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The Kerr spacetime is one of the most widely known solutions to Einstein's vacuum field equations and is commonly used to describe a black hole with mass $m$ and spin $a$. Astrophysical observations in the electromagnetic spectrum as well as detected gravitational wave signals indicate that it can be used to describe the spacetime around candidates for rotating black holes. While the geodesic structure of the Kerr spacetime is already well known for decades, using exact analytic solutions to the equations of motion for applications to astrophysical problems has only attracted attention relatively recently. Here, these applications mainly focus on predicting observations for the shadow, the photon rings, and characteristic structures in the accretion disk. Using the exact analytic solutions to investigate exact gravitational lensing of light and gravitational waves emitted by sources outside the accretion disk has only received limited attention so far. Therefore, the focus of this paper will be to address this question. For this purpose we assume that we have a standard observer in the domain of outer communication. We introduce a local orthonormal tetrad to relate the constants of motion of light rays and high-frequency gravitational waves detected by the observer to latitude-longitude coordinates on the observer's celestial sphere. In this parameterisation we derive the radius coordinates of the photon orbits and their latitudinal projections onto the observer's celestial sphere as functions of the celestial longitude. We use the latitude-longitude coordinates to classify the different types of motion, and solve the equations of motion analytically using elementary and Jacobi's elliptic functions as well as Legendre's elliptic integrals. We use the analytic solutions to write down an exact lens equation, and to calculate the redshift and the travel time., Comment: 78 pages, 9 figures, abstract abbreviated (full abstract can be found in the pdf); transfers investigation in arXiv:2112.10260 to the Kerr spacetime

The integration of superconducting niobium and tantalum into superconducting quantum devices has been increasingly explored over the past few years. Recent developments have shown that two-level-systems (TLS) in the surface oxides of these superconducting films are a leading source of decoherence in quantum circuits, and understanding the surface oxidation kinetics of these materials is key to enabling scalability of these technologies. We analyze the nature of atmospheric oxidation of both niobium and a-tantalum surfaces at time scales relevant to fabrication, from sub-minute to two-week atmospheric exposure, employing a combination of x-ray photoelectron spectroscopy and transmission electron microscopy to monitor the growth of the surface oxides. The oxidation kinetics are modeled according to the Cabrera-Mott model of surface oxidation, and the model growth parameters are reported for both films. Our results indicate that niobium surface oxidation follows a consistent regime of inverse logarithmic growth for the entire time scale of the study, whereas a-Ta surface oxidation shows a clear transition between two inverse logarithmic growth regimes at time t = 1 hour, associated with the re-coordination of the surface oxide as determined by x-ray photoelectron spectroscopy analysis. Our findings provide a more complete understanding of the differences in atmospheric surface oxidation between Nb and a-Ta, particularly at short time scales, paving the way for the development of more robust fabrication control for quantum computing architectures., Comment: 15 pages, 5 figures, 4 embedded tables

The task of predicting long-term patient outcomes using supervised machine learning is a challenging one, in part because of the high variance of each patient's trajectory, which can result in the model over-fitting to the training data. Temporal difference (TD) learning, a common reinforcement learning technique, may reduce variance by generalising learning to the pattern of state transitions rather than terminal outcomes. However, in healthcare this method requires several strong assumptions about patient states, and there appears to be limited literature evaluating the performance of TD learning against traditional supervised learning methods for long-term health outcome prediction tasks. In this study, we define a framework for applying TD learning to real-time irregularly sampled time series data using a Semi-Markov Reward Process. We evaluate the model framework in predicting intensive care mortality and show that TD learning under this framework can result in improved model robustness compared to standard supervised learning methods. and that this robustness is maintained even when validated on external datasets. This approach may offer a more reliable method when learning to predict patient outcomes using high-variance irregular time series data., Comment: To be published in the Proceedings of the 4th Machine Learning for Health symposium, Proceedings of Machine Learning Research (PMLR)

Theoretical and empirical evidence suggests that joint graph embedding algorithms induce correlation across the networks in the embedding space. In the Omnibus joint graph embedding framework, previous results explicitly delineated the dual effects of the algorithm-induced and model-inherent correlations on the correlation across the embedded networks. Accounting for and mitigating the algorithm-induced correlation is key to subsequent inference, as sub-optimal Omnibus matrix constructions have been demonstrated to lead to loss in inference fidelity. This work presents the first efforts to automate the Omnibus construction in order to address two key questions in this joint embedding framework: the correlation-to-OMNI problem and the flat correlation problem. In the flat correlation problem, we seek to understand the minimum algorithm-induced flat correlation (i.e., the same across all graph pairs) produced by a generalized Omnibus embedding. Working in a subspace of the fully general Omnibus matrices, we prove both a lower bound for this flat correlation and that the classical Omnibus construction induces the maximal flat correlation. In the correlation-to-OMNI problem, we present an algorithm -- named corr2Omni -- that, from a given matrix of estimated pairwise graph correlations, estimates the matrix of generalized Omnibus weights that induces optimal correlation in the embedding space. Moreover, in both simulated and real data settings, we demonstrate the increased effectiveness of our corr2Omni algorithm versus the classical Omnibus construction., Comment: 34 pages, 8 figures

Wolf-Rayet stars (WRs) are one of the final evolutionary stages of massive stars and immediate progenitors of stellar-mass black holes. Their multiplicity forms an important anchor point in single and binary population models for predicting gravitational-wave progenitors. Recent spectroscopic campaigns have suggested incompatible multiplicity fractions and period distributions for N- and C-rich Galactic WRs (WNs and WCs) at short as well as long orbital periods, in contradiction with evolutionary model predictions. In this work, we employed infrared interferometry using the $K$-band instrument GRAVITY at the VLTI to investigate the multiplicity of WRs at long periods and explore the nature of their companions. We present a survey of 39 Galactic WRs, including 11 WN, 15 WC and 13 H-rich WN (WNh) stars. We detected wide companions with GRAVITY for only four stars: WR 48, WR 89, WR 93 and WR 115. Combining with spectroscopic studies, we arrived at multiplicity fractions of $f^{\rm WN}_{\rm obs} = 0.55\pm0.15$, $f^{\rm WC}_{\rm obs} = 0.40\pm0.13$ and $f^{\rm WNh}_{\rm obs} = 0.23\pm0.12$. In addition, we also found other features in the GRAVITY dataset such as (i) a diffuse extended component in over half the WR sample; (ii) five known spectroscopic binaries resolved in differential phase data and (iii) spatially resolved winds in four stars: WR 16, WR 31a, WR 78 and WR 110. Our survey reveals a lack of intermediate (few 100s d) and long- (few years to decades) period WR systems. The 200-d peak in the period distributions of WR+OB and BH+OB binaries predicted by Case B mass-transfer binary evolution models is not seen in our data. The rich companionship of their O-type progenitors in this separation range suggest that the WR progenitor stars expand and interact with their companions, most likely through unstable mass-transfer, resulting in either a short-period system or a merger., Comment: Accepted in A&A

The inner parts of the hot discs surrounding massive young stellar objects (MYSOs) are still barely explored due to observational limitations in terms of angular resolution, scarcity of diagnostic lines and the embedded and rare nature of these targets. We present the first K-band spectro-interferometric observations toward the MYSO G033.3891, which based on former kinematic evidence via the CO bandhead emission is known to host an accreting disc. Using the high spectral resolution mode (R$\sim$4000) of the GRAVITY/VLTI, we spatially resolve the emission of the inner dusty disc and the crucial gaseous interface between the star and the dusty disc. Using detailed modelling on the K-band dust continuum and tracers known to be associated with the ionised and molecular gaseous interface (Br$\gamma$, CO), we report on the smallest scales of accretion/ejection. The new observations in combination with our geometric and kinematic models employed to fit former high spectral resolution observations on the source (R$\sim$30,000; CRIRES/VLTI) allow us to constrain the size of the inner gaseous disc both spatially and kinematically via the CO overtone emission at only 2 au. Our models reveal that both Br$\gamma$ and CO emissions are located well within the dust sublimation radius (5~au) as traced by the hot 2.2~$\mu$m dust continuum. Our paper provides the first case study where the tiniest scales of gaseous accretion around the MYSO G033.3891 are probed both kinematically and spatially via the CO bandhead emission. This analysis of G033.3891 stands as only the second instance of such investigation within MYSOs, underscoring the gradual accumulation of knowledge regarding how massive young stars gain their mass, while further solidifying the disc nature of accretion at the smallest scales of MYSOs., Comment: Accepted for publication in A&A (9 pages, 5 figures, 2 tables)

We present the first polarized neutron transmission image of a model Ne\'el ferrimagnetic material, polycrystalline terbium iron garnet (Tb$_{3}$Fe$_{5}$O$_{12}$, TbIG for short), as it is taken through its compensation temperature $T_{comp}$ where, according to the theory of ferrimagnetism, the internal magnetization should vanish. Our polarized neutron imaging data and the additional supporting measurements using neutron spin echo spectroscopy and SQUID magnetometry are all consistent with a vanishing internal magnetization at $T_{comp}$., Comment: 15 pages, 15 figures

The LHS 6343 system consists of a resolved M-dwarf binary with an evolved, negligibly irradiated brown dwarf, LHS 6343 C, orbiting the primary star. Such brown dwarf eclipsing binaries present rare and unique opportunities to calibrate sub-stellar evolutionary and atmosphere models since mass, radius, temperature and luminosity can be directly measured. We update this brown dwarf's mass (62.6+/-2.2 MJup) and radius (0.788+/-0.043 RJup) using empirical stellar relations and a Gaia DR3 distance. We use Hubble Space Telescope/WFC3 observations of an LHS 6343 C secondary eclipse to obtain a NIR emission spectrum, which matches to a spectral type of T1.5+/-1. We combine this spectrum with existing Kepler and Spitzer/IRAC secondary eclipse photometry to perform atmospheric characterization using the ATMO-2020, Sonora-Bobcat and BT-Settl model grids. ATMO-2020 models with strong non-equilibrium chemistry yield the best fit to observations across all modelled bandpasses while predicting physical parameters consistent with Gaia-dependant analogs. BT-Settl predicts values slightly more consistent with such analogs but offers a significantly poorer fit to the WFC3 spectrum. Finally, we obtain a semi-empirical measurement of LHS 6343 C's apparent luminosity by integrating its observed and modelled spectral energy distribution. Applying knowledge of the system's distance yields a bolometric luminosity of log(Lbol/Lsun) = -4.77+/-0.03 and, applying the Stefan-Boltzmann law for the known radius, an effective temperature of 1303+/-29 K. We also use the ATMO-2020 and Sonora-Bobcat evolutionary model grids to infer an age for LHS 6343 C of 2.86 +0.40-0.33 Gyr and 3.11 +0.50-0.38 Gyr respectively., Comment: Accepted for publication in ApJ

This workshop will grow and consolidate a community of interdisciplinary CSCW researchers focusing on the topic of contestable AI. As an outcome of the workshop, we will synthesize the most pressing opportunities and challenges for contestability along AI value chains in the form of a research roadmap. This roadmap will help shape and inspire imminent work in this field. Considering the length and depth of AI value chains, it will especially spur discussions around the contestability of AI systems along various sites of such chains. The workshop will serve as a platform for dialogue and demonstrations of concrete, successful, and unsuccessful examples of AI systems that (could or should) have been contested, to identify requirements, obstacles, and opportunities for designing and deploying contestable AI in various contexts. This will be held primarily as an in-person workshop, with some hybrid accommodation. The day will consist of individual presentations and group activities to stimulate ideation and inspire broad reflections on the field of contestable AI. Our aim is to facilitate interdisciplinary dialogue by bringing together researchers, practitioners, and stakeholders to foster the design and deployment of contestable AI., Comment: 5 pages, 0 figure, to be held as a workshop at CSCW'24

We consider a perimeter defense problem in a rooted full tree graph environment in which a single defending vehicle seeks to defend a set of specified vertices, termed as the perimeter from mobile intruders that enter the environment through the tree's leaves. We adopt the technique of competitive analysis to characterize the performance of an online algorithm for the defending vehicle. We first derive fundamental limits on the performance of any online algorithm relative to that of an optimal offline algorithm. Specifically, we give three fundamental conditions for finite, 2, and 3/2 competitive ratios in terms of the environment parameters. We then design and analyze three classes of online algorithms that have provably finite competitiveness under varying environmental parameter regimes. Finally, we give a numerical visualization of these regimes to better show the comparative strengths and weaknesses of each algorithm., Comment: 8 pages, 5 figures

Super-resolution ultrasound imaging through microbubble (MB) localisation and tracking, also known as ultrasound localisation microscopy, allows non-invasive sub-diffraction resolution imaging of microvasculature in animals and humans. The number of MBs localised from the acquired contrast-enhanced ultrasound (CEUS) images and the localisation precision directly influence the quality of the resulting super-resolution microvasculature images. However, non-negligible noise present in the CEUS images can make localising MBs challenging. To enhance the MB localisation performance, we propose a Multi-Frame Deconvolution (MF-Decon) framework that can exploit the spatiotemporal coherence inherent in the CEUS data, with new spatial and temporal regularisers designed based on total variation (TV) and regularisation by denoising (RED). Based on the MF-Decon framework, we introduce two novel methods: MF-Decon with spatial and temporal TVs (MF-Decon+3DTV) and MF-Decon with spatial RED and temporal TV (MF-Decon+RED+TV). Results from in silico simulations indicate that our methods outperform two widely used methods using deconvolution or normalised cross-correlation across all evaluation metrics, including precision, recall, $F_1$ score, mean and standard localisation errors. In particular, our methods improve MB localisation precision by up to 39% and recall by up to 12%. Super-resolution microvasculature maps generated with our methods on a publicly available in vivo rat brain dataset show less noise, better contrast, higher resolution and more vessel structures., Comment: 26 pages, 1 table, 7 figures

We present the generalised design of low-complexity, small aperture optical terminals intended for kilometre-scale, terrestrial, free-space laser links between fixed and dynamic targets. The design features single-mode fibre coupling of the free-space beam, assisted by a fast-steering, tip/tilt mirror that enables first-order turbulence suppression and fine target tracking. The total power throughput over the free-space link and the scintillation index in fibre are optimised. The optimal tip/tilt correction bandwidth and range, aperture size, and focal length for a given link are derived using analytical atmospheric turbulence modelling and numerical simulations., Comment: 11 pages, 6 figures

Quasi-Lie bialgebras are natural extensions of Lie-bialgebras, where the cobracket satisfies the co-Jacobi relation up to some natural obstruction controlled by a skew-symmetric 3-tensor $\phi$. This structure was introduced by Drinfeld while studying deformation theory of universal enveloping algebras and has since seen many other applications in algebra and geometry. In this paper we study the derivation complex of strongly homotopy quasi-Lie bialgebra, both in the unwheeled (i.e standard) and wheeled case, and compute its cohomology in terms of Kontsevich graph complexes.

Deep learning-based object detection algorithms enable the simultaneous classification and localization of any number of objects in image data. Many of these algorithms are capable of operating in real-time on high resolution images, attributing to their widespread usage across many fields. We present an end-to-end object detection pipeline designed for real-time rare event searches for the Migdal effect, using high-resolution image data from a state-of-the-art scientific CMOS camera in the MIGDAL experiment. The Migdal effect in nuclear scattering, crucial for sub-GeV dark matter searches, has yet to be experimentally confirmed, making its detection a primary goal of the MIGDAL experiment. Our pipeline employs the YOLOv8 object detection algorithm and is trained on real data to enhance the detection efficiency of nuclear and electronic recoils, particularly those exhibiting overlapping tracks that are indicative of the Migdal effect. When deployed online on the MIGDAL readout PC, we demonstrate our pipeline to process and perform the rare event search on 2D image data faster than the peak 120 frame per second acquisition rate of the CMOS camera. Applying these same steps offline, we demonstrate that we can reduce a sample of 20 million camera frames to around 1000 frames while maintaining nearly all signal that YOLOv8 is able to detect, thereby transforming a rare search into a much more manageable search. Our studies highlight the potential of pipelines similar to ours significantly improving the detection capabilities of experiments requiring rapid and precise object identification in high-throughput data environments.

Exploring the conduction mechanism in the chalcogenide perovskite BaZrS$_3$ is of significant interest due to its potential suitability as a top absorber layer in silicon-based tandem solar cells and other optoelectronic applications. Theoretical and experimental studies anticipate native ambipolar doping in BaZrS$_3$, although experimental validation remains limited. This study reveals a transition from highly insulating behavior to n-type conductivity in BaZrS$_3$ through annealing in an S-poor environment. BaZrS$_3$ thin films are synthesized $\textit{via}$ a two step process: co-sputtering of Ba-Zr followed by sulfurization at 600 $^{\circ}$C, and subsequent annealing in high vacuum. UV-Vis measurement reveal a red-shift in the absorption edge concurrent with sample color darkening after annealing. The increase in defect density with vacuum annealing, coupled with low activation energy and n-type character of defects, strongly suggests that sulfur vacancies (V$_{\mathrm{S}}$) are responsible, in agreement with theoretical predictions. The shift of the Fermi level towards conduction band minimum, quantified by Hard X-ray Photoelectron Spectroscopy (Ga K$\alpha$, 9.25 keV), further corroborates the induced n-type of conductivity in annealed samples. Our findings indicate that vacuum annealing induces V$_{\mathrm{S}}$ defects that dominate the charge transport, thereby making BaZrS$_3$ an n-type semiconductor under S-poor conditions. This study offers crucial insights into understanding the defect properties of BaZrS$_3$, facilitating further improvements for its use in solar cell applications., Comment: 19 pages (single column), 6 figures in main manuscript and 8 figures in supplementary information

Industry 4.0 and beyond will rely heavily on sustainable Business Decision Modelling (BDM) that can be accelerated by blockchain and Digital Twin (DT) solutions. BDM is built on models and frameworks refined by key identification factors, data analysis, and mathematical or computational aspects applicable to complex business scenarios. Gaining actionable intelligence from collected data for BDM requires a carefully considered infrastructure to ensure data transparency, security, accessibility and sustainability. Organisations should consider social, economic and environmental factors (based on the triple bottom line approach) to ensure sustainability when integrating such an infrastructure. These sustainability features directly impact BDM concerning resource optimisation, stakeholder engagement, regulatory compliance and environmental impacts. To further understand these segments, taxonomies are defined to evaluate blockchain and DT sustainability features based on an in-depth review of the current state-of-the-art research. Detailed comparative evaluations provide insight into the reachability of the sustainable solution in terms of ideologies, access control and performance overheads. Several research questions are put forward to motivate further research that significantly impacts BDM. Finally, a case study based on an exemplary supply chain management system is presented to show the interoperability of blockchain and DT with BDM., Comment: 34 pages, 19 figures, 7 tables

We provide a method for calculating anharmonic lattice dynamics, by building a surrogate model based on Gaussian Processes (GPs). Due to the underlying Gaussian form of a GP, the model is infinitely differentiable. This allows us to train the model trained directly on forces (the derivative of PESs) reducing the evaluations required for a given accuracy. We can extend this differentiation to directly calculate second and third order force-constants using automatic differentiation (AD). For the five model materials we study, we find that the force-constants are in close agreement with a standard finite-displacement approach. Our method appears to be linear scaling in the number of atoms at predicting both second and third-order (anharmonic) force-constants., Comment: 12 pages, 5 figures

Recent Large Language Models (LLMs) have shown the ability to generate content that is difficult or impossible to distinguish from human writing. We investigate the ability of differently-sized LLMs to replicate human writing style in short, creative texts in the domain of Showerthoughts, thoughts that may occur during mundane activities. We compare GPT-2 and GPT-Neo fine-tuned on Reddit data as well as GPT-3.5 invoked in a zero-shot manner, against human-authored texts. We measure human preference on the texts across the specific dimensions that account for the quality of creative, witty texts. Additionally, we compare the ability of humans versus fine-tuned RoBERTa classifiers to detect AI-generated texts. We conclude that human evaluators rate the generated texts slightly worse on average regarding their creative quality, but they are unable to reliably distinguish between human-written and AI-generated texts. We further provide a dataset for creative, witty text generation based on Reddit Showerthoughts posts., Comment: Accepted to *SEM 2024 (StarSEM) conference

Two-dimensional hybrid organic-inorganic metal halide perovskites offer enhanced stability for perovskite-based applications. Their crystal structure's soft and ionic nature gives rise to strong interactions between charge carriers and ionic rearrangements. Here, we investigate the interaction of photo-generated electrons and ionic polarizations in single-crystal 2D perovskite butylammonium lead iodide, varying the inorganic lammelae thickness in the 2D single crystals. We determined the directionality of the transition dipole moments of the relevant phonon modes (in the 0.3-3 THz range) by angle-and-polarization dependent THz transmission measurements. We find a clear anisotropy of the in-plane photoconductivity, with a 10% reduction along the axis parallel with the transition dipole moment of the most strongly coupled phonon. Detailed calculations, based on Feynman polaron theory, indicate that the anisotropy originates from directional electron-phonon interactions., Comment: 50 pages, 5 figures main text, 13 figures in supplementary information

Massive stars (those larger than 8 solar masses at formation) have radiative envelopes that cannot sustain a dynamo, the mechanism that produces magnetic fields in lower-mass stars. Despite this, approximately 7\% of massive stars have observed magnetic fields, the origin of which is debated. We used multi-epoch interferometric and spectroscopic observations to characterize HD 148937, a binary system of two massive stars. We found that only one star is magnetic and that it appears younger than its companion. The system properties and a surrounding bipolar nebula can be reproduced with a model in which two stars merged (in a previous triple system) to produce the magnetic massive star. Our results provide observational evidence that magnetic fields form in at least some massive stars through stellar mergers., Comment: Full paper with supplementary materials. 59 pages, 18 figures

F\"orster resonance energy transfer (FRET) is a quantum mechanical phenomenon involving the non-radiative transfer of energy between coupled electric dipoles. Due to the strong dependence of FRET on the distance between the dipoles, it is frequently used as a ``molecular ruler" in biology, chemistry, and physics. This is done by placing dipolar molecules called dyes on molecules of interest. In time-resolved confocal single-molecule FRET (smFRET) experiments, the joint distribution of the FRET efficiency and the donor fluorescence lifetime can reveal underlying molecular conformational dynamics via deviation from their theoretical F\"orster relationship. This deviation is referred to as a dynamic shift. Quantifying the dynamic shift caused by the motion of the fluorescent dyes is essential to decoupling the dynamics of the studied molecules and the dyes. We develop novel Langevin models for the dye linker dynamics, including rotational dynamics, based on first principle physics and proper dye linker chemistry to match accessible volumes predicted by molecular dynamics simulations. By simulating the dyes' stochastic translational and rotational dynamics, we show that the observed dynamic shift can largely be attributed to the mutual orientational dynamics of the electric dipole moments associated with the dyes, not their accessible volume. Our models provide the most up-to-date and accurate estimation of FRET., Comment: 14 Pages, 7 Figures

The CG-2 beamline at the High Flux Isotope Reactor (HFIR) exhibits a notable discrepancy between observed count rates and the count rates we would expect based on a Monte-Carlo neutron ray-trace simulation. These simulations consistently predict count rates approximately five times greater than those observed in four separate experimental runs involving different instrument configurations. This discrepancy suggests that certain factors are causing losses in measurements that are not adequately accounted for in the simulation, in particular guide reflectivity or misalignment. To investigate these discrepancies, a high-dimensional simulation parameter approach is applied in order to understand the losses. Region of Interest (ROI) groups along the instrument are assigned to different surfaces of the guide components within the simulation. This allows the parameters of those guide components to be varied as a group to minimize the complexity of the search space. The result is an optimization of simulation parameters using an iterative scheme that aims to minimize the difference between experimentally measured count rates and simulated count rates across all tested collimator combinations. This proposed methodology holds the potential to reveal previously unrecognized sources of intensity loss in the CG-2 beamline at HFIR and improve the accuracy of simulations, leading to enhanced understanding and performance of the beamline for various scientific applications., Comment: 14 pages, 19 figures

This white paper presents the current status of the R&D for Analysis Facilities (AFs) and attempts to summarize the views on the future direction of these facilities. These views have been collected through the High Energy Physics (HEP) Software Foundation's (HSF) Analysis Facilities forum, established in March 2022, the Analysis Ecosystems II workshop, that took place in May 2022, and the WLCG/HSF pre-CHEP workshop, that took place in May 2023. The paper attempts to cover all the aspects of an analysis facility.

In this paper we present EgoLifter, a novel system that can automatically segment scenes captured from egocentric sensors into a complete decomposition of individual 3D objects. The system is specifically designed for egocentric data where scenes contain hundreds of objects captured from natural (non-scanning) motion. EgoLifter adopts 3D Gaussians as the underlying representation of 3D scenes and objects and uses segmentation masks from the Segment Anything Model (SAM) as weak supervision to learn flexible and promptable definitions of object instances free of any specific object taxonomy. To handle the challenge of dynamic objects in ego-centric videos, we design a transient prediction module that learns to filter out dynamic objects in the 3D reconstruction. The result is a fully automatic pipeline that is able to reconstruct 3D object instances as collections of 3D Gaussians that collectively compose the entire scene. We created a new benchmark on the Aria Digital Twin dataset that quantitatively demonstrates its state-of-the-art performance in open-world 3D segmentation from natural egocentric input. We run EgoLifter on various egocentric activity datasets which shows the promise of the method for 3D egocentric perception at scale., Comment: ECCV 2024 camera ready version. Project page: https://egolifter.github.io/

We introduce SceneScript, a method that directly produces full scene models as a sequence of structured language commands using an autoregressive, token-based approach. Our proposed scene representation is inspired by recent successes in transformers & LLMs, and departs from more traditional methods which commonly describe scenes as meshes, voxel grids, point clouds or radiance fields. Our method infers the set of structured language commands directly from encoded visual data using a scene language encoder-decoder architecture. To train SceneScript, we generate and release a large-scale synthetic dataset called Aria Synthetic Environments consisting of 100k high-quality in-door scenes, with photorealistic and ground-truth annotated renders of egocentric scene walkthroughs. Our method gives state-of-the art results in architectural layout estimation, and competitive results in 3D object detection. Lastly, we explore an advantage for SceneScript, which is the ability to readily adapt to new commands via simple additions to the structured language, which we illustrate for tasks such as coarse 3D object part reconstruction., Comment: see project page, https://projectaria.com/scenescript

Managing complex Cyber-Physical Energy Systems (CPES) requires solving various optimization problems with multiple objectives and constraints. As distributed control architectures are becoming more popular in CPES for certain tasks due to their flexibility, robustness, and privacy protection, multi-objective optimization must also be distributed. For this purpose, we present MO-COHDA, a fully distributed, agent-based algorithm, for solving multi-objective optimization problems of CPES. MO-COHDA allows an easy and flexible adaptation to different use cases and integration of custom functionality. To evaluate the effectiveness of MO-COHDA, we compare it to a central NSGA-2 algorithm using multi-objective benchmark functions from the ZDT problem suite. The results show that MO-COHDA can approximate the reference front of the benchmark problems well and is suitable for solving multi-objective optimization problems. In addition, an example use case of scheduling a group of generation units while optimizing three different objectives was evaluated to show how MO-COHDA can be easily applied to real-world optimization problems in CPES., Comment: Submitted to Energy Informatics Review (ACM SIGEnergy)

We describe a set of network analysis methods based on the rows of the Krylov subspace matrix computed from a network adjacency matrix via power iteration using a non-random initial vector. We refer to these node-specific row vectors as Krylov subspace trajectories. While power iteration using a random initial starting vector is commonly applied to the network adjacency matrix to compute eigenvector centrality values, this application only uses the final vector generated after numerical convergence. Importantly, use of a random initial vector means that the intermediate results of power iteration are also random and lack a clear interpretation. To the best of our knowledge, use of intermediate power iteration results for network analysis has been limited to techniques that leverage just a single pre-convergence solution, e.g., Power Iteration Clustering. In this paper, we explore methods that apply power iteration with a non-random inital vector to the network adjacency matrix to generate Krylov subspace trajectories for each node. These non-random trajectories provide important information regarding network structure, node importance, and response to perturbations. We have created this short preprint in part to generate feedback from others in the network analysis community who might be aware of similar existing work.

Conversions between neutrons $n$ and Dark Matter candidate sterile neutrons $n'$ have been proposed as a mechanism for Baryon Number $\mathcal{B}$ violation. In the case that there is a small mass difference $\Delta{m}$ between the $n$ and the $n'$ states, oscillations can be induced by compensating for $\Delta{m}$ with a magnetic field. A search for such neutron oscillations was performed at the Spallation Neutron Source by looking for anomalous neutron transmission through a strongly absorbing cadmium wafer inside of a $6.6$~T magnet. The approach described here saw no regenerated neutrons above background, which provides an improved limit for neutron - sterile neutrons transformations for a range of $\Delta{m}$ between $0.1$~neV and $1000$~neV., Comment: 18 pages, 15 figures