MOTIVATION: Copy-number variants (CNVs) are a form of genetic structural variation with increasing importance in complex human disorders. Both DNA sequencing and microarray data can be used to detect CNVs, which can be used in genetic association tests. Unlike genotypes, CNV detection in microarrays requires the use of observed intensity signals at each probe, which limits the imputability for analyses that span multiple array types. Thus far, a consensus set of probes (those present on all arrays) has been used to circumvent the problem of differing array-specific sensitivities. This has led to excessive reduction in overall sensitivity since arrays can have an undesirably low probe overlap. To overcome this limitation, we developed MarkerMatch, a proximity-based algorithm that matches probes across different genotyping microarrays to maximize the number of probes considered in the CNV calling algorithm, thereby increasing the resolution and sensitivity while preserving precision. RESULTS: By analyzing CNV calls from 4906 individuals genotyped across three different arrays, we show that the MarkerMatch approach improves sensitivity by increasing the density of probes available for CNV calling while maintaining precision or improving it relative to the current practice (e.g. use of consensus probes only). We further demonstrate that MarkerMatch matches the CNV detection from current practice in terms of F1 score and PPV for larger CNVs. We also optimize MarkerMatch parameters, DMAX and Method, and find an optimal DMAX setting at 10 kb, with no clear optimal candidate based on Method, indicating that parameters for this metric should be determined on a use case basis. AVAILABILITY: The R package for MarkerMatch is available at: https://github.com/FranjoIM/MarkerMatch. The code used for analysis and implementation is available at: https://doi.org/10.5281/zenodo.18460979. The live notebook is available at https://fivankovic.notion.site/2026-markermatch.

As any other type of data, GPS traces contain noise, anomaly, and sometimes unexpected values. Normally, researchers and data engineers analysts would start dealing with GPS data by removing those noises and outliers. However, in this work, we take the opposite direction. We focus on analyzing those unexpected values rather than discarding them. Interestingly, we discovered useful findings from an insight look at the noise in GPS trajectories. The intuition behind those discoveries is that when unexpected GPS readings are observed several times around a specific location, we study the nature of that location rather than thrown away those reading. By doing so, we are able to tell the type of area around those readings. For example, we can infer that a driver is passing by a tall building or through a forest based on the pattern of noise in the GPS readings. We are also able to question the quality of the underlying road map. Our findings and discoveries are based on the analysis of real GPS data for the Microsoft shuttles.

This paper addresses the challenge of Web Services performance in eBay Item Visibility Framework (IVF). There are two types of performance challenges when Web Services are introduced in IVF. First, if IVF is a caller to other eBay internal Web Services for Cross Border Trade business, what can we do to keep IVF machines healthy and fully functional in the instance other Web Services take more than three seconds to return the response? Second, as a Web Services provider to serve item visibility information to other applications, how can IVF return response of hundred items to clients in milliseconds? Both questions are discussed in this paper. Solution and results are presented with conclusion.