Статті в журналах: "Sensitivity metric"

1

Hill, Allen, and Julie Nantel. "Sensitivity of discrete symmetry metrics: Implications for metric choice." PLOS ONE 17, no. 5 (May 19, 2022): e0268581. http://dx.doi.org/10.1371/journal.pone.0268581.

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Gait asymmetry is present in several pathological populations, including those with Parkinson’s disease, Huntington’s disease, and stroke survivors. Previous studies suggest that commonly used discrete symmetry metrics, which compare single bilateral variables, may not be equally sensitive to underlying effects of asymmetry, and the use of a metric with low sensitivity could result in unnecessarily low statistical power. The purpose of this study was to provide a comprehensive assessment of the sensitivity of commonly used discrete symmetry metrics to better inform design of future studies. Monte Carlo simulations were used to estimate the statistical power of each symmetry metric at a range of asymmetry magnitudes, group/condition variabilities, and sample sizes. Power was estimated by repeated comparison of simulated symmetric and asymmetric data with a paired t-test, where the proportion of significant results is equivalent to the power. Simulation results confirmed that not all common discrete symmetry metrics are equally sensitive to reference effects of asymmetry. Multiple symmetry metrics exhibit equivalent sensitivities, but the most sensitive discrete symmetry metric in all cases is a bilateral difference (e.g. left—right). A ratio (e.g. left/right) has poor sensitivity when group/condition variability is not small, but a log-transformation produces increased sensitivity. Additionally, two metrics which included an absolute value in their definitions showed increased sensitivity when the absolute value was removed. Future studies should consider metric sensitivity when designing analyses to reduce the possibility of underpowered research.

2

Torn, Ryan D., and Gregory J. Hakim. "Ensemble-Based Sensitivity Analysis." Monthly Weather Review 136, no. 2 (February 1, 2008): 663–77. http://dx.doi.org/10.1175/2007mwr2132.1.

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Abstract The sensitivity of forecasts to observations is evaluated using an ensemble approach with data drawn from a pseudo-operational ensemble Kalman filter. For Gaussian statistics and a forecast metric defined as a scalar function of the forecast variables, the effect of observations on the forecast metric is quantified by changes in the metric mean and variance. For a single observation, expressions for these changes involve a product of scalar quantities, which can be rapidly evaluated for large numbers of observations. This technique is applied to determining climatological forecast sensitivity and predicting the impact of observations on sea level pressure and precipitation forecast metrics. The climatological 24-h forecast sensitivity of the average pressure over western Washington State shows a region of maximum sensitivity to the west of the region, which tilts gently westward with height. The accuracy of ensemble sensitivity predictions is tested by withholding a single buoy pressure observation from this region and comparing this perturbed forecast with the control case where the buoy is assimilated. For 30 cases, there is excellent agreement between these forecast differences and the ensemble predictions, as measured by the forecast metric. This agreement decreases for increasing numbers of observations. Nevertheless, by using statistical confidence tests to address sampling error, the impact of thousands of observations on forecast-metric variance is shown to be well estimated by a subset of the O(100) most significant observations.

3

Neralić, Luka, and Richard E. Wendell. "Generalized tolerance sensitivity and DEA metric sensitivity." Croatian Operational Research Review 6, no. 1 (March 30, 2015): 169–80. http://dx.doi.org/10.17535/crorr.2015.0014.

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4

Mishra, Siddhartha, Nicholas Monath, Michael Boratko, Ariel Kobren, and Andrew McCallum. "An Evaluative Measure of Clustering Methods Incorporating Hyperparameter Sensitivity." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7788–96. http://dx.doi.org/10.1609/aaai.v36i7.20747.

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Clustering algorithms are often evaluated using metrics which compare with ground-truth cluster assignments, such as Rand index and NMI. Algorithm performance may vary widely for different hyperparameters, however, and thus model selection based on optimal performance for these metrics is discordant with how these algorithms are applied in practice, where labels are unavailable and tuning is often more art than science. It is therefore desirable to compare clustering algorithms not only on their optimally tuned performance, but also some notion of how realistic it would be to obtain this performance in practice. We propose an evaluation of clustering methods capturing this ease-of-tuning by modeling the expected best clustering score under a given computation budget. To encourage the adoption of the proposed metric alongside classic clustering evaluations, we provide an extensible benchmarking framework. We perform an extensive empirical evaluation of our proposed metric on popular clustering algorithms over a large collection of datasets from different domains, and observe that our new metric leads to several noteworthy observations.

5

Gamboa, Fabrice, Thierry Klein, Agnès Lagnoux, and Leonardo Moreno. "Sensitivity analysis in general metric spaces." Reliability Engineering & System Safety 212 (August 2021): 107611. http://dx.doi.org/10.1016/j.ress.2021.107611.

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6

Chu, Xiu-qin. "Contrast-sensitivity-function-based clutter metric." Optical Engineering 51, no. 6 (June 5, 2012): 067003. http://dx.doi.org/10.1117/1.oe.51.6.067003.

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7

Jude Chukwura Obi. "A comparative study of several classification metrics and their performances on data." World Journal of Advanced Engineering Technology and Sciences 8, no. 1 (February 28, 2023): 308–14. http://dx.doi.org/10.30574/wjaets.2023.8.1.0054.

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Six classification metrics namely, Accuracy, Precision, Recall (Sensitivity), Specificity, F1-Score and Area Under the Curve have been studied in this work. A classification model based on the Support Vector Machine, was used to obtain a confusion matrix, which provided the needed information for calculating the different classification metrics. Twenty different datasets were used to assess the performances of the classification metrics. Accuracy and Area Under the Curve are the two metrics that consistently gave a classification result given each dataset used in the study. Although accuracy appears to be marginally better that AUC, it was discovered that in some cases where sensitivity is zero, accuracy yielded a high correct classification result. This goes further to implying that prior to choosing accuracy as a preferred metric for classification, investigation should be carried out to find out what sensitivity and specificity are. Where there are high values for sensitivity and specificity, the study shows that a choice of accuracy as a preferred classification metric leads to a high percentage of correct classification result.

8

Paiva, João R. B., Alana S. Magalhães, Pedro H. F. Moraes, Júnio S. Bulhões, and Wesley P. Calixto. "Stability Metric Based on Sensitivity Analysis Applied to Electrical Repowering System." Energies 14, no. 22 (November 22, 2021): 7824. http://dx.doi.org/10.3390/en14227824.

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Stability metrics are used to quantify a system’s ability to maintain equilibrium under disturbances. We did not identify the proposition of a stability metric using sensitivity analysis within the literature. This work proposes a system stability metric and its application to an electrical repowering system. The methodology for applying the proposed metric comprises: (i) system parameters sensitivity analysis and spider diagram construction, (ii) determining the array containing the line segments inclination angles of each spider diagram curve, and (iii) stability calculation using the array mean and maximum inclination value of a line segment. After simulating the model built for the electrical repowering system and applying the methodology, we obtain results regarding the sensitivity indices and stability values of system inputs relative to their outputs, considering the original system and with reduced parameters. Using the stability study, it was possible to determine different stability categories for the system parameters, which indicates the need for different analysis levels.

9

Lehuta, Sigrid, Stéphanie Mahévas, Pascal Le Floc’h, and Pierre Petitgas. "A simulation-based approach to assess sensitivity and robustness of fisheries management indicators for the pelagic fishery in the Bay of Biscay." Canadian Journal of Fisheries and Aquatic Sciences 70, no. 12 (December 2013): 1741–56. http://dx.doi.org/10.1139/cjfas-2013-0066.

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Indicators are widely promoted as means to monitor ecosystem status or to evaluate fisheries management performance. “Which indicators are most relevant as decision-support tools in fisheries management?” still remains a topical question. Indicators should be metrics related to fish populations and fleets and should be sensitive to management strategies. However, given the complexity of the processes involved, it is often difficult to unequivocally interpret variations in metrics. A simulation approach was used to study metric properties and to identify robust and relevant fishery indicators. By applying sensitivity analysis methods, simulation designs were built that cross a variety of management scenarios and uncertainty hypotheses. Bio-economic outputs were simulated using a mechanistic model (ISIS-Fish), and their properties were statistically analyzed. This approach was applied to the pelagic fishery of the Bay of Biscay. The analysis of metric properties highlighted the major factors driving variations in each metric and identified the important sources of uncertainty that need to be reduced to allow the use of metrics as indicators. Although very few metrics gave robust indications of management performance, sensitivity indices evidenced how management performances could be improved, and spatially disaggregated metrics provided insights into the mechanisms underlying management performance.

10

Jensen, Hector A., and Abdon E. Sepulveda. "Design Sensitivity Metric for Structural Dynamic Response." AIAA Journal 36, no. 9 (September 1998): 1686–93. http://dx.doi.org/10.2514/2.572.

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11

Farrell, Joyce E., and Andrew E. Fitzhugh. "Discriminability metric based on human contrast sensitivity." Journal of the Optical Society of America A 7, no. 10 (October 1, 1990): 1976. http://dx.doi.org/10.1364/josaa.7.001976.

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12

Jensen, Hector A., and Abdon E. Sepulveda. "Design sensitivity metric for structural dynamic response." AIAA Journal 36 (January 1998): 1686–93. http://dx.doi.org/10.2514/3.14023.

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13

Taffel, Myles T., Lyndon Luk, Justin M. Ream, and Andrew B. Rosenkrantz. "Exploratory Study of Apparent Diffusion Coefficient Histogram Metrics in Assessing Pancreatic Malignancy." Canadian Association of Radiologists Journal 70, no. 4 (November 2019): 416–23. http://dx.doi.org/10.1016/j.carj.2019.07.001.

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Purpose To evaluate whole-lesion 3D-histogram apparent diffusion coefficient (ADC) metrics for assessment of pancreatic malignancy. Methods Forty-two pancreatic malignancies (36 pancreatic adenocarcinoma [PDAC], 6 pancreatic neuroendocrine [PanNET]) underwent abdominal magnetic resonance imaging (MRI) with diffusion-weighted imaging before endoscopic ultrasound biopsy or surgical resection. Two radiologists independently placed 3D volumes of interest to derive whole-lesion histogram ADC metrics. Mann-Whitney tests and receiver operating characteristic analyses were used to assess metrics’ diagnostic performance for lesion histology, T-stage, N-stage, and grade. Results Whole-lesion ADC histogram metrics lower in PDACs than PanNETs for both readers ( P ≤ .026) were mean ADC (area under the curve [AUC] = 0.787-0.792), mean of the bottom 10th percentile (mean0-10) (AUC = 0.787-0.880), mean of the 10th-25th percentile (mean10-25) (AUC = 0.884-0.917) and mean of the 25th-50th percentile (mean25-50) (AUC = 0.829-0.829). For mean10-25 (metric with highest AUC for identifying PDAC), for reader 1 a threshold > 0.94 × 10−3 mm2/s achieved sensitivity 94% and specificity 83%, and for reader 2 a threshold > 0.82 achieved sensitivity 97% and specificity 67%. Metrics lower in nodal status ≥ N1 than N0 for both readers ( P ≤ .043) were mean0-10 (AUC = 0.789-0.822) and mean10-25 (AUC = 0.800-0.822). For mean10-25 (metric with highest AUC for identifying N0), for reader 1 a threshold <1.17 achieved sensitivity 87% and specificity 67%, and for reader 2 a threshold <1.04 achieved sensitivity 87% and specificity 83%. No metric was associated with T-stage ( P > .195) or grade ( P > .215). Conclusion Volumetric ADC histogram metrics may serve as non-invasive biomarkers of pancreatic malignancy. Mean10-25 outperformed standard mean for lesion histology and nodal status, supporting the role of histogram analysis.

14

Gomes, Viviane M., Joao R. B. Paiva, Marcio R. C. Reis, Gabriel A. Wainer, and Wesley P. Calixto. "Mechanism for Measuring System Complexity Applying Sensitivity Analysis." Complexity 2019 (April 8, 2019): 1–12. http://dx.doi.org/10.1155/2019/1303241.

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This work proposes a complexity metric which maps internal connections of the system and its relationship with the environment through the application of sensitivity analysis. The proposed methodology presents (i) system complexity metric, (ii) system sensitivity metric, and (iii) two models as case studies. Based on the system dynamics, the complexity metric maps the internal connections through the states of the system and the metric of sensitivity evaluates the contribution of each parameter to the output variability. The models are simulated in order to quantify the complexity and the sensitivity and to analyze the behavior of the systems leading to the assumption that the system complexity is closely linked to the most sensitive parameters. As findings from results, it may be observed that systems may exhibit high performance as a result of optimized configurations given by their natural complexity.

15

Anderson, P. S. L., and S. N. Patek. "Mechanical sensitivity reveals evolutionary dynamics of mechanical systems." Proceedings of the Royal Society B: Biological Sciences 282, no. 1804 (April 7, 2015): 20143088. http://dx.doi.org/10.1098/rspb.2014.3088.

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A classic question in evolutionary biology is how form–function relationships promote or limit diversification. Mechanical metrics, such as kinematic transmission (KT) in linkage systems, are useful tools for examining the evolution of form and function in a comparative context. The convergence of disparate systems on equivalent metric values (mechanical equivalence) has been highlighted as a source of potential morphological diversity under the assumption that morphology can evolve with minimal impact on function. However, this assumption does not account for mechanical sensitivity—the sensitivity of the metric to morphological changes in individual components of a structure. We examined the diversification of a four-bar linkage system in mantis shrimp (Stomatopoda), and found evidence for both mechanical equivalence and differential mechanical sensitivity. KT exhibited variable correlations with individual linkage components, highlighting the components that influence KT evolution, and the components that are free to evolve independently from KT and thereby contribute to the observed pattern of mechanical equivalence. Determining the mechanical sensitivity in a system leads to a deeper understanding of both functional convergence and morphological diversification. This study illustrates the importance of multi-level analyses in delineating the factors that limit and promote diversification in form–function systems.

16

Smirnov, S., and D. Sotnikov. "Proximity of Bachelier and Samuelson Models for Different Metrics." Review of Business and Economics Studies 9, no. 3 (November 22, 2021): 52–76. http://dx.doi.org/10.26794/2308-944x-2021-9-3-52-76.

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This paper proposes a method of comparing the prices of European options, based on the use of probabilistic metrics, with respect to two models of price dynamics: Bachelier and Samuelson. In contrast to other studies on the subject, we consider two classes of options: European options with a Lipschitz continuous payout function and European options with a bounded payout function. For these classes, the following suitable probability metrics are chosen: the Fortet-Maurier metric, the total variation metric, and the Kolmogorov metric. It is proved that their computation can be reduced to computation of the Lambert in case of the Fortet-Mourier metric, and to the solution of a nonlinear equation in other cases. A statistical estimation of the model parameters in the modern oil market gives the order of magnitude of the error, including the magnitude of sensitivity of the option price, to the change in the volatility.

17

Pinto, Danna, Anat Prior, and Elana Zion Golumbic. "Assessing the Sensitivity of EEG-Based Frequency-Tagging as a Metric for Statistical Learning." Neurobiology of Language 3, no. 2 (2022): 214–34. http://dx.doi.org/10.1162/nol_a_00061.

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Abstract Statistical learning (SL) is hypothesized to play an important role in language development. However, the measures typically used to assess SL, particularly at the level of individual participants, are largely indirect and have low sensitivity. Recently, a neural metric based on frequency-tagging has been proposed as an alternative measure for studying SL. We tested the sensitivity of frequency-tagging measures for studying SL in individual participants in an artificial language paradigm, using non-invasive electroencephalograph (EEG) recordings of neural activity in humans. Importantly, we used carefully constructed controls to address potential acoustic confounds of the frequency-tagging approach, and compared the sensitivity of EEG-based metrics to both explicit and implicit behavioral tests of SL. Group-level results confirm that frequency-tagging can provide a robust indication of SL for an artificial language, above and beyond potential acoustic confounds. However, this metric had very low sensitivity at the level of individual participants, with significant effects found only in 30% of participants. Comparison of the neural metric to previously established behavioral measures for assessing SL showed a significant yet weak correspondence with performance on an implicit task, which was above-chance in 70% of participants, but no correspondence with the more common explicit 2-alternative forced-choice task, where performance did not exceed chance-level. Given the proposed ubiquitous nature of SL, our results highlight some of the operational and methodological challenges of obtaining robust metrics for assessing SL, as well as the potential confounds that should be taken into account when using the frequency-tagging approach in EEG studies.

18

Jiang, Liting, Peng Jin, and Peng Lei. "Color discrimination metric based on cone cell sensitivity." Optics Express 23, no. 11 (June 1, 2015): A741. http://dx.doi.org/10.1364/oe.23.00a741.

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Dolgun, Anil, Haydar Demirhan, Andrew Gill, Dion Grieger, Stella Stylianou, and Stelios Georgiou. "A new rank sensitivity metric for decision support." Statistics and Its Interface 12, no. 4 (2019): 573–83. http://dx.doi.org/10.4310/sii.2019.v12.n4.a7.

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20

Kurtek, Sebastian, and Karthik Bharath. "Bayesian sensitivity analysis with the Fisher–Rao metric." Biometrika 102, no. 3 (July 15, 2015): 601–16. http://dx.doi.org/10.1093/biomet/asv026.

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Krzanowski, W. J. "Sensitivity in Metric Scaling and Analysis of Distance." Biometrics 62, no. 1 (July 4, 2005): 239–44. http://dx.doi.org/10.1111/j.1541-0420.2005.00398.x.

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Evans, Jeffrey J., and Cynthia S. Hood. "A network performance sensitivity metric for parallel applications." International Journal of High Performance Computing and Networking 7, no. 1 (2011): 8. http://dx.doi.org/10.1504/ijhpcn.2011.038706.

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23

Jha, Sumit Kumar, Rickard Ewetz, Alvaro Velasquez, Arvind Ramanathan, and Susmit Jha. "Shaping Noise for Robust Attributions in Neural Stochastic Differential Equations." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (June 28, 2022): 9567–74. http://dx.doi.org/10.1609/aaai.v36i9.21190.

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Neural SDEs with Brownian motion as noise lead to smoother attributions than traditional ResNets. Various attribution methods such as saliency maps, integrated gradients, DeepSHAP and DeepLIFT have been shown to be more robust for neural SDEs than for ResNets using the recently proposed sensitivity metric. In this paper, we show that neural SDEs with adaptive attribution-driven noise lead to even more robust attributions and smaller sensitivity metrics than traditional neural SDEs with Brownian motion as noise. In particular, attribution-driven shaping of noise leads to 6.7%, 6.9% and 19.4% smaller sensitivity metric for integrated gradients computed on three discrete approximations of neural SDEs with standard Brownian motion noise: stochastic ResNet-50, WideResNet-101 and ResNeXt-101 models respectively. The neural SDE model with adaptive attribution-driven noise leads to 25.7% and 4.8% improvement in the SIC metric over traditional ResNets and Neural SDEs with Brownian motion as noise. To the best of our knowledge, we are the first to propose the use of attributions for shaping the noise injected in neural SDEs, and demonstrate that this process leads to more robust attributions than traditional neural SDEs with standard Brownian motion as noise.

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Rajendran, S., and S. Subramanian. "Mesh distortion sensitivity of 8-node plane elasticity elements based on parametric, metric, parametric-metric, and metric-parametric formulations." Structural Engineering and Mechanics 17, no. 6 (June 25, 2004): 767–88. http://dx.doi.org/10.12989/sem.2004.17.6.767.

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25

Saha, Soumadeep, Utpal Garain, Arijit Ukil, Arpan Pal, and Sundeep Khandelwal. "MedTric : A clinically applicable metric for evaluation of multi-label computational diagnostic systems." PLOS ONE 18, no. 8 (August 10, 2023): e0283895. http://dx.doi.org/10.1371/journal.pone.0283895.

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When judging the quality of a computational system for a pathological screening task, several factors seem to be important, like sensitivity, specificity, accuracy, etc. With machine learning based approaches showing promise in the multi-label paradigm, they are being widely adopted to diagnostics and digital therapeutics. Metrics are usually borrowed from machine learning literature, and the current consensus is to report results on a diverse set of metrics. It is infeasible to compare efficacy of computational systems which have been evaluated on different sets of metrics. From a diagnostic utility standpoint, the current metrics themselves are far from perfect, often biased by prevalence of negative samples or other statistical factors and importantly, they are designed to evaluate general purpose machine learning tasks. In this paper we outline the various parameters that are important in constructing a clinical metric aligned with diagnostic practice, and demonstrate their incompatibility with existing metrics. We propose a new metric, MedTric that takes into account several factors that are of clinical importance. MedTric is built from the ground up keeping in mind the unique context of computational diagnostics and the principle of risk minimization, penalizing missed diagnosis more harshly than over-diagnosis. MedTric is a unified metric for medical or pathological screening system evaluation. We compare this metric against other widely used metrics and demonstrate how our system outperforms them in key areas of medical relevance.

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Liu, Heng, Fengchun Lei, and Lidong Wang. "Li-Yorke Sensitivity of Set-Valued Discrete Systems." Journal of Applied Mathematics 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/260856.

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Consider the surjective, continuous mapf:X→Xand the continuous mapf¯of𝒦(X)induced byf, whereXis a compact metric space and𝒦(X)is the space of all nonempty compact subsets ofXendowed with the Hausdorff metric. In this paper, we give a short proof that iff¯is Li-Yoke sensitive, thenfis Li-Yorke sensitive. Furthermore, we give an example showing that Li-Yorke sensitivity offdoes not imply Li-Yorke sensitivity off¯.

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Novikova, Evgenia, Elena Doynikova, Diana Gaifulina, and Igor Kotenko. "Construction and Analysis of Integral User-Oriented Trustworthiness Metrics." Electronics 11, no. 2 (January 12, 2022): 234. http://dx.doi.org/10.3390/electronics11020234.

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Trustworthiness metrics help users to understand information system’s or a device’s security, safety, privacy, resilience, and reliability level. These metrics have different types and natures. The challenge consists of the integration of these metrics into one clear, scalable, sensitive, and reasonable metric representing overall trustworthiness level, useful for understanding if the users can trust the system or for the comparison of the devices and information systems. In this research, the authors propose a novel algorithm for calculation of an integral trustworthiness risk score that is scalable to any number of metrics, considers their criticality, and does not perform averaging in a case when all metrics are of equal importance. The obtained trustworthiness risk score could be further transformed to trustworthiness level. The authors analyze the resulting integral metric sensitivity and demonstrate its advantages on the series of experiments.

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Qu, Xin, Alex Hall, Anthony M. DeAngelis, Mark D. Zelinka, Stephen A. Klein, Hui Su, Baijun Tian, and Chengxing Zhai. "On the Emergent Constraints of Climate Sensitivity." Journal of Climate 31, no. 2 (January 2018): 863–75. http://dx.doi.org/10.1175/jcli-d-17-0482.1.

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Differences among climate models in equilibrium climate sensitivity (ECS; the equilibrium surface temperature response to a doubling of atmospheric CO2) remain a significant barrier to the accurate assessment of societally important impacts of climate change. Relationships between ECS and observable metrics of the current climate in model ensembles, so-called emergent constraints, have been used to constrain ECS. Here a statistical method (including a backward selection process) is employed to achieve a better statistical understanding of the connections between four recently proposed emergent constraint metrics and individual feedbacks influencing ECS. The relationship between each metric and ECS is largely attributable to a statistical connection with shortwave low cloud feedback, the leading cause of intermodel ECS spread. This result bolsters confidence in some of the metrics, which had assumed such a connection in the first place. Additional analysis is conducted with a few thousand artificial metrics that are randomly generated but are well correlated with ECS. The relationships between the contrived metrics and ECS can also be linked statistically to shortwave cloud feedback. Thus, any proposed or forthcoming ECS constraint based on the current generation of climate models should be viewed as a potential constraint on shortwave cloud feedback, and physical links with that feedback should be investigated to verify that the constraint is real. In addition, any proposed ECS constraint should not be taken at face value since other factors influencing ECS besides shortwave cloud feedback could be systematically biased in the models.

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Seltzer, Michael H., Ken A. Frank, and Anthony S. Bryk. "The Metric Matters: The Sensitivity of Conclusions About Growth in Student Achievement to Choice of Metric." Educational Evaluation and Policy Analysis 16, no. 1 (March 1994): 41–49. http://dx.doi.org/10.3102/01623737016001041.

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A central means of assessing the health of educational systems involves using longitudinal data to examine patterns of academic growth across a series of grades (e.g., the extent to which the range of achievement among students widens or narrows over time and whether students tend to “slump” in particular grades). The purpose of this article is to illustrate, through a series of analyses based on a longitudinal study of reading achievement in the Chicago Public Schools, that the conclusions one draws about patterns of academic growth and, in turn, the decisions one makes regarding the kinds of interventions that may be needed can be extremely sensitive to the metric used (i.e., whether achievement is measured using grade equivalents or based on item response theory metrics).

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Kim, J. G., I. Biederman, and O. Amir. "Greater Sensitivity to Nonaccidental than Metric Differences in Relations." Journal of Vision 11, no. 11 (September 23, 2011): 860. http://dx.doi.org/10.1167/11.11.860.

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Mirsky, H. P., J. Stelling, R. A. Harvey, S. R. Taylor, and F. J. Doyle. "Distribution-based sensitivity metric for highly variable biochemical systems." IET Systems Biology 5, no. 1 (January 1, 2011): 50–57. http://dx.doi.org/10.1049/iet-syb.2009.0064.

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BRANDT, ROBERT, and MISHA VOROBYEV. "Metric Analysis of Threshold Spectral Sensitivity in the Honeybee." Vision Research 37, no. 4 (February 1997): 425–39. http://dx.doi.org/10.1016/s0042-6989(96)00195-2.

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33

Jiang, Yongxi, Xiaofang Yang, and Tianxiu Lu. "Sensitivity of Uniformly Convergent Mapping Sequences in Non-Autonomous Discrete Dynamical Systems." Fractal and Fractional 6, no. 6 (June 7, 2022): 319. http://dx.doi.org/10.3390/fractalfract6060319.

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Let H be a compact metric space. The metric of H is denoted by d. And let (H,f1,∞) be a non-autonomous discrete system where f1,∞={fn}n=1∞ is a mapping sequence. This paper discusses infinite sensitivity, m-sensitivity, and m-cofinitely sensitivity of f1,∞. It is proved that, if fn(n∈N) are feebly open and uniformly converge to f:H→H, fi∘f=f∘fi for any i∈{1,2,…}, and ∑i=1∞D(fi,f)<∞, then (H,f) has the above sensitive property if and only if (H,f1,∞) has the same property where D(·,·) is the supremum metric.

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Karalis, Vangelis D. "An In Silico Approach toward the Appropriate Absorption Rate Metric in Bioequivalence." Pharmaceuticals 16, no. 5 (May 10, 2023): 725. http://dx.doi.org/10.3390/ph16050725.

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In bioequivalence, the maximum plasma concentration (Cmax) is traditionally used as a metric for the absorption rate, despite the fact that there are several concerns. The idea of “average slope” (AS) was recently introduced as an alternative metric to reflect absorption rate. This study aims to further extend the previous findings and apply an in silico approach to investigate the kinetic sensitivity of AS and Cmax. This computational analysis was applied to the C-t data of hydrochlorothiazide, donepezil, and amlodipine, which exhibit different absorption kinetics. Principal component analysis (PCA) was applied to uncover the relationships between all bioequivalence metrics. Monte Carlo simulations of bioequivalence trials were performed to investigate sensitivity. The appropriate programming codes were written in Python for the PCA and in MATLAB® for the simulations. The PCA verified the desired properties of AS and the unsuitability of Cmax to reflect absorption rate. The Monte Carlo simulations showed that AS is quite sensitive to detecting differences in absorption rate, while Cmax has almost negligible sensitivity. Cmax fails to reflect absorption rate, and its use in bioequivalence gives only a false impression. AS has the appropriate units, is easily calculated, exhibits high sensitivity, and has the desired properties of absorption rate.

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Vincze, István, László Endrényi, and László Tóthfalusi. "Bioequivalence metrics for absorption rates: linearity, specificity, sensitivity." Acta Pharmaceutica Hungarica 89, no. 1 (April 9, 2019): 17–21. http://dx.doi.org/10.33892/aph.2019.89.17-21.

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Aims:In order to ensure the therapeutic equivalence of generic products, it would be important to contrast measures additional to Cmax in order to assess differences in absorption rates. Our aim was to compare partial AUC (PAUC), Swing, and PTF to Cmax in terms of sensitivity, specificity and linearity under identical kinetic conditions. Methods:Single-dose and multiple-dose concentration curves were generated assuming one-compartment models. Kinetic sensitivity curves were obtained by gradually changing the absorption rate constant and keeping all other parameters fixed. Results:A metric should reflect specifically the investigated kinetic feature (e.g., the rate of absorption), be linearly related to it, and should exhibit high kinetic sensitivity. Cmax is related nonlinearly to the rate of absorption, is nonspecific to it (reflects also the extent of absorption as well as the rates of disposition processes), lacks kinetic sensitivity even following a single administration. Compared to Cmax, PAUC was always more sensitive under every investigated condition. Swing and PTF showed high kinetic sensitivity but, in contrast to PAUC, they could be evaluated only in multiple-dose studies. Conclusion:Under identical conditions, different metrics provide widely differing point estimates. Differences in kinetic sensitivity among bioequivalence metrics should be accounted for when results of different metrics are compared.

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Laud, Peeter, Alisa Pankova, and Martin Pettai. "A Framework of Metrics for Differential Privacy from Local Sensitivity." Proceedings on Privacy Enhancing Technologies 2020, no. 2 (April 1, 2020): 175–208. http://dx.doi.org/10.2478/popets-2020-0023.

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AbstractThe meaning of differential privacy (DP) is tightly bound with the notion of distance on databases, typically defined as the number of changed rows. Considering the semantics of data, this metric may be not the most suitable one, particularly when a distance comes out as larger than the data owner desired (which would undermine privacy). In this paper, we give a mechanism to specify continuous metrics that depend on the locations and amounts of changes in a much more nuanced manner. Our metrics turn the set of databases into a Banach space. In order to construct DP information release mechanisms based on our metrics, we introduce derivative sensitivity, an analogue to local sensitivity for continuous functions. We use this notion in an analysis that determines the amount of noise to be added to the result of a database query in order to obtain a certain level of differential privacy, and demonstrate that derivative sensitivity allows us to employ powerful mechanisms from calculus to perform the analysis for a variety of queries. We have implemented the analyzer and evaluated its efficiency and precision.

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Azar, C., and D. J. A. Johansson. "On the relationship between metrics to compare greenhouse gases – the case of IGTP, GWP and SGTP." Earth System Dynamics 3, no. 2 (November 2, 2012): 139–47. http://dx.doi.org/10.5194/esd-3-139-2012.

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Abstract. Metrics for comparing greenhouse gases are analyzed, with a particular focus on the integrated temperature change potential (IGTP) following a call from IPCC to investigate this metric. It is shown that the global warming potential (GWP) and IGTP are asymptotically equal when the time horizon approaches infinity when standard assumptions about a constant background atmosphere are used. The difference between IGTP and GWP is estimated for different greenhouse gases using an upwelling diffusion energy balance model with different assumptions on the climate sensitivity and the parameterization governing the rate of ocean heat uptake. It is found that GWP and IGTP differ by some 10% for CH4 (for a time horizon of less than 500 yr), and that the relative difference between GWP and IGTP is less for gases with a longer atmospheric life time. Further, it is found that the relative difference between IGTP and GWP increases with increasing rates of ocean heat uptake and increasing climate sensitivity since these changes increase the inertia of the climate system. Furthermore, it is shown that IGTP is equivalent to the sustained global temperature change potential (SGTP) under standard assumptions when estimating GWPs. We conclude that while it matters little for abatement policy whether IGTP, SGTP or GWP is used when making trade-offs, it is more important to decide whether society should use a metric based on time integrated effects such as GWP, a "snapshot metric" as GTP, or metrics where both economics and physical considerations are taken into account. Of equal importance is the question of how to choose the time horizon, regardless of the chosen metric. For both these overall questions, value judgments are needed.

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Burgess, Hayley, Joan Kramer, Elizabeth Hofammann, and Mandelin Cooper. "Clinical Metrics for a Large Healthcare System’s Antimicrobial Management Program." Infection Control & Hospital Epidemiology 41, S1 (October 2020): s7. http://dx.doi.org/10.1017/ice.2020.478.

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Background: Clinical metrics and outcomes for evaluation of antimicrobial management programs (AMP) are challenging and inconsistent throughout the United States. Here, we present the results of the development of clinical metrics to measure and trend AMP outcomes within 161 acute-care facilities affiliated with a large healthcare system. Methods: Key AMP metrics were implemented in 2018 using 2017 as baseline: use of fluoroquinolones in UTIs, dosing of vancomycin, de-escalation, and intravenous (IV)-to-oral conversion of targeted drugs. Fluoroquinolone (FQ) and UTI metric evaluated all inpatients who received at least 1 dose of a FQ based on barcoded medication administration (BCMA) data and urinary tract infections were based on cystitis ICD-10 coding. Vancomycin dosing metric evaluated inpatient vancomycin troughs within therapeutic range during the admission. De-escalation metric evaluated for patients on a broad-spectrum antibiotic with a positive culture and sensitivity to narrower antibiotics. The IV-to-oral ratio was used to monitor targeted medications. Nonantimicrobial medications appropriate for IV-to- oral conversion were included in the ratio. Goals were established for each metric using the 75th percentile and ranges for “at goal,” “close to goal,” and “not at goal” were established using green–yellow–red color coding. Metrics were monitored via a systemwide dashboard that included all affiliated facilities. Data were shared monthly to key stakeholders including physicians, pharmacists, and senior leadership. Results: From 2017 to the third quarter of 2019, the FQ and UTI metric decreased 55%. This reduction in the FQ usage in UTI metric correlated with a reduction of 26.7 days of therapy (DOT) per 1,000 days present for FQ and a 50% reduction in FQ DOT for all affiliated facilities. The vancomycin dosing metric improved 2.9% from 75.2% of patients to 78.1% of patients with at least 1 vancomycin trough within range during the admission, which represents ~2,000 more patients with dosing in the target range over baseline. The de-escalation metric improved by 7% overall from 2018 to the third quarter of 2019, which translates to ~1,600 more patients with therapy de-escalated. The IV-to-oral ratio metric improved 5.5%, which means that ~180,000 more oral dosages were administered. Conclusions: Implementing AMP program clinical metrics in a large health system positively influenced antimicrobial medication therapy management for patients. Monitoring of process metrics should be considered for all AMP programs to advance antibiotic stewardship.Funding: NoneDisclosures: None

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Aladahalli, Chandankumar, Jonathan Cagan, and Kenji Shimada. "Objective Function Effect Based Pattern Search—An Implementation for 3D Component Layout." Journal of Mechanical Design 129, no. 3 (December 21, 2005): 255–65. http://dx.doi.org/10.1115/1.2406096.

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Generalized pattern search (GPS) algorithms have been used successfully to solve three-dimensional (3D) component layout problems. These algorithms use a set of patterns and successively decreasing step sizes of these patterns to explore the search space before converging to good local minima. A shortcoming of conventional GPS algorithms is the lack of recognition of the fact that patterns affect the objective function by different amounts and hence it might be efficient to introduce them into the search in a certain order rather than introduce all of them at the beginning of the search. To address this shortcoming, it has been shown by the authors in previous work that it is more efficient to schedule patterns in decreasing order of their effect on the objective function. The effect of the patterns on the objective function was estimated by the a priori expectation of the objective function change due to the patterns. However, computing the a priori expectation is expensive, and to practically implement the scheduling of patterns, an inexpensive estimate of the effect on the objective function is necessary. This paper introduces a metric for geometric layout called the sensitivity metric that is computationally inexpensive, to estimate the effect of pattern moves on the objective function. A new pattern search algorithm that uses the sensitivity metric to schedule patterns is shown to perform as well as the pattern search algorithm that used the a priori expectation of the objective function change. Though the sensitivity metric applies to the class of geometric layout or placement problems, the foundation and approach is useful for developing metrics for other optimization problems.

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Gallart, Francesc, Jana von Freyberg, María Valiente, James W. Kirchner, Pilar Llorens, and Jérôme Latron. "Technical note: An improved discharge sensitivity metric for young water fractions." Hydrology and Earth System Sciences 24, no. 3 (March 6, 2020): 1101–7. http://dx.doi.org/10.5194/hess-24-1101-2020.

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Abstract. Recent virtual and experimental investigations have shown that the young water fraction Fyw (i.e. the proportion of catchment outflow younger than circa 2–3 months) increases with discharge in most catchments. The discharge sensitivity of Fyw has been defined as the rate of increase in Fyw with increasing discharge (Q) and has been estimated by the linear regression slope between Fyw and Q, hereafter called DS(Q). The combined use of both metrics, Fyw and DS(Q), provides a promising method for catchment inter-comparison studies that seek to understand streamflow generation processes. Here we explore the discharge sensitivity of Fyw in the intensively sampled small Mediterranean research catchment Can Vila. Intensive sampling of high flows at Can Vila allows young water fractions to be estimated for the far upper tail of the flow frequency distribution. These young water fractions converge toward 1 at the highest flows, illustrating a conceptual limitation in the linear regression method for estimating DS(Q) as a metric of discharge sensitivity: Fyw cannot grow with discharge indefinitely, since the fraction of young water in discharge can never be larger than 1. Here we propose to quantify discharge sensitivity by the parameter of an exponential-type equation that expresses how Fyw varies with discharge. The exponential parameter (Sd) approximates DS(Q) at moderate discharges where Fyw is well below 1; however, the exponential equation and its discharge sensitivity metric better capture the non-linear relationship between Fyw and Q and are robust with respect to changes in the range of sampled discharges, allowing comparisons between catchments with strongly contrasting flow regimes.

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Wei, Kuang, Matthew Cieslak, Clint Greene, Scott T. Grafton, and Jean M. Carlson. "Sensitivity analysis of human brain structural network construction." Network Neuroscience 1, no. 4 (December 2017): 446–67. http://dx.doi.org/10.1162/netn_a_00025.

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Network neuroscience leverages diffusion-weighted magnetic resonance imaging and tractography to quantify structural connectivity of the human brain. However, scientists and practitioners lack a clear understanding of the effects of varying tractography parameters on the constructed structural networks. With diffusion images from the Human Connectome Project (HCP), we characterize how structural networks are impacted by the spatial resolution of brain atlases, total number of tractography streamlines, and grey matter dilation with various graph metrics. We demonstrate how injudicious combinations of highly refined brain parcellations and low numbers of streamlines may inadvertently lead to disconnected network models with isolated nodes. Furthermore, we provide solutions to significantly reduce the likelihood of generating disconnected networks. In addition, for different tractography parameters, we investigate the distributions of values taken by various graph metrics across the population of HCP subjects. Analyzing the ranks of individual subjects within the graph metric distributions, we find that the ranks of individuals are affected differently by atlas scale changes. Our work serves as a guideline for researchers to optimize the selection of tractography parameters and illustrates how biological characteristics of the brain derived in network neuroscience studies can be affected by the choice of atlas parcellation schemes.

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Dawkins, Bryan A., Trang T. Le, and Brett A. McKinney. "Theoretical properties of distance distributions and novel metrics for nearest-neighbor feature selection." PLOS ONE 16, no. 2 (February 8, 2021): e0246761. http://dx.doi.org/10.1371/journal.pone.0246761.

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The performance of nearest-neighbor feature selection and prediction methods depends on the metric for computing neighborhoods and the distribution properties of the underlying data. Recent work to improve nearest-neighbor feature selection algorithms has focused on new neighborhood estimation methods and distance metrics. However, little attention has been given to the distributional properties of pairwise distances as a function of the metric or data type. Thus, we derive general analytical expressions for the mean and variance of pairwise distances for Lq metrics for normal and uniform random data with p attributes and m instances. The distribution moment formulas and detailed derivations provide a resource for understanding the distance properties for metrics and data types commonly used with nearest-neighbor methods, and the derivations provide the starting point for the following novel results. We use extreme value theory to derive the mean and variance for metrics that are normalized by the range of each attribute (difference of max and min). We derive analytical formulas for a new metric for genetic variants, which are categorical variables that occur in genome-wide association studies (GWAS). The genetic distance distributions account for minor allele frequency and the transition/transversion ratio. We introduce a new metric for resting-state functional MRI data (rs-fMRI) and derive its distance distribution properties. This metric is applicable to correlation-based predictors derived from time-series data. The analytical means and variances are in strong agreement with simulation results. We also use simulations to explore the sensitivity of the expected means and variances in the presence of correlation and interactions in the data. These analytical results and new metrics can be used to inform the optimization of nearest neighbor methods for a broad range of studies, including gene expression, GWAS, and fMRI data.

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Burke, Eleanor J. "Understanding the Sensitivity of Different Drought Metrics to the Drivers of Drought under Increased Atmospheric CO2." Journal of Hydrometeorology 12, no. 6 (November 1, 2011): 1378–94. http://dx.doi.org/10.1175/2011jhm1386.1.

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Abstract A perturbed physics Hadley Centre climate model ensemble was used to study changes in drought on doubling atmospheric CO2. The drought metrics analyzed were based on 1) precipitation anomalies, 2) soil moisture anomalies, and 3) the Palmer drought severity index (PDSI). Drought was assumed to occur 17% of the time under single CO2. On doubling CO2, in general, PDSI drought occurs more often than soil moisture drought, which occurs more often than precipitation drought. This paper explores the relative sensitivity of each drought metric to changes in the main drivers of drought, namely precipitation and available energy. Drought tends to increase when the mean precipitation decreases, the mean available energy increases, the standard deviation of precipitation increases, and the standard deviation of available energy decreases. Simple linear approximations show that the sensitivity of drought to changes in mean precipitation is similar for the three different metrics. However, the sensitivity of drought to changes in the mean available energy (which is projected to increase under increased atmospheric CO2) is highly dependent on metric: with PDSI drought the most sensitive, soil moisture less sensitive, and precipitation independent of available energy. Drought metrics are only slightly sensitive to changes in the variability of the drivers. An additional driver of drought is the response of plants to increased CO2. This process reduces evapotranspiration and increases soil moisture, and generally causes less soil moisture drought. In contrast, the associated increase in available energy generally causes an increase in PDSI drought. These differing sensitivities need to be taken into consideration when developing adaptation strategies.

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Ovalle, Anaelia, Evan Czyzycki, and Cho-Jui Hsieh. "Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (June 26, 2023): 16292–93. http://dx.doi.org/10.1609/aaai.v37i13.27006.

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Intentionally crafted adversarial samples have effectively exploited weaknesses in deep neural networks. A standard method in adversarial robustness assumes a framework to defend against samples crafted by minimally perturbing a sample such that its corresponding model output changes. These sensitivity attacks exploit the model's sensitivity toward task-irrelevant features. Another form of adversarial sample can be crafted via invariance attacks, which exploit the model underestimating the importance of relevant features. Previous literature has indicated a tradeoff in defending against both attack types within a strictly L-p bounded defense. To promote robustness toward both types of attacks beyond Euclidean distance metrics, we use metric learning to frame adversarial regularization as an optimal transport problem. Our preliminary results indicate that regularizing over invariant perturbations in our framework improves both invariant and sensitivity defense.

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Murphy, Kimberly A., and Laura M. Justice. "Lexical-Level Predictors of Reading Comprehension in Third Grade: Is Spelling a Unique Contributor?" American Journal of Speech-Language Pathology 28, no. 4 (November 19, 2019): 1597–610. http://dx.doi.org/10.1044/2019_ajslp-18-0299.

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Purpose Considerable research effort has focused on understanding reading comprehension and reading comprehension difficulties. The purpose of this correlational study was to add to the small but growing body of literature on the role that spelling may play in reading comprehension, by investigating the full range of lexical-level literacy skills and whether spelling makes a unique contribution. This study also explored whether these relations vary with the spelling scoring metric. Method Data were collected from 63 children attending Grade 3 in a Midwestern state. In addition to measuring reading comprehension, word recognition, and vocabulary, 4 spelling scoring metrics were examined: the number of words spelled correctly, the number of correct letter sequences (CLS), and Spelling Sensitivity Scores for elements and for words. Results All spelling metrics were significantly correlated with reading comprehension. Results of hierarchical regressions showed that spelling was a significant, unique predictor of reading comprehension when the CLS metric was used. The scoring metrics were differentially related to reading comprehension. Metrics that gave credit based on orthographic precision only (number of words spelled correctly and CLS) were more highly related to reading comprehension than metrics that scored not only on orthographic accuracy but also on phonological and morphological accuracy (Spelling Sensitivity Scores for elements and for words). Conclusion These results indicate that spelling is related to reading comprehension and have theoretical and clinical implications for the use of spelling assessment. Supplemental Material https://doi.org/10.23641/asha.9947216

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Yang, Bofan, Lin Zhang, Bo Zhang, Wenfeng Wang, and Minglinag Zhang. "Resilience Metric of Equipment System: Theory, Measurement and Sensitivity Analysis." Reliability Engineering & System Safety 215 (November 2021): 107889. http://dx.doi.org/10.1016/j.ress.2021.107889.

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Oh, HyungSeon, and Robert J. Thomas. "Estimation of a Sensitivity-Based Metric for Detecting Market Power." Journal of Energy Engineering 136, no. 2 (June 2010): 32–41. http://dx.doi.org/10.1061/(asce)ey.1943-7897.0000019.

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Christy, John R., and Richard T. McNider. "Satellite bulk tropospheric temperatures as a metric for climate sensitivity." Asia-Pacific Journal of Atmospheric Sciences 53, no. 4 (November 2017): 511–18. http://dx.doi.org/10.1007/s13143-017-0070-z.

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Li, Jingzhen, Xiaojing Ma, Igbe Tobore, Yuhang Liu, Abhishek Kandwal, Lei Wang, Jingyi Lu, et al. "A Novel CGM Metric-Gradient and Combining Mean Sensor Glucose Enable to Improve the Prediction of Nocturnal Hypoglycemic Events in Patients with Diabetes." Journal of Diabetes Research 2020 (November 2, 2020): 1–8. http://dx.doi.org/10.1155/2020/8830774.

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Nocturnal hypoglycemia is a serious complication of insulin-treated diabetes, and it is often asymptomatic. A novel CGM metric-gradient was proposed in this paper, and a method of combining mean sensor glucose (MSG) and gradient was presented for the prediction of nocturnal hypoglycemia. For this purpose, the data from continuous glucose monitoring (CGM) encompassing 1,921 patients with diabetes were analyzed, and a total of 302 nocturnal hypoglycemic events were recorded. The MSG and gradient values were calculated, respectively, and then combined as a new metric (i.e., MSG+gradient). In addition, the prediction was conducted by four algorithms, namely, logistic regression, support vector machine, random forest, and long short-term memory. The results revealed that the gradient of CGM showed a downward trend before hypoglycemic events happened. Additionally, the results indicated that the specificity and sensitivity based on the proposed method were better than the conventional metrics of low blood glucose index (LBGI), coefficient of variation (CV), mean absolute glucose (MAG), lability index (LI), etc., and the complex metrics of MSG+LBGI, MSG+CV, MSG+MAG, and MSG+LI, etc. Specifically, the specificity and sensitivity were greater than 96.07% and 96.03% at the prediction horizon of 15 minutes and greater than 87.79% and 90.07% at the prediction horizon of 30 minutes when the proposed method was adopted to predict nocturnal hypoglycemic events in the aforementioned four algorithms. Therefore, the proposed method of combining MSG and gradient may enable to improve the prediction of nocturnal hypoglycemic events. Future studies are warranted to confirm the validity of this metric.

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Durrant, J. T., Kaylee Nyborg, Mark C. Anderson, Kent L. Gee, Grant W. Hart, and Lucas K. Hall. "Challenges in measuring and quantifying sonic booms from Falcon-9 booster landings." Journal of the Acoustical Society of America 154, no. 4_supplement (October 1, 2023): A148. http://dx.doi.org/10.1121/10.0023079.

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Sonic booms from Falcon-9 booster landings contribute to the overall noise of the vehicle, but obtaining high-fidelity acoustical measurements and robust metric calculations of each sonic boom pose several challenges. This paper discusses three such challenges: sonic boom metric variability from differences in vehicle trajectory and meteorology, poor low-frequency response of low-sensitivity microphones, and contamination of the sonic boom by the landing burn noise. To quantify the variability between measurements, boom metrics are compared across four separate launches from Vandenberg Space Force Base. Boom metrics calculated from microphone stations at the same location differ by as much as 10 dB from launch to launch. Next, a digital pole-shift filter helps correct the low-frequency response of low-sensitivity microphones near the pad. These filters are adjusted so the waveforms more closely match those from microphones with a superior low-frequency response. Last, to obtain a clean sonic boom waveform, tracking the noise source location and peak frequency may help to distinguish where the sonic boom ends and landing burn noise starts. However, due to similar frequency content and close spacing in time, separating the boom from the landing burn noise near the pad remains a challenge for some booms. [Work supported by USACE.]

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