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Isolated planetary-mass objects share their mass range with planets but do not orbit a star. They lack the necessary mass to support fusion in their cores and thermally radiate their heat from formation as they cool, primarily at infrared wavelengths. Many isolated planetary-mass objects show variations in their infrared brightness consistent with non-uniform atmospheric features modulated by their rotation. SIMP J013656.5+093347.3 is a rapidly rotating isolated planetary-mass object, and previous infrared monitoring suggests complex atmospheric features rotating in and out of view. The physical nature of these features is not well understood, with clouds, temperature variations, thermochemical instabilities, and infrared-emitting aurora all proposed as contributing mechanisms. Here we report JWST time-resolved low-resolution spectroscopy from 0.8 - 11 micron of SIMP J013656.5+093347.3 which supports the presence of three specific features in the atmosphere: clouds, hot spots, and changing carbon chemistry. We show that no single mechanism can explain the variations in the time-resolved spectra. When combined with previous studies of this object indicating patchy clouds and aurorae, these measurements reveal the rich complexity of the atmosphere of SIMP J013656.5+093347.3. Gas giant planets in the solar system, specifically Jupiter and Saturn, also have multiple cloud layers and high-altitude hot spots, suggesting these phenomena are also present in worlds both within and beyond our solar-system., Comment: Accepted to ApJ Letters

Giant planets on long period orbits around the nearest stars are among the easiest to directly image. Unfortunately these planets are difficult to fully constrain by indirect methods, e.g., transit and radial velocity (RV). In this study, we present the discovery of a super-Jupiter, HD 222237 b, orbiting a star located $11.445\pm0.002$ pc away. By combining RV data, Hipparcos and multi-epoch Gaia astrometry, we estimate the planetary mass to be ${5.19}_{-0.58}^{+0.58}\,M_{\rm Jup}$, with an eccentricity of ${0.56}_{-0.03}^{+0.03}$ and a period of ${40.8}_{-4.5}^{+5.8}$ yr, making HD 222237 b a promising target for imaging using the Mid-Infrared Instrument (MIRI) of JWST. A comparative analysis suggests that our method can break the inclination degeneracy and thus differentiate between prograde and retrograde orbits of a companion. We further find that the inferred contrast ratio between the planet and the host star in the F1550C filter ($15.50\,\mu \rm m$) is approximately $1.9\times10^{-4}$, which is comparable with the measured limit of the MIRI coronagraphs. The relatively low metallicity of the host star ($\rm-0.32\,dex$) combined with the unique orbital architecture of this system presents an excellent opportunity to probe the planet-metallicity correlation and the formation scenarios of giant planets., Comment: 18 pages, 10 figures, 3 tables, accepted for publication in MNRAS

State-of-the-art techniques for 3D reconstruction are largely based on volumetric scene representations, which require sampling multiple points to compute the color arriving along a ray. Using these representations for more general inverse rendering -- reconstructing geometry, materials, and lighting from observed images -- is challenging because recursively path-tracing such volumetric representations is expensive. Recent works alleviate this issue through the use of radiance caches: data structures that store the steady-state, infinite-bounce radiance arriving at any point from any direction. However, these solutions rely on approximations that introduce bias into the renderings and, more importantly, into the gradients used for optimization. We present a method that avoids these approximations while remaining computationally efficient. In particular, we leverage two techniques to reduce variance for unbiased estimators of the rendering equation: (1) an occlusion-aware importance sampler for incoming illumination and (2) a fast cache architecture that can be used as a control variate for the radiance from a high-quality, but more expensive, volumetric cache. We show that by removing these biases our approach improves the generality of radiance cache based inverse rendering, as well as increasing quality in the presence of challenging light transport effects such as specular reflections., Comment: Website: https://benattal.github.io/flash-cache/

We present the phase one report of the Bright Star Subtraction (BSS) pipeline for the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). This pipeline is designed to create an extended PSF model by utilizing observed stars, followed by subtracting this model from the bright stars present in LSST data. Running the pipeline on Hyper Suprime-Cam (HSC) data shows a correlation between the shape of the extended PSF model and the position of the detector within the camera's focal plane. Specifically, detectors positioned closer to the focal plane's edge exhibit reduced circular symmetry in the extended PSF model. To mitigate this effect, we present an algorithm that enables users to account for the location dependency of the model. Our analysis also indicates that the choice of normalization annulus is crucial for modeling the extended PSF. Smaller annuli can exclude stars due to overlap with saturated regions, while larger annuli may compromise data quality because of lower signal-to-noise ratios. This makes finding the optimal annulus size a challenging but essential task for the BSS pipeline. Applying the BSS pipeline to HSC exposures allows for the subtraction of, on average, 100 to 700 stars brighter than 12th magnitude measured in g-band across a full exposure, with a full HSC exposure comprising ~100 detectors., Comment: 11 pages, 8 figures, published in Proc. SPIE 13101, Software and Cyberinfrastructure for Astronomy VIII, 131013N (25 July 2024)

Background: Neonatal seizures are a neurological emergency that require urgent treatment. They are hard to diagnose clinically and can go undetected if EEG monitoring is unavailable. EEG interpretation requires specialised expertise which is not widely available. Algorithms to detect EEG seizures can address this limitation but have yet to reach widespread clinical adoption. Methods: Retrospective EEG data from 332 neonates was used to develop and validate a seizure-detection model. The model was trained and tested with a development dataset ($n=202$) that was annotated with over 12k seizure events on a per-channel basis. This dataset was used to develop a convolutional neural network (CNN) using a modern architecture and training methods. The final model was then validated on two independent multi-reviewer datasets ($n=51$ and $n=79$). Results: Increasing dataset and model size improved model performance: Matthews correlation coefficient (MCC) and Pearson's correlation ($r$) increased by up to 50% with data scaling and up to 15% with model scaling. Over 50k hours of annotated single-channel EEG was used for training a model with 21 million parameters. State-of-the-art was achieved on an open-access dataset (MCC=0.764, $r=0.824$, and AUC=0.982). The CNN attains expert-level performance on both held-out validation sets, with no significant difference in inter-rater agreement among the experts and among experts and algorithm ($\Delta \kappa < -0.095$, $p>0.05$). Conclusion: With orders of magnitude increases in data and model scale we have produced a new state-of-the-art model for neonatal seizure detection. Expert-level equivalence on completely unseen data, a first in this field, provides a strong indication that the model is ready for further clinical validation.

We present the Bright Star Subtraction (BSS) pipeline for the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). This pipeline generates an extended PSF model using observed stars and subtracts the model from the bright stars in LSST data. When testing the pipeline on Hyper Suprime-Cam (HSC) data, we find that the shape of the extended PSF model depends on the location of the detector on the camera's focal plane. The closer a detector is to the edge of the focal plane, the less the extended PSF model is circularly symmetric. We introduce an algorithm that allows the user to consider the location dependency of the model., Comment: 4 pages, 1 figure; Astronomical Data Analysis Software & Systems XXXIII proceeding

Multi-planet systems exhibit a diversity of architectures that diverge from the solar system and contribute to the topic of exoplanet demographics. Radial velocity (RV) surveys form a crucial component of exoplanet surveys, as their long observational baselines allow searches for more distant planetary orbits. This work provides a significantly revised architecture for the multi-planet system HD 134606 using both HARPS and UCLES RVs. We confirm the presence of previously reported planets b, c, and d with periods $12.0897^{+0.0019}_{-0.0018}$, $58.947^{+0.056}_{-0.054}$, and $958.7^{+6.3}_{-5.9}$ days, and masses $9.14^{+0.65}_{-0.63}$, $11.0\pm1$, and $44.5\pm2.9$ Earth masses respectively, with the planet d orbit significantly revised to over double that originally reported. We report two newly detected super-Earths, e and f, with periods $4.31943^{+0.00075}_{-0.00068}$ and $26.9^{+0.019}_{-0.017}$ days, and masses $2.31^{+0.36}_{-0.35}$ and $5.52^{+0.74}_{-0.73}$ Earth masses, respectively. In addition, we identify a linear trend in the RV time series, and the cause of this acceleration is deemed to be a newly detected sub-stellar companion at large separation. HD 134606 now displays four low mass planets in a compact region near the star, one gas giant further out in the Habitable Zone, an additional massive companion in the outer regime, and a low mass M dwarf stellar companion at large separation, making it an intriguing target for system formation/evolution studies. The location of planet d in the Habitable Zone proves to be an exciting candidate for future space-based direct imaging missions, whereas continued RV observations of this system are recommended for understanding the nature of the massive, long period companion., Comment: 23 pages, 10 figures, 4 tables, accepted for publication in the Astronomical Journal

Diffusion models have achieved great progress in face generation. However, these models amplify the bias in the generation process, leading to an imbalance in distribution of sensitive attributes such as age, gender and race. This paper proposes a novel solution to this problem by balancing the facial attributes of the generated images. We mitigate the bias by localizing the means of the facial attributes in the latent space of the diffusion model using Gaussian mixture models (GMM). Our motivation for choosing GMMs over other clustering frameworks comes from the flexible latent structure of diffusion model. Since each sampling step in diffusion models follows a Gaussian distribution, we show that fitting a GMM model helps us to localize the subspace responsible for generating a specific attribute. Furthermore, our method does not require retraining, we instead localize the subspace on-the-fly and mitigate the bias for generating a fair dataset. We evaluate our approach on multiple face attribute datasets to demonstrate the effectiveness of our approach. Our results demonstrate that our approach leads to a more fair data generation in terms of representational fairness while preserving the quality of generated samples.

LISA, the Laser Interferometer Space Antenna, will usher in a new era in gravitational-wave astronomy. As the first anticipated space-based gravitational-wave detector, it will expand our view to the millihertz gravitational-wave sky, where a spectacular variety of interesting new sources abound: from millions of ultra-compact binaries in our Galaxy, to mergers of massive black holes at cosmological distances; from the beginnings of inspirals that will venture into the ground-based detectors' view to the death spiral of compact objects into massive black holes, and many sources in between. Central to realising LISA's discovery potential are waveform models, the theoretical and phenomenological predictions of the pattern of gravitational waves that these sources emit. This white paper is presented on behalf of the Waveform Working Group for the LISA Consortium. It provides a review of the current state of waveform models for LISA sources, and describes the significant challenges that must yet be overcome., Comment: 239 pages, 11 figures, white paper from the LISA Consortium Waveform Working Group, invited for submission to Living Reviews in Relativity, updated with comments from community

Construction of the new all-silicon Inner Tracker (ITk), developed by the ATLAS collaboration for the High Luminosity LHC, started in 2020 and is expected to continue till 2028. The ITk detector will include 18,000 highly segmented and radiation hard n+-in-p silicon strip sensors (ATLAS18), which are being manufactured by Hamamatsu Photonics. Mechanical and electrical characteristics of produced sensors are measured upon their delivery at several institutes participating in a complex Quality Control (QC) program. The QC tests performed on each individual sensor check the overall integrity and quality of the sensor. During the QC testing of production ATLAS18 strip sensors, an increased number of sensors that failed the electrical tests was observed. In particular, IV measurements indicated an early breakdown, while large areas containing several tens or hundreds of neighbouring strips with low interstrip isolation were identified by the Full strip tests, and leakage current instabilities were measured in a long-term leakage current stability setup. Moreover, a high surface electrostatic charge reaching a level of several hundreds of volts per inch was measured on a large number of sensors and on the plastic sheets, which mechanically protect these sensors in their paper envelopes. Accumulated data indicates a clear correlation between observed electrical failures and the sensor charge-up. To mitigate the above-described issues, the QC testing sites significantly modified the sensor handling procedures and introduced sensor recovery techniques based on irradiation of the sensor surface with UV light or application of intensive flows of ionized gas. In this presentation, we will describe the setups implemented by the QC testing sites to treat silicon strip sensors affected by static charge and evaluate the effectiveness of these setups in terms of improvement of the sensor performance.

In this work, we present a methodology and a corresponding code-base for constructing mock integral field spectrograph (IFS) observations of simulated galaxies in a consistent and reproducible way. Such methods are necessary to improve the collaboration and comparison of observation and theory results, and accelerate our understanding of how the kinematics of galaxies evolve over time. This code, SimSpin, is an open-source package written in R, but also with an API interface such that the code can be interacted with in any coding language. Documentation and individual examples can be found at the open-source website connected to the online repository. SimSpin is already being utilised by international IFS collaborations, including SAMI and MAGPI, for generating comparable data sets from a diverse suite of cosmological hydrodynamical simulations., Comment: 20 pages, 15 figures, 2 tables. Accepted for publication in PASA. 30/08/23

An emerging application of Raman spectroscopy is monitoring the state of chemical reactors during biologic drug production. Raman shift intensities scale linearly with the concentrations of chemical species and thus can be used to analytically determine real-time concentrations using non-destructive light irradiation in a label-free manner. Chemometric algorithms are used to interpret Raman spectra produced from complex mixtures of bioreactor contents as a reaction evolves. Finding the optimal algorithm for a specific bioreactor environment is challenging due to the lack of freely available Raman mixture datasets. The RaMix Python package addresses this challenge by enabling the generation of synthetic Raman mixture datasets with controllable noise levels to assess the utility of different chemometric algorithm types for real-time monitoring applications. To demonstrate the capabilities of this package and compare the performance of different chemometric algorithms, 48 datasets of simulated spectra were generated using the RaMix Python package. The four tested algorithms include partial least squares regression (PLS), a simple neural network, a simple convolutional neural network (simple CNN), and a 1D convolutional neural network with a ResNet architecture (ResNet). The performance of the PLS and simple CNN model was found to be comparable, with the PLS algorithm slightly outperforming the other models on 83\% of the data sets. The simple CNN model outperforms the other models on large, high noise datasets, demonstrating the superior capability of convolutional neural networks compared to PLS in analyzing noisy spectra. These results demonstrate the promise of CNNs to automatically extract concentration information from unprocessed, noisy spectra, allowing for better process control of industrial drug production. Code for this project is available at github.com/DexterAntonio/RaMix., Comment: 7 pages, 7 figures

Common and important applications of person identification occur at distances and viewpoints in which the face is not visible or is not sufficiently resolved to be useful. We examine body shape as a biometric across distance and viewpoint variation. We propose an approach that combines standard object classification networks with representations based on linguistic (word-based) descriptions of bodies. Algorithms with and without linguistic training were compared on their ability to identify people from body shape in images captured across a large range of distances/views (close-range, 100m, 200m, 270m, 300m, 370m, 400m, 490m, 500m, 600m, and at elevated pitch in images taken by an unmanned aerial vehicle [UAV]). Accuracy, as measured by identity-match ranking and false accept errors in an open-set test, was surprisingly good. For identity-ranking, linguistic models were more accurate for close-range images, whereas non-linguistic models fared better at intermediary distances. Fusion of the linguistic and non-linguistic embeddings improved performance at all, but the farthest distance. Although the non-linguistic model yielded fewer false accepts at all distances, fusion of the linguistic and non-linguistic models decreased false accepts for all, but the UAV images. We conclude that linguistic and non-linguistic representations of body shape can offer complementary identity information for bodies that can improve identification in applications of interest., Comment: 9 pages, 5 figures, 4 tables

Face recognition algorithms perform more accurately than humans in some cases, though humans and machines both show race-based accuracy differences. As algorithms continue to improve, it is important to continually assess their race bias relative to humans. We constructed a challenging test of 'cross-race' face verification and used it to compare humans and two state-of-the-art face recognition systems. Pairs of same- and different-identity faces of White and Black individuals were selected to be difficult for humans and an open-source implementation of the ArcFace face recognition algorithm from 2019 (5). Human participants (54 Black; 51 White) judged whether face pairs showed the same identity or different identities on a 7-point Likert-type scale. Two top-performing face recognition systems from the Face Recognition Vendor Test-ongoing performed the same test (7). By design, the test proved challenging for humans as a group, who performed above chance, but far less than perfect. Both state-of-the-art face recognition systems scored perfectly (no errors), consequently with equal accuracy for both races. We conclude that state-of-the-art systems for identity verification between two frontal face images of Black and White individuals can surpass the general population. Whether this result generalizes to challenging in-the-wild images is a pressing concern for deploying face recognition systems in unconstrained environments., Comment: 8 pages, 6 figures

High-quality reconstruction of controllable 3D head avatars from 2D videos is highly desirable for virtual human applications in movies, games, and telepresence. Neural implicit fields provide a powerful representation to model 3D head avatars with personalized shape, expressions, and facial parts, e.g., hair and mouth interior, that go beyond the linear 3D morphable model (3DMM). However, existing methods do not model faces with fine-scale facial features, or local control of facial parts that extrapolate asymmetric expressions from monocular videos. Further, most condition only on 3DMM parameters with poor(er) locality, and resolve local features with a global neural field. We build on part-based implicit shape models that decompose a global deformation field into local ones. Our novel formulation models multiple implicit deformation fields with local semantic rig-like control via 3DMM-based parameters, and representative facial landmarks. Further, we propose a local control loss and attention mask mechanism that promote sparsity of each learned deformation field. Our formulation renders sharper locally controllable nonlinear deformations than previous implicit monocular approaches, especially mouth interior, asymmetric expressions, and facial details., Comment: Accepted at CVPR 2023

Face identity masking algorithms developed in recent years aim to protect the privacy of people in video recordings. These algorithms are designed to interfere with identification, while preserving information about facial actions. An important challenge is to preserve subtle actions in the eye region, while obscuring the salient identity cues from the eyes. We evaluated the effectiveness of identity-masking algorithms based on Canny filters, applied with and without eye enhancement, for interfering with identification and preserving facial actions. In Experiments 1 and 2, we tested human participants' ability to match the facial identity of a driver in a low resolution video to a high resolution facial image. Results showed that both masking methods impaired identification, and that eye enhancement did not alter the effectiveness of the Canny filter mask. In Experiment 3, we tested action preservation and found that neither method interfered significantly with driver action perception. We conclude that relatively simple, filter-based masking algorithms, which are suitable for application to low quality video, can be used in privacy protection without compromising action perception., Comment: 8 pages, 8 figures

Volumetric scene representations enable photorealistic view synthesis for static scenes and form the basis of several existing 6-DoF video techniques. However, the volume rendering procedures that drive these representations necessitate careful trade-offs in terms of quality, rendering speed, and memory efficiency. In particular, existing methods fail to simultaneously achieve real-time performance, small memory footprint, and high-quality rendering for challenging real-world scenes. To address these issues, we present HyperReel -- a novel 6-DoF video representation. The two core components of HyperReel are: (1) a ray-conditioned sample prediction network that enables high-fidelity, high frame rate rendering at high resolutions and (2) a compact and memory-efficient dynamic volume representation. Our 6-DoF video pipeline achieves the best performance compared to prior and contemporary approaches in terms of visual quality with small memory requirements, while also rendering at up to 18 frames-per-second at megapixel resolution without any custom CUDA code., Comment: Project page: https://hyperreel.github.io/

We analyze 5108 AFGKM stars with at least five high precision radial velocity points as well as Gaia and Hipparcos astrometric data utilizing a novel pipeline developed in previous work. We find 914 radial velocity signals with periods longer than 1000\,d. Around these signals, 167 cold giants and 68 other types of companions are identified by combined analyses of radial velocity, astrometry, and imaging data. Without correcting for detection bias, we estimate the minimum occurrence rate of the wide-orbit brown dwarfs to be 1.3\%, and find a significant brown dwarf valley around 40 $M_{\rm Jup}$. We also find a power-law distribution in the host binary fraction beyond 3 au similar to that found for single stars, indicating no preference of multiplicity for brown dwarfs. Our work also reveals nine sub-stellar systems (GJ 234 B, GJ 494 B, HD 13724 b, HD 182488 b, HD 39060 b and c, HD 4113 C, HD 42581 d, HD 7449 B, and HD 984 b) that have previously been directly imaged, and many others that are observable at existing facilities. Depending on their ages we estimate that an additional 10-57 sub-stellar objects within our sample can be detected with current imaging facilities, extending the imaged cold (or old) giants by an order of magnitude., Comment: 39 pages, 14 figures, 5 tables, published in The Astrophysical Journal Supplement Series

Deep convolutional neural networks (DCNNs) have achieved human-level accuracy in face identification (Phillips et al., 2018), though it is unclear how accurately they discriminate highly-similar faces. Here, humans and a DCNN performed a challenging face-identity matching task that included identical twins. Participants (N=87) viewed pairs of face images of three types: same-identity, general imposter pairs (different identities from similar demographic groups), and twin imposter pairs (identical twin siblings). The task was to determine whether the pairs showed the same person or different people. Identity comparisons were tested in three viewpoint-disparity conditions: frontal to frontal, frontal to 45-degree profile, and frontal to 90-degree profile. Accuracy for discriminating matched-identity pairs from twin-imposters and general imposters was assessed in each viewpoint-disparity condition. Humans were more accurate for general-imposter pairs than twin-imposter pairs, and accuracy declined with increased viewpoint disparity between the images in a pair. A DCNN trained for face identification (Ranjan et al., 2018) was tested on the same image pairs presented to humans. Machine performance mirrored the pattern of human accuracy, but with performance at or above all humans in all but one condition. Human and machine similarity scores were compared across all image-pair types. This item-level analysis showed that human and machine similarity ratings correlated significantly in six of nine image-pair types [range r=0.38 to r=0.63], suggesting general accord between the perception of face similarity by humans and the DCNN. These findings also contribute to our understanding of DCNN performance for discriminating high-resemblance faces, demonstrate that the DCNN performs at a level at or above humans, and suggest a degree of parity between the features used by humans and the DCNN.

We introduce a new algorithm, Regression based Supervised Learning (RSL), for learning per instance Neural Network (NN) defined heuristic functions for classical planning problems. RSL uses regression to select relevant sets of states at a range of different distances from the goal. RSL then formulates a Supervised Learning problem to obtain the parameters that define the NN heuristic, using the selected states labeled with exact or estimated distances to goal states. Our experimental study shows that RSL outperforms, in terms of coverage, previous classical planning NN heuristics functions while requiring two orders of magnitude less training time.

The landscape of privacy laws and regulations around the world is complex and ever-changing. National and super-national laws, agreements, decrees, and other government-issued rules form a patchwork that companies must follow to operate internationally. To examine the status and evolution of this patchwork, we introduce the Government Privacy Instructions Corpus, or GPI Corpus, of 1,043 privacy laws, regulations, and guidelines, covering 182 jurisdictions. This corpus enables a large-scale quantitative and qualitative examination of legal foci on privacy. We examine the temporal distribution of when GPIs were created and illustrate the dramatic increase in privacy legislation over the past 50 years, although a finer-grained examination reveals that the rate of increase varies depending on the personal data types that GPIs address. Our exploration also demonstrates that most privacy laws respectively address relatively few personal data types, showing that comprehensive privacy legislation remains rare. Additionally, topic modeling results show the prevalence of common themes in GPIs, such as finance, healthcare, and telecommunications. Finally, we release the corpus to the research community to promote further study., Comment: 14 pages, 7 figures, 7 tables

The ubiquity of digital music consumption has made it possible to extract information about modern music that allows us to perform large scale analysis of stylistic change over time. In order to uncover underlying patterns in cultural evolution, we examine the relationship between the established characteristics of different genres and styles, and the introduction of novel ideas that fuel this ongoing creative evolution. To understand how this dynamic plays out and shapes the cultural ecosystem, we compare musical artifacts to their contemporaries to identify novel artifacts, study the relationship between novelty and commercial success, and connect this to the changes in musical content that we can observe over time. Using Music Information Retrieval (MIR) data and lyrics from Billboard Hot 100 songs between 1974-2013, we calculate a novelty score for each song's aural attributes and lyrics. Comparing both scores to the popularity of the song following its release, we uncover key patterns in the relationship between novelty and audience reception. Additionally, we look at the link between novelty and the likelihood that a song was influential given where its MIR and lyrical features fit within the larger trends we observed.

This report describes a set of neonatal electroencephalogram (EEG) recordings graded according to the severity of abnormalities in the background pattern. The dataset consists of 169 hours of multichannel EEG from 53 neonates recorded in a neonatal intensive care unit. All neonates received a diagnosis of hypoxic-ischaemic encephalopathy (HIE), the most common cause of brain injury in full term infants. For each neonate, multiple 1-hour epochs of good quality EEG were selected and then graded for background abnormalities. The grading system assesses EEG attributes such as amplitude and frequency, continuity, sleep--wake cycling, symmetry and synchrony, and abnormal waveforms. Background severity was then categorised into 4 grades: normal or mildly abnormal EEG, moderately abnormal EEG, severely abnormal EEG, and inactive EEG. The data can be used as a reference set of multi-channel EEG for neonates with HIE, for EEG training purposes, or for developing and evaluating automated grading algorithms.

Holographic near-eye displays offer unprecedented capabilities for virtual and augmented reality systems, including perceptually important focus cues. Although artificial intelligence--driven algorithms for computer-generated holography (CGH) have recently made much progress in improving the image quality and synthesis efficiency of holograms, these algorithms are not directly applicable to emerging phase-only spatial light modulators (SLM) that are extremely fast but offer phase control with very limited precision. The speed of these SLMs offers time multiplexing capabilities, essentially enabling partially-coherent holographic display modes. Here we report advances in camera-calibrated wave propagation models for these types of holographic near-eye displays and we develop a CGH framework that robustly optimizes the heavily quantized phase patterns of fast SLMs. Our framework is flexible in supporting runtime supervision with different types of content, including 2D and 2.5D RGBD images, 3D focal stacks, and 4D light fields. Using our framework, we demonstrate state-of-the-art results for all of these scenarios in simulation and experiment., Comment: Project page with more details: http://www.computationalimaging.org/publications/time-multiplexed-neural-holography/

Facial morphs created between two identities resemble both of the faces used to create the morph. Consequently, humans and machines are prone to mistake morphs made from two identities for either of the faces used to create the morph. This vulnerability has been exploited in "morph attacks" in security scenarios. Here, we asked whether the "other-race effect" (ORE) -- the human advantage for identifying own- vs. other-race faces -- exacerbates morph attack susceptibility for humans. We also asked whether face-identification performance in a deep convolutional neural network (DCNN) is affected by the race of morphed faces. Caucasian (CA) and East-Asian (EA) participants performed a face-identity matching task on pairs of CA and EA face images in two conditions. In the morph condition, different-identity pairs consisted of an image of identity "A" and a 50/50 morph between images of identity "A" and "B". In the baseline condition, morphs of different identities never appeared. As expected, morphs were identified mistakenly more often than original face images. Moreover, CA participants showed an advantage for CA faces in comparison to EA faces (a partial ORE). Of primary interest, morph identification was substantially worse for cross-race faces than for own-race faces. Similar to humans, the DCNN performed more accurately for original face images than for morphed image pairs. Notably, the deep network proved substantially more accurate than humans in both cases. The results point to the possibility that DCNNs might be useful for improving face identification accuracy when morphed faces are presented. They also indicate the significance of the ORE in morph attack susceptibility in applied settings., Comment: 4 figures, 11 pages

We exploit memory effect speckle correlations for the imaging of incoherent linear (single-photon) fluorescent sources behind scattering tissue. While memory effect-based imaging techniques have been heavily studied in the past, for thick scattering layers and complex illumination patterns these correlations are weak, limiting the practice applicability of the idea. In this work, we introduce a Spatial Light Modulator (SLM) between the tissue sample and the imaging sensor and capture multiple modulations of the speckle pattern. We show that by correctly designing the modulation pattern and the reconstruction algorithm we can greatly enhance statistical correlations in the data. We exploit this to demonstrate the reconstruction of mega-pixel wide fluorescent patterns behind scattering tissue.

We present an overview of the "FAIR Guiding Principles for scientific data management and stewardship", first published in 2016, and how they relate to astronomical data management. In particular, we discuss the connection between the FAIR principles and IVOA standards, and how data management systems with these standards implemented are close to compliance. We then look at what extra components are required to make astronomical data FAIR. Finally, we give a case study of the All-Sky Virtual Observatory (Australia's node of the VO) and their implementation of the FAIR principles., Comment: 10 pages, 1 figure, to be published in Proc. ADASS XXXI (2021)

The Gamma-ray Module, GMOD, is a miniaturised novel gamma-ray detector which will be the primary scientific payload on the Educational Irish Research Satellite (EIRSAT-1) 2U CubeSat mission. GMOD comprises a compact (25mm $\times$ 25mm $\times$ 40mm) cerium bromide scintillator coupled to a tiled array of 4$\times$4 silicon photomultipliers, with front-end readout provided by the IDE3380 SIPHRA. This paper presents the detailed GMOD design and the accommodation of the instrument within the restrictive CubeSat form factor. The electronic and mechanical interfaces are compatible with many off-the-shelf CubeSat systems and structures. The energy response of the GMOD engineering qualification model has been determined using radioactive sources, and an energy resolution of 5.4% at 662keV has been measured. EIRSAT-1 will perform on-board processing of GMOD data. Trigger results, including light-curves and spectra, will be incorporated into the spacecraft beacon and transmitted continuously. Inexpensive hardware can be used to decode the beacon signal, making the data accessible to a wide community. GMOD will have scientific capability for the detection of gamma-ray bursts, in addition to the educational and technology demonstration goals of the EIRSAT-1 mission. The detailed design and measurements to date demonstrate the capability of GMOD in low Earth orbit, the scalability of the design for larger CubeSats and as an element of future large gamma-ray missions., Comment: 31 pages, 16 figures. This is an author accepted manuscript of an article accepted for publication in Experimental Astronomy. The final open-access version will be available at: https://doi.org/10.1007/s10686-022-09842-z

Due to the critical importance of Industrial Control Systems (ICS) to the operations of cities and countries, research into the security of critical infrastructure has become increasingly relevant and necessary. As a component of both the research and application sides of smart city development, accurate and precise modeling, simulation, and verification are key parts of a robust design and development tools that provide critical assistance in the prevention, detection, and recovery from abnormal behavior in the sensors, controllers, and actuators which make up a modern ICS system. However, while these tools have potential, there is currently a need for helper-tools to assist with their setup and configuration, if they are to be utilized widely. Existing state-of-the-art tools are often technically complex and difficult to customize for any given IoT/ICS processes. This is a serious barrier to entry for most technicians, engineers, researchers, and smart city planners, while slowing down the critical aspects of safety and security verification. To remedy this issue, we take a case study of existing simulation toolkits within the field of water management and expand on existing tools and algorithms with simplistic automated retrieval functionality using a much more in-depth and usable customization interface to accelerate simulation scenario design and implementation, allowing for customization of the cyber-physical network infrastructure and cyber attack scenarios. We additionally provide a novel in-tool-assessment of network's resilience according to graph theory path diversity. Further, we lay out a roadmap for future development and application of the proposed tool, including expansions on resiliency and potential vulnerability model checking, and discuss applications of our work to other fields relevant to the design and operation of smart cities., Comment: 8 pages, 13 figures, 1 table