scholarly journals Comparison between XBT data and TOPEX/Poseidon satellite altimetry in the Ligurian-Tyrrhenian area

2003 ◽  
Vol 21 (1) ◽  
pp. 123-135 ◽  
Author(s):  
S. Vignudelli ◽  
P. Cipollini ◽  
F. Reseghetti ◽  
G. Fusco ◽  
G. P. Gasparini ◽  
...  
Keyword(s):  
Pilot Project ◽  
Atlantic Water ◽  
Data Sets ◽  
Tyrrhenian Sea ◽  
Satellite Altimeter ◽  
Ligurian Sea ◽  
Data Set ◽  
Additional Information ◽  
Forecasting System ◽  
Computational Errors

Abstract. From September 1999 to December 2000, eXpendable Bathy-Thermograph (XBT) profiles were collected along the Genova-Palermo shipping route in the framework of the Mediterranean Forecasting System Pilot Project (MFSPP). The route is virtually coincident with track 0044 of the TOPEX/Poseidon satellite altimeter, crossing the Ligurian and Tyrrhenian basins in an approximate N–S direction. This allows a direct comparison between XBT and altimetry, whose findings are presented in this paper. XBT sections reveal the presence of the major features of the regional circulation, namely the eastern boundary of the Ligurian gyre, the Bonifacio gyre and the Modified Atlantic Water inflow along the Sicily coast. Twenty-two comparisons of steric heights derived from the XBT data set with concurrent realizations of single-pass altimetric heights are made. The overall correlation is around 0.55 with an RMS difference of less than 3 cm. In the Tyrrhenian Sea the spectra are remarkably similar in shape, but in general the altimetric heights contain more energy. This difference is explained in terms of oceanographic signals, which are captured with a different intensity by the satellite altimeter and XBTs, as well as computational errors. On scales larger than 100 km, the data sets are also significantly coherent, with increasing coherence values at longer wavelengths. The XBTs were dropped every 18–20 km along the track: as a consequence, the spacing scale was unable to resolve adequately the internal radius of deformation (< 20 km). Furthermore, few XBT drops were carried out in the Ligurian Sea, due to the limited north-south extent of this basin, so the comparison is problematic there. On the contrary, the major features observed in the XBT data in the Tyrrhenian Sea are also detected by TOPEX/Poseidon. The manuscript is completed by a discussion on how to integrate the two data sets, in order to extract additional information. In particular, the results emphasize their complementariety in providing a dynamically complete description of the observed structures. Key words. Oceanography: general (descriptive and regional oceanography) Oceanography: physical (sea level variations; instruments and techniques)

scholarly journals Protein function prediction in genomes: Critical assessment of coiled-coil predictions based on protein structure data

2019 ◽  
Author(s):  
Dominic Simm ◽  
Klas Hatje ◽  
Stephan Waack ◽  
Martin Kollmar
Keyword(s):  
Protein Function ◽  
Protein Function Prediction ◽  
Functional Characterization ◽  
Coiled Coil ◽  
Imbalanced Data ◽  
Large Data ◽  
Data Sets ◽  
Data Set ◽  
Protein Motifs ◽  
Additional Information

AbstractCoiled-coil regions were among the first protein motifs described structurally and theoretically. The beauty and simplicity of the motif gives hope to detecting coiled-coil regions with reasonable accuracy and precision in any protein sequence. Here, we re-evaluated the most commonly used coiled-coil prediction tools with respect to the most comprehensive reference data set available, the entire Protein Data Base (PDB), down to each amino acid and its secondary structure. Apart from the thirtyfold difference in number of predicted coiled-coils the tools strongly vary in their predictions, across structures and within structures. The evaluation of the false discovery rate and Matthews correlation coefficient, a widely used performance metric for imbalanced data sets, suggests that the tested tools have only limited applicability for large data sets. Coiled-coil predictions strongly impact the functional characterization of proteins, are used for functional genome annotation, and should therefore be supported and validated by additional information.

scholarly journals Comparing Findings From a Friends of Cancer Research Exploratory Analysis of Real-World End Points With the Cancer Analysis System in England

2021 ◽  
pp. 1155-1168
Author(s):  
Pia Horvat ◽  
Christen M. Gray ◽  
Alexandrina Lambova ◽  
Jennifer B. Christian ◽  
Laura Lasiter ◽  
...  
Keyword(s):  
Health Care ◽  
Cancer Research ◽  
Real World ◽  
Pilot Project ◽  
Data Sets ◽  
Patient Privacy ◽  
Data Set ◽  
End Points ◽  
Analysis System ◽  
Cas A

PURPOSE This study compared real-world end points extracted from the Cancer Analysis System (CAS), a national cancer registry with linkage to national mortality and other health care databases in England, with those from diverse US oncology data sources, including electronic health care records, insurance claims, unstructured medical charts, or a combination, that participated in the Friends of Cancer Research Real-World Evidence Pilot Project 1.0. Consistency between data sets and between real-world overall survival (rwOS) was assessed in patients with immunotherapy-treated advanced non–small-cell lung cancer (aNSCLC). PATIENTS AND METHODS Patients with aNSCLC, diagnosed between January 2013 and December 2017, who initiated treatment with approved programmed death ligand-1 (PD-[L]1) inhibitors until March 2018 were included. Real-world end points, including rwOS and real-world time to treatment discontinuation (rwTTD), were assessed using Kaplan-Meier analysis. A synthetic data set, Simulacrum, on the basis of conditional random sampling of the CAS data was used to develop and refine analysis scripts while protecting patient privacy. RESULTS Characteristics (age, sex, and histology) of the 2,035 patients with immunotherapy-treated aNSCLC included in the CAS study were broadly comparable with US data sets. In CAS, a higher proportion (46.7%) of patients received a PD-(L)1 inhibitor in the first line than in US data sets (18%-30%). Median rwOS (11.4 months; 95% CI, 10.4 to 12.7) and rwTTD (4.9 months; 95% CI, 4.7 to 5.1) were within the range of US-based data sets (rwOS, 8.6-13.5 months; rwTTD, 3.2-7.0 months). CONCLUSION The CAS findings were consistent with those from US-based oncology data sets. Such consistency is important for regulatory decision making. Differences observed between data sets may be explained by variation in health care settings, such as the timing of PD-(L)1 approval and reimbursement, and data capture.

scholarly journals Assessing the performance of the Cretan Sea ecosystem model with the use of high frequency M3A buoy data set

2003 ◽  
Vol 21 (1) ◽  
pp. 365-375 ◽  
Author(s):  
G. Triantafyllou ◽  
G. Petihakis ◽  
I. J. Allen
Keyword(s):  
High Frequency ◽  
Optimal Parameter ◽  
Extended Period ◽  
Pilot Project ◽  
Ecosystem Model ◽  
Data Set ◽  
Forecasting System ◽  
Buoy Data ◽  
First Time ◽  
Cretan Sea

Abstract. During the Mediterranean Forecasting System Pilot Project a buoy was deployed in the Cretan Sea and for the first time high-frequency physical and biogeochemical data were collected over an extended period, providing a unique opportunity for the evaluation of an ecosystem model. The model both tuned and validated in the Cretan Sea in the past, is explored and quantified. In addition, the optimal parameter set is determined while the effects of high-frequency forcing are explored. The model results are satisfactory, especially at the upper part of the water column, while the inability of 1-D modelling in fully exploring the hydrodynamics of the particular area is depicted and further developments are suggested. Key words. Oceanography; general (numerical modeling) – Oceanography; biological and chemical (ecosystems and ecology)

scholarly journals Comparison of Different Image Data Augmentation Approaches

2021 ◽  
Vol 7 (12) ◽  
pp. 254
Author(s):  
Loris Nanni ◽  
Michelangelo Paci ◽  
Sheryl Brahnam ◽  
Alessandra Lumini
Keyword(s):  
Image Classification ◽  
Data Augmentation ◽  
Image Data ◽  
Research Literature ◽  
Discrete Wavelet ◽  
Data Sets ◽  
Data Set ◽  
Additional Information ◽  
Comparable Performance ◽  
Data Points

Convolutional neural networks (CNNs) have gained prominence in the research literature on image classification over the last decade. One shortcoming of CNNs, however, is their lack of generalizability and tendency to overfit when presented with small training sets. Augmentation directly confronts this problem by generating new data points providing additional information. In this paper, we investigate the performance of more than ten different sets of data augmentation methods, with two novel approaches proposed here: one based on the discrete wavelet transform and the other on the constant-Q Gabor transform. Pretrained ResNet50 networks are finetuned on each augmentation method. Combinations of these networks are evaluated and compared across four benchmark data sets of images representing diverse problems and collected by instruments that capture information at different scales: a virus data set, a bark data set, a portrait dataset, and a LIGO glitches data set. Experiments demonstrate the superiority of this approach. The best ensemble proposed in this work achieves state-of-the-art (or comparable) performance across all four data sets. This result shows that varying data augmentation is a feasible way for building an ensemble of classifiers for image classification.

scholarly journals Variability of mesoscale features in the Mediterranean Sea from XBT data analysis

2003 ◽  
Vol 21 (1) ◽  
pp. 21-32 ◽  
Author(s):  
G. Fusco ◽  
G. M. R. Manzella ◽  
A. Cruzado ◽  
M. Gačić ◽  
G. P. Gasparini ◽  
...  
Keyword(s):  
General Circulation ◽  
Eastern Mediterranean ◽  
Thermal Structure ◽  
Pilot Project ◽  
Atlantic Water ◽  
Levantine Basin ◽  
Thermal Features ◽  
Main Circulation ◽  
Forecasting System ◽  
The Mediterranean

Abstract. During the period 1998–2000, the Mediterranean Forecasting System Pilot Project, aiming to build a forecasting system for the physical state of the sea, has been carried out. A ship-of-opportunity programme sampled the Mediterranean upper ocean thermal structure by means of eXpendable Bathy-Thermographs (XBTs), along seven tracks, from September 1999 to May 2000. The tracks were designed to detect some of the main circulation features, such as the stream of surface Atlantic water flowing from the Alboran Sea to the Eastern Levantine Basin. The cyclonic gyres in the Liguro-Provenal Basin, the southern Adriatic and Ionian Seas and the anticyclonic gyres in the Levantine Basin were also features to be detected. The monitoring system confirmed a long-term persistence of structures (at least during the entire observing period), which were previously thought to be transient features. In particular, in the Levantine Basin anticyclonic Shikmona and Ierapetra Gyres have been observed during the monitoring period. In order to identify the major changes in the thermal structures and the dynamical implications, the XBT data are compared with historical measurements collected in the 1980s and 1990s. The results indicate that some thermal features are being restored to the situation that existed in the 1980s, after the changes induced by the so-called "Eastern Mediterranean Transient". Key words. Oceanography: physical (eddies and mesoscale processes; general circulation; instruments and techniques)

scholarly journals Closing the Performance Gap between Siamese Networks for Dissimilarity Image Classification and Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5809
Author(s):  
Loris Nanni ◽  
Giovanni Minchio ◽  
Sheryl Brahnam ◽  
Davide Sarraggiotto ◽  
Alessandra Lumini
Keyword(s):  
Image Classification ◽  
Image Data ◽  
Support Vector ◽  
Final Decision ◽  
Data Sets ◽  
Data Set ◽  
Additional Information ◽  
New Strategy ◽  
Fully Connected ◽  
Siamese Networks

In this paper, we examine two strategies for boosting the performance of ensembles of Siamese networks (SNNs) for image classification using two loss functions (Triplet and Binary Cross Entropy) and two methods for building the dissimilarity spaces (FULLY and DEEPER). With FULLY, the distance between a pattern and a prototype is calculated by comparing two images using the fully connected layer of the Siamese network. With DEEPER, each pattern is described using a deeper layer combined with dimensionality reduction. The basic design of the SNNs takes advantage of supervised k-means clustering for building the dissimilarity spaces that train a set of support vector machines, which are then combined by sum rule for a final decision. The robustness and versatility of this approach are demonstrated on several cross-domain image data sets, including a portrait data set, two bioimage and two animal vocalization data sets. Results show that the strategies employed in this work to increase the performance of dissimilarity image classification using SNN are closing the gap with standalone CNNs. Moreover, when our best system is combined with an ensemble of CNNs, the resulting performance is superior to an ensemble of CNNs, demonstrating that our new strategy is extracting additional information.

scholarly journals A Compact Rayleigh Autonomous Lidar (CORAL) for the middle atmosphere

2021 ◽  
Vol 14 (2) ◽  
pp. 1715-1732
Author(s):  
Bernd Kaifler ◽  
Natalie Kaifler
Keyword(s):  
Middle Atmosphere ◽  
Temperature Profiles ◽  
Atmospheric Gravity Wave ◽  
Data Set ◽  
Additional Information ◽  
Wave Event ◽  
System Data ◽  
Forecasting System ◽  
Safety Features ◽  
Sky Conditions

Abstract. The Compact Rayleigh Autonomous Lidar (CORAL) is the first fully autonomous middle atmosphere lidar system to provide density and temperature profiles from 15 to approximately 90 km altitude. From October 2019 to October 2020, CORAL acquired temperature profiles on 243 out of the 365 nights (66 %) above Río Grande, southern Argentina, a cadence which is 3–8 times larger as compared to conventional human-operated lidars. The result is an unprecedented data set with measurements on 2 out of 3 nights on average and high temporal (20 min) and vertical (900 m) resolution. The first studies using CORAL data have shown, for example, the evolution of a strong atmospheric gravity wave event and its impact on the stratospheric circulation. We describe the instrument and its novel software which enables automatic and unattended observations over periods of more than a year. A frequency-doubled diode-pumped pulsed Nd:YAG laser is used as the light source, and backscattered photons are detected using three elastic channels (532 nm wavelength) and one Raman channel (608 nm wavelength). Automatic tracking of the laser beam is realized by the implementation of the conical scan (conscan) method. The CORAL software detects blue sky conditions and makes the decision to start the instrument based on local meteorological measurements, detection of stars in all-sky images, and analysis of European Center for Medium-range Weather Forecasts Integrated Forecasting System data. After the instrument is up and running, the strength of the lidar return signal is used as additional information to assess sky conditions. Safety features in the software allow for the operation of the lidar even in marginal weather, which is a prerequisite to achieving the very high observation cadence.

scholarly journals distance based measure of data quality

2014 ◽  
Vol 11 (2) ◽  
Author(s):  
Pavol Král’ ◽  
Lukáš Sobíšek ◽  
Mária Stachová
Keyword(s):  
Decision Making ◽  
Data Quality ◽  
Weighting Function ◽  
Training Data ◽  
Data Sets ◽  
Belief Functions ◽  
Data Set ◽  
Additional Information ◽  
The Ideal

Data quality can be seen as a very important factor for the validity of information extracted from data sets using statistical or data mining procedures. In the paper we propose a description of data quality allowing us to characterize data quality of the whole data set, as well as data quality of particular variables and individual cases. On the basis of the proposed description, we define a distance based measure of data quality for individual cases as a distance of the cases from the ideal one. Such a measure can be used as additional information for preparation of a training data set, fitting models, decision making based on results of analyses etc. It can be utilized in different ways ranging from a simple weighting function to belief functions.

scholarly journals IMPROVED BACKPROPAGATION LEARNING IN NEURAL NETWORKS WITH WINDOWED MOMENTUM

2002 ◽  
Vol 12 (03n04) ◽  
pp. 303-318 ◽  
Author(s):  
ERNEST ISTOOK ◽  
TONY MARTINEZ
Keyword(s):  
Neural Network ◽  
Standard Technique ◽  
Presentation Order ◽  
Convergence Time ◽  
Data Sets ◽  
Data Set ◽  
Acceptable Solution ◽  
Additional Information ◽  
Network Training ◽  
Improved Accuracy

Backpropagation, which is frequently used in Neural Network training, often takes a great deal of time to converge on an acceptable solution. Momentum is a standard technique that is used to speed up convergence and maintain generalization performance. In this paper we present the Windowed Momentum algorithm, which increases speedup over Standard Momentum. Windowed Momentum is designed to use a fixed width history of recent weight updates for each connection in a neural network. By using this additional information, Windowed Momentum gives significant speedup over a set of applications with same or improved accuracy. Windowed Momentum achieved an average speedup of 32% in convergence time on 15 data sets, including a large OCR data set with over 500,000 samples. In addition to this speedup, we present the consequences of sample presentation order. We show that Windowed Momentum is able to overcome these effects that can occur with poor presentation order and still maintain its speedup advantages.

scholarly journals Closing the Performance Gap between Siamese Networks for Dissimilarity Image Classification and Convolutional Neural Networks

2021 ◽  
Author(s):  
Loris Nanni ◽  
Giovanni Minchio ◽  
Sheryl Brahnam ◽  
Davide Sarraggiotto ◽  
Alessandra Lumini
Keyword(s):  
Image Classification ◽  
Image Data ◽  
Support Vector ◽  
Final Decision ◽  
Data Sets ◽  
Data Set ◽  
Additional Information ◽  
New Strategy ◽  
Fully Connected ◽  
Siamese Networks

In this paper, we examine two strategies for boosting the performance of ensembles of Siamese networks (SNNs) for image classification using two loss functions (Triplet and Binary Cross Entropy) and two methods for building the dissimilarity spaces (FULLY and DEEPER). With FULLY, the distance between a pattern and a prototype is calculated by comparing two images using the fully connected layer of the Siamese network. With DEEPER, each pattern is described using a deeper layer combined with dimensionality reduction. The basic design of the SNNs takes advantage of supervised k-means clustering for building the dissimilarity spaces that train a set of support vector machines, which are then combined by sum rule for a final decision. The robustness and versatility of this approach are demonstrated on several cross-domain image data sets, including a portrait data set, two bioimage and two animal vocalization data sets. Results show that the strategies employed in this work to increase the performance of dissimilarity image classification using SNN is closing the gap with standalone CNNs. Moreover, when our best system is combined with an ensemble of CNNs, the resulting performance is superior to an ensemble of CNNs, demonstrating that our new strategy is extracting additional information.

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