scholarly journals Learning sparse representations on the sphere

2019 ◽  
Vol 621 ◽  
pp. A73
Author(s):  
F. Sureau ◽  
F. Voigtlaender ◽  
M. Wust ◽  
J.-L. Starck ◽  
G. Kutyniok
Keyword(s):  
Learning Approach ◽  
Data Sets ◽  
Complex Structures ◽  
Representation System ◽  
Scientific Application ◽  
The Past ◽  
Representation Systems ◽  
Data Representations ◽  
Adaptive Approaches ◽  
Parametrized Family

Many representation systems on the sphere have been proposed in the past, such as spherical harmonics, wavelets, or curvelets. Each of these data representations is designed to extract a specific set of features, and choosing the best fixed representation system for a given scientific application is challenging. In this paper, we show that one can directly learn a representation system from given data on the sphere. We propose two new adaptive approaches: the first is a (potentially multiscale) patch-based dictionary learning approach, and the second consists in selecting a representation from among a parametrized family of representations, the α-shearlets. We investigate their relative performance to represent and denoise complex structures on different astrophysical data sets on the sphere.

scholarly journals Handwritten digits recognition with decision tree classification: a machine learning approach

2019 ◽  
Vol 9 (5) ◽  
pp. 4446
Author(s):  
Tsehay Admassu Assegie ◽  
Pramod Sekharan Nair
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Classification Model ◽  
Learning Approach ◽  
Data Sets ◽  
The Past ◽  
Decision Tree Classification ◽  
Machine Learning Approach ◽  
Future Data ◽  
Class Labels

Handwritten digits recognition is an area of machine learning, in which a machine is trained to identify handwritten digits. One method of achieving this is with decision tree classification model. A decision tree classification is a machine learning approach that uses the predefined labels from the past known sets to determine or predict the classes of the future data sets where the class labels are unknown. In this paper we have used the standard kaggle digits dataset for recognition of handwritten digits using a decision tree classification approach. And we have evaluated the accuracy of the model against each digit from 0 to 9.

Sicker and Poorer—The Consequences of Being Uninsured: A Review of the Research on the Relationship between Health Insurance, Medical Care Use, Health, Work, and Income

2003 ◽  
Vol 60 (2_suppl) ◽  
pp. 3S-75S ◽  
Author(s):  
Jack Hadley
Keyword(s):  
Health Insurance ◽  
Medical Care ◽  
Data Sets ◽  
Work Effort ◽  
Services Research ◽  
The Past ◽  
Medical Care Use ◽  
Process Studies ◽  
Annual Earnings ◽  
The Relationship

Health services research conducted over the past 25 years makes a compelling case that having health insurance or using more medical care would improve the health of the uninsured. The literature's broad range of conditions, populations, and methods makes it difficult to derive a precise quantitative estimate of the effect of having health insurance on the uninsured's health. Some mortality studies imply that a 4% to 5% reduction in the uninsured's mortality is a lower bound; other studies suggest that the reductions could be as high as 20% to 25%. Although all of the studies reviewed suffer from methodological flaws of varying degrees, there is substantial qualitative consistency across studies of different medical conditions conducted at different times and using different data sets and statistical methods. Corroborating process studies find that the uninsured receive fewer preventive and diagnostic services, tend to be more severely ill when diagnosed, and receive less therapeutic care. Other literature suggests that improving health status from fair or poor to very good or excellent would increase both work effort and annual earnings by approximately 15% to 20%.

scholarly journals Searching for g modes

2018 ◽  
Vol 617 ◽  
pp. A108 ◽  
Author(s):  
T. Appourchaux ◽  
P. Boumier ◽  
J. W. Leibacher ◽  
T. Corbard
Keyword(s):  
Time Series ◽  
Radial Velocity ◽  
Time Shift ◽  
Data Sets ◽  
Mode Amplitude ◽  
The Past ◽  
Mode Detection ◽  
Velocity Calibration ◽  
Remaining Time ◽  
Soho Spacecraft

Context. The recent claims of g-mode detection have restarted the search for these potentially extremely important modes. These claims can be reassessed in view of the different data sets available from the SoHO instruments and ground-based instruments. Aims. We produce a new calibration of the GOLF data with a more consistent p-mode amplitude and a more consistent time shift correction compared to the time series used in the past. Methods. The calibration of 22 yr of GOLF data is done with a simpler approach that uses only the predictive radial velocity of the SoHO spacecraft as a reference. Using p modes, we measure and correct the time shift between ground- and space-based instruments and the GOLF instrument. Results. The p-mode velocity calibration is now consistent to within a few percent with other instruments. The remaining time shifts are within ±5 s for 99.8% of the time series.

Fingerprint Presentation Attack Detection Using Transfer Learning Approach

2021 ◽  
Vol 17 (1) ◽  
pp. 53-67
Author(s):  
Rajneesh Rani ◽  
Harpreet Singh
Keyword(s):  
Transfer Learning ◽  
Attack Detection ◽  
Classification Error ◽  
Learning Approach ◽  
Biometric Authentication ◽  
The Past ◽  
Liveness Detection ◽  
Fingerprint Liveness Detection ◽  
Software And Hardware ◽  
Presentation Attack Detection

In this busy world, biometric authentication methods are serving as fast authentication means. But with growing dependencies on these systems, attackers have tried to exploit these systems through various attacks; thus, there is a strong need to protect authentication systems. Many software and hardware methods have been proposed in the past to make existing authentication systems more robust. Liveness detection/presentation attack detection is one such method that provides protection against malicious agents by detecting fake samples of biometric traits. This paper has worked on fingerprint liveness detection/presentation attack detection using transfer learning for which the authors have used a pre-trained NASNetMobile model. The experiments are performed on publicly available liveness datasets LivDet 2011 and LivDet 2013 and have obtained good results as compared to state of art techniques in terms of ACE(average classification error).

scholarly journals Moving Forward With Accelerometer-Assessed Physical Activity: Two Strategies to Ensure Meaningful, Interpretable, and Comparable Measures

2018 ◽  
Vol 30 (4) ◽  
pp. 450-456 ◽  
Author(s):  
Alex V. Rowlands
Keyword(s):  
Physical Activity ◽  
Black Box ◽  
Data Sets ◽  
Accelerometer Data ◽  
Future Health ◽  
The Past ◽  
Acceleration Data ◽  
Night And Day ◽  
Very High ◽  
Made In

Significant advances have been made in the measurement of physical activity in youth over the past decade. Monitors and protocols promote very high compliance, both night and day, and raw measures are available rather than “black box” counts. Consequently, many surveys and studies worldwide now assess children’s physical behaviors (physical activity, sedentary behavior, and sleep) objectively 24 hours a day, 7 days a week using accelerometers. The availability of raw acceleration data in many of these studies is both an opportunity and a challenge. The richness of the data lends itself to the continued development of innovative metrics, whereas the removal of proprietary outcomes offers considerable potential for comparability between data sets and harmonizing data. Using comparable physical activity outcomes could lead to improved precision and generalizability of recommendations for children’s present and future health. The author will discuss 2 strategies that he believes may help ensure comparability between studies and maximize the potential for data harmonization, thereby helping to capitalize on the growing body of accelerometer data describing children’s physical behaviors.

scholarly journals A Machine Learning Approach to Coreference Resolution of Noun Phrases

2001 ◽  
Vol 27 (4) ◽  
pp. 521-544 ◽  
Author(s):  
Wee Meng Soon ◽  
Hwee Tou Ng ◽  
Daniel Chung Yong Lim
Keyword(s):  
Machine Learning ◽  
Noun Phrase ◽  
State Of The Art ◽  
Noun Phrases ◽  
Learning Approach ◽  
Data Sets ◽  
Coreference Resolution ◽  
Machine Learning Approach

In this paper, we present a learning approach to coreference resolution of noun phrases in unrestricted text. The approach learns from a small, annotated corpus and the task includes resolving not just a certain type of noun phrase (e.g., pronouns) but rather general noun phrases. It also does not restrict the entity types of the noun phrases; that is, coreference is assigned whether they are of “organization,” “person,” or other types. We evaluate our approach on common data sets (namely, the MUC-6 and MUC-7 coreference corpora) and obtain encouraging results, indicating that on the general noun phrase coreference task, the learning approach holds promise and achieves accuracy comparable to that of nonlearning approaches. Our system is the first learning-based system that offers performance comparable to that of state-of-the-art nonlearning systems on these data sets.

scholarly journals A One-shot Learning Approach to Image Classification using Genetic Programming

2020 ◽  
Author(s):  
Harith Al-Sahaf ◽  
Mengjie Zhang ◽  
M Johnston
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Genetic Programming ◽  
Image Classification ◽  
Local Binary Patterns ◽  
Support Vector ◽  
Learning Approach ◽  
Data Sets ◽  
Domain Specific ◽  
International Publishing

In machine learning, it is common to require a large number of instances to train a model for classification. In many cases, it is hard or expensive to acquire a large number of instances. In this paper, we propose a novel genetic programming (GP) based method to the problem of automatic image classification via adopting a one-shot learning approach. The proposed method relies on the combination of GP and Local Binary Patterns (LBP) techniques to detect a predefined number of informative regions that aim at maximising the between-class scatter and minimising the within-class scatter. Moreover, the proposed method uses only two instances of each class to evolve a classifier. To test the effectiveness of the proposed method, four different texture data sets are used and the performance is compared against two other GP-based methods namely Conventional GP and Two-tier GP. The experiments revealed that the proposed method outperforms these two methods on all the data sets. Moreover, a better performance has been achieved by Naïve Bayes, Support Vector Machine, and Decision Trees (J48) methods when extracted features by the proposed method have been used compared to the use of domain-specific and Two-tier GP extracted features. © Springer International Publishing 2013.

Experimenting on the past: a case study on changing analysability in English ly-adverbs

2017 ◽  
Vol 21 (2) ◽  
pp. 317-340 ◽  
Author(s):  
HENDRIK DE SMET ◽  
FREEK VAN DE VELDE
Keyword(s):  
Negative Correlation ◽  
Historical Data ◽  
Frequency Change ◽  
Data Sets ◽  
The Past ◽  
Analogous Composition ◽  
Using Data ◽  
Complex Words ◽  
Experimental Findings

While it is undoubtedly true that historical data do not lend themselves well to the reproduction of experimental findings, the availability of increasingly extensive data sets has brought some experimenting within practical reach. This means that certain predictions based on a combination of synchronic observations and uniformitarian thinking are now testable. Synchronic evidence shows a negative correlation between analysability in morphologically complex words and various measures of frequency. It is therefore expected that when the frequency of morphologically complex items changes, their analysability will change along with this. If analysability decreases, this should in turn be reflected in decreasing sensitivity to priming by items with analogous composition. The latter prediction is in principle testable on diachronic data, offering a way of verifying the diachronic effect of frequency change on analysability. In this spirit, the present article examines the relation between changing frequency and priming sensitivity, as a proxy to analysability. This is done for a sample of 250 English ly-adverbs, such as roughly, blindly, publicly, etc. over the period 1950–2005, using data from the Hansard Corpus. Some of the expected relations between frequency and analysability can be shown to hold, albeit with great variation across lexical items. At the same time, much of the variation in our measure of analysability cannot be accounted for by frequency or frequency change alone.

Pursuing Forecasts of the Behavior and Arrival of Coronal Mass Ejections through Modeling and Observations

2017 ◽  
Vol 13 (S335) ◽  
pp. 58-64 ◽  
Author(s):  
Hebe Cremades
Keyword(s):  
Remote Sensing ◽  
Coronal Mass Ejections ◽  
Empirical Models ◽  
Time Of Arrival ◽  
Data Sets ◽  
Radio Waves ◽  
Weather Forecasts ◽  
The Past ◽  
Increasing Demand ◽  
Solar Disturbances

AbstractSophisticated instrumentation dedicated to studying and monitoring our Sun’s activity has proliferated in the past few decades, together with the increasing demand of specialized space weather forecasts that address the needs of commercial and government systems. As a result, theoretical and empirical models and techniques of increasing complexity have been developed, aimed at forecasting the occurrence of solar disturbances, their evolution, and time of arrival to Earth. Here we will review groundbreaking and recent methods to predict the propagation and evolution of coronal mass ejections and their driven shocks. The methods rely on a wealth of data sets provided by ground- and space-based observatories, involving remote-sensing observations of the corona and the heliosphere, as well as detections of radio waves.

scholarly journals An uracil-linked hydroxyflavone probe for the recognition of ATP

2018 ◽  
Vol 14 ◽  
pp. 747-755 ◽  
Author(s):  
Márton Bojtár ◽  
Péter Zoltán Janzsó-Berend ◽  
Dávid Mester ◽  
Dóra Hessz ◽  
Mihály Kállay ◽  
...  
Keyword(s):  
Fluorescence Spectroscopy ◽  
Electrostatic Interactions ◽  
Theoretical Calculations ◽  
Structural Similarity ◽  
Living Systems ◽  
Selective Detection ◽  
Complex Structures ◽  
Fluorescent Chemosensors ◽  
The Past ◽  
Physiological Processes

Background: Nucleotides are essential molecules in living systems due to their paramount importance in various physiological processes. In the past years, numerous attempts were made to selectively recognize and detect these analytes, especially ATP using small-molecule fluorescent chemosensors. Despite the various solutions, the selective detection of ATP is still challenging due to the structural similarity of various nucleotides. In this paper, we report the conjugation of a uracil nucleobase to the known 4’-dimethylamino-hydroxyflavone fluorophore. Results: The complexation of this scaffold with ATP is already known. The complex is held together by stacking and electrostatic interactions. To achieve multi-point recognition, we designed the uracil-appended version of this probe to include complementary base-pairing interactions. The theoretical calculations revealed the availability of multiple complex structures. The synthesis was performed using click chemistry and the nucleotide recognition properties of the probe were evaluated using fluorescence spectroscopy. Conclusions: The first, uracil-containing fluorescent ATP probe based on a hydroxyflavone fluorophore was synthesized and evaluated. A selective complexation with ATP was observed and a ratiometric response in the excitation spectrum.

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