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We introduce a novel adaptive eigenvalue filtering strategy to stabilize and accelerate the optimization of Neo-Hookean energy and its variants under the Projected Newton framework. For the first time, we show that Newton's method, Projected Newton with eigenvalue clamping and Projected Newton with absolute eigenvalue filtering can be unified using ideas from the generalized trust region method. Based on the trust-region fit, our model adaptively chooses the correct eigenvalue filtering strategy to apply during the optimization. Our method is simple but effective, requiring only two lines of code change in the existing Projected Newton framework. We validate our model outperforms stand-alone variants across a number of experiments on quasistatic simulation of deformable solids over a large dataset., Comment: SIGGRAPH Asia 2024 (Conference track). Project page: https://www.cs.columbia.edu/cg/trust-region/

Mapping the conformational dynamics of proteins is crucial for elucidating their functional mechanisms. While Molecular Dynamics (MD) simulation enables detailed time evolution of protein motion, its computational toll hinders its use in practice. To address this challenge, multiple deep learning models for reproducing and accelerating MD have been proposed drawing on transport-based generative methods. However, existing work focuses on generation through transport of samples from prior distributions, that can often be distant from the data manifold. The recently proposed framework of stochastic interpolants, instead, enables transport between arbitrary distribution endpoints. Building upon this work, we introduce EquiJump, a transferable SO(3)-equivariant model that bridges all-atom protein dynamics simulation time steps directly. Our approach unifies diverse sampling methods and is benchmarked against existing models on trajectory data of fast folding proteins. EquiJump achieves state-of-the-art results on dynamics simulation with a transferable model on all of the fast folding proteins.

Electron density prediction stands as a cornerstone challenge in molecular systems, pivotal for various applications such as understanding molecular interactions and conducting precise quantum mechanical calculations. However, the scaling of density functional theory (DFT) calculations is prohibitively expensive. Machine learning methods provide an alternative, offering efficiency and accuracy. We introduce a novel SE(3)-equivariant architecture, drawing inspiration from Slater-Type Orbitals (STO), to learn representations of molecular electronic structures. Our approach offers an alternative functional form for learned orbital-like molecular representation. We showcase the effectiveness of our method by achieving SOTA prediction accuracy of molecular electron density with 30-70\% improvement over other work on Molecular Dynamics data.

Nonparametric procedures are more powerful for detecting interaction in two-way ANOVA when the data are non-normal. In this paper, we compute null critical values for the aligned rank-based tests (APCSSA/APCSSM) where the levels of the factors are between 2 and 6. We compare the performance of these new procedures with the ANOVA F-test for interaction, the adjusted rank transform test (ART), Conover's rank transform procedure (RT), and a rank-based ANOVA test (raov) using Monte Carlo simulations. The new procedures APCSSA/APCSSM are comparable with existing competitors in all settings. Even though there is no single dominant test in detecting interaction effects for non-normal data, nonparametric procedure APCSSM is the most highly recommended procedure for Cauchy errors settings.

We have conducted extremely ultra-deep pencil beam observations for new satellites around both Uranus and Neptune. Tens of images on several different nights in 2021, 2022 and 2023 were obtained and shifted and added together to reach as faint as 26.9 and 27.2 magnitudes in the r-band around Uranus and Neptune, respectively. One new moon of Uranus, S/2023 U1, and two new moons of Neptune, S/2021 N1 and S/2002 N5, were found. S/2023 U1 was 26.6 mags, is about 7 km in diameter and has a distant, eccentric and inclined retrograde orbit similar to Caliban and Stephano, implying these satellites are fragments from a once larger parent satellite. S/2023 U1 almost completely overlaps Stephano in orbital phase space. S/2021 N1 was 26.9 mags, about 14 km in size and has a retrograde orbit similar to Neso and Psamathe, indicating they are a dynamical family. We find S/2021 N1 is in a Kozai-Lidov orbital resonance. S/2002 N5 was 25.9 mags, is about 23 km in size and it makes a family of distant prograde satellites with Sao and Laomedeia. All three new moons show for the first time dynamical groups of moons exist around both Uranus and Neptune. The creation of these groups likely produced dust that could be the source of red material seen on the leading hemispheres of some larger inner satellites like Titania, Oberon and Umbriel. We also detected all known outer moons of Uranus and Neptune on multiple nights. This survey mostly completes the outer satellites of Uranus to about 8 km and Neptune to about 14 km in diameter. The size distributions of satellite dynamical families around the giant planets shows a strong steepening in the power law size distribution smaller than 5 km in diameter. The satellites of a family become much more common smaller than 5 km and their size distribution is consistent with a collisional break-up of a once larger parent satellite., Comment: Accepted Astronomical Journal

Semi-supervised 3D object detection is a common strategy employed to circumvent the challenge of manually labeling large-scale autonomous driving perception datasets. Pseudo-labeling approaches to semi-supervised learning adopt a teacher-student framework in which machine-generated pseudo-labels on a large unlabeled dataset are used in combination with a small manually-labeled dataset for training. In this work, we address the problem of improving pseudo-label quality through leveraging long-term temporal information captured in driving scenes. More specifically, we leverage pre-trained motion-forecasting models to generate object trajectories on pseudo-labeled data to further enhance the student model training. Our approach improves pseudo-label quality in two distinct manners: first, we suppress false positive pseudo-labels through establishing consistency across multiple frames of motion forecasting outputs. Second, we compensate for false negative detections by directly inserting predicted object tracks into the pseudo-labeled scene. Experiments on the nuScenes dataset demonstrate the effectiveness of our approach, improving the performance of standard semi-supervised approaches in a variety of settings.

Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape pioneers a novel approach to AI-powered journaling by integrating passively collected behavioral patterns such as conversational engagement, sleep, and location with Large Language Models (LLMs). This integration creates a highly personalized and context-aware journaling experience, enhancing self-awareness and well-being by embedding behavioral intelligence into AI. We present an 8-week exploratory study with 20 college students, demonstrating the MindScape app's efficacy in enhancing positive affect (7%), reducing negative affect (11%), loneliness (6%), and anxiety and depression, with a significant week-over-week decrease in PHQ-4 scores (-0.25 coefficient), alongside improvements in mindfulness (7%) and self-reflection (6%). The study highlights the advantages of contextual AI journaling, with participants particularly appreciating the tailored prompts and insights provided by the MindScape app. Our analysis also includes a comparison of responses to AI-driven contextual versus generic prompts, participant feedback insights, and proposed strategies for leveraging contextual AI journaling to improve well-being on college campuses. By showcasing the potential of contextual AI journaling to support mental health, we provide a foundation for further investigation into the effects of contextual AI journaling on mental health and well-being., Comment: arXiv admin note: text overlap with arXiv:2404.00487

In 1947, four months before the famous Shelter Island conference, Richard Feynman wrote a lengthy letter to his former MIT classmate Theodore Welton, reporting on his efforts to develop a path integral describing the propagation of a Dirac particle. While these efforts never came to fruition, and were shortly abandoned in favor of a very different method of dealing with the electron propagator appearing in in QED, the letter is interesting both from the historical viewpoint of revealing what Feynman was thinking about during that period just before the development of QED, and for its scientific ideas. It also contains at the end some philosophical remarks, which Feynman wraps up with the comment, ``Well enough for the baloney.'' In this article I present a transcription of the letter along with editorial notes, and a facsimile of the original handwritten document. I also briefly comment on Feynman's efforts and discuss their relation to some later work.

The magnetic properties of hole quantum dots in Ge are sensitive to their shape due to the interplay between strong spin-orbit coupling and confinement. We show that the split-off band, surrounding SiGe layers, and hole-hole interactions have a strong influence on calculations of the effective $g$ factor of a lithographic quantum dot in a Ge/SiGe heterostructure. Comparing predictions from a model including these effects to raw magnetospectroscopy data, we apply maximum-likelihood estimation to infer the shape of a quantum dot with up to four holes. We expect that methods like this will be useful in assessing qubit-to-qubit variability critical to further scaling quantum computing technologies based on spins in semiconductors., Comment: 9 pages, 6 figures

A charged particle in a suitably strong magnetic field spirals along the field lines while slowly drifting transversely. This note provides a brief derivation of an effective Lagrangian formulation for the guiding center approximation that captures this dynamics without resolving the gyro motion. It also explains how the effective Lagrangian may, for special magnetic fields, admit a ``quasisymmetry'' which can give rise to a conserved quantity helpful for plasma confinement in fields lacking a geometric isometry. The aim of this note is to offer a pedagogical introduction and some perspectives on this well established subject., Comment: 9 pages

Partial penalized tests provide flexible approaches to testing linear hypotheses in high dimensional generalized linear models. However, because the estimators used in these tests are local minimizers of potentially non-convex folded-concave penalized objectives, the solutions one computes in practice may not coincide with the unknown local minima for which we have nice theoretical guarantees. To close this gap between theory and computation, we introduce local linear approximation (LLA) algorithms to compute the full and reduced model estimators for these tests and develop theory specifically for the LLA solutions. We prove that our LLA algorithms converge to oracle estimators for the full and reduced models in two steps with overwhelming probability. We then leverage this strong oracle result and the asymptotic properties of the oracle estimators to show that the partial penalized test statistics evaluated at the two-step LLA solutions are approximately chi-square in large samples, giving us guarantees for the tests using specific computed solutions and thereby closing the theoretical gap. We conduct simulation studies to assess the finite-sample performance of our testing procedures, finding that partial penalized tests using the LLA solutions agree with tests using the oracle estimators, and demonstrate our testing procedures in a real data application.

We present multiwavelength observations of the Swift short $\gamma$-ray burst GRB 231117A, localized to an underlying galaxy at redshift $z = 0.257$ at a small projected offset ($\sim 2~$kpc). We uncover long-lived X-ray (Chandra) and radio/millimeter (VLA, MeerKAT, and ALMA) afterglow emission, detected to $\sim 37~$days and $\sim 20~$days (rest frame), respectively. We measure a wide jet ($\sim 10.4^\circ$) and relatively high circumburst density ($\sim 0.07~{\rm cm}^{-3}$) compared to the short GRB population. Our data cannot be easily fit with a standard forward shock model, but they are generally well fit with the incorporation of a refreshed forward shock and a reverse shock at $< 1~$day. We incorporate GRB 231117A into a larger sample of 132 X-ray detected events, 71 of which were radio-observed (17 cm-band detections), for a systematic study of the distributions of redshifts, jet and afterglow properties, galactocentric offsets, and local environments of events with and without detected radio afterglows. Compared to the entire short GRB population, the majority of radio-detected GRBs are at relatively low redshifts ($z < 0.6$) and have high circumburst densities ($> 10^{-2}~{\rm cm}^{-3}$), consistent with their smaller ($< 8~$kpc) projected galactocentric offsets. We additionally find that 70% of short GRBs with opening angle measurements were radio-detected, indicating the importance of radio afterglows in jet measurements, especially in the cases of wide ($> 10^\circ$) jets where observational evidence of collimation may only be detectable at radio wavelengths. Owing to improved observing strategies and the emergence of sensitive radio facilities, the number of radio-detected short GRBs has quadrupled in the past decade., Comment: 30 pages, 11 figures, submitted to ApJ

Ranked Choice Voting (RCV) and Single Transferable Voting (STV) are widely valued; but are complex to understand due to intricate per-round vote transfers. Questions like determining how far a candidate is from winning or identifying effective election strategies are computationally challenging as minor changes in voter rankings can lead to significant ripple effects - for example, lending support to a losing candidate can prevent their votes from transferring to a more competitive opponent. We study optimal strategies - persuading voters to change their ballots or adding new voters - both algorithmically and theoretically. Algorithmically, we develop efficient methods to reduce election instances while maintaining optimization accuracy, effectively circumventing the computational complexity barrier. Theoretically, we analyze the effectiveness of strategies under both perfect and imperfect polling information. Our algorithmic approach applies to the ranked-choice polling data on the US 2024 Republican Primary, finding, for example, that several candidates would have been optimally served by boosting another candidate instead of themselves.

Effective representation of 2D images is fundamental in digital image processing, where traditional methods like raster and vector graphics struggle with sharpness and textural complexity respectively. Current neural fields offer high-fidelity and resolution independence but require predefined meshes with known discontinuities, restricting their utility. We observe that by treating all mesh edges as potential discontinuities, we can represent the magnitude of discontinuities with continuous variables and optimize. Based on this observation, we introduce a novel discontinuous neural field model that jointly approximate the target image and recovers discontinuities. Through systematic evaluations, our neural field demonstrates superior performance in denoising and super-resolution tasks compared to InstantNGP, achieving improvements of over 5dB and 10dB, respectively. Our model also outperforms Mumford-Shah-based methods in accurately capturing discontinuities, with Chamfer distances 3.5x closer to the ground truth. Additionally, our approach shows remarkable capability in handling complex artistic drawings and natural images.

We installed the next-generation automated laser adaptive optics system, Robo-AO-2, on the University of Hawaii 2.2-m telescope on Maunakea in 2023. We engineered Robo-AO-2 to deliver robotic, diffraction-limited observations at visible and near-infrared wavelengths in unprecedented numbers. This new instrument takes advantage of upgraded components, manufacturing techniques and control; and includes a parallel reconfigurable natural guide star wavefront sensor with which to explore hybrid wavefront sensing techniques. We present the results of commissioning in 2023 and 2024., Comment: Proceedings of SPIE, 13097-15

The Near-Earth binary asteroid Didymos was the target of a planetary defense demonstration mission DART in September 2022. The smaller binary component, Dimorphos, was impacted by the spacecraft in order to measure momentum transfer in kinetic impacts into rubble piles. DART and associated Earth-based observation campaigns have provided a wealth of scientific data on the Didymos-Dimorphos binary. DART revealed a largely oblate and ellipsoidal shape of Dimorphos before the impact, while the post-impact observations suggest that Dimorphos now has a prolate shape. Here we add those data points to the known properties of small binary asteroids and propose new paradigms of the radiative binary YORP (BYORP) effect as well as tidal dissipation in small binaries. We find that relatively spheroidal bodies like Dimorphos made of small debris may experience a weaker and more size-dependent BYORP effect than previously thought. This could explain the observed values of period drift in several well-characterized binaries. We also propose that energy dissipation in small binaries is dominated by relatively brief episodes of large-scale movement of (likely surface) materials, rather than long-term steady-state tidal dissipation. We propose that one such episode was triggered on Dimorphos by the DART impact. Depending on the longevity of this high-dissipation regime, it is possible that Dimorphos will be more dynamically relaxed in time for the Hera mission than it was in the weeks following the impact., Comment: Accepted for PSJ

3D Gaussian Splatting (3DGS) is a promising technique for 3D reconstruction, offering efficient training and rendering speeds, making it suitable for real-time applications.However, current methods require highly controlled environments (no moving people or wind-blown elements, and consistent lighting) to meet the inter-view consistency assumption of 3DGS. This makes reconstruction of real-world captures problematic. We present SpotLessSplats, an approach that leverages pre-trained and general-purpose features coupled with robust optimization to effectively ignore transient distractors. Our method achieves state-of-the-art reconstruction quality both visually and quantitatively, on casual captures. Additional results available at: https://spotlesssplats.github.io

We discuss general aspects of charge conjugation symmetry in Euclidean lattice field theories, including its dynamical gauging. Our main focus is $O(2) = U(1)\rtimes \mathbb{Z}_2 $ gauge theory, which we construct using a non-abelian generalization of the Villain formulation via gauging the charge conjugation symmetry of pure $U(1)$ gauge theory. We describe how to construct gauge-invariant non-local operators in a theory with gauged charge conjugation symmetry, and use it to define Wilson and 't Hooft lines as well as non-invertible symmetry operators. Our lattice discretization preserves the higher-group and non-invertible symmetries of $O(2)$ gauge theory, which we explore in detail. In particular, these symmetries give rise to selection rules for extended operators and their junctions, and constrain the properties of the worldvolume degrees of freedom on twist vortices (also known as Alice or Cheshire strings). We propose a phase diagram of the theory coupled to dynamical magnetic monopoles and twist vortices, where the various generalized symmetries are typically only emergent., Comment: 78 pages, 20 figures

We propose a new method to accurately approximate the Pompeiu-Hausdorff distance from a triangle soup A to another triangle soup B up to a given tolerance. Based on lower and upper bound computations, we discard triangles from A that do not contain the maximizer of the distance to B and subdivide the others for further processing. In contrast to previous methods, we use four upper bounds instead of only one, three of which newly proposed by us. Many triangles are discarded using the simpler bounds, while the most difficult cases are dealt with by the other bounds. Exhaustive testing determines the best ordering of the four upper bounds. A collection of experiments shows that our method is faster than all previous accurate methods in the literature.

Constructing well-behaved Laplacian and mass matrices is essential for tetrahedral mesh processing. Unfortunately, the \emph{de facto} standard linear finite elements exhibit bias on tetrahedralized regular grids, motivating the development of finite-volume methods. In this paper, we place existing methods into a common construction, showing how their differences amount to the choice of simplex centers. These choices lead to satisfaction or breakdown of important properties: continuity with respect to vertex positions, positive semi-definiteness of the implied Dirichlet energy, positivity of the mass matrix, and unbiased-ness on regular grids. Based on this analysis, we propose a new method for constructing dual-volumes which explicitly satisfy all of these properties via convex optimization., Comment: SGP 2024

Graph partitioning (GP) and vertex connectivity have traditionally been two distinct fields of study. This paper introduces the highly connected graph partitioning (HCGP) problem, which partitions a graph into compact, size balanced, and $Q$-(vertex) connected parts for any $Q\geq 1$. This problem is valuable in applications that seek cohesion and fault-tolerance within their parts, such as community detection in social networks and resiliency-focused partitioning of power networks. Existing research in this fundamental interconnection primarily focuses on providing theoretical existence guarantees of highly connected partitions for a limited set of dense graphs, and do not include canonical GP considerations such as size balance and compactness. This paper's key contribution is providing a general modeling and algorithmic approach for HCGP, inspired by recent work in the political districting problem, a special case of HCGP with $Q=1$. This approach models $Q$-connectivity constraints as mixed integer programs for any $Q\geq 1$ and provides an efficient branch-and-cut method to solve HCGP. When solution time is a priority over optimality, this paper provides a heuristic method specifically designed for HCGP with $Q=2$. A computational analysis evaluates these methods using a test bed of instances from various real-world graphs. In this analysis, the branch-and-cut method finds an optimal solution within one hour in $82.8\%$ of the instances solved. For $Q=2$, small and sparse instances are challenging for the heuristic, whereas large and sparse instances are challenging for the exact method. Furthermore, this study quantifies the computational cost of ensuring higher connectivity using the branch-and-cut approach, compared to a baseline of ensuring $1$-connectivity. Overall, this work serves as an effective tool to partition a graph into resilient and cohesive parts.

Energy systems models, critical for power sector decision support, incur non-linear memory and runtime penalties when scaling up under typical formulations. Even hardware improvements cannot make large models tractable, requiring omission of detail which affects siting, cost, and emission outputs to an unknown degree. Recent algorithmic innovations have enabled large scale, high resolution modeling. Newly tractable, granular systems can be compared with coarse ones for better understanding of inaccuracies from low resolution. Here we use a state of the art model to quantify the impact of resolution on results salient to policymakers and planners, affording confidence in decision quality. We find more realistic siting in recommendations from high resolution energy systems models, improving emissions, reliability, and price outcomes. Errors are generally stronger from low spatial resolution. When models have low resolution in multiple dimensions, errors are introduced by the coarser of temporal or spatial resolution. We see no diminishing returns in accuracy for several key metrics when increasing resolution. We recommend using computationally efficient techniques to maximize granularity and allocating resolution without leaving any aspect (spatial, temporal, operational) of systems unduly coarse.

Volume-preserving hyperelastic materials are widely used to model near-incompressible materials such as rubber and soft tissues. However, the numerical simulation of volume-preserving hyperelastic materials is notoriously challenging within this regime due to the non-convexity of the energy function. In this work, we identify the pitfalls of the popular eigenvalue clamping strategy for projecting Hessian matrices to positive semi-definiteness during Newton's method. We introduce a novel eigenvalue filtering strategy for projected Newton's method to stabilize the optimization of Neo-Hookean energy and other volume-preserving variants under high Poisson's ratio (near 0.5) and large initial volume change. Our method only requires a single line of code change in the existing projected Newton framework, while achieving significant improvement in both stability and convergence speed. We demonstrate the effectiveness and efficiency of our eigenvalue projection scheme on a variety of challenging examples and over different deformations on a large dataset., Comment: SIGGRAPH 2024 (Conference track). Project page: https://www.cs.columbia.edu/cg/abs-psd/

In recent years, mathematical models have become an indispensable tool in the planning, evaluation, and implementation of public health interventions. Models must often provide detailed information for many levels of population stratification. Such detail comes at a price: in addition to the computational costs, the number of considered input parameters can be large, making effective study design difficult. To address these difficulties, we propose a novel technique to reduce the dimension of the model input space to simplify model-informed intervention planning. The method works by first applying a dimension reduction technique on the model output space. We then develop a method which allows us to map each reduced output to a corresponding vector in the input space, thereby reducing its dimension. We apply the method to the HIV Optimization and Prevention Economics (HOPE) model, to validate the approach and establish proof of concept.

We propose a novel technique for adding geometric details to an input coarse 3D mesh guided by a text prompt. Our method is composed of three stages. First, we generate a single-view RGB image conditioned on the input coarse geometry and the input text prompt. This single-view image generation step allows the user to pre-visualize the result and offers stronger conditioning for subsequent multi-view generation. Second, we use our novel multi-view normal generation architecture to jointly generate six different views of the normal images. The joint view generation reduces inconsistencies and leads to sharper details. Third, we optimize our mesh with respect to all views and generate a fine, detailed geometry as output. The resulting method produces an output within seconds and offers explicit user control over the coarse structure, pose, and desired details of the resulting 3D mesh., Comment: SIGGRAPH Asia 2024

Recent discoveries of Earth-sized planets transiting nearby M dwarfs have made it possible to characterize the atmospheres of terrestrial planets via follow-up spectroscopic observations. However, the number of such planets receiving low insolation is still small, limiting our ability to understand the diversity of the atmospheric composition and climates of temperate terrestrial planets. We report the discovery of an Earth-sized planet transiting the nearby (12 pc) inactive M3.0 dwarf Gliese 12 (TOI-6251) with an orbital period ($P_{\rm{orb}}$) of 12.76 days. The planet, Gliese 12b, was initially identified as a candidate with an ambiguous $P_{\rm{orb}}$ from TESS data. We confirmed the transit signal and $P_{\rm{orb}}$ using ground-based photometry with MuSCAT2 and MuSCAT3, and validated the planetary nature of the signal using high-resolution images from Gemini/NIRI and Keck/NIRC2 as well as radial velocity (RV) measurements from the InfraRed Doppler instrument on the Subaru 8.2 m telescope and from CARMENES on the CAHA 3.5 m telescope. X-ray observations with XMM-Newton showed the host star is inactive, with an X-ray-to-bolometric luminosity ratio of $\log L_{\rm X}/L_{\rm bol} \approx -5.7$. Joint analysis of the light curves and RV measurements revealed that Gliese 12b has a radius of 0.96 $\pm$ 0.05 $R_\oplus$, a 3$\sigma$ mass upper limit of 3.9 $M_\oplus$, and an equilibrium temperature of 315 $\pm$ 6 K assuming zero albedo. The transmission spectroscopy metric (TSM) value of Gliese 12b is close to the TSM values of the TRAPPIST-1 planets, adding Gliese 12b to the small list of potentially terrestrial, temperate planets amenable to atmospheric characterization with JWST., Comment: 29 pages (20 pages in main body), 13 figures (10 figures in main body). Equal contributions from M. K. and A. F.. Accepted for Publication in ApJL at 2024 March 21

The near-infrared calibration unit (NI-CU) on board Euclid's Near-Infrared Spectrometer and Photometer (NISP) is the first astronomical calibration lamp based on light-emitting diodes (LEDs) to be operated in space. Euclid is a mission in ESA's Cosmic Vision 2015-2025 framework, to explore the dark universe and provide a next-level characterisation of the nature of gravitation, dark matter, and dark energy. Calibrating photometric and spectrometric measurements of galaxies to better than 1.5% accuracy in a survey homogeneously mapping ~14000 deg^2 of extragalactic sky requires a very detailed characterisation of near-infrared (NIR) detector properties, as well their constant monitoring in flight. To cover two of the main contributions - relative pixel-to-pixel sensitivity and non-linearity characteristics - as well as support other calibration activities, NI-CU was designed to provide spatially approximately homogeneous (<12% variations) and temporally stable illumination (0.1%-0.2% over 1200s) over the NISP detector plane, with minimal power consumption and energy dissipation. NI-CU is covers the spectral range ~[900,1900] nm - at cryo-operating temperature - at 5 fixed independent wavelengths to capture wavelength-dependent behaviour of the detectors, with fluence over a dynamic range of >=100 from ~15 ph s^-1 pixel^-1 to >1500 ph s^-1 pixel^-1. For this functionality, NI-CU is based on LEDs. We describe the rationale behind the decision and design process, describe the challenges in sourcing the right LEDs, as well as the qualification process and lessons learned. We also provide a description of the completed NI-CU, its capabilities and performance as well as its limits. NI-CU has been integrated into NISP and the Euclid satellite, and since Euclid's launch in July 2023 has started supporting survey operations., Comment: Paper accepted for publication in A&A as part of the special issue 'Euclid on Sky', which contains Euclid key reference papers and first results from the Euclid Early Release Observations

The Near-Infrared Spectrometer and Photometer (NISP) on board the Euclid satellite provides multiband photometry and R>=450 slitless grism spectroscopy in the 950-2020nm wavelength range. In this reference article we illuminate the background of NISP's functional and calibration requirements, describe the instrument's integral components, and provide all its key properties. We also sketch the processes needed to understand how NISP operates and is calibrated, and its technical potentials and limitations. Links to articles providing more details and technical background are included. NISP's 16 HAWAII-2RG (H2RG) detectors with a plate scale of 0.3" pix^-1 deliver a field-of-view of 0.57deg^2. In photo mode, NISP reaches a limiting magnitude of ~24.5AB mag in three photometric exposures of about 100s exposure time, for point sources and with a signal-to-noise ratio (SNR) of 5. For spectroscopy, NISP's point-source sensitivity is a SNR = 3.5 detection of an emission line with flux ~2x10^-16erg/s/cm^2 integrated over two resolution elements of 13.4A, in 3x560s grism exposures at 1.6 mu (redshifted Ha). Our calibration includes on-ground and in-flight characterisation and monitoring of detector baseline, dark current, non-linearity, and sensitivity, to guarantee a relative photometric accuracy of better than 1.5%, and relative spectrophotometry to better than 0.7%. The wavelength calibration must be better than 5A. NISP is the state-of-the-art instrument in the NIR for all science beyond small areas available from HST and JWST - and an enormous advance due to its combination of field size and high throughput of telescope and instrument. During Euclid's 6-year survey covering 14000 deg^2 of extragalactic sky, NISP will be the backbone for determining distances of more than a billion galaxies. Its NIR data will become a rich reference imaging and spectroscopy data set for the coming decades., Comment: Paper submitted as part of the A&A special issue 'Euclid on Sky', which contains Euclid key reference papers and first results from the Euclid Early Release Observations

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance., Comment: Accepted for publication in the A&A special issue`Euclid on Sky'

Neuromorphic architectures mimicking biological neural networks have been proposed as a much more efficient alternative to conventional von Neumann architectures for the exploding compute demands of AI workloads. Recent neuroscience theory on intelligence suggests that Cortical Columns (CCs) are the fundamental compute units in the neocortex and intelligence arises from CC's ability to store, predict and infer information via structured Reference Frames (RFs). Based on this theory, recent works have demonstrated brain-like visual object recognition using software simulation. Our work is the first attempt towards direct CMOS implementation of Reference Frames for building CC-based neuromorphic processors. We propose NeRTCAM (Neuromorphic Reverse Ternary Content Addressable Memory), a CAM-based building block that supports the key operations (store, predict, infer) required to perform inference using RFs. NeRTCAM architecture is presented in detail including its key components. All designs are implemented in SystemVerilog and synthesized in 7nm CMOS, and hardware complexity scaling is evaluated for varying storage sizes. NeRTCAM system for biologically motivated MNIST inference with a storage size of 1024 entries incurs just 0.15 mm^2 area, 400 mW power and 9.18 us critical path latency, demonstrating the feasibility of direct CMOS implementation of CAM-based Reference Frames., Comment: Accepted and Presented at Neuro-Inspired Computational Elements (NICE) Conference, La Jolla, CA. 2024

The gravitational path integral is usually implemented with a covariant action by analogy with other gauge field theories, but the gravitational case is different in important ways. A key difference is that the integrand has an essential singularity, which occurs at zero lapse where the spacetime metric degenerates. The lapse integration contour required to impose the local time reparametrization constraints must run from $-\infty$ to $+\infty$, yet must not pass through zero. This raises the question: what is the correct integration contour, and why? We study that question by starting with the reduced phase space path integral, which involves no essential singularity. We observe that if the momenta are to be integrated before the lapse, to obtain a configuration space path integral, the lapse contour should pass below the origin in the complex lapse plane. This contour is also consistent with the requirement that quantum field fluctuation amplitudes have the usual short distance vacuum form, and with obtaining the Bekenstein-Hawking horizon entropy from a Lorentzian path integral. We close with a discussion of related issues, including potential obstacles to deriving a nonperturbative covariant gravitational path integral., Comment: 7 pages; v2: added comments on related prior work and gauge accessibility

We present UV/optical/NIR observations and modeling of supernova (SN) 2024ggi, a type II supernova (SN II) located in NGC 3621 at 7.2 Mpc. Early-time ("flash") spectroscopy of SN 2024ggi within +0.8 days of discovery shows emission lines of H I, He I, C III, and N III with a narrow core and broad, symmetric wings (i.e., IIn-like) arising from the photoionized, optically-thick, unshocked circumstellar material (CSM) that surrounded the progenitor star at shock breakout. By the next spectral epoch at +1.5 days, SN 2024ggi showed a rise in ionization as emission lines of He II, C IV, N IV/V and O V became visible. This phenomenon is temporally consistent with a blueward shift in the UV/optical colors, both likely the result of shock breakout in an extended, dense CSM. The IIn-like features in SN 2024ggi persist on a timescale of $t_{\rm IIn} = 3.8 \pm 1.6$ days at which time a reduction in CSM density allows the detection of Doppler broadened features from the fastest SN material. SN 2024ggi has peak UV/optical absolute magnitudes of $M_{\rm w2} = -18.7$ mag and $M_{\rm g} = -18.1$ mag that are consistent with the known population of CSM-interacting SNe II. Comparison of SN 2024ggi with a grid of radiation hydrodynamics and non-local thermodynamic equilibrium (nLTE) radiative-transfer simulations suggests a progenitor mass-loss rate of $\dot{M} = 10^{-2}$M$_{\odot}$ yr$^{-1}$ ($v_w$ = 50 km/s), confined to a distance of $r < 5\times 10^{14}$ cm. Assuming a wind velocity of $v_w$ = 50 km/s, the progenitor star underwent an enhanced mass-loss episode in the last ~3 years before explosion., Comment: Accepted for publication in ApJ. arXiv admin note: text overlap with arXiv:2306.04721, arXiv:2403.02382

Mercury is notoriously difficult to form in solar system simulations, due to its small mass and iron-rich composition. Smooth particle hydrodynamics simulations of collisions have found that a Mercury-like body could be formed by one or multiple giant impacts, but due to the chaotic nature of collisions it is difficult to create a scenario where such impacts will take place. Recent work has found more success forming Mercury analogues by adding additional embryos near Mercury's orbit. In this work, we aim to form Mercury by simulating the formation of the solar system in the presence of the giant planets Jupiter and Saturn. We test out the effect of an inner disk of embryos added on to the commonly-used narrow annulus of initial material. We form Mercury analogues with core-mass fractions (CMF) $> 0.4$ in $\sim 10\%$ of our simulations, and twice that number of Mercury analogues form during the formation process, but are unstable and do not last to the end of the simulations. Mercury analogues form at similar rates for both disks with and without an inner component, and most of our Mercury analogues have lower CMF than that of Mercury, $\sim 0.7$, due to significant accretion of debris material. We suggest that a more in-depth understanding of the fraction of debris mass that is lost to collisional grinding is necessary to understand Mercury's formation, or some additional mechanism is required to stop this debris from accreting., Comment: 15 pages, accepted for publication in ApJ

We present an ultraviolet-to-infrared search for the electromagnetic (EM) counterpart to GW190425, the second-ever binary neutron star (BNS) merger discovered by the LIGO-Virgo-KAGRA Collaboration (LVK). GW190425 was more distant and had a larger localization area than GW170817, therefore we use a new tool teglon to redistribute the GW190425 localization probability in the context of galaxy catalogs within the final localization volume. We derive a 90th percentile area of 6,688 deg$^{2}$, a $\sim$1.5$\times$ improvement relative to the LIGO/Virgo map, and show how teglon provides an order of magnitude boost to the search efficiency of small ($\leq$1 deg$^{2}$) field-of-view instruments. We combine our data with all publicly reported imaging data, covering 9,078.59 deg$^2$ of unique area and 48.13% of the LIGO/Virgo-assigned localization probability, to calculate the most comprehensive kilonova, short gamma-ray burst (sGRB) afterglow, and model-independent constraints on the EM emission from a hypothetical counterpart to GW190425 to date under the assumption that no counterpart was found in these data. If the counterpart were similar to AT 2017gfo, there was a 28.4% chance that it would have been detected in the combined dataset. We are relatively insensitive to an on-axis sGRB, and rule out a generic transient with a similar peak luminosity and decline rate as AT 2017gfo to 30% confidence. Finally, across our new imaging and all publicly-reported data, we find 28 candidate optical counterparts that we cannot rule out as being associated with GW190425, finding that 4 such counterparts discovered within the localization volume and within 5 days of merger exhibit luminosities consistent with a kilonova., Comment: 41 pages, 11 figures, Submitted to ApJ

MindScape aims to study the benefits of integrating time series behavioral patterns (e.g., conversational engagement, sleep, location) with Large Language Models (LLMs) to create a new form of contextual AI journaling, promoting self-reflection and well-being. We argue that integrating behavioral sensing in LLMs will likely lead to a new frontier in AI. In this Late-Breaking Work paper, we discuss the MindScape contextual journal App design that uses LLMs and behavioral sensing to generate contextual and personalized journaling prompts crafted to encourage self-reflection and emotional development. We also discuss the MindScape study of college students based on a preliminary user study and our upcoming study to assess the effectiveness of contextual AI journaling in promoting better well-being on college campuses. MindScape represents a new application class that embeds behavioral intelligence in AI.

Diagnostic imaging has gained prominence as potential biomarkers for early detection and diagnosis in a diverse array of disorders including cancer. However, existing methods routinely face challenges arising from various factors such as image heterogeneity. We develop a novel imaging-based distributional data analysis (DDA) approach that incorporates the probability (quantile) distribution of the pixel-level features as covariates. The proposed approach uses a smoothed quantile distribution (via a suitable basis representation) as functional predictors in a scalar-on-functional quantile regression model. Some distinctive features of the proposed approach include the ability to: (i) account for heterogeneity within the image; (ii) incorporate granular information spanning the entire distribution; and (iii) tackle variability in image sizes for unregistered images in cancer applications. Our primary goal is risk prediction in Hepatocellular carcinoma that is achieved via predicting the change in tumor grades at post-diagnostic visits using pre-diagnostic enhancement pattern mapping (EPM) images of the liver. Along the way, the proposed DDA approach is also used for case versus control diagnosis and risk stratification objectives. Our analysis reveals that when coupled with global structural radiomics features derived from the corresponding T1-MRI scans, the proposed smoothed quantile distributions derived from EPM images showed considerable improvements in sensitivity and comparable specificity in contrast to classification based on routinely used summary measures that do not account for image heterogeneity. Given that there are limited predictive modeling approaches based on heterogeneous images in cancer, the proposed method is expected to provide considerable advantages in image-based early detection and risk prediction., Comment: 16, 2 figures, 3 tables

We present ultraviolet/optical/near-infrared observations and modeling of Type II supernovae (SNe II) whose early-time ($\delta t < 2$ days) spectra show transient, narrow emission lines from shock ionization of confined ($r < 10^{15}$ cm) circumstellar material (CSM). The observed electron-scattering broadened line profiles (i.e., IIn-like) of HI, He I/II, C III/IV, and N III/IV/V from the CSM persist on a characteristic timescale ($t_{\rm IIn}$) that marks a transition to a lower-density CSM and the emergence of Doppler-broadened features from the fast-moving SN ejecta. Our sample, the largest to date, consists of 39 SNe with early-time IIn-like features in addition to 35 "comparison" SNe with no evidence of early-time IIn-like features, all with ultraviolet observations. The total sample consists of 50 unpublished objects with 474 previously unpublished spectra and 50 multiband light curves, collected primarily through the Young Supernova Experiment and Global Supernova Project collaborations. For all sample objects, we find a significant correlation between peak ultraviolet brightness and both $t_{\rm IIn}$ and the rise time, as well as evidence for enhanced peak luminosities in SNe II with IIn-like features. We quantify mass-loss rates and CSM density for the sample through matching of peak multiband absolute magnitudes, rise times, $t_{\rm IIn}$ and optical SN spectra with a grid of radiation hydrodynamics and non-local thermodynamic equilibrium (nLTE) radiative-transfer simulations. For our grid of models, all with the same underlying explosion, there is a trend between the duration of the electron-scattering broadened line profiles and inferred mass-loss rate: $t_{\rm IIn} \approx 3.8[\dot{M}/(0.01 \textrm{M}_{\odot} \textrm{yr}^{-1})]$ days., Comment: 58 pages, 24 figures, submitted to ApJ. Supplementary figures available on Github (https://github.com/wynnjacobson-galan/Flash_Spectra_Sample). Data release following publication

The dimension of the Hilbert space of a quantum gravitational system can be written formally as a path integral partition function over Lorentzian metrics. We implement this in a 2+1 dimensional simplicial minisuperspace model in which the system is a spatial topological disc, and recover by contour deformation through a Euclidean saddle the entropy of the de Sitter static patch, up to discretization artifacts. The model illustrates the importance of integration over both positive and negative lapse to enforce the gravitational constraints, and of restriction to complex metrics for which the fluctuation integrals would converge. Although a strictly Lorentzian path integral is oscillatory, an exponentially large partition function results from unavoidable imaginary contributions to the action. These arise from analytic continuation of the simplicial (Regge) action for configurations with codimension-2 simplices where the metric fails to be Lorentzian. In particular, the dominant contribution comes from configurations with contractible closed timelike curves that encircle the boundary of the disc, in close correspondence with recent continuum results., Comment: 24 pages, 12 figures, published version

MoodCapture presents a novel approach that assesses depression based on images automatically captured from the front-facing camera of smartphones as people go about their daily lives. We collect over 125,000 photos in the wild from N=177 participants diagnosed with major depressive disorder for 90 days. Images are captured naturalistically while participants respond to the PHQ-8 depression survey question: \textit{``I have felt down, depressed, or hopeless''}. Our analysis explores important image attributes, such as angle, dominant colors, location, objects, and lighting. We show that a random forest trained with face landmarks can classify samples as depressed or non-depressed and predict raw PHQ-8 scores effectively. Our post-hoc analysis provides several insights through an ablation study, feature importance analysis, and bias assessment. Importantly, we evaluate user concerns about using MoodCapture to detect depression based on sharing photos, providing critical insights into privacy concerns that inform the future design of in-the-wild image-based mental health assessment tools.

Benchmark dose analysis aims to estimate the level of exposure to a toxin that results in a clinically-significant adverse outcome and quantifies uncertainty using the lower limit of a confidence interval for this level. We develop a novel framework for benchmark dose analysis based on monotone additive dose-response models. We first introduce a flexible approach for fitting monotone additive models via penalized B-splines and Laplace-approximate marginal likelihood. A reflective Newton method is then developed that employs de Boor's algorithm for computing splines and their derivatives for efficient estimation of the benchmark dose. Finally, we develop and assess three approaches for calculating benchmark dose lower limits: a naive one based on asymptotic normality of the estimator, one based on an approximate pivot, and one using a Bayesian parametric bootstrap. The latter approaches improve upon the naive method in terms of accuracy and are guaranteed to return a positive lower limit; the approach based on an approximate pivot is typically an order of magnitude faster than the bootstrap, although they are both practically feasible to compute. We apply the new methods to make inferences about the level of prenatal alcohol exposure associated with clinically significant cognitive defects in children using data from an NIH-funded longitudinal study. Software to reproduce the results in this paper is available at https://github.com/awstringer1/bmd-paper-code.