James G. Flocks, Nicholas F. Ferina, and Jack L. Kindinger
This paper provides a summary of previous studies and a synthesis of the surficial geology of the MississippiAlabama shelf, located between the modern Mississippi River Delta and the Florida carbonate platform. Presently, sedimentation processes on the shelf are a function of prevailing winds and currents; however, in the past, the shelf was the focus of numerous delta cycles. Major episodes of deposition and erosion on the shelf have been primarily in response to oscillations in sea level. As sea level fell during the last ice age, deltas moved across the shelf to the shelf edge, incising river valleys across the middle shelf and creating stacked delta sequences on the slope. The delta complexes regressed during the last sea-level rise, infilling valleys while also providing sediments for erosion. Shoals were formed throughout these processes and are found along the shelf and modern shoreline. Data collected from the shelf, incorporated into this summary, include bathymetric, geophysical, and sediment cores. The purpose of the report is to integrate past studies with archived data to provide a comprehensive overview of the geology and geomorphology of the shelf. In addition, areas of further study are identified in the summary as a bulleted list of future needs and goals.
Int'l Conf. Genetic and Evolutionary Methods | GEM'14 |
A Novel Autoregressive Model for System State Prediction
De Z. Li1, Fathy Ismail1, and Wilson Wang2
Department of Mechanical & Mechatronics Engineering, University of Waterloo, Waterloo, Ontario, Canada
Department of Mechanical Engineering, Lakehead University, Thunder Bay, Ontario, Canada
Abstract - Autoregressive (AR) model is one of the commonly used predictors for system state forecasting. Several training methods have been used to optimize AR model parameters, such as least square estimate and maximum likelihood estimate; however, both of these techniques are sensitive to noisy samples and to outliers. To address these problems, a novel AR predictor, NAR, is proposed in this work to improve the prediction accuracy and reduce the effect of noise and outliers. In NAR the model parameters of AR are trained using an adaptive least square estimate (ALSE) method. The proposed ALSE is used to learn samples characteristics more effectively. In each training epoch, the ALSE can discern the samples associated with their fitting accuracy. The samples with larger errors will be assigned a larger penalty value in the cost function; however the penalties of difficult-to-predict samples will be reduced to improve the overall prediction accuracy. The effectiveness of the developed NAR predictor is demonstrated by simulation tests. Test results show that the proposed NAR predictor can capture system dynamics effectively and track system characteristics accurately.
Formal Veriﬁcation of Improved Numeric Comparison Protocol for
Secure Simple Paring in Bluetooth Using ProVerif
Kenichi Arai and Toshinobu Kaneko
Department of Electrical Engineering, Faculty of Science and Technology, Tokyo University of Science
2641 Yamazaki, Noda, Chiba, 278-8510 Japan
Abstract— Recently, research has been conducted on automatic veriﬁcation of cryptographic security protocols with the formal method. An automatic veriﬁer is very useful because the risk of human error in such complicated protocols can be reduced. In this paper, we introduce our formalization of an improved Numeric Comparison protocol for Secure
Simple Pairing in Bluetooth proposed by Yeh et al. and verify its security using ProVerif as an automatic cryptographic protocol veriﬁer. As a result, we show that this improved protocol is subject to attacks. Moreover, we propose countermeasures against these attacks on this improved protocol.
Our proposal provides this improved protocol with a higher level of security.
For 200 years the newspaper front page dominated public thinking. In the
last 20 years that picture has changed. Today television news is watched more often than
people read newspapers, than people listen to radio, than people read or gather any
other form of communication. The reason: people are lazy. With television you just
sit-watch-listen. The thinking is done for you.
The year 1960 was the year that television became the most important thing in politics.
After refusing to wear makeup and campaigning for hours beforehand, Richard Nixon appeared
weary, sick, and sloppy next to the well-rested and confident John Kennedy. Seventy million
people tuned into the first televised presidential debate, and after it was over, John
Kennedy moved into the lead and never looked back.
Having learned his lesson, when he ran for president again in 1968, Nixon hired a
28-year-old local television producer from Cleveland to be the media advisor to his
campaign. His name was Roger Ailes, and hed take Richard Nixon from the sickly sideliner to
the polished, professional candidate who made it to the White House.
Of all the iPhone’s talents, its iPoddishness may be the most successful. This function, after all, gets the most impressive battery life (40 to 80 hours of playback, depending on the model). There’s enough room on your phone to store thousands of songs. And iTunes Radio means that you’ll never run out of music to listen to—and you’ll never have to pay a penny for it.
To enter iPod Land, open the Music app. On a new phone, it’s at the lower-right corner of the screen.
There’s another way to get to iPod mode. Just swipe upward from the bottom of the screen. That opens the Control Center, whose central feature is the music playback controls and a volume control.
The Music program begins with lists—lots of lists. The icons at the bottom of the screen represent your starter lists. You can rearrange or swap them, but you start out with Radio, Playlists, Artists, Songs, and More. Here’s what they all do.