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The central molecular zone (CMZ) of our Galaxy exhibits widespread emission from SiO and various complex organic molecules (COMs), yet the exact origin of such emission is uncertain. Here we report the discovery of a unique class of long ($>$0.5 pc) and narrow ($<$0.03 pc) filaments in the emission of SiO 5$-$4 and eight additional molecular lines, including several COMs, in our ALMA 1.3 mm spectral line observations toward two massive molecular clouds in the CMZ, which we name as slim filaments. However, these filaments are not detected in the 1.3 mm continuum at the 5$\sigma$ level. Their line-of-sight velocities are coherent and inconsistent with being outflows. The column densities and relative abundances of the detected molecules are statistically similar to those in protostellar outflows but different from those in dense cores within the same clouds. Turbulent pressure in these filaments dominates over self gravity and leads to hydrostatic inequilibrium, indicating that they are a different class of objects than the dense gas filaments in dynamical equilibrium ubiquitously found in nearby molecular clouds. We argue that these newly detected slim filaments are associated with parsec-scale shocks, likely arising from dynamic interactions between shock waves and molecular clouds. The dissipation of the slim filaments may replenish SiO and COMs in the interstellar medium and lead to their widespread emission in the CMZ., Comment: 14 pages, 9 figures, 2 tables

Natural Language Processing (NLP) has become a cornerstone in many critical sectors, including healthcare, finance, and customer relationship management. This is especially true with the development and use of advanced models such as GPT-based architectures and BERT, which are widely used in decision-making processes. However, the black-box nature of these advanced NLP models has created an urgent need for transparency and explainability. This review explores explainable NLP (XNLP) with a focus on its practical deployment and real-world applications, examining its implementation and the challenges faced in domain-specific contexts. The paper underscores the importance of explainability in NLP and provides a comprehensive perspective on how XNLP can be designed to meet the unique demands of various sectors, from healthcare's need for clear insights to finance's emphasis on fraud detection and risk assessment. Additionally, this review aims to bridge the knowledge gap in XNLP literature by offering a domain-specific exploration and discussing underrepresented areas such as real-world applicability, metric evaluation, and the role of human interaction in model assessment. The paper concludes by suggesting future research directions that could enhance the understanding and broader application of XNLP.

Astronomers have used observations of the Galactic gas and dust via infrared, microwave, and radio to study molecular clouds in extreme environments such as the Galactic center. More recently, X- ray telescopes have opened up a new wavelength range in which to study these molecular clouds. Previous flaring events from SgrA* propagate X-rays outwards in all directions, and these X-rays interact with the surrounding molecular gas, illuminating different parts of the clouds over time. We use a combination of X-ray observations from Chandra and molecular gas tracers (line data from Herschel and the Submillimeter Array) to analyze specific features in the Sticks cloud, one of three clouds in the Three Little Pigs system in the Central Molecular Zone (Galactic longitude and latitude of 0.106 and -0.082 degrees respectively). We also present a novel X-ray tomography method we used to create 3-D map of the Sticks cloud. By combining X-ray and molecular tracer observations, we are able to learn more about the environment inside the Sticks cloud., Comment: 11 pages, 8 figures

Environmental noise that couples longitudinally to a quantum system dephases that system and can limit its coherence lifetime. Performance using quantum superposition in clocks, information processors, communication networks, and sensors depends on careful state and external field selection to lower sensitivity to longitudinal noise. In many cases time varying external control fields--such as the Hahn echo sequence originally developed for nuclear magnetic resonance applications--can passively correct for longitudinal errors. There also exist continuous versions of passive correction called continuous dynamical decoupling (CDD), or spin-locking depending on context. However, treating quantum systems under CDD as qubits has not been well explored. Here, we develop universal single-qubit gates that are ``fast'' relative to perturbative Rabi gates and applicable to any CDD qubit architecture. We demonstrate single-qubit gates with fidelity $\mathcal{F}=0.9947(1)$ on a frequency tunable CDD transmon superconducting circuit operated where it is strongly sensitive to longitudinal noise, thus establishing this technique as a potentially useful tool for operating qubits in applications requiring high fidelity under non-ideal conditions., Comment: 10 pages, 7 figures

HD 60435 is a well-known rapidly oscillating (roAp) Ap star with a series of alternating even and odd degree modes, making it a prime asteroseismic target. It is also an oblique pulsator with rotational inclination, $i$, and magnetic/pulsation obliquity, $\beta$, such that both magnetic/pulsation poles are viewed over the rotation period, $P_{\rm rot} = 7.679696$ d, determined from rotational light variations. While some roAp stars have stable pulsation mode amplitudes over decades, HD 60435 is known to have amplitude variations on time scales as short as 1 d. We show from 5 yr of {\it TESS} observations that there is strong amplitude modulation on this short time scale with possible mode interactions. Most remarkably, HD 60435 stopped pulsating during the time span of the {\it TESS} observations. This is the first time that any pulsating star has been observed to cease pulsating entirely. That has implications for mode interaction, excitation and damping, and is relevant to the problem of why only some stars in many pulsation instability strips pulsate, while others do not. During a 24.45-d time span of the {\it TESS} data when there was mode stability for a dipole mode and a quadrupole mode, the oblique pulsator model constrained $i$ and $\beta$, which we used to model those modes with a magnetic pulsation model from which we determined a polar field strength of 4 kG, in good agreement with a known magnetic measurement. We modelled the frequency separations showing that they can constrain the global metallicity, something that is not possible from spectroscopy of the highly peculiar Ap atmosphere., Comment: 19 pages plus an appendix. 13 figures. Accepted for publication in MNRAS

We present Atacama Large Millimeter/submillimeter Array (ALMA) Band 6 (1.3 mm) observations of dense cores in three massive molecular clouds within the Central Molecular Zone (CMZ) of the Milky Way, including the Dust Ridge cloud e, Sgr C, and the 20 km s-1 cloud, at a spatial resolution of 2000 au. Among the 834 cores identified from the 1.3 mm continuum, we constrain temperatures and linewidths of 253 cores using local thermodynamic equilibrium (LTE) methods to fit the H2CO and/or CH3CN spectra. We determine their masses using the 1.3 mm dust continuum and derived temperatures, and then evaluate their virial parameters using the H2CO and/or CH3CN linewidths and construct the core mass functions (CMFs). We find that the contribution of external pressure is crucial for the virial equilibrium of the dense cores in the three clouds, which contrasts with the environment in the Galactic disk where dense cores are already bound even without the contribution of external pressure. We also find that the CMFs show a Salpeter-like slope in the high-mass (>~3-6 Msun) end, a change from previous works with our new temperature estimates. Combined with the possible top-heavy initial mass functions (IMFs) in the CMZ, our result suggests that gas accretion and further fragmentation may play important roles in transforming the CMF to the IMF., Comment: ApJ accepted. The 4 figure sets with numerous panels will be published on the AAS journal website

The fine structures of solar radio bursts reveal complex dynamics in the corona, yet the observed characteristics of these sub-second bursts are additionally complicated by radio wave scattering in the turbulent solar corona. We examine the impact of anisotropic turbulence in radio-wave propagation simulations with non-radial magnetic field structures in shaping the morphology, time-characteristics, and source position of fine structures. The apparent sources are found to move along the direction of the magnetic-field lines and not along the density gradient, whereas the major axis of the scattered source is perpendicular to the local magnetic field (the scattering anisotropy axis). Using a dipolar magnetic field structure of an active region, we reproduce observed radio fine structure source motion parallel to the solar limb associated with a coronal loop and provide a natural explanation for puzzling observations of solar radio burst position motions with LOFAR. Furthermore, the anisotropy aligned with a dipolar magnetic field causes the apparent source images to bifurcate into two distinct components, with characteristic sizes smaller than in unmagnetized media. The temporal broadening induced by scattering reduces the observed frequency drift rate of fine structures, depending on the contribution of scattering to the time profile. The findings underscore the role of magnetic field geometry and anisotropic scattering for the interpretation of solar radio bursts and highlight that anisotropic scattering produces more than a single source., Comment: 16 pages, 12 figures. Accepted in The Astrophysical Journal

Modern AI (i.e., Deep Learning and its variants) is here to stay. However, its enigmatic black box nature presents a fundamental challenge to the traditional methods of test and validation (T&E). Or does it? In this paper we introduce a Digital Engineering (DE) approach to T&E (DE-T&E), combined with generative AI, that can achieve requisite mil spec statistical validation as well as uncover potential deleterious Black Swan events that might otherwise not be uncovered until it is too late. An illustration of these concepts is presented for an advanced modern radar example employing deep learning AI.

The Central Molecular Zone (CMZ) is the largest reservoir of dense molecular gas in the Galaxy and is heavily obscured in the optical and near-IR. We present an overview of the far-IR dust continuum, where the molecular clouds are revealed, provided by Herschel in the inner 40\deg($|l| <$ 20\deg) of the Milky Way with a particular focus on the CMZ. We report a total dense gas ($N$(H$_2$) $> 10^{23}$ cm$^{-2}$) CMZ mass of M=$2\substack{+2 \\ -1} \times 10^7$ M$_{\odot}$ and confirm that there is a highly asymmetric distribution of dense gas, with about 70-75% at positive longitudes. We create and publicly release complete fore/background-subtracted column density and dust temperature maps in the inner 40\deg ($|l| <$ 20\deg) of the Galaxy. We find that the CMZ clearly stands out as a distinct structure, with an average mass per longitude that is at least $3\times$ higher than the rest of the inner Galaxy contiguously from 1.8\deg $> \ell >$ -1.3\deg. This CMZ extent is larger than previously assumed, but is consistent with constraints from velocity information. The inner Galaxy's column density peaks towards the SgrB2 complex with a value of about 2 $\times$ 10$^{24}$ cm$^{-2}$, and typical CMZ molecular clouds are about N(H$_2$)=10$^{23}$ cm$^{-2}$. Typical CMZ dust temperatures range from about $12-35$ K with relatively little variation. We identify a ridge of warm dust in the inner CMZ that potentially traces the base of the northern Galactic outflow seen with MEERKAT., Comment: Submitted to ApJ, 16 pages, project website: https://centralmolecularzone.github.io/3D_CMZ/

The Central Molecular Zone (CMZ) is the way station at the heart of our Milky Way Galaxy, connecting gas flowing in from Galactic scales with the central nucleus. Key open questions remain about its 3-D structure, star formation properties, and role in regulating this gas inflow. In this work, we identify a hierarchy of discrete structures in the CMZ using column density maps from Paper I (Battersby et al., submitted). We calculate the physical ($N$(H$_2$), $T_{\rm{dust}}$, mass, radius) and kinematic (HNCO, HCN, and HC$_3$N moments) properties of each structure as well as their bolometric luminosities and star formation rates (SFRs). We compare these properties with regions in the Milky Way disk and external galaxies. Despite the fact that the CMZ overall is well below the Gao-Solomon dense gas star-formation relation (and in modest agreement with the Schmidt-Kennicutt relation), individual structures on the scale of molecular clouds generally follow these star-formation relations and agree well with other Milky Way and extragalactic regions. We find that individual CMZ structures require a large external pressure ($P_e$/k$_B$ $> 10^{7-9}$ K cm$^{-3}$) to be considered bound, however simple estimates suggest that most CMZ molecular-cloud-sized structures are consistent with being in pressure-bounded virial equilibriuim. We perform power-law fits to the column density probability distribution functions (N-PDFs) of the inner 100 pc, SgrB2, and the outer 100 pc of the CMZ as well as several individual molecular cloud structures and find generally steeper power-law slopes ($-9<\alpha<-2$) compared with the literature ($-6 < \alpha < -1$)., Comment: Accepted to ApJ, 25 pages, project website: https://centralmolecularzone.github.io/3D_CMZ/

A comprehensive 3-D model of the central 300 pc of the Milky Way, the Central Molecular Zone (CMZ) is of fundamental importance in understanding energy cycles in galactic nuclei, since the 3-D structure influences the location and intensity of star formation, feedback, and black hole accretion. Current observational constraints are insufficient to distinguish between existing 3-D models. Dust extinction is one diagnostic tool that can help determine the location of dark molecular clouds relative to the bright Galactic Center emission. By combining Herschel and Spitzer observations, we developed three new dust extinction techniques to estimate the likely near/far locations for each cloud in the CMZ. We compare our results to four geometric CMZ orbital models. Our extinction methods show good agreement with each other, and with results from spectral line absorption analysis from Walker et al. (submitted). Our near/far results for CMZ clouds are inconsistent with a projected version of the Sofue (1995) two spiral arms model, and show disagreement in position-velocity space with the Molinari et al. (2011) closed elliptical orbit. Our results are in reasonable agreement with the Kruijssen et al. (2015) open streams. We find that a simplified toy-model elliptical orbit which conserves angular momentum shows promising fits in both position-position and position-velocity space. We conclude that all current CMZ orbital models lack the complexity needed to describe the motion of gas in the CMZ, and further work is needed to construct a complex orbital model to accurately describe gas flows in the CMZ., Comment: Accepted by ApJ; 38 pages; Project series website with relevant code, data, and results: https://centralmolecularzone.github.io/3D_CMZ/

The Milky Way's Central Molecular Zone (CMZ) is the largest concentration of dense molecular gas in the Galaxy, the structure of which is shaped by the complex interplay between Galactic-scale dynamics and extreme physical conditions. Understanding the 3-D geometry of this gas is crucial as it determines the locations of star formation and subsequent feedback. We present a catalogue of clouds in the CMZ using Herschel data. Using archival data from the APEX and MOPRA CMZ surveys, we measure averaged kinematic properties of the clouds at 1mm and 3mm. We use archival ATCA data of the H$_{2}$CO (1$_{1,0}$ - 1$_{1,1}$) 4.8 GHz line to search for absorption towards the clouds, and 4.85 GHz GBT C-band data to measure the radio continuum emission. We measure the absorption against the continuum to provide new constraints for the line-of-sight positions of the clouds relative to the Galactic centre, and find a highly asymmetric distribution, with most clouds residing in front of the Galactic centre. The results are compared with different orbital models, and we introduce a revised toy model of a vertically-oscillating closed elliptical orbit. We find that most models describe the PPV structure of the gas reasonably well, but find significant inconsistencies in all cases regarding the near vs. far placement of individual clouds. Our results highlight that the CMZ is likely more complex than can be captured by these simple geometric models, along with the need for new data to provide further constraints on the true 3-D structure of the CMZ., Comment: Submitted to ApJ, 30 pages, project website: https://centralmolecularzone.github.io/3D_CMZ/

Microwave-optical quantum transduction is a key enabling technology in quantum networking, but has been plagued by a formidable technical challenge. As most microwave-optical-transduction techniques rely on three-wave mixing processes, the processes consume photons from a driving telecom-band (pump) laser to convert input microwave photons into telecom-band photons detuned from the laser by this microwave frequency. However, cleanly separating out single photons detuned only a few GHz away from a classically bright laser in the same spatial mode requires frequency filters of unprecedented extinction over a very narrow transition band, straining the capabilities of today's technology. Instead of confronting this challenge directly, we show how one may achieve the same transduction objective with comparable efficiency using a four-wave mixing process in which $pairs$ of pump photons are consumed to produce transduced optical photons widely separated in frequency from the pump. We develop this process by considering higher-order analogues of photoelasticity and electrostriction than those used in conventional optomechanics, and examine how the efficiency of this process can be made to exceed conventional optomechanical couplings., Comment: 5 pages + 2 more for appendices, and 1 figure

Replicating causal estimates across different cohorts is crucial for increasing the integrity of epidemiological studies. However, strong assumptions regarding unmeasured confounding and effect modification often hinder this goal. By employing an instrumental variable (IV) approach and targeting the local average treatment effect (LATE), these assumptions can be relaxed to some degree; however, little work has addressed the replicability of IV estimates. In this paper, we propose a novel survey weighted LATE (SWLATE) estimator that incorporates unknown sampling weights and leverages machine learning for flexible modeling of nuisance functions, including the weights. Our approach, based on influence function theory and cross-fitting, provides a doubly-robust and efficient framework for valid inference, aligned with the growing "double machine learning" literature. We further extend our method to provide bounds on a target population ATE. The effectiveness of our approach, particularly in non-linear settings, is demonstrated through simulations and applied to a Mendelian randomization analysis of the relationship between triglycerides and cognitive decline.

Though larger vessels may be well-equipped to deal with wavy conditions, smaller vessels are often more susceptible to disturbances. This paper explores the development of a nonlinear model predictive control (NMPC) system for Uncrewed Surface Vessels (USVs) in wavy conditions to minimize average roll. The NMPC is based on a prediction method that uses information about the vessel's dynamics and an assumed wave model. This method is able to mitigate the roll of an under-actuated USV in a variety of conditions by adjusting the weights of the cost function. The results show a reduction of 39% of average roll with a tuned controller in conditions with 1.75-metre sinusoidal waves. A general and intuitive tuning strategy is established. This preliminary work is a proof of concept which sets the stage for the leveraging of wave prediction methodologies to perform planning and control in real time for USVs in real-world scenarios and field trials., Comment: 6 pages, 10 figures. To appear in Proceedings of the 2024 IEEE/RSJ International Conference on Robots and Systems (IROS), October 2024

In a recent class of phase field crystal (PFC) models, the density order parameter is coupled to powers of its mean field. This effectively introduces a phenomenology of higher-order direct correlation functions acting on long wavelengths, which is required for modelling solid-liquid-vapor systems. The present work generalizes these models by incorporating, into a single-field theory, higher-order direct correlations, systematically constructed in reciprocal space to operate across long {\it and} short wavelengths. The correlation kernels introduced are also readily adaptable to describe distinct crystal structures. We examine the three-phase equilibrium properties and phase diagrams of the proposed model, and reproduce parts of the aluminum phase diagram as an example of its versatile parametrization. We assess the dynamics of the model, showing that it allows robust control of the interface energy between the vapor and condensed phases (liquid and solid). We also examine the dynamics of solid-vapor interfaces over a wide range of parameters and find that dynamical artifacts reported in previous PFC models do not occur in the present formalism. Additionally, we demonstrate the capacity of the proposed formalism for computing complex microstructures and defects such as dislocations, grain boundaries, and voids in solid-liquid-vapor systems, all of which are expected to be crucial for investigating rapid solidification processes.

We obtain an infinite-dimensional cone of singular twisted Hilbert spaces $Z(\varphi)$ which are isomorphic to their duals but not to their conjugate duals. We do that by showing that the subset of all bi-Lipschitz maps from $[0, \infty)$ to $\mathbb{R}$ is coneable. We also provide a characterization of the Kalton-Peck space among all twisted Hilbert spaces of the form $Z(\varphi)$, which gives a partial answer to a conjecture of F. Cabello S\'anchez and J. Castillo.

We present different techniques for applying Combinatorial Nullstellensatz to polynomials over finite fields. For examples, we generalize theorems from Noga Alon's paper on the subject, and present a few of our own.

The central regions of the Milky Way constitute a unique laboratory for a wide swath of astrophysical studies, consequently the inner $\sim$400 pc has been the target of numerous large surveys at all accessible wavelengths. In this paper we present a catalog of sources at 25 and 37 $\mu$m located within all of the regions observed with the SOFIA/FORCAST instrument in the inner $\sim$200 pc of the Galaxy. The majority of the observations were obtained as part of the SOFIA Cycle 7 Galactic Center Legacy program survey, which was designed to complement the Spitzer/MIPS 24 $\mu$m catalog in regions saturated in the MIPS observations. Due to the wide variety of source types captured by our observations at 25 and 37 $\mu$m, we do not limit the FORCAST source catalog to unresolved point sources, or treat all sources as if they are point-like sources. The catalog includes all detectable sources in the regions, resulting in a catalog of 950 sources, including point sources, compact sources, and extended sources. We also provide the user with metrics to discriminate between the source types., Comment: 29 pages, 13 figures, Accepted to ApJ

We give further evidence that the matrix-tensor model studied in \cite{belin2023} is dual to AdS$_{3}$ gravity including the sum over topologies. This provides a 3D version of the duality between JT gravity and an ensemble of random Hamiltonians, in which the matrix and tensor provide random CFT$_2$ data subject to a potential that incorporates the bootstrap constraints. We show how the Feynman rules of the ensemble produce a sum over all three-manifolds and how surgery is implemented by the matrix integral. The partition functions of the resulting 3d gravity theory agree with Virasoro TQFT (VTQFT) on a fixed, hyperbolic manifold. However, on non-hyperbolic geometries, our 3d gravity theory differs from VTQFT, leading to a difference in the eigenvalue statistics of the associated ensemble. As explained in \cite{belin2023}, the Schwinger-Dyson (SD) equations of the matrix-tensor integral play a crucial role in understanding how gravity emerges in the limit that the ensemble localizes to exact CFT's. We show how the SD equations can be translated into a combinatorial problem about three-manifolds., Comment: Equations 1.1 and 2.1 corrected. Section 3.1 revised. Typos fixed and references added. 75 pages

Human children far exceed modern machine learning algorithms in their sample efficiency, achieving high performance in key domains with much less data than current models. This ''data gap'' is a key challenge both for building intelligent artificial systems and for understanding human development. Egocentric video capturing children's experience -- their ''training data'' -- is a key ingredient for comparison of humans and models and for the development of algorithmic innovations to bridge this gap. Yet there are few such datasets available, and extant data are low-resolution, have limited metadata, and importantly, represent only a small set of children's experiences. Here, we provide the first release of the largest developmental egocentric video dataset to date -- the BabyView dataset -- recorded using a high-resolution camera with a large vertical field-of-view and gyroscope/accelerometer data. This 493 hour dataset includes egocentric videos from children spanning 6 months - 5 years of age in both longitudinal, at-home contexts and in a preschool environment. We provide gold-standard annotations for the evaluation of speech transcription, speaker diarization, and human pose estimation, and evaluate models in each of these domains. We train self-supervised language and vision models and evaluate their transfer to out-of-distribution tasks including syntactic structure learning, object recognition, depth estimation, and image segmentation. Although performance in each scales with dataset size, overall performance is relatively lower than when models are trained on curated datasets, especially in the visual domain. Our dataset stands as an open challenge for robust, humanlike AI systems: how can such systems achieve human-levels of success on the same scale and distribution of training data as humans?, Comment: 9 pages, 2 figures, 4 tables and SI. Submitted to NeurIPS Datasets and Benchmarks