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Observing exoplanets through transmission spectroscopy supplies detailed information on their atmospheric composition, physics, and chemistry. Prior to JWST, these observations were limited to a narrow wavelength range across the near-ultraviolet to near-infrared, alongside broadband photometry at longer wavelengths. To understand more complex properties of exoplanet atmospheres, improved wavelength coverage and resolution are necessary to robustly quantify the influence of a broader range of absorbing molecular species. Here we present a combined analysis of JWST transmission spectroscopy across four different instrumental modes spanning 0.5-5.2 micron using Early Release Science observations of the Saturn-mass exoplanet WASP-39b. Our uniform analysis constrains the orbital and stellar parameters within sub-percent precision, including matching the precision obtained by the most precise asteroseismology measurements of stellar density to-date, and further confirms the presence of Na, K, H$_2$O, CO, CO$_2$, and SO$_2$ atmospheric absorbers. Through this process, we also improve the agreement between the transmission spectra of all modes, except for the NIRSpec PRISM, which is affected by partial saturation of the detector. This work provides strong evidence that uniform light curve analysis is an important aspect to ensuring reliability when comparing the high-precision transmission spectra provided by JWST., Comment: 34 pages, 11 figures, 3 tables. Nat Astron (2024)

In this note we study line and conic arrangements associated to sextactic and type 9 points on the Fermat cubic $F$ and we provide explicit coordinates for each of the 72 type 9 points on $F$., Comment: 9 pages

The $\beta$ Pictoris system is the closest known stellar system with directly detected gas giant planets, an edge-on circumstellar disc, and evidence of falling sublimating bodies and transiting exocomets. The inner planet, $\beta$ Pictoris c, has also been indirectly detected with radial velocity (RV) measurements. The star is a known $\delta$ Scuti pulsator, and the long-term stability of these pulsations opens up the possibility of indirectly detecting the gas giant planets through time delays of the pulsations due to a varying light travel time. We search for phase shifts in the $\delta$ Scuti pulsations consistent with the known planets $\beta$ Pictoris b and c and carry out an analysis of the stellar pulsations of $\beta$ Pictoris over a multi-year timescale. We used photometric data collected by the BRITE-Constellation, bRing, ASTEP, and TESS to derive a list of the strongest and most significant $\delta$ Scuti pulsations. We carried out an analysis with the open-source python package maelstrom to study the stability of the pulsation modes of $\beta$ Pictoris in order to determine the long-term trends in the observed pulsations. We did not detect the expected signal for $\beta$ Pictoris b or $\beta$ Pictoris c. The expected time delay is 6 seconds for $\beta$ Pictoris c and 24 seconds for $\beta$ Pictoris b. With simulations, we determined that the photometric noise in all the combined data sets cannot reach the sensitivity needed to detect the expected timing drifts. An analysis of the pulsational modes of $\beta$ Pictoris using maelstrom showed that the modes themselves drift on the timescale of a year, fundamentally limiting our ability to detect exoplanets around $\beta$ Pictoris via pulsation timing., Comment: 16 pages, 16 figures, 4 tables, accepted for publication in A&A

Growing regulatory and societal pressures demand increased transparency in AI, particularly in understanding the decisions made by complex machine learning models. Counterfactual Explanations (CFs) have emerged as a promising technique within Explainable AI (xAI), offering insights into individual model predictions. However, to understand the systemic biases and disparate impacts of AI models, it is crucial to move beyond local CFs and embrace global explanations, which offer a~holistic view across diverse scenarios and populations. Unfortunately, generating Global Counterfactual Explanations (GCEs) faces challenges in computational complexity, defining the scope of "global," and ensuring the explanations are both globally representative and locally plausible. We introduce a novel unified approach for generating Local, Group-wise, and Global Counterfactual Explanations for differentiable classification models via gradient-based optimization to address these challenges. This framework aims to bridge the gap between individual and systemic insights, enabling a deeper understanding of model decisions and their potential impact on diverse populations. Our approach further innovates by incorporating a probabilistic plausibility criterion, enhancing actionability and trustworthiness. By offering a cohesive solution to the optimization and plausibility challenges in GCEs, our work significantly advances the interpretability and accountability of AI models, marking a step forward in the pursuit of transparent AI.

We present PPCEF, a novel method for generating probabilistically plausible counterfactual explanations (CFs). PPCEF advances beyond existing methods by combining a probabilistic formulation that leverages the data distribution with the optimization of plausibility within a unified framework. Compared to reference approaches, our method enforces plausibility by directly optimizing the explicit density function without assuming a particular family of parametrized distributions. This ensures CFs are not only valid (i.e., achieve class change) but also align with the underlying data's probability density. For that purpose, our approach leverages normalizing flows as powerful density estimators to capture the complex high-dimensional data distribution. Furthermore, we introduce a novel loss that balances the trade-off between achieving class change and maintaining closeness to the original instance while also incorporating a probabilistic plausibility term. PPCEF's unconstrained formulation allows for efficient gradient-based optimization with batch processing, leading to orders of magnitude faster computation compared to prior methods. Moreover, the unconstrained formulation of PPCEF allows for the seamless integration of future constraints tailored to specific counterfactual properties. Finally, extensive evaluations demonstrate PPCEF's superiority in generating high-quality, probabilistically plausible counterfactual explanations in high-dimensional tabular settings. This makes PPCEF a powerful tool for not only interpreting complex machine learning models but also for improving fairness, accountability, and trust in AI systems.

Correlations obtained from neutrino oscillation data on mixing parameters may help to validate neutrino mixing schemes. In this context, we explore how correlations of neutrino oscillation parameters affect the $\rm{TM}_1$ and $\rm{TM}_2$ mixing scenarios., Comment: 6 pages, 3 figures, submitted to Proceedings of "XXX Cracow EPIPHANY Conference on Precision Physics at High Energy Colliders"

Hot Jupiters are among the best-studied exoplanets, but it is still poorly understood how their chemical composition and cloud properties vary with longitude. Theoretical models predict that clouds may condense on the nightside and that molecular abundances can be driven out of equilibrium by zonal winds. Here we report a phase-resolved emission spectrum of the hot Jupiter WASP-43b measured from 5-12 $\mu$m with JWST's Mid-Infrared Instrument (MIRI). The spectra reveal a large day-night temperature contrast (with average brightness temperatures of 1524$\pm$35 and 863$\pm$23 Kelvin, respectively) and evidence for water absorption at all orbital phases. Comparisons with three-dimensional atmospheric models show that both the phase curve shape and emission spectra strongly suggest the presence of nightside clouds which become optically thick to thermal emission at pressures greater than ~100 mbar. The dayside is consistent with a cloudless atmosphere above the mid-infrared photosphere. Contrary to expectations from equilibrium chemistry but consistent with disequilibrium kinetics models, methane is not detected on the nightside (2$\sigma$ upper limit of 1-6 parts per million, depending on model assumptions)., Comment: 61 pages, 13 figures, 4 tables. This preprint has been submitted to and accepted in principle for publication in Nature Astronomy without significant changes

We report precise radial velocity (RV) observations of HD 212657 (= K2-167), a star shown by K2 to host a transiting sub-Neptune-sized planet in a 10 day orbit. Using Transiting Exoplanet Survey Satellite (TESS) photometry, we refined the planet parameters, especially the orbital period. We collected 74 precise RVs with the HARPS-N spectrograph between August 2015 and October 2016. Although this planet was first found to transit in 2015 and validated in 2018, excess RV scatter originally limited mass measurements. Here, we measure a mass by taking advantage of reductions in scatter from updates to the HARPS-N Data Reduction System (2.3.5) and our new activity mitigation method called CCF Activity Linear Model (CALM), which uses activity-induced line shape changes in the spectra without requiring timing information. Using the CALM framework, we performed a joint fit with RVs and transits using EXOFASTv2 and find $M_p = 6.3_{-1.4}^{+1.4}$ $M_{\oplus}$ and $R_p = 2.33^{+0.17}_{-0.15}$ $R_{\oplus}$, which places K2-167 b at the upper edge of the radius valley. We also find hints of a secondary companion at a $\sim$ 22 day period, but confirmation requires additional RVs. Although characterizing lower-mass planets like K2-167 b is often impeded by stellar variability, these systems especially help probe the formation physics (i.e. photoevaporation, core-powered mass loss) of the radius valley. In the future, CALM or similar techniques could be widely applied to FGK-type stars, help characterize a population of exoplanets surrounding the radius valley, and further our understanding of their formation., Comment: 20 pages, 16 figures, accepted for publication in MNRAS

Designing predictive models for subjective problems in natural language processing (NLP) remains challenging. This is mainly due to its non-deterministic nature and different perceptions of the content by different humans. It may be solved by Personalized Natural Language Processing (PNLP), where the model exploits additional information about the reader to make more accurate predictions. However, current approaches require complete information about the recipients to be straight embedded. Besides, the recent methods focus on deterministic inference or simple frequency-based estimations of the probabilities. In this work, we overcome this limitation by proposing a novel approach to capture the uncertainty of the forecast using conditional Normalizing Flows. This allows us to model complex multimodal distributions and to compare various models using negative log-likelihood (NLL). In addition, the new solution allows for various interpretations of possible reader perception thanks to the available sampling function. We validated our method on three challenging, subjective NLP tasks, including emotion recognition and hate speech. The comparative analysis of generalized and personalized approaches revealed that our personalized solutions significantly outperform the baseline and provide more precise uncertainty estimates. The impact on the text interpretability and uncertainty studies are presented as well. The information brought by the developed methods makes it possible to build hybrid models whose effectiveness surpasses classic solutions. In addition, an analysis and visualization of the probabilities of the given decisions for texts with high entropy of annotations and annotators with mixed views were carried out., Comment: 10 pages, 8 figures, SENTIRE'23 (ICDM 2023)

The observed pattern of fermion masses and mixing is an outstanding puzzle in particle physics, generally known as the flavor problem. Over the years, guided by precision neutrino oscillation data, discrete flavor symmetries have often been used to explain the neutrino mixing parameters, which look very different from the quark sector. In this review, we discuss the application of non-Abelian finite groups to the theory of neutrino masses and mixing in the light of current and future neutrino oscillation data. We start with an overview of the neutrino mixing parameters, comparing different global fit results and limits on normal and inverted neutrino mass ordering schemes. Then, we discuss a general framework for implementing discrete family symmetries to explain neutrino masses and mixing. We discuss CP violation effects, giving an update of CP predictions for trimaximal models with nonzero reactor mixing angle and models with partial $\mu-\tau$ reflection symmetry, and constraining models with neutrino mass sum rules. The connection between texture zeroes and discrete symmetries is also discussed. We summarize viable higher-order groups, which can explain the observed pattern of lepton mixing where the non-zero $\theta_{13}$ plays an important role. We also review the prospects of embedding finite discrete symmetries in the Grand Unified Theories and with extended Higgs fields. Models based on modular symmetry are also briefly discussed. A major part of the review is dedicated to the phenomenology of flavor symmetries and possible signatures in the current and future experiments at the intensity, energy, and cosmic frontiers. In this context, we discuss flavor symmetry implications for neutrinoless double beta decay, collider signals, leptogenesis, dark matter, as well as gravitational waves., Comment: This version is accepted for publication in Progress in Particle and Nuclear Physics

Enhancing low-light images while maintaining natural colors is a challenging problem due to camera processing variations and limited access to photos with ground-truth lighting conditions. The latter is a crucial factor for supervised methods that achieve good results on paired datasets but do not handle out-of-domain data well. On the other hand, unsupervised methods, while able to generalize, often yield lower-quality enhancements. To fill this gap, we propose Dimma, a semi-supervised approach that aligns with any camera by utilizing a small set of image pairs to replicate scenes captured under extreme lighting conditions taken by that specific camera. We achieve that by introducing a convolutional mixture density network that generates distorted colors of the scene based on the illumination differences. Additionally, our approach enables accurate grading of the dimming factor, which provides a wide range of control and flexibility in adjusting the brightness levels during the low-light image enhancement process. To further improve the quality of our results, we introduce an architecture based on a conditional UNet. The lightness value provided by the user serves as the conditional input to generate images with the desired lightness. Our approach using only few image pairs achieves competitive results compared to fully supervised methods. Moreover, when trained on the full dataset, our model surpasses state-of-the-art methods in some metrics and closely approaches them in others.

The first JWST observations of TRAPPIST-1 c showed a secondary eclipse depth of 421+/-94 ppm at 15 um, which is consistent with a bare rock surface or a thin, O2-dominated, low CO2 atmosphere (Zieba et al. 2023). Here, we further explore potential atmospheres for TRAPPIST-1 c by comparing the observed secondary eclipse depth to synthetic spectra of a broader range of plausible environments. To self-consistently incorporate the impact of photochemistry and atmospheric composition on atmospheric thermal structure and predicted eclipse depth, we use a two-column climate model coupled to a photochemical model, and simulate O2-dominated, Venus-like, and steam atmospheres. We find that a broader suite of plausible atmospheric compositions are also consistent with the data. For lower pressure atmospheres (0.1 bar), our O2-CO2 atmospheres produce eclipse depths within 1$\sigma$ of the data, consistent with the modeling results of Zieba et al. (2023). However, for higher-pressure atmospheres, our models produce different temperature-pressure profiles and are less pessimistic, with 1-10 bar O2, 100 ppm CO2 models within 2.0-2.2$\sigma$ of the measured secondary eclipse depth, and up to 0.5% CO2 within 2.9$\sigma$. Venus-like atmospheres are still unlikely. For thin O2 atmospheres of 0.1 bar with a low abundance of CO2 ($\sim$100 ppm), up to 10% water vapor can be present and still provide an eclipse depth within 1$\sigma$ of the data. We compared the TRAPPIST-1 c data to modeled steam atmospheres of $\leq$ 3 bar, which are 1.7-1.8$\sigma$ from the data and not conclusively ruled out. More data will be required to discriminate between possible atmospheres, or to more definitively support the bare rock hypothesis., Comment: 15 pages, accepted to APJL

State-of-the-art models can perform well in controlled environments, but they often struggle when presented with out-of-distribution (OOD) examples, making OOD detection a critical component of NLP systems. In this paper, we focus on highlighting the limitations of existing approaches to OOD detection in NLP. Specifically, we evaluated eight OOD detection methods that are easily integrable into existing NLP systems and require no additional OOD data or model modifications. One of our contributions is providing a well-structured research environment that allows for full reproducibility of the results. Additionally, our analysis shows that existing OOD detection methods for NLP tasks are not yet sufficiently sensitive to capture all samples characterized by various types of distributional shifts. Particularly challenging testing scenarios arise in cases of background shift and randomly shuffled word order within in domain texts. This highlights the need for future work to develop more effective OOD detection approaches for the NLP problems, and our work provides a well-defined foundation for further research in this area., Comment: 11 pages, 3 figures, Association for Computational Linguistics

Self-supervised methods have been proven effective for learning deep representations of 3D point cloud data. Although recent methods in this domain often rely on random masking of inputs, the results of this approach can be improved. We introduce PointCAM, a novel adversarial method for learning a masking function for point clouds. Our model utilizes a self-distillation framework with an online tokenizer for 3D point clouds. Compared to previous techniques that optimize patch-level and object-level objectives, we postulate applying an auxiliary network that learns how to select masks instead of choosing them randomly. Our results show that the learned masking function achieves state-of-the-art or competitive performance on various downstream tasks. The source code is available at https://github.com/szacho/pointcam.

Seven rocky planets orbit the nearby dwarf star TRAPPIST-1, providing a unique opportunity to search for atmospheres on small planets outside the Solar System (Gillon et al., 2017). Thanks to the recent launch of JWST, possible atmospheric constituents such as carbon dioxide (CO2) are now detectable (Morley et al., 2017, Lincowski et al., 2018}. Recent JWST observations of the innermost planet TRAPPIST-1 b showed that it is most probably a bare rock without any CO2 in its atmosphere (Greene et al., 2023). Here we report the detection of thermal emission from the dayside of TRAPPIST-1 c with the Mid-Infrared Instrument (MIRI) on JWST at 15 micron. We measure a planet-to-star flux ratio of fp/fs = 421 +/- 94 parts per million (ppm) which corresponds to an inferred dayside brightness temperature of 380 +/- 31 K. This high dayside temperature disfavours a thick, CO2-rich atmosphere on the planet. The data rule out cloud-free O2/CO2 mixtures with surface pressures ranging from 10 bar (with 10 ppm CO2) to 0.1 bar (pure CO2). A Venus-analogue atmosphere with sulfuric acid clouds is also disfavoured at 2.6 sigma confidence. Thinner atmospheres or bare-rock surfaces are consistent with our measured planet-to-star flux ratio. The absence of a thick, CO2-rich atmosphere on TRAPPIST-1 c suggests a relatively volatile-poor formation history, with less than 9.5 +7.5 -2.3 Earth oceans of water. If all planets in the system formed in the same way, this would indicate a limited reservoir of volatiles for the potentially habitable planets in the system., Comment: Published in Nature on June 19th. 2023, 10 figures, 4 tables

Semiconductor quantum dots (QDs) enable the generation of single and entangled photons, useful for various applications in photonic quantum technologies. Specifically for quantum communication via fiber-optical networks, operation in the telecom C-band centered around 1550$\,$nm is ideal. The direct generation of QD-photons in this spectral range and with high quantum-optical quality, however, remained challenging. Here, we demonstrate the coherent on-demand generation of indistinguishable photons in the telecom C-band from single QD devices consisting of InAs/InP QD-mesa structures heterogeneously integrated with a metallic reflector on a silicon wafer. Using pulsed two-photon resonant excitation of the biexciton-exciton radiative cascade, we observe Rabi rotations up to pulse areas of $4\pi$ and a high single-photon purity in terms of $g^{(2)}(0)=0.005(1)$ and $0.015(1)$ for exciton and biexciton photons, respectively. Applying two independent experimental methods, based on fitting Rabi rotations in the emission intensity and performing photon cross-correlation measurements, we consistently obtain preparation fidelities at the $\pi$-pulse exceeding 80$\%$. Finally, performing Hong-Ou-Mandel-type two-photon interference experiments we obtain a photon-indistinguishability of the full photon wave packet of up to $35(3)\%$, representing a significant advancement in the photon-indistinguishability of single photons emitted directly in the telecom C-band., Comment: Main text: 24 pages (including 4 figures, 1 table, and 56 citations); Supporting Information: 18 pages (including 10 figures and 3 tables)

NeRF is a popular model that efficiently represents 3D objects from 2D images. However, vanilla NeRF has a few important limitations. NeRF must be trained on each object separately. The training time is long since we encode the object's shape and color in neural network weights. Moreover, NeRF does not generalize well to unseen data. In this paper, we present MultiPlaneNeRF -- a first model that simultaneously solves all the above problems. Our model works directly on 2D images. We project 3D points on 2D images to produce non-trainable representations. The projection step is not parametrized, and a very shallow decoder can efficiently process the representation. Using existing images as part of NeRF can significantly reduce the number of parameters since we train only a small implicit decoder. Furthermore, we can train MultiPlaneNeRF on a large data set and force our implicit decoder to generalize across many objects. Consequently, we can only replace the 2D images (without additional training) to produce a NeRF representation of the new object. In the experimental section, we demonstrate that MultiPlaneNeRF achieves comparable results to state-of-the-art models for synthesizing new views and has generalization properties.

Photochemical hazes are expected to form in hot Jupiter atmospheres and may explain the strong scattering slopes and muted spectral features observed in the transmission spectra of many hot Jupiters. Absorption and scattering by photochemical hazes have the potential to drastically alter temperature structure and atmospheric circulation of these planets but have previously been neglected in general circulation models (GCMs). We present GCM simulations of hot Jupiter HD 189733b that include photochemical hazes as a radiatively active tracer fully coupled to atmospheric dynamics. The influence of haze radiative feedback strongly depends on the assumed haze optical properties. For soot hazes, two distinct thermal inversions form, separated by a local temperature minimum around 10$^{-5}$ bar caused by upwelling on the dayside mixing air with low haze abundance upwards. The equatorial jet broadens and slows down. The horizontal distribution of hazes remains relatively similar to simulations with radiatively passive tracers. For Titan-type hazes, the equatorial jet accelerates and extends to much lower pressures, resulting in a dramatically different 3D distribution of hazes compared to radiatively passive or soot hazes. Further experimental and observational studies to constrain the optical properties of photochemical hazes will therefore be crucial for understanding the role of hazes in exoplanet atmospheres. In the dayside emission spectrum, for both types of hazes the amplitude of near-infrared features is reduced, while the emitted flux at longer wavelengths ($>$4 $\mu$m) increases. Haze radiative feedback leads to increased phase curve amplitudes in many infrared wavelength regions, mostly due to stronger dayside emission., Comment: 31 pages, 18 figures, accepted for publication in ApJ

The presence of minor bodies in exoplanetary systems is in most cases inferred through infra-red excesses, with the exception of exocomets. Even if over 35 years have passed since the first detection of exocomets around beta Pic, only ~ 25 systems are known to show evidence of evaporating bodies, and most of them have only been observed in spectroscopy. With the appearance of new high-precision photometric missions designed to search for exoplanets, such as CHEOPS, a new opportunity to detect exocomets is available. Combining data from CHEOPS and TESS we investigate the lightcurve of 5 Vul, an A-type star with detected variability in spectroscopy, to search for non periodic transits that could indicate the presence of dusty cometary tails in the system. While we did not find any evidence of minor bodies, the high precision of the data, along with the combination with previous spectroscopic results and models, allows for an estimation of the sizes and spatial distribution of the exocomets., Comment: Accepted for publication in MNRAS

There are no planets intermediate in size between Earth and Neptune in our Solar System, yet these objects are found around a substantial fraction of other stars. Population statistics show that close-in planets in this size range bifurcate into two classes based on their radii. It is hypothesized that the group with larger radii (referred to as "sub-Neptunes") is distinguished by having hydrogen-dominated atmospheres that are a few percent of the total mass of the planets. GJ 1214b is an archetype sub-Neptune that has been observed extensively using transmission spectroscopy to test this hypothesis. However, the measured spectra are featureless, and thus inconclusive, due to the presence of high-altitude aerosols in the planet's atmosphere. Here we report a spectroscopic thermal phase curve of GJ 1214b obtained with JWST in the mid-infrared. The dayside and nightside spectra (average brightness temperatures of 553 $\pm$ 9 and 437 $\pm$ 19 K, respectively) each show >3$\sigma$ evidence of absorption features, with H$_2$O as the most likely cause in both. The measured global thermal emission implies that GJ 1214b's Bond albedo is 0.51 $\pm$ 0.06. Comparison between the spectroscopic phase curve data and three-dimensional models of GJ 1214b reveal a planet with a high metallicity atmosphere blanketed by a thick and highly reflective layer of clouds or haze., Comment: Published online in Nature on May 10, 2023

Quadratic harnesses are time-inhomogeneous Markov polynomial processes with linear conditional expectations and quadratic conditional variances with respect to the past-future filtrations. Typically they are determined by five numerical constants hidden in the form of conditional variances. In this paper we derive infinitesimal generators of such processes, extending previously known results. The infinitesimal generators are identified through a solution of a q-commutation equation in the algebra Q of infinite sequences of polynomials in one variable. The solution is a special element in Q, whose coordinates satisfy a three-term recurrence and thus define a system of orthogonal polynomials. It turns out that the respective orthogonality measure uniquely determines the infinitesimal generator (acting on polynomials or bounded functions with bounded continuous second derivative) as an integro-differential operator with the explicit kernel, where the integration is with respect to this measure.

In this paper we investigate the game chromatic number for complete multipartite graphs. We devise several strategies for Alice, and one strategy for Bob, and we prove their optimality in all complete multipartite graphs with no singletons. All the strategies presented are computable in linear time, and the values of the game chromatic number depend directly only on the number and the sizes of sets in the partition.

Single indistinguishable photons at telecom C-band wavelengths are essential for quantum networks and the future quantum internet. However, high-throughput technology for single-photon generation at 1550 nm remained a missing building block to overcome present limitations in quantum communication and information technologies. Here, we demonstrate the high-throughput fabrication of quantum-photonic integrated devices operating at C-band wavelengths based on epitaxial semiconductor quantum dots. Our technique enables the deterministic integration of single pre-selected quantum emitters into microcavities based on circular Bragg gratings. Respective devices feature the triggered generation of single photons with ultra-high purity and record-high photon indistinguishability. Further improvements in yield and coherence properties will pave the way for implementing single-photon non-linear devices and advanced quantum networks at telecom wavelengths., Comment: 10 pages, 4 figures, Supplemental Material: 23 pages, 21 figures

Traditional 3D face models are based on mesh representations with texture. One of the most important models is FLAME (Faces Learned with an Articulated Model and Expressions), which produces meshes of human faces that are fully controllable. Unfortunately, such models have problems with capturing geometric and appearance details. In contrast to mesh representation, the neural radiance field (NeRF) produces extremely sharp renders. But implicit methods are hard to animate and do not generalize well to unseen expressions. It is not trivial to effectively control NeRF models to obtain face manipulation. The present paper proposes a novel approach, named NeRFlame, which combines the strengths of both NeRF and FLAME methods. Our method enables high-quality rendering capabilities of NeRF while also offering complete control over the visual appearance, similar to FLAME. Unlike conventional NeRF-based architectures that utilize neural networks to model RGB colors and volume density, NeRFlame employs FLAME mesh as an explicit density volume. As a result, color values are non-zero only in the proximity of the FLAME mesh. This FLAME backbone is then integrated into the NeRF architecture to predict RGB colors, allowing NeRFlame to explicitly model volume density and implicitly model RGB colors.

The recent surge in popularity of deep generative models for 3D objects has highlighted the need for more efficient training methods, particularly given the difficulties associated with training with conventional 3D representations, such as voxels or point clouds. Neural Radiance Fields (NeRFs), which provide the current benchmark in terms of quality for the generation of novel views of complex 3D scenes from a limited set of 2D images, represent a promising solution to this challenge. However, the training of these models requires the knowledge of the respective camera positions from which the images were viewed. In this paper, we overcome this limitation by introducing HyperNeRFGAN, a Generative Adversarial Network (GAN) architecture employing a hypernetwork paradigm to transform a Gaussian noise into the weights of a NeRF architecture that does not utilize viewing directions in its training phase. Consequently, as evidenced by the findings of our experimental study, the proposed model, despite its notable simplicity in comparison to existing state-of-the-art alternatives, demonstrates superior performance on a diverse range of image datasets where camera position estimation is challenging, particularly in the context of medical data.

We present observations of the 1.35+/-0.07 Earth-radius planet L 98-59 c using Wide Field Camera~3 on the Hubble Space Telescope. L 98-59 is a nearby (10.6 pc), bright (H=7.4 mag), M3V star that harbors three small, transiting planets. As one of the closest known transiting multi-planet systems, L 98-59 offers one of the best opportunities to probe and compare the atmospheres of rocky planets that formed in the same stellar environment. We measured the transmission spectrum of L 98-59 c during a single transit, with the extracted spectrum showing marginal evidence for wavelength-dependent transit depth variations which would indicate the presence of an atmosphere. Forward modeling was used to constrain possible atmospheric compositions of the planet based on the shape of the transmission spectrum. Although L 98-59 is a fairly quiet star, we have seen evidence for stellar activity, and therefore we cannot rule out a scenario where the source of the signal originates with inhomogeneities on the host-star surface. While intriguing, our results are inconclusive and additional data is needed to verify any atmospheric signal. Fortunately, additional data will soon be collected from both HST and JWST. Should this result be confirmed with additional data, L 98-59 c would be the first planet smaller than 2 Earth-radii with a detected atmosphere, and among the first small planets with a known atmosphere to be studied in detail by the JWST., Comment: Submitted to AAS Journals

Taking inspiration from recent developments in visual generative tasks using diffusion models, we propose a method for end-to-end speech-driven video editing using a denoising diffusion model. Given a video of a talking person, and a separate auditory speech recording, the lip and jaw motions are re-synchronized without relying on intermediate structural representations such as facial landmarks or a 3D face model. We show this is possible by conditioning a denoising diffusion model on audio mel spectral features to generate synchronised facial motion. Proof of concept results are demonstrated on both single-speaker and multi-speaker video editing, providing a baseline model on the CREMA-D audiovisual data set. To the best of our knowledge, this is the first work to demonstrate and validate the feasibility of applying end-to-end denoising diffusion models to the task of audio-driven video editing., Comment: 8 Pages, code and project page available here: https://danbigioi.github.io/DiffusionVideoEditing/

Talking face generation has historically struggled to produce head movements and natural facial expressions without guidance from additional reference videos. Recent developments in diffusion-based generative models allow for more realistic and stable data synthesis and their performance on image and video generation has surpassed that of other generative models. In this work, we present an autoregressive diffusion model that requires only one identity image and audio sequence to generate a video of a realistic talking human head. Our solution is capable of hallucinating head movements, facial expressions, such as blinks, and preserving a given background. We evaluate our model on two different datasets, achieving state-of-the-art results on both of them.