The Cyprus Institute

About: The Cyprus Institute is a other organization based out in Nicosia, Cyprus. It is known for research contribution in the topics: Aerosol & Environmental science. The organization has 418 authors who have published 1252 publications receiving 32586 citations.

Examination of Organic Vapor Adsorption onto Alkali Metal and Halide Atomic Ions by using Ion Mobility Mass Spectrometry.

Abstract: We utilize ion mobility-mass spectrometry with an atmospheric pressure differential mobility analyzer coupled to a time-of-flight mass spectrometer (DMA-MS) to examine the formation of ion-vapor molecule complexes with seed ions of K+, Rb+, Cs+, Br-, and I- exposed to n-butanol and n-nonane vapor under subsaturated conditions. Ion-vapor molecule complex formation is indicated by a shift in the apparent mobility of each ion. Measurement results are compared to predicted mobility shifts based upon the Kelvin-Thomson equation, which is commonly used in predicting rates of ion-induced nucleation. We find that n-butanol at saturation ratios as low as 0.03 readily binds to all seed ions, leading to mobility shifts in excess of 35%. Conversely, the binding of n-nonane is not detectable for any ion for saturation ratios in the 0-0.27 range. An inverse correlation between the ionic radius of the initial seed and the extent of n-butanol uptake is observed, such that at elevated n-butanol concentrations, the smallest ion (K+) has the smallest apparent mobility and the largest (I-) has the largest apparent mobility. Though the differences in behavior of the two vapor molecules types examined and the observed effect of ionic seed radius are not accounted for by the Kelvin-Thomson equation, its predictions are in good agreement with measured mobility shifts for Rb+, Cs+, and Br- in the presence of n-butanol (typically within 10% of measurements).

Enhancing Facial Data Diversity With Style-Based Face Aging

Abstract: A significant limiting factor in training fair classifiers relates to the presence of dataset bias. In particular, face datasets are typically biased in terms of attributes such as gender, age, and race. If not mitigated, bias leads to algorithms that exhibit unfair behaviour towards such groups. In this work, we address the problem of increasing the diversity of face datasets with respect to age. Concretely, we propose a novel, generative style-based architecture for data augmentation that captures fine-grained aging patterns by conditioning on multi-resolution age-discriminative representations. By evaluating on several age-annotated datasets in both single- and cross-database experiments, we show that the proposed method outperforms state-of-the-art algorithms for age transfer, especially in the case of age groups that lie in the tails of the label distribution. We further show significantly increased diversity in the augmented datasets, outperforming all compared methods according to established metrics.

High dynamic range video for cultural heritage documentation and experimental archaeology

Abstract: Video recording and photography are frequently used to document Cultural Heritage (CH) objects and sites. High Dynamic Range (HDR) imaging is increasingly being used as it allows a wider range of light to be considered that most current technologies are unable to natively acquire and reproduce. HDR video content however has only recently become possible at desirable, high definition resolution and dynamic range. In this paper we explore the potential use of a 20 f-stop HDR video camera for CH documentation and experimental archaeology purposes. We discuss data acquisition of moving caustics, flames, distant light and in participating media. Comparisons of Low Dynamic Range (LDR) and HDR content are made to illustrate the additional data that this new technology is able to capture, and the benefits this is likely to bring to CH documentation and experimental archaeology.

Automatic detection and removal of muscle artifacts from scalp EEG recordings in patients with epilepsy

Abstract: Automatic detection and removal of muscle artifacts plays an important role in long-term scalp electroencephalography (EEG) monitoring, and muscle artifact detection algorithms have been intensively investigated. This paper proposes an algorithm for automatic muscle artifacts detection and removal using canonical correlation analysis (CCA) and wavelet transform (WT) in epochs from long-term EEG recordings. The proposed method first performs CCA analysis and then conducts wavelet decomposition on the canonical components within a specific frequency range and selects a subset of the wavelet coefficients for subsequent processing. A set of features, including the mean of wavelet coefficients and the canonical component autocorrelation values, are extracted from the above analysis and subsequently used as input in a random forest (RF) classifier. The RF classifier produces a similarity measure between observations and selects a subset of the most important features by comparing the original data with a set of synthetic data that is constructed based on the latter. The RF predictor output is finally used in combination with unsupervised clustering algorithms to discriminate between contaminated and non-contaminated EEG epochs. The proposed method is evaluated in epochs of 30 min from scalp EEG recordings obtained from three patients with epilepsy and yields a sensitivity of 71% and 80%, as well as a specificity of 81% and 85% for k-means and spectral clustering, respectively.