42. Does the facial lymph node require removal during neck dissection for oral cancer?

British Journal of Oral and Maxillofacial Surgery · 2023

• Medicine
• Surgery
• Internal medicine

David Greenwood

Géotechnique · 2021

• Geology
• Geotechnical engineering
• Engineering

Probabilistic sentence satisfiability: An approach to PSAT

Artificial Intelligence · 2019-11-04 · 8 citations

BRECCIA: A novel multi-source fusion framework for dynamic geospatial data analysis

2017-11-01 · 6 citations

Geospatial Intelligence analysis involves the combination of multi-source information expressed in logical form (as sentences or statements), computational form (as numerical models of physics or other processes), and sensor data (as measurements from transducers). Each of these forms has its own way to describe uncertainty or error: e.g., frequency models, algorithmic truncation, floating point roundoff, Gaussian distributions, etc. We propose BRECCIA, a Geospatial Intelligence analysis system, which receives information from humans (as logical sentences), simulations (e.g., weather or environmental predictions), and sensors (e.g. cameras, weather stations, microphones, etc.), where each piece of information has an associated uncertainty; BRECCIA then provides responses to user queries based on a new probabilistic logic system which determines a coherent overall response to the query and the probability of that response; this new method avoids the exponential complexity of previous approaches. In addition, BRECCIA attempts to identify concrete mechanisms (proposed actions) to acquire new data dynamically in order to reduce the uncertainty of the query response. The basis for this is a novel approach to probabilistic argumentation analysis <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

Parallelization and Performance of the NIM Weather Model on CPU, GPU, and MIC Processors

Bulletin of the American Meteorological Society · 2017-03-15 · 40 citations

Abstract The design and performance of the Non-Hydrostatic Icosahedral Model (NIM) global weather prediction model is described. NIM is a dynamical core designed to run on central processing unit (CPU), graphics processing unit (GPU), and Many Integrated Core (MIC) processors. It demonstrates efficient parallel performance and scalability to tens of thousands of compute nodes and has been an effective way to make comparisons between traditional CPU and emerging fine-grain processors. The design of the NIM also serves as a useful guide in the fine-grain parallelization of the finite volume cubed (FV3) model recently chosen by the National Weather Service (NWS) to become its next operational global weather prediction model. This paper describes the code structure and parallelization of NIM using standards-compliant open multiprocessing (OpenMP) and open accelerator (OpenACC) directives. NIM uses the directives to support a single, performance-portable code that runs on CPU, GPU, and MIC systems. Performance results are compared for five generations of computer chips including the recently released Intel Knights Landing and NVIDIA Pascal chips. Single and multinode performance and scalability is also shown, along with a cost–benefit comparison based on vendor list prices.

Gaussian processes for multi-sensor environmental monitoring

2015-09-01 · 18 citations

Efficiently monitoring environmental conditions across large indoor spaces (such as warehouses, factories or data centers) is an important problem with many applications. Deployment of a sensor network across the space can provide very precise readings at discrete locations. However, construction of a continuous model from this discrete sensor data is a challenge. The challenge is made harder by economic and logistical constraints that may limit the number of sensor motes in the network. The required model, therefore, must be able to interpolate sparse data and give accurate predictions at unsensed locations, as well as provide some notion of the uncertainty on those predictions. We propose a Gaussian process based model to answer both of these issues. We use Gaussian processes to model temperature and humidity distributions across an indoor space as functions of a 3-dimensional point. We study the model selection process and show that good results can be obtained, even with sparse sensor data. Deployment of a sensor network across an indoor lab provides real-world data that we use to construct an environmental model of the lab space. We seek to refine the model obtained from the initial deployment by using the uncertainty estimates provided by the Gaussian process methodology to modify sensor distribution such that each sensor is most advantageously placed. We explore multiple sensor placement techniques and experimentally validate a near-optimal criterion.

Numerical Weather Prediction Optimization

Elsevier eBooks · 2015-01-01 · 5 citations

Contributors

Elsevier eBooks · 2015-01-01

Parallelization and Performance of the NIM Weather Model Running on GPUs

EGU General Assembly Conference Abstracts · 2014-05-01

Directive-Based Parallelization of the NIM Weather Model for GPUs

2014-11-01 · 19 citations

The NIM is a performance-portable model that runs on CPU, GPU and MIC architectures with a single source code. The single source plus efficient code design allows application scientists to maintain the Fortran code, while computer scientists optimize performance and portability using OpenMP, OpenACC, and F2CACC directives. The F2C-ACC compiler was developed in 2008 at NOAA's Earth System Research Laboratory (ESRL) to support GPU parallelization before commercial Fortran GPU compilers were available. Since then, a number of vendors have built GPU compilers that are compliant to the emerging OpenACC standard. The paper will compare parallelization and performance of NIM using the F2C-ACC, Cray and PGI Fortran GPU compilers.