Stellar pulsation and granulation as noise sources in exoplanet transit spectroscopy in the ARIEL space mission

Stellar variability from pulsations and granulation presents a source of correlated noise that can impact the accuracy and precision of multiband photometric transit observations of exoplanets. This can potentially cause biased measurements in the transmission or emission spectrum or underestimation of the final error bars on the spectrum. ARIEL is a future space telescope and instrument designed to perform a transit spectroscopic survey of a large sample of exoplanets. In this paper, we perform simulations to assess the impact of stellar variability arising from pulsations and granulation on ARIEL observations of GJ 1214b and HD 209458b. We take into account the correlated nature of stellar noise, quantify it, and compare it to photon noise. In the range 1.95–7.8 μ m, stellar pulsation and granulation noise has insignificant impact compared to photon noise for both targets. In the visual range, the contribution increases significantly but remains small in absolute terms and will have minimal impact on the transmission spectra of the targets studied. The impact of pulsation and granulation will be greatest for planets with low scale height atmospheres and long transit times around bright stars

HyperReenact: One-Shot Reenactment via Jointly Learning to Refine and Retarget Faces

In this paper, we present our method for neural face reenactment, called HyperReenact, that aims to generate realistic talking head images of a source identity, driven by a target facial pose. Existing state-of-the-art face reenactment methods train controllable generative models that learn to synthesize realistic facial images, yet producing reenacted faces that are prone to significant visual artifacts, especially under the challenging condition of extreme head pose changes, or requiring expensive few-shot fine-tuning to better preserve the source identity characteristics. We propose to address these limitations by leveraging the photorealistic generation ability and the disentangled properties of a pretrained StyleGAN2 generator, by first inverting the real images into its latent space and then using a hypernetwork to perform: (i) refinement of the source identity characteristics and (ii) facial pose re-targeting, eliminating this way the dependence on external editing methods that typically produce artifacts. Our method operates under the one-shot setting (i.e., using a single source frame) and allows for cross-subject reenactment, without requiring any subject-specific fine-tuning. We compare our method both quantitatively and qualitatively against several state-of-the-art techniques on the standard benchmarks of VoxCeleb1 and VoxCeleb2, demonstrating the superiority of our approach in producing artifact-free images, exhibiting remarkable robustness even under extreme head pose changes. We make the code and the pretrained models publicly available at: https://github.com/StelaBou/HyperReenact .Comment: Accepted for publication in ICCV 2023. Project page: https://stelabou.github.io/hyperreenact.github.io/ Code: https://github.com/StelaBou/HyperReenac

HyperReenact: One-Shot Reenactment via Jointly Learning to Refine and Retarget Faces

In this paper, we present our method for neural face reenactment, called HyperReenact, that aims to generate realistic talking head images of a source identity, driven by a target facial pose. Existing state-of-the-art face reenactment methods train controllable generative models that learn to synthesize realistic facial images, yet producing reenacted faces that are prone to significant visual artifacts, especially under the challenging condition of extreme head pose changes, or requiring expensive few-shot fine-tuning to better preserve the source identity characteristics. We propose to address these limitations by leveraging the photorealistic generation ability and the disentangled properties of a pretrained StyleGAN2 generator, by first inverting the real images into its latent space and then using a hypernetwork to perform:(i) refinement of the source identity characteristics and (ii) facial pose re-targeting, eliminating this way the dependence on external editing methods that typically produce artifacts. Our method operates under the one-shot setting (ie, using a single source frame) and allows for cross-subject reenactment, without requiring any subject-specific fine-tuning. We compare our method both quantitatively and qualitatively against several state-of-the-art techniques on the standard benchmarks of VoxCeleb1 and VoxCeleb2, demonstrating the superiority of our approach in producing artifact-free images, exhibiting remarkable robustness even under extreme head pose changes

Characterization of the optical properties of the buried contact of the JWST MIRI Si:As infrared blocked impurity band detectors

The Mid-Infrared Instrument MIRI on-board the James Webb Space Telescope uses three Si:As impurity band conduction detector arrays. MIRI medium resolution spectroscopic measurements (R$\sim$3500-1500) in the 5~$\mu m$ to 28~$\mu m$ wavelength range show a 10-30\% modulation of the spectral baseline; coherent reflections of infrared light within the Si:As detector arrays result in fringing. We quantify the shape and impact of fringes on spectra of optical sources observed with MIRI during ground testing and develop an optical model to simulate the observed modulation. We use our optical model in conjunction with the MIRI spectroscopic data to show that the properties of the buried contact inside the MIRI Si:As detector have a significant effect on the fringing behavior.Comment: 11 pages, 7 figures, SPIE Astronomical Telescopes + Instrumentation 2020, submitted to SPI

Hunting IoT Cyberattacks With AI - Powered Intrusion Detection

The rapid progression of the Internet of Things allows the seamless integration of cyber and physical environments, thus creating an overall hyper-connected ecosystem. It is evident that this new reality provides several capabilities and benefits, such as real-time decision-making and increased efficiency and productivity. However, it also raises crucial cybersecurity issues that can lead to disastrous consequences due to the vulnerable nature of the Internet model and the new cyber risks originating from the multiple and heterogeneous technologies involved in the loT. Therefore, intrusion detection and prevention are valuable and necessary mechanisms in the arsenal of the loT security. In light of the aforementioned remarks, in this paper, we introduce an Artificial Intelligence (AI)-powered Intrusion Detection and Prevention System (IDPS) that can detect and mitigate potential loT cyberattacks. For the detection process, Deep Neural Networks (DNNs) are used, while Software Defined Networking (SDN) and Q-Learning are combined for the mitigation procedure. The evaluation analysis demonstrates the detection efficiency of the proposed IDPS, while Q- Learning converges successfully in terms of selecting the appropriate mitigation action

A 3D Drizzle Algorithm for JWST and Practical Application to the MIRI Medium Resolution Spectrometer

We describe an algorithm for application of the classic `drizzle' technique to produce 3d spectral cubes using data obtained from the slicer-type integral field unit (IFU) spectrometers on board the James Webb Space Telescope. This algorithm relies upon the computation of overlapping volume elements (composed of two spatial dimensions and one spectral dimension) between the 2d detector pixels and the 3d data cube voxels, and is greatly simplified by treating the spatial and spectral overlaps separately at the cost of just 0.03% in spectrophotometric fidelity. We provide a matrix-based formalism for the computation of spectral radiance, variance, and covariance from arbitrarily dithered data and comment on the performance of this algorithm for the Mid-Infrared Instrument's Medium Resolution IFU Spectrometer (MIRI MRS). We derive a series of simplified scaling relations to account for covariance between cube spaxels in spectra extracted from such cubes, finding multiplicative factors ranging from 1.5 to 3 depending on the wavelength range and kind of data cubes produced. Finally, we discuss how undersampling produces periodic amplitude modulations in the extracted spectra in addition to those naturally produced by fringing within the instrument; reducing such undersampling artifacts below 1% requires a 4-point dithering strategy and spectral extraction radii of 1.5 times the PSF FWHM or greater.Comment: 16 pages, 12 figures. Revised version resubmitted to A