Receiver Operator Characteristic (ROC) curves are commonly applied as metrics for quantifying the performance of binary fault detection systems. An ROC curve provides a visual representation of a detection system’s True Positive Rate versus False Positive Rate sensitivity as the detection threshold is varied. The area under the curve provides a measure of fault detection performance independent...

v. dannon showed that spherical curves in e4 can be given by frenet-like equations, and he thengave an integral characterization for spherical curves in e4 . in this paper, lorentzian spherical timelike andspacelike curves in the space time 41 r are shown to be given by frenet-like equations of timelike andspacelike curves in the euclidean space e3 and the minkowski 3-space 31 r . thus, finding...

abstract to estimate spatial variability of soil hydraulic functions, scaling methods were developed and have been widely used. among these functions, physically based methods have been found more desirable because of possibility of estimating soil hydraulic functions from soil physical properties. in this paper, a new and physically based method has been described for scaling soil hydraulic co...

The receiver operating characteristic (ROC) curve is an important tool to gauge the performance of classifiers. In certain situations of high-throughput data analysis, the data is heavily class-skewed, i.e. most features tested belong to the true negative class. In such cases, only a small portion of the ROC curve is relevant in practical terms, rendering the ROC curve and its area under the cu...

The hyperelliptic curve cryptosystem is one of the emerging cryptographic primitives of the last years. This system offers the same security as established public-key cryptosystems, such as those based on RSA or elliptic curves, with much shorter operand length. Consequently, this system allows highly efficient computation of the underlying field arithmetic. However, until recently the common b...

Supervised learning algorithms perform common tasks including classification, ranking, scoring, and probability estimation. We investigate how scoring information, often produced by these models, is utilized by an evaluation measure. The ROC curve represents a visualization of the ranking performance of classifiers. However, they ignore the scores which can be quite informative. While this igno...

Space filling curves (SFCs) are widely used in the design of indexes for spatial and temporal data. Clustering is a key metric for an SFC, that measures how well the curve preserves locality in moving from higher dimensions to a single dimension. We present the onion curve, an SFC whose clustering performance is provably close to optimal for cube and nearcube shaped query sets, irrespective of ...

conclusions in our center, prolonged length of stay in the icu correlated positively with euro score. the overall predictive performance of euro score is acceptable and provides both surgeons and intensivists with a good estimate of patient risk in terms of icu stay. background risk stratification models allow preoperative assessment of individual patients cardiac surgical risk and enable analy...

Introduction: Rectal toxicity is a dose limiting issue in prostate cancer radiotherapy. Prediction of these effects may be used to tailor the therapy. The purpose of this work was to develop predictive radiomic models based on clinical, dosimetric and radiomic features extracted from rectal wall magnetic resonance image (MRI).   Materials and Methods: This st...