Deep Learning Goes to Boot Camp: The U.S. Army wants to Team Humans and Robots on the Battlefield

IEEE Spectrum ◽  
2021 ◽  
Vol 58 (10) ◽  
pp. 56-62
Author(s):  
Evan Ackerman
Keyword(s):  
Deep Learning ◽  
Boot Camp ◽  
The U.S

scholarly journals Marine Corps Boot Camp during World War II: THE GATEWAY TO THE CORPS’ S U C C E S S AT I WO J I M A

2021 ◽  
Vol 7 (1) ◽  
pp. 46-63
Author(s):  
Jessica Anderson-Colon
Keyword(s):  
World War Ii ◽  
Marine Corps ◽  
Boot Camp ◽  
World War ◽  
The U.S

Was the Marine Corps’ success at Iwo Jima a matter of leadership, bravado, or fundamental training? This article examines the efficacy of boot camp, replacement training, and unit training as it relates to the success of the U.S. Marines on Iwo Jima. During World War II, the exploits of the Marines on Iwo Jima have been commended, but the reality of wartime exigencies inevitably placed a strain on the quality of men slated for the Service. However, the Marine Corps’ emphasis on the fundamentals during boot camp proved the necessary ingredient for victory. Beyond leadership or lore, this article asserts that Marine Corps boot camp provided an elemental gateway to success on Iwo Jima.

scholarly journals Forecasting type-specific seasonal influenza after 26 weeks in the United States using influenza activities in other countries

2019 ◽  
Author(s):  
S. B. Choi ◽  
J. Kim ◽  
I. Ahn
Keyword(s):  
United States ◽  
Time Series ◽  
Deep Learning ◽  
Seasonal Influenza ◽  
Prediction Models ◽  
Time Lag ◽  
The United States ◽  
Surveillance Data ◽  
Influenza Surveillance ◽  
The U.S

AbstractTo identify countries that have seasonal patterns similar to the time series of influenza surveillance data in the United States and other countries, and to forecast the 2018–2019 seasonal influenza outbreak in the U.S. using linear regression, auto regressive integrated moving average, and deep learning. We collected the surveillance data of 164 countries from 2010 to 2018 using the FluNet database. Data for influenza-like illness (ILI) in the U.S. were collected from the Fluview database. This cross-correlation study identified the time lag between the two time-series. Deep learning was performed to forecast ILI, total influenza, A, and B viruses after 26 weeks in the U.S. The seasonal influenza patterns in Australia and Chile showed a high correlation with those of the U.S. 22 weeks and 28 weeks earlier, respectively. The R2 score of DNN models for ILI for validation set in 2015–2019 was 0.722 despite how hard it is to forecast 26 weeks ahead. Our prediction models forecast that the ILI for the U.S. in 2018–2019 may be later and less severe than those in 2017–2018, judging from the influenza activity for Australia and Chile in 2018. It allows to estimate peak timing, peak intensity, and type-specific influenza activities for next season at 40th week. The correlation for seasonal influenza among Australia, Chile, and the U.S. could be used to decide on influenza vaccine strategy six months ahead in the U.S.

scholarly journals A Plastic Contamination Image Dataset for Deep Learning Model Development and Training

2020 ◽  
Vol 2 (2) ◽  
pp. 317-321
Author(s):  
Mathew G. Pelletier ◽  
Greg A. Holt ◽  
John D. Wanjura
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Detection System ◽  
Low Cost ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Image Dataset ◽  
The U.S ◽  
Image Datasets ◽  
Color Cameras

The removal of plastic contamination in cotton lint is an issue of top priority for the U.S. cotton industry. One of the main sources of plastic contamination appearing in marketable cotton bales is plastic used to wrap cotton modules on cotton harvesters. To help mitigate plastic contamination at the gin, automatic inspection systems are needed to detect and control removal systems. Due to significant cost constraints in the U.S. cotton ginning industry, the use of low-cost color cameras for detection of plastic contamination has been successfully adopted. However, some plastics of similar color to background are difficult to detect when utilizing traditional machine learning algorithms. Hence, current detection/removal system designs are not able to remove all plastics and there is still a need for better detection methods. Recent advances in deep learning convolutional neural networks (CNNs) show promise for enabling the use of low-cost color cameras for detection of objects of interest when placed against a background of similar color. They do this by mimicking the human visual detection system, focusing on differences in texture rather than color as the primary detection paradigm. The key to leveraging the CNNs is the development of extensive image datasets required for training. One of the impediments to this methodology is the need for large image datasets where each image must be annotated with bounding boxes that surround each object of interest. As this requirement is labor-intensive, there is significant value in these image datasets. This report details the included image dataset as well as the system design used to collect the images. For acquisition of the image dataset, a prototype detection system was developed and deployed into a commercial cotton gin where images were collected for the duration of the 2018–2019 ginning season. A discussion of the observational impact that the system had on reduction of plastic contamination at the commercial gin, utilizing traditional color-based machine learning algorithms, is also included.

scholarly journals Cluster Analysis of SARS-CoV-2 Gene using Deep Learning Autoencoder: Gene Profiling for Mutations and Transitions

2021 ◽  
Author(s):  
Jun Miyake ◽  
Takaaki Sato ◽  
Shunsuke Baba ◽  
Hayato Nakamura ◽  
Hirohiko Niioka ◽  
...  
Keyword(s):  
United States ◽  
Cluster Analysis ◽  
Deep Learning ◽  
The United States ◽  
Gene Profiling ◽  
Type B ◽  
Type D ◽  
Type C ◽  
The U.S ◽  
Over Time

We report on a method for analyzing the variant of coronavirus genes using autoencoder. Since coronaviruses have mutated rapidly and generated a large number of genotypes, an appropriate method for understanding the entire population is required. The method using autoencoder meets this requirement and is suitable for understanding how and when the variants emarge and disappear. For the over 30,000 SARS-CoV-2 ORF1ab gene sequences sampled globally from December 2019 to February 2021, we were able to represent a summary of their characteristics in a 3D plot and show the expansion, decline, and transformation of the virus types over time and by region. Based on ORF1ab genes, the SARS-CoV-2 viruses were classified into five major types (A, B, C, D, and E in the order of appearance): the virus type that originated in China at the end of 2019 (type A) practically disappeared in June 2020; two virus types (types B and C) have emerged in the United States and Europe since February 2020, and type B has become a global phenomenon. Type C is only prevalent in the U.S. and is suspected to be associated with high mortality, but this type also disappeared at the end of June. Type D is only found in Australia. Currently, the epidemic is dominated by types B and E.

“Riding a Stick of Dynamite”

2020 ◽  
pp. 27-32
Author(s):  
Joseph B. Atkins
Keyword(s):  
World War Ii ◽  
Military Service ◽  
South Pacific ◽  
Boot Camp ◽  
World War ◽  
The South ◽  
Battle Of Okinawa ◽  
The U.S

This chapter details Harry Dean's military service in the U.S. Navy during World War II. He was one of several actors who served in the South Pacific, including Lee Van Cleef, Lee Marvin, and Harry Dean's future acting teacher Jeff Corey. Following a description of life in Navy boot camp, the chapter discusses service on an LST (landing ship, tank), which Harry Dean described as "riding a stick of dynamite." He was ship's cook on the hardware-carrying USS LST-970, which saw service in the Battle of Okinawa -- the last major battle of the war -- and faced the death-defying missions of Japan's kamikaze pilots. The Navy lost more ships in this battle than at any other time in its history. "I was damn lucky I didn't get blown up or killed," Harry Dean said about the experience.

When the U.S. catches a cold, Canada sneezes: A lower-bound tale told by deep learning

2020 ◽  
Vol 117 ◽  
pp. 103926
Author(s):  
Vadym Lepetyuk ◽  
Lilia Maliar ◽  
Serguei Maliar
Keyword(s):  
Deep Learning ◽  
Lower Bound ◽  
The U.S

Historical Introduction

1977 ◽  
Vol 35 ◽  
pp. 2-5
Author(s):  
R. D. Heidenreich
Keyword(s):  
Electron Emission ◽  
Secondary Electron ◽  
Secondary Electron Emission ◽  
50Th Anniversary ◽  
Secondary Emission ◽  
Wave Nature ◽  
Nobel Prize In Physics ◽  
Primary Electrons ◽  
The U.S ◽  
Mutual Agreement

This program has been organized by the EMSA to commensurate the 50th anniversary of the experimental verification of the wave nature of the electron. Davisson and Germer in the U.S. and Thomson and Reid in Britian accomplished this at about the same time. Their findings were published in Nature in 1927 by mutual agreement since their independent efforts had led to the same conclusion at about the same time. In 1937 Davisson and Thomson shared the Nobel Prize in physics for demonstrating the wave nature of the electron deduced in 1924 by Louis de Broglie.The Davisson experiments (1921-1927) were concerned with the angular distribution of secondary electron emission from nickel surfaces produced by 150 volt primary electrons. The motivation was the effect of secondary emission on the characteristics of vacuum tubes but significant deviations from the results expected for a corpuscular electron led to a diffraction interpretation suggested by Elasser in 1925.

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