radiance Fields (NeRFs) to the physical properties of the Sun and demonstrate that non-ecliptic viewpoints could be reconstructed from observations limited to the solar ecliptic. To validate our approach, we train on simulations of solar EUV emission that provide a ground truth for all viewpoints. Our model accurately reconstructs the simulated 3D structure of the Sun, achieving a peak signal-to-noise ratio of 43.3 dB and a mean absolute relative error of 0.3\% for non-ecliptic viewpoints. Our method provides a consistent 3D reconstruction of the Sun from a limited number of viewpoints, thus highlighting the potential to create a virtual instrument for satellite observations of the Sun. Its extension to real observations will provide the missing link to compare the Sun to other stars and to improve space-weather forecasting.Comment: Accepted at Machine Learning and the Physical Sciences workshop, NeurIPS 202

Due Regard Encounter Model

Approved for public release; distribution is unlimited. Lexington Airspace encounter models describe encounter situations that may occur between aircraft in the airspace and are a critical component of safety assessment of sense and avoid (SAA) systems for Unmanned Aircraft Systems (UASs). Some UAS will fly in international airspace under due regard and may encounter other aircraft during these operations. In these types of encounters, the intruder aircraft is likely receiving air traffic control (ATC) services, but the UAS is not. Thus, there is a need for a due regard encounter model that can be used to generate these types of encounters. This report describes the development of a due regard encounter model. In order to build the model, Lincoln Laboratory collected data for aircraft flying in international airspace using the Enhanced Traffic Management System (ETMS) data feed that was provided by the Volpe Center. Lincoln processed these data, and extracted important features to construct the model. The model is based on Bayesian networks that represent the probabilistic relationship between variables that describe how aircraft behave. The model is used to construct random aircraft trajectories that ar

AIAA's A. ircraft Techno.logy,.Inter.a.t. ion, and Operations (ATIO) 2002 Technical AIAA 2002-5871

This paper describes the requirements for integrating a noise modeling capability into air transportation system simulations. In order to address community concerns, noise impact should be analyzed with appropriate models in simulation environments. Coupling a noise modeling capability with these simulators will lead to better understanding of what impact certain flight operations may have on local communities. Described within this paper are the general data requirements that a noise modeling tool must receive from a simulator. At a minimum, the simulator must provide data to the noise model that may be categorized under environmental conditions, flight path information including aircraft and engine performance, and grid set-up in order to analyze noise impact. An application of these requirements to the integration of a noise model with an air traffic control tower simulator is presented. Complexities in obtaining and adapting these data types from the simulator are examined. It is anticipated that the details of these requirements may be used to facilitate the integration of a noise modeling capability into other air transportation system simulation environments

A case study on the corporate social responsibility policies and practices of Petron Corporation and Pilipinas Shell Petroleum Corporation

The study identified the CSR policies and practices of Petron Corporation and Pilipinas Shell Corporation in terms of their specific programs and projects, primary motivations, evaluation methods and reporting guidelines in the field of social responsibility. It covered policies and practices of the periods 2000 to 2006. The result of the study showned that majority of Petron and Shell\u27s CSR activities can be clssified under the environment and social category of the World Bank Diagnostic Tool. The study also showed that establing goodwill and a good reputation in the community are Perton and Shell\u27s primary motivations of their social investment

Impact on emotional and social well being of adolescents whose mothers are diagnosed with breast cancer

A non-experimental descriptive research design was used in the study, which involved 30 respondents in selected areas of Cavite, namely Imus, Bacoor, General Trias and Carmona, selected using purposive sampling technique. A questionnaire was used as the research tool and the data obtained was tallied and encoded in the computer for statistical computation using frequency, percentage, mean and t-test of independent variable. The study concluded that, 1) Most of the respondents are female adolescents whose age ranges 17-20 years old, presently in the high school level and belongs to a nuclear family; 2) Both of the factors emotional and social well being had the mean of 2.83 and 2.80 (high), respectively. This means that there is a high impact on emotional and social well being of adolescents whose mothers are diagnosed with breast cancer. This suggests that the respondents agree with the statements in the questionnaire under this aspect of impact on emotional and social well being; 2) There was no significant difference in the impact on the emotional well being of adolescents whose mothers are diagnosed with breast cancer when grouped according to age, gender, family structure and educational attainment. These factors do not influence the adolescents when their mothers are diagnosed with breast cancer. Likewise, this means that the family structure and level of educational attainment were not significantly related to the impact on emotional well being of adolescents whose mothers are diagnosed with breast cancer; 3) There was no significant difference in the impact of social well-being when grouped according to the demographic variables mentioned. These factors do not affect the social well being of adolescents whose mothers are diagnosed with breast cancer

Sun Neural Radiance Fields (SuNeRFs): From Images to 4D Models of the Solar Atmosphere

EUV-observing instruments are limited in their numbers and have mainly been constrained to viewing the Sun from the ecliptic. For example, the Solar Dynamics Observatory (SDO; 2010-present) provides images of the Sun in EUV from the perspective of the Earth-Sun line. Two additional viewpoints are provided by the STEREO twin satellites pulling Ahead (STEREO-A; 2006-present) and falling Behind (STEREO-B; 2006-2014) of Earth's orbit. No satellites observe the solar poles directly. However, a complete image of the 3D Sun is required to fully understand the dynamics of the Sun (from eruptive events to space weather in the solar system), to forecast EUV radiation to protect our assets in space, to relate the Sun to other stars in the universe, and to generalize our knowledge of the Sun-Earth system to other host stars. To maximize the science return of multiple viewpoints, we propose a novel approach that unifies and smoothly integrates data from multiple perspectives into a consistent 3D representation of the solar corona. More specifically, we leverage Neural Radiance Fields (NeRFs) which are neural networks that achieve state-of-the-art 3D scene representation and generate novel views from a limited number of input images. We adapted a Sun NeRF (SuNeRF) to generate a physically-consistent representation of the 3D Sun, with the inclusion of radiative transfer and geometric ray sampling that matches the physical reality of optically thin plasma in the solar atmosphere. SuNeRFs leverage existing multi-viewpoint observations and act as virtual instruments that can fly out of the ecliptic, that can view the poles, and that can be placed anywhere in the solar system to generate novel views. Our pipeline is an example of how novel deep learning techniques can be used to significantly enhance observational capabilities by the creation of virtual instruments