Methods for Recognizing Images of Heterogeneous Objects in Small Training Sample

2020 ◽  
Author(s):  
I.E. Shishkin ◽  
A.N. Grekov
Keyword(s):  
Training Sample ◽  
Heterogeneous Objects

Machete

2021 ◽  
Vol 28 (1) ◽  
pp. 1-46
Author(s):  
Eugene M. Taranta II ◽  
Corey R. Pittman ◽  
Mehran Maghoumi ◽  
Mykola Maslych ◽  
Yasmine M. Moolenaar ◽  
...  
Keyword(s):  
Video Game ◽  
Recognition Accuracy ◽  
Training Sample ◽  
Low Latency ◽  
Computationally Efficient ◽  
Extensive Evaluation ◽  
Segmentation Accuracy ◽  
Alternative Techniques ◽  
High Recognition Accuracy ◽  
Continuous Dynamic

We present Machete, a straightforward segmenter one can use to isolate custom gestures in continuous input. Machete uses traditional continuous dynamic programming with a novel dissimilarity measure to align incoming data with gesture class templates in real time. Advantages of Machete over alternative techniques is that our segmenter is computationally efficient, accurate, device-agnostic, and works with a single training sample. We demonstrate Machete’s effectiveness through an extensive evaluation using four new high-activity datasets that combine puppeteering, direct manipulation, and gestures. We find that Machete outperforms three alternative techniques in segmentation accuracy and latency, making Machete the most performant segmenter. We further show that when combined with a custom gesture recognizer, Machete is the only option that achieves both high recognition accuracy and low latency in a video game application.

scholarly journals A novel simple risk model to predict the prognosis of patients with paraquat poisoning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yanxia Gao ◽  
Liwen Liu ◽  
Tiegang Li ◽  
Ding Yuan ◽  
Yibo Wang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Latent Class ◽  
Risk Model ◽  
Risk Groups ◽  
Training Sample ◽  
Risk Indicators ◽  
Hospital Death ◽  
Gamma Glutamyl Transferase ◽  
Creatine Kinase Mb ◽  
Glutamyl Transferase

AbstractTo identify risk factors and develop a simple model to predict early prognosis of acute paraquat (PQ) poisoning patients, we performed a retrospective cohort study of acute PQ poisoning patients (n = 1199). Patients (n = 913) with PQ poisoning from 2011 to 2018 were randomly divided into training (n = 609) and test (n = 304) samples. Another two independent cohorts were used as validation samples for a different time (n = 207) and site (n = 79). Risk factors were identified using a logistic model with Markov Chain Monte Carlo (MCMC) simulation and further evaluated using a latent class analysis. The prediction score was developed based on the training sample and was evaluated using the testing and validation samples. Eight factors, including age, ingestion volume, creatine kinase-MB [CK-MB], platelet [PLT], white blood cell [WBC], neutrophil counts [N], gamma-glutamyl transferase [GGT], and serum creatinine [Cr] were identified as independent risk indicators of in-hospital death events. The risk model had C statistics of 0.895 (95% CI 0.855–0.928), 0.891 (95% CI 0.848–0.932), and 0.829 (95% CI 0.455–1.000), and predictive ranges of 4.6–98.2%, 2.3–94.9%, and 0–12.5% for the test, validation_time, and validation_site samples, respectively. In the training sample, the risk model classified 18.4%, 59.9%, and 21.7% of patients into the high-, average-, and low-risk groups, with corresponding probabilities of 0.985, 0.365, and 0.03 for in-hospital death events. We developed and evaluated a simple risk model to predict the prognosis of patients with acute PQ poisoning. This risk scoring system could be helpful for identifying high-risk patients and reducing mortality due to PQ poisoning.

scholarly journals Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks

2021 ◽  
Vol 2021 (4) ◽  
Author(s):  
Jack Y. Araz ◽  
Michael Spannowsky
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Top Quark ◽  
Feature Space ◽  
Training Sample ◽  
Bayesian Techniques ◽  
Training Sample Size ◽  
Event Reconstruction ◽  
Neural Network Classifiers ◽  
Quark Jets

Abstract Ensemble learning is a technique where multiple component learners are combined through a protocol. We propose an Ensemble Neural Network (ENN) that uses the combined latent-feature space of multiple neural network classifiers to improve the representation of the network hypothesis. We apply this approach to construct an ENN from Convolutional and Recurrent Neural Networks to discriminate top-quark jets from QCD jets. Such ENN provides the flexibility to improve the classification beyond simple prediction combining methods by linking different sources of error correlations, hence improving the representation between data and hypothesis. In combination with Bayesian techniques, we show that it can reduce epistemic uncertainties and the entropy of the hypothesis by simultaneously exploiting various kinematic correlations of the system, which also makes the network less susceptible to a limitation in training sample size.

scholarly journals Meta-Apo improves accuracy of 16S-amplicon-based prediction of microbiome function

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Gongchao Jing ◽  
Yufeng Zhang ◽  
Wenzhi Cui ◽  
Lu Liu ◽  
Jian Xu ◽  
...  
Keyword(s):  
16S Rrna ◽  
Large Scale ◽  
Low Cost ◽  
Human Microbiome ◽  
Amplicon Sequencing ◽  
Training Sample ◽  
Rrna Gene ◽  
16S Amplicon Sequencing ◽  
Cross Platform ◽  
Functional Profiles

Abstract Background Due to their much lower costs in experiment and computation than metagenomic whole-genome sequencing (WGS), 16S rRNA gene amplicons have been widely used for predicting the functional profiles of microbiome, via software tools such as PICRUSt 2. However, due to the potential PCR bias and gene profile variation among phylogenetically related genomes, functional profiles predicted from 16S amplicons may deviate from WGS-derived ones, resulting in misleading results. Results Here we present Meta-Apo, which greatly reduces or even eliminates such deviation, thus deduces much more consistent diversity patterns between the two approaches. Tests of Meta-Apo on > 5000 16S-rRNA amplicon human microbiome samples from 4 body sites showed the deviation between the two strategies is significantly reduced by using only 15 WGS-amplicon training sample pairs. Moreover, Meta-Apo enables cross-platform functional comparison between WGS and amplicon samples, thus greatly improve 16S-based microbiome diagnosis, e.g. accuracy of gingivitis diagnosis via 16S-derived functional profiles was elevated from 65 to 95% by WGS-based classification. Therefore, with the low cost of 16S-amplicon sequencing, Meta-Apo can produce a reliable, high-resolution view of microbiome function equivalent to that offered by shotgun WGS. Conclusions This suggests that large-scale, function-oriented microbiome sequencing projects can probably benefit from the lower cost of 16S-amplicon strategy, without sacrificing the precision in functional reconstruction that otherwise requires WGS. An optimized C++ implementation of Meta-Apo is available on GitHub (https://github.com/qibebt-bioinfo/meta-apo) under a GNU GPL license. It takes the functional profiles of a few paired WGS:16S-amplicon samples as training, and outputs the calibrated functional profiles for the much larger number of 16S-amplicon samples.

scholarly journals Change by challenge: A common genetic basis behind childhood cognitive development and cognitive training

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Bruno Sauce ◽  
John Wiedenhoeft ◽  
Nicholas Judd ◽  
Torkel Klingberg
Keyword(s):  
Cognitive Development ◽  
Brain Plasticity ◽  
Training Sample ◽  
Age Related ◽  
Central Piece ◽  
Gene Environment ◽  
Related Development ◽  
Explained Variation ◽  
Genetic And Environmental Factors ◽  
Cognitive Challenges

AbstractThe interplay of genetic and environmental factors behind cognitive development has preoccupied multiple fields of science and sparked heated debates over the decades. Here we tested the hypothesis that developmental genes rely heavily on cognitive challenges—as opposed to natural maturation. Starting with a polygenic score (cogPGS) that previously explained variation in cognitive performance in adults, we estimated its effect in 344 children and adolescents (mean age of 12 years old, ranging from 6 to 25) who showed changes in working memory (WM) in two distinct samples: (1) a developmental sample showing significant WM gains after 2 years of typical, age-related development, and (2) a training sample showing significant, experimentally-induced WM gains after 25 days of an intense WM training. We found that the same genetic factor, cogPGS, significantly explained the amount of WM gain in both samples. And there was no interaction of cogPGS with sample, suggesting that those genetic factors are neutral to whether the WM gains came from development or training. These results represent evidence that cognitive challenges are a central piece in the gene-environment interplay during cognitive development. We believe our study sheds new light on previous findings of interindividual differences in education (rich-get-richer and compensation effects), brain plasticity in children, and the heritability increase of intelligence across the lifespan.

scholarly journals Integrated Framework for Detecting the Areas Prone to Flooding Generated by Flash-Floods in Small River Catchments

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 758 ◽  
Author(s):  
Romulus Costache ◽  
Alina Barbulescu ◽  
Quoc Bao Pham
Keyword(s):  
Analytical Hierarchy Process ◽  
Process Model ◽  
Buffer Zone ◽  
Training Sample ◽  
Flash Floods ◽  
Operating Characteristics ◽  
Conditioning Factors ◽  
Flood Susceptibility ◽  
Hierarchy Process ◽  
Very High

In the present study, the susceptibility to flash-floods and flooding was studied across the Izvorul Dorului River basin in Romania. In the first phase, three ensemble models were used to determine the susceptibility to flash-floods. These models were generated by a combination of three statistical bivariate methods, namely frequency ratio (FR), weights of evidence (WOE), and statistical index (SI), with fuzzy analytical hierarchy process (FAHP). The result obtained from the application of the FAHP-WOE model had the best performance highlighted by an Area Under Curve—Receiver Operating Characteristics Curve (AUC-ROC) value of 0.837 for the training sample and another of 0.79 for the validation sample. Furthermore, the results offered by FAHP-WOE were weighted on the river network level using the flow accumulation method, through which the valleys with a medium, high, and very high torrential susceptibility were identified. Based on these valleys’ locations, the susceptibility to floods was estimated. Thus, in the first stage, a buffer zone of 200 m was delimited around the identified valleys along which the floods could occur. Once the buffer zone was established, ten flood conditioning factors were used to determine the flood susceptibility through the analytical hierarchy process model. Approximately 25% of the total delimited area had a high and very high flood susceptibility.

EM Algorithm Based on Fuzzification and its Application

2012 ◽  
Vol 532-533 ◽  
pp. 1445-1449
Author(s):  
Ting Ting Tong ◽  
Zhen Hua Wu
Keyword(s):  
Em Algorithm ◽  
Mixed Model ◽  
Image Data ◽  
Training Sample ◽  
Model Parameters ◽  
Parameter Learning ◽  
Weighted Degree ◽  
The Em Algorithm ◽  
The Impact

EM algorithm is a common method to solve mixed model parameters in statistical classification of remote sensing image. The EM algorithm based on fuzzification is presented in this paper to use a fuzzy set to represent each training sample. Via the weighted degree of membership, different samples will be of different effect during iteration to decrease the impact of noise on parameter learning and to increase the convergence rate of algorithm. The function and accuracy of classification of image data can be completed preferably.

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