scholarly journals An Assessment of Reprocessed GPS/MET Observations Spanning 1995–1997

2021 ◽  
Author(s):  
Anthony James Mannucci ◽  
Chi On Ao ◽  
Byron A. Iijima ◽  
Thomas K. Meehan ◽  
Panagiotis Vergados ◽  
...  
Keyword(s):  
Single Frequency ◽  
Data Sets ◽  
Frequency Data ◽  
Dual Frequency ◽  
Altitude Range ◽  
Data Set ◽  
Encrypted Data ◽  
Temperature Bias ◽  
Insight Into ◽  
Frequency Processing

Abstract. We have performed an analysis of reprocessed GPS/MET data spanning 1995–1997 generated by CDAAC in 2007. CDAAC developed modified dual-frequency processing methods for the encrypted data (AS-on) during 1995–1997. We compared the CDAAC data set to the MERRA-2 reanalysis, separately for AS-on and AS-off, focusing on the altitude range 10–30 km. MERRA-2 did not assimilate GPS/MET data in the period 1995–1997. To gain insight into the CDAAC data set, we developed a single-frequency data set for GPS/MET, which is unaffected by the presence of encryption. We find excellent agreement between the more limited single frequency data set and the CDAAC data set: the bias between these two data sets is consistently less than 0.25 % in refractivity, whether or not AS is on. Given the different techniques applied between the CDAAC and JPL data sets, agreement suggests that the CDAAC AS-on processing and the single frequency processing are not biased in an aggregate sense greater than 0.25 % in refractivity, which corresponds approximately to a temperature bias less than 0.5 K. Since the profiles contained in the new single frequency data set are not a subset of the CDAAC profiles, the combination of the CDAAC data set, consisting of 9,579 profiles, and the new single-frequency data set, consisting of 4,729 profiles, yields a total number of 11,531 unique profiles from combining the JPL and CDAAC data sets. All numbers are after quality control has been applied by the respective processing activities.

scholarly journals GAINESIS: Generative Artificial Intelligence NEtlists SynthesIS

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 245
Author(s):  
Konstantinos G. Liakos ◽  
Georgios K. Georgakilas ◽  
Fotis C. Plessas ◽  
Paris Kitsos
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Power Analysis ◽  
Public Libraries ◽  
Data Sets ◽  
Hardware Trojan ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Encrypted Data ◽  
Adversarial Network

A significant problem in the field of hardware security consists of hardware trojan (HT) viruses. The insertion of HTs into a circuit can be applied for each phase of the circuit chain of production. HTs degrade the infected circuit, destroy it or leak encrypted data. Nowadays, efforts are being made to address HTs through machine learning (ML) techniques, mainly for the gate-level netlist (GLN) phase, but there are some restrictions. Specifically, the number and variety of normal and infected circuits that exist through the free public libraries, such as Trust-HUB, are based on the few samples of benchmarks that have been created from circuits large in size. Thus, it is difficult, based on these data, to develop robust ML-based models against HTs. In this paper, we propose a new deep learning (DL) tool named Generative Artificial Intelligence Netlists SynthesIS (GAINESIS). GAINESIS is based on the Wasserstein Conditional Generative Adversarial Network (WCGAN) algorithm and area–power analysis features from the GLN phase and synthesizes new normal and infected circuit samples for this phase. Based on our GAINESIS tool, we synthesized new data sets, different in size, and developed and compared seven ML classifiers. The results demonstrate that our new generated data sets significantly enhance the performance of ML classifiers compared with the initial data set of Trust-HUB.

scholarly journals Kinematic PPP Using Mixed GPS/Glonass Single-Frequency Observations

2019 ◽  
Vol 54 (3) ◽  
pp. 97-112
Author(s):  
Mostafa Hamed ◽  
Ashraf Abdallah ◽  
Ashraf Farah
Keyword(s):  
Low Cost ◽  
Satellite System ◽  
Single Frequency ◽  
Future Research ◽  
Frequency Data ◽  
Dual Frequency ◽  
Trajectory Data ◽  
Average Improvement ◽  
Kinematic Ppp ◽  
Global Navigation Satellite

Abstract Nowadays, Precise Point Positioning (PPP) is a very popular technique for Global Navigation Satellite System (GNSS) positioning. The advantage of PPP is its low cost as well as no distance limitation when compared with the differential technique. Single-frequency receivers have the advantage of cost effectiveness when compared with the expensive dual-frequency receivers, but the ionosphere error makes a difficulty to be completely mitigated. This research aims to assess the effect of using observations from both GPS and GLONASS constellations in comparison with GPS only for kinematic purposes using single-frequency observations. Six days of the year 2018 with single-frequency data for the Ethiopian IGS station named “ADIS” were processed epoch by epoch for 24 hours once with GPS-only observations and another with GPS/GLONASS observations. In addition to “ADIS” station, a kinematic track in the New Aswan City, Aswan, Egypt, has been observed using Leica GS15, geodetic type, dual-frequency, GPS/GLONASS GNSS receiver and single-frequency data have been processed. Net_Diff software was used for processing all the data. The results have been compared with a reference solution. Adding GLONASS satellites significantly improved the satellite number and Position Dilution Of Precision (PDOP) value and accordingly improved the accuracy of positioning. In the case of “ADIS” data, the 3D Root Mean Square Error (RMSE) ranged between 0.273 and 0.816 m for GPS only and improved to a range from 0.256 to 0.550 m for GPS/GLONASS for the 6 processed days. An average improvement ratio of 24%, 29%, 30%, and 29% in the east, north, height, and 3D position components, respectively, was achieved. For the kinematic trajectory, the 3D position RMSE improved from 0.733 m for GPS only to 0.638 m for GPS/GLONASS. The improvement ratios were 7%, 5%, 28%, and 13% in the east, north, height, and 3D position components, respectively, for the kinematic trajectory data. This opens the way to add observations from the other two constellations (Galileo and BeiDou) for more accuracy in future research.

scholarly journals Panoramic stitching of heterogeneous single-cell transcriptomic data

2018 ◽  
Author(s):  
Brian Hie ◽  
Bryan Bryson ◽  
Bonnie Berger
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Data Sets ◽  
Cell Type ◽  
Data Set ◽  
Wide Range ◽  
Data Set Integration ◽  
Biological Patterns ◽  
Insight Into ◽  
Comprehensive Reference

AbstractResearchers are generating single-cell RNA sequencing (scRNA-seq) profiles of diverse biological systems1–4 and every cell type in the human body.5 Leveraging this data to gain unprecedented insight into biology and disease will require assembling heterogeneous cell populations across multiple experiments, laboratories, and technologies. Although methods for scRNA-seq data integration exist6,7, they often naively merge data sets together even when the data sets have no cell types in common, leading to results that do not correspond to real biological patterns. Here we present Scanorama, inspired by algorithms for panorama stitching, that overcomes the limitations of existing methods to enable accurate, heterogeneous scRNA-seq data set integration. Our strategy identifies and merges the shared cell types among all pairs of data sets and is orders of magnitude faster than existing techniques. We use Scanorama to combine 105,476 cells from 26 diverse scRNA-seq experiments across 9 different technologies into a single comprehensive reference, demonstrating how Scanorama can be used to obtain a more complete picture of cellular function across a wide range of scRNA-seq experiments.

Verification of statistical oncological endpoints on encrypted data: Confirming the feasibility of real-world data sharing without the need to reveal protected patient information.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18725-e18725
Author(s):  
Ravit Geva ◽  
Barliz Waissengrin ◽  
Dan Mirelman ◽  
Felix Bokstein ◽  
Deborah T. Blumenthal ◽  
...  
Keyword(s):  
Data Sharing ◽  
Real World ◽  
Clinical Decision Making ◽  
Homomorphic Encryption ◽  
Data Sets ◽  
Real World Data ◽  
Data Set ◽  
Raw Data ◽  
Encrypted Data ◽  
World Data

e18725 Background: Healthcare data sharing is important for the creation of diverse and large data sets, supporting clinical decision making, and accelerating efficient research to improve patient outcomes. This is especially vital in the case of real world data analysis. However, stakeholders are reluctant to share their data without ensuring patients’ privacy, proper protection of their data sets and the ways they are being used. Homomorphic encryption is a cryptographic capability that can address these issues by enabling computation on encrypted data without ever decrypting it, so the analytics results are obtained without revealing the raw data. The aim of this study is to prove the accuracy of analytics results and the practical efficiency of the technology. Methods: A real-world data set of colorectal cancer patients’ survival data, following two different treatment interventions, including 623 patients and 24 variables, amounting to 14,952 items of data, was encrypted using leveled homomorphic encryption implemented in the PALISADE software library. Statistical analysis of key oncological endpoints was blindly performed on both the raw data and the homomorphically-encrypted data using descriptive statistics and survival analysis with Kaplan-Meier curves. Results were then compared with an accuracy goal of two decimals. Results: The difference between the raw data and the homomorphically encrypted data results, regarding all variables analyzed was within the pre-determined accuracy range goal, as well as the practical efficiency of the encrypted computation measured by run time, are presented in table. Conclusions: This study demonstrates that data encrypted with Homomorphic Encryption can be statistical analyzed with a precision of at least two decimal places, allowing safe clinical conclusions drawing while preserving patients’ privacy and protecting data owners’ data assets. Homomorphic encryption allows performing efficient computation on encrypted data non-interactively and without requiring decryption during computation time. Utilizing the technology will empower large-scale cross-institution and cross- stakeholder collaboration, allowing safe international collaborations. Clinical trial information: 0048-19-TLV. [Table: see text]

Hanging on to wisdom of the “old”

2016 ◽  
Vol 24 (4) ◽  
pp. 11-13
Author(s):  
Brian Beal
Keyword(s):  
Older Workers ◽  
Holistic Approach ◽  
Work Capacity ◽  
Active Ageing ◽  
Data Sets ◽  
Data Set ◽  
Content Type ◽  
Section Data ◽  
Using Data ◽  
Insight Into

Purpose The purpose of this paper is to shed light on developments in Norwegian companies’ active ageing policies, and hence offer insight into what characterizes those Norwegian companies offering measures to retain their older workers. Design/methodology/approach The research questions are investigated using data from two surveys carried out among a representative sample of Norwegian companies in 2005 and 2010. The two data sets are analyzed both separately and jointly, being merged to obtain a pooled cross-section data set. Both multivariate logistic and linear regression are applied. Findings The proportion of companies in Norway offering retention measures, as well as the extensiveness of their retention efforts (the number of different measures offered), has increased considerably from 2005 to 2010. What characterizes these companies however are surprisingly similar in 2005 and 2010. Their retention efforts seem to be part of a holistic approach to active ageing. Offering a number of different retention measures is more common among companies having initiated “measures to facilitate lifelong learning” and “measures to prevent health problems or reduced work capacity”. Originality/value The employers’ perspective has received little attention in previous research and the authors are the first to report on developments in Norwegian companies’ retention efforts over time. Knowledge about what characterizes employers offering such measures will be important for future efforts to increase employments rates among older workers, which is an aim for most European countries.

scholarly journals The use of genetic programming to develop a predictor of swash excursion on sandy beaches

2018 ◽  
Vol 18 (2) ◽  
pp. 599-611 ◽  
Author(s):  
Marinella Passarella ◽  
Evan B. Goldstein ◽  
Sandro De Muro ◽  
Giovanni Coco
Keyword(s):  
Genetic Programming ◽  
Sandy Beaches ◽  
Coastal Hazards ◽  
Coastal Processes ◽  
Published Data ◽  
Data Sets ◽  
Prediction Errors ◽  
Data Set ◽  
Physical Insight ◽  
Insight Into

Abstract. We use genetic programming (GP), a type of machine learning (ML) approach, to predict the total and infragravity swash excursion using previously published data sets that have been used extensively in swash prediction studies. Three previously published works with a range of new conditions are added to this data set to extend the range of measured swash conditions. Using this newly compiled data set we demonstrate that a ML approach can reduce the prediction errors compared to well-established parameterizations and therefore it may improve coastal hazards assessment (e.g. coastal inundation). Predictors obtained using GP can also be physically sound and replicate the functionality and dependencies of previous published formulas. Overall, we show that ML techniques are capable of both improving predictability (compared to classical regression approaches) and providing physical insight into coastal processes.

scholarly journals Comparison of HDO measurements from Envisat/MIPAS with observations by Odin/SMR and SCISAT/ACE-FTS

2011 ◽  
Vol 4 (9) ◽  
pp. 1855-1874 ◽  
Author(s):  
S. Lossow ◽  
J. Steinwagner ◽  
J. Urban ◽  
E. Dupuy ◽  
C. D. Boone ◽  
...  
Keyword(s):  
Thermal Emission ◽  
Absolute Amount ◽  
Data Sets ◽  
Altitude Range ◽  
Data Set ◽  
High Consistency ◽  
The Tropics ◽  
The Antarctic ◽  
Good Agreement

Abstract. Measurements of thermal emission in the mid-infrared by Envisat/MIPAS allow the retrieval of HDO information roughly in the altitude range between 10 km and 50 km. From June 2002 to March 2004 MIPAS performed measurements in the full spectral resolution mode. To assess the quality of the HDO data set obtained during that period comparisons with measurements by Odin/SMR and SCISAT/ACE-FTS were performed. Comparisons were made on profile-to-profile basis as well as using seasonal and monthly averages. All in all the comparisons yield favourable results. The largest deviations between MIPAS and ACE-FTS are observed below 15 km, where relative deviations can occasionally exceed 100%. Despite these deviations in the absolute amount of HDO the latitudinal structures observed by both instruments are consistent in this altitude range. Between 15 km and 20 km there is less good agreement, in particular in the Antarctic during winter and spring. Also in the tropics some deviations are found. Above 20 km there is a high consistency in the structures observed by all three instruments. MIPAS and ACE-FTS typically agree within 10%, with MIPAS mostly showing higher abundances than ACE-FTS. Both data sets show considerably more HDO than SMR. This bias can be explained basically by uncertainties in spectroscopic parameters. Above 40 km, where the MIPAS HDO retrieval reaches its limits, still good agreement with the structures observed by SMR is found for most seasons. This puts some confidence in the MIPAS data at these altitudes.

scholarly journals Combined SAGE II-GOMOS ozone profile data set 1984–2011 and trend analysis of the vertical distribution of ozone

2013 ◽  
Vol 13 (4) ◽  
pp. 10661-10700 ◽  
Author(s):  
E. Kyrölä ◽  
M. Laine ◽  
V. Sofieva ◽  
J. Tamminen ◽  
S.-M. Päivärinta ◽  
...  
Keyword(s):  
Time Series ◽  
Vertical Distribution ◽  
Diurnal Variation ◽  
Data Sets ◽  
Positive Trend ◽  
Vertical Resolution ◽  
Altitude Range ◽  
Profile Data ◽  
Data Set ◽  
Ozone Profile

Abstract. We have studied data from two satellite occultation instruments in order to generate a high vertical resolution homogeneous ozone time series of 26 yr. The Stratospheric Aerosol and Gas Experimen (SAGE) II solar occultation instrument from 1984–2005 and the Global Ozone Monitoring by Occultation of Stars instrument (GOMOS) from 2002–2012 measured ozone profiles in the stratosphere and mesosphere. Global coverage, good vertical resolution and the self calibrating measurement method make data from these instruments valuable for the detection of changes in vertical distribution of ozone over time. As both instruments share a common measurement period from 2002–2005, it is possible to intercalibrate the data sets. We investigate how well these measurements agree with each other and combine all the data to produce a new stratospheric ozone profile data set. Above 55 km SAGE II measurements show much less ozone than the GOMOS nighttime measurements as a consequence of the well-known diurnal variation of ozone in the mesosphere. Between 35–55 km SAGE II sunrise and sunset measurements differ from each other. Sunrise measurements show 2% less ozone than GOMOS whereas sunset measurements show 4% more ozone than GOMOS. Differences can be explained qualitatively by the diurnal variation of ozone in the stratosphere recently observed by SMILES and modelled by chemical transport models. For 25–35 km SAGE II sunrise and sunset and GOMOS agree within 1%. The observed ozone bias between collocated measurements of SAGE II sunrise/sunset and GOMOS night measurements is used to align the two data sets. The combined data set covers the time period 1984–2011, latitudes 60° S–60° N and the altitude range of 20–60 km. Profile data are given on a 1 km vertical grid, and with a resolution of one month in time and ten degrees in latitude. The combined ozone data set is analyzed by fitting a time series model to the data. We assume a linear trend with an inflexion point (so-called "hockey stick" form). The best estimate for the point of inflexion was found to be the year 1997 for ozone between altitudes 35 and 45 km. At all latitudes and altitudes from 25 km to 50 km we find a clear change in ozone trend before and after the inflexion time. From 38 km to 45 km a negative trend of 0–3% per decade at the equator has changed to a small positive trend of 0–2% per decade except in the altitude range of 30–35 km where the ozone loss has even increased. At mid-latitudes the negative trend of 4–10% per decade has changed to to a small positive trend of 0–2% per decade.

scholarly journals Decomposing conditioned avoidance performance with computational models

2020 ◽  
Author(s):  
Angelos-Miltiadis Krypotos ◽  
Geert Crombez ◽  
Ann Meulders ◽  
Nathalie Claes ◽  
Johan Vlaeyen
Keyword(s):  
Computational Models ◽  
Aversive Stimulus ◽  
Avoidance Performance ◽  
Conditioned Avoidance ◽  
Direct Application ◽  
Data Sets ◽  
Frequency Data ◽  
Data Set ◽  
Laboratory Procedures ◽  
Underlying Processes

Avoidance towards innocuous stimuli is a key characteristic across anxiety-related disorders and chronic pain. Insights into the relevant learning processes of avoidance are often gained via laboratory procedures, where individuals learn to avoid stimuli or movements that have been previously associated with an aversive stimulus. Typically, statistical analyses of data gathered with conditioned avoidance procedures include frequency data, for example, the number of times a participant has avoided an aversive stimulus. Here, we argue that further insights into the underlying processes of avoidance behavior could be unraveled using computational models of behavior. We then demonstrate how computational models could be used by reanalysing a previously published avoidance data set and interpreting the key findings. We conclude our article by listing some challenges in the direct application of computational modeling to avoidance data sets.

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