MISSING 411 EBOOK
Missing by David Paulides, , CreateSpace edition, in English. Book Details Author: David Paulides Pages: Publisher: CreateSpace Independent Publishing Platform Brand: English ISBN: Publication Date: Release Date: [Ebook] download game of thrones a feast of ice and fire the official compa. Where can I download the Missing book series by David Paulides at a reasonable Can you get the David Paulides Missing books on Kindle or as ebooks?.
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By David Paulides Missing North America and. Beyond: Stories of people who have disappeared in remote locations of. Click here if your download doesn" t. Download eBooks Missing Eastern United States [PDF, ePub] by David Paulides Complete Read Online "Click Visit button" to access full FREE ebook. Download >> Missing Western United States & Canada.  Can you get the David Paulides Missing books on Kindle or as ebooks?.
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Topography does play a part into the age of the victims and certain clusters have specific age and sex consistency that is baffling. The manuscript for the research was extremely large so the story was split between two books, Missing Western United States and Canada and Missing Eastern United States.
The Eastern version will be released in late March and will include a list of all missing people in each edition and a concluding chapter that draws both books together for conclusions. Some of the issues that are discussed in each edition: Chances are, they will find a way to trivialize or ignore the disturbing evidence accumulated by David Paulides, a former law man turned investigative journalist.
The paper trail uncovered by Paulides through sheer doggedness is impressive, the evidence indisputable. People are vanishing without a trace from our national parks and forests, yet government agencies are saying nothing.
At a minimum, this story deserves space on the front page of every newspaper in the country, and it warrants a formal high level inquiry by the federal agencies whose files leave little doubt that something very strange is unfolding in our wilderness.
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Show related SlideShares at end. WordPress Shortcode. Published in: Full Name Comment goes here. Are you sure you want to Yes No. The approach presented in this paper provides a novel and comprehensive approach toward large-scale, faster, and real-time road traffic prediction. The road traffic characteristics that we predict are flow, speed, and occupancy.
GPUs provide massively parallel computing power to speed up computations. Big data leverages distributed and high performance computing HPC technologies, such as GPUs, to manage and analyze data.
Big data and HPC technologies are converging to address their individual limitations and exploit their synergies [ 60 , 61 ].
In-memory computing allows faster analysis of data by the use of random access memories RAMs as opposed to the secondary memories. The road traffic dataset provides five-minute interval traffic data on the freeways. The dataset is used for the training of deep convolution neural networks.
To the best of our knowledge, this is the largest data, in terms of the time duration, that has been used in a deep learning based study.
Big data veracity issues have been discussed in detail and methods to address the incompleteness and errors in data have been described. Several combinations of the input attributes of the data along with various network configurations of the deep learning models are investigated for the training and prediction purposes.
Different configuration sets of the deep learning networks have been executed multiple times, where the batch sizes and the number of epochs have been varied with different combinations, and each combination has been executed multiple times. These multiple configurations show consistency of the accuracy of the results.
The training of a deep model is a compute intensive job, particularly when the size of the dataset is large. The use of GPUs provides a speedy deep learning training process, and we verified this by comparing the execution time performance of the training process on GPUs with CPUs.
Moreover, we explored the possibility of real-time prediction by saving the pre-trained deep learning models for traffic prediction using the complete 11 years of data, and subsequently using it on smaller datasets for near real-time traffic predictions using GPUs. This is a first step towards the real-time prediction of road traffic and will be further explored in our future work.
Missing The Devil's in the Details
For the accuracy evaluation of our deep prediction models, we used three well-known evaluation metrics: mean absolute error MAE , mean absolute percentage error MAPE , and root mean squared error RMSE.
Additionally, we have provided the comparison of actual and predicted values of the road traffic characteristics. The paper contributes novel deep learning models, algorithms, implementation and an analytics methodology, and a software tool for smart cities, big data, HPC, and their convergence.
The paper also serves as a preliminary investigation into the convergence of big data and higher performance computing [ 60 ] using the transportation case study. These convergence issues will be further explored in the future with the aim of devising novel multidisciplinary technologies for transportation and other sectors.
The rest of this paper is organized as follows. Section 2 reviews the work done by others in the area of traffic behavior prediction. The details about our methodology and the deep learning model are given in Section 3. The input dataset and pre-processing details are also described in this section. Section 4 discusses the results.
The proposed approach for the near real-time prediction of road traffic is discussed in Section 5. Finally, we conclude in Section 6 and give directions for future work.
Literature Review A great deal of works have been done on traffic modeling, analysis, and prediction, and in the broader area of transportation. Some of these works have already been mentioned and discussed in Section 1. Our focus in this paper is on the road traffic characteristics prediction using deep learning approaches, and, hence, in the rest of this section, we review the notable works relevant to our main focus area in this paper.
A deep learning approach to predict traffic flow for short intervals on road networks is proposed in [ 26 ]. A traffic prediction method based on long short-term memory LSTM was used by the authors for prediction purpose.
An origin destination correlation ODC matrix is used as input to the training algorithm. The dataset used for this process is retrieved from the Beijing Traffic Management Bureau and is collected from more than observation stations or sensors containing around 26 million records.
Five-minute interval data from 1 January to 30 June are collected where the data for first five months are used for training and the rest of the data were are for testing purposes.
Input data are used to predict the flow in , , , and min time intervals.
The authors selected three observation points with high, medium, and low flow rates to compare the actual flow and predicted flow values on those observation points. MRE values for min interval flow prediction reported in this work are 6. Overall, it performs better than other older machine learning models. Therefore, it is concluded that LSTM is an appropriate choice for long time intervals. Another traffic flow prediction approach has been proposed in [ 23 ] that uses autoencoders for training, testing and to make predictions.
The model is named as stacked autoencoder SAE model. Data for this purpose are also collected from PeMS [ 62 ].
They used weekdays Monday—Friday data for first three months of giving vehicles flow on a highway on five-minute interval basis. Data for first two months are used for training and remaining one-month data are used for the testing. By using five-minute interval data, the authors predicted the aggregate flow for , , , and min intervals in this model.
Three months of data for all the highways available in the system are used in this model. Although this is a large amount of data that requires gigabytes of storage capacity, the key point to mention is that data from one highway can only be used to predict the flow on that particular highway. Therefore, no big data technology is used to store or process the input datasets.But these cases are so truly bizarre and strange.
They used LSTM to predict speed on road networks for different short intervals of one to four minutes using historical data. These allegations are generally ignored by authorities until pressured by facts presented through secondary autopsies that families requested and paid for. Yet, little analysis has concentrated on understanding the conditions under which institutional structures within non-majoritarian systems lend themselves to partisanship and how this subsequently influences public policy, especially within the context of international organisations.
So where Paulides notices a geographic cluster of three disappearances of toddlers twenty years apart that happened to all take place in June, he sees a pattern.