Analysis of OD Flows (Raw Data)

In a recent paper, Structural Analysis of Network Traffic Flows, we analyzed the set of Origin Destination traffic flows from the Sprint-Europe and Abilene backbone networks. This report presents the complete set of results from analyzing data from both networks. The results in this report are specific to the Sprint-1 and Abilene datasets studied in the above paper. The following results are presented here: 1 Rows of Principal Matrix (V) 2 1.1 Sprint-1 Dataset ................................ 2 1.2 Abilene Dataset.................................. 9 2 Set of Eigenflows 14 2.1 Sprint-1 Dataset.................................. 14 2.2 Abilene Dataset................................... 21 3 Classifying Eigenflows 26 3.1 Sprint-1 Dataset.................................. 26 3.2 Abilene Datase.................................... 44Centre National de la Recherche Scientifique (CNRS) France; Sprint Labs; Office of Naval Research (N000140310043); National Science Foundation (ANI-9986397, CCR-0325701

Correspondence analysis of raw data

Correspondence analysis has found extensive use in ecology, archeology, linguistics and the social sciences as a method for visualizing the patterns of association in a table of frequencies or nonnegative ratio-scale data. Inherent to the method is the expression of the data in each row or each column relative to their respective totals, and it is these sets of relative values (called profiles) that are visualized. This ‘relativization’ of the data makes perfect sense when the margins of the table represent samples from sub-populations of inherently different sizes. But in some ecological applications sampling is performed on equal areas or equal volumes so that the absolute levels of the observed occurrences may be of relevance, in which case relativization may not be required. In this paper we define the correspondence analysis of the raw ‘unrelativized’ data and discuss its properties, comparing this new method to regular correspondence analysis and to a related variant of non-symmetric correspondence analysis.Abundance data, biplot, Bray-Curtis dissimilarity, profile, size and shape, visualisation

Efficient SAR Raw Data Compression in Frequency Domain

SAR raw data compression is necessary to reduce huge amounts of SAR data for a memory on board a satellite, space shuttle or aircraft and for later downlink to a ground station. In view of interferometric and polarimetric applications for SAR data, it becomes more and more important to pay attention to phase errors caused by data compression. Herein, a detailed comparison of block adaptive quantization in time domain (BAQ) and in frequency domain (FFT-BAQ) is given. Inclusion of raw data compression in the processing chain allows an efficient use of the FFT-BAQ and makes implementation for on-board data compression feasible. The FFT-BAQ outperforms the BAQ in terms of signal-to-quantization noise ratio and phase error and allows a direct decimation of the oversampled data equivalent to FIR-filtering in time domain. Impacts on interferometric phase and coherency are also given

Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU

In this paper, we present that security threats coming with existing GPU memory management strategy are overlooked, which opens a back door for adversaries to freely break the memory isolation: they enable adversaries without any privilege in a computer to recover the raw memory data left by previous processes directly. More importantly, such attacks can work on not only normal multi-user operating systems, but also cloud computing platforms. To demonstrate the seriousness of such attacks, we recovered original data directly from GPU memory residues left by exited commodity applications, including Google Chrome, Adobe Reader, GIMP, Matlab. The results show that, because of the vulnerable memory management strategy, commodity applications in our experiments are all affected

Raw and Count Data Comparability of Hip-Worn ActiGraph GT3X+ and Link Accelerometers

To enable inter- and intrastudy comparisons it is important to ascertain comparability among accelerometer models. Purpose: The purpose of this study was to compare raw and count data between hip-worn ActiGraph GT3X+ and GT9X Link accelerometers. Methods: Adults (n = 26 (n = 15 women); age, 49.1 T 20.0 yr) wore GT3X+ and Link accelerometers over the right hip for an 80-min protocol involving 12–21 sedentary, household, and ambulatory/exercise activities lasting 2–15 min each. For each accelerometer, mean and variance of the raw (60 Hz) data for each axis and vector magnitude (VM) were extracted in 30-s epochs. A machine learning model (Montoye 2015) was used to predict energy expenditure in METs from the raw data. Raw data were also processed into activity counts in 30-s epochs for each axis and VM, with Freedson 1998 and 2011 count-based regression models used to predictMETs. Time spent in sedentary, light, moderate, and vigorous intensities was derived from predicted METs from each model. Correlations were calculated to compare raw and count data between accelerometers, and percent agreement was used to compare epoch-by-epoch activity intensity. Results: For raw data, correlations for mean acceleration were 0.96 T 0.05, 0.89 T 0.16, 0.71 T 0.33, and 0.80 T 0.28, and those for variance were 0.98 T 0.02, 0.98 T 0.03, 0.91 T 0.06, and 1.00 T 0.00 in the X, Y, and Z axes and VM, respectively. For count data, corresponding correlations were 1.00 T 0.01, 0.98 T 0.02, 0.96 T 0.04, and 1.00 T 0.00, respectively. Freedson 1998 and 2011 count-based models had significantly higher percent agreement for activity intensity (95.1% T 5.6% and 95.5% T 4.0%) compared with theMontoye 2015 raw data model (61.5% T 27.6%; P G 0.001). Conclusions: Count data were more highly comparable than raw data between accelerometers. Data filtering and/or more robust raw data models are needed to improve raw data comparability between ActiGraph GT3X+ and Link accelerometers