826 research outputs found
Space Weather FX
No full textSpace Weather FX is a vodcast (video podcast) series that explores the science of space weather and how it can impact our every day lives. Episodes include Space Weather and its Effects, Connecting the Sun and Earth, When Space Weather Attacks, Stratospheric Sudden Warming, A Tour of Haystack's Radars, GPS and Space Weather, It Came from the Sun, and The Big Picture. The site also contain links to space weather information and educational materials. The episodes will run on one of four free video players. Educational levels: Intermediate elementary, Middle school, High school, Undergraduate lower division, Informal education, General public
Calibration of FX hybrid model with time-dependent parameters
No full textThis thesis captures the calibration of a FX hybrid model: The FX Black-Scholes Hull-White model. The main focus is on the calibration of the parameters in the Hull-White process: The mean reversion and the volatility parameter. The latter is commonly calibrated as a time-dependent parameter, whilst the mean reversion parameter is not. This thesis covers the calibration of the mean reversion as a time-dependent parameter. A known method from the Literature is researched, where we calibrate the mean reversion independently from the volatility parameter to the ratio of two swaptions with the same expiry but different tenor. In our research this method is extended to the negative interest rate environment by assuming that the swap rate follows a shifted lognormal distribution instead of a lognormal distribution. We show that a specific set of swaptions can be chosen, so that the calibration problem is simplified. This choice leads to sequential calibration of convex optimization problems. Numerical results of calibration to artificial and market data are presented, where we compare our method to picking the mean reversion parameter arbitrarily. The findings suggest that the choice of mean reversion parameter affects the calibration procedure. Therefore, we argue that a calibration method for the mean reversion would be appropriate. Besides the focus on the Hull-White process, the calibration of the volatility parameter in the FX Black-Scholes process of the hybrid model is investigated. For the calibration of the FX volatility parameter ATM options on FX rates are used. Numerical results of calibration to artificial and market data are shown. A typical problem of calibration in the industry is discussed: Precision for late maturities. The results in this thesis suggest that this problem could be solved. For research on homogeneity constraints on the time-dependent parameters, the performance of delta-hedging is investigated. The delta-hedging is performed in a simple Black-Scholes world with time-dependent volatility. We show that given a fixed amount hedges beforehand and interest rate zero, there exists an optimal distribution of time points that will lead to equal variance on the profit and loss for every volatility function that has equal implied volatility from t0 to maturity T. Using this strategy, the homogeneity of the volatility parameter has no impact on the performance of delta-hedging. Therefore, in this thesis no homogeneity constraints are used for the calibration of the time-dependent parameters.Applied Mathematic
FX: an RNA-Seq analysis tool on the cloud
No full textFX is an RNA-Seq analysis tool, which runs in parallel on cloud computing infrastructure, for the estimation of gene expression levels and genomic variant calling. In the mapping of short RNASeq reads, FX uses a transcriptome-based reference primarily, generated from similar to 160 000 mRNA sequences from RefSeq, UCSC and Ensembl databases. This approach reduces the misalignment of reads originating from splicing junctions. Unmapped reads not aligned on known transcripts are then mapped on the human genome reference. FX allows analysis of RNA-Seq data on cloud computing infrastructures, supporting access through a user-friendly web interface.
Availability: FX is freely available on the web at (http://fx.gmi.ac.kr), and can be installed on local Hadoop clusters. Guidance for the installation and operation of FX can be found under the 'Documentation' menu on the website
FX: An RNA-seq analysis tool on the cloud
No full textSummary: FX is an RNA-Seq analysis tool, which runs in parallel on cloud computing infrastructure, for the estimation of gene expression levels and genomic variant calling. In the mapping of short RNA-Seq reads, FX uses a transcriptome-based reference primarily, generated from ~160 000 mRNA sequences from RefSeq, UCSC and Ensembl databases. This approach reduces the misalignment of reads originating from splicing junctions. Unmapped reads not aligned on known transcripts are then mapped on the human genome reference. FX allows analysis of RNA-Seq data on cloud computing infrastructures, supporting access through a user-friendly web interface. © The Author 2012. Published by Oxford University Press. All rights reserved
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FX Law and Regulations in Korea ::Problems and Prospects /
No full textIn FX Law and Regulations in Korea: Problems and Prospects , Min-woo Kang offers a comprehensive and thorough discussion of the FX regulatory system in Korea, with a special focus on its chronic problems and possible remedies under the overhauled legal system. The author has provided technical analysis on each provision of the complex Korean law, which is commonly accepted as too convoluted, even for legal professionals. Fully utilising a host of legal materials as well as documents in the relevant economic theory, Min-woo Kang convincingly provides the rationale for FX regulation and a robust argument for amending the current Korean law in a significant way. This piece sheds a light on the path Korean lawmakers and regulatory authorities will take. Academics and practitioners interested in the Korean FX law will find this a good reference
A brief history of the NPS Field Experimentation program: Spanning STAN, TNT and JIFX
No full textThe NPS Field Experimentation Program was initiated in FY02 to provide an opportunity for NPS faculty and students to evaluate new technologies from their research in a field environment. These efforts were continued and integrated to create a formal decade long cooperative field experimental effort with USSOCOM (S&T /J9 and SORDAC) that began in FY03 as STAN (Surveillance and Targeting Acquisition Network) and culminated as TNT (Tactical Network Topology) in 2013. After TNT, sponsorship of NPS FX transitioned to OSD (AT&L) and the Department of Homeland Security (DHS). The NPS Joint Interagency Field Experimentation (JIFX) program exists today to support the S&T needs of all of the COCOMs, interagency, and first responders. This technical report serves to briefly document the history of the NPS FX program from STAN through current day JIFX. This document reflects the opinions of the author and does not represent the official policy or position of the Naval Postgraduate School, the United States Navy, or any other government organization.Approved for public release; distribution is unlimited.Naval Postgraduate School, Monterey, CA 93943-500
Mean error (ME) and root mean square error (RMSE) of the FX model (Fredlund and Xing, 1994) for the tested eight soils from saturation to oven dryness: FX-1 and FX-3 indicate that the parameters were obtained by fitting the models to the measurements in the suction range of 0 to 100 kPa and 0 to 300 kPa, respectively.
No full text<p>Mean error (ME) and root mean square error (RMSE) of the FX model (Fredlund and Xing, 1994) for the tested eight soils from saturation to oven dryness: FX-1 and FX-3 indicate that the parameters were obtained by fitting the models to the measurements in the suction range of 0 to 100 kPa and 0 to 300 kPa, respectively.</p
WMR prediction using recurrent neural networks on FX limit order book data
No full textThis thesis investigates the application of machine learning models on foreign exchange data around the WM/R 4pm Closing Spot Rate (colloquially known as the WMR Fix). Due to the nature of the market dynamics around the WMR Fix, inefficiencies can occur and therefore some predictability might be expected. We aim to find these inefficiencies. This is done by applying machine learning models, specifically recurrent neural networks, on limit order book data of foreign exchange (FX). The focus will be on the Euro - US dollar exchange rate.Applied Mathematics | Financial Engineerin
Mean error (ME) and root mean square error (RMSE) of the FX model (Fredlund and Xing, 1994) and KCGS model (Khlosi et al., 2006) for the tested 16 soils from Campbell and Shiozawa (1992) and Prebble (1991) from saturation to oven dryness: FX-1 and KCGS-1 indicate that the parameters were obtained by fitting the FX model and KCGS model to the measurements in the suction range of 0 to 100 kPa, respectively.
No full text<p>FX-3 indicates that the parameters were obtained by fitting the FX model to the measurements in the suction range of 0 to 300 kPa.</p><p>Mean error (ME) and root mean square error (RMSE) of the FX model (Fredlund and Xing, 1994) and KCGS model (Khlosi et al., 2006) for the tested 16 soils from Campbell and Shiozawa (1992) and Prebble (1991) from saturation to oven dryness: FX-1 and KCGS-1 indicate that the parameters were obtained by fitting the FX model and KCGS model to the measurements in the suction range of 0 to 100 kPa, respectively.</p
Exponential convergence of coupled diffusion processes
No full textThe coupled diffusion process, which describes Brownian motors, is an important model in the physics related to biophenomena. We address the exponential convergence of the Markov semigroup of a coupled diffusion process, and show the spectral gap inequality and Log-Sobolev inequality by comparing them with those of related diffusion processes. At the end of the paper, we provide sufficient conditions for a coupled diffusion process to converge exponentially. (C) 2005 American Institute of Physics.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000229749100036&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Physics, MathematicalSCI(E)1ARTICLE6null4
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