An investigation for loss functions widely used in machine learning

Nie, Feiping; Hu, Zhanxuan; Li, Xuelong

doi:10.4310/cis.2018.v18.n1.a2

Cited by 41 publications

(24 citation statements)

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“…Repetition was done 20 times for the full model and 10 times for the short model with the default threshold of 0.01. Logistic loss/Log loss was calculated in the “MLmetrics” package and the best models were shown with the plots [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia

et al. 2021

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Background and objectives Diagnosis of Pulmonary Rifampicin Resistant Tuberculosis (RR-TB) with the Drug-Susceptibility Test (DST) is costly and time-consuming. Furthermore, GeneXpert for rapid diagnosis is not widely available in Indonesia. This study aims to develop and evaluate the CUHAS-ROBUST model performance, an artificial-intelligence-based RR-TB screening tool. Methods A cross-sectional study involved suspected all type of RR-TB patients with complete sputum Lowenstein Jensen DST (reference) and 19 clinical, laboratory, and radiology parameter results, retrieved from medical records in hospitals under the Faculty of Medicine, Hasanuddin University Indonesia, from January 2015-December 2019. The Artificial Neural Network (ANN) models were built along with other classifiers. The model was tested on participants recruited from January 2020-October 2020 and deployed into CUHAS-ROBUST (index test) application. Sensitivity, specificity, and accuracy were obtained for assessment. Results A total of 487 participants (32 Multidrug-Resistant/MDR 57 RR-TB, 398 drug-sensitive) were recruited for model building and 157 participants (23 MDR and 21 RR) in prospective testing. The ANN full model yields the highest values of accuracy (88% (95% CI 85–91)), and sensitivity (84% (95% CI 76–89)) compare to other models that show sensitivity below 80% (Logistic Regression 32%, Decision Tree 44%, Random Forest 25%, Extreme Gradient Boost 25%). However, this ANN has lower specificity among other models (90% (95% CI 86–93)) where Logistic Regression demonstrates the highest (99% (95% CI 97–99)). This ANN model was selected for the CUHAS-ROBUST application, although still lower than the sensitivity of global GeneXpert results (87.5%). Conclusion The ANN-CUHAS ROBUST outperforms other AI classifiers model in detecting all type of RR-TB, and by deploying into the application, the health staff can utilize the tool for screening purposes particularly at the primary care level where the GeneXpert examination is not available. Trial registration NCT04208789.

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Section: Methodsmentioning

confidence: 99%

Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia

et al. 2021

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“…In particular, we would rather have a small but regular error rather than very large errors a little less frequently since this would completely affect the precision of the flux measurement. Common loss functions are being investigated in [17].…”

Section: Loss Function For Training the Deep Learning Neural Networkmentioning

confidence: 99%

A New Method Based on a Multilayer Perceptron Network to Determine In-Orbit Satellite Attitude for Spacecrafts without Active ADCS Like UVSQ-SAT

et al. 2021

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Climate change is largely determined by the radiation budget imbalance at the Top Of the Atmosphere (TOA), which is generated by the increasing concentrations of greenhouse gases (GHGs). As a result, the Earth Energy Imbalance (EEI) is considered as an Essential Climate Variable (ECV) that has to be monitored continuously from space. However, accurate TOA radiation measurements remain very challenging. Ideally, EEI monitoring should be performed with a constellation of satellites in order to resolve as much as possible spatio-temporal fluctuations in EEI which contain important information on the underlying mechanisms driving climate change. The monitoring of EEI and its components (incoming solar, reflected solar, and terrestrial infrared fluxes) is the main objective of the UVSQ-SAT pathfinder nanosatellite, the first of its kind in the construction of a future constellation. UVSQ-SAT does not have an active determination system of its orientation with respect to the Sun and the Earth (i.e., the so-called attitude), a prerequisite in the calculation of EEI from the satellite radiation measurements. We present a new effective method to determine the UVSQ-SAT’s in-orbit attitude using its housekeeping and scientific sensors measurements and a well-established deep learning algorithm. One of the goals is to estimate the satellite attitude with a sufficient accuracy for retrieving the radiative fluxes (incoming solar, reflected solar, terrestrial infrared) on each face of the satellite with an uncertainty of less than ±5 Wm−2 (1σ). This new method can be extended to any other satellites with no active attitude determination or control system. To test the accuracy of the method, a ground-based calibration experiment with different attitudes is performed using the Sun as the radiative flux reference. Based on the deep learning estimation of the satellite ground-based attitude, the uncertainty on the solar flux retrieval is about ±16 Wm−2 (1σ). The quality of the retrieval is mainly limited by test conditions and the number of data samples used in training the deep learning system during the ground-based calibration. The expected increase in the number of training data samples will drastically decrease the uncertainty in the retrieved radiative fluxes. A very similar algorithm will be implemented and used in-orbit for UVSQ-SAT.

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“…One of the most commonly used loss function in regression tasks is Mean Squared Error or L2 [26]. MSE is the sum of squared distances between the real value and predicted values, it is defined as:…”

Section: Loss Functionmentioning

confidence: 99%

Predicting file lifetimes for data placement in multi-tiered storage systems for HPC

ThomasLuis

GougeaudSebastien

RubiniStéphane

et al. 2021

SIGOPS Oper. Syst. Rev.

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The emergence of Exascale machines in HPC will have the foreseen consequence of putting more pressure on the storage systems in place, not only in terms of capacity but also bandwidth and latency. With limited budget we cannot imagine using only storage class memory, which leads to the use of a heterogeneous tiered storage hierarchy. In order to make the most efficient use of the high performance tier in this storage hierarchy, we need to be able to place user data on the right tier and at the right time. In this paper, we assume a 2-tier storage hierarchy with a high performance tier and a high capacity archival tier. Files are placed on the high performance tier at creation time and moved to capacity tier once their lifetime expires (that is once they are no more accessed). The main contribution of this paper lies in the design of a file lifetime prediction model solely based on its path based on the use of Convolutional Neural Network. Results show that our solution strikes a good trade-off between accuracy and under-estimation. Compared to previous work, our model made it possible to reach an accuracy close to previous work (around 98.60% compared to 98.84%) while reducing the underestimations by almost 10x to reach 2.21% (compared to 21.86%). The reduction in underestimations is crucial as it avoids misplacing files in the capacity tier while they are still in use.

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An investigation for loss functions widely used in machine learning

Cited by 41 publications

References 0 publications

Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia

Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia

A New Method Based on a Multilayer Perceptron Network to Determine In-Orbit Satellite Attitude for Spacecrafts without Active ADCS Like UVSQ-SAT

Predicting file lifetimes for data placement in multi-tiered storage systems for HPC

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