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Let $p$ be an odd prime integer, $F/\mathbb{Q}$ be an imaginary quadratic field, and $\Psi$ be a small slope cuspidal Bianchi modular form over $F$ which is non-ordinary at $p$. In this article, we first construct a $p$-adic distribution $L^{\mathrm{As}}_{p}(\Psi)$ that interpolates the twisted critical $L$-values of Asai (or twisted tensor) $L$-function of $\Psi$, generalizing the works of Loeffler-Williams from the ordinary case to the non-ordinary case. To construct this distribution, we use the Betti analogue of the Euler system machinery, developed by Loeffler-Williams, as well as techniques analogous to those used by Loeffler-Zerbes for interpolating the twists of Beilinson-Flach elements arising in the Euler system associated with Rankin-Selberg convolutions of elliptic modular forms. We also use the interpolation method developed by Amice-V\'elu, Perrin-Riou, and B\"uy\"ukboduk-Lei in the construction. Furthermore, assume that $p$ splits as $\mathfrak{p}\overline{\mathfrak{p}}$ in $F$. Let $\Psi$ be a cuspidal Bianchi eigenform of level $\mathcal{N}$, where $\mathcal{N}\subset\mathcal{O}_{F}$ is an ideal coprime to $p$, such that $\Psi$ is non-ordinary at $\mathfrak{p}$ and ordinary at $\overline{\mathfrak{p}}$. We can then attach unbounded $p$-adic distributions $L_{p}^{\mathrm{As}}(\Psi^{\tilde{\alpha}})$ and $L_{p}^{\mathrm{As}}(\Psi^{\tilde{\beta}})$ to the $p$-stabilizations $\Psi^{\tilde{\alpha}}$ and $\Psi^{\tilde{\beta}}$ of $\Psi$ respectively. Another objective of this article is to decompose these unbounded $p$-adic distributions into the linear combination of bounded measures as done by Pollack, Sprung, and Lei-Loeffler-Zerbes in the elliptic modular forms case., Comment: 43 pages, comments are welcome!

Bayesian Neural Networks (BNNs) offer robust uncertainty quantification in model predictions, but training them presents a significant computational challenge. This is mainly due to the problem of sampling multimodal posterior distributions using Markov Chain Monte Carlo (MCMC) sampling and variational inference algorithms. Moreover, the number of model parameters scales exponentially with additional hidden layers, neurons, and features in the dataset. Typically, a significant portion of these densely connected parameters are redundant and pruning a neural network not only improves portability but also has the potential for better generalisation capabilities. In this study, we address some of the challenges by leveraging MCMC sampling with network pruning to obtain compact probabilistic models having removed redundant parameters. We sample the posterior distribution of model parameters (weights and biases) and prune weights with low importance, resulting in a compact model. We ensure that the compact BNN retains its ability to estimate uncertainty via the posterior distribution while retaining the model training and generalisation performance accuracy by adapting post-pruning resampling. We evaluate the effectiveness of our MCMC pruning strategy on selected benchmark datasets for regression and classification problems through empirical result analysis. We also consider two coral reef drill-core lithology classification datasets to test the robustness of the pruning model in complex real-world datasets. We further investigate if refining compact BNN can retain any loss of performance. Our results demonstrate the feasibility of training and pruning BNNs using MCMC whilst retaining generalisation performance with over 75% reduction in network size. This paves the way for developing compact BNN models that provide uncertainty estimates for real-world applications., Comment: 22 pages, 11 figures

The Sersic law reproduces very well the surface brightness profile of early-type galaxies, and therefore is routinely used in observational and theoretical works. Unfortunately, its deprojection can not be expressed in terms of elementary functions for generic values of the shape parameter $n$. Over the years, different families of approximate deprojection formulae have been proposed, generally based on fits of the numerical deprojection over some radial range. We searched for a very simple, accurate, and theoretically motivated deprojection formula of the Sersic law, without free parameters, not based on fits of the numerical deprojection, and holding for generic $n > 1$. The formula has been found by requiring it to reproduce the analytical expressions for the inner and outer asymptotic expansions of the deprojected Sersic law of given $n$, and by matching the two expansions at intermediate radii with the request that the total luminosity coincides with that of the original Sersic profile of same $n$. The resulting formula is algebraically very simple, by construction its inner and outer parts are the exact (asymptotic) deprojection of the Sersic law, and it depends on two coefficients that are analytical functions of $n$ of immediate evaluation. The accuracy of the formula over the whole radial range is very good and increases for increasing $n$, with maximum relative deviations from the true numerical deprojection of $\simeq 8\,10^{-3}$ for the de Vaucouleurs profile. In the Appendix, the extension of the proposed formula to profiles with $n <1$ is also presented and discussed. The formula obtained is a useful tool of simple use in the modeling of early-type galaxies. Its ellipsoidal generalization is immediate., Comment: 8 pages, 3 figures, accepted for publication in A&A

Accurate diagnostic coding of medical notes is crucial for enhancing patient care, medical research, and error-free billing in healthcare organizations. Manual coding is a time-consuming task for providers, and diagnostic codes often exhibit low sensitivity and specificity, whereas the free text in medical notes can be a more precise description of a patients status. Thus, accurate automated diagnostic coding of medical notes has become critical for a learning healthcare system. Recent developments in long-document transformer architectures have enabled attention-based deep-learning models to adjudicate medical notes. In addition, contrastive loss functions have been used to jointly pre-train large language and image models with noisy labels. To further improve the automated adjudication of medical notes, we developed an approach based on i) models for ICD-10 diagnostic code sequences using a large real-world data set, ii) large language models for medical notes, and iii) contrastive pre-training to build an integrated model of both ICD-10 diagnostic codes and corresponding medical text. We demonstrate that a contrastive approach for pre-training improves performance over prior state-of-the-art models for the MIMIC-III-50, MIMIC-III-rare50, and MIMIC-III-full diagnostic coding tasks.

Traditional wavefront control in high-energy, high-intensity laser systems usually lacks real-time capability, failing to address dynamic aberrations. This limits experimental accuracy due to shot-to-shot fluctuations and necessitates long cool-down phases to mitigate thermal effects, particularly as higher repetition rates become essential, e.g. in Inertial Fusion research. This paper details the development and implementation of a real-time capable adaptive optics system at the Apollon laser facility. Inspired by astronomical adaptive optics, the system uses a fiber-coupled 905 nm laser diode as a pilot beam that allows for spectral separation, bypassing the constraints of pulsed lasers. A GPU-based controller, built on the open-source CACAO framework, manages a loop comprising a bimorph deformable mirror and high-speed Shack-Hartmann sensor. Initial tests showed excellent stability and effective aberration correction. However, integration into the Apollon laser revealed critical challenges unique to the laser environment that must be resolved to ensure safe operation with amplified shots., Comment: 15 pages, 17 figures, 2 tables; to be published in High Power Laser Science and Engineering

In Laser Powder Bed Fusion (LPBF), the applied laser energy produces high thermal gradients that lead to unacceptable final part distortion. Accurate distortion prediction is essential for optimizing the 3D printing process and manufacturing a part that meets geometric accuracy requirements. This study introduces data-driven parameterized reduced-order models (ROMs) to predict distortion in LPBF across various machine process settings. We propose a ROM framework that combines Proper Orthogonal Decomposition (POD) with Gaussian Process Regression (GPR) and compare its performance against a deep-learning based parameterized graph convolutional autoencoder (GCA). The POD-GPR model demonstrates high accuracy, predicting distortions within $\pm0.001mm$, and delivers a computational speed-up of approximately 1800x., Comment: 7 pages, 4 figures, NeurIPS Machine Learning for Physical Sciences workshop

Accurate prediction over long time horizons is crucial for modeling complex physical processes such as wave propagation. Although deep neural networks show promise for real-time forecasting, they often struggle with accumulating phase and amplitude errors as predictions extend over a long period. To address this issue, we propose a novel loss decomposition strategy that breaks down the loss into separate phase and amplitude components. This technique improves the long-term prediction accuracy of neural networks in wave propagation tasks by explicitly accounting for numerical errors, improving stability, and reducing error accumulation over extended forecasts., Comment: 6 pages, 4 figures, NeurIPS Machine Learning for Physical Sciences workshop

Dynamic programming (DP) is a fundamental and powerful algorithmic paradigm taught in most undergraduate (and many graduate) algorithms classes. DP problems are challenging for many computer science students because they require identifying unique problem structures and a refined understanding of recursion. In this paper, we present dpvis, a Python library that helps students understand DP through a frame-by-frame animation of dynamic programs. dpvis can easily generate animations of dynamic programs with as little as two lines of modifications compared to a standard Python implementation. For each frame, dpvis highlight the cells that have been read from and written to during an iteration. Moreover, dpvis allows users to test their understanding by prompting them with questions about the next operation performed by the algorithm. We deployed dpvis as a learning tool in an undergraduate algorithms class, and report on the results of a survey. The survey results suggest that dpvis is especially helpful for visualizing the recursive structure of DP. Although some students struggled with the installation of the tool (which has been simplified since the reported deployment), essentially all other students found the tool to be useful for understanding dynamic programs. dpvis is available at https://github.com/itsdawei/dpvis., Comment: Published as a conference paper at Technical Symposium on Computer Science Education (SIGCSE TS '25); dpvis is available at https://dpvis.readthedocs.io/en/latest/

The scenario approach is widely used in robust control system design and chance-constrained optimization, maintaining convexity without requiring assumptions about the probability distribution of uncertain parameters. However, the approach can demand large sample sizes, making it intractable for safety-critical applications that require very low levels of constraint violation. To address this challenge, we propose a novel yet simple constraint scaling method, inspired by large deviations theory. Under mild nonparametric conditions on the underlying probability distribution, we show that our method yields an exponential reduction in sample size requirements for bilinear constraints with low violation levels compared to the classical approach, thereby significantly improving computational tractability. Numerical experiments on robust pole assignment problems support our theoretical findings.

Multi-site MRI studies often suffer from site-specific variations arising from differences in methodology, hardware, and acquisition protocols, thereby compromising accuracy and reliability in clinical AI/ML tasks. We present PRISM (Privacy-preserving Inter-Site MRI Harmonization), a novel Deep Learning framework for harmonizing structural brain MRI across multiple sites while preserving data privacy. PRISM employs a dual-branch autoencoder with contrastive learning and variational inference to disentangle anatomical features from style and site-specific variations, enabling unpaired image translation without traveling subjects or multiple MRI modalities. Our modular design allows harmonization to any target site and seamless integration of new sites without the need for retraining or fine-tuning. Using multi-site structural MRI data, we demonstrate PRISM's effectiveness in downstream tasks such as brain tissue segmentation and validate its harmonization performance through multiple experiments. Our framework addresses key challenges in medical AI/ML, including data privacy, distribution shifts, model generalizability and interpretability. Code is available at https://github.com/saranggalada/PRISM, Comment: This work has been submitted to ISBI 2025

We present significant upgrades to the VAMPIRES instrument, a visible-light (600 nm to 800 nm) high-contrast imaging polarimeter integrated within SCExAO on the Subaru telescope. Key enhancements include new qCMOS detectors, coronagraphs, polarization optics, and a multiband imaging mode, improving sensitivity, resolution, and efficiency. These upgrades position VAMPIRES as a powerful tool for studying sub-stellar companions, accreting protoplanets, circumstellar disks, stellar jets, stellar mass-loss shells, and solar system objects. The instrument achieves angular resolutions from 17 mas to 21 mas and Strehl ratios up to 60\%, with 5$\sigma$ contrast limits of $10^{\text{-}4}$ at 0.1'' to $10^{\text{-}6}$ beyond 0.5''. We demonstrate these capabilities through spectro-polarimetric coronagraphic imaging of the HD 169142 circumstellar disk, ADI+SDI imaging of the sub-stellar companion HD 1160B, narrowband H$\alpha$ imaging of the R Aqr emission nebula, and spectro-polarimetric imaging of Neptune., Comment: 36 pages, 33 figures, accepted to PASP

We analyze near-infrared integral field spectropolarimetry of the AB Aurigae protoplanetary disk and protoplanet (AB Aur b), obtained with SCExAO/CHARIS in 22 wavelength channels covering the J, H, and K passbands ($\lambda_{\rm o}$ = 1.1--2.4 $\mu m$) over angular separations of $\rho$ $\approx$ 0.13" to 1.1" ($\sim$20--175 au). Our images resolve spiral structures in the disk in each CHARIS channel. At the longest wavelengths, the data may reveal an extension of the western spiral seen in previous polarimetric data at $\rho$ $<$ 0.3" out to larger distances clockwise from the protoplanet AB Aur b, coincident with the ALMA-detected $CO$ gas spiral. While AB Aur b is detectable in complementary total intensity data, it is a non-detection in polarized light at $\lambda$ $>$ 1.3 $\mu $m. While the observed disk color is extremely red across $JHK$, the disk has a blue intrinsic scattering color consistent with small dust grains. The disk's polarization spectrum is redder than AB Aur b's total intensity spectrum. The polarization fraction peaks at $\sim$ 0.6 along the major disk axis. Radiative transfer modeling of the CHARIS data shows that small, porous dust grains with a porosity of $p$ = 0.6--0.8 better reproduce the scattered-light appearance of the disk than more compact spheres ($p$ = 0.3), especially the polarization fraction. This work demonstrates the utility of integral field spectropolarimetry to characterize structures in protoplanetary disks and elucidate the properties of the disks' dust., Comment: 23 pages, 17 Figures, accepted to AAS Journals

Long tail problems frequently arise in the medical field, particularly due to the scarcity of medical data for rare conditions. This scarcity often leads to models overfitting on such limited samples. Consequently, when training models on datasets with heavily skewed classes, where the number of samples varies significantly, a problem emerges. Training on such imbalanced datasets can result in selective detection, where a model accurately identifies images belonging to the majority classes but disregards those from minority classes. This causes the model to lack generalizability, preventing its use on newer data. This poses a significant challenge in developing image detection and diagnosis models for medical image datasets. To address this challenge, the One Shot GANs model was employed to augment the tail class of HAM10000 dataset by generating additional samples. Furthermore, to enhance accuracy, a novel metric tailored to suit One Shot GANs was utilized., Comment: 15 pages, 6 Figures, 9 Tables and additional 6 Tables in Ancillary Data

Real-time control (RTC) is pivotal for any Adaptive Optics (AO) system, including high-contrast imaging of exoplanets and circumstellar environments. It is the brain of the AO system, and what wavefront sensing and control (WFS\&C) techniques need to work with to achieve unprecedented image quality and contrast, ultimately advancing our understanding of exoplanetary systems in the context of high contrast imaging (HCI). Developing WFS\&C algorithms first happens in simulation or a lab before deployment on-sky. The transition to on-sky testing is often challenging due to the different RTCs used. Sharing common RTC standards across labs and telescope instruments would considerably simplify this process. A data architecture based on the interprocess communication method known as shared memory is ideally suited for this purpose. The CACAO package, an example of RTC based on shared memory, was initially developed for the Subaru-SCExAO instrument and now deployed on several benches and instruments. This proceeding discusses the challenges, requirements, implementation strategies, and performance evaluations associated with integrating a shared memory-based RTC. The Santa Cruz Extreme AO Laboratory (SEAL) bench is a platform for WFS\&C development for large ground-based segmented telescopes. Currently, SEAL offers the user a non-real-time version of CACAO, a shared-memory based RTC package initially developed for the Subaru-SCExAO instrument, and now deployed on several benches and instruments. We show here the example of the SEAL RTC upgrade as a precursor to both RTC upgrade at the 3-m Shane telescopes at Lick Observatory (Shane-AO) and a future development platform for the Keck II AO. This paper is aimed at specialists in AO, astronomers, and WFS\&C scientists seeking a deeper introduction to the world of RTCs., Comment: SPIE Astronomical Telescope and Intrumentation 2024

Photonic lanterns are waveguide devices enabling high throughput single mode spectroscopy and high angular resolution. We aim to present the first on-sky demonstration of a photonic lantern (PL) operating in visible light, to measure its throughput and assess its potential for high-resolution spectroscopy of compact objects. We used the SCExAO instrument (a double stage extreme AO system installed at the Subaru telescope) and FIRST mid-resolution spectrograph (R 3000) to test the visible capabilities of the PL on internal source and on-sky observations. The best averaged coupling efficiency over the PL field of view was measured at 51% +/- 10% with a peak at 80%. We also investigate the relationship between coupling efficiency and the Strehl ratio for a PL, comparing them with those of a single-mode fiber (SMF). Findings show that in the AO regime, a PL offers better coupling efficiency performance than a SMF, especially in the presence of low spatial frequency aberrations. We observed Ikiiki (alpha Leo - mR = 1.37) and `Aua (alpha Ori - mR = -1.17) at a frame rate of 200 Hz. Under median seeing conditions (about 1 arcsec measured in H band) and large tip/tilt residuals (over 20 mas), we estimated an average light coupling efficiency of 14.5% +/- 7.4%, with a maximum of 42.8% at 680 nm. We were able to reconstruct both star's spectra, containing various absorption lines. The successful demonstration of this device opens new possibilities in terms of high throughput single-mode fiber-fed spectroscopy in the Visible. The demonstrated on-sky coupling efficiency performance would not have been achievable with a single SMF injection setup under similar conditions, partly because the residual tip/tilt alone exceeded the field of view of a visible SMF (18 mas at 700 nm). Thus emphasizing the enhanced resilience of PL technology to such atmospheric disturbances. The additional, Comment: Accepted in Astronomy and Astrophysics journal on 9/11/2024

We describe the motivation, design, and early results for our 42-night, 125 star Subaru/SCExAO direct imaging survey for planets around accelerating stars. Unlike prior large surveys, ours focuses only on stars showing evidence for an astrometric acceleration plausibly due to the dynamical pull of an unseen planet or brown dwarf. Our program is motivated by results from a recent pilot program that found the first planet jointly discovered from direct imaging and astrometry and resulted in a planet and brown dwarf discovery rate substantially higher than previous unbiased surveys like GPIES. The first preliminary results from our program reveal multiple new companions; discovered planets and brown dwarfs can be further characterized with follow-up data, including higher-resolution spectra. Finally, we describe the critical role this program plays in supporting the Roman Space Telescope Coronagraphic Instrument, providing a currently-missing list of targets suitable for the CGI technological demonstration without which the CGI tech demo risks failure., Comment: 13 pages, 7 figures; Proc. SPIE in press

Recent advancements in speech-based topic segmentation have highlighted the potential of pretrained speech encoders to capture semantic representations directly from speech. Traditionally, topic segmentation has relied on a pipeline approach in which transcripts of the automatic speech recognition systems are generated, followed by text-based segmentation algorithms. In this paper, we introduce an end-to-end scheme that bypasses this conventional two-step process by directly employing semantic speech encoders for segmentation. Focused on the broadcasted news domain, which poses unique challenges due to the diversity of speakers and topics within single recordings, we address the challenge of accessing topic change points efficiently in an end-to-end manner. Furthermore, we propose a new benchmark for spoken news topic segmentation by utilizing a dataset featuring approximately 1000 hours of publicly available recordings across six European languages and including an evaluation set in Hindi to test the model's cross-domain performance in a cross-lingual, zero-shot scenario. This setup reflects real-world diversity and the need for models adapting to various linguistic settings. Our results demonstrate that while the traditional pipeline approach achieves a state-of-the-art $P_k$ score of 0.2431 for English, our end-to-end model delivers a competitive $P_k$ score of 0.2564. When trained multilingually, these scores further improve to 0.1988 and 0.2370, respectively. To support further research, we release our model along with data preparation scripts, facilitating open research on multilingual spoken news topic segmentation.

The petaling effect, induced by pupil fragmentation from the telescope spider, drastically affects the performance of high contrast instruments by inducing core splitting on the PSF. Differential piston/tip/tilt aberrations within each optically separated fragment of the pupil are poorly measured by commonly used Adaptive Optics (AO) systems. We here pursue a design of dedicated low-order wavefront sensor -- or petalometers -- to complement the main AO. Interferometric devices sense differential aberrations between fragments with optimal sensitivity; their weakness though is their limitation to wrapped phase measurements. We show that by combining multiple spectral channels, we increase the capture range for petaling aberrations beyond several microns, enough to disambiguate one-wave wrapping errors made by the main AO system. We propose here to implement a petalometer from the multi-wavelength imaging mode of the VAMPIRES visible-light instrument, deployed on SCExAO at the Subaru Telescope. The interferometric measurements obtained in four spectral channels through a 7 hole non-redundant mask allow us to effiiently reconstruct diffierential piston between pupil petals., Comment: Paper 13097-89 from SPIE Astronomical Telescopes + Instrumentation, 2024, Yokohama, Japan

The Zernike wavefront sensor (ZWFS) stands out as one of the most sensitive optical systems for measuring the phase of an incoming wavefront, reaching photon efficiencies close to the fundamental limit. This quality, combined with the fact that it can easily measure phase discontinuities, has led to its widespread adoption in various wavefront control applications, both on the ground but also for future space-based instruments. Despite its advantages, the ZWFS faces a significant challenge due to its extremely limited dynamic range, making it particularly challenging for ground-based operations. To address this limitation, one approach is to use the ZWFS after a general adaptive optics (AO) system; however, even in this scenario, the dynamic range remains a concern. This paper investigates two optical configurations of the ZWFS: the conventional setup and its phase-shifted counterpart, which generates two distinct images of the telescope pupil. We assess the performance of various reconstruction techniques for both configurations, spanning from traditional linear reconstructors to gradient-descent-based methods. The evaluation encompasses simulations and experimental tests conducted on the Santa cruz Extreme Adaptive optics Lab (SEAL) bench at UCSC. Our findings demonstrate that certain innovative reconstruction techniques introduced in this study significantly enhance the dynamic range of the ZWFS, particularly when utilizing the phase-shifted version.

Integrated optics are used to achieve astronomical interferometry inside robust and compact materials, improving the instruments stability and sensitivity. In order to perform differential phase measurements at the H$\alpha$ line (656.3 nm) with the 600-800 nm spectro-interferometer FIRST, a photonic integrated circuit (PIC) is being developed. This PIC performs the visible combination of the beams coming from the telescope pupil sub-apertures. In this work, TEEM Photonics waveguides fabricated by $K_+:Na_+$ ion exchange in glass are characterized in terms of single-mode range and mode field diameter. The waveguide diffused index profile is modeled on Beamprop software. FIRST beam combiner building blocks are simulated, especially achromatic directional couplers and passive $\pi/2$ phase shifters for a potential ABCD interferometric combination., Comment: Proceeding. arXiv admin note: text overlap with arXiv:2409.03476

Integrated optics are used to achieve astronomical interferometry inside robust and compact materials, improving the instrument's stability and sensitivity. In order to perform differential phase measurements at the H$\alpha$ line (656.3 nm) with the 600-800 nm spectro-interferometer FIRST, a photonic integrated circuit (PIC) is being developed in collaboration with TEEM Photonics. This PIC performs the interferometric combination of the beams coming from sub-apertures selected in the telescope pupil, thus implementing the pupil remapping technique to restore the diffraction limit of the telescope. In this work, we report on the latest developments carried out within the FIRST project to produce a high performance visible PIC. The PICs are manufactured by TEEM Photonics, using their technology based on $K_+:Na_+$ ion exchange in glass. The first part of the study consists in the experimental characterization of the fundamental properties of the waveguides, in order to build an accurate model, which is the basis for the design of more complex functions. In the second part, theoretical designs and their optimization for three types of combiner architectures are presented: symmetric directional coupler, asymmetric directional couplers and ABCD cells including achromatic phase shifters., Comment: Preprint

We present follow-up SCExAO/CHARIS $H$ and $K$-band (R $\sim$ 70) high-contrast integral field spectroscopy and Keck/NIRC2 photometry of directly-imaged brown dwarf companion HD 33632 Ab and new radial-velocity data for the system from the SOPHIE spectrograph, complemented by Hipparcos and Gaia astrometry. These data enable more robust spectral characterization compared to lower-resolution spectra from the discovery paper and more than double the available astrometric and radial-velocity baseline. HD 33632 Ab's spectrum is well reproduced by a field L8.5--L9.5 dwarf. Using the Exo-REM atmosphere models, we derive a best-fit temperature, surface gravity and radius of $T_{\rm eff}$ = 1250 $K$, log(g) = 5, and $R$ = 0.97 $R_{\rm J}$ and a solar C/O ratio. Adding the SOPHIE radial-velocity data enables far tighter constraints on the companion's orbital properties (e.g. $i$=${46.6}_{-5.7}^{+2.9}$$^{o}$) and dynamical mass (${51.7}_{-2.5}^{+2.6}$$M_{\rm J}$) than derived from imaging data and \textit{Gaia} eDR3 astrometry data alone. HD 33632 Ab should be a prime target for multi-band imaging and spectroscopy with the James Webb Space Telescope and the Roman Space Telescope's Coronagraphic Instrument, shedding detailed light on HD 33632 Ab's clouds and chemistry and providing a key reference point for understanding young exoplanet atmospheres., Comment: 20 pages, 11 figures, 7 tables; submitted to AAS Journals

The facility adaptive optics of the Subaru Telescope AO188 recently received some long-awaited upgrades: a new 3224-actuator deformable mirror (DM) from ALPAO (hence the name change to AO3000 or AO3k), an upgraded GPU-based real-time computer, a visible nonlinear curvature wavefront sensor and a near-infrared wavefront sensor (NIR WFS), closing the loop at up to 2~kHz. The wavefront sensors were added in 2023, while the DM will be installed at the beginning of 2024. With these new features, AO3k will provide extreme-AO level of correction to all the instruments on the IR Nasmyth platform: The NIR-MIR camera and spectrograph IRCS, the high-resolution Doppler spectrograph IRD, and the high-contrast instrument SCExAO. AO3k will also support laser tomography (LTAO), delivering high Strehl ratio imaging with large sky coverage. The high Strehl will especially benefit SCExAO for high-contrast imaging, both in infrared and visible. The second stage extreme AO will no longer have to chase large residual atmospheric turbulence, and will focus on truly high-contrast techniques to create and stabilize dark holes, as well as coherent differential imaging techniques. We will finally be able to leverage the several high performance coronagraphs tested in SCExAO, even in the visible. AO3k will answer crucial questions as a precursor for future adaptive optics systems for ELTs, especially as a technology demonstrator for the HCI Planetary Systems Imager on the Thirty Meter Telescope. A lot of questions are still unanswered on the on-sky behavior of high actuator counts DMs, NIR wavefront sensing, the effect of rolling shutters or persistence. We present here the first on-sky results of AO3k, before the system gets fully offered to the observers in the second half of 2024. These results give us some insight on the great scientific results we hope to achieve in the future., Comment: 15 pages, 11 figures, proceedings of the SPIE Astronomical Telescopes and Instrumentation 2024 (Yokohama, Japan)

GLINT is a nulling interferometer downstream of the SCExAO extreme-adaptive-optics system at the Subaru Telescope (Hawaii, USA), and is a pathfinder instrument for high-contrast imaging of circumstellar environments with photonic technologies. GLINT is effectively a testbed for more stable, compact, and modular instruments for the era of 30m-class telescopes. GLINT is now undergoing an upgrade with a new photonic chip for more achromatic nulls, and for phase information to enable fringe tracking. Here we provide an overview of the motivations for the GLINT project and report on the design of the new chip, the on-site installation, and current status., Comment: Proc. SPIE Astronomical Telescopes + Instrumentation 2024 (Yokohama, Japan), Optical and Infrared Interferometry and Imaging IX

Exo-NINJA will realize nearIR R$\sim$4000 diffraction-limited narrow-field spectro-imaging for characterization of exoplanets and circumstellar disk structures. It uniquely combines mid-R spectroscopy, high throughput, and spatial resolution, in contrast to CHARIS, which does spectro-imaging, and REACH, which is single-point (no spatial resolution). Exo-NINJA's spectro-imaging at the telescope diffraction limit will characterize exoplanet atmospheres, detect and map (spatially and spectrally) gas accretion on protoplanets, and also detect exoplanets at small angular separation ($\lambda$/D) from their host star by spectro-astrometry. Exo-NINJA will link two instruments at the Subaru Telescope using a high-throughput hexagonal multi-mode fiber bundle (hexabundle). The fiber coupling resides between the high contrast imaging system SCExAO, which combines ExAO and coronagraph, and the medium-resolution spectrograph NINJA (R$=$4000 at JHK bands). Exo-NINJA will provide an end-to-end throughput of 20% compared to the 1.5% obtained with REACH. Exo-NINJA is scheduled for implementation on the Subaru Telescope's NasIR platform in 2025; we will present a concise overview of its future installation, laboratory tests such as the throughput and focal ratio degradation (FRD) performance of optical fiber imaging hexabundles, in the NIR and the trade-offs for fiber choices for the NINJA-SCExAO hexabundle fiber cable, and the expected on sky performance., Comment: Proc Spie, in press

A Photonic Lantern (PL) is a novel device that efficiently converts a multi-mode fiber into several single-mode fibers. When coupled with an extreme adaptive optics (ExAO) system and a spectrograph, PLs enable high throughput spectroscopy at high angular resolution. The Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) system of the Subaru Telescope recently acquired a PL that converts its multi-mode input into 19 single-mode outputs. The single mode outputs feed a R~4,000 spectrograph optimized for the 600 to 760 nm wavelength range. We present here the integration of the PL on SCExAO, and study the device performance in terms of throughput, field of view, and spectral reconstruction. We also present the first on-sky demonstration of a Visible PL coupled with an ExAO system, showing a significant improvement of x12 in throughput compared to the use of a sole single-mode fiber. This work paves the way towards future high throughput photonics instrumentation at small angular resolution., Comment: Proceeding published in SPIE Astronomical Telescopes + Instrumentation (2024) - Paper number 13096-25

In this article, we calculate the $S$-matrix elements for the process $e^{+} e^{-}\rightarrow t \bar{t}$ mediated by SM photon, $Z$ boson and an additional $Z^{'}$ boson indicating the contribution from new physics. We calculate the amplitude square using two component spinor formalism and four component spinor formalism and show the equivalence of the results using the two formalisms. We also establish the relations between the couplings of $Z^{'}$ boson to fermions in the two component spinor formalism and four component spinor formalism.

We consider the problem of online multi-agent Nash social welfare (NSW) maximization. While previous works of Hossain et al. [2021], Jones et al. [2023] study similar problems in stochastic multi-agent multi-armed bandits and show that $\sqrt{T}$-regret is possible after $T$ rounds, their fairness measure is the product of all agents' rewards, instead of their NSW (that is, their geometric mean). Given the fundamental role of NSW in the fairness literature, it is more than natural to ask whether no-regret fair learning with NSW as the objective is possible. In this work, we provide a complete answer to this question in various settings. Specifically, in stochastic $N$-agent $K$-armed bandits, we develop an algorithm with $\widetilde{\mathcal{O}}\left(K^{\frac{2}{N}}T^{\frac{N-1}{N}}\right)$ regret and prove that the dependence on $T$ is tight, making it a sharp contrast to the $\sqrt{T}$-regret bounds of Hossain et al. [2021], Jones et al. [2023]. We then consider a more challenging version of the problem with adversarial rewards. Somewhat surprisingly, despite NSW being a concave function, we prove that no algorithm can achieve sublinear regret. To circumvent such negative results, we further consider a setting with full-information feedback and design two algorithms with $\sqrt{T}$-regret: the first one has no dependence on $N$ at all and is applicable to not just NSW but a broad class of welfare functions, while the second one has better dependence on $K$ and is preferable when $N$ is small. Finally, we also show that logarithmic regret is possible whenever there exists one agent who is indifferent about different arms.

High-spin states in the transitional $^{217}$Ac nucleus are established up to 3.8 MeV excitation energy and $I^{\pi} =$ 41/2$^+$ with the addition of around 20 new transitions. The structure of the yrast and near-yrast states below the 29/2$^+$ isomer is revisited. The inconsistencies in the level schemes reported earlier are resolved. The level structure above the 29/2$^+$ isomer is established for the first time. Large-basis shell-model calculations with the KHPE interaction are performed to compare the experimentally observed level energies with the theoretical predictions. A comparison with the systematics of the N = 128 isotones suggests that the yrast structures result from a weak coupling of the odd proton to the even-even 216Ra core, which is consistent with the shell-model configurations. Furthermore, alpha decay of the 29/2$^+$ isomer is revisited and the decay scheme established from this work is discussed in the framework of the shell model., Comment: 11 pages, 9 figures

This paper offers a newly created integral approach for operators employing the orthogonal modified Laguerre polynomials and P\u{a}lt\u{a}nea basis. These operators approximate the functions over the interval $[0,\infty)$. Further, the moments are established for the proposed operators, and the universal Korovkin's theorem is used to derive the approximation properties of the operators. We examine convergence using a variety of analytical methods, including the Lipschitz class, Peetre's K-functional, the second-order modulus of smoothness, and the modulus of continuity. Moreover, an asymptotic formula associated with the Voronovskaja-type is established. The approximation is estimated through the weighted modulus of continuity, and convergence of the proposed operators in weighted spaces of functions is investigated as well. Ultimately, we employ numerical examples and visual representations to validate the theoretical findings.

We present a multi-wavelength (1.16$\mu$m-2.37$\mu$m) view of the debris disk around BD+45$^\circ$598, using the Subaru Coronagraphic Extreme Adaptive Optics system paired with the Coronagraphic High Angular Resolution Imaging Spectrograph. With an assumed age of 23 Myr, this source allows us to study the early evolution of debris disks and search for forming planets. We fit a scattered light model to our disk using a differential evolution algorithm, and constrain its geometry. We find the disk to have a peak density radius of $R_0 = 109.6$ au, an inclination of $i = 88.1^\circ$, and position angle $PA = 111.0^\circ$. While we do not detect a substellar companion in the disk, our calculated contrast limits indicate sensitivity to planets as small as $\sim 10 M_{\rm Jup}$ at a projected separation of 12 au of the star, and as small as $\sim 4 M_{\rm Jup}$ beyond 38 au. When measuring intensity as a function of wavelength, the disk color constrains the minimum dust grain size within a range of $\sim0.13$ to 1.01$\mu$m., Comment: 22 pages, 12 figures; Accepted to AJ

There is a significant need for precise and reliable forecasting of the far-field noise emanating from shipping vessels. Conventional full-order models based on the Navier-Stokes equations are unsuitable, and sophisticated model reduction methods may be ineffective for accurately predicting far-field noise in environments with seamounts and significant variations in bathymetry. Recent advances in reduced-order models, particularly those based on convolutional and recurrent neural networks, offer a faster and more accurate alternative. These models use convolutional neural networks to reduce data dimensions effectively. However, current deep-learning models face challenges in predicting wave propagation over long periods and for remote locations, often relying on auto-regressive prediction and lacking far-field bathymetry information. This research aims to improve the accuracy of deep-learning models for predicting underwater radiated noise in far-field scenarios. We propose a novel range-conditional convolutional neural network that incorporates ocean bathymetry data into the input. By integrating this architecture into a continual learning framework, we aim to generalize the model for varying bathymetry worldwide. To demonstrate the effectiveness of our approach, we analyze our model on several test cases and a benchmark scenario involving far-field prediction over Dickin's seamount in the Northeast Pacific. Our proposed architecture effectively captures transmission loss over a range-dependent, varying bathymetry profile. This architecture can be integrated into an adaptive management system for underwater radiated noise, providing real-time end-to-end mapping between near-field ship noise sources and received noise at the marine mammal's location., Comment: 14 pages, 18 figures

The Jeans equations do not form a closed system, and to solve them a parametrization relating the velocity moments is often adopted. For axisymmetric models, a phenomenological choice (the "$b$-ansatz") is widely used for the relation between the vertical ($\sigma_z^2$) and radial ($\sigma_R^2$) components of the velocity dispersion tensor, thus breaking their identity present in two-integral systems. However, the way in which the ansatz affects the resulting kinematical fields can be quite complicated, so that the analysis of these fields is usually performed only after numerically computing them. We present here a general procedure to study the properties of the ansatz-dependent fields $\overline{v_{\varphi}^2}$, $\Delta = \overline{v_{\varphi}^2} - \sigma_z^2$ and $\Delta_R = \overline{v_{\varphi}^2} - \sigma_R^2$. Specifically, the effects of the $b$-ansatz can be determined before solving the Jeans equations once the behaviour over the ($R,z$)-plane of three easy-to-build ansatz-independent functions is known. The procedure also constrains the ansatz to exclude unphysical results (as a negative $\overline{v_{\varphi}^2}$). The method is illustrated by discussing the cases of three well-known galaxy models: the Miyamoto & Nagai and Satoh disks, and the Binney logarithmic halo, for which the regions and the constraints on the ansatz values can be determined analytically; a two-component (Miyamoto & Nagai plus logarithmic halo) model is also discussed., Comment: 17 pages, 8 figures, Accepted for publication in MNRAS

Locomotion mechanics of legged robots are suitable when pacing through difficult terrains. Recognising terrains for such robots are important to fully yoke the versatility of their movements. Consequently, robotic terrain classification becomes significant to classify terrains in real time with high accuracy. The conventional classifiers suffer from overfitting problem, low accuracy problem, high variance problem, and not suitable for live dataset. On the other hand, classifying a growing dataset is difficult for convolution based terrain classification. Supervised recurrent models are also not practical for this classification. Further, the existing recurrent architectures are still evolving to improve accuracy of terrain classification based on live variable-length sensory data collected from legged robots. This paper proposes a new semi-supervised method for terrain classification of legged robots, avoiding preprocessing of long variable-length dataset. The proposed method has a stacked Long Short-Term Memory architecture, including a new loss regularization. The proposed method solves the existing problems and improves accuracy. Comparison with the existing architectures show the improvements., Comment: Preliminary draft of the work published in IEEE conference 2023

Crashes and delays at Railroad Highway Grade Crossings (RHGC), where highways and railroads intersect, pose significant safety concerns for the U.S. Federal Railroad Administration (FRA). Despite the critical importance of addressing accidents and traffic delays at highway-railroad intersections, there is a notable dearth of research on practical solutions for managing these issues. In response to this gap in the literature, our study introduces an intelligent system that leverages machine learning and computer vision techniques to enhance safety at Railroad Highway Grade crossings (RHGC). This research proposed a Non-Maximum Suppression (NMS)- based ensemble model that integrates a variety of YOLO variants, specifically YOLOv5S, YOLOv5M, and YOLOv5L, for grade-crossing object detection, utilizes segmentation techniques from the UNet architecture for detecting approaching rail at a grade crossing. Both methods are implemented on a Raspberry Pi. Moreover, the strategy employs high-definition cameras installed at the RHGC. This framework enables the system to monitor objects within the Region of Interest (ROI) at crossings, detect the approach of trains, and clear the crossing area before a train arrives. Regarding accuracy, precision, recall, and Intersection over Union (IoU), the proposed state-of-the-art NMS-based object detection ensemble model achieved 96% precision. In addition, the UNet segmentation model obtained a 98% IoU value. This automated railroad grade crossing system powered by artificial intelligence represents a promising solution for enhancing safety at highway-railroad intersections., Comment: 11 pages, 11 figures, conference

In the healthcare domain, Magnetic Resonance Imaging (MRI) assumes a pivotal role, as it employs Artificial Intelligence (AI) and Machine Learning (ML) methodologies to extract invaluable insights from imaging data. Nonetheless, the imperative need for patient privacy poses significant challenges when collecting data from diverse healthcare sources. Consequently, the Deep Learning (DL) communities occasionally face difficulties detecting rare features. In this research endeavor, we introduce the Ensemble-Based Federated Learning (EBFL) Framework, an innovative solution tailored to address this challenge. The EBFL framework deviates from the conventional approach by emphasizing model features over sharing sensitive patient data. This unique methodology fosters a collaborative and privacy-conscious environment for healthcare institutions, empowering them to harness the capabilities of a centralized server for model refinement while upholding the utmost data privacy standards.Conversely, a robust ensemble architecture boasts potent feature extraction capabilities, distinguishing itself from a single DL model. This quality makes it remarkably dependable for MRI analysis. By harnessing our groundbreaking EBFL methodology, we have achieved remarkable precision in the classification of brain tumors, including glioma, meningioma, pituitary, and non-tumor instances, attaining a precision rate of 94% for the Global model and an impressive 96% for the Ensemble model. Our models underwent rigorous evaluation using conventional performance metrics such as Accuracy, Precision, Recall, and F1 Score. Integrating DL within the Federated Learning (FL) framework has yielded a methodology that offers precise and dependable diagnostics for detecting brain tumors., Comment: 6

The healthcare industry has been revolutionized by the convergence of Artificial Intelligence of Medical Things (AIoMT), allowing advanced data-driven solutions to improve healthcare systems. With the increasing complexity of Artificial Intelligence (AI) models, the need for Explainable Artificial Intelligence (XAI) techniques become paramount, particularly in the medical domain, where transparent and interpretable decision-making becomes crucial. Therefore, in this work, we leverage a custom XAI framework, incorporating techniques such as Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Gradient-weighted Class Activation Mapping (Grad-Cam), explicitly designed for the domain of AIoMT. The proposed framework enhances the effectiveness of strategic healthcare methods and aims to instill trust and promote understanding in AI-driven medical applications. Moreover, we utilize a majority voting technique that aggregates predictions from multiple convolutional neural networks (CNNs) and leverages their collective intelligence to make robust and accurate decisions in the healthcare system. Building upon this decision-making process, we apply the XAI framework to brain tumor detection as a use case demonstrating accurate and transparent diagnosis. Evaluation results underscore the exceptional performance of the XAI framework, achieving high precision, recall, and F1 scores with a training accuracy of 99% and a validation accuracy of 98%. Combining advanced XAI techniques with ensemble-based deep-learning (DL) methodologies allows for precise and reliable brain tumor diagnoses as an application of AIoMT., Comment: 7 pages, 8 figures

We present the direct-imaging discovery of a substellar companion (a massive planet or low-mass brown dwarf) to the young, $\gamma$ Doradus-type variable star, HIP 39017 (HD 65526). The companion's SCExAO/CHARIS JHK ($1.1-2.4\mu$m) spectrum and Keck/NIRC2 L$^{\prime}$ photometry indicate that it is an L/T transition object. A comparison of the JHK+L$^{\prime}$ spectrum to several atmospheric model grids finds a significantly better fit to cloudy models than cloudless models. Orbit modeling with relative astrometry and precision stellar astrometry from Hipparcos and Gaia yields a semi-major axis of $23.8^{+8.7}_{-6.1}$ au, a dynamical companion mass of $30^{+31}_{-12}$~M$_J$, and a mass ratio of $\sim$1.9\%, properties most consistent with low-mass brown dwarfs. However, its mass estimated from luminosity models is a lower $\sim$13.8 $M_{\rm J}$ due to an estimated young age ($\lesssim$ 115 Myr); using a weighted posterior distribution informed by conservative mass constraints from luminosity evolutionary models yields a lower dynamical mass of $23.6_{-7.4}^{+9.1}$~M$_J$ and a mass ratio of $\sim$1.4\%. Analysis of the host star's multi-frequency $\gamma$ Dor-type pulsations, astrometric monitoring of HIP 39017b, and Gaia Data Release 4 astrometry of the star will clarify the system age and better constrain the mass and orbit of the companion. This discovery further reinforces the improved efficiency of targeted direct-imaging campaigns informed by long-baseline, precision stellar astrometry., Comment: Published in AJ: April 9, 2024