This book concerns the problem of data smoothing. There are many smoothing techniques,
yet the kernel smoothing seems to be one of the most important and widely used ones. In …

Network security is becoming increasingly important in our daily lives—not only for
organizations but also for individuals. Intrusion detection systems have been widely used to …

Paroxysmal atrial fibrillation (PAF) is a cardiac arrhythmia that can eventually lead to heart
failure or stroke if left untreated. Early detection of PAF is therefore crucial to prevent any …

Surprise Adequacy (SA) is one of the emerging and most promising adequacy criteria for
Deep Learning (DL) testing. As an adequacy criterion, it has been used to assess the …

In today's transparent markets, e-commerce providers often have to adjust their prices within
short time intervals, eg, to take frequently changing prices of competitors into account …

For over a century, reduced order models (ROMs) have been a fundamental discipline of
theoretical fluid mechanics. Early examples include Galerkin models inspired by the Orr …

The problem of network disintegration, such as suppression of an epidemic spread and
destabilization of terrorist networks, possesses extensive applications and has lately been …

Kernel density estimation and kernel regression are powerful but computationally expensive
techniques: a direct evaluation of kernel density estimates at M evaluation points given N …

Kernel density estimates are important for a broad variety of applications. Their construction
has been well-studied, but existing techniques are expensive on massive datasets and/or …

Polynomial chaos expansion (PCE) is a widely used approach for establishing the surrogate
model of a time-consuming performance function for the convenience of uncertainty …