high bias
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2022 ◽  
Vol 169 ◽  
pp. 108897
Author(s):  
Matthew Dunbrack ◽  
Christopher Stewart ◽  
Anna Erickson
Keyword(s):  

Diagnostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 166
Author(s):  
Sudip Paul ◽  
Maheshrao Maindarkar ◽  
Sanjay Saxena ◽  
Luca Saba ◽  
Monika Turk ◽  
...  

Background and Motivation: Diagnosis of Parkinson’s disease (PD) is often based on medical attention and clinical signs. It is subjective and does not have a good prognosis. Artificial Intelligence (AI) has played a promising role in the diagnosis of PD. However, it introduces bias due to lack of sample size, poor validation, clinical evaluation, and lack of big data configuration. The purpose of this study is to compute the risk of bias (RoB) automatically. Method: The PRISMA search strategy was adopted to select the best 39 AI studies out of 85 PD studies closely associated with early diagnosis PD. The studies were used to compute 30 AI attributes (based on 6 AI clusters), using AP(ai)Bias 1.0 (AtheroPointTM, Roseville, CA, USA), and the mean aggregate score was computed. The studies were ranked and two cutoffs (Moderate-Low (ML) and High-Moderate (MH)) were determined to segregate the studies into three bins: low-, moderate-, and high-bias. Result: The ML and HM cutoffs were 3.50 and 2.33, respectively, which constituted 7, 13, and 6 for low-, moderate-, and high-bias studies. The best and worst architectures were “deep learning with sketches as outcomes” and “machine learning with Electroencephalography,” respectively. We recommend (i) the usage of power analysis in big data framework, (ii) that it must undergo scientific validation using unseen AI models, and (iii) that it should be taken towards clinical evaluation for reliability and stability tests. Conclusion: The AI is a vital component for the diagnosis of early PD and the recommendations must be followed to lower the RoB.


Author(s):  
Xiaomin Cui ◽  
Shaojie Hu ◽  
Takashi Kimura

Abstract Lateral spin valves are ideal nanostructures for investigating spin-transport physics phenomena and promoting the development of future spintronic devices owing to dissipation-less pure spin current. The magnitude of the spin accumulation signal is well understood as a barometer for characterizing spin current devices. Here, we develop a novel fabrication method for lateral spin valves based on ferromagnetic nanopillar structures using a multi-angle deposition technique. We demonstrate that the spin-accumulation signal is effectively enhanced by reducing the lateral dimension of the nonmagnetic spin channel. The obtained results can be quantitatively explained by the confinement of the spin reservoir by considering spin diffusion into the leads. The temperature dependence of the spin accumulation signal and the influence of the thermal spin injection under a high bias current are also discussed.


2021 ◽  
Vol 14 (11) ◽  
pp. 7133-7153
Author(s):  
Denise Degen ◽  
Cameron Spooner ◽  
Magdalena Scheck-Wenderoth ◽  
Mauro Cacace

Abstract. Geophysical process simulations play a crucial role in the understanding of the subsurface. This understanding is required to provide, for instance, clean energy sources such as geothermal energy. However, the calibration and validation of the physical models heavily rely on state measurements such as temperature. In this work, we demonstrate that focusing analyses purely on measurements introduces a high bias. This is illustrated through global sensitivity studies. The extensive exploration of the parameter space becomes feasible through the construction of suitable surrogate models via the reduced basis method, where the bias is found to result from very unequal data distribution. We propose schemes to compensate for parts of this bias. However, the bias cannot be entirely compensated. Therefore, we demonstrate the consequences of this bias with the example of a model calibration.


2021 ◽  
Vol 893 (1) ◽  
pp. 012002
Author(s):  
A. Indrawati ◽  
D. F. Andarini ◽  
N. Cholianawati ◽  
Sumaryati

Abstract Forest fires have an impact on air quality and visibility. Visibility can be associated with a highly visual indicator of air pollution. This research aims to analyze the relationship between the PM10 concentration and visibility during the forest firest events and normal conditions in Palangkaraya from 2000 to 2014 by using a regression method. The relative humidity data was used to filter the PM10 and visibility. Furthermore, the equation resulted from the regression analysis was used to predict PM10 concentration in Palangka Raya. The result showed that the regression pattern tends to form a logarithmic function. Specifically, without filtering data, the coefficient correlation (r-value) during the forest fire events and normal conditions are 0.69 and 0.5, respectively. Meanwhile, a data filtering method gives a higher relationship between PM10 and visibility, with the r-value of 0.7 for the forest fire events and 0.68 for the normal condition. On the other hand, the prediction of PM10 concentration indicates a high bias value due to the other influenced factors that have not been included in this study.


Author(s):  
Md Belal Hossain ◽  
Lucy Mosquera ◽  
Mohammad Karim

Introduction: The instrumental variable (IV)-based methods (e.g., two-stage least square [2SLS], two-stage residual inclusion [2SRI], and nonparametric causal bound [NPCB]) can be used to address non-adherence in pragmatic trials. These methods require assumptions, e.g., exclusion restriction, although they are known to handle unmeasured confounding. The inverse probability-weighted per-protocol [IPW-PP] method is useful in the same setting but requires different assumptions (no unmeasured confounding). Although all these methods aim to address the same problem, comprehensive simulations to compare their performance are absent in the literature. We performed extensive simulations when (1) confounding is present, (2) confounder is unmeasured but exclusion restriction is met, (3) exclusion restriction is violated, and (4) non-adherence is one-sided and differential. Method: We compared the performance in terms of bias, standard error (SE), mean squared error (MSE), and 95% confidence interval coverage probability. Results: For setting-1, IPW-PP outperforms IV-methods in terms of bias, SE, MSE, and coverage for <80% non-adherence but produces high bias beyond that point. IPW-PP also has high biases, but 2SLS and 2SRI work well for setting-2. For setting-3, 2SLS and 2SRI perform the worst in all scenarios; IPW-PP produces unbiased estimates when necessary confounders are measured and adjusted. For setting-4, IPW-PP has less bias, but 2SLS and 2SRI have higher SE and MSE. NPCB has wider bounds in all scenarios. We also analyze a two-arm trial to estimate the effect of vitamin A supplementation on childhood mortality after addressing non-adherence. Conclusion: We need to be cautious using the IPW-PP when non-adherence is very high or strong unmeasured confounding and should avoid using the IV methods when the exclusion restriction assumption is violated or high differential non-adherence. Since assumptions are different and often untestable for IPW-PP and IV methods, we suggest analyzing data using both methods for a robust conclusion.


Author(s):  
Charles R. Sampson ◽  
Efren A. Serra ◽  
John A. Knaff ◽  
Joshua H. Cossuth

AbstractThe U.S. Navy is keenly interested in analyses and predictions of waves at sea due to their effects on important tasks such as shipping, base preparedness and disaster relief. U.S. Tropical Cyclone (TC) Forecast Centers routinely disseminate wind probabilities consistent with official TC forecasts worldwide, but do not do the same for wave forecasts. These probabilities are especially important at longer leads where TC forecast accuracy diminishes. This work describes global wave probabilities consistent with both the official TC forecasts and their wind probabilities. Real-time runs for 84 TCs between May 2018 and March 2019, with probabilities generated for 12-ft and 18-ft significant wave heights are used to calculate verification statistics. This results in 347, 319, 261, 214, 155, and 112 verification cases at lead times of 1, 2, 3, 4, and 5 days where each verification case consists of a 20x20 degree latitude longitude grid around the verifying TC position. When compared with wave probabilities generated solely by a global numerical weather prediction model, the wind probability-based algorithm demonstrates improved consistency with official forecasts and provides additional benefits. Those benefits include an improved capability to discriminate between 12-ft and 18-ft significant wave events and non-events. The verification statistics also shows that the wind probability-based algorithm has a consistent high bias. How these biases can be reduced in future efforts is also discussed.


2021 ◽  
Vol 6 (1) ◽  
pp. 36-41
Author(s):  
Arsandi Akhmad ◽  
Lukas Lukas ◽  
Bagus Mahawan

The purpose of this research is to develop a fraud detection model on loan transactions at failed banks in the context of deposit and deposit guarantees mandated to the Indonesia Deposit Insurance Corporation (IDIC). The data used in this study is the data of a bank in the Jakarta area that had liquidated at the end of 2015. Meanwhile, data on loan transaction ranges ranged from 2010 to 2015. This research also focuses on improving the performance of detection models by using feature selection. With the feature selection, it expected that the impact of the reduced performance of the model exposed to high variance and high bias due to the many features used can handled better.


2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Carlos David Ardón Muñoz ◽  
Bryan José Morales Calderón
Keyword(s):  

Se realizó un estudio comparativo entre dos arquitecturas de redes neuronales, InceptionV3 y ResNet50, para determinar cuál es la mejor en la clasificación de la severidad del Virus del Mosaico Dorado en frijol. Para esto, se recolectaron y clasificaron manualmente 3409 imágenes de hojas de frijol. Las muestras se organizaron en dos formas de agrupación, tres categorías (alto, medio y bajo) y dos categorías (alto y bajo). Además, los entrenamientos se realizaron con dos niveles de extracción de características: todas las capas y desde capas intermedias. Los resultados de los entrenamientos sobre los datos distribuidos en tres categorías produjeron modelos con high-bias. Por otro lado, los modelos entrenados sobre el conjunto de datos con dos categorías produjeron las exactitudes más altas sobre el conjunto de pruebas al extraer características desde capas intermedias (ResNet50=96.68% e InceptionV3=94.47%). Con una prueba de McNemar se determinó que la diferencia es estadísticamente significativa a un nivel de significancia del 5%. Por tanto, ResNet50 con extracción de características desde capas intermedias posee la exactitud más alta en la tarea estudiada.


2021 ◽  
Author(s):  
Melvin Ikwubuo ◽  
Jinkwan Song ◽  
Jong Guen Lee

Abstract Combustion dynamics has been a significant problem for a lean, premixed, prevaporized (LPP) combustor. Understanding the acoustic characteristics of combustor components is essential to modeling thermoacoustic behavior in a gas turbine combustion system. Acoustic characteristics such as impedance and scattering matrix elements are experimentally determined for different-shape orifices with an emphasis on the effect of the flow field on them. These orifices are used to represent premixed swirl cups in LP combustors. The validity and limitation of two different methodologies are evaluated by comparing measured results with those of others. Consistent with analytical predictions, the measured resistance through an orifice increases as the bias flow increases. Different types of orifices considered in this study behave similarly to a thin orifice at high bias flow even though the discharge coefficients vary as much as 30% between them. The conventional method produces impedance values independent of waves reflected from the end boundary condition only when the scattering elements at the orifice downstream are roughly equal to those upstream of the orifice. However, the scattering matrix method produces impedance values that are not affected by the source or reflected waves at the system’s boundary. The scattering matrix measurements show that the reflection and transmission elements increases and decreases, respectively, as the bias flow through an orifice increases.


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