Detection of out-of-distribution samples using binary neuron activation patterns

Deep neural networks (DNN) have outstanding performance in various applications. Despite numerous efforts of the research community, out-of-distribution (OOD) samples remain a significant limitation of DNN classifiers. The ability to identify previously unseen inputs as novel is crucial in safety-critical applications such as self-driving cars, unmanned aerial vehicles, and robots. Existing approaches to detect OOD samples treat a DNN as a black box and evaluate the confidence score of the output predictions. Unfortunately, this method frequently fails, because DNNs are not trained to reduce their confidence for OOD inputs. In this work, we introduce a novel method for OOD detection. Our method is motivated by theoretical analysis of neuron activation patterns (NAP) in ReLU-based architectures. The proposed method does not introduce a high computational overhead due to the binary representation of the activation patterns extracted from convolutional layers. The extensive empirical evaluation proves its high performance on various DNN architectures and seven image datasets

Combating noisy labels in object detection datasets

The quality of training datasets for deep neural networks is a key factor contributing to the accuracy of resulting models. This is even more important in difficult tasks such as object detection. Dealing with errors in these datasets was in the past limited to accepting that some fraction of examples is incorrect or predicting their confidence and assigning appropriate weights during training. In this work, we propose a different approach. For the first time, we extended the confident learning algorithm to the object detection task. By focusing on finding incorrect labels in the original training datasets, we can eliminate erroneous examples in their root. Suspicious bounding boxes can be re-annotated in order to improve the quality of the dataset itself, thus leading to better models without complicating their already complex architectures. We can effectively point out 99\% of artificially disturbed bounding boxes with FPR below 0.3. We see this method as a promising path to correcting well-known object detection datasets.Comment: 10 pages, 8 figures, submitted to CVPR 2023 Conferenc

Constraining the near-core rotation of the gamma Doradus star 43 Cygni using BRITE-Constellation data

Photometric time series of the $\gamma$ Dor star 43 Cyg obtained with the BRITE-Constellation nano-satellites allow us to study its pulsational properties in detail and to constrain its interior structure. We aim to find a g-mode period spacing pattern that allows us to determine the near-core rotation rate of 43 Cyg and redetermine the star's fundamental atmospheric parameters and chemical composition. We conducted a frequency analysis using the 156-days long data set obtained with the BRITE-Toronto satellite and employed a suite of MESA/GYRE models to derive the mode identification, asymptotic period spacing and near-core rotation rate. We also used high-resolution, high signal-to-noise ratio spectroscopic data obtained at the 1.2m Mercator telescope with the HERMES spectrograph to redetermine the fundamental atmospheric parameters and chemical composition of 43 Cyg using the software Spectroscopy Made Easy (SME). We detected 43 intrinsic pulsation frequencies and identified 18 of them to be part of a period spacing pattern consisting of prograde dipole modes with an asymptotic period spacing $\Delta \Pi_{l=1}$ of $2970^{+700}_{-570} \rm s$. The near-core rotation rate was determined to be $f_{\rm rot} = 0.56^{+0.12}_{-0.14}\,\rm d^{-1}$. The atmosphere of 43 Cyg shows solar chemical composition at an effective temperature of 7150 $\pm$ 150 K, a log g of 4.2 $\pm$ 0.6 dex and a projected rotational velocity, $v {\rm sin}i$, of 44 $\pm$ 4 kms$^{-1}$. The morphology of the observed period spacing patterns shows indications of the presence of a significant chemical gradient in the stellar interior.Comment: 9 pages, 8 figures, accepted by A&

Investigating the origin of optical flares from the TeV blazar S4 0954+65

Aims. We aim to investigate the extreme variability properties of the TeV blazar S4 0954+65 using optical photometric and polarisation observations carried out between 2017 and 2023 using three ground-based telescopes. Methods. We examined an extensive dataset comprised of 138 intraday (observing duration shorter than a day) light curves (LCs) of S4 0954+65 for flux, spectral, and polarisation variations on diverse timescales. For the variable LCs, we computed the minimum variability timescales. We investigated flux-flux correlations and colour variations to look for spectral variations on long (several weeks to years) timescales. Additionally, we looked for connections between optical R-band flux and polarisation degree. Results. We found significant variations in 59 out of 138 intraday LCs. We detected a maximum change of 0.58$\pm$0.11 in V-band magnitude within $\sim$2.64 h and a corresponding minimum variability timescale of 18.21$\pm$4.87 mins on 2017 March 25. During the course of our observing campaign, the source brightness changed by $\sim$4 magnitudes in V and R bands; however, we did not find any strong spectral variations. The slope of the relative spectral energy distribution was 1.37$\pm$0.04. The degree of polarisation varied from $\sim$ 3% to 39% during our monitoring. We observed a change of $\sim$120 degrees in polarisation angle (PA) within $\sim$3 h on 2022 April 13. No clear correlation was found between optical flux and the degree of polarisation. Conclusions. The results of our optical flux, colour, and polarisation study provide hints that turbulence in the relativistic jet could be responsible for the intraday optical variations in the blazar S4 0954+65. However, the long-term flux variations may be caused by changes in the Doppler factor.Comment: 9 pages, 10 figures, 4 tables, 4 appendix, Astronomy & Astrophysics journal (in press