相关论文: "Discrimination of virgin olive oils with differen…

This paper proposes a machine learning-based methodology for the classification of various oil samples based on their dielectric properties, utilizing a microwave resonant sensor. The dielectric behaviour of oils, governed by their…

We develop a method for separating quasars from other variable point sources using SDSS Stripe 82 light curve data for ~10,000 variable objects. To statistically describe quasar variability, we use a damped random walk model parametrized by…

Variational autoencoders (VAEs) are one class of generative probabilistic latent-variable models designed for inference based on known data. We develop three variations on VAEs by introducing a second parameterized encoder/decoder pair and,…

The influence of ns-laser wavelength to discriminate ancient painting techniques such as are fresco, casein, animal glue, egg yolk and oil was investigated in this work. This study was carried out with a single shot laser on samples covered…

The purpose of this paper is to use absorbance data obtained by human tasting and an ultraviolet-visible (UV-Vis) scanning spectrophotometer to predict the attributes of grape juice (GJ) and to classify the wine's origin, respectively. The…

We propose a dense indirect visual odometry method taking as input externally estimated optical flow fields instead of hand-crafted feature correspondences. We define our problem as a probabilistic model and develop a generalized-EM…

Optical diagnostic diagrams are frequently ambiguous as a test of the photoionization or fast shock models of the narrow line regions of active galaxies. Here, we present a set of UV line ratio diagrams which can discriminate between pure…

A carbon-to-oxygen ratio (C/O) of around unity is believed to act as a natural separator of water- and methane-dominated spectra when characterizing exoplanet atmospheres. In this paper we quantify the C/O ratios at which this separation…

Electron and scanning probe microscopy produce vast amounts of data in the form of images or hyperspectral data, such as EELS or 4D STEM, that contain information on a wide range of structural, physical, and chemical properties of…

Despite their successes, deep neural networks may make unreliable predictions when faced with test data drawn from a distribution different to that of the training data, constituting a major problem for AI safety. While this has recently…

Identification and classification of environmental forensics, with the petroleum forensics as the main application, requires an effective technology or method to distinguish between the closely located forensics as they share many main…

Variational autoencoders (VAEs) are influential generative models with rich representation capabilities from the deep neural network architecture and Bayesian method. However, VAE models have a weakness that assign a higher likelihood to…

Extracellular vesicles (EVs) have drawn rapidly increasing attention as the next-generation diagnostic biomarkers and therapeutic agents. However, the heterogeneous nature of EVs necessitates advanced methods for profiling EVs at the…

A significant fraction (~30%) of the high-energy gamma-ray sources listed in the second Fermi LAT catalog (2FGL) are still of unknown origin, being not yet associated with counterparts at low energies. We recently developed a new…

Volatile organic compounds (VOCs) represent a novel but underexplored modality for emotion recognition. This paper presents a systematic evidence synthesis and exploratory investigation of VOC-based affective computing using low-cost…

To reduce the adverse impacts of the unknown colored noise on the performance degradation of the direction-of-arrival (DOA) estimation, we propose a new gridless DOA estimation method based on fourth-order cumulant (FOC)in this letter. We…

Variational Auto-Encoders (VAEs) have become very popular techniques to perform inference and learning in latent variable models as they allow us to leverage the rich representational power of neural networks to obtain flexible…

Out-of-distribution (OOD) robustness is difficult to diagnose when target-domain labels are unavailable. We consider a more restrictive source-only variant of unsupervised accuracy estimation: selecting robust checkpoints using only…

We have used data from the Galaxy Evolution Explorer to study the different components of the diffuse ultraviolet background in the region between the Galactic latitudes 70-80 degree. We find an offset at zero dust column density (E(B - V)…

The Voyagers (V) 1 and 2 Ultraviolet Spectrometers (UVS) data harvest covers outer planets encounters, heliosphere sky-background measurements, and stellar spectrophotometry. Because their operation period overlaps with many ultraviolet…