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A pressing environmental question facing the ocean is the potential impact of possible deep-sea mining activities. This work presents our initial results in developing an ocean and plume modeling system for the Bismark Sea where deep sea mining operations will probably take place. We employ the MSEAS modeling system to both simulate the ocean and to downscale initial conditions from a global system...
The method of compressive sensing is applied to moving source data created by simulation to estimate the mode wavenumbers, mode depth functions and mode amplitudes without any environmental acoustic information, such as sound speed profile or bottom properties. The method needs in principle only data covering a short range span and is thus applicable to a range-dependent environment to estimate the...
In this work we develop and demonstrate a probabilistic generative model for phytoplankton communities. The proposed model takes counts of a set of phytoplankton taxa in a timeseries as its training data, and models communities by learning sparse co-occurrence structure between the taxa. Our model is probabilistic, where communities are represented by probability distributions over the species, and...
Arctic coastal morphology is increasingly affected by changes to the climate. As the season length for shorefast ice decreases and temperatures warm permafrost, coastlines are increasingly susceptible to erosion from storm waves. Such coastal erosion is significant since the majority of the population centers and infrastructure in the Arctic are located near the coasts. Stakeholders and decision makers...
This study investigates near-shore circulation and wave characteristics applied to a case-study site in Monterey Bay, California. We integrate physics-based models to resolve wave conditions (based on inputs from a global wave model, wind data from an operational weather platform, and predictions from a regional flow model) together with a linear machine learning algorithm that combines forecasts...
Modeling the spatial variation of resources is necessary because it gives an estimate of what to expect during their exploration and exploitation. We focus on the spatial modeling of polymetallic nodules found in the deep sea regions of the Clarion-Clipperton zone in the Pacific. The data from this region available in the open domain is sparse, which warrants modeling techniques that can efficiently...
The U.S. National Weather Service (NWS) operates an independent array of Tsunami Gauges in Alaska and California. NWS Tsunami Gauges are designed to provide real-time monitoring capability to assess the hazard potential of tsunamis. The gauges are developed at the U.S. National Tsunami Warning Center (NTWC) and encompass two distinct designs. The Alpha version utilizes a high precision radar sensor,...
The National Tsunami Warning Center (NTWC) and the Pacific Tsunami Warning Center (PTWC) collaborate to provide tsunami warning service and mutual backup to tsunami-threatened areas throughout the United States and many other countries throughout the world. With mostly home-grown analysis systems, NTWC staff rapidly locates, sizes, and otherwise characterizes major earthquakes. We determine each earthquake's...
A sequential space carving method has been developed for the 3-D reconstruction of objects from multitude of forward-look sonar images captured at known sonar poses [1]. The 3-D space within common viewing volume of several camera poses is divided into small volumetric pixels (voxels). Projecting each onto various images, all voxels satisfying certain consistency measure are maintained. Conversely,...
We examine how model errors in previous Madden-Julian Oscillation (MJO) events can affect the simulation of subsequent MJO events due to increased errors that develop in the upper-ocean before the MJO initiation stage. Two fully coupled numerical simulations with 45-km and 27-km horizontal resolutions were integrated for a two-month period from November to December 2011 using the Navy's limited area...
Shell Exploration & Production Company is working with academic, non-profit, and federal stakeholders in the Gulf of Mexico to develop and implement long term environmental offshore monitoring programs. One such program, started in 2008 between Shell and the National Oceanic and Atmospheric Administration, has expanded to include new collaborators working together to operate multiple underwater...
To enhance the safety of various marine operations in many coastal regions, ocean weather monitoring and prediction systems are playing an increasingly important role over the last couple of decades. To provide marine forecasts in Cook Inlet, Alaska, which has extremely complex geometry and the largest tidal fluctuations has in the U.S., a wave forecasting system is developed. High-resolution 36-hour...
In this paper, we address interesting questions about how feng shui influences house price from a data perspective. First, is feng shui likely to influence house price? Second, how do different feng shui features, e.g., house shape, master bedroom location, and other interior room arrangements, influence the price? Third, can we automatically diagnose the feng shui problems of a house? From a dataset...
Throughput prediction is one of good solutions to improve quality of mobile applications (e.g., YouTube or Netflix) for video streaming delivery services in mobile networks. This is because such applications require monitoring the network performances to control content quality, thus guarantee quality of service (QoS) and quality of experience (QoE). In this paper, we propose a history-based TCP throughput...
Context-Aware Recommendation Systems has gained lots of attention in both industry and academic research. Factorization Machines (FM) based recommendation has been successfully used in sparse industrial datasets for user personalized video recommendations. FM is a collaborative filtering technique for predicting a target such as user rating, given observations of interaction between some users and...
The task of visual relationship recognition (VRR) is recognizing multiple objects and their relationships in an image. A fundamental difficulty of this task is class-number scalability, since the number of possible relationships we need to consider causes combinatorial explosion. Another difficulty of this task is modeling how to avoid outputting semantically redundant relationships. To overcome these...
In this paper we overview the modern forecast methods of monthly sunspot numbers, such as McNish-Lincoln and Hathaway-Wilson-Reichmann standard curve-fitting. Their disadvantages are presented, leading us to the necessity of researching a new technique for the solar activity prediction. For the long-term forecast we propose to use the established nonlinear dynamo model based on negative effective...
Most automatic speech recognition (ASR) systems are incapable of generating punctuation, making it difficult to read the transcribed output and less appropriate for tasks such as dictation. This paper introduces a procedure to automatically insert punctuation into unpunctuated sentences by using a bidirectional recurrent neural network with attention mechanism and Part-of-Speech (POS) Tags. Using...
Human pose forecasting is an important problem in computer vision with applications to human-robot interaction, visual surveillance, and autonomous driving. Usually, forecasting algorithms use 3D skeleton sequences and are trained to forecast for a few milliseconds into the future. Long-range forecasting is challenging due to the difficulty of estimating how long a person continues an activity. To...
Road pixel segmentation in airborne data is an important and challenging task. Recently, a sophisticated and robust approach based on superpixels and minimum cost paths has been published. In order to find out which of the numerous features are most essential, we propose a forward-search wrapper approach for feature selection which was tested with two different classifiers and with both generic and...
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